271

Terrestrial and Inland

Water Systems

Coordinating Lead Authors:

Josef Settele (Germany), Robert Scholes (South Africa)

Lead Authors:

Richard A. Betts (UK), Stuart Bunn (Australia), Paul Leadley (France), Daniel Nepstad (USA),

Jonathan T. Overpeck (USA), Miguel Angel Taboada (Argentina)

Contributing Authors:

Rita Adrian (Germany), Craig Allen (USA), William Anderegg (USA), Celine Bellard (France),

Paulo Brando (Brazil), Louise P. Chini (New Zealand), Franck Courchamp (France),

Wendy Foden (South Africa), Dieter Gerten (Germany), Scott Goetz (USA), Nicola Golding (UK),

Patrick Gonzalez (USA), Ed Hawkins (UK), Thomas Hickler (Germany), George Hurtt (USA),

Charles Koven (USA), Josh Lawler (USA), Heike Lischke (Switzerland), Georgina M. Mace (UK),

Melodie McGeoch (Australia), Camille Parmesan (USA), Richard Pearson (UK),

Beatriz Rodriguez-Labajos (Spain), Carlo Rondinini (Italy), Rebecca Shaw (USA), Stephen Sitch

(UK), Klement Tockner (Germany), Piero Visconti (UK), Marten Winter (Germany)

Review Editors:

Andreas Fischlin (Switzerland), José M. Moreno (Spain), Terry Root (USA)

Volunteer Chapter Scientists:

Martin Musche (Germany), Marten Winter (Germany)

This chapter should be cited as:

Settele

, J., R. Scholes, R. Betts, S. Bunn, P. Leadley, D. Nepstad, J.T. Overpeck, and M.A. Taboada, 2014:

Terrestrial and inland water systems. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability.

Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the

Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach,

M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy,

S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United

Kingdom and New York, NY, USA, pp. 271-359.

272

Executive Summary ........................................................................................................................................................... 274

4.1. Past Assessments ..................................................................................................................................................... 278

4.2. A Dynamic and Inclusive View of Ecosystems ........................................................................................................ 278

4.2.1. Ecosystems, Adaptation, Thresholds, and Tipping Points ................................................................................................................... 278

4.2.2. Methods and Models Used ............................................................................................................................................................... 279

4.2.3. Paleoecological Evidence .................................................................................................................................................................. 279

4.2.4. Multiple Stressors Interacting with Climate Change ......................................................................................................................... 283

4.2.4.1.Land Use and Cover Change ................................................................................................................................................ 283

Box 4-1. Future Land Use Changes .................................................................................................................................. 284

4.2.4.2.Nitrogen Deposition ............................................................................................................................................................. 285

4.2.4.3.Tropospheric Ozone .............................................................................................................................................................. 286

4.2.4.4.Rising Carbon Dioxide .......................................................................................................................................................... 287

4.2.4.5.Diffuse and Direct Radiation ................................................................................................................................................ 288

4.2.4.6.Invasive and Alien Species ................................................................................................................................................... 288

4.3. Vulnerability of Terrestrial and Freshwater Ecosystems to Climate Change .......................................................... 290

4.3.1. Changes in the Disturbance Regime ................................................................................................................................................. 290

4.3.2. Observed and Projected Change in Ecosystems ................................................................................................................................ 290

4.3.2.1.Phenology ............................................................................................................................................................................ 291

4.3.2.2.Primary Productivity ............................................................................................................................................................. 292

4.3.2.3.Biomass and Carbon Stocks ................................................................................................................................................. 293

4.3.2.4.Evapotranspiration and Water Use Efﬁciency ....................................................................................................................... 294

4.3.2.5.Changes in Species Range, Abundance, and Extinction ........................................................................................................ 294

4.3.3. Impacts on and Risks for Major Systems .......................................................................................................................................... 301

4.3.3.1.Forests and Woodlands ........................................................................................................................................................ 301

Box 4-2. Tree Mortality and Climate Change ................................................................................................................... 306

4.3.3.2.Dryland Ecosystems: Savannas, Shrublands, Grasslands, and Deserts .................................................................................. 308

Box 4-3. A Possible Amazon Basin Tipping Point ............................................................................................................. 309

4.3.3.3.Rivers, Lakes, Wetlands, and Peatlands ................................................................................................................................. 312

4.3.3.4.Tundra, Alpine, and Permafrost Systems ............................................................................................................................... 314

Box 4-4. Boreal–Tundra Biome Shift ................................................................................................................................ 316

4.3.3.5.Highly Human-Modiﬁed Systems ......................................................................................................................................... 317

4.3.4. Impacts on Key Ecosystem Services .................................................................................................................................................. 319

4.3.4.1.Habitat for Biodiversity ........................................................................................................................................................ 319

4.3.4.2.Timber and Pulp Production ................................................................................................................................................. 320

4.3.4.3.Biomass-Derived Energy ....................................................................................................................................................... 320

4.3.4.4.Pollination, Pest, and Disease Regulation ............................................................................................................................. 320

4.3.4.5.Moderation of Climate Change, Variability, and Extremes ................................................................................................... 321

Table of Contents

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4.4. Adaptation and Its Limits ....................................................................................................................................... 321

4.4.1. Autonomous Adaptation by Ecosystems and Wild Organisms .......................................................................................................... 321

4.4.1.1.Phenological ........................................................................................................................................................................ 321

4.4.1.2.Evolutionary and Genetic ..................................................................................................................................................... 322

4.4.1.3.Migration of Species ............................................................................................................................................................ 324

4.4.2. Human-Assisted Adaptation ............................................................................................................................................................. 324

4.4.2.1.Reduction of Non-Climate Stresses and Restoration of Degraded Ecosystems ..................................................................... 324

4.4.2.2.The Size, Location, and Layout of Protected Areas ............................................................................................................... 324

4.4.2.3.Landscape and Watershed Management ............................................................................................................................. 324

4.4.2.4.Assisted Migration ............................................................................................................................................................... 325

4.4.2.5.Ex Situ Conservation ............................................................................................................................................................ 326

4.4.3. Consequences and Costs of Inaction and Beneﬁts of Action ............................................................................................................ 326

4.4.4. Unintended Consequences of Adaptation and Mitigation ................................................................................................................ 327

4.5. Emerging Issues and Key Uncertainties .................................................................................................................. 328

References ......................................................................................................................................................................... 328

Frequently Asked Questions

4.1: How do land use and land cover changes cause changes in climate? .............................................................................................. 282

4.2: What are the non-greenhouse gas effects of rising carbon dioxide on ecosystems? ........................................................................ 287

4.3: Will the number of invasive alien species increase as a result of climate change? ........................................................................... 289

4.4: How does climate change contribute to species extinction? ............................................................................................................ 295

4.5: Why does it matter if ecosystems are altered by climate change? .................................................................................................... 319

4.6: Can ecosystems be managed to help them and people to adapt to climate change? ...................................................................... 325

4.7: What are the economic costs of changes in ecosystems due to climate change? ............................................................................. 326

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Chapter 4 Terrestrial and Inland Water Systems

Executive Summary

The planet’s biota and ecosystem processes were strongly affected by past climate changes at rates of climate change lower

than those projected during the 21st century under high warming scenarios (e.g., Representative Concentration Pathway 8.5

(RCP8.5)) (high confidence). Most ecosystems are vulnerable to climate change even at rates of climate change projected under

low- to medium-range warming scenarios (e.g., RCP2.6 to RCP6.0).

The paleoecological record shows that global climate changes

comparable in magnitudes to those projected for the 21st century under all scenarios resulted in large-scale biome shifts and changes in

community composition; and that for rates projected under RCP6 and 8.5 were associated with species extinctions in some groups (high

confidence). {4.2.3}

Climate change is projected to be a powerful stressor on terrestrial and freshwater ecosystems in the second half of the 21st

century, especially under high-warming scenarios such as RCP6.0 and RCP8.5 (high confidence). Direct human impacts such as

land use and land use change, pollution, and water resource development will continue to dominate the threats to most

freshwater (high confidence) and terrestrial (medium confidence) ecosystems globally over the next 3 decades. Changing climate

exacerbates other impacts on biodiversity (high confidence).

Ecosystem changes resulting from climate change may not be fully apparent

for several decades, owing to long response times in ecological systems (medium confidence). Model-based projections imply that under low to

moderate warming scenarios (e.g., RCP2.6 to RCP6.0), direct land cover change will continue to dominate over (and conceal) climate-induced

change as a driver of ecosystem change at the global scale; for higher climate change scenarios, some model projections imply climate-driven

ecosystem changes sufficiently extensive to equal or exceed direct human impacts at the global scale (medium confidence). In high-altitude

and high-latitude freshwater and terrestrial ecosystems, climate changes exceeding those projected under RCP2.6 will lead to major changes in

species distributions and ecosystem function, especially in the second half of the 21st century (high confidence). {4.2.4, 4.3.2.5, 4.3.3, 4.3.3.1,

4.3.3.3, 4.4.1.1}

When terrestrial ecosystems are substantially altered (in terms of plant cover, biomass, phenology, or plant group dominance),

either through the effects of climate change or through other mechanisms such as conversion to agriculture or human settlement,

the local, regional, and global climates are also affected (high confidence).

The feedbacks between terrestrial ecosystems and climate

include, among other mechanisms, changes in surface albedo, evapotranspiration, and greenhouse gas (GHG) emissions and uptake. The physical

effects on the climate can be opposite in direction to the GHG effects, and can materially alter the net outcome of the ecosystem change on the

global climate (high confidence). The regions where the climate is affected may extend beyond the location of the ecosystem that has changed.

{4.2.4.1, 4.3.3.4}

Rising water temperatures, due to global warming, will lead to shifts in freshwater species distributions and worsen water quality

problems, especially in those systems experiencing high anthropogenic loading of nutrients (high confidence).

Climate change-

induced changes in precipitation will substantially alter ecologically important attributes of flow regimes in many rivers and wetlands and

exacerbate impacts from human water use in developed river basins (medium confidence). {4.3.3.3, Box CC-RF}

Many plant and animal species have moved their ranges, altered their abundance, and shifted their seasonal activities in response

to observed climate change over recent decades (high confidence). They are doing so now in many regions and will continue to do

so in response to projected future climate change (high confidence).

The broad patterns of species and biome shifts toward the poles and

higher in altitude in response to a warming climate are well established for periods thousands of years in the past (very high confidence). These

general patterns of range shifts have also been observed over the last few decades in some well-studied species groups such as insects and

birds and can be attributed to observed climatic changes (high confidence). Interactions between changing temperature, precipitation, and land

use can sometimes result in range shifts that are downhill or away from the poles. Certainty regarding past species movements in response to

changing climate, coupled with projections from a variety of models and studies, provides high confidence that such species movements will be

the norm with continued warming. Under all RCP climate change scenarios for the second half of the 21st century, with high confidence:

(1) community composition will change as a result of decreases in the abundances of some species and increases in others; and (2) the

seasonal activity of many species will change differentially, disrupting life cycles and interactions between species. Composition and seasonal

change will both alter ecosystem function. {4.2.1, 4.2.3, 4.3.2, 4.3.2.1, 4.3.2.5, 4.3.3, 4.4.1.1}

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Many species will be unable to move fast enough during the 21st century to track suitable climates under mid- and high-range

rates of climate change (i.e., RCP4.5, RCP6.0, and RCP8.5 scenarios) (medium confidence).

The climate velocity (the rate of movement

of the climate across the landscape) will exceed the maximum velocity at which many groups of organisms, in many situations, can disperse or

migrate, except after mid-century in the RCP2.6 scenario. Populations of species that cannot keep up with their climate niche will find themselves

in unfavorable climates, unable to reach areas of potentially suitable climate. Species occupying extensive flat landscapes are particularly

vulnerable because they must disperse over longer distances than species in mountainous regions to keep pace with shifting climates. Species

with low dispersal capacity will also be especially vulnerable: examples include many plants (especially trees), many amphibians, and some

small mammals. For example, the maximum observed and modeled dispersal and establishment rates for mid- and late-successional tree

species are insufficient to track climate change except in mountainous areas, even at moderate projected rates of climate change. Barriers to

dispersal, such as habitat fragmentation, prior occupation of habitat by competing species, and human-made impediments such as dams on

rivers and urbanized areas on land, reduce the ability of species to migrate to more suitable climates (high confidence). Intentional and

accidental anthropogenic transport can speed dispersal. {4.3.2.5, 4.3.3.3}

Large magnitudes of climate change will reduce the populations, vigor, and viability of species with spatially restricted populations,

such as those confined to small and isolated habitats, mountaintops, or mountain streams, even if the species has the biological

capacity to move fast enough to track suitable climates (high confidence).

The adverse effects on restricted populations are modest for

low magnitudes of climate change (e.g., RCP2.6) but very severe for the highest magnitudes of projected climate change (e.g., RCP8.5).

{4.3.2.5, 4.3.3.4, 4.3.4.1}

The capacity of many species to respond to climate change will be constrained by non-climate factors (high confidence), including

but not limited to the simultaneous presence of inhospitable land uses, habitat fragmentation and loss, competition with alien species, exposure

to new pests and pathogens, nitrogen loading, and tropospheric ozone. {4.2.4.6, 4.3.3.5, Figure 4-4}

The establishment, growth, spread, and survival of populations of invasive alien species have increased (high confidence), but

the ability to attribute alien species invasion to climate change is low in most cases. Some invasive alien species have traits that favor

their survival and reproduction under changing climates. Future movement of species into areas where they were not present historically will

continue to be driven mainly by increased dispersal opportunities associated with human activities and by increased disturbances from natural

and anthropogenic events, in some cases facilitated and promoted by climate change. {4.2.4.6, Figure 4-4}

A large fraction of terrestrial and freshwater species face increased extinction risk under projected climate change during and

beyond the 21st century, especially as climate change interacts with other pressures, such as habitat modification, overexploitation,

pollution, and invasive species (high confidence).

The extinction risk is increased under all RCP scenarios, and the risk increases with both

the magnitude and rate of climate change. While there is medium confidence that recent warming contributed to the extinction of some species

of Central American amphibians, there is generally very low confidence that observed species extinctions can be attributed to recent climate

change. Models project that the risk of species extinctions will increase in the future owing to climate change, but there is low agreement

concerning the fraction of species at increased risk, the regional and taxonomic focus for such extinctions and the time frame over which

extinctions could occur. Modeling studies and syntheses since the AR4 broadly confirm that a large proportion of species are projected to be at

increased risk of extinction at all but the lowest levels of climate warming (RCP2.6). Some aspects leading to uncertainty in the quantitative

projections of extinction risks were not taken into account in previous models; as more realistic details are included, it has been shown that the

extinction risks may be either under- or overestimated when based on simpler models. {4.3.2.5}

Terrestrial and freshwater ecosystems have sequestered about a quarter of the carbon dioxide (CO

) emitted to the atmosphere

by human activities in the past 3 decades (high confidence).

The net fluxes out of the atmosphere and into plant biomass and soils show

large year-to-year variability; as a result there is low confidence in the ability to determine whether the net rate at which carbon has been

taken up by terrestrial ecosystems at the global scale has changed between the decades 1991–2000 and 2001–2010. There is high confidence

that the factors causing the current increase in land carbon include the positive effects of rising CO

on plant productivity, a warming climate,

nitrogen deposition, and recovery from past disturbances, but low confidence regarding the relative contribution by each of these and other

factors. {4.2.4.1, 4.2.4.2, 4.2.4.4, 4.3.2.2, 4.3.2.3, WGI AR5 6.3.1, 6.3.2.6}

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Chapter 4 Terrestrial and Inland Water Systems

The natural carbon sink provided by terrestrial ecosystems is partially offset at the decadal time scale by carbon released

through the conversion of natural ecosystems (principally forests) to farm and grazing land and through ecosystem degradation

(high confidence). Carbon stored in the terrestrial biosphere is vulnerable to loss back to the atmosphere as a result of the direct

and indirect effects of climate change, deforestation, and degradation (high confidence). The net transfer of CO

from the

atmosphere to the land is projected to weaken during the 21st century (medium confidence). The direct effects of climate change on

stored terrestrial carbon include high temperatures, drought, and windstorms; indirect effects include increased risk of fires and pest and disease

outbreaks. Experiments and modeling studies provide medium confidence that increases in CO

up to about 600 ppm will continue to enhance

photosynthesis and plant water use efficiency, but at a diminishing rate; and high confidence that low availability of nutrients, particularly

nitrogen, will limit the response of many natural ecosystems to rising CO

. There is medium confidence that other factors associated with

global change, including high temperatures, rising ozone concentrations, and in some places drought, decrease plant productivity by amounts

comparable in magnitude to the enhancement by rising CO

. There are few field-scale experiments on ecosystems at the highest CO

concentrations projected by RCP8.5 for late in the century, and none of these include the effects of other potential confounding factors.

{4.2.4, 4.2.4.1, 4.2.4.2, 4.2.4.3, 4.2.4.4, 4.3.2.2, 4.3.3.1, Box 4-3, Box CC-VW, WGI AR5 6.4.3.3}

Increases in the frequency or intensity of ecosystem disturbances such as droughts, wind storms, fires, and pest outbreaks have

been detected in many parts of the world and in some cases are attributed to climate change (medium confidence). Changes in

the ecosystem disturbance regime beyond the range of natural variability will alter the structure, composition, and functioning

of ecosystems (high confidence).

Ecological theory and experimentation predict that ecological change resulting from altered disturbance

regimes will be manifested as relatively abrupt and spatially patchy transitions in ecosystem structure, composition, and function, rather than

gradual and spatially uniform shifts in location or abundance of species (medium confidence). {4.2.4.6, 4.3.3, 4.3.2.5, Box 4-3, Box 4-4,

Figure 4-10}

Increased tree death has been observed in many places worldwide, and in some regions has been attributed to climate change

(high confidence). In some places it is sufficiently intense and widespread as to result in forest dieback (low confidence). Forest

dieback is a major environmental risk, with potentially large impacts on climate, biodiversity, wood production, water quality, amenity, and

economic activity. In detailed regional studies in western and boreal North America, the tree mortality observed over the past few decades has

been attributed to the effects of high temperatures and drought, or to changes in the distribution and abundance of insect pests and

pathogens related, in part, to warming (high confidence). Tree mortality and associated forest dieback will become apparent in many regions

sooner than previously anticipated (medium confidence). Earlier projections of increased tree growth and enhanced forest carbon sequestration

due to increased growing season duration, rising CO

concentration, and atmospheric nitrogen deposition must be balanced by observations

and projections of increasing tree mortality and forest loss due to fires and pest attacks. The consequences for the provision of timber and other

wood products are projected to be highly variable between regions and products, depending on the balance of the positive versus negative

effects of global change. {4.3.2, 4.3.3.1, 4.3.3.4, 4.3.3.5, 4.3.4, 4.3.4.2, Box 4-2, Box 4-3}

There is a high risk that the large magnitudes and high rates of climate change associated with low-mitigation climate scenarios

(RCP4.5 and higher) will result within this century in abrupt and irreversible regional-scale change in the composition, structure,

and function of terrestrial and freshwater ecosystems, for example in the Amazon (low confidence) and Arctic (medium confidence),

leading to substantial additional climate change.

There are plausible mechanisms, supported by experimental evidence, observations, and

model results, for the existence of ecosystem tipping points in both boreal-tundra Arctic systems and the rainforests of the Amazon basin.

Continued climate change will transform the species composition, land cover, drainage, and permafrost extent of the boreal-tundra system,

leading to decreased albedo and the release of GHGs (medium confidence). Adaptation measures will be unable to prevent substantial change

in the boreal-Arctic system (high confidence). Climate change alone is not projected to lead to abrupt widespread loss of forest cover in the

Amazon during this century a (medium confidence), but a projected increase in severe drought episodes, together with land use change and

forest fire, would cause much of the Amazon forest to transform to less dense, drought- and fire-adapted ecosystems, and in doing so put a

large stock of biodiversity at elevated risk, while decreasing net carbon uptake from the atmosphere (low confidence). Large reductions in

deforestation, as well as wider application of effective wildfire management, lower the risk of abrupt change in the Amazon, as well as the

impacts of that change (medium confidence). {4.2.4.1, 4.3.3.1.1, 4.3.3.1.3, 4.3.3.4, Figure 4-8, Box 4-3, Box 4-4}

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Management actions can reduce, but not eliminate, the risk of impacts to terrestrial and freshwater ecosystems due to climate

change, as well as increase the inherent capacity of ecosystems and their species to adapt to a changing climate (high confidence).

The capacity for natural adaptation by ecosystems and their constituent organisms is substantial, but for many ecosystems and species it will

be insufficient to cope with projected rates and magnitudes of climate change in the 21st century without substantial loss of species and

ecosystem services, under medium-range warming (e.g., RCP6.0) or high-range warming scenarios (e.g., RCP8.5) (medium confidence). The

capacity for ecosystems to adapt to climate change can be increased by reducing the other stresses operating on them; reducing the rate and

magnitude of climate change; reducing habitat fragmentation and increasing connectivity; maintaining a large pool of genetic diversity and

functional evolutionary processes; assisted translocation of slow moving organisms or those whose migration is impeded, along with the

species on which they depend; and manipulation of disturbance regimes to keep them within the ranges necessary for species persistence and

sustained ecosystem functioning. {4.4, 4.4.1, 4.4.2}

Adaptation responses to climate change in the urban and agricultural sectors can have unintended negative outcomes for

terrestrial and freshwater ecosystems (medium confidence). For example, adaptation responses to counter increased variability of water

supply, such as building more and larger impoundments and increased water extraction, will in many cases worsen the direct effects of climate

change in freshwater ecosystems. {4.3.3.3, 4.3.4.6}

Widespread transformation of terrestrial ecosystems in order to mitigate climate change, such as carbon sequestration through

planting fast-growing tree species into ecosystems where they did not previously occur, or the conversion of previously

uncultivated or non-degraded land to bioenergy plantations, will lead to negative impacts on ecosystems and biodiversity (high

confidence).

For example, the land use scenario accompanying the mitigation scenario RCP2.6 features a large expansion of biofuel production,

displacing natural forest cover. {4.2.4.1, 4.4.4}

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Chapter 4 Terrestrial and Inland Water Systems

4.1. Past Assessments

The topics assessed in this chapter were last assessed by the IPCC in

2007, principally in WGII AR4 Chapters 3 (Kundzewicz et al., 2007) and

(Fischlin et al., 2007), but also in WGII AR4 Sections 1.3.4 and 1.3.5

(Rosenzweig et al., 2007). The WGII AR4 SPM stated “Observational

evidence from all continents and most oceans shows that many natural

systems are being affected by regional climate changes, particularly

temperature increases,” though they noted that documentation of

observed changes in tropical regions and the Southern Hemisphere was

sparse (Rosenzweig et al., 2007). Fischlin et al. (2007) found that 20 to

30% of the plant and animal species that had been assessed to that time

were considered to be at increased risk of extinction if the global average

temperature increase exceeds 2°C to 3°C above the preindustrial level

with medium confidence, and that substantial changes in structure and

functioning of terrestrial, marine, and other aquatic ecosystems are very

likely under that degree of warming and associated atmospheric CO

concentration. No time scale was associated with these findings. The

carbon stocks in terrestrial ecosystems were considered to be at high

risk from climate change and land use change. The report warned that

the capacity of ecosystems to adapt naturally to the combined effect of

climate change and other stressors is likely to be exceeded if greenhouse

gas (GHG) emission continued at or above the then-current rate.

4.2. A Dynamic and Inclusive View

of Ecosystems

There are three aspects of the contemporary scientific view of ecosystems

that are important to know for policy purposes. First, ecosystems usually

have imprecise and variable boundaries. They span a wide range of

spatial scales, nested within one another, from the whole biosphere,

down through its major ecosystem types (biomes), to local and possibly

short-lived associations of organisms. Second, the human influence on

ecosystems is globally pervasive. Humans are regarded as an integral,

rather than separate, part of social-ecological systems (Gunderson and

Holling, 2001; Berkes et al., 2003). Ecosystems are connected across

boundaries through the movement of energy, materials, and organisms,

and subsidies between terrestrial and freshwater systems are known

to be particularly important (Polis et al., 1997; Loreau et al., 2003). As

a consequence, human activities in terrestrial systems can significantly

impact freshwater ecosystems and their biota (Allan, 2004). The dynamics

of socio-ecological systems are governed not only by biophysical

processes such as energy flows, material cycles, competition, and

predation, but also by social processes such as economics, politics,

culture, and individual preferences (Walker and Salt, 2006). Third,

ecologists do not view ecosystems as necessarily inherently static and

at equilibrium in the absence of a human disturbance (Hastings, 2004).

Ecosystems vary over time and space in the relative magnitude of their

components and fluxes, even under a constant environment, owing to

internal dynamics (Scheffer, 2009). Furthermore, attempts to restrict

this intrinsic variation—or that resulting from externally generated

disturbances—are frequently futile, and may damage the capacity of

the ecosystem to adapt to a changing environment (Folke et al.,

2004). This contrasts with the popular view that ecosystems exhibit a

“balance of Nature” and benefit from being completely protected from

disturbance.

4.2.1. Ecosystems, Adaptation, Thresholds,

and Tipping Points

The term “adaptation” has different meanings in climate policy, ecology,

and evolutionary biology. In climate policy (see Glossary) it implies

human actions intended to reduce negative outcomes. In ecology,

ecosystems are said to be adaptive because their composition or function

can change in response to a changing environment, without necessarily

involving deliberate human actions (see Section 4.4.1). In evolutionary

biology, adaptation means a change in the genetic properties of a

population of individuals as a result of natural selection (Section 4.4.1.2),

a possibility seen since the Fourth Assessment Report as increasingly

relevant to climate change.

The notion of thresholds has become a prominent ecological and political

concern (Knapp, A.K. et al., 2008; Lenton et al., 2008; Leadley et al.,

2010). To avoid policy confusion, three types of threshold need to be

distinguished. The first reflects a human preference that the ecosystem

stays within certain bounds, such as above a certain forest cover. These

can be, by definition, negotiated. The second type reflects fundamental

biological or physical properties, for instance the temperature at which

frozen soils thaw (see Box 4-4) or the physiological tolerance limits of

species. The third type is caused by system dynamics: the point at which

the net effect of all the positive and negative feedback loops regulating

the system is sufficiently large and positive that a small transgression

becomes sufficiently amplified to lead to a change in ecosystem state

called a regime shift (Lenton et al., 2008). The new state exhibits different

dynamics, mean composition, sensitivity to environmental drivers, and

flows of ecosystem services relative to the prior state. This type of

threshold is called a “tipping point” (defined in the Glossary as a level

of change in system properties beyond which a system reorganizes,

often abruptly, and persists in its new state even if the drivers of the

change are abated ) and is important in the context of climate change

because its onset may be abrupt, hard to predict precisely, and effectively

irreversible (Scheffer et al., 2009; Leadley et al., 2010; Barnosky et al.,

2012; Brook et al., 2013; Hughes et al., 2013). Many examples of tipping

points have now been identified (Scheffer, 2009). Regional-scale

ecosystem tipping points have not occurred in the recent past, but there

is good evidence for tipping points in the distant past (Section 4.2.3)

and there is concern that they could occur in the near future (see Boxes

4-3 and 4-4).

The early detection and prediction of ecosystem thresholds, particularly

tipping points, is an area of active research. There are indications (Scheffer,

2009) that an increase in ecosystem variability signals the impending

approach of a threshold. In practice, such signals may not be detectable

against background noise and uncertainty until the threshold is crossed

(Biggs et al., 2009). The dynamics of ecosystems are complex and our

present level of knowledge is inadequate to predict all ecosystem

outcomes with confidence, even if the future climate were precisely

known.

Field observations over the past century in numerous locations in boreal,

temperate, and tropical ecosystems have detected biome shifts, the

replacement at a location of one suite of species by another (high

confidence). The effect is usually of biomes moving upward in elevation

and to higher latitudes (Gonzalez et al., 2010; see Figure 4-1). These shifts

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Terrestrial and Inland Water Systems Chapter 4

have often been attributed to anthropogenic climate change, as biome

distribution is known to broadly reflect climate zones, and the shifts have

been observed in areas without major human disturbance (medium

confidence; see Table 4-1). Projections of future vegetation distribution

under climate change indicate that many biomes could shift substantially,

including in areas where ecosystems are largely undisturbed by direct

human land use (Figure 4-2). The extent of the shift increases with

increasing global mean warming, without a sudden threshold (Scholze

et al., 2006; Pereira et al., 2010; Rehfeldt et al., 2012).

4.2.2. Methods and Models Used

Analysis of the current and past impacts of climate change on terrestrial

and freshwater ecosystems and their projection into the future relies

on three general approaches: inference from analogous situations in

the past or elsewhere in the present; manipulative experimentation,

deliberately altering one of a few factors at a time; and models with a

mechanistic or statistical basis. Studies of the relatively distant past

are discussed in depth in Section 4.2.3. Inferences from present spatial

patterns in relation to climate is at the core of climate envelope niche

modeling, a well-established but limited statistical technique for making

projections of the future distribution under equilibrium conditions (Elith

and Leathwick, 2009). Representing the rate of change during the non-

equilibrium conditions that will prevail over the next century requires a

more mechanistic approach, of which there are some examples (e.g.,

Keith et al., 2008; Kearney and Porter, 2009). Changes in ecosystem

function are usually determined by experimentation (see examples in

Section 4.3.3) and are modeled using mechanistic models, in many

cases with relatively high uncertainty (Seppelt et al., 2011).

4.2.3. Paleoecological Evidence

Paleoclimatic observations and modeling indicate that the Earth’s climate

has always changed on a wide range of time scales. In many cases,

particularly over the last million years, it has changed in ways that are

well understood in terms of both patterns and causes (Jansen et al.,

2007; see WGI AR5 Chapter 5). Paleoecological records demonstrate with

high confidence that the planet’s biota (both terrestrial and aquatic),

DE: Desert

RW: Tropical woodland

RD: Tropical deciduous broadleaf forest

Biomes

IC: Ice

BC: Boreal conifer forest

UA: Tundra and alpine

TC: Temperate conifer forest

TB: Temperate broadleaf forest

TM: Temperate mixed forest

TS: Temperate shrubland

TG: Temperate grassland

RG: Tropical grassland

RE: Tropical evergreen broadleaf forest

1-22: See Table 4-1

Figure 4-1 | Locations of observed biome shifts during the 20th century, listed in Table 4-1, derived from Gonzalez et al. (2010). The color of each semicircle indicates the

retracting biome (top for North America, Europe, Asia; bottom for Africa and New Zealand) and the expanding biome (bottom for North America, Europe, Asia; top for Africa and

New Zealand), according to published ﬁeld observations. Biomes, from poles to equator: ice (IC), tundra and alpine (UA), boreal conifer forest (BC), temperate conifer forest (TC),

temperate broadleaf forest (TB), temperate mixed forest (TM), temperate shrubland (TS), temperate grassland (TG), desert (DE), tropical grassland (RG), tropical woodland (RW),

tropical deciduous broadleaf forest (RD), tropical evergreen broadleaf forest (RE). The background is the potential biome according to the MC1 dynamic global vegetation model

under the 1961–1990 climate. No shift was observed on locations 10, 11, 16, and 23 (see Table 4-1).

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Chapter 4 Terrestrial and Inland Water Systems

carbon cycle, and associated feedbacks and services have responded to

this climatic change, particularly when the climatic change was as large

as that projected during the 21st century under mid- to high-end radiative

forcing pathways (e.g., MacDonald et al., 2008; Claussen, 2009; Arneth

et al., 2010; Dawson et al., 2011; Willis and MacDonald, 2011). Excellent

examples of past large climate change events that drove large ecological

change, as well as recovery periods in excess of a million years, include

the events that led to the Earth’s five mass extinctions in the distant past

(i.e., during the Ordovician, about 443 Ma, the Devonian, about 359 Ma,

the Permian, about 251 Ma, the Triassic, about 200 Ma, and the

Cretaceous, about 65 Ma; Barnosky et al., 2011). Major ecological

change was also driven by climate change during the Paleocene-Eocene

Thermal Maximum (PETM, 56 Ma; Wing et al., 2005; Jaramillo et al., 2010;

Wing and Currano, 2013), the early Eocene Climatic Optimum (EECO, 53

to 50 Ma; Woodburne et al., 2009), the Pliocene (5.3 to 2.6 Ma; Haywood

and Valdes, 2006; Haywood et al., 2011), and the Last Glacial Maximum

(LGM) to Holocene transition between 21 and 6 ka (MacDonald et al.,

2008; Clark et al., 2009; Gill et al., 2009; Williams, J.W. et al., 2010;

Prentice et al., 2011; Daniau et al., 2012). The paleoecological record thus

provides high confidence that large global climate change, comparable

in magnitude to that projected for the 21st century, can result in large

ecological changes, including large-scale biome shifts, reshuffling of

communities, and species extinctions.

Rapid, regional warming before and after the Younger Dryas cooling

event (11.7 to 12.9 ka) provides a relatively recent analogy for climate

change at a rate approaching, for many regions, that projected for the

21st century for all Representative Concentration Pathways (RCPs; Alley

et al., 2003; Steffensen et al., 2008). Ecosystems and species responded

rapidly during the Younger Dryas by shifting distributions and abundances,

and there were some notable large animal extinctions, probably

exacerbated by human activities (Gill et al., 2009; Dawson et al., 2011).

In some regions, species became locally or regionally extinct (extirpated),

but there is no evidence for climate-driven global-scale extinctions

during this period (Botkin et al., 2007; Willis, K.J. et al., 2010). However,

the Younger Dryas climate changes differ from those projected for the

future because they were regional rather than global; may have only

regionally exceeded rates of warming projected for the future; and

started from a baseline substantially colder than present (Alley et al.,

2003). The mid-Holocene, about 6 ka, provides a very recent example

of the effects of modest climate change. Regional mean warming during

this period (mean annual temperature about 0.5°C to 1.0°C above

Location Reference Plots

Time

period

Shift

type

Retracting

biome

Expanding

biome

Temp. change

(ºC century

–

)

Precip. change

(% century

–

)

1. Alaska Range, Alaska, USA Lloyd and Fastie (2003) 18 1800 – 2000 L UA BC 1.1* 3

. Baltic Coast, Sweden Walther et al. (2005) 71944 – 2003 L

C TB

.6* 8

. Becca di Viou, Italy Leonelli et al. (2011) 11700 – 2008 E UA

.9* – 6

4. Garibaldi, British Columbia, Canada Brink (1959) 1 1860 – 1959 E UA

BC 0.7* 16*

. Goulet Sector, Québec, Canada Payette and Filion (1985) 21880 – 1980 E UA

.4* 19*

6. Green Mountains, Vermont, USA Beckage et al. (2008) 33 1962 – 2005 E

BC TB 1.6* 6

. Jasper, Alberta, Canada Luckman and Kavanagh (2000) 1 1700 – 1994 E UA

.6 21*

8. Kenai Mountains, Alaska, USA Dial et al. (2007) 31951–1996E UA

BC 0.7 6

9. Kluane Range, Yukon, Canada Danby and Hik (2007) 2 1800 – 2000 E UA

BC 0.7 5

0. Low Peninsula, Québec, Canada Payette and Filion (1985) 11750 – 1980 N— — 1.4* 19*

11. Mackenzie Mountains, Northwest

erritories, Canada

Szeicz and Macdonald (1995) 13 1700 – 1990 N— — 1.4* 3

12. Montseny Mountains, Catalonia, Spain Peñuelas and Boada (2003) 50 1945 – 2001 E UA

TB 1.2* – 3

3. Napaktok Bay, Labrador, Canada Payette (2007) 21750 – 2000 L UA

.1* 5

14. Noatak, Alaska, USA Suarez et al. (1999) 18 1700 – 1990 L UA

BC 0.6 19*

15. Putorana Mountains, Russian Federation Kirdyanov et al. (2012) 10 1500 – 2000 E UA

BC 0.3 10

16. Rahu Saddle, New Zealand Cullen et al. (2001) 71700 – 2000 N— — 0.6* 3

17. Rai-Iz, Urals, Russian Federation Devi et al. (2008) 144 1700 – 2002 E UA

BC 0.3 35*

18. Sahel, Sudan, Guinea zones; Senegal Gonzalez (2001) 135 1945 – 1993 L RW RG 0.4* – 48*

19. Sahel, Burkina Faso, Chad, Mali, Mauritania,

Niger

Gonzalez et al. (2012) 14 1960 – 2000 L RW RG – 0.01* to 0.8* – 31* to 9

20. Scandes, Sweden Kullman and Öberg (2009) 123 1915 – 2007 E UA

BC 0.8* 25*

21. Sierra Nevada, California, USA Millar et al. (2004) 10 1880 – 2002 E UA

TC – 0.1 21*

22. South Island, New Zealand Wardle and Coleman (1992) 22 1980 – 1990 E TS

TB 0.6* 3

23. Yambarran, Northern Territory, Australia Sharp and Bowman (2004) 33 1948 – 2000 N— — – 0.06 35*

Table 4-1 | Biome shifts of the 20th century from published ﬁ eld research that examined trends over periods >30years for biomes in areas where climate (rather than land use

change or other factors) predominantly inﬂ uenced vegetation, derived from a systematic analysis of published studies (Gonzalez et al., 2010). Pre-AR4 publications are included

to provide a comprehensive review. Shift type: elevational (E), latitudinal (L), examined but not detected (N). The biome abbreviations match those in Figure 4-1. Rate of change

in temperature (Temp.) and fractional rate of change in precipitation (Precip.) are derived from linear least squares regression of 1901– 2002 data (Mitchell and Jones, 2005;

Gonzalez et al., 2010). The table provides general regional climate trends at 50 km spatial resolution because the references do not give uniform site-speciﬁ c climate data to

compare across locations. The regional trends are consistent with local trends reported in each reference. *Rate signiﬁ cant at P ≤ 0.05.

281

Terrestrial and Inland Water Systems Chapter 4

(d) Model agreement on climate change-driven biome shift between 1990 and 2100

RCP2.6 land use scenario (IMAGE model)

(a)

Projected primary vegetation cover in 2100

Primary vegetation cover in 2005

Percent of model agreement

ercent of primary vegetation*

over in grid cell

(b)

Comparison of panels (a), (b) and (c)

shows the effect of direct

human-induced vegetation change

through land use, without the effects

of climate change

biome shift is

projected to occur

due to climate

change

previously undisturbed

by human activities

no primary vegetation

Primary

vegetation

100

Figure 4-2 | Projections of climate change-driven biome shifts in the context of direct human land use. (a) Fraction of land covered by primary vegetation in 2005 (Hurtt et al.,

2011); (b) Fraction of land covered by primary vegetation in 2100 under the RCP2.6 land use scenario, with no effect of climate change (Hurtt et al., 2011); (c) Fraction of land

covered by primary vegetation in 2100 under the RCP6.0 land use scenario, with no effect of climate change (Hurtt et al., 2011). (d) Fraction of simulations showing climate

change-driven biome shift for any level of global warming between 1990 and 2100, with no direct anthropogenic land use change, using the MC1 vegetation model under 9

CMIP3 climate projections (3 GCMs, each forced by the SRES A2, A1B, and B1 scenarios; Gonzalez et al., 2010); Comparison of colored areas in (d) with those in (a) shows

where climate-driven biome shifts would occur in current areas of primary vegetation. Comparison of (b) and (c) with (a) illustrates two scenarios of how primary vegetation

could change due to direct human land use, irrespective of the effects of climate change. (b) shows the land use scenario associated with RCP2.6, in which global climate

change is projected to be smaller than that driving the biome shifts in (d) as a result of mitigation measures, some of which involved land use. (c) shows the land use scenario

associated with RCP6.0, in which global climate change is projected to be larger than RCP2.6 so biome shifts similar to those in (d) may occur alongside the projected land use

changes in (c). For example, climate change-driven biome shift is projected in many Arctic land areas (d) which are unaffected by direct human land use at the present day (a)

and in the RCP2.6 and 6.0 land use scenarios (b, c), indicating that climate change is the dominant inﬂuence on Arctic land ecosystems in these scenarios. In contrast, in Borneo,

none of the GCMs analysed by Gonzalez et al. (2010) project climate change-driven biome shift (d), and instead a reduction in primary vegetation cover occurs in the mitigation

scenario RCP2.6 as a consequence of direct human land use (b). A smaller reduction occurs in RCP6.0. Land use is therefore projected to be the dominant driver of change in

Borneo in these scenarios. In the boreal forest regions of North America, Europe, and north-west Asia, climate change-driven biome shift (d) is projected in regions already

subject to some inﬂuence of present-day human land use (a), and increased land use leading to further reductions in primary vegetation occur in both RCP2.6 (b) and RCP6.0

(c). Hence in these boreal forest regions, both climate change and land use are projected to be drivers of ecosystem change in these scenarios. Further details of the RCP land

use/cover scenarios are given in Box 4-1, Figure 4-3, and Table 4-2.

282

Chapter 4 Terrestrial and Inland Water Systems

reindustrial in some continental-scale regions; see WGI AR5 Section

5.5.1) was the same order of magnitude as the warming the Earth has

experienced over the 20th century. Ecological effects were small

compared to periods with larger climate excursions, but even this small

warming was characterized by frequent fires in drier parts of the Amazon

(Mayle and Power, 2008), development of lush vegetation and lakes in

a wetter Sahara (Watrin et al., 2009), temperate deciduous forests in

Europe expanding further north and up to higher elevations (Prentice

et al., 1996), and large-scale migration of Boreal Forest into a warmer

tundra (Jackson and Overpeck, 2000). Past climate change, even more

modest than mid-range projected future change, also clearly impacted

inland water systems (e.g., Smol and Douglas, 2007a; Battarbee et al.,

2009; Beilman et al., 2009). However, there are no exact analogs for

future climate change: none of the well-studied past periods of large

climate change involved simultaneously the rates, magnitude, and

spatial scale of climate and atmospheric carbon dioxide (CO

) change

projected for the 21st century and beyond (Jansen et al., 2007; Schulte

et al., 2010; Wing and Currano, 2013; see WGI AR5 Chapter 5). Direct

analogy with the paleoecological record is also unwarranted because

future climate change will interact with other global changes such as

land use change, invasive species, pollution, and overexploitation of

natural resources (Pereira et al., 2010). There is high confidence that

these interactions will be important: the paleoecological record provides

medium confidence (medium evidence, high agreement) that exploitation

by humans helped drive many large mammal species to extinction during

periods of climate change in the past (Lorenzen et al., 2011).

It has been demonstrated that state-of-the-art vegetation models are able

to simulate much of the biome-level equilibrium response of terrestrial

vegetation to large paleoclimate change (Prentice et al., 1996, 2011;

Salzmann et al., 2008). The same types of models predict large changes in

species ranges, ecosystem function, and carbon storage when forced by

21st century climate change, although the future situation is complicated

by land use and other factors absent in the paleoenvironmental case

(Sitch et al., 2008; Cheaib et al., 2012; see WGI AR5 Section 6.4). Thus,

the paleoecological record and models that have been tested against it

provide a coherent message that biomes will alter their functioning and

composition in response to changing and often novel future climates:

they will move as species mixtures change (Section 4.3.2.5 has more

specific information on projected migration rates), novel plant communities

will emerge, and significant carbon stock changes will take place

(Williams and Jackson, 2007; MacDonald, 2010; Prentice et al., 2011;

illis and MacDonald, 2011). The paleoecological record and models

provide high confidence that it will be difficult or impossible to maintain

many ecological systems in their current states if global warming exceeds

2°C to 3°C, raising questions about the long-term viability of some

current protected areas and conservation schemes, particularly where

the objective is to maintain present-day species mixtures (Jackson and

Hobbs, 2009; Hickler et al., 2012).

Much of the complex, time-dependent change at regional scales has

not yet been simulated by models. The paleoecological record indicates

that vegetation in many parts of the world has the potential to respond

within years to a few decades to climate change (e.g., Mueller, A.D. et al.,

2009; Watrin et al., 2009; Williams et al., 2009; Harrison and Goni, 2010).

This record provides a critical opportunity for model evaluation that

should be more thoroughly exploited to gain confidence in time-

dependent simulations of future change, particularly given the complex

role that interacting climate change and vegetation disturbance has

played in the past (e.g., Jackson et al., 2009; Marlon et al., 2009;

Williams et al., 2009; Daniau et al., 2010; Dawson et al., 2011). The

paleoecological record also highlights the importance of including the

direct effects of changing atmospheric CO

levels in efforts to simulate

future ecosystem functioning and plant species competition (Prentice

et al., 2011; Woillez et al., 2011; Bond and Midgley, 2012; Claussen et

al., 2013).

The paleoeclimatic record also reveals that past radiative climate forcing

change was slower than that anticipated for the 21st century (see WGI

AR5 Chapters 5, 8, and 12), but even these slower changes often drove

surprisingly abrupt, nonlinear, regional-scale change in terrestrial and

inland water systems (e.g., Harrison and Goni, 2010; Williams et al.,

2011), as did even slower climate change during the most recent

Holocene interglacial (e.g., Booth et al., 2005; Kropelin et al., 2008;

Williams, J.W. et al., 2010; Williams et al., 2011). In all cases, specific

periods of abrupt ecological response were regionally distinct in nature

and were less synchronous for small, slow changes in forcing (e.g.,

during the Holocene) than for the global-scale rapid changes listed at

the start of this section. State-of-the-art climate and Earth System

Models (ESMs) are unable to simulate the full range of abrupt change

observed in many of these periods (e.g., Valdes, 2011). Thus there is

high confidence that these models may not capture some aspects of

future abrupt climate change and associated ecosystem impacts (Leadley

et al., 2010).

Frequently Asked Questions

FAQ 4.1 | How do land use and land cover changes cause changes in climate?

Land use change affects the local as well as the global climate. Different forms of land cover and land use can cause

warming or cooling and changes in rainfall, depending on where they occur in the world, what the preceding land

cover was, and how the land is now managed. Vegetation cover, species composition, and land management practices

(such as harvesting, burning, fertilizing, grazing, or cultivation) inﬂuence the emission or absorption of greenhouse

gases. The brightness of the land cover affects the fraction of solar radiation that is reﬂected back into the sky, instead

of being absorbed, thus warming the air immediately above the surface. Vegetation and land use patterns also inﬂuence

water use and evapotranspiration, which alter local climate conditions. Effective land use strategies can also help to

mitigate climate change.

283

Terrestrial and Inland Water Systems Chapter 4

4.2.4. Multiple Stressors Interacting with Climate Change

The climatic and non-climatic drivers of ecosystem change need to be

distinguished if the joint and separate attribution of changes to their

causes is to be performed (see Chapter 18). In this section we elaborate

on factors affecting ecosystems, operating simultaneously with climate

change. These factors share underlining drivers with one another and

with climate change to varying degrees; together they form a syndrome

known as “global change.” The individual effects of climate change,

habitat loss and fragmentation, chemical pollution, overharvesting, and

invasive alien species are increasingly well documented (Millennium

Ecosystem Assessment, 2005c; Settele et al., 2010a) but much less is

known about their combined consequences. Ecosystem changes may

occur in cascades, where a change in one factor precipitates increased

vulnerability with respect to other factors (Wookey et al., 2009) or

propagates through the ecosystem as a result of species interactions

(Gilman et al., 2010). Multiple stressors can act in a non-additive way

(Shaw et al., 2002; Settele et al., 2010b; Larsen et al., 2011), potentially

invalidating findings and interventions based on single-factor analysis.

For instance, Larsen et al. (2011) demonstrated that non-additive

interactions among the climate factors in a multifactor experiment were

frequent and most often antagonistic, leading to smaller effects than

predicted from the sum of single factor effects. Leuzinger et al. (2011)

and Dieleman et al. (2012) have synthesized multifactor experiments

and demonstrated that, in general, the effect size is reduced when more

factors are involved, but Leuzinger et al. (2011) suggest that multifactor

models tend to show the opposite tendency.

4.2.4.1. Land Use and Cover Change

Land use and cover change (LUCC) is both a cause (WGI AR5 Section

6.1.2) and a consequence of climate change. It is the major driver of

current ecosystem and biodiversity change (Millennium Ecosystem

Assessment, 2005b) and a key cause of changes in freshwater systems

(Section 4.3.3.3). In tropical and subtropical areas of Asia, Africa,

Oceania, and South America, the dominant contemporary changes are

conversion of forests and woodlands to annual and perennial agriculture,

grazing pastures, industrial logging, and commercial plantations,

followed by conversion of savannas, grasslands, and pastures to annual

agriculture (Hosonuma et al., 2012; Macedo et al., 2012). In Europe

there is net conversion of agricultural lands to forest (Rounsevell and

Reay, 2009; Miyake et al., 2012). Conversion of peatlands to agriculture

has been an important source of carbon to the atmosphere in Southeast

Asia (Limpens et al., 2008; Hooijer et al., 2010; see Section 4.3.3.3).

Contemporary drivers of LUCC include rising demand for food, fiber, and

bioenergy and changes in lifestyle and technologies (Hosonuma et al.,

2012; Macedo et al., 2012). By mid-century climate change is projected

to become a major driver of land cover change (Leadley et al., 2010).

Non-climate environmental changes such as nitrogen deposition, air

pollution, and altered disturbance regimes are also implicated in LUCC.

Some of the underlying drivers of LUCC are also direct or indirect drivers

of climate change (Cui and Graf, 2009; McAlpine et al., 2009; Mishra et

al., 2010; Schwaiger and Bird, 2010; van der Molen et al., 2011; Groisman

et al., 2012); this cause-and-effect entanglement of climate change and

LUCC can confound the detection of climate change and make attribution

o one or the other difficult. Local-to-regional climate change was at

least partly attributed to LUCC in 11 of 26 studies reviewed for this

chapter, generally with limited evidence and low confidence. (Direct

climate effects attributed to LUCC: Cui and Graf, 2009; Li et al., 2009;

McAlpine et al., 2009; Zhang et al., 2009; Fall et al., 2010; Jin et al.,

2010; Mishra et al., 2010; Schwaiger and Bird, 2010; Wu et al., 2010;

Carmo et al., 2012; Groisman et al., 2012. No climate effects studied:

Suarez et al., 1999; Saurral et al., 2008; Tseng and Chen, 2008; Wang et

al., 2008; Cochrane and Barber, 2009; Jia, B. et al., 2009; Rounsevell and

Reay, 2009; Graiprab et al., 2010; Martin et al., 2010; Wiley et al., 2010;

Clavero et al., 2011; Dai et al., 2011; Gao and Liu, 2011; Viglizzo et al.,

2011; Yoshikawa and Sanga-Ngoie, 2011).

LUCC (and land use itself) contributes to changes in the climate through

altering the GHG concentrations in the atmosphere, surface and cloud

albedos, surface energy balance, wind profiles, and evapotranspiration,

among other mechanisms. The phrase “biophysical effects” is shorthand

for the effect vegetation has on the climate other than through its role

as a source or sink of GHGs. These effects are now well documented,

significant, and are increasingly included in models of global and regional

climate change. The GHG and biophysical effects of vegetation can be

opposite in sign (de Noblet-Ducoudre et al., 2012) and operate at

different scales. For instance, conversion of forest to non-forest generally

releases CO

from biomass and soils to the atmosphere (causing warming

globally), but may result in an increase in seasonally averaged albedo

(local and global cooling, Davin et al., 2007) and a decrease in

transpiration (local, but not global warming). Findell et al. (2007)

concluded on the basis of model studies that the non-GHG climate

impacts of LUCC were generally minor, but nevertheless significant in

some regions. Brovkin et al. (2013), projecting the overall effect of LUCC

on climate change for the 21st century, found LUCC to be a small driver

globally, but locally important. Most global climate models suggest

local average cooling effects following forest conversion to croplands

and pastures (Pitman et al., 2009; Longobardi et al., 2012). Satellite

observations suggest that the effect of conversion of the Brazilian savannas

(cerrado) to pasture was to induce a local warming that was partly

reversed when the pasture was subsequently converted to sugarcane

(Loarie et al., 2011). Several modeling studies suggest that the global

surface air temperature response to deforestation depends on the latitude

at which deforestation occurs. High-latitude deforestation results in

global cooling, low-latitude deforestation causes global warming, and

the mid-latitude response is mixed (Bathiany et al., 2010; Davin and de

Noblet-Ducoudre, 2010; van der Molen et al., 2011; Longobardi et al.,

2012), with some exceptions documented for boreal forests (Spracklen

et al., 2008). Boreal and tropical forests influence the climate for different

reasons: boreal forests have low albedo (i.e., reflect less solar radiation,

especially in relation to a snowy background; Levis, 2010; Mishra et al.,

2010; Longobardi et al., 2012) and tropical forests pump more water

and aerosols into the atmosphere than non-forest systems in similar

climates (Davin and de Noblet-Ducoudre, 2010; Delire et al., 2011; Pielke

et al., 2011). The implications of these findings for afforestation as a

climate mitigation action are discussed in Section 4.3.4.5. Forests may

also influence regional precipitation through biophysical effects (Butt

et al., 2011; Pielke et al., 2011; see Section 4.3.3).

In summary, changes in land cover have biophysical effects on the

climate, sometimes opposite in direction to GHG-mediated effects,

284

Chapter 4 Terrestrial and Inland Water Systems

Box 4-1 | Future Land Use Changes

Assessment of climate change effects on terrestrial and inland freshwater ecosystems requires the simultaneous consideration of

land use and cover change (LUCC). The world is undergoing important shifts in land use, driven by accelerating demand for food,

feed, ﬁber, and fuel. The main underlying driver is the rate at which per capita consumption is growing, particularly in emerging

economies (Tilman et al., 2011). Policy shifts in developed countries favoring biofuel production have also contributed (Searchinger et

al., 2008; Lapola et al., 2010; Miyake et al., 2012). Agricultural commodity prices have risen and may stay high through 2020 (OECD

and FAO, 2010), owing to (1) demand growth outpacing supply growth, exacerbated by climate-related crop failure (Lobell et al.,

2011); (2) decline in the rate of improvement in agricultural productivity (Ray et al., 2012); (3) shortage of arable land not already

under cultivation, especially in the temperate zone; (4) growing pressure on as-yet uncultivated ecosystems on soils that are potentially

suitable for cultivation and that are concentrated in tropical latitudes, especially South America and Africa (Lambin and Meyfroidt,

2011); and (5) declining area under cultivation in temperate zones, mainly in developed countries. The shortage of arable land in

temperate systems could put pressure on marginal or sensitive landscapes, mainly in Latin America’s cerrados and grasslands (Brazil,

Argentina) and in African savannas (Sudan, Democratic Republic of Congo, Mozambique, Tanzania, Madagascar) (Lambin and

Meyfroidt, 2011).

Deforestation in developing countries correlates with the export of agricultural commodities (DeFries et al., 2010). Future LUCC

remains uncertain, as it depends on economic trends and policies themselves dependent on complex political and social processes,

including climate policy. By 2012, the deforestation rate in the Brazilian Amazon had declined by 77% below its 1996–2005 average

(Nepstad et al., 2009; INPE, 2013) as a result of policy and market signals (Soares-Filho et al., 2010). This single trend represents a

1.5% reduction in global anthropogenic carbon emissions (Nepstad et al., 2013).

RCP Model and references Key assumptions /drivers Land use /cover outcomes

8.5 MESSAGE; Riahi et al. (2007) • No climate change mitigation actions; radiative forcing still

rising at 2100.

• Strong increase in agricultural resource use driven by the

increasing population (rises to 12 billion people by 2100).

• Yield improvements and intensiﬁ cation assumed to account for

most of production increases.

• Increase in cultivated land by about 305 million ha from 2000

to 2100.

• Forest cover declines by 450 million ha from 2000 to 2100.

• Arable land use in developed countries slightly decreased — all

of the net increases occur in developing countries.

6.0 AIM; Fujino et al. (2006),

Hijioka et al. (2008)

• Mitigation actions taken late in the century to stabilize radiative

forcing at 6 W m

−2

after 2100.

• Population growth and economic growth.

• Increasing food demand drives cropland expansion .

• Urban land use increases.

• Cropland area expands.

• Grassland area declines.

• Total forested area extent remains constant.

4.5 GCAM; Smith and Wigley

(2006), Wise et al. (2009)

• Mitigation stabilizes radiative forcing at 4.5 W m

−2

before 2100.

• Assumes that global greenhouse gas emissions prices are

invoked to limit emissions and therefore radiative forcing.

Emissions pricing assumes all carbon emissions are charged an

equal penalty price, so reductions in land use change carbon

emissions available as mitigation.

• Food demand is met through crop yield improvements, dietary

shifts, production efﬁ ciency, and international trade.

• Preservation of large stocks of terrestrial carbon in forests.

• Overall expansion in forested area.

• Agricultural land declines slightly due to afforestation.

2.6 IMAGE; van Vuuren et al.

(2006), van Vuuren et al. (2007)

• Overall trends in land use and land cover are determined mainly

by demand, trade, and production of agricultural products and

bioenergy.

• Expansion of croplands largely due to bioenergy production.

• Production of animal products is met through shift from

extensive to more intensive animal husbandry.

• Much agriculture relocates from high-income to low-income

regions.

• Increase in bioenergy production, new area for bioenergy crops

near current agricultural areas.

• Pasture largely constant.

Table 4-2 | Summary of drivers and outcomes of Land Use and Land Cover Change (LUCC) scenarios associated with Representative Concentration Pathways (RCPs;

Hurtt et al., 2011). RCPs are identiﬁ ed with the radiative forcing by 2100 (8.5, 6.0, 4.5, and 2.6 W m

–2

) and by the name of the model used to generate the associated

land use /cover scenarios (MESSAGE (Model for Energy Supply Strategy Alternatives and their General Environmental Impact), AIM (Asia-Paciﬁ c Integrated Model),

GCAM (Global Change Assessment Model), and IMAGE (Integrated Model to Assess the Global Environment); see Hurtt et al. (2011) for further details).

Continued next page

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Terrestrial and Inland Water Systems Chapter 4

which can materially alter the net outcome of the land cover change

on the global climate (high confidence).

4.2.4.2. Nitrogen Deposition

The global nitrogen cycle has been strongly perturbed by human activity

over the past century (Gruber and Galloway, 2008; Canfield et al., 2010).

Activities such as fertilizer production and fossil fuel burning currently

transform 210 TgN yr

–1

of nitrogen gas in the atmosphere into reactive

forms of nitrogen (N

) that can be readily used by plants and

microorganisms in land and in the ocean, slightly more than the non-

anthropogenic transformation of 203 TgN yr

–1

(Fowler et al., 2013). Most

of the transformations of anthropogenic N

are on land (Fowler et al.,

2013). The human-caused flow from land to oceans in rivers is 40 to

70 TgN yr

–1

, additional to the estimated natural flux of 30 TgN yr

–1

(Galloway et al., 2008; Fowler et al., 2013). Many of the sources of

additional nitrogen share root causes with changes in the carbon cycle,

such as increased use of fossil fuels and expansion and intensification

of global agriculture. Nitrogen deposition, CO

concentrations, and

temperatures are therefore increasing together at global scales (Steffen

et al., 2011). Regional trends in nitrogen fluxes differ substantially:

nitrogen fertilizer use and nitrogen deposition are stable or declining

in some regions, such as Western Europe; but nitrogen deposition and

its impacts on biodiversity and ecosystem functioning are projected to

increase substantially over the next several decades in other regions,

especially in the tropics (Galloway et al., 2008) owing to increased

needs for food and energy for growing populations in emerging

economies (e.g., Zhu et al., 2005).

Experiments and observations, most of which are in temperate and boreal

Europe and North America, show a consistent pattern of increase in the

Box 4-1 (continued)

Each of the four main Representative Concentration Pathways (RCPs) used for future climate projections has a spatially explicit future

land use scenario consistent with both the emissions scenario and the underlying associated socioeconomic scenario simulated by

integrated assessment models, as well as conditions in 2005 (Hurtt et al., 2011; see also Table 4-2, Figure 4-2, Figure 4-3). In scenarios

where cropland and pasture are projected to decrease, they are replaced with secondary vegetation. Tropical and boreal forest regions

are both projected to undergo declining primary forest cover in most RCPs, but in RCP6.0 total forest area remains approximately

constant and in RCP4.5 total forest area expands because of increased secondary forest. The extent to which primary vegetation is

replaced by secondary vegetation, crops, or pasture varies between the RCPs (Figure 4-3), with no simple linear relationship between

the extent of vegetation change and the level of total radiative forcing. Larger reductions in primary vegetation cover are projected in

RCP8.5, owing to a general absence of proactive measures to control land cover change in that scenario. Large reductions are also

projected in RCP2.6 owing to widespread conversion of land to biofuel crops (Figure 4-2). Smaller reductions are foreseen in RCP6.0

and RCP4.5, with the latter involving conservation of primary forest and afforestation as mitigation measures.

RCP8.5

2000 2100 2000 2100 2000 2100

RCP6.0 RCP4.5 RCP2.6Historical

Year

1500 1600 1700 1800 1900 2000 2100

Fraction of global land area

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Urban

Cropland

Pasture

Secondary non-forest

Secondary forest

Primary non-forest

Primary forest

Figure 4-3 | Proportion of global land cover occupied by primary and secondary vegetation (forest and non-forest), cropland, pasture, and urban land, from satellite

data and historical reconstructions up to 2005 (Klein Goldewijk et al., 2010, 2011), and from scenarios associated with the RCPs from 2005 to 2100 (Hurtt et al., 2011).

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Chapter 4 Terrestrial and Inland Water Systems

ominance of a few nitrogen-loving plant species and loss of overall

plant species richness at nitrogen deposition loads exceeding between 5

and 20 kgN ha

–

(Power et al., 2006; Clark and Tilman, 2008; Bobbink

et al., 2010; but see Stevens, C.J. et al., 2010). Nitrogen deposition is

currently above these limits in much of Europe, eastern North America,

and southern Asia (Galloway et al., 2008), including in many protected

areas (Bleeker et al., 2011).

The impacts of nitrogen deposition are often first manifested in freshwater

ecosystems because they collect and concentrate the excess nitrogen

(and phosphorus) from the land, as well as from sewage and industrial

effluents. Primary production in freshwater ecosystems can be either

nitrogen and phosphorus limited or both (Elser et al., 2007), but the

biodiversity and capacity of freshwater ecosystems to deliver high-

quality water, recreational amenity, and fisheries services is severely

reduced by the addition of nutrients beyond their capacity to process

them. Excessive loading of nitrogen and phosphorus is widespread in

the lakes of the Northern Hemisphere (NH; Bergström and Jansson,

2006), although reduced nitrogen loading including deposition was

observed between 1988 and 2003 in Sweden (Weyhenmeyer et al.,

2007). The observed symptoms include a shift from nitrogen limitation

of phytoplankton in lakes to phosphorus limitation (Elser et al., 2009).

Since the AR4, an increasing number of studies have models, observations,

and experiments to understand and predict the interactive effects of

nitrogen deposition, climate change, and CO

on ecosystem function.

Interactions between nitrogen and other global change factors are

widespread, strong, and complex (Rustad, 2008; Thompson et al., 2008;

Langley and Megonigal, 2010; Gaudnik et al., 2011; Eisenhauer et al.,

2012; Hoover et al., 2012; but see Zavaleta et al., 2003, for evidence of

additive effects). In a study of plant-pollinator relationships, the

combination of nitrogen deposition, CO

enrichment, and warming

resulted in larger negative impacts on pollinator populations than could

be predicted from the individual effects (Hoover et al., 2012). In a

perennial grassland species, nitrogen limitation constrained the response

to rising CO

(Reich et al., 2006). Broadly, the overall body of research

shows that ecosystem function is mediated by complex interactions

between these factors, such that many ecosystem responses remain

difficult to understand and predict (Churkina et al., 2010; Norby and

Zak, 2011).

In forests in many parts of the world, experiments, observations, and

models suggest that the observed increase in productivity and carbon

storage is due to combinations of nitrogen deposition, climate change,

fertilization effects of rising CO

, and forest management (Huang et al.,

2007; Magnani et al., 2007; Pan et al., 2009; Churkina et al., 2010;

Bellassen et al., 2011; Bontemps et al., 2011; de Vries and Posch, 2011;

Eastaugh et al., 2011; Norby and Zak, 2011; Shanin et al., 2011; Lu et

al., 2012). N deposition and rising CO

appear to have generally

dominated in much of the NH. However, the direct effects of rising

temperature and changes in precipitation may exceed nitrogen and CO

as key drivers of ecosystem primary productivity in a few decades time.

In grasslands, however, experiments show that plant productivity is

increased more by nitrogen addition (within the projected range for

this century) than by elevated CO

, also within its projected range, and

that nitrogen effects increase with increasing precipitation (Lee et al.,

2010).

n contrast to forests and temperate grasslands, nitrogen deposition and

warming can have negative effects on productivity in other terrestrial

ecosystems, such as moss-dominated ecosystems (Limpens et al., 2011).

The interactions between nitrogen deposition and climate change remain

difficult to understand and predict (Menge and Field, 2007; Ma et al.,

2011), in part owing to shifts in plant species composition (Langley and

Megonigal, 2010) and the complex dynamics of coupled carbon, nitrogen,

and phosphorus cycles (Menge and Field, 2007; Niboyet et al., 2011).

Analyses using the multi-factor biodiversity change model GLOBIO3

suggest that nitrogen deposition will continue to be a significant

contributing factor to terrestrial biodiversity loss in the first third of the

21st century but will be a less important factor than climate change in

this period, and a much smaller driver than habitat loss due to expansion

of agricultural lands (Alkemade et al., 2009). Models that explicitly take

into account interactive effects of climate change and nitrogen deposition

on plant communities project that nitrogen deposition impacts will

continue to be important, but climate change effects will begin to

dominate other factors by the middle of the 21st century (Belyazid et

al., 2011).

4.2.4.3. Tropospheric Ozone

The concentration of ozone in the troposphere (the part of the atmosphere

adjacent to the Earth’s surface) has risen over the past 150 years from

a global average of 20 to 30 ppb to 30 to 50 ppb, with high spatial and

temporal variability (Horowitz, 2006; Oltmans et al., 2006; Cooper et al.,

2010; WGI AR5 Figure 2.7). This is due to (1) increasing anthropogenic

emissions of gases that react in the atmosphere to form ozone (Denman

et al., 2007) and (2) the increased mixing of stratospheric ozone into

the troposphere as a result of climate change (Hegglin and Shepherd,

2009). The key ozone precursor gases are volatile organic compounds

(VOCs) and oxides of nitrogen (NO

). Intercontinental transport of these

precursors contributes to rising global background ozone concentrations,

including in regions where local ozone precursor emissions are decreasing

(Dentener et al., 2010). Global sources of VOC are predominantly

biogenic (BVOC), especially forests (Hoyle et al., 2011).

Negative effects of the current levels of ozone have been widely

documented (Mills et al., 2011). A meta-analysis of more than 300

articles addressing the effect of ozone on tree growth (Wittig et al.,

2009)—focused largely on NH temperate and boreal species—

concluded that current levels of tropospheric ozone suppress growth

by 7% relative to preindustrial levels. Modeling studies that extrapolate

experimentally measured dose-response relationships suggest a 14 to

23% contemporary reduction in Gross Primary Productivity (GPP)

worldwide, with higher values in some regions (Sitch et al., 2007) and

1 to 16% reduction of Net Primary Productivity (NPP) in temperate

forests (Ainsworth et al., 2012).

The mechanisms by which ozone (O

) affects plant growth are now better

known (Hayes et al., 2007; Ainsworth et al., 2012). Chronic exposure to

at levels above about 40 ppb generally reduces stomatal conductance

and impairs the activity of photosynthetic enzymes (The Royal Society,

2008), although in some cases ozone exposure increases stomatal

conductance (Wilkinson and Davies, 2010). For the species studied,

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Terrestrial and Inland Water Systems Chapter 4

arbon assimilation rates and leaf area are generally reduced, while

respiration increases and leaf senescence is accelerated—all leading to

a reduction in NPP. Conifers are less sensitive than broad-leafed species.

In a modeling study, lower stomatal conductance due to O

exposure

increased river runoff by reducing the loss of soil moisture through

transpiration, but observational studies that measured runoff in relation

to ozone exposure show divergent trends on this issue (McLaughlin et

al., 2007; Wittig et al., 2007; Mills et al., 2009; Huntingford et al., 2011).

A modeling study (Sitch et al., 2007) suggests that the negative effects

of rising O

on plant productivity could offset 17 to 31% of the projected

increase in global carbon storage due to increasing CO

concentrations

over the 21st century, but the possible interactive effects between CO

and O

are poorly understood (The Royal Society, 2008). Reduced

stomatal conductance, widely observed under elevated CO

, should help

protect plants from ozone damage. Some chamber experiments

(Bernacchi et al., 2006) and model studies (Klingberg et al., 2011)

suggest this to be the case. The one plot-scale study of CO

and O

interactions in a temperate forest (Karnosky et al., 2005; Hofmockel et

al., 2011) suggests that the effects of O

and CO

are not independent

and may partly compensate for one another.

There is genotypic variation in plant sensitivity to O

(Ainsworth et al.,

2012). Other than changing cultivars or species, few management actions

promoting adaptation to higher levels of O

are currently available

(Wilkinson and Davies, 2010; Teixiera et al., 2011). Research into

developing ozone resistant varieties and chemical protectants against

damage may provide management options in the future (Wilkinson and

Davies, 2010; Ainsworth et al., 2012).

4.2.4.4. Rising Carbon Dioxide

Rising atmospheric CO

concentrations affect ecosystems directly and

through biological and chemical processes. The consequences for the

global carbon cycle are discussed in WGI AR5 Box 6.3; the discussion

here focusses on impacts on terrestrial and inland water systems. Paleo

records over the Late Quaternary (past Myr) show that changes in the

atmospheric CO

content between 180 and 280 ppmv had ecosystem-

scale effects worldwide (Prentice and Harrison, 2009).

In contrast to the oceans, changes in CO

concentrations in inland

waters are influenced primarily by biological processes, such as inputs

f terrestrial organic matter, particularly dissolved organic carbon (DOC),

and bacterial respiration (van de Waal et al., 2010; Aufdenkampe et al.,

2011). Carbon can, however, become limiting during intense algal

blooms, especially in the surface waters of stratified lakes and reservoirs,

and rising atmospheric CO

concentrations may stimulate higher algal

production under these conditions (van de Waal et al., 2010). Higher

concentrations can lead to increases in the C:N and C:P ratios of

phytoplankton, though the trophic consequences of this are difficult to

predict because zooplankton may alter their feeding behavior to select

higher quality forms of algae or increase feeding rate (Urabe et al., 2003;

van de Waal et al., 2010).

Over the past 2 decades, and especially since AR4, experimental

investigation of elevated CO

effects on plants and ecosystems has used

mainly Free Air CO

Enrichment (FACE) techniques (Leakey et al., 2009).

FACE is considered more realistic than earlier approaches using enclosed

chambers, because plant community and atmospheric interactions and

below-ground conditions are more like those of natural systems. Plants

with a C

photosynthetic system, which includes most species but

excludes warm-region grasses, show an increase in photosynthesis

under elevated CO

, the precise magnitude of which varies between

species. Acclimation (“down-regulation”) occurs under long-term

exposure, leading to cessation of effects in some (Norby and Zak, 2011)

but not all studies (Leakey et al., 2009). The C

photosynthetic system

found in most tropical grasses and some important crops is not directly

affected by elevated CO

, but C

plant productivity generally increases

under elevated CO

because of increased water use efficiency (WUE).

Transpiration is decreased under elevated CO

in many species, due to

reduced opening of stomatal apertures, leading to greater WUE (Leakey

et al., 2009; Leuzinger and Körner, 2010; De Kauwe et al., 2013).

Increasing WUE is corroborated by studies of stable carbon isotopes

(Barbosa et al., 2010; Koehler et al., 2010; Silva et al., 2010; Maseyk et

al., 2011). The WUE increase does not acclimate to higher CO

in the

medium term, that is, over several years (Leakey et al., 2009). Satellite

observations from 1982–2010 show an 11% increase in green foliage

cover in warm, arid environments (where WUE is most important) after

correcting for the effects of precipitation variability (Donohue et al.,

2013); gas exchange theory predicts 5 to 10% greening resulting from

rising CO

over this period.

The interactive effects of elevated CO

and other global changes (such as

climate change, nitrogen deposition, and biodiversity loss) on ecosystem

function are extremely complex. Generally, nitrogen use efficiency is

Frequently Asked Questions

FAQ 4.2 | What are the non–greenhouse gas effects of rising carbon dioxide on ecosystems?

Carbon dioxide (CO

) is an essential building block of the process of photosynthesis. Simply put, plants use sunlight

and water to convert CO

into energy. Higher CO

concentrations enhance photosynthesis and growth (up to a point),

and reduce the water used by the plant. This means that water remains longer in the soil or recharges rivers and

aquifers. These effects are mostly beneﬁcial; however, high CO

also has negative effects, in addition to causing global

warming. High CO

levels cause the nitrogen content of forest vegetation to decline and can increase their chemical

defenses, reducing their quality as a source of food for plant-eating animals. Furthermore, rising CO

causes ocean

waters to become acidic (see FAQ 6.3), and can stimulate more intense algal blooms in lakes and reservoirs.

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Chapter 4 Terrestrial and Inland Water Systems

ncreased under higher CO

(

Leakey et al., 2009) although, in some tree

FACE experiments, productivity increases as a result of enhanced CO

sustained by increased nitrogen uptake rather than increased nitrogen

use efficiency (Finzi et al., 2007). In one 10-year temperate grassland

experiment in Minnesota, elevated CO

halved the loss of species richness

expected from nitrogen addition (Reich, 2009), whereas no such benefit

was reported for an alpine grassland in France (Bloor et al., 2010) or a

Danish heathland ecosystem (Kongstad et al., 2012).

Elevated CO

can affect plant response to other stresses, such as high

temperature (Lloyd and Farquhar, 2008) and drought. Ozone exposure

decreases with lower stomatal conductance (Sitch et al., 2007). In

savannas, faster growth rates under higher CO

can allow woody plants

to grow tall enough between successive fires to escape the flames

(Bond and Midgley, 2001; Scheiter and Higgins, 2009). Differential

species responses to elevated CO

appear to be altering competition

(Dawes et al., 2011), for example, increasing the likelihood of faster-

growing species such as lianas out-competing slower-growing species

such as trees (Mohan et al., 2006; Potvin et al., 2007; Lewis et al., 2009a).

Experimental studies have shown that elevated CO

leads to increased

leaf C:N ratios in woody plants, forbs, and C

grasses (but not C

grasses),

which may decrease their quality as food and increase herbivorous

insect feeding rates and changes to their density and community structure

(Sardans et al., 2012). Plants may also become more toxic to herbivores

under elevated CO

levels, through increased concentrations of carbon-

and nitrogen-based defenses (Lindroth, 2010; Cavagnaro et al., 2011).

Our understanding of ecosystem responses to elevated CO

is incomplete

in some respects. The majority of FACE experiments apply upper

concentrations of approximately 550 ppmv, which is below the

concentrations projected by 2100 under higher emissions scenarios. The

physiology of photosynthesis suggests that direct CO

effects saturate

at levels of approximately 700 ppmv (Long et al., 2004). Most elevated

experiments impose a sudden increase of CO

concentration as

opposed to the gradual rise experienced in reality. Most large-scale

FACE experiments have been conducted in temperate locations (e.g.,

Hickler et al., 2008); there are currently no large-scale tropical or boreal

FACE experiments. The magnitude of CO

effects decreases as the spatial

scale of study increases (Leuzinger et al., 2011).The scale of controlled

experiments is limited to approximately 100 m

. Extrapolation to larger

scales ignores large-scale atmospheric feedbacks (Körner et al., 2007)

and catchment-scale hydrological effects (see Box CC-VW). Overall,

there is medium confidence (much evidence, medium agreement) that

increases in CO

up to about 600 ppm will continue to enhance

photosynthesis and plant water use efficiency, but at a diminishing rate.

effects are a first-order influence on model projections of ecosystem

and hydrological responses to anthropogenic climate change (Sitch et

al., 2008; Lapola et al., 2009; Friend et al., 2013).The direct effect of CO

on plant physiology, independent of its role as a GHG, means that

assessing climate change impacts on ecosystems and hydrology solely

in terms of global mean temperature rise (or equivalently, expressing

GHG effects solely in terms of radiative forcing) is an oversimplification

(Huntingford et al., 2011; Betts et al., 2012). A 2°C rise in global mean

temperature, for example, may have a different net impact on ecosystems

depending on the change in CO

concentration accompanying the rise

(

e.g., Good et al., 2011a). A high climate sensitivity and/or a higher

proportion of non-CO

GHGs would imply a relatively low CO

rise at

2°C global warming, so the offsetting effects of CO

fertilization and

increased water use efficiency would be smaller than for low climate

sensitivity and/or a lower proportion of non-CO

GHGs.

.2.4.5. Diffuse and Direct Radiation

The quantity and size distribution of aerosols in the atmosphere alters

both the amount of solar radiation reaching the Earth’s surface and the

proportions of direct versus diffuse radiation. In some regions, direct

radiation has been reduced by up to 30 W m

–2

over the industrial era,

with an accompanying increase in diffuse radiation of up to 20 W m

–2

(Kvalevåg and Myhre, 2007). The global mean direct and diffuse radiation

changes due to aerosols are −3.3 and +0.9 W m

−2

, respectively

(Kvalevåg and Myhre, 2007). For a constant total radiation, an increased

fraction received as diffuse radiation theoretically increases net

photosynthesis because a smaller fraction of the vegetation canopy is

light-saturated, making photosynthesis more light efficient at the

canopy scale (Knohl and Baldocchi, 2008; Kanniah et al., 2012). In a

global model that included this effect, an increase in diffuse fraction of

solar radiation due to volcanic and anthropogenic aerosols and cloud

cover was simulated to lead to approximately a 25% increase in the

strength of the global land carbon sink between 1960 and 1999; however,

under a scenario of climate change and decreased anthropogenic

aerosol concentration, this enhancement declined to near zero by the

end of the 21st century (Mercado et al., 2009), All RCPs project

decreased aerosol concentrations due to air quality protection measures,

as already seen in some countries. The influence of the form of radiation

on plant growth and the land carbon budget is a potentially important

unintended consequence of solar radiation management schemes that

involve the injection of aerosols into the stratosphere to reduce radiant

forcing (see WGI AR5 Section 7.7), but this topic is at present insufficiently

researched for adequate assessment.

4.2.4.6. Invasive and Alien Species

Since the IPCC AR4, the number of observations of the spread and

establishment of alien species attributed to climate change has increased

for several taxa (e.g., Walther et al., 2009) and for particular areas,

including mountain tops and polar regions (McDougall et al., 2011;

Chown et al., 2012). Species invasions have increased over the last

several decades (very high confidence), and the aggressive expansion

of plant and animal species beyond their historical range is having

increasingly negative impacts on ecosystem services and biodiversity

(high confidence; Brook, 2008; Burton et al., 2010; McGeoch et al., 2010;

Simberloff et al., 2013). Climate change will exacerbate some invasion

impacts and ameliorate others (Peterson et al., 2008; Bradley et al., 2009;

Britton et al., 2010; Bellard et al., 2013). Although there is increasing

evidence that some species invasions have been assisted by climate

change, there is low confidence that species invasions have in general

been assisted by recent climatic trends because of the overwhelming

importance of human-facilitated dispersal in mediating invasions. The

spread of alien species has several causes, including habitats made

favorable by climate change (Walther et al., 2009), deliberate species

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Terrestrial and Inland Water Systems Chapter 4

transfer, and accidental transfer due to increased global movement of

goods.

In most cases climate change increases the likelihood of the establishment,

growth, spread, and survival of invasive species populations (Dukes et

al., 2009; Walther et al., 2009; Bradley et al., 2010; Huang et al., 2011;

Chown et al., 2012). Some degree of climate/habitat match has been

found to be a prerequisite of establishment success across seven major

plant and animal groups (Hayes and Barry, 2008). A range of alien

species responses and local consequences are expected (e.g., Rahel and

Olden, 2008; Frelich et al., 2012; Haider et al., 2012; West et al., 2012).

Invasive species, compared to native species, may have traits that favor

their survival, reproduction, and adaptation under changing climates;

invasive plants in particular tend to have faster growth rates and are

particularly favored when resources are not limited (medium to high

confidence; van Kleunen et al., 2010; Willis, C.G. et al., 2010; Buswell et

al., 2011; Davidson et al., 2011; Zerebecki and Sorte, 2011; Haider et al.,

2012; Matzek, 2012). Some invasive plants are more drought tolerant

(Crous et al., 2012; Matzek, 2012; Perry et al., 2012), and on average

they have higher overall metabolic rates, foliar nitrogen concentrations,

and photosynthetic rates than their native counterparts (Leishman et

al., 2007).

Extreme climate events provide opportunities for invasion by generating

disturbances and redistributing available resources (Diez et al., 2012) and

changing connectivity between different ecosystems. Current warming

has already enabled many invasive alien species, including plant,

vertebrate, invertebrate, and single-cell taxa, to extend their distributions

into new areas (high confidence for plants and insects; Walther et al.,

2009; Smith et al., 2012). However, population declines and range

contractions are predicted for some invasive species in parts of their

ranges (Bradley et al., 2009; Sobek-Swant et al., 2012; Taylor et al., 2012;

Bertelsmeier et al., 2013). The expansion of invasive species in some areas

and contraction in others will contribute to community reorganization

and the formation of novel ecosystems and interactions in both terrestrial

and freshwater habitats (high confidence; e.g., Britton et al., 2010;

Kiesecker, 2011; Martinez, 2012; see also Section 4.3.2.5). For example,

invasive grasses may be favored over native ones with increasing

temperatures (Parker-Allie et al., 2009; Chuine et al., 2012; Sandel and

Dangremond, 2012).

In a few cases, benefits to biodiversity and society may result from the

interactive effects of climate change and invasive species, such as

increases in resources available to some threatened species (Caldow et al.,

2007), forest structural recovery (Bolte and Degen, 2010), and available

biomass for timber and fuel (van Wilgen and Richardson, 2012). The

effect of invasions on net changes in carbon stocks are situation specific

and may be either positive or negative (Williams, A.L. et al., 2007). Rising

levels will increase the growth rates of most invasive plant species

(Mainka and Howard, 2010; but see Section 4.2.4.4).The effectiveness

of invasive alien species management for sequestering carbon is uncertain

and context specific (Peltzer et al., 2010). Longer term, indirect effects

of invasive alien species will be more important than direct, short-term

effects, for instance, as a result of changes in soil carbon stocks and tree

community composition (low to medium confidence; Peltzer et al., 2010).

Synergistic interactions occur between climate change and invasive

alien species, along with landscape change, habitat disturbance, and

human-facilitated breakdown of dispersal barriers (Brook et al., 2008;

Angeler and Goedkoop, 2010; Bradley et al., 2010; Winder, M. et al.,

2011). Climate change and invasive alien plant species generally

increase the risk and intensity of fire, and the interaction is being

reported more frequently as a direct result of higher temperatures and

increased invasive plant biomass (high confidence; Abatzoglou and

Kolden, 2011). In freshwater systems, alien species establishment and

survival, species interactions, and disease virulence will change as a

result of changes in frequency of high-flow events, increasing water

temperature, water properties, and water demand (medium confidence;

Schnitzler et al., 2007; Rahel and Olden, 2008; Britton et al., 2010).

A range of climate change-related variables (extreme events and changes

in precipitation, temperature, and CO

) will continue to exacerbate the

establishment and spread of pests, vectors, and pathogens and negatively

impact production systems (medium confidence; Robinet and Roques,

2010; Clements and Ditommaso, 2011). Warming has contributed to the

spread of many invasive insect species, such as the mountain pine

Frequently Asked Questions

FAQ 4.3 | Will the number of invasive alien species increase as a result of climate change?

Some invasive plants and insects have already been shown to beneﬁt from climate change and will establish and

spread into new regions (where they are “aliens”), once they are introduced. The number of newly arrived species

and the abundance of some already established alien species will increase because climate change will improve

conditions for them. At the same time, increasing movement of people and goods in the modern world, combined

with land use changes worldwide, increases the likelihood that alien species are accidentally transported to new

locations and become established there. There are many actions that can be taken to reduce, but not eliminate, the

risk of alien species invasions, such as the treatment of ballast water in cargo ships and wood products, strict quarantine

applied to crop and horticultural products, and embargos on the trade and deliberate introduction of known invader

species. Some invasive species will suffer from climate change and are expected to decrease in range and population

size in some regions. Generally, increased establishment success and spread will be most visible for those alien species

that have characteristics favored by the changing climate, such as those that are drought tolerant or able to take

advantage of higher temperatures.

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Chapter 4 Terrestrial and Inland Water Systems

ark beetle, and resulted in forest destruction (high confidence; Raffa

et al., 2008). The interactions between crop growth, climate change, and

pest or pathogen dynamics are difficult to predict (West et al., 2012).

Management strategies may become less effective as a consequence of

the decoupling of biocontrol relationships and less effective mechanical

control as biomass and/or population size of invasive species increases

(low to medium confidence; Hellmann et al., 2008).

4.3. Vulnerability of Terrestrial and Freshwater

Ecosystems to Climate Change

The vulnerability of ecosystems to climate change, that is, their propensity

to be adversely affected, is determined by the sensitivity of ecosystem

processes to the particular elements of climate undergoing change and

the degree to which the system (including its coupled social elements) can

maintain its structure, composition, and function in the presence of such

change, either by tolerating or adapting to it. Tolerance and adaptability

both interact with exposure, which in the case of terrestrial and freshwater

ecosystems means the magnitude and rate of climate change relative

to ranges of climatic conditions and rates of change under which the

ecosystem developed and its organisms evolved. Chapter 19 provides

a full discussion on vulnerability concepts.

4.3.1. Changes in the Disturbance Regime

The species composition at a given location is determined by three

considerations: the ability of species to reach the location; the physiological

tolerance of the species in relation to the range of conditions experienced

there; and interactions with other species, including competitors,

symbionts, predators, prey, and pathogens. Occasional disturbances

relieve competition, create opportunities for the establishment and

success of less dominant species, and may facilitate dispersal. Moderate

disturbance is thus important in maintaining diversity and ecosystem

function (Connell, 1978). Exposure to disturbances keeps tolerance of

disturbance in the population high. Fire, floods, and strong winds are

all examples of biodiversity-sustaining climate disturbances, provided

that their frequency and intensity do not deviate greatly above or below

the regime to which the species are adapted. Average environmental

conditions may be less of a determinant of species range and abundance

than the extreme conditions, such as the occurrence of exceptionally

cold or hot days or droughts exceeding a certain duration (Zimmermann

et al., 2009). The projected changes in probability of extremes are

typically disproportionately larger than the projected changes in

the mean (see IPCC, 2012; but also Diffenbaugh et al., 2005). Biotic

disturbances, such as pest and pathogen outbreaks are also often

implicated in ecosystem change, and may be enabled by climate change.

It is suggested that ecosystem regime shifts resulting from climate

change (alone or in interaction with other factors) will often be triggered

by changes in the disturbance regime, rather than by physiological

tolerance for the mean conditions (Thonicke et al., 2001). A “disturbance

regime” refers to the totality of different types of disturbance events in

a system, each characterized by its probability of occurrence, intensity,

and other relevant attributes, such as its seasonal pattern. A corollary

is that disturbance-related change is abrupt rather than gradual. Change

n the fire disturbance regime is emerging as a key proximal mechanism

and early indicator of terrestrial ecosystem change (Girardin et al., 2009;

Johnstone et al., 2010). Changes in the fire regime have in some cases

been attributed to climate change (Littell et al., 2009). Regional trends

in fire occurrence have been observed since 2000 (Giglio et al., 2013),

but interpreting their significance requires a longer term perspective

(e.g., Bergeron et al., 2010).

4.3.2. Observed and Projected Change in Ecosystems

This section highlights key observed changes in terrestrial and freshwater

ecosystems over the recent past, as well as changes projected during

the 21st century. For observations, we assess the degree of confidence

that change has been detected, and separately the confidence we have

in attributing the change to climate change (Figure 4-4). Confidence in

detection is considered to be very high when there is high agreement

between many independent studies, species, ecosystems, or regions

and where there is robust evidence that the changes over time are

statistically significant (see Chapter 18; Mastrandrea et al., 2010). Note

that a slightly different definition of detection is used here than in Chapter

18, because detection here is based solely on the presence of a temporal

trend and does not attempt to distinguish natural from climate-related

variation. Confidence in attribution to climate change is very high when

three tests are satisfied: changes correspond to a sound mechanistic

understanding of responses to climate change; the time series of

observations is sufficiently long to detect trends correlated with climate

change; and confounding factors can be accounted for or are of limited

importance. In the sections that provide the details of the assessment

of detection and attribution, estimated levels of confidence are given

even in cases where the capacity for detection or attribution capacity

is low or very low, because changes in these ecosystem properties or

processes could have large impacts on biodiversity or ecosystem services

at regional to global scales. In all cases the estimates of confidence levels

are based on global and cross-taxon assessments, so the positioning

may be different for specific taxa or regions. Some of the sections include

assessments of model-based projections of future change; the confidence

assessment of detection and attribution does not extend to these.

A key message arising from the analysis of detection and attribution is

that climate impacts on the functioning of organisms and ecosystems

are clearest when temperature is a principal driver, changes are relatively

rapid, and confounding factors play a small role. At one end of the

spectrum, the large warming signal over the last several decades in

much of the Arctic tundra combined with minimal human impacts is

associated with high confidence in detection of an increase in shrubs

and permafrost thawing and high confidence in the attribution to climate

warming (Section 4.3.3.1.1). Likewise, the phenology of most organisms

is sensitive to temperature, confounding effects are often small, and the

response is rapid, leading to high confidence in detection and attribution

of changes in phenology to warming (Section 4.3.2.1). At the opposite

end of the spectrum, species extinctions are very difficult to attribute to

climate change (Section 4.3.2.5), in part because other factors dominate

recent extinctions. This does not mean that climate has not played an

important contributing role; indeed it has been argued that the low

level of confidence in attribution is due to the lack of studies looking

for climate signals in extinctions (Cahill et al., 2013). Similarly there is

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Terrestrial and Inland Water Systems Chapter 4

very good evidence that species composition is changing in cultural

landscapes, but the important role of other factors, for example, land

management and nitrogen deposition, makes attribution of a contribution

to recent warming difficult. This analysis indicates that responses in

most species and ecosystem levels will become more apparent over

time because (1) observed organism-level changes will have long-term

impacts on ecosystem functioning (high confidence; Sections 4.3.2.1,

4.3.2.5, 4.3.3) and (2) warming signals can be detected in ecosystems

where the recent warming has been strong and confounding factors

are minimal. In addition, the absence of observed changes does not

preclude confident projections of future change for three reasons: climate

change projected for the 21st century substantially exceeds the changes

experienced over the past century in medium to high scenarios (all but

RCP2.6); ecosystem responses to climate change may be nonlinear; and

change may be apparent only after considerable time lags (Jones et al.,

2009).

4.3.2.1. Phenology

Further evidence from ground-based and satellite studies, focused

mainly on the NH (Northern Hemisphere), supports the AR4 conclusion

that shifts in phenology have occurred over recent decades. “Spring

advancement”—earlier occurrence of spring events, such as breeding,

bud burst, breaking hibernation, flowering, migration—is seen in

hundreds of plant and animal species in many regions (Menzel et al.,

2006; Cleland et al., 2007; Parmesan, 2007; Primack et al., 2009; Cook

et al., 2012a; Peñuelas et al., 2013), although magnitudes of change

vary considerably and some species show no change (Parmesan, 2007).

Apparent discrepancies between two estimates of overall NH spring

advancement noted in AR4 (-2.3 days per decade, Parmesan and Yohe,

2003; -5.1 days per decade, Root et al., 2003) are largely resolved when

methodological differences are accounted for, particularly the inclusion

of species that do not show phenological changes (Parmesan, 2007). A

combined analysis of 203 species suggests NH spring advancement of

-2.8 ± 0.35 days per decade (Parmesan, 2007).

4.3.2.1.1. Plants

Spring advancement is seen across the NH including North America

(e.g., Cook et al., 2008, 2012b), Europe (e.g., Menzel et al., 2006; Cook

et al., 2012b), Asia (e.g., Primack et al., 2009; Ma and Zhou, 2012), and

the High Arctic (Høye et al., 2007). Changes are generally larger at higher

latitudes. A meta-analysis indicates mean NH spring advancement of

-1.1 ± 0.16 days per decade for herbs and grasses (85 species), -1.1 ±

0.68 days per decade for shrubs (6 species), and -3.3 ± 0.87 days per

decade for trees (16 species), over a record period of 35 to 132 years,

depending on the study. The warming trends detected in the well-mixed

surface waters (epilimnion) of many lakes in North America, Eurasia,

and Africa (Adrian et al., 2009) are associated with the earlier onset of

spring phytoplankton blooms (Winder and Schindler, 2004; Winder and

Sommer, 2012). Satellite data also indicate a general tendency of spring

advancement, though there is variation between satellite studies,

especially at local scales, due to the use of different instruments and

methods (e.g., White et al., 2009). A study using the Advanced Very High

Resolution Radiometer (AVHRR) suggests that for vegetation between

30ºN and 80ºN, the start of the growing season advanced by -5.2 days

Very low

Low Medium

Degree of conﬁdence in detection

High Very high

Low Medium

Degree of conﬁdence in attribution

High Very high

1. Changes in evapotranspiration (Section 4.3.2.4)

2. Increased tree mortality (Section 4.3.3.1, Box 4-2)

3. Increased extinctions (Section 4.3.2.5)

4. Increased primary productivity (Section 4.3.2.2) and carbon stocks

(Section 4.3.2.3)

. Changes in phenology (Section 4.3.2.1)

6. Species range shifts (Section 4.3.2.5)

7. Invasive species (Section 4.2.4.6)

. Flow-related impacts on freshwater ecosystems (Section 4.3.3.3)

9. Cultural landscapes – species composition changes (Section 4.3.3.5)

10. Tundra – increase in shrubs, melting of permafrost (Section 4.3.3.4,

Box 4-4)

11. Boreal – tree mortality (Section 4.3.3.1.1, Box 4-4)

12. Amazon – tree mortality (Section 4.3.3.1.3, Box 4-3)

13. Savannahs – woody encroachment (Section 4.3.3.2.2)

4. Evolutionary and genetic adaptation (Section 4.4.1)

Evidence of change in species and ecosystems

Impacts on major systems including early signs of regime shifts

Adaptation

Figure 4-4 | Conﬁdence in detection of change and attribution of observed responses of terrestrial ecosystems to climate change. Conﬁdence levels are based on expert

judgment of the available literature following the IPCC uncertainty guidance (Mastrandrea et al., 2010), attribution criteria outlined in Chapter 18, and detection criteria deﬁned

in the text. The symbols in the ﬁgure represent global and cross-taxon assessments; the positioning may be different for speciﬁc taxa or regions. Details of the assessments that

were used in positioning each of the points can be found in the sections given in parentheses.

292

Chapter 4 Terrestrial and Inland Water Systems

etween 1999 and 1982 and advanced a further -0.2 days by 2008;

while the growing season end was delayed by 6.6 days between 1982

and 2008 (Jeong et al., 2011). Studies with a more recent satellite

instrument, the Moderate Resolution Imaging Spectrometer (MODIS),

also show spring advancement (e.g., Ahl et al., 2006). The relatively short

duration of satellite observations makes trend detection particularly

sensitive to the choice of analysis period.

4.3.2.1.2. Animals

Many new studies provide further evidence of changes in animal

phenology (e.g., amphibians: Kusano and Inoue, 2008; Phillimore et al.,

2010; birds: Pulido, 2007; Thorup et al., 2007; mammals: Adamik and Kral,

2008; Lane et al., 2012; insects: Robinet and Roques, 2010; freshwater

plankton: Adrian et al., 2009). Changes in breeding phenology are

reported from various regions and different taxa (e.g., Parmesan, 2006,

2007; Post et al., 2008; Primack et al., 2009). In the NH several studies

show advancements of egg laying dates in birds (e.g., Parmesan, 2007:

-3.7 ± 0.7 days per decade, in 41 species). In contrast, a delay of the

mean breeding date by 2.8 to 3.7 days between 1950 and 2004 was seen

for two of nine seabirds in the Eastern Antarctic, linked to decreased sea

ice extent (Barbraud and Weimerskirch, 2006). Spring arrival dates have

advanced for many migratory birds (e.g., Thorup et al., 2007). Patterns

of changes in autumn migration in birds are mostly not consistent

(delayed, advanced, no change) across analyzed species and regions

and appear to be highly related to non-climatic variables (e.g., Sokolov,

2006; Adamik and Pietruszkova, 2008).

A large body of evidence therefore shows that, in NH temperate, boreal,

and Arctic regions, spring advancement has occurred in many plant and

animal species over the last several decades (high confidence due to

robust evidence but only medium agreement when examined across all

species and regions; Figure 4-4).

Understanding of the drivers of phenological change has also improved

further since AR4. Many observational studies find a correlation with

higher temperatures (Cook et al., 2012a). Experimental manipulation

generally supports this (e.g., plants: Cleland et al., 2012; bird egg-laying:

Visser et al., 2009; insects: Musolin et al., 2010; Kollberg et al., 2013).

Some individual studies find good agreement between experimental

warming and in situ observations (e.g., Gunderson et al., 2012) although

a meta-analysis suggests that experiments can substantially under-

predictadvances in the timing of flowering and leafing of plants in

comparison with observational studies (Wolkovich et al., 2012).

Observational data can also be affected by methodological issues; for

example, flipper-tagging of penguins can alter their migratory behavior

(Saraux et al., 2011). Rates of warming across a season may also be

important (Schaper et al., 2012). Models can be used to explain

relationships between observed phenological changes and environmental

variables. For example, a model based on water temperature captured

the observed temporal and spatial variation in Daphnia phenology in

NH lakes (Straile et al., 2012). Other environmental factors related to

temperature, such as timing of snowmelt, snow cover, and snow depth,

can play a role. Snowmelt changes led to earlier flowering and appearances

of plants and arthropods in Greenland between 1996 and 2005 (Høye et

al., 2007) and earlier flowering in an alpine plant in the Rocky Mountains,

SA, between 1975 and 2008 (Hülber et al., 2010; Lambert et al., 2010).

Earlier snowmelts decreased floral resources and hence affected insect

population dynamics in mountain ranges in the USA in the years 1980,

1985, 1986, and 1989 (Boggs and Inouye, 2012). In Colorado, USA, the

yellow-bellied marmot emerged earlier from hibernation due to

snowmelts becoming earlier over 1976–2008 (Ozgul et al., 2010) while

in Alberta, Canada, Columbian ground squirrels emerged later over

1992–2012 owing to delayed snowmelts associated with increased

late-season snowstorms (Lane et al., 2012). Delayed emergence from

hibernation was associated with decreased population growth rate

(Lane et al., 2012). Food availability can be important; for example, in

the Yukon area, Canada, the date of giving birth in North American

squirrels (Tamiascurus hudsonicus) advanced by an average of -18 days

over the period 1989–1998, coinciding with increasing abundance of

white spruce cones, their major food source (Réale et al., 2003).

Phenological response can differ with migration strategy in birds, for

example short-distance migrants show greater advancements in spring

arrivals than long distant migrants (e.g., Saino et al., 2009; but see

Parmesan, 2006 for different patterns). In a temperate region

(Massachusetts, USA), declining sizes of populations and migrating

cohorts of North American Passerine birds account for a large part of

the variation in migration times between 1970 and 2002 (Miller-Rushing

et al., 2008). The remaining variation was explained by climatic variables,

migration distance, and date. The variation in bird migration phenology

change can also be related to differing patterns of feather changes during

moulting times, food availability at stop-over places, and differing health

conditions of individual species (Gordo, 2007).

Although a number of non-climatic influences on phenology are also

identified, an increased number of observational and experimental studies,

across many organism types, suggest that warming has contributed to

the overall spring advancement observed in the NH (high confidence due

to high agreement and medium evidence).

4.3.2.2. Primary Productivity

Primary production, the process of plant growth, is fundamental to the

global carbon cycle (see Section 4.3.2.3) and underpins provisioning

ecosystem services such as food, timber, and grazing. Trends in the

amount, seasonal timing, variability, location, and type of primary

production are therefore important indicators of ecosystem function.

Well-established theory, experimentation, and observation all agree that

primary production is directly sensitive to most aspects of climate change,

is indirectly affected via the effects of climate on pests and diseases,

and is responsive to many of the other changes simultaneously taking

place in the world, such as described in Section 4.2.4. The diverse and

frequently nonlinear form of responses to the factors influencing

primary production, combined with the complexity of interactions

between them, means that at a given location the net outcome can be

an increase, no change, or a decrease in productivity.

The concentration of CO

in the atmosphere shows clear patterns in

space and time largely related to the primary productivity of the land

and oceans. The contribution by terrestrial ecosystems to these patterns

can be estimated using isotope measurements, emission databases, and

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Terrestrial and Inland Water Systems Chapter 4

odels (Canadell et al., 2007). It consists of a sink term, due to increased

net ecosystem production, plus a source term due to land use change.

During the decade 2000–2009, land net primary productivity at the

global scale continued to be enhanced about 5% relative to the estimated

preindustrial level, leading to a land sink of 2.6 + 1.2 PgC yr

–1

(these

values are from WGI AR5 Section 6.3.2.6; the uncertainty range is 2

standard deviations; for the primary literature see also Raupach et al.,

2008; Le Quéré et al., 2009). The net uptake of carbon by the land is

highly variable year to year, mainly in response to climate variation and

major volcanic eruptions (Peylin et al., 2005; Sitch et al., 2008; Mercado

et al., 2009). Given the uncertainty range, it is not possible to conclude

whether the rate of carbon uptake by the residual land sink has

increased or decreased over the past 2 decades (Raupach et al., 2008;

WGI AR5 Section 6.3.2.6). Coupled Model Intercomparison Project Phase

5 (CMIP5) model projections, using the RCP scenarios, suggest that the

rate of net carbon uptake by terrestrial ecosystems will decrease during

the 21st century except under the RCP4.5 scenario, and by the greatest

amount under RCP8.5. There is greater uncertainty between models

than between scenarios; in some models terrestrial ecosystems become

a net source of CO

to the atmosphere (WGI AR5 Section 6.4.3.2,

especially Figure 6.26).

It is possible to downscale the land sink estimate continentally, using

inversion modeling techniques and the growing network of precision

atmospheric observations. There is high agreement and medium evidence

that the net land uptake in natural and semi-natural terrestrial ecosystems

is broadly distributed around the world, almost equally between

forested and non-forested ecosystems, but is offset in the tropics by a

large carbon emission flux resulting from land use change, principally

deforestation (Pan et al., 2011).

The observed trends in Normalized Difference Vegetation Index (NDVI),

a satellite proxy for primary productivity, are discussed under various

ecosystem-specific discussions above and below. In some cases the

trends are sufficiently strong and consistent to support a confident

statement about the underlying phenomenon, but in many cases they

are not. This may mean that no change has occurred, or simply reflect

inadequacies in the indicator, method of analysis, and length of the

record in relation to the high interannual variability. AR4 reported a

trend of increasing seasonally accumulated NDVI (“greening”) at high

northern latitudes (Fischlin et al., 2007; based on Sitch et al., 2007),

but subsequent observations show a lower rate and no geographical

uniformity (Goetz et al., 2007). More than 25% of high-latitude North

American forest areas, excluding areas recently disturbed by fire,

showed a decline in greenness and no systematic change in growing

season length, particularly after 2000 (Goetz et al., 2007). NDVI trend

analyses in rangelands show varying patterns around the world, with

substantial disagreement between studies (Millennium Ecosystem

Assessment, 2005a; Bai et al., 2008; Beck, H.E. et al., 2011; Fensholt et

al., 2012). There is agreement that the Sahel showed widespread NDVI

increase between the mid-1980s and about 2000, along with an

increase in rainfall, but no consensus on whether the detected signal

represents increased productivity by grasses, trees, or herbs; and to what

degree it reveals land management efforts or responses to climate

(Anyamba and Tucker, 2005; Prince et al., 2007; Hellden and Tottrup,

2008; Seaquist et al., 2009). In the period 2000–2009 no NDVI trend

was apparent in the Sahel (Samanta et al., 2011).

ree rings record changes in tree growth over approximately the past

millennium. Many tree ring records show accelerated tree growth during

much of the 20th century (Briffa et al., 2008), which often correlates with

rising temperature. Variations in tree ring width, density, and isotopic

composition arise from many factors, including temperature, moisture

stress, CO

fertilization, N deposition, and O

damage, but also stand

structure and management. Direct CO

effects, inferred from the

ring record once the effects of drought and temperature have been

accounted for, have been proposed for approximately 20% of the sites

in the International Tree Ring Data Base (Gedalof and Berg, 2010) and

studied in detail at some sites (Koutavas, 2008). Since the 1980s, a

number of tree ring records show a decline in tree growth (Wilson et al.,

2007). Several possible causes have been suggested for this, including

increasing water stress and O

damage; but the most recent rings in

most published tree ring chronologies date from before the 1990s

(Gedalof and Berg, 2010), so tree ring-based conclusions for the past

2 decades are based on a relatively small body of evidence and may

therefore be biased. Recent tree ring studies were often specifically

designed to examine growth in response to environmental changes

(Gedalof and Berg, 2010) and may therefore not be representative of

global tree growth. Direct repeated measurements of tree girth increment

in forest monitoring plots (discussed in Section 4.3.2.3) are an alternate

data source for recent decades.

Primary production in freshwater lakes has been observed to increase

in some Arctic (Michelutti et al., 2005) and boreal lakes, but to decrease

in Lake Tanganyika in the tropics (O’Reilly et al., 2003). In both cases

the changes were attributed by the authors to climate change.

In summary, there is high confidence that net terrestrial ecosystem

productivity at the global scale has increased relative to the preindustrial

era. There is low confidence in attribution of these trends to climate change.

Most studies speculate that rising CO

concentrations are contributing

to this trend through stimulation of photosynthesis, but there is no clear,

consistent signal of a climate change contribution (Figure 4-4).

4.3.2.3. Biomass and Carbon Stocks

The forest biomass carbon stock can be estimated from the routine forest

monitoring that takes place for management and research purposes.

Forest inventories were generally designed to track timber volumes;

inferring total biomass and ecosystem carbon stocks requires further

information and assumptions, which make absolute values less certain,

but have a lesser effect on trend detection. Forest inventory systems are

well developed for NH temperate and boreal forest (Nabuurs et al., 2010;

Ryan et al., 2010; Wang, B. et al., 2010). Data for tropical and Southern

Hemisphere forests and woodlands also exist (Maniatis et al., 2011;

Tomppo et al., 2010) but are typically less available and comprehensive

(Romijn et al., 2012). More and better data may become available as a

result of advances in remote sensing (e.g., Baccini et al., 2012) and

increased investment in forest monitoring through initiatives such as the

Reduced Emissions from Deforestation and Degradation (REDD) of the

United Nations Framework Convention on Climate Change (UNFCCC).

Forests have increased in biomass and carbon stocks over the past half

century in Europe (Ciais et al., 2008; Luyssaert et al., 2010) and the USA

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Chapter 4 Terrestrial and Inland Water Systems

(

Birdsey et al., 2006). Canadian managed forests increased in biomass

only slightly during 1998–2008, because growth was offset by significant

losses due to fires and beetle outbreaks (Stinson et al., 2011). Several

dozen sites across the moist tropics have been monitored to estimate

forest biomass changes. In the Amazon (Phillips et al., 2009) forest

biomass has generally increased in recent decades, dropping temporarily

after a drought in 2005. Globally, for the period 2000–2007, recently

undisturbed forests are estimated to have withdrawn 2.30 ± 0.49 PgC

–1

from the atmosphere, while formerly cleared tropical forests, now

regrowing, withdrew an additional 1.72 ± 0.54 PgC yr

–1

(Pan et al.,

2011). The global terrestrial carbon sink is partly offset by the losses of

forest carbon stocks to the atmosphere through land use change, largely

in the tropics, of 1.1 ± 0.8 PgC yr

–1

(2000–2009, WGI AR5 Section 6.3.2.6).

The carbon stock in global soils, including litter and peatlands is 1500

to 2400 PgC, with permanently frozen soils adding another 1700 PgC

(Davidson and Janssens, 2006). The soil carbon stock is thus more than

10 times greater than the carbon stock in forest biomass (Kindermann

et al., 2008). Changes in the size of the soil carbon stock result from

changes in the net balance of inputs and losses over a period of many

years. Inputs derive from primary production, discussed in Section 4.3.2.2,

and are mostly modestly increasing under climate change. Losses result

principally through the respiration of soil microbes, which increases with

increasing temperature. The present and future temperature sensitivity

of microbial respiration remains uncertain (Davidson and Janssens,

2006). An analysis of long-term respiration measurements from the soil

around the world suggests that it has increased over the past 2 decades

by an amount of 0.1 PgC yr

–1

, some of which may be due to increased

productivity (Bond-Lamberty and Thomson, 2010). If soil respiration were

to exceed terrestrial net primary production globally and on a sustained

basis, the present net terrestrial sink would become a net source,

accelerating the rate of CO

build-up in the atmosphere (Luo, 2007).

The carbon stock in freshwater systems is also quite high in global terms.

Annual rates of storage (0.03 to 0.07 PgC yr

–1

) may be trivial compared

with sequestration by soils and terrestrial vegetation, but lake sediments

are preserved over longer time scales (+10 kyr compared with decades

to centuries), and Holocene storage of carbon in lake sediments has

been estimated at 820 Pg (Cole et al., 2007). Manmade impoundments

represent an increasing and short-lived additional carbon store with

conservative annual estimates of 0.16 to 0.2 PgC yr

–1

(Cole et al., 2007).

A short-duration study of the temperature sensitivity of decomposition

in flooded coastal soils, extrapolated to the 21st century, suggested that

increases in respiration would exceed increases in future production

(Kirwan and Blum, 2011). Further detail on wetland soil carbon stocks

can be found in Section 4.3.3.3 on peatlands and on permafrost carbon

stocks in Box 4-4 and in Chapter 28.

In summary, biomass and soil carbon stocks in terrestrial ecosystems

are currently increasing (high confidence) but are vulnerable to loss to

the atmosphere as a result of rising temperature, drought, and fire

projected in the 21st century (Figure 4-4). Measurements of increased

tree growth over the last several decades, a large sink for carbon, are

consistent with this but confounding factors such as N deposition,

afforestation, and land management make attribution of these trends

to climate change difficult (low confidence).

4.3.2.4. Evapotranspiration and Water Use Efficiency

Evapotranspiration (ET) includes evaporation from the ground and

vegetation surfaces, and transpiration through plant stomata. Both are

affected by multiple factors (Luo et al., 2008) including temperature,

solar (shortwave) and thermal (longwave) radiation, humidity, soil

moisture, and terrestrial water storage; transpiration is additionally

affected by CO

concentration through its influence on plant stomatal

conductance. Studies using lysimeters, evaporation pans, the balance

of observed precipitation and runoff, and model reconstructions indicate

both increases and decreases in ET in different regions and between

approximately 1950 and the present (Huntington, 2008; Teuling et al.,

2009; Douville et al., 2013). Flux tower records have at most 15 years

duration (FLUXNET, 2012), so there are insufficient data to calculate large-

scale, long-term trends. ET can also be estimated from meteorological

observations or simulated with models constrained by observations.

Estimates of ET from 1120 globally (but non-uniformly) distributed

stations indicate that global land mean ET increased by approximately

2.2% between 1982 and 2002, a rate of increase of 0.75 mm yr

–2

(Wang, K. et al., 2010). Other studies, using data-constrained models,

indicated global ET rises of between 0.25 and 1.1 mm yr

–

during the

1980s and 1990s (Jung et al., 2010; Vinukollu et al., 2011; Zeng et al.,

2012), possibly linked with increased surface solar radiation and

thermal radiation (Wild et al., 2008) or warming (Jung et al., 2010).

There has been no significant ET trend since approximately 2000

(Jung et al., 2010; Vinukollu et al., 2011; Zeng et al., 2012), possibly due

to soil moisture limitation (Jung et al., 2010). Overall, there is low

confidence in both detection and attribution of long-term trends in ET

(Figure 4-4).

Experiments show that rising CO

decreases transpiration and increases

intrinsic water use efficiency (iWUE, the ratio of photosynthesis to

stomatal conductance; Leakey et al., 2009). Some modeling studies

suggest that, over the 20th century, the effects of CO

on decreasing

transpiration are of comparable size but opposite to the effects of rising

temperature (Gerten et al., 2008; Peng et al., 2013). However, the

observed general increase in ET argues that reduced transpiration cannot

be the dominant factor (Huntington, 2008). A meta-analysis of studies

at 47 sites across five ecosystem types (Peñuelas et al., 2011) suggests

that iWUE for mature trees increased by 20.5% between the 1970s and

2000s. Increased iWUE since preindustrial times (1850 or before) has

also been found at several forest sites (Andreu-Hayles et al., 2011;

Gagen et al., 2011; Loader et al., 2011; Nock et al., 2011) and also in a

temperate semi-natural grassland since 1857 (Koehler et al., 2010),

although in one boreal tree species iWUE ceased to increase after 1970

(Gagen et al., 2011).

4.3.2.5. Changes in Species Range, Abundance, and Extinction

Species respond to climate change through genotypic adaptation and

phenotypic plasticity; by moving out of unfavorable and into favorable

climates; or by going locally or globally extinct (Dawson et al., 2011;

Bellard et al., 2012; Peñuelas et al., 2013; see also Section 4.2.3). These

responses to climate change can potentially have large impacts on

biodiversity and ecosystem services. Genotypic adaptation in the face of

strong selection pressure from climate change is typically accompanied

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Terrestrial and Inland Water Systems Chapter 4

by large reductions in abundance (see Section 4.4.1.2). Species range

shifts are accompanied by changes in abundance, local extinctions, and

colonization that can alter ecosystem services when they affect dominant

species such as trees, keystone species such as pollinators, or species

that are vectors for diseases (Zarnetske et al., 2012). Global extinctions

result in the permanent loss of unique forms of life.

Substantial evidence has accumulated since AR4 reinforcing the conclusion

that the geographical ranges of many terrestrial and freshwater plant

and animal species have moved over the last several decades in response

to warming and that this movement is projected to accelerate over the

coming decades under high rates of climate change. Some changes in

species abundances appear to be linked to climate change in a predictable

manner, with species abundances increasing in areas where climate has

become more favorable and vice versa. In contrast, uncertainties

concerning attribution to climate change of recent global species

extinctions, and in projections of future extinctions, have become more

apparent since the AR4.

4.3.2.5.1. Observed species range shifts

The number of studies looking at observed range shifts and the breadth

of species examined have greatly increased since AR4. The most

important advances since AR4 concern improvements in understanding

the relationship between range shifts and changes in climate over the

last several decades. The “uphill and poleward” view of species range shifts

in response to recent warming (Parmesan and Yohe, 2003; Parmesan,

2006; Fischlin et al., 2007; Chen et al., 2011) is a useful simplification

of species responses; however, responses to warming are conditioned

by changes in precipitation, land use, species interactions, and many

other factors. Investigations of the mechanisms underlying observed

range shifts show that climate signals can often be detected, but the

impacts of and interactions between changing temperature, precipitation,

and land use often result in range shifts that are downhill or away from

the poles (Rowe et al., 2010; Crimmins et al., 2011; Hockey et al., 2011;

McCain and Colwell, 2011; Rubidge et al., 2011; Pauli et al., 2012; Tingley

et al., 2012; Zhu et al., 2012). There are large differences in the ability

of species groups (i.e., broad taxonomic categories of species) and

species within these groups to track changes in climate through range

shifts (Angert et al., 2011; Mattila et al., 2011; Chen et al., 2011). For

example, butterflies appear to be able track climate better than birds

(community shifts: Devictor et al., 2012; but see Chen et al., 2011 for

range shifts) while some plants appear to be lagging far behind climate

trends except in mountainous areas (Bertrand et al., 2011; Doxford and

Freckleton, 2012; Gottfried et al., 2012; Zhu et al., 2012; Telwala et al.,

2013). There is growing evidence that responses at the “trailing edge”

of species distributions (i.e., local extinction in areas where climate has

become unfavorable) are often less pronounced than responses at the

“leading edge” (i.e., colonization of areas where climate has become

favorable), which may be related to differences in the rates of local

extinction vs. colonization processes (Doak and Morris, 2010; Chen et

al., 2011; Brommer et al., 2012; Sunday et al., 2012) and difficulties in

detecting local extinction with confidence (Thomas et al., 2006).

Rising water temperatures are also implicated in species range shifts

in river fish communities (e.g., Comte and Grenouillet, 2013), combined

with a decrease in recruitment and survival as well as range contraction

of cold-water species such as salmonids (Bartholow, 2005; Bryant, 2009;

Ficke et al., 2007; Jonsson and Jonsson, 2009; Hague et al., 2011). Shifts

in freshwater fish species range toward higher elevation and upstream

(Hickling et al., 2006; Comte and Grenouillet, 2013) also are not keeping

pace with the rate of warming in streams and rivers. While these

changes in river temperature regimes may also open up new habitat at

higher latitudes (or altitudes) for migratory (Reist et al., 2006) and cool-

and warm-water species of fish (Tisseuil et al., 2012), there is high

confidence that range contraction threatens the long-term persistence

of some fully aquatic species.

Rates of recent climate change have varied greatly across the globe,

ranging from rapid warming to cooling (Burrows et al., 2011; Dobrowski

et al., 2013). Taking this spatial variation into account should enhance

the ability to detect climate-related range shifts. A recent synthesis of

range shifts indicates that terrestrial animal species have moved at rates

that correspond better with changes in temperature when climate is

measured only in the regions where the range shifts were observed

Frequently Asked Questions

FAQ 4.4 | How does climate change contribute to species extinction?

There is a consensus that climate change over the coming century will increase the risk of extinction for many species.

When a species becomes extinct, a unique and irreplaceable life form is lost. Even local extinctions can impair the

healthy functioning of ecosystems.

Under the fastest rates and largest amounts of projected climate change, many species will be unable to move fast

enough to track suitable environments, which will greatly reduce their chances of survival. Under the lowest projected

rates and amounts of climate change, and with the assistance of effective conservation actions, the large majority of

species will be able to adapt to new climates, or move to places that improve their chances of survival. Loss of habitat

and the presence of barriers to species movement increase the risk of extinctions as a result of climate change.

Climate change may have already contributed to the extinction of a small number of species, such as frogs and toads

in Central America, but the role of climate change in these recent extinctions is the subject of considerable debate.

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Chapter 4 Terrestrial and Inland Water Systems

(

Chen et al., 2011), providing greater confidence in attribution of the

range shifts to climate change. Average range shifts across taxa and

regions in this study were approximately 17 km poleward and 11 m up

in altitude per decade, velocities that are two to three times greater than

previous estimates (compare with Parmesan and Yohe, 2003; Fischlin et

al., 2007), but these responses differ greatly among species groups.

However, this approach remains a simplification, as the climate drivers

of species range changes, for example, temperature and precipitation,

have frequently shifted in different geographical directions (Dobrowski

et al., 2013). Disentangling these conflicting climate signals can help

explain complex responses of species ranges to changes in climate

(Tingley et al., 2012). Overall, studies since AR4 show that species range

changes result from interactions among climate drivers and between

climate and non-climate factors. It is the greater understanding of these

interactions, combined with increased geographical scope, that leads

to high confidence that several well-studied species groups, such as

insects and birds, have shifted their ranges over significant distances

(tens of kilometers or more) over the last several decades, and that these

range shifts can be attributed to changes in climate. But for many other

species groups range shifts are more difficult to attribute to changes in

climate because the climate signal is small, there are many confounding

factors, differences between expected and observed range shifts are

large, or variability within or between studies is high. Thus there is only

medium confidence in detection and attribution when examined across

all species and all regions.

4.3.2.5.2. Future range shifts

Projections of climate change impacts on future species range shifts

since the AR4 have been dominated by studies using Ecological Niche

Models (ENMs) that project future ranges based on correlative models

of current relationships between environmental factors and species

distribution (Peterson et al., 2011). A variety of process-based models

are starting to be more widely used to make projections of future species

distributions (Buckley et al., 2010; Beale and Lennon, 2012; Cheaib et

al., 2012; Higgins et al., 2012; Foden et al., 2013). Model comparisons

show that correlative models generally predict larger range shifts than

process-based models for trees (Morin and Thuiller, 2009; Kearney et

al., 2010; Cheaib et al., 2012). For other species groups that have been

studied, differences in projections between model types show no clear

tendency (Kearney et al., 2009; Buckley et al., 2010; Bateman et al.,

2012). There has been some progress in model validation: projected

species shifts are broadly coherent with species responses to climate

change in the paleontological record and with observed recent species

shifts (see Section 4.2.2 and above in this section), but further validation

is needed (Green et al., 2008; Pearman et al., 2008; Nogues-Bravo et al.,

2010; Dawson et al., 2011). Modeling studies typically do not account

for a number of key mechanisms mediating range shifts, such as genetic

adaptation and phenotypic plasticity (see Section 4.4.1.2), species

interactions, or human-mediated effects. An important limitation in most

studies is that realistic species displacement rates are not accounted

for (i.e., rates at which species are able to shift their ranges through

dispersal and establishment); as such, they only indicate changes in the

location of favorable and unfavorable climates, from which potential

shifts in species distribution can be inferred, but not rates of change

(Bateman et al., 2013).

nalyses and models developed since AR4 permit the estimation of the

ability of a wide range of species to track climate change. Figure 4-5

provides a synthesis of the projected abilities of several species groups

to track climate change. This analysis is based on (1) past and future

climate velocity, which is a measure of the rate of climate displacement

across a landscape and provides an indication of the speed at which an

organism would need to move in order to keep pace with the changing

climatic conditions (Loarie et al., 2009; Burrows et al., 2011; Chen et

al., 2011; Sandel et al., 2011; Feeley and Rehm, 2012; Dobrowski et al.,

2013); and (2) species displacement rates across landscapes for a broad

range of species (e.g., Stevens, V.M. et al., 2010; Nathan et al., 2011;

Barbet-Massin et al., 2012; Kappes and Haase, 2012; Meier et al., 2012;

Schloss et al., 2012; see additional references in Figure 4-5 legend).

Comparisons of these rates indicate whether species are projected to

be able to track climate as it changes. When species displacement

capacity exceeds climate velocity it is inferred that species will be able

to keep pace with climate change; when displacement capacity is lower

than projected climate velocities then they will not, within the bounds

of uncertainty of both parameters. This simplified analysis is coherent

with more sophisticated model analyses of climate-induced species

displacement across landscapes, some of which have evaluated additional

constraints such as demographics, habitat fragmentation, or competition

(e.g., Meier et al., 2012; Schloss et al., 2012).

Rates of climate change over the 20th century and projected for the

21st century are shown in Figure 4-5a. Rates of climate change for

global land surfaces are given for IPCC AR5 climate projections under

a wide range of GHG emissions scenarios (i.e., WGI AR5 Chapter 12;

Knutti and Sedláček, 2012). Rates of global warming for land surfaces

have averaged approximately 0.03°C yr

–1

since 1980, but have slowed

over the last decade and a half (WGI AR5 Chapter 2). At the low end of

projected future rates of warming, rates decrease over time, reaching

near zero by the end of the century (RCP2.6). At the high end, projected

rates increase over time, exceeding 0.06°C yr

–1

by the end of the century

(RCP8.5), and perhaps above 0.08°C yr

–1

at the upper bound.

Climate velocity is defined as the rate of change in climate over time

(e.g., °C yr

–1

, if only temperature is considered) divided by the rate of

change in climate over distance (e.g., °C km

–1

, if only temperature is

considered) and therefore depends on regional rates of climate change

and the degree of altitudinal relief (Figure 4-5b; Loarie et al., 2009;

Dobrowski et al., 2013). For example, climate velocity for temperature

is low in mountainous areas because the change in temperature over

short distances is large (e.g., Rocky Mountains, Andes, Alps, Himalayas;

Figure 4-5b, leftmost axis). Climate velocity for temperature is generally

high in flat areas because the rate of change in temperature over

distance is low (e.g., parts of the USA Midwest, Amazon basin, West

Africa, central Australia; Figure 4-5b, rightmost axis). In flat areas, climate

velocity can exceed 8 km yr

–1

for the highest rates of projected climate

change (RCP8.5). We have focused on climate velocity for temperature

change, but several analyses also account for precipitation change.

Rates of displacement vary greatly within and among species groups

(Figure 4-5c). Some species groups, notably herbaceous plants and trees,

generally have very low displacement capacity. Other species groups

such as butterflies, birds (not shown), and large vertebrates generally

have a very high capacity to disperse across landscapes, nonetheless

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Terrestrial and Inland Water Systems Chapter 4

0246810

(a) Climate change scenarios

(km yr

–1

)

(km yr

–1

)

Rate of climate change for global land and freshwater areas (°C yr

–1

)

(°C yr

–1

)

Conversion

= 110 km°C

−1

Flat areas

30 km °C

−1

Global

verage

3.8 km°C

−1

Mountain

reas

Observed

Historical

RCP2.6 (+1.0°C)

RCP2.6-low

RCP8.5-high

RCP4.5 (+1.8°C)

RCP6.0 (+2.2°C)

RCP8.5 (+3.7°C)

−0.04

−0.02

0.00

0.02

0.04

0.06

0.08

0.00

0.02

.04

0.06

0.08

0.10

0.00

0.02

.04

0.06

0.08

0.10

1900 1950 2000 2050

Historical

Projected

2100

Lower

bound

Upper

bound

Projected mean

Estimated speed at which

species group can move

Median

Unable to keep up Able to keep up

(a) Rate of climate change

(b) Estimate of climate velocity to determine rate of displacement

Rate of temperature change

under RCP8.5 scenario

between 2050 and 2100

(Mean projected increase in global temperature for the period 2081–2100

(

WGI, Chapter 12))

0246810

Trees

Herbaceous

plants

Split-hoofed

mammals

Carnivorous

mammals

Rodents

Primates

Plant-feeding

insects

Freshwater

molluscs

Mountain areas

Global average

Flat areas

Figure 4-5 | (a) Rates of climate change, (b) corresponding climate velocities, and (c) rates of displacement of several terrestrial and freshwater species groups in the absence of

human intervention. Horizontal and vertical pink bands illustrate the interpretation of this ﬁgure. Climate velocities for a given range of rates of climate change are determined by

tracing a band from the range of rates in (a) to the points of intersection with the three climate velocity scalars in (b). Comparisons with species displacement rates are made by

tracing vertical bands from the points of intersection on the climate velocity scalars down to the species displacement rates in (c). Species groups with displacement rates below

the band are projected to be unable to track climate in the absence of human intervention. (a) Observed rates of climate change for global land areas are derived from Climatic

Research Unit/Hadley Centre gridded land-surface air temperature version 4 (CRUTEM4) climate data reanalysis; all other rates are calculated based on the average of Coupled

Model Intercomparison Project Phase 5 (CMIP5) climate model ensembles for the historical period (gray shading indicates model uncertainty) and for the future based on the four

Representative Concentration Pathway (RCP) emissions scenarios. Data were smoothed using a 20-year sliding window, and rates are means of between 17 and 30 models using

one member per model. Global average temperatures at the end of the 21st century for the four RCP scenarios are from WGI AR5 Chapter 12. (b) Estimates of climate velocity for

temperature were synthesized from historical and projected future relationships between rates of temperature change and climate velocity (historical: Burrows et al., 2011; Chen

et al., 2011; Dobrowski et al., 2013; projected future: Loarie et al., 2009; Sandel et al., 2011; Feeley and Rehm, 2012). The three scalars are climate velocities that are

representative of mountainous areas (left), averaged across global land areas (center), and large ﬂat regions (right). (c) Rates of displacement are given with an estimate of the

median (black bars) and range (boxes = approximately 95% of observations or models for herbaceous plants, trees, and plant-feeding insects or median ± 1.5 inter-quartile

range for mammals). Displacement rates for herbaceous plants were derived from paleobotanical records, modern plant invasion rates, and genetic analyses (Kinlan and Gaines,

2003). Displacement estimates for trees are based on reconstructed rates of tree migration during the Holocene (Clark, 1998; Clark et al., 2003; Kinlan and Gaines, 2003;

McLachlan et al., 2005; Nathan, 2006; Pearson, 2006) and modeled tree dispersal and establishment in response to future climate change (Higgins et al., 2003; Iverson et al.,

2004; Epstein et al., 2007; Goetz et al., 2011; Nathan et al., 2011; Meier et al., 2012; Sato and Ise, 2012). Displacement rates for mammals were based on modeled dispersal

rates of a wide range of mammal species (mean of Schloss et al., 2012 for Western Hemisphere mammals and rates calculated from global assessments of dispersal distance by

Santini et al., 2013 and generation length by Paciﬁci et al., 2013). Displacement rates for phytophagous insects are based on observed dispersal distances and genetic analyses

(Peterson and Denno, 1998; Kinlan and Gaines, 2003; Schneider, 2003; Berg et al., 2010; Chen et al., 2011). The estimate of median displacement rate for this group exceeds the

highest rates on the axis. These displacement rates do not take into account limitations imposed by host plants. Displacement estimates for freshwater molluscs correspond to the

range of passive plus active dispersal rates for upstream movement (Kappes and Haase, 2012).

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Chapter 4 Terrestrial and Inland Water Systems

ome species in these groups have low dispersal capacity. Current and

future rates of climate change correspond to climate velocities that exceed

rates of displacement for several species groups for most climate

change scenarios. This is particularly true for mid- and late-successional

trees that have maximum displacement rates that are on the order of

tens to a few hundreds of meters per year. Overall, many plant species

are foreseen to be able to track climates only in mountainous areas at

medium to high rates of warming, though there is uncertainty concerning

the potential role of long-distance dispersal (Pearson, 2006). Primates

generally have substantially higher dispersal capacity than trees;

however, a large fraction of primates are found in regions with very

high climate velocities, in particular the Amazon basin, thereby putting

them at high risk of being unable to track climates even at relatively

low rates of climate change (Schloss et al., 2012). On a global average,

many rodents, as well as some carnivores and freshwater molluscs, are

projected to be unable to track climate at very high rates of climate

change (i.e., >0.06°C yr

–1

). These projected differences in species ability

to keep pace with future climate change are broadly coherent with

observations of species ability or inability to track recent global warming

(see Section 4.3.2.5.1).

Humans can increase species displacement rates by intentionally or

unintentionally dispersing individuals or propagules. For example, many

economically important tree species may be deliberately moved on large

scales as part of climate adaptation strategies in forestry in some regions

(Lindner et al., 2010). Human activities can also substantially reduce

displacement rates. In particular, habitat loss and fragmentation typically

reduces displacement rates, sometimes substantially (Eycott et al., 2012;

Hodgson et al., 2012; Meier et al., 2012; Schloss et al., 2012). The degree

to which habitat fragmentation slows displacement depends on many

factors, including the spatial pattern of the fragments and corridors,

maximum dispersal distances, population dynamics, and the suitability of

intervening modified habitats as stepping-stones (Pearson and Dawson,

2003). Species and habitat dependencies may also speed or hinder

species displacement. For example, host plants are projected to move

much more slowly than most herbivorous insects, substantially slowing

displacement of the insects if they are unable to switch host plants

(Schweiger et al., 2012). Likewise, many habitats are structured by slow

moving plants, so habitat shifts are projected to lag behind climate

change (Hickler et al., 2012; Jones et al., 2012), which will in turn mediate

the movements of habitat specialists.

There are significant uncertainties in climate velocities, measured estimates

of dispersal and establishment rates, and model formulations. Climate

velocities are calculated using a variety of methods and spatial resolutions,

making direct comparisons difficult and leading to low confidence in

estimates of climate velocities in Figure 4-5b (limited evidence and

medium agreement). The lowest estimates of global average climate

velocity (Figure 4-5b, center axis) are about half the best estimate values

we show on the climate velocity axes (Loarie et al., 2009), while the

highest estimates are about four times higher (Burrows et al., 2011),

but high estimates may be artefacts of using very large spatial resolutions

(Dobrowski et al., 2013). In addition, the climate velocities used in Figure

4-5 are based on temperature alone, and recent analyses indicate that

including more climate factors increases climate velocity (Feeley and

Rehm, 2012; Dobrowski et al., 2013). Species displacement rates are

calculated based on a very wide range of methods including rates of

isplacement in the paleontological record, rates of current range shifts

due to climate warming, models of dispersal and establishment, maximum

observed dispersal distances and genetic analyses (e.g., Kinlan and

Gaines, 2003; Stevens, V.M. et al., 2010). There are often large differences

in estimates of dispersal rates across methods due to intrinsic uncertainties

in the methods and differences in the mechanisms included (Kinlan and

Gaines, 2003; Stevens, V.M. et al., 2010). For example, estimates of tree

displacement rates are frequently based on models or observations

that explicitly or implicitly include both dispersal of seeds and biotic

and abiotic factors controlling establishment of adult trees. Displacement

rates of trees are often more strongly limited by establishment than

dispersal (Higgins et al., 2003; Meier et al., 2012). It is reasonable to

expect that limits on establishment could also be important for other

species groups, but often only dispersal rates have been calculated,

leading to an overestimation of displacement rates. For trees there is

medium confidence in projections of their displacement rates due to

the large number of studies of past, current, and future displacement

rates (robust evidence and medium agreement). Less is known for other

broad species groups such as mammals, so there is only low confidence

in estimates of their displacement capacity. Estimates for other groups,

such as freshwater molluscs, are based on very little data, so estimates

of their dispersal capacity are poorly constrained.

Despite large uncertainties in displacement capacity and climate velocity,

the rates of displacement required to track the highest rates of climate

change (RCP8.5) are so high that many species will be unable to do so

(high confidence). Moderate rates of projected climate change (RCP4.5

and RCP6.0) would allow more species to track climate, but would still

exceed the capacity of many species to track climate (medium confidence).

The lowest rates of projected climate change (RCP2.6) would allow

most species to track climate toward the end of the century (high

confidence). This analysis highlights the importance of rates of climate

change as an important component of climate change impacts on

species and ecosystems. For example, differences in the magnitude of

climate change between scenarios are small at mid-21st century (WGI

AR5 Chapter 12), but the differences in rates of climate change are

large. At mid-century, it is projected that species would need to move

little at the lowest rates of climate change (RCP2.6), but will need to

move approximately 70 km per decade in flat areas in order to track

climate at the highest rates of climate change (RCP8.5).

Species that cannot move fast enough to keep pace with the rate of

climate change will lose favorable climate space and experience large

range contractions (Warren et al., 2013), whereas displacement that

keeps pace with climate change greatly increases the fraction of species

that can maintain or increase their range size (Menéndez et al., 2008;

Pateman et al., 2012). Mountains provide an extremely important

climate refuge for many species because the rate of displacement

required to track climate is low (Figure 4-5b; Colwell et al., 2008; Engler

et al., 2011; Gottfried et al., 2012; Pauli et al., 2012; but see Dullinger

et al., 2012). However, species that already occur near mountaintops

(or other boundaries) are among the most threatened by climate change

because they cannot move upwards (Ponniah and Hughes, 2004; Thuiller

et al., 2005; Raxworthy et al., 2008; Engler et al., 2011; Sauer et al.,

2011). The consequences of losing favorable climate space are not yet

well understood. The extent to which adaptive responses might allow

persistence in areas of unfavorable climates is discussed in Section

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Terrestrial and Inland Water Systems Chapter 4

.4.1.2. In the absence of adaptation, losing favorable climate space is

projected to lead to reduced fitness, declining abundance, and local

extinction, with potentially large effects on biodiversity and ecosystem

services (see evidence of early signs of this for trees in Box 4-2).

4.3.2.5.3. Observed changes in abundance and local extinctions

Observations of range shifts imply changes in abundance, that is,

colonization at the “leading edge” and local extinction at the “trailing

edge” of ranges. Evidence that the attribution of these responses to

recent changes in climate can be made with high confidence for several

species groups is reviewed here (Section 4.3.2.5), in AR4, and by Cahill

et al. (2013). Changes in abundance, as measured by changes in the

population size of individual species or shifts in community structure

within existing range limits, have also occurred in response to recent

global warming (high confidence; Thaxter et al., 2010; Bertrand et al.,

2011; Naito and Cairns, 2011; Rubidge et al., 2011; Devictor et al., 2012;

Tingley et al., 2012; Vadadi-Fulop et al., 2012; Cahill et al., 2013; Ruiz-

Labourdette et al., 2013). Confident attribution to recent global warming

is hindered by confounding factors such as disease, land use change,

and invasive species (Cahill et al., 2013). New tentative conclusions

since AR4 are that climate-related changes in abundance and local

extinctions appear to be more strongly related to species interactions

than to physiological tolerance limits (low confidence; Cahill et al.,

2013) and that precipitation can be a stronger driver of abundance

change than temperature in many cases (Tian et al., 2011; Tingley et al.,

2012). This gives weight to concerns that biological interactions, which

are poorly known and modeled, may play a critical role in mediating

the impacts of future climate change on species abundance and local

extinctions (Dunn et al., 2009; Bellard et al., 2012; Hannah, 2012; Urban

et al., 2012; Vadadi-Fulop et al., 2012).

A few examples illustrate the types of change in abundance that are

being observed and the challenges in attributing these to recent global

warming. Some of the clearest examples of climate-related changes in

species populations come from high-latitude ecosystems where non-

climate drivers are of lesser importance. For example, both satellite data

and a large number of long-term observations indicate that shrub

abundance is generally increasing over broad areas of Arctic tundra,

which is coherent with predicted shifts in community structure due to

warming (Epstein et al., 2007; Goetz et al., 2011; Myers-Smith et al.,

2011). In the Antarctic, two native vascular plants, Antarctic pearlwort

(Colobanthus quitensis) and Antarctic hair grass (Deschampsia antarctica),

have become more prolific over recent decades, perhaps because they

benefit more from warming of soils than do mosses (Hill et al., 2011).

Penguin populations have declined in several areas of the Antarctic,

including a recent local extinction of an Emperor penguin (Aptenodytes

forsteri) population that has been attributed to regional changes in

climate (Trathan et al., 2011). The attribution of these declines to

changes in regional climate is well supported, but the link to global

warming is tenuous (Barbraud et al., 2011).

Mountains also provide good examples of changes in abundance that

can be linked to climate because very strong climate gradients are found

there. AR4 highlighted these responses, and the case for changes in

abundance, in particular plants, has become stronger since then. For

xample, Pauli et al. (2012) reported an increase in species richness from

plant communities of mountaintops in the European boreal and

temperate zones due to increasing temperatures and a decrease in

species richness on the Mediterranean mountain tops, probably due to a

decrease in the water availability in southern Europe. An increase in the

population size of warm-adapted species at high altitudes also appears

to be attributable to increasing temperatures (Gottfried et al., 2012).

However, these attributions are complicated by other anthropogenic

influences such as changes in grazing pressure, atmospheric N deposition,

and forest management practices (Gottfried et al., 2012). Altitudinal

gradients in local and global extinctions of amphibians also contributed

to the attribution of these extinctions to recent global warming, although

this attribution remains controversial (see Section 4.3.2.5.5).

4.3.2.5.4. Projected changes in abundance and local extinction

Ecological niche models do not predict population changes, but the

shifts in suitable climates can be used to infer areas where species

populations might decline or increase. These models project that local

extinction risk by the end of the 21st century due to climate change will

vary widely, ranging from almost no increase in local extinction risk

within the current range for some species or species groups to greatly

increased risk of local extinctions in more than 95% of the present-day

range for others (Settele et al., 2008; Bellard et al., 2012). Projected

local colonization rates are equally variable. There has been progress in

coupling species distribution models and species abundance models for

a wide range of organisms (Keith et al., 2008; Midgley et al., 2010;

Matthews et al., 2011; Schippers et al., 2011; Oliver et al., 2012a;

Renwick et al., 2012). These hybrid approaches predict extinction risk

directly, rather than by inference from changes in climate suitability

(Fordham et al., 2012). The main conclusions from these studies are that

changes in species abundance and local extinction risk as a result of

climate change can range from highly positive to highly negative, and

are determined by a combination of factors, including its environmental

niche, demographics, and life history traits, as well as interactions

among these factors (Aiello-Lammens et al., 2011; Clavero et al., 2011;

Conlisk et al., 2012; Fordham et al., 2012; Swab et al., 2012).

Changes in abundances will also be accompanied by changes in genetic

diversity (see also Section 4.4.1.2). At the intraspecific level, future climate

change is projected to induce losses of genetic diversity when it results

in range contraction (Balint et al., 2011; Pauls et al., 2013). In addition,

there is theoretical and observational evidence this loss of genetic

diversity will depend on rates of migration and range contraction

(Arenas et al., 2012). In these cases, reductions in genetic diversity may

then decrease the ability of species to adapt to further climate change

or other global changes. Climate change may also compound losses of

genetic diversity that are already occurring due to other global changes

such as the introduction of alien species or habitat fragmentation

(Winter et al., 2009; see also Section 4.2.4.6).

4.3.2.5.5. Observed global extinctions

Global species extinctions, many of them caused by human activities,

are now occurring at rates that approach or exceed the upper limits of

300

Chapter 4 Terrestrial and Inland Water Systems

bserved natural rates of extinction in the fossil record (Barnosky et al.,

2011). However, across all taxa there is only low confidence that rates

of species extinctions have increased over the last several decades

(birds: Szabo et al., 2012; but see Kiesecker, 2011, for amphibians). Most

extinctions over the last several centuries have been attributed to habitat

loss, overexploitation, pollution, or invasive species, and these are the

most important current drivers of extinctions (Millennium Ecosystem

Assessment, 2005b; Hofmann and Todgham, 2010; Cahill et al., 2013). Of

the more than 800 global extinctions documented by the International

Union for Conservation of Nature (IUCN), only 20 have been tenuously

linked to recent climate change (Cahill et al., 2013; see also Hoffmann

et al., 2010; Szabo et al., 2012). Molluscs, especially freshwater molluscs,

have by far the highest rate of documented extinctions of all species

groups (Barnosky et al., 2011). Mollusc extinctions are attributed

primarily to invasive species, habitat modification, and pollution;

changes in climate are rarely evoked as a driver (Lydeard et al., 2004;

Regnier et al., 2009; Chiba and Roy, 2011; but see a few cases in Kappes

and Haase, 2012; Cahill et al., 2013). Freshwater fish have the highest

documented extinction rates of all vertebrates, and again very few have

been attributed to changing climate, even tenuously (Burkhead, 2012;

Cahill et al., 2013). In contrast, changes in climate have been identified

as one of the key drivers of extinctions of amphibians (Pounds et al., 2006).

There have been more than 160 probable extinctions of amphibians

documented over the last 2 decades, many of them in Central America

(Pounds et al., 2006; Kiesecker, 2011). The most notable cases have been

the golden toad (Bufo periglenes) and Monteverde harlequin frog

(Atelopus varius) of Central America, which belong to a group of

amphibians with high rates of extinction previously ascribed to global

warming with “very high confidence” (Pounds et al., 2006; Fischlin et

al., 2007). This case has raised a number of important issues about

attribution because (1) the proximate causes of extinction of these and

other Central American frogs appear to be an extremely virulent invasive

fungal infection and land use change, with regional changes in climate

as a potential contributing factor, and (2) changes in regional climate

may have been related to natural climate fluctuations rather than

anthropogenic climate change (Sodhi et al., 2008; Lips et al., 2008;

Anchukaitis and Evans, 2010; Bustamante et al., 2010; Collins, 2010;

Vredenburg et al., 2010; Kiesecker, 2011; McKenzie and Peterson, 2012;

McMenamin and Hannah, 2012). Owing to low agreement among

studies there is only medium confidence in detection of extinctions and

attribution of Central American amphibian extinctions to climate

change. While this case highlights difficulties in attribution of extinctions

to recent global warming, it also points to a growing consensus that it

is the interaction of climate change with other global change pressures

that poses the greatest threat to species (Brook et al., 2008; Pereira et

al., 2010; Hof et al., 2011b). Overall, there is very low confidence that

observed species extinctions can be attributed to recent climate warming,

owing to the very low fraction of global extinctions that have been

ascribed to climate change and tenuous nature of most attributions.

4.3.2.5.6 Projected future species extinctions

Projections of future extinctions due to climate change have received

considerable attention since AR4. AR4 stated with medium confidence

“that approximately 20–30% of the plant and animal species assessed

to date are at increasing risk of extinction as global mean temperatures

xceed a warming of 2-3°C above preindustrial levels” (Fischlin et al.,

2007). All model-based analyses since AR4 broadly confirm this concern,

leading to high confidence that climate change will contribute to

increased extinction risk for terrestrial and freshwater species over the

coming century (Pereira et al., 2010; Sinervo et al., 2010; Pearson, 2011;

Warren et al., 2011, 2012; Bellard et al., 2012; Hannah, 2012; Ihlow et

al., 2012; Sekercioglu et al., 2012; Wearn et al., 2012; Foden et al., 2013).

Most studies indicate that extinction risk rises rapidly with increasing

levels of climate change, but some do not (Pereira et al., 2010). The

limited number of studies that have directly compared land use and

climate change drivers have concluded that projected land use change

will continue to be a more important driver of extinction risk throughout

the 21st century (Pereira et al., 2010). There is, however, broad agreement

that land use, and habitat fragmentation in particular, will pose serious

impediments to species adaptation to climate change as it is projected

to reduce the capacity of many species to track climate (see Section

4.3.2.5.3). These considerations lead to the assessment that future

species extinctions are a high risk because the consequences of climate

change are potentially severe, widespread, and irreversible, as extinctions

constitute the permanent loss of unique life forms.

There is, however, low agreement concerning the overall fraction of

species at risk, the taxa and places most at risk, and the time scale for

climate change-driven extinctions to occur. Part of this uncertainty arises

from differences in extinction risks within and between modeling studies:

this uncertainty has been evaluated in AR4 and subsequent syntheses

(Pereira et al., 2010; Warren et al., 2011; Bellard et al., 2012; Cameron,

2012). All studies project increased extinction risk by the end of the 21st

century due to climate change, but as indicated in AR4 the range of

estimates is large. Recent syntheses indicate that model-based estimates

of the fraction of species at substantially increased risk of extinction

due to 21st century climate change range from below 1% to above 50%

of species in the groups that have been studied (Pereira et al., 2010;

Bellard et al., 2012; Cameron, 2012; Foden et al., 2013). Differences in

modeling methods, species groups, and climate scenarios between

studies make comparisons between estimates difficult (Pereira et al.,

2010; Warren et al., 2011; Cameron, 2012).

Many papers published since AR4 argue that the uncertainty may be

even higher than indicated in syntheses of model projections, due to

limitations in the ability of current models to evaluate extinction risk (e.g.,

Kuussaari et al., 2009; Pereira et al., 2010; Dawson et al., 2011; McMahon

et al., 2011; Pearson, 2011; Araujo and Peterson, 2012; Bellard et al., 2012;

Fordham et al., 2012; Hannah, 2012; Kramer et al., 2012; Zurell et al.,

2012; Halley et al., 2013; Moritz and Agudo, 2013). Models frequently

do not account for genetic and phenotypic adaptive capacity, dispersal

capacity, population dynamics, the effects of habitat fragmentation and

loss, community interactions, micro-refugia, and the effects of rising CO

concentrations, all of which could play a major role in determining

species vulnerability to climate change, causing models to either over-

or underestimate risk. In addition, difficulties in model validation, large

variation in the climate sensitivity of species groups, and uncertainties

about time scales linking extinction risks to range reductions also lead

to large uncertainty in model-based estimates of extinction risk.

A variety of studies since AR4 illustrate how accounting for these factors

alters estimates of extinction risk. Accounting for biotic interactions

301

Terrestrial and Inland Water Systems Chapter 4

uch as pollination or predator-prey networks can increase modeled

extinction risks, at least for certain areas and species groups (Schweiger

et al., 2008; Urban et al., 2008; Hannah, 2012; Nakazawa and Doi,

2012), or can decrease extinction risk (Menéndez et al., 2008; Pateman

et al., 2012). Accounting for climatic variation at fine spatial scales may

increase (Randin et al., 2009; Gillingham et al., 2012; Suggitt et al., 2012;

Dobrowski et al., 2013; Franklin et al., 2013) or decrease (Trivedi et al.,

2008; Engler et al., 2011; Shimazaki et al., 2012) the persistence of small

populations under future climate change. Several recent studies indicate

that correlative species distribution models (the type of model most

frequently used for evaluating species extinction risk) tend to be much

more pessimistic concerning plant species range contractions and the

inferred extinction risks due to climate change when compared to

mechanistic models that explicitly account for the interactions between

climate change and protective effects of rising CO

concentrations on

plants (Morin and Thuiller, 2009; Kearney et al., 2010; Cheaib et al.,

2012). Models that account for population dynamics indicate that some

species populations, such as those of polar bears (Hunter et al., 2010),

will decline precipitously over the course of the next century due to

climate change, greatly increasing extinction risk, while others may not

(Keith et al., 2008). Phenotypic plasticity in one very well-studied

temperate bird population has been estimated to be sufficient to keep

extinction risk low even with projected warming exceeding 2–3°C

(Vedder et al., 2013), but this and other studies suggest that capacity for

adaptation is often substantially lower in species with long generation

times (see Section 4.4.1.2). There is evidence that interactions between

physiological tolerances and regional climate change will lead to large

taxonomic and spatial variation in extinction risk (Deutsch et al., 2008;

Sinervo et al., 2010). Even species whose populations are not projected

to decline rapidly over the next century can face a substantial “extinction

debt,” that is, will be in unfavorable climates that over a period of many

centuries are projected to lead to large reductions in population size and

increase the risk of extinction (Dullinger et al., 2012). Finally, evidence

from the paleontological record indicating very low extinction rates over

the last several hundred thousand years of substantial natural fluctuations

in climate—with a few notable exceptions such as large land animal

extinctions during the Holocene—has led to concern that forecasts of

very high extinction rates due entirely to climate change may be

overestimated (Botkin et al., 2007; Dawson et al., 2011; Hof et al.,

2011a; Willis and MacDonald, 2011; Moritz and Agudo, 2013). However,

as indicated in Section 4.2.3, no past climate changes are precise

analogs of future climate change in terms of speed, magnitude, and

spatial scale; nor did they occur alongside the habitat modification,

overexploitation, pollution, and invasive species that are characteristic

of the 21st century. Therefore the paleontological record cannot easily

be used to assess future extinction risk due to climate change.

4.3.3. Impacts on and Risks for Major Systems

This section covers impacts of climate change on broad categories of

terrestrial and freshwater ecosystems of the world. We have placed a

particular emphasis on those ecosystems that have high exposure to

climate change or that may be pushed past thresholds or “tipping

points” by climate change. Two geographical regions of particularly high

risk have been identified in recent studies: (1) tropics, due to the limited

capacity of species to adapt to moderate global warming and (2) high

orthern latitude systems, because temperature increases are projected

to be large. There has been a tendency to oppose these two points of

view, but there is a high risk in both types of systems, albeit for different

reasons (Corlett, 2011). Tropical species, which experienced low inter-

and intra-annual climate variability, have evolved within narrow thermal

limits, and are already near their upper thermal limits (ectotherms:

Deutsch et al., 2008; Huey et al., 2012; birds: Sekercioglu et al., 2012;

trees: Corlett, 2011). On this basis, tropical species and ecosystems are

predicted to be more sensitive to climate change than species and

ecosystems that have evolutionary histories of climatic variability (e.g.,

Arctic and boreal ecosystems; Beaumont et al., 2011). However, there

are physiological, evolutionary, and ecological arguments that tropical

species and ecosystem sensitivities to climate change are complex and

may not be particularly high compared to other systems (Gonzalez et

al., 2010; Corlett, 2011; Laurance et al., 2011; Gunderson and Leal, 2012;

Walters et al., 2012). High-latitude systems have the greatest projected

exposure to rising temperatures (WGI AR5 Chapter 12; Diffenbaugh and

Giorgi, 2012), which all else being equal would put them at higher risk.

The greatest degree of recent climate warming has occurred at high

northern latitudes (Burrows et al., 2011) and the strongest and clearest

signals of recent climate warming impacts on ecosystems come from

these regions. A comparison of modeled biome level vulnerability

indicated that temperate and high northern latitude systems are also

the most vulnerable in the future (Gonzalez et al., 2010).

Several potential tipping points (see Section 4.2.1) with regional and

global consequences have been identified (Scheffer, 2009); two are

elaborated in Boxes 4-3 (Amazon dieback) and 4-4 (tundra-boreal

regime shift). An assessment by the authors of this chapter of the top risks

in relation to climate change and terrestrial and freshwater ecosystems

is presented in Table 4-3.

4.3.3.1. Forests and Woodlands

Forests and woodlands are principal providers of timber, pulp, bioenergy,

water, food, medicines, and recreation opportunities and can play

prominent roles in cultural traditions. Forests are the habitat of a large

fraction of the Earth’s terrestrial plant and animal species, with the

highest concentrations and levels of endemism found in tropical regions

(Gibson et al., 2011). Climate change and forests interact strongly; air

temperature, solar radiation, rainfall, and atmospheric CO

concentrations

are major drivers of forest productivity and forest dynamics, and forests

help control climate through the large amounts of carbon they can remove

from the atmosphere or release, through absorption or reflection of

solar radiation (albedo), cooling through evapotranspiration, and the

production of cloud-forming aerosols (Arneth et al., 2010; Pan et al.,

2011; Pielke et al., 2011).

Combinations of ground-based observations, atmospheric carbon

budgets, and satellite measurements indicate with high confidence that

forests are currently a net sink for carbon at the global scale. It is

estimated that intact and regrowing forests currently contain 860 ± 70

PgC and sequestered 4.0 ± 0.7 PgC yr

–1

globally between 2000 and

2007 (WGI AR5 Chapter 6; Canadell et al., 2007; Pan et al., 2011; Le

Quéré et al., 2012). The carbon taken up by intact and regrowing forests

was counterbalanced by a release due to land use change of 2.8 ± 0.4

302

Chapter 4 Terrestrial and Inland Water Systems

PgC yr

–1

over this same period due mostly to tropical deforestation and

forest degradation associated with logging and fire, resulting in a net

carbon balance for global forests of 1.1±0.8 PgC yr

–1

The future of the interaction between climate and forests is unclear.

The carbon taken up by intact and regrowing forests appears to have

stabilized compared to the 1990s, after having increased in the 1970s

and 1980s (Canadell et al., 2007; Pan et al., 2011). There is medium

confidence that the terrestrial carbon sink is weakening. The drivers

behind the forest carbon sink vary greatly across regions. They include

forest regrowth and stimulation of carbon sequestration by climate

change, rising atmospheric CO

concentrations, and nitrogen deposition

Near term

(2030 – 2040)

Present

ong term

(

2080 – 2100)

2°C

4°C

Very

low

ery

high

Medium

ear term

(2030 – 2040)

Long term

(2080 – 2100)

able 4-3 | Key risks for terrestrial and freshwater ecosystems from climate change and the potential for reducing risk through mitigation and adaptation. Key risks are identiﬁed

based on assessment of the literature and expert judgments by chapter authors, with evaluation of evidence and agreement in supporting chapter sections. Each key risk is

characterized as very low to very high. Risk levels are presented in three time frames: the present, near term (here, assessed over 2030–2040), and longer term (here, assessed

over 2080–2100). For the near term era of committed climate change, projected levels of global mean temperature increase do not diverge substantially across emission

cenarios. For the longer term era of climate options, risk levels are presented for global mean temperature increase of 2°C and 4°C above pre-industrial levels. For each

imeframe, risk levels are estimated for a continuation of current adaptation and for a hypothetical highly adapted state. Relevant climate variables are indicated by icons. For a

iven key risk, change in risk level through time and across magnitudes of climate change is illustrated, but because the assessment considers potential impacts on different

physical, biological, and human systems, risk levels should not necessarily be used to evaluate relative risk across key risks, sectors, or regions.

Reduction in terrestrial carbon sink: Carbon stored in terrestrial ecosystems is vulnerable

o loss back into the atmosphere. Key mechanisms include an increase in ﬁre frequency due to

climate change and the sensitivity of ecosystem respiration to rising temperatures.

(medium conﬁdence)

[

4.2.4, 4.3.2, 4.3.3]

Adaptation prospects include managing

land use (including deforestation), ﬁre,

nd other disturbances and non-climatic

stressors.

Present

2°C

4°C

Very

low

ery

high

Medium

Tree mortality and forest loss: Tree mortality has been observed to have increased in

many places and has been attributed in some cases to direct climate effects and indirect

effects due to pests and diseases. The dead trees increase the risk of forest ﬁres.

(medium conﬁdence)

[4.3.3.1, Box 4-2]

Adaption options include more effective

management of ﬁre, pests, and

pathogens.

Present

2°C

4°C

Very

low

Very

high

Medium

Increased risk of species extinction: A large fraction of the species that have been assessed are

vulnerable to extinction as a result of climate change, often in interaction with other threats. Species

with an intrinsically low dispersal rate, especially when occupying ﬂat landscapes where the projected

climate velocity is high, and species in isolated habitats such as mountain tops, islands, or small

protected areas are especially at risk. Cascading effects through organism interactions, and especially

those vulnerable to timing (phenological) changes, amplify the risk. (high conﬁdence)

[

4.3.2.5, 4.3.3.3, 4.3.2.1, 4.4.2]

Adaptation options include reducing

habitat modiﬁcation, habitat

fragmentation, pollution,

over-exploitation, and invasive species;

protected area expansion, assisted

dispersal, ex situ conservation.

Present

2°C

4°C

Very

low

Very

high

Medium

Invasion by non-native species: Disruptions of species interactions and the increase in

physiological stress as a result of being near the edge or outside of the historical climate niche

increases the vulnerability of ecosystems to invasion by non-native (alien) species, especially in the

presence of increased long-distance dispersal opportunities. In the extreme this can result in biome

shifts, with consequent changes in the spectrum of ecosystem services provided. (high conﬁdence)

[4.2.4.6]

Climate is one driver among many.

Adaptation options are limited, largely

based on reducing other stresses and

measures to slow the unintended arrival of

aliens. Intensive direct intervention in

controlling emergent invasive species is an

option, but could be overwhelmed by the

rapidly rising number of cases.

Near term

(2030 – 2040)

Long term

(2080 – 2100)

Near term

(2030 – 2040)

Long term

(2080 – 2100)

Near term

(2030 – 2040)

Long term

(2080 – 2100)

Present

2°C

4°C

Very

igh

Medium

oreal tipping point: Arctic ecosystems are vulnerable to abrupt change related to the

thawing of permafrost and spread of shrubs in tundra and increase in pests and ﬁres in

oreal forests. (medium conﬁdence)

[

4.3.3.1.1, Box 4-4]

There are few adaptation options in the

Arctic.

Present

2°C

4°C

ery

low

Very

igh

edium

Amazon tipping point: Moist Amazon forests could change abruptly to less

carbon-dense drought and ﬁre-adapted ecosystems. (low conﬁdence)

[

4.3.3.1.3, Box 4-3]

Policy and market measures to reduce

deforestation and ﬁre.

Near term

(2030 – 2040)

Long term

(2080 – 2100)

ey risk Adaptation issues & prospects

Climatic

drivers

isk & potential for

adaptation

imeframe

recipitation

limate-related drivers of impacts

arming

rend

xtreme

emperature

evel of risk & potential for adaptation

otential for additional adaptation

to reduce risk

isk level with

urrent adaptation

isk level with

igh adaptation

rying

rend

303

Terrestrial and Inland Water Systems Chapter 4

(

Pan et al., 2011; see also Sections 4.2.4.1, 4.2.4.2, 4.2.4.4). Most models

suggest that rising temperatures, drought, and fires will lead to forests

becoming a weaker sink or a net carbon source before the end of the

century (Sitch et al., 2008; Bowman et al., 2009). Fires play a dominant

role in driving forest dynamics in many parts of the world; forest

susceptibility to fire is projected to change little for the lowest emissions

scenario (RCP2.6), but substantially for the high emissions scenario

(RCP8.5; Figure 4-6). There is low agreement on whether climate change

will cause fires to become more or less frequent in individual locations

(Figure 4-6). Climate change-mediated disease and insect outbreaks

could exacerbate climate-driven increases in fire susceptibility (Kurz et

al., 2008). The greatest risks for large positive feedbacks from forests to

climate through changes in disturbance regimes arise from widespread

tree mortality and fire in tropical forests and low-latitude areas of boreal

forests, as well as northward expansion of boreal forests into Arctic

tundra (Lenton et al., 2008; Kriegler et al., 2009; Good et al., 2011b).

Recent evidence suggests (low confidence) that the stimulatory effects

of global warming and rising CO

concentrations on tree growth may

have already peaked in many regions (Charru et al., 2010; Silva et al.,

2010; Silva and Anand, 2013) and that warming and changes in

precipitation are increasing tree mortality in a wide range of forest

systems, acting via heat stress, drought stress, pest outbreaks, and a

wide range of other indirect impact mechanisms (Allen, C.D. et al., 2010;

Box 4-2). Detection of a coherent global signal is hindered by the lack

of long-term observations in many regions and attribution to climate

change is difficult because of the multiplicity of mechanisms mediating

mortality (Allen, C.D. et al., 2010).

Deforestation has slowed over the last decade (Meyfroidt and Lambin,

2011). This includes substantial reductions in tropical deforestation in

some regions, such as the Brazilian Amazon, where deforestation rates

declined rapidly after peaking in 2005 (Nepstad et al., 2009; INPE,

2013). Growing pressure for new crop (Section 4.4.4) and grazing land

will continue to drive tropical deforestation (medium confidence),

although recent policy experiments and market-based interventions in

land use demonstrate the potential to reduce deforestation (Meyfroidt

and Lambin, 2011; Westley et al., 2011; Nepstad et al., 2013).

4.3.3.1.1. Boreal forests

Most projections suggest a poleward expansion of forests into tundra

regions, accompanied by a general shift in composition toward more

temperate plant functional types (e.g., evergreen needleleaf being

replaced by deciduous broadleaf; or in colder regions, deciduous

needleleaf replaced by evergreen needleleaf (Lloyd et al., 2011; Pearson

et al., 2013). Projections of climate-driven changes in boreal forests over

the next few centuries remain uncertain on some issues, partly as a

result of different processes of change being considered in different

models. In particular, the inclusion or exclusion of fire and insects makes

a big difference, possibly making the boreal forest more susceptible to

a rapid, nonlinear, or abrupt decline in some regions (Bernhardt et al.,

2011; Mann et al., 2012; Scheffer et al., 2012; see WGI AR5 Chapter 12).

Recent observed change (Box 4-2) and dynamic vegetation modeling

(e.g., Sitch et al., 2008) suggest that regions of the boreal forest could

experience widespread forest dieback, although there is low confidence

wing to conflicting results (Sitch et al., 2008; Gonzalez et al, 2010) and

poor understanding of relevant mechanisms (WGI AR5 Section 12.5.5.6).

If such shifts were to occur, they would put the boreal carbon sink at

risk (Pan et al., 2011; Mann et al., 2012).

Whereas boreal forest productivity has been expected to increase as a

result of warming (Hari and Kulmata, 2008; Bronson et al., 2009; Zhao

and Running, 2010; Van Herk et al., 2011), and early analyses of satellite

observations confirmed this trend in the 1980s (medium confidence),

more recent and longer-term assessments indicate with high confidence

that many areas of boreal forest have instead experienced productivity

declines (high confidence; Goetz et al., 2007; Parent and Verbyla, 2010;

Beck, P.S.A. et al., 2011; de Jong et al., 2011). The best evidence to date

indicates that these “browning trends” are due to warming-induced

drought, specifically the greater drying power of air (vapor pressure

deficit; Williams et al., 2013), inducing photosynthetic down-regulation

of boreal tree species, particularly conifer species, most of which are not

adapted to the warmer conditions (Welp et al., 2007; Bonan, 2008; Van

Herk et al., 2011). Satellite evidence for warming-induced productivity

declines has been corroborated by tree ring studies (Barber et al., 2000;

Hogg et al., 2008; Beck, P.S.A. et al., 2011; Porter and Pisaric, 2011;

Griesbauer and Green, 2012) and long-term tree demography plots in

more continental and densely forested areas (Peng et al., 2011; Ma et

al., 2012). Conversely, productivity has increased at the boreal-tundra

ecotone, where more mesic (moist) conditions may be generating the

expected warming-induced positive growth response (Rupp et al., 2001;

McGuire et al., 2007; Goldblum and Rigg, 2010; Beck, P.S.A. et al., 2011).

The complexity of boreal forest response also involves tree age and size,

with younger trees and stands perhaps being more able to benefit from

warming where other factors are not limiting (Girardin et al., 2011, 2012).

Where they occur, warming and drying, coupled with productivity

declines, insect disturbance, and associated tree mortality, also favor

greater fire disturbance (high confidence). The boreal biome fire regime

has intensified regionally in recent decades, exemplified by increases in

the extent of area burned but also a longer fire season and more

episodic fires that burn with greater energy output or intensity (Girardin

and Mudelsee, 2008; Macias Fauria and Johnson, 2008; Kasischke et

al., 2010; Turetsky et al., 2011; Mann et al., 2012; Girardin et al., 2013a).

The latter is particularly important because more severe burning

consumes soil organic matter to greater depth, often to mineral soil,

providing conditions that favor recruitment of deciduous species that

in some regions of the North American boreal forest replace what was

previously evergreen conifer forest (Johnstone et al., 2010; Bernhardt

et al., 2011). Fire-mediated composition changes in post-fire succession

influence a host of ecosystem feedbacks to climate, including changes

in net ecosystem carbon balance (Bond-Lamberty et al., 2007; Goetz et

al., 2007; Welp et al., 2007; Euskirchen et al., 2009) as well as albedo

and energy balance (Randerson et al., 2006; Jin et al., 2012; O’Halloran

et al., 2012). The extent to which the net effect of these feedbacks will

exacerbate or mitigate additional warming is not well known over the

larger geographic domain of the boreal biome, except via modeling

studies that are relatively poorly constrained owing to sparse in situ

observations.

The vulnerability of the boreal biome to this cascading series of interacting

processes (Wolken et al., 2011), and their ultimate influence on climate

304

Chapter 4 Terrestrial and Inland Water Systems

Change in ﬁre frequency (% per century)

Multi-model agreement

Multi-model agreement on change in

ﬁre probability 1971–2000 to 2070–2099

–50 0 +50

–4

90%

–1000 –100

–10

–1

10 100 1000

Decrease Increase

67%

67% 90%

Low

agreement

–2

246

8 10

12 24 50

Forest Fire Danger Index (FFDI)

orest Fire Danger Index (FFDI)

Change in FFDI

Change in ﬁre frequency

1951–2000 to 2051–2100

Change in Forest Fire Danger Index (FFDI) 1970–1999 to 2070–2099

Fire counts per year

Change in ﬁre frequency between 2004 and 2100

1970–1999, HadGEM2-ES 2070–2099, RCP2.6, HadGEM2-ES

RCP2.6, HadGEM2-ES

A1B, three-model mean

(h) (i)

(

(g)

(e)(d) (f)

(

b) (c)

A2, GISSB1, GISS

2070–2099, RCP8.5, HadGEM2-ES

RCP8.5, HadGEM2-ES

Figure 4-6 | Projected changes in meteorological ﬁre danger, ﬁre probability, and ﬁre frequency with different methods and climate models. (a) 30-year annual mean McArthur

Forest Fire Danger Index (FFDI) and change simulated with the Hadley Centre Global Environmental Model version 2 Earth System conﬁguration (HadGEM2-ES) for 1970–1999,

with areas of no vegetation excluded (Betts et al., 2013). (b) As (a) for 2070–2099, Representative Concentration Pathway 2.6 (RCP2.6). (c) as (a) for 2070–2099, RCP8.5. (d)

Change in ﬁre frequency by 2051–2100 relative to 1951–2000, SRES A1B, simulated with the MC1 vegetation model driven by three GCMs (Commonwealth Scientiﬁc and

Industrial Research Organisation (CSIRO)-Mk3.0, Met Ofﬁce Hadley Centre Coupled Model version 3 (HadCM3), Model for Interdisciplinary Research On Climate (MIROC)

3.2medres; mean over three simulations; Gonzalez et al., 2010). (e) Difference between (b) and (a): change in FFDI by 2070–2099 relative to 1970–1999 in HadGEM2-ES,

RCP2.6. (f) Difference between (c) and (a): change in FFDI by 2070–2099 relative to 1970–1999 in HadGEM2-ES, RCP8.5. (g) Agreement on changes in ﬁre probability by

2070–2099 relative to 1971–2000 (Moritz et al., 2012) simulated with a statistical model using climate projections from 16 Coupled Model Intercomparison Project Phase 3

(CMIP3) GCMs, Special Report on Emission Scenarios (SRES) A2. (h) Change in ﬁre frequency by 2100 relative to 2004, SRES B1, simulated using climate and land cover

projections from the Goddard Institute of Space Studies General Circulation Model (GISS GCM) (AR4 version) and Integrated Model to Assess the Global Environment Integrated

Assessment Model (IMAGE IAM) (Pechony and Shindell, 2010). (i) As (h) for SRES A2. Changes in FFDI (a), (b), (c), (e), (f) and ﬁre probability (g) arise entirely from changes in

meteorological quantities, whereas changes in ﬁre frequency (d), (h), (i) depend on both meteorological quantities and vegetation.

305

Terrestrial and Inland Water Systems Chapter 4

eedbacks, differs between North America and northern Eurasia (high

confidence). The latter is dominated by deciduous conifer (larch) forest,

extending from western Russia across central to eastern Siberia—a

region more than twice the size of the North American boreal biome,

most of it underlain by permafrost. In terms of post-fire succession

analogous to the North American boreal biome, larch function more like

deciduous species than evergreen conifers, with greater density and

biomass gain in more severely burned areas, given adequate seed

survival through fire events or post-fire seed dispersal (Zyryanova, 2007;

Osawa et al., 2010; Alexander et al., 2012). Although the fire regime has

intensified in the last 100 years in Siberia, as well as in parts of North

America (Soja et al., 2007; Ali et al., 2012; Mann et al., 2012; Marlon et

al., 2013), the likelihood of regime shifts in larch forests is currently

unknown, partly because larch are self-replacing (albeit at different

densities) and partly because it is largely dependent on the fate of

permafrost across the region. In summary, an increase in tree mortality

is observed in many boreal forests, with the clearest indicators of this

in North America. However, tree health in boreal forests varies greatly

among regions, which coupled with insufficient temporal coverage

means that there is low confidence in the detection and attribution of

a clear temporal trend in tree mortality at the global scale (Figure 4-4).

The vulnerability of permafrost to thawing and degradation with climate

warming is critical not only for determining the rate of a boreal-tundra

biome shift and its associated net feedback to climate, but also for

predicting the degree to which the mobilization of very large carbon

stores frozen for centuries could provide additional warming (high

confidence; Schuur et al., 2008, 2009, 2013; Tarnocai et al., 2009;

Romanovsky et al., 2010; Schaefer et al., 2011; see WGI AR5 Chapters

6 and 12; see also Section 4.3.3.4). The extent and rate of permafrost

degradation varies with temperature gradients from warmer discontinuous

permafrost areas to colder, more continuous areas, but also with the

properties of the soil composition and biology (e.g., Mackelprang et al.,

2011). The degree of thermokarsting (melting of ice-rich soil) associated

with different substrates and associated topographic relief is variable

because boreal vegetation in later successional stages (evergreen conifers

in North America) insulates permafrost from air temperature increases;

soils with differing silt and gravel content tend to have different ice

content that, when melted, produces different degradation and

deformation rates; and because of other factors such as the reduction

of insulation provided by vegetation cover and soil organic layers due

to increased fire (Jorgenson et al., 2010; Grosse et al., 2011). This

variability and vulnerability is poorly represented in ESMs (McGuire et

al., 2012) and is thus the emphasis of research initiatives currently

underway. Carbon management strategies to keep permafrost intact,

for example, by removing forest cover to expose the land surface to

winter temperatures (Zimov et al., 2009), are impractical, not only

because of the vast spatial domain underlain by permafrost, but also

because of the broad societal and ecological impacts that would result.

4.3.3.1.2. Temperate forests

The largest areas of temperate forest are found in eastern North America,

Europe, and eastern Asia. The overall trend for forests in these regions

has until recently been an increase in growth rates of trees and in total

carbon stocks. This has been attributed to a combination of increasing

rowing season length, rising atmospheric CO

oncentrations, nitrogen

deposition, and forest management—specifically regrowth following

formerly more intensive harvesting regimes (Ciais et al., 2008). The

relative contribution of these factors has been the subject of substantial

and unresolved debate (Boisvenue and Running, 2006). Most temperate

forests are managed such that any change is and will be to a large

extent anthropogenic.

The world’s temperate forests act as an important carbon sink (high

confidence due to robust evidence and high agreement), absorbing 0.70

± 0.08 PgC yr

–1

from 1990 to 1999 and 0.80 ± 0.09 from 2000 to 2007

(Pan et al., 2011).This represents 34% of global carbon accumulation

in intact forests and 65% of the global net forest carbon sink (total sink

minus total emissions from land use).

Recent indications are that temperate forests and trees are beginning

to show signs of climate stress, including a reversal of tree growth

enhancement in some regions (North America: Silva et al., 2010; Silva

and Anand, 2013; Europe: Charru et al., 2010; Bontemps et al., 2011;

Kint et al., 2012); increasing tree mortality (Allen, C.D. et al., 2010; Box

4-2); and changes in fire regimes, insect outbreaks, and pathogen attacks

(Adams et al., 2012; Edburg et al., 2012). In northeastern France,

widespread recent declines in growth rates of European beech (Fagus

sylvatica L.) have been attributed to decreasing water availability

(Charru et al., 2010). These trends threaten the substantial role of

temperate forests as net carbon sinks, but it is still unclear to what

extent the observations are representative for temperate forests as a

whole. Several studies find that tree growth rates in temperate forests

passed their peak in the late 20th century and that the decline in tree

growth rates can be attributed to climatic factors, especially drought or

heat waves (Charru et al., 2010; Silva et al., 2010). Extreme climate

events have had a major impact on temperate forests over the last

decade (Ciais et al., 2005; Witte et al., 2011; Kasson and Livingston,

2012). Extensive forest fires occurred in Russia during the exceptionally

hot and dry summer of 2010 (Witte et al., 2011). The complex interactions

between climate and forest management in determining susceptibility

to extreme events make it difficult to unequivocally attribute these

events to recent climate warming (Allen, C.D. et al., 2010). There is

low confidence (limited evidence, medium agreement) that climate

change is threatening the temperate forest carbon sink directly or

indirectly.

At the biome level, there remains considerable uncertainty in the sign

and the magnitude of the carbon cycle response of temperate forests

to climate change. A comparison of Dynamic Global Vegetation

Models (DGVMs) showed that for identical end of 21st century climate

projections, temperate forests are variously projected to substantially

increase in total (biomass plus soil) carbon storage, especially through

gains in forest cover; or decrease due to reductions in total carbon

storage per hectare and loss of tree cover (Sitch et al., 2008). Projections

for eastern Asia are less variable: temperate forests remain carbon

sinks over the coming century, with carbon storage generally

peaking by mid-century and then declining (Sitch et al., 2008; Peng

et al., 2009; Ni, 2011). However, regional vegetation models for China

predict a substantial northward shift of temperate forest (Weng and

Zhou, 2006; Ni, 2011). There is little indication from either models or

observations that the responses of temperate forests to climate change

306

Chapter 4 Terrestrial and Inland Water Systems

Box 4-2 | Tree Mortality and Climate Change

Extensive tree mortality and widespread forest dieback (high mortality rates at a regional scale) linked to drought and temperature

stress have been documented recently on all vegetated continents (Allen, C.D. et al., 2010; Figure 4-7). However, appropriate ﬁeld

data sets are currently lacking for many regions (Anderegg et al., 2013a), leading to low conﬁdence in our ability to detect a global

trend. Nevertheless, long-term increasing tree mortality rates associated with temperature increases and drought have been

documented in boreal and temperate forests in western North America (van Mantgem et al., 2009; Peng et al., 2011). Increased levels

of tree mortality following drought episodes have also been detected in multiple tropical forests (Kraft et al., 2010; Phillips et al.,

2010) and Europe (Carnicer et al., 2011). Episodes of widespread dieback (high mortality rates at a regional scale) have been

observed in multiple vegetation types, particularly in western North America, Australia, and southern Europe (Raffa et al., 2008;

Carnicer et al., 2011; Anderegg et al., 2013a). Some widespread dieback events have occurred concomitant with infestation outbreaks

(Hogg et al., 2008; Raffa et al., 2008; Michaelian et al., 2011), where insect populations are also directly inﬂuenced by climate, such

as population release by warmer winter temperatures (Bentz et al., 2010). Although strong attribution of extensive tree mortality to

recent warming has been made in a few studies, the paucity of long-term studies of the mechanisms driving mortality means that

there is low conﬁdence that this attribution can be made at the global scale.

Localities compiled through 2009 (summarized and listed in Allen et al., 2010)

Global forest cover

Other wooded regions

Examples not included in Allen et al., 2010, largely from post-2009 publications

Broad areas described by particular post-2009 publications

Localities c

omp

iled thro

ugh

2009

(su

mmarized and listed in Alle

et al

, 2010)

Global forest cove

Other wooded re

gio

Examples not included in Allen

et al

, 2010, l

arg

ely

from

pos

t-2009

blications

Broad areas described by

rticular

pos

t-2009

blication

Figure 4-7 | Locations of substantial drought- and heat-induced tree mortality around the globe since 1970 (global forest cover and other wooded regions based on

FAO, 2005). Studies compiled through 2009 (red dots) are summarized and listed in Allen, C.D. et al. (2010). Localities and measurement networks not included in Allen,

C.D. et al. (2010), which are largely from post-2009 publications, have been added to this map (white dots and shapes). New locality references by region: Africa: Mehl

et al., 2010; van der Linde et al., 2011; Fauset et al., 2012; Gonzalez et al., 2012; Kherchouche et al., 2012; Asia: Dulamsuren et al., 2009; Kharuk et al., 2013; Liu et al.,

2013; Zhou et al., 2013; Australasia: Brouwers et al., 2012; Fensham et al., 2012; Keith et al., 2012; Matusick et al., 2012; Brouwers et al., 2013; Matusick et al., 2013;

Europe: Innes, 1992; Peterken and Mountford, 1996; Linares et al., 2009; Galiano et al., 2010; Vennetier and Ripert, 2010; Aakala et al., 2011; Carnicer et al., 2011;

Linares et al., 2011; Sarris et al., 2011; Marini et al., 2012; Cailleret et al., 2013; Vilà-Cabrera et al., 2013; North America: Fahey, 1998; Minnich, 2007; Klos et al., 2009;

Ganey and Vojta, 2011; Michaelian et al., 2011; Peng et al., 2011; DeRose and Long, 2012; Fellows and Goulden, 2012; Kaiser et al., 2012; Millar et al., 2012; Garrity et

al., 2013; Kukowski et al., 2013; Williams et al., 2013; Worrall et al., 2013; South America: Enquist and Enquist, 2011; Lewis et al., 2011; Saatchi et al., 2013.

Continued next page

307

Terrestrial and Inland Water Systems Chapter 4

are characterized by tipping points (Bonan, 2008). There is low

confidence (medium evidence, low agreement) on long-term, climate-

driven changes in temperate forest biomass and geographical range

shifts.

At the species level, models predict that the potential climatic space for

most tree species will shift poleward and to higher altitude in response

to climate change (Dale et al., 2010; Ogawa-Onishi et al., 2010; Hickler

et al., 2012). Associated long-term projected range shifts generally vary

from several kilometers to several tens of kilometers per decade, most

probably faster than natural migration (e.g., Chmura et al., 2011; see

also Section 4.3.2.5). Therefore, assisted migration has been suggested

as an adaptation measure (see Section 4.4.2.4). Such shifts would alter

biodiversity and ecosystem services from temperate forests (e.g., Dale

et al., 2010). Multi-model comparisons for temperate forests, however,

illustrate that there are differences in species response and that models

differ greatly in the severity of projected climate change impacts on

species ranges (Morin and Thuiller, 2009; Kearney et al., 2010; Kramer

et al., 2010; Cheaib et al., 2012). Tree growth models project increased

tree growth at the poleward and high altitudinal range limits over most

of the 21st century in China (Ni, 2011). New approaches to modeling

tree responses, based on the sensitivity of key life-history stages, suggest

that climate change impacts on reproduction could be a major limitation

on temperate tree distributions (Morin et al., 2007). Comparisons with

paleoecological data have helped improve confidence in the ability of

models to project future changes in species ranges (Pearman et al.,

2008; Allen, J.R.M. et al., 2010; Garreta et al., 2010). Model projections

are qualitatively coherent with observations that temperate forest

species are moving up in altitude, probably due to climate warming at

the end of the 20th century (Lenoir et al., 2008). There is medium

confidence (medium evidence, medium agreement) that temperate tree

species are migrating poleward and to higher altitudes.

4.3.3.1.3. Tropical forests

Climate change effects on tropical forests interact with the direct

influences of humans and are understood largely through field studies

of the responses of forests to extreme weather events and through

models that are able to simulate a growing number of ecological and

atmospheric processes (Malhi et al., 2008; Davidson et al., 2012).

A key uncertainty in our understanding of future impacts of climate

change on tropical forests is the strength of direct CO

effects on

photosynthesis and transpiration (see Section 4.3.2.4). These responses

will play an important role in determining tropical forest trends as

temperatures and atmospheric CO

concentrations rise. There is a

physiological basis for arguing that photosynthesis will increase

sufficiently to offset the inhibitory effects of higher temperatures on

forest productivity (Lloyd and Farquhar, 2008), although heightened

photosynthesis does not necessarily translate into an increase in overall

forest biomass (Körner and Basler, 2010). DGVMs and the current

generation of ESMs, including those used within CMIP5 (e.g., Jones et

al., 2011; Powell et al., 2013), generally use formulations for CO

effects

on photosynthesis and transpiration based on laboratory-scale work

(Jarvis, 1976; Farquhar et al., 1980; Ball et al., 1987; Stewart, 1988;

Collatz et al., 1992; Leuning, 1995; Haxeltine and Prentice, 1996; Cox

et al., 1998) that predates larger ecosystem-scale studies, although

some models have been calibrated on the basis of more recent data

(Jones et al., 2011).

A second important source of uncertainty is the rate of future CO

rise and

climate change (Betts et al., 2012). Modeled simulations of future climate

in tropical forest regions indicate with high confidence (robust evidence,

high agreement) that temperature will increase. Future precipitation

change, in contrast, is highly uncertain and varies considerably between

Box 4-2 (continued)

Forest dieback has inﬂuenced the species composition, structure and age demographics, and successional trajectories in affected

forests, and in some cases led to decreased plant species diversity and increased risk of invasion (Kane et al., 2011; Anderegg et al.,

2012). Widespread tree mortality also has multiple effects on biosphere-atmosphere interactions and could play an important role in

future carbon-cycle feedbacks through complex effects on forest biophysical properties and biogeochemical cycles (Breshears et al.,

2005; Kurz et al., 2008; Anderson et al., 2011).

Projections of tree mortality due to climate stress and potential thresholds of widespread forest loss are currently highly uncertain

(McDowell et al., 2011). Most current vegetation models have little-to-no mechanistic representation of tree mortality (Fisher et al.,

2010; McDowell et al., 2011). Nonetheless, a global analysis of tree hydraulic safety margins found that 70% of surveyed tree species

operate close to their limits of water stress tolerance (Choat et al., 2012), indicating that vulnerability to drought and temperature

stress will not be limited to arid and semiarid forests. Furthermore, time scales of tree and plant community recovery following

drought are largely unknown, but preliminary evidence from several forests indicates that full recovery times may be longer than

drought return intervals, leading to “compounding” effects of multiple droughts (Mueller et al., 2005; Anderegg et al., 2013b; Saatchi

et al., 2013). Projected increases in temperature are also expected to facilitate expansion of insect pest outbreaks poleward and in

altitude, which may also cause or contribute to tree mortality (Bentz et al., 2010).

308

Chapter 4 Terrestrial and Inland Water Systems

limate models (WGI AR5 Annex 1: Atlas of Global and Regional Climate

Projections), although there is medium confidence (medium evidence,

medium agreement) that some tropical regions, such as the eastern

Amazon Basin, will experience lower precipitation and more severe

drought (Malhi et al., 2009a; Shiogama et al., 2011). The range of

possible shifts in the moist tropical forest envelope is large, sensitive to

the responsiveness of water use efficiency (WUE) to rising concentrations

of atmospheric CO

, and varies depending on the climate and vegetation

model that is used (Scholze et al., 2006; Sitch et al., 2008; Zelazowski

et al., 2011). Recent model studies (Malhi et al., 2009a; Cox et al., 2013;

Huntingford et al., 2013) indicate that the future geographical range

of moist tropical forests as determined by its shifting climatological

envelope is less likely to undergo major retractions or expansions by

2100 than was suggested in AR4. Since AR4, there is new evidence of

more frequent severe drought episodes in the Amazon region that are

associated with sea surface temperature increases in the tropical North

Atlantic (medium confidence; Marengo et al., 2011). There is low

confidence, however, that these droughts or the observed sea surface

temperatures can be attributed to climate change.

Networks of long-term forest plots reveal that lianas and fast-growing

tree species are increasing, as is forest biomass (Phillips et al., 2002,

2005; Lewis et al., 2009a,b, 2011). Faster tree growth is consistent with

increasing WUE associated with the rising concentration of CO

, but

also with changes in solar radiation and the ratio of diffuse to direct

radiation (Lewis et al., 2009a; Mercado et al., 2009; Brando et al., 2010;

see also Section 4.2.4.5). There is low confidence (limited evidence,

medium agreement) that the composition and biomass of Amazon and

African forests are changing through the rise in atmospheric CO

. The

potential suppression of photosynthesis and tree growth in tropical

forests through rising air temperatures is supported by physiological

and eddy covariance studies (Doughty and Goulden, 2008; Lloyd and

Farquhar, 2008; Wood et al., 2012), but is not yet observed as changes

in forest biomass (except Clark et al., 2003).

Since AR4, there is new experimental and observational evidence of

ecological thresholds of drought and fire in moist tropical forests that

points to an important indirect role of climate change in driving large-

scale changes in these ecosystems, and to the importance of extreme

drought events (see Box 4-3). Forest tree mortality increased abruptly

above a critical level of soil moisture depletion in two rainfall exclusion

experiments (Nepstad et al., 2007; Fisher et al., 2008) and above a critical

level of weather-related fire intensity in a prescribed burn experiment

(Brando et al., 2012). These experimental results were corroborated by

observations of increased tree mortality during the severe 2005 drought

in the Amazon (Phillips et al., 2009) and extensive forest fire (Alencar

et al., 2006, 2011; Aragão et al., 2008; Box 4-3). There is high confidence

(medium evidence, high agreement) that moist tropical forests have

many tree species that are vulnerable to drought- and fire-induced

mortality during extreme dry periods.

There is also a growing body of evidence that severe weather events

interact with land use to influence moist tropical forest fire regimes.

Many moist tropical forests are not susceptible to fire during typical

rainfall years because of high moisture content of fine fuels (Cochrane,

2003). Selective logging, drought, and fire itself can reduce this fire

resistance by killing trees, thinning the canopy, and allowing greater

eating of the forest interior (Uhl and Kauffman, 1990; Curran et al.,

2004; Ray et al., 2005; Box 4-3). Land use also often increases the

ignition sources in tropical landscapes (Silvestrini et al., 2011). These

relationships are not yet represented fully in coupled climate-vegetation

models. There is high confidence (robust evidence, high agreement) that

forest fire frequency and severity is increasing through the interaction

between severe droughts and land use. There is medium confidence

(medium evidence, high agreement) that tree mortality in the Amazon

region is increasing through severe drought and increased forest fire

occurrence and low confidence that this can be attributed to warming

(Figure 4-4).

Dry tropical forests are defined by strong seasonality in rainfall

distribution (Mooney et al., 1995) and have been reduced to an estimated

1 million km

globally through human activities (Miles et al., 2006). Half

of the world’s remaining dry tropical forests are located in South America.

Using five climate model simulations for the 2040–2069 period under

the IS92a “business-as-usual scenario,” Miles et al. (2006) found that

approximately one-third of the remaining area of tropical dry forests in

the Americas will be exposed to higher temperatures and lower rainfall

through climate change. Climate change, deforestation, fragmentation,

fire, or human pressure place virtually all (97%) of the remaining

tropical dry forests at risk of replacement or degradation (Miles et al.,

2006). In a regional study a dynamic vegetation model (Integrated

Biosphere Simulator (IBIS)) under A2 and B2 scenarios projected by a

global climate model (Hadley Centre Regional Model 3 (HadRM3))

found that most of the dry forests of India would be outside of their

climate envelopes later in this century (Chaturvedi et al., 2011). There

is low confidence in our understanding of climate change effects on dry

forests globally.

4.3.3.2. Dryland Ecosystems:

Savannas, Shrublands, Grasslands, and Deserts

The following sections treat a wide range of terrestrial ecosystems

covering a large part of the land surface, whose common features are

that they typically exhibit strong water stress for several months each

year and grass-like plants and herbs are a major part of their vegetation

cover. Thus the principal land use often involves grazing by domestic

livestock or wild herbivores.

4.3.3.2.1. Savannas

Savannas are mixtures of coexisting trees and grasses, covering about

a quarter of the global land surface, including tropical and temperate

forms. Savannas are characterized by annual to decadal fires (Archibald

et al., 2009) of relatively low intensity, which are an important factor

in maintaining the tree-grass proportions (Beerling and Osborne, 2006),

but also constitute a major and climate-sensitive global source of fire-

related emissions from land to atmosphere (Schultz et al., 2008; van

der Werf et al., 2010). The geographical distribution of savannas is

determined by temperature, the seasonal availability of water, fire, and

soil conditions (Ellery et al., 1991; Walker and Langridge, 1997; Staver

et al., 2011) and is therefore inferred to be susceptible to climate

change. In parts of Central Africa, forests have been observed to be

309

Terrestrial and Inland Water Systems Chapter 4

Box 4-3 | A Possible Amazon Basin Tipping Point

Since AR4, our understanding of the potential of a large-scale, climate-driven, self-reinforcing transition of Amazon forests to a dry

stable state (known as the Amazon “forest dieback”) has improved. Modeling studies indicate that the likelihood of a climate-driven

forest dieback by 2100 is lower than previously thought (Malhi et al., 2009b; Cox et al., 2013; Good et al., 2013; Huntingford et al.,

2013), although lower rainfall and more severe drought is expected in the eastern Amazon (Malhi et al., 2009a). There is now

medium conﬁdence (medium evidence, medium agreement) that climate change alone (i.e., through changes in the climate envelope,

without invoking ﬁre and land use) will not drive large-scale forest loss by 2100 although shifts to drier forest types are predicted in

the eastern Amazon (Mahli et al., 2009a). Meteorological ﬁre danger is projected to increase in some models (Golding and Betts,

2008; Betts et al., 2013; Figure 4-6). Field studies and regional observations have provided new evidence of critical ecological

thresholds and positive feedbacks between climate change and land use activities that could drive a ﬁre-mediated, self-reinforcing

dieback during the next few decades (Figure 4-8). There is now medium conﬁdence (medium evidence, high agreement) that severe

drought episodes, land use, and ﬁre interact synergistically to drive the transition of mature Amazon forests to low-biomass, low-

statured ﬁre-adapted woody vegetation.

Amazon

River

Grass

invasion

Forest ﬁres

Severe

drought

Global warming

Rising atmospheric CO

Logging and

clearing

Tree

death

Andes Mountains

Figure 4-8 | The forests of the Amazon Basin are being altered through severe droughts, land use (deforestation, logging), and increased frequencies of forest ﬁre.

Some of these processes are self-reinforcing through positive feedbacks, and create the potential for a large-scale tipping point. For example, forest ﬁre kills trees,

increasing the likelihood of subsequent burning. This effect is magniﬁed when tree death allows forests to be invaded by ﬂammable grasses. Deforestation provides

ignition sources to ﬂammable forests, contributing to this dieback. Climate change contributes to this tipping point by increasing drought severity, reducing rainfall and

raising air temperatures, particularly in the eastern Amazon Basin (medium conﬁdence; medium evidence, medium agreement).

Continued next page

310

Chapter 4 Terrestrial and Inland Water Systems

moving into adjacent savannas and grasslands (Mitchard et al., 2009),

possibly due to depopulation and changes in the fire regime. In northern

Australia, forest is expanding into former savanna areas (Brook and

Bowman, 2006; Bowman et al., 2011; Tng et al., 2012). It has been

projected that drying and greater seasonality, acting in conjunction with

increased fire, could lead to former forested areas becoming savannas

in parts of the Amazon basin (Malhi et al., 2009b; Box 4-3). In many

places around the world the savanna boundary is moving into former

grasslands on elevation gradients; in other words, into areas inferred

to be formerly too cool for trees (Breshears, 2006).

The proportion of trees and grasses in savannas is considered unstable

under some conditions (De Michele et al., 2011; Staver et al., 2011). The

differential effects of climate change, rising CO

, fire, and herbivory on

trees and grasses have the potential to alter the tree cover in savannas,

possibly abruptly. There is evidence from many parts of the world that

the tree cover and biomass in savannas has increased over the past

century and in some places, on all continents, continues to do so

(robust evidence, high agreement; Moleele et al., 2002; Angassa and

Oba, 2008; Cabral et al., 2009; Wigley et al., 2009; Witt et al., 2009; Lunt

et al., 2010; Rohde and Hoffman, 2012). The general consequences are

more carbon stored per unit land area in form of tree biomass and soil

organic matter (Hughes et al., 2006; Liao et al., 2006; Knapp et al., 2007;

Throop and Archer, 2008; Boutton et al., 2009), changes in hydrology

(Muñoz-Robles et al., 2011), and reduced grazing potential (Scholes and

Archer, 1997). Increasing tree cover in savannas has been attributed to

changes in land management (Joubert et al., 2008; Van Auken, 2009),

rising CO

(Bond and Midgley, 2012; Buitenwerf et al., 2012), climate

variability and change (Eamus and Palmer, 2007; Fensham et al., 2009),

or several of these factors acting in combination (Ward, 2005). As yet,

there are no studies that definitively attribute the relative importance

of the climate- and non-climate-related causes of woody plant biomass

increase in savannas (and the invasion of trees into former grasslands),

but there is medium agreement and robust evidence that climate

change and rising CO

are contributing factors in many cases. The

increased growth rate of C

photosynthetic system trees relative to C

Box 4-3 (continued)

Most primary forests of the Amazon Basin have damp ﬁne fuel layers and low susceptibility to ﬁre, even during annual dry seasons

(Uhl and Kauffman, 1990; Ray et al., 2005). Forest susceptibility to ﬁre increases through canopy thinning and greater sunlight

penetration caused by tree mortality associated with selective logging (Uhl and Kauffman, 1990; Ray et al., 2005; Barlow and Peres,

2008), previous forest ﬁre (Balch et al., 2008; Brando et al., 2012), severe drought (Alencar et al., 2006), or drought-induced tree

mortality (Nepstad et al., 2007; da Costa et al., 2010). The impact of ﬁre on tree mortality is also weather dependent. Under very dry,

hot conditions, ﬁre-related tree mortality can increase sharply (Brando et al., 2012). Under some circumstances, tree damage is

sufﬁcient to allow light-demanding, ﬂammable grasses to establish in the forest understory, increasing forest susceptibility to further

burning (Veldman and Putz, 2011). There is high conﬁdence (robust evidence, high agreement) that logging, severe drought, and

previous ﬁre increase Amazon forest susceptibility to burning.

Landscape level processes further increase the likelihood of forest ﬁre. Fire ignition sources are more common in agricultural and

grazing lands than in forested landscapes (Silvestrini et al., 2011) (high conﬁdence: robust evidence, high agreement), and forest

conversion to grazing and crop lands can inhibit regional rainfall through changes in albedo and evapotranspiration (Costa et al.,

2007; Butt et al., 2011; Knox et al., 2011) (low conﬁdence: medium evidence, low agreement) or through smoke, which can inhibit

rainfall under some circumstances (Andreae et al., 2004) (medium conﬁdence: medium evidence, medium agreement). Apart from

these landscape processes, climate change could increase the incidence of severe drought episodes (Mahli et al. 2009b; Shiogama et

al., 2011).

If recent patterns of deforestation (through 2005), logging, severe drought, and forest ﬁre continue into the future, more than half of

the region’s forests will be cleared, logged, burned, or exposed to drought by 2030, even without invoking positive feedbacks with

regional climate, releasing 20 ± 10 PgC to the atmosphere (Nepstad et al., 2008) (low conﬁdence: low evidence, medium agreement)

(Figure 4-8). The likelihood of a tipping point being reached may decline if extreme droughts (such as 1998, 2005, and 2010)

(Marengo et al., 2011) become less frequent, if land management ﬁres are suppressed, if forest ﬁres are extinguished on a large scale

(Soares-Filho et al., 2012), if deforestation declines, or if cleared lands are reforested (Nepstad et al., 2008). The 77% decline in

deforestation in the Brazilian Amazon with 80% of the region’s forest still standing (INPE, 2013) demonstrates that policy-led

avoidance of a ﬁre-mediated tipping point is plausible.

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Terrestrial and Inland Water Systems Chapter 4

rasses under rising CO

ould relieve the demographic bottleneck that

keeps trees trapped within the flame zone of the grasses, a hypothesis

supported by elevated CO

experiments with savanna saplings (Kgope

et al., 2010).

A model of grasslands, savannas, and forests suggests that rising CO

does increase the likelihood of abrupt shifts to woodier states, but the

transition will take place at different CO

concentrations in different

environments (Higgins and Scheiter, 2012). On the other hand,

observation of contrasts in the degree of savanna thickening between

land parcels with the same CO

exposure but different land use histories,

topographic position, or soil depth (Wiegand et al., 2005; Wu and Archer,

2005) imply that land management, water balance, and microclimate

are also important. Tree cover in savannas is rainfall-constrained

(Sankaran et al., 2005), suggesting that future increases in rainfall

projected for most but not all savanna areas (WGI AR5 Annex I: Atlas

of Global and Regional Climate Projections) could lead to increased tree

biomass.

4.3.3.2.2. Grasslands and shrublands

Rangelands (partly overlapping with savannas) cover approximately

30% of the Earth’s ice-free land surface and hold an equivalent amount

of the world’s terrestrial carbon (Booker et al., 2013). Much evidence

from around the world shows that dry grasslands and shrublands are

highly responsive in terms of primary production, species composition,

and carbon balance to changes in water balance (precipitation and

evaporative demand) within the range of projected climate changes

(high confidence) (e.g., Sala et al., 1988; Snyman and Fouché, 1993; Fay

et al., 2003; Peñuelas et al., 2004, 2007; Prieto et al., 2009; Peters et al.,

2010; Martí-Roura et al., 2011; Booker et al., 2013; Wu and Chen, 2013).

Rainfall amount and timing have large effects on a wide range of

biological processes in grasslands and shrublands, including seed

germination, seedling establishment, plant growth, flowering time, root

mass, community composition, population and community dynamics

production, decomposition and respiration, microbial processes and

carbon, plant, and soil nutrient contents (e.g., Fay et al., 2003; Peñuelas

et al., 2004, 2007; Beier et al., 2008; Sardans et al., 2008a,b; Sowerby

et al., 2008; Liu et al., 2009; Miranda et al., 2009; Albert et al., 2011,

2012; Selsted et al., 2012; Walter et al., 2012).

Precipitation changes were as important for mountain flora in Europe

as temperature changes, and the greatest composition changes will

probably occur when decreased precipitation accompany warming

(Engler et al., 2011). Responses of shrublands to drought may be driven

partly by changes in the soil microbial community (Jensen et al., 2003)

or changes in soil fauna (Maraldo et al., 2008). An increase in drought

frequency, without an increase in drought severity, leads to loss of soil

carbon in moist, carbon-rich moorlands, due to changes in soil structure

or soil microbial community leading to increased hydrophobicity and

soil respiration (Sowerby et al., 2008, 2010). Simulated increased spring

temperature and decreased summer precipitation had a general negative

effect on plant survival and plant growth, irrespective of the macroclimatic

niche characteristics of the species. Against expectation, species with

ranges extending into drier regions did not generally perform better

under drier conditions (Bütof et al., 2012).

hanging climate and land use have resulted in increased aridity and

a higher frequency of droughts in drylands around the world, with

increasing dominance of abiotic controls of land degradation (in contrast

to direct human- or herbivore-driven degradation) and changes in

hydrology and the erosion of soil by wind (Ravi et al., 2010). In mixed shrub

grasslands, the influence of drought periods could produce transient

pulses of carbon that are much larger than the pulses produced by fire

(Martí-Roura et al., 2011). Most studies of changes in arid systems between

grasslands and shrublands have focused on plant-soil feedbacks that

favor shrub growth. Summers drier than three-quarters of current rainfall

decreased grass seedling recruitment to negligible values (Peters et al.,

2010). Management cannot reliably increase carbon uptake in arid and

semiarid rangelands, which is most often controlled by abiotic factors

not easily changed by management of grazing or vegetation (Booker

et al., 2013).

Other factors being equal, grasslands and shrublands in cool areas are

expected to respond to warming with increased primary production,

while those in hot areas are expected to show decreased production

(limited evidence, low agreement). A shift to more woody vegetation

states expected to occur (locally but not globally) in tropical grasslands

of the African continent (Higgins and Scheiter, 2012). The response to

warming and drought depends on site, year, and plant species, as shown

by manipulation experiments (Peñuelas et al., 2004, 2007; Gao and

Giorgi, 2008; Grime et al., 2008; Shinoda et al., 2010; Wu and Chen,

2013). In most temperate and Arctic regions, the capacity to support

richer (i.e., more diverse) communities is projected to increase with rising

temperature, while decreases in water availability suggest a decline in

capacity to support species-rich communities in most tropical and

subtropical regions (Sommer et al., 2010). Warming may cause an

asymmetrical response of soil carbon and nitrogen cycles, causing

nitrogen limitation that reduces acclimation in plant production (Beier

et al., 2008).

Some grasslands are exposed to elevated levels of nitrogen deposition,

which alters species composition, increases primary production up to a

point, and decreases it thereafter (see Section 4.2.4.2; Bobbink et al.,

2010; Cleland and Harpole, 2010; Gaudnik et al., 2011). In a study of

162 plots over 25 years, nitrogen deposition drove grassland composition

at the local scale, in interaction with climate, whereas climate changes

were the predominant driver at the regional scale (Gaudnik et al., 2011).

Nitrogen mineralization in shrublands under either arid or wet conditions

is more sensitive to periodic droughts than systems under more mesic

conditions (Emmett et al., 2004). Decreased tissue concentrations of

phosphorus were also associated with warming and drought (Peñuelas et

al., 2004, 2012; Beier et al., 2008). Strong interactions between warming

and disturbances have been observed, leading to increased nitrogen

leaching from shrubland ecosystems (Beier et al., 2004).

Most grasslands and shrublands are characterized by relatively frequent

but low-intensity fires, which affect their plant species composition and

demographics (e.g., Gibson and Hulbert, 1987; Gill et al., 1999; Uys et

al., 2004; de Torres Curth et al., 2012). Species composition changes

may be as important in determining ecosystem impacts as the direct

effects of climate on plant (Suttle et al., 2007). Fire frequency, duration,

and intensity are influenced primarily by climate and secondarily by

management (Pitman et al., 2007; Lenihan et al., 2008; Archibald et al.,

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Chapter 4 Terrestrial and Inland Water Systems

009; Giannakopoulos et al., 2009; Armenteras-Pascual et al., 2011),

and are therefore sensitive to climate change; the duration of the fire

season is also projected to broaden (Clarke et al., 2013). Changes in fire

frequency may interact with changes in rainfall seasonality: for instance,

if fires are followed by rainy spring periods in northwestern Patagonia,

as occurs with more frequent El Niño-Southern Oscillation (ENSO)

phenomena, there are more recruitment windows for shrubs (Ghermandi

et al., 2010). Relatively little is known regarding the combined effect of

climate change and increased grazing by large mammals, or on the

consequences for pastoral livelihoods that depend on rangelands

(Thornton et al., 2009).

4.3.3.2.3. Deserts

The deserts of the world, defined as land areas with an arid or hyperarid

climate regime, occupy 35% of the global land surface. Species

composition in desert areas is expected to shift in response to climate

warming (Ooi et al., 2009; Kimball et al., 2010). Deserts are sparsely

populated, but the people who do live there are among the poorest

in the world (Millennium Ecosystem Assessment, 2005a). There is

medium agreement but limited evidence that the present extent of

deserts will increase in the coming decades, despite the projected

increase in rainfall at a global scale, as a result of the strengthening

of the Hadley Circulation, which determines the location of the broad

band of hot deserts approximately 15°N to 30°N and 15°S to 30°S of

the equator (Mitas and Clement, 2005; Seidel et al., 2008; Johanson

and Fu, 2009; Lu et al., 2009; Zhou et al., 2011). There may be a

feedback to the global climate from an increase in desert extent, which

differs in sign between deserts closer to the equator than 20° and those

closer to the pole: in model simulations, extension of the near-equator

“hot deserts” causes warming, while extension of the near-boreal

“cold deserts” causes cooling, in both cases largely through albedo-

mediated effects (Alkama et al., 2012). Deserts are expected to become

warmer and drier at faster rates than other terrestrial regions (Lapola

et al., 2009; Stahlschmidt et al., 2011). Most deserts are already

extremely hot, and therefore further warming likely to be physiologically

injurious rather than beneficial. The ecological dynamics in deserts are

rainfall event-driven (Holmgren et al., 2006), often involving the

concatenation of a number of quasi-independent events. Some desert

tolerance mechanisms (e.g., biological adaptations by long-lived taxa)

may be outpaced by global climate change (Lapola et al., 2009;

Stahlschmidt et al., 2011).

4.3.3.2.4. Mediterranean-type ecosystems

Mediterranean-type ecosystems occur on most continents, and are

characterized by cool, wet winters and hot, dry summers. They were

identified as being among the most likely to be impacted by climate

change in AR4 and received extensive coverage (Fischlin et al., 2007).

Since then, further evidence has accumulated of climate risks to these

systems from rising temperature (Giorgi and Lionello, 2008), rainfall

change (declining in most but not all cases), increased drought (Sections

23.2.3, 25.2), and increased fire frequency (Section 23.4.4). There have

been observed shifts in phenology (Gordo and Sanz, 2010), range

contraction of Mediterranean species (Pauli et al., 2012), declines in the

ealth and growth rate of dominant tree species (Allen, C.D. et al., 2010;

Sarris et al., 2011; Brouwers et al., 2012; see also Section 23.4.4), and

increased risk of erosion and desertification, especially in very dry areas

(Lindner et al., 2010; Shakesby, 2011). Model projections show further

species range contractions in the 21st century under all climate change

scenarios. This will result in losses of biodiversity (medium confidence)

(Maiorano et al., 2011; Kuhlmann et al., 2012; see also Sections 23.6.4,

25.1).

4.3.3.3. Rivers, Lakes, Wetlands, and Peatlands

Freshwater ecosystems are considered to be among the most

threatened on the planet (Dudgeon et al., 2006; Vörösmarty et al., 2010).

Fragmentation of rivers by dams and the alteration of natural flow

regimes have led to major impacts on freshwater biota (Pringle, 2001;

Bunn and Arthington, 2002; Nilsson et al., 2005; Reidy Liermann et al.,

2012). Floodplains and wetland areas have become occupied for intensive

urban and agricultural land use to the extent that many are functionally

disconnected from their rivers (Tockner et al., 2008). Pollution from cities

and agriculture, especially nutrient loading, has resulted in declines in

water quality and the loss of essential ecosystem services (Allan, 2004).

As a direct consequence of these and other impacts, freshwaters have

some of the highest rates of extinction of any ecosystem for those

species groups assessed for the IUCN Red List (estimated as much as

4% per decade for some groups, such as crayfish, mussels, fishes, and

amphibians in North America) (Dudgeon et al., 2006), with estimates that

roughly 10,000 to 20,000 freshwater species are extinct or imperilled

as a consequence of human activity (Strayer and Dudgeon, 2010). This

is a particular concern given that freshwater habitats support 6% of all

described species (Dudgeon et al., 2006), including approximately 40%

of the world’s fish diversity and a third of the vertebrate diversity (Balian

et al., 2008).

It is very likely that these stressors to freshwater ecosystems will

continue to dominate as human demand for water resources grows,

accompanied by increased urbanization and expansion of irrigated

agriculture (Vörösmarty et al., 2000; Malmqvist et al., 2008; Dise, 2009).

However, climate change will have significant additional impacts (high

confidence), from altered thermal regimes, altered precipitation and

flow regimes, and, in the case of coastal wetlands, sea level rise. Specific

aquatic habitats that are most vulnerable to these direct climate effects,

especially rising temperatures, are those at high altitude and high latitude,

including Arctic and sub-Arctic bog communities on permafrost, and alpine

and Arctic streams and lakes (see Section 4.3.3.4; Klanderud and Totland,

2005; Smith et al., 2005; Smol and Douglas, 2007b). It is noteworthy

that these high-latitude systems currently experience a relatively low

level of threat from other human activities (Vörösmarty et al., 2010). It

is likely that the shrinkage and disappearance of glaciers will lead to

the reduction of local and regional freshwater biodiversity, with 11 to

38% of the regional macroinvertebrate species pool expected to be lost

following complete disappearance of glaciers (Jacobsen et al., 2012;

Box CC-RF). Shrinkage of glaciers and the loss of small glaciers will

most likely reduce beta diversity at the species and the genetic level, as

predicted for the Pyrenees (Finn et al., 2013). Dryland rivers and wetlands,

many already experiencing severe water stress from human consumptive

use, are also likely to be further impacted by decreased and more variable

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Terrestrial and Inland Water Systems Chapter 4

recipitation and higher temperatures. Headwater stream systems in

general are also vulnerable to the effects of warming because their

temperature regimes closely track air temperatures (Caissie, 2006).

There is widespread evidence of rising stream and river temperatures

over the past few decades (Langan et al., 2001; Morrison et al., 2002;

Webb and Nobilis, 2007; Chessman, 2009; Ormerod, 2009; Kaushal et

al., 2010; van Vliet et al., 2011; Markovic et al., 2013; but see Arismendi

et al., 2012). Rising water temperature has been linked by observational

and experimental studies to shifts in invertebrate community composition,

including declines in cold stenothermic species (Brown et al., 2007;

Durance and Ormerod, 2007; Chessman, 2009; Ormerod, 2009). Rising

temperature is also implicated in species range shifts (e.g., Comte and

Grenouillet, 2013), implying changes in the composition of river fish

communities (Daufresne and Boet, 2007; Buisson et al., 2008; Comte

et al., 2013), especially in headwater streams where species are more

sensitive to warming (e.g., Buisson and Grenouillet, 2009).

Rising temperatures in the well-mixed surface waters in many temperate

lakes, resulting in reduced periods of ice formation (Livingstone and

Adrian, 2009; Weyhenmeyer et al., 2011) and earlier onset and increased

duration and stability of the thermocline during summer (Winder and

Schindler, 2004), are projected to favor a shift in dominance to smaller

phytoplankton (Parker et al., 2008; Winder et al., 2009; Yvon-Durocher

et al., 2011) and cyanobacteria (Wiedner et al., 2007; Jöhnk et al., 2008;

Paerl et al., 2011), especially in those ecosystems experiencing high

anthropogenic loading of nutrients (Wagner and Adrian, 2009); with

impacts to water quality, food webs, and productivity (O’Reilly et al.,

2003; Verburg et al., 2003; Gyllström et al., 2005; Parker et al., 2008;

Shimoda et al., 2011). Prolonged stratification and associated anaerobic

conditions near the sediment-water interface can increase the internal

loading of phosphorus, particularly in eutrophic lakes (Søndergaard et

al., 2003; Wilhelm and Adrian, 2008; Wagner and Adrian, 2009).

In many freshwater ecosystems, the input of dissolved organic carbon

through runoff from the catchment has increased, inducing changes in

water color (Hongve et al., 2004; Evans et al., 2005; Erlandsson et al.,

2008). Soil recovery from acidification and changed hydrological

conditions (partly linked to increased precipitation) appear to be the

main factors driving this development (Evans et al., 2005; Monteith et

al., 2007). The resulting increased light attenuation can lead to lower

algal concentrations and loss of submersed vegetation (Ask et al., 2009;

Karlsson et al., 2009).

Emergent aquatic macrophytes are likely to expand their northward

distribution and percentage cover in boreal lakes and wetlands, posing

an increasing overgrowth risk for sensitive macrophyte species (Alahuhta

et al., 2011). Long-term shifts in macroinvertebrate communities have

also been observed in European lakes where temperatures have

increased (Burgmer et al., 2007), noting that warming may increase

species richness in smaller temperate water bodies, especially those at

high altitude (Rosset et al., 2010). Although less studied, it has been

proposed that tropical ectothermic (“cold blooded”) organisms will be

particularly vulnerable because they will approach critical maximum

temperatures proportionately faster than species in high-latitude

environments, despite lower rates of warming (Deutsch et al., 2008;

Hamilton, 2010; Laurance et al., 2011).

here is growing evidence that climate-induced changes in precipitation

will significantly alter ecologically important attributes of hydrologic

regimes in rivers and wetlands, and exacerbate impacts from human

water use in developed river basins (high confidence in detection, medium

confidence in attribution; see Box CC-RF; Xenopoulos et al., 2005;

Aldous et al., 2011). Freshwater ecosystems in Mediterranean-montane

ecoregions (e.g., Australia, California, and South Africa) are projected

to experience a shortened wet season and prolonged, warmer summer

season (Klausmeyer and Shaw, 2009), increasing the vulnerability of

fish communities to drought (Magalhães et al., 2007; Hermoso and

Clavero, 2011) and floods (Meyers et al., 2010). Shifts in hydrologic

regimes in snowmelt systems, including earlier runoff and declining base

flows in summer (Stewart et al., 2005; Stewart, 2009), are projected to

alter freshwater ecosystems, through changes in physical habitat and

water quality (Bryant, 2009). Declining rainfall and increased interannual

variability will most likely increase low-flow and dry-spell duration in

dryland regions, leading to reduced water quality in remnant pools

(Dahm et al., 2003), reduction in floodplain egg and seed banks (Capon,

2007; Jenkins and Boulton, 2007), the loss of permanent aquatic refugia

for fully aquatic species and water birds (Johnson et al., 2005; Bond et

al., 2008; Sheldon et al., 2010), altered freshwater food webs (Ledger

et al., 2013), and drying out of wetlands (Davis, J.L. et al., 2010).

Climate-induced changes in precipitation will probably be an important

factor altering peatland vegetation in temperate and boreal regions, with

decreasing wetness during the growing season generally associated with

a shift from a Sphagnum dominated to vascular plant dominated

vegetation type and a general decline of carbon sequestration in the

long term (Limpens et al., 2008). Mire ecosystems (i.e., bogs, transition

bogs, and fens) in central Europe face severe climate-induced risk, with

increased summer temperatures being particularly important (Essl et al.,

2012). Decreased dry season precipitation and longer dry seasons in

major tropical peatland areas in Southeast Asia are projected to result

in lower water tables more often and for longer periods, with an increased

risk of fire (Li et al., 2007; Rieley et al., 2008; Frolking et al., 2011).

Peatlands contain large stocks of carbon that are vulnerable to change

through land use and climate change. Although peatlands cover only

about 3% of the land surface, they hold the equivalent of half of the

atmosphere’s carbon (as CO

), or one-third of the world’s soil carbon

stock (400 to 600 Pg) (Limpens et al., 2008; Frolking et al., 2011; Page

et al., 2011). About 14 to 20% of the world’s peatlands are currently

used for agriculture (Oleszczuk et al., 2008) and many, particularly

peat swamp forests in Southeast Asia, are undergoing rapid major

transformations through drainage and burning in preparation for oil

palm and other crops or through unintentional burning (Limpens et al.,

2008; Hooijer et al., 2010). Deforestation, drainage, and burning in

Indonesian peat swamp forests can release 59.4 ± 10.2 Mg CO

–1

over 25 years (Murdiyarso et al., 2010), contributing significantly

to global GHG emissions, especially during periods of intense drought

associated with ENSO when burning is more common (Page et al.,

2002). Anthropogenic disturbance has changed peatlands from being

a weak global carbon sink to a source (Frolking et al., 2011), though

interannual variability is large. Fluvial export can also be a significant

contributor to carbon losses that has been largely overlooked to date,

with recent estimates of DOC export from degraded tropical peatlands

50% higher than in intact systems (Moore et al., 2013). Conserving

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Chapter 4 Terrestrial and Inland Water Systems

eatland areas not yet developed for biofuels or other crops, or

rewetting and restoring degraded peatlands to preserve their carbon

store, are potential mitigation strategies.

Sea level rise will lead to direct losses of coastal wetlands with associated

impacts on water birds and other wildlife species dependent on fresh

water (BMT WBM, 2010; Pearlstine et al., 2010; Traill et al., 2010), but

the impact will probably be relatively small compared with the degree

of direct and indirect human-induced destruction (Nicholls, 2004). River

deltas and associated wetlands are particularly vulnerable to rising sea

level, and this threat is further compounded by trapping of sediment in

reservoirs upstream and subsidence from removal of oil, gas, and water

(Syvitski et al., 2009; see Section 5.4.2.7). Lower river flows might

exacerbate the impact of sea level rise and thus salinization on freshwater

ecosystems close to the ocean (Ficke et al., 2007).

4.3.3.4. Tundra, Alpine, and Permafrost Systems

The High Arctic region, with tundra-dominated landscapes, has warmed

more than the global average over the last century (Kaufman et al.,

2009; see WGI AR5 Chapter 2). Changes consistent with warming are

evident in the freshwater and terrestrial ecosystems and permafrost of

the region (Hinzman et al., 2005; Axford et al., 2009; Jia, G.J. et al., 2009;

Post et al., 2009; Prowse and Brown, 2010; Romanovsky et al., 2010;

Walker et al., 2012). Most of the Arctic has experienced recent change

in vegetation photosynthetic capacity, particularly adjacent to rapidly

retreating sea ice (Bhatt et al., 2010). Changes in terrestrial environments

in Antarctica have also been reported. Vieira et al. (2010) show that in

in the Maritime Antarctic permafrost temperatures are close to thaw.

Permafrost warming has been observed in continental Antarctica

(Guglielmin and Cannone, 2012) and for the Palmer archipelago

(Bockheim et al., 2013).

Continued warming is projected to cause the terrestrial vegetation and

lake systems of the Arctic to change substantially (high confidence).

Continued expansion in woody vegetation cover in tundra regions over

the 21st century is projected by the CMIP5 ESMs (Bosio et al., 2012; see

WGI AR5 Chapter 6), by dynamic global vegetation models driven by

other climate model projections, and by observationally based statistical

models (Pearson et al., 2013). Changes may be complex (see Box 4-4)

and in some cases involve nonlinear and threshold responses to warming

and other climatic change (Hinzman et al., 2005; Mueller, D.R. et al., 2009;

Bonfils et al., 2012). Arctic vegetation change is expected to continue

long after any stabilization of global mean temperature (see WGI AR5

Chapter 6; Falloon et al., 2012). In some regions, reduced surface albedo

due to increased vegetation cover is projected to cause further local

warming even in scenarios of stabilized GHG concentrations (Falloon

et al., 2012).

In the Arctic tundra biome (in contrast to the boreal forests discussed

in Section 4.3.3.1.1), vegetation productivity has systematically

increased over the past few decades in both North America and

northern Eurasia (Goetz et al., 2007; Stow et al., 2007; Jia, G.J. et al.,

2009; de Jong et al., 2011; Myers-Smith et al., 2011; Elmendorf et al.,

2012). This phenomenon is amplified by retreat of coastal sea ice (Bhatt

et al., 2010) and has been widely discussed in the context of increased

hrub growth and expansion over the last half century (Forbes et al.,

2010; Myers-Smith et al., 2011). Deciduous shrubs and graminoids

respond to warming with increased growth (Walker, 2006; Epstein et

al., 2008; Euskirchen et al., 2009; Lantz et al., 2010). Analyses of satellite

time series data show the increased productivity trend is not unique to

shrub-dominated tundra areas (Jia, G.J. et al., 2009; Beck and Goetz,

2011); thus greening is a response shared by multiple vegetation

communities and continued changes in the tundra biome can be

expected irrespective of shrub presence. The very large spatial scale over

which these changes are occurring, the strong warming signal over

much of the Arctic for the last 5 decades (Burrows et al., 2011), and the

absence of strong confounding factors means that detection of these

changes in Arctic systems and their attribution to global warming can

be made with high confidence, despite the relatively short time frame

of most observations (Figure 4-4).

Shrub expansion and height changes are particularly important because

they trap snow, mediate winter soil temperature and summer moisture

regimes, increase nutrient mineralization, and produce a positive feedback

for additional shrub growth (Sturm et al., 2005; Lawrence et al., 2007;

Bonfils et al., 2012). Although increased shrub cover and height produce

shadowing that reduce ground heat flux and active layer depth, they also

reduce surface albedo, increase energy absorption and evapotranspiration

(Chapin III et al., 2005; Blok et al., 2010), and produce feedbacks that

reinforce shrub densification and regional warming (Lawrence and

Swenson, 2011; Bonfils et al., 2012). On balance, these feedbacks can

act to partially offset one another, but when coupled with warmer and

wetter conditions they act to increase active layer depth and permafrost

thaw (Yi et al., 2007; Bonfils et al., 2012).

The Arctic tundra biome is experiencing increasing fire disturbance and

permafrost degradation. Both of these processes facilitate conditions

for woody species establishment in tundra areas, either through

incremental migration or via more rapid long-distance dispersal to areas

reinitialized by burning (Epstein et al., 2007; Goetz et al., 2011). When

already present at the boreal-tundra ecotone, shrub and tree species

show increased productivity with warmer conditions (Devi et al., 2008;

Andreu-Hayles et al., 2011; Elmendorf et al., 2012). Tundra fires not only

emit large quantities of combusted carbon formerly stored in vegetation

and organic soils (Mack et al., 2011; Rocha and Shaver, 2011), but also

increase active layer depth during summer months (Racine et al., 2004;

Liljedahl et al., 2007; Jorgenson et al., 2010), produce landforms

associated with thawing of ice-rich permafrost, and can create conditions

that alter vegetation succession (Racine et al., 2004; Lantz et al., 2009;

Higuera et al., 2011).

It is virtually certain that the area of NH permafrost will continue to

decline over the first half of the 21st century (see WGI AR5 Chapter 12)

in all RCP scenarios (Figure 4-9; Caesar et al., 2013; Koven et al., 2013).

In the RCP2.6 scenario of an early stabilization of CO

concentrations,

the permafrost area is projected to stabilize at a level approximately 20%

below the 20th century area, and then begin a slight recovering trend. In

RCP4.5, in which CO

concentration is stabilized at approximately 550

ppmv by the mid-21st century, the simulations that extend beyond 2100

show permafrost continuing to decline for at least another 250 years.

In the RCP8.5 scenario of ongoing CO

rise, the permafrost area is

simulated to approach zero by the middle of the 22nd century in

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Terrestrial and Inland Water Systems Chapter 4

simulations that extend beyond 2100. RCP8.5 simulations that ended

at 2100 showed continued permafrost decline in the late 21st century,

although at slower rates in some cases as the remaining permafrost

area decreases (Figure 4-9.).

Frozen soils and permafrost currently hold about 1700 PgC, more than

twice the carbon than the atmosphere, and thus represent a particularly

large vulnerability to climate change (i.e., warming) (see WGI AR5

Chapter 6). Although the Arctic is currently a net carbon sink, continued

warming will act to turn the Arctic to a net carbon source, which will in

turn create a potentially strong positive feedback to accelerate Arctic

(and global) warming with additional releases of CO

, CH

, and perhaps

O, from the terrestrial biosphere into the atmosphere (high confidence;

Schuur et al., 2008, 2009; Maslin et al., 2010; McGuire et al., 2010;

O’Connor et al., 2010; Schaefer et al., 2011; see WGI AR5 Chapter 6 for

detailed treatment of biogeochemistry, including feedbacks). Moreover,

this feedback is already accelerating due to climate-induced increases

in fire (McGuire et al., 2010; O’Donnell et al., 2011). The rapid retreat

of snow cover and resulting spread of shrubs and trees into areas

currently dominated by tundra has begun, and will continue to serve

1900 2000 22002100 2300

Permafrost area (10

)

1900 2000 22002100 2300

Permafrost area (10

)

900 2000 22002100 2300

Permafrost area (10

)

HadGEM2-ES

IPSL-CM5A-LR

IPSL-CMA5A-MR

MICROC-ESM-CHEM

MICROC-ESM

MICROC5

MPI-ESM-LR

MRI-CGCM3

BCC-CSM1-1

INM-CM4

NorESM1-M

(a) RCP2.6 modeled permafrost extent

(b) RCP4.5 modeled permafrost extent

CCSM4

CESM1-CAM5

CanESM2

GFDL-ESM2G

GISS-E2-R

HadCM3

HadGEM2-CC

Figure 4-9 | CMIP5 multi-model simulated area of Northern Hemisphere permafrost in the upper 3 m of soil, from 1850 to 2100 or 2300 depending on extent of individual

simulations. Each panel shows historical (1850–2005) and projected (2005–2100 or 2300) simulations for (a) Representative Concentration Pathway 2.6 (RCP2.6), (b) RCP4.5,

and (c) RCP8.5. The observed current permafrost extent is 15 × 10

. (Based on Koven et al., 2013, with analysis extended to 2300 following Caesar et al., 2013).

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Chapter 4 Terrestrial and Inland Water Systems

Box 4-4 | Boreal-Tundra Biome Shift

Changes in a suite of ecological processes currently underway across the broader Arctic region are consistent with Earth System

Model (ESM) predictions of climate-induced geographic shifts in the range extent and functioning of the tundra and boreal forest

biomes (Figure 4-10). Until now, these changes have been gradual shifts across temperature and moisture gradients, rather than

abrupt. Responses are expressed through gross and net primary production, microbial respiration, ﬁre and insect disturbance,

vegetation composition, species range expansion and contraction, surface energy balance and hydrology, active layer depth and

permafrost thaw, and a range of other inter-related variables. Because the high northern latitudes are warming more rapidly than

other parts of the Earth, due at least in part to Arctic ampliﬁcation (Serreze and Francis, 2006), the rate of change in these ecological

processes are sufﬁciently rapid that they can be documented in situ (Hinzman et al., 2005; Post et al., 2009; Peng et al., 2011;

Elmendorf et al., 2012) as well as from satellite observations (Goetz et al., 2007; Beck, P.S.A. et al., 2011; Xu et al., 2013) and captured

in ESMs (McGuire et al., 2010).

ConiferDead treeShrub Fire

Tundra

Boreal

forest

Coastal

sea

Sea ice

Gap opening between land and

sea ice alters coastal circulation

Decrease

in albedo

Shorter duration

of snow cover

Shrub

encroachment

and densiﬁcation

Warmer spring

and summer

Increased ﬁre

intensity

emissions

Replacement

of conifers by

broadleaf forest

Sea ice retreating

Insulating organic layer contains a large

carbon stock that decomposes faster

under warmer climate

Lakes drain or form as permafrost

thaws and drainage changes

New wetlands emit CH

Permafrost is thawing in warmer regions

Permafrost

Organic layer

Soil carbon export to rivers

increases with permafrost thaw

Global

warming

Figure 4-10 | Tundra–boreal biome shift. Earth System Models predict a northward shift of Arctic vegetation with climate warming, as the boreal biome migrates into

what is currently tundra. Observations of shrub expansion in tundra, increased tree growth at the tundra–forest transition, and tree mortality at the southern extent of

the boreal forest in recent decades are consistent with model projections. Vegetation changes associated with a biome shift, which is facilitated by intensiﬁcation of the

ﬁre regime, will modify surface energy budgets, and net ecosystem carbon balance, permafrost thawing, and methane emissions, with net feedbacks to additional

climate change.

Continued next page

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Terrestrial and Inland Water Systems Chapter 4

as a positive feedback accelerating high-latitude warming (Chapin III

et al., 2005; Bonfils et al., 2012).

There is medium confidence that rapid change in the Arctic is affecting

its animals. For example, seven of 19 sub-populations of the polar bear

are declining in number, while four are stable, one is increasing, and the

remaining seven have insufficient data to identify a trend (Vongraven

and Richardson, 2011). Declines of two of the sub-populations are

linked to reductions in sea ice (Vongraven and Richardson, 2011). Polar

bear populations are projected to decline greatly in response to

continued Arctic warming (Hunter et al., 2010; Stirling and Derocher,

2012), and it is expected that the populations of other Arctic animals

will be affected dramatically by climate change, often in complex but

potentially dramatic ways (e.g., Post et al., 2009; Sharma et al., 2009;

Gallant et al., 2012; Gilg et al., 2012; Post and Brodie, 2012; Gauthier

et al., 2013; Nielsen and Wall, 2013; Prost et al., 2013; White et al.,

2013). Simple niche-based or climatic envelope models have difficulty

in capturing the full complexity of these future changes (MacDonald,

2010).

There is high confidence that alpine systems are already showing a high

sensitivity to ongoing climate change and will be highly vulnerable to

change in the future. In western North America, warming, glacier retreat,

snowpack decline, and drying of soils are already causing a large increase

in mountain forest mortality and wildfire, plus other ecosystem impacts

(e.g., Westerling et al., 2006; Crimmins et al., 2009; van Mantgem et al.,

2009; Pederson et al., 2010; Muhlfeld et al., 2011; Brusca et al., 2013;

Williams et al., 2013), and disturbance will continue to be an important

agent of climate-induced change in this region (Littell et al., 2010).

Globally, tree line altitude appears to be changing, although not always

in simple ways (Harsch et al., 2009; Tingley et al., 2012) and may

sometimes be due to factors not related to climate change. Responses

to climate change in high-altitude ecosystems are taking place in Africa,

Asia, Europe, and elsewhere (Cannone et al., 2007, 2008; Yasuda et al.,

2007; Lenoir et al., 2008, 2010; Britton et al., 2009; Chen et al., 2009,

2011; Cui and Graf, 2009; Normand et al., 2009; Allen, C.D. et al., 2010;

Eggermont et al., 2010; Engler et al., 2011; Kudo et al., 2011; Laurance

et al., 2011; Dullinger et al., 2012). For example, in a study of permanent

plots from 1994 to 2004 in the Austrian high Alps, a range contraction

of subnival to nival plant species was indicated at the downslope edge,

and an expansion of alpine pioneer species at the upslope edge (Pauli

et al., 2007). Thermophilous vascular plant species were observed to

colonize in alpine mountain-top vegetation across Europe during the

past decade (Gottfried et al., 2012). As with the Arctic, permafrost

thawing in alpine systems could provide a strong positive feedback (e.g.,

Tibet; Cui and Graf, 2009).

4.3.3.5. Highly Human-Modified Systems

About a quarter of the land surface is now occupied by ecosystems

highly modified by human activities. In this section we assess the

vulnerability to climate change only of those modified systems not dealt

with elsewhere, that is, excluding agriculture (Chapter 7), freshwater

fisheries (Chapter 3), and urban areas (Chapter 8).

4.3.3.5.1. Plantation forestry

Plantation forests are established through afforestation or reforestation,

often with tree crop replacement (Dohrenbusch and Bolte, 2007; FAO,

2010). They differ from natural or semi-natural forests (Section 4.3.3.1)

by generally being even-aged, having a reduced species diversity

(sometimes of non-native species), and being dedicated to the production

of timber, pulp, and/or bioenergy. Plantation forests contribute 7% to

the global forest area (FAO, 2010), an increase of 5 million hectares

between 2000 and 2010 (FAO, 2010). Most recent plantations have

been established by afforestation of non-forest areas in the tropics and

subtropics and some temperate regions, particularly China (Kirilenko

and Sedjo, 2007; FAO, 2010). Afforestation usually results in net CO

uptake from the atmosphere (Canadell and Raupach, 2008; Van Minnen

et al., 2008) but does not necessarily result in a reduction in global

warming (Bala et al., 2007; see Section 4.3.4.5).

Growth rates in plantation forests have generally increased during the

last decades but the variability is large. In forests that are not highly

Box 4-4 (continued)

Gradual changes in composition resulting from decreased evergreen conifer productivity and increased mortality, as well as increased

deciduous species productivity, can be facilitated by more rapid shifts associated with ﬁre disturbance where it can occur (Mack et al.,

2008; Johnstone et al., 2010; Roland et al., 2013). Each of these interacting processes, as well as insect disturbance and associated

tree mortality, are tightly coupled with warming-induced drought (Choat et al., 2012; Ma et al., 2012; Anderegg et al., 2013a). Similarly,

gradual productivity increases at the boreal-tundra ecotone are facilitated by long distance dispersal into areas disturbed by tundra

ﬁre and thermokarsting (Tchebakova et al., 2009; Brown, 2010; Hampe, 2011). In North America these coupled interactions set the

stage for changes in ecological processes, already documented, consistent with a biome shift characterized by increased deciduous

composition in the interior boreal forest and evergreen conifer migration into tundra areas that are, at the same time, experiencing

increased shrub densiﬁcation. The net feedback of these ecological changes to climate is multi-faceted, complex, and not yet well

known across large regions except via modeling studies, which are often poorly constrained by observations.

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Chapter 4 Terrestrial and Inland Water Systems

ater limited, increased growth is consistent with higher temperatures

and extended growing seasons. As in the case of forests in general, clear

attribution is difficult because of the interaction of multiple environmental

drivers as well as changes in forest management (e.g., Boisvenue and

Running, 2006; Ciais et al., 2008; Dale et al., 2010; see also Section

4.3.3.1). In Europe much of the increase has been attributed to recovery

following previously more intense harvesting (Ciais et al., 2008; Lindner

et al., 2010).

Several studies using forest yield models suggest future increases in

forest production (Kirilenko and Sedjo, 2007). These results may

overestimate the positive effects of elevated CO

(Kirilenko and Sedjo,

2007; see Section 4.2.4.4). The effects of disturbances such as wildfires,

forest pests, pathogens, and windstorms, which are major drivers of

forest dynamics, are poorly represented in the models (Loustau, 2010;

see also Section 4.3.3.1 and Box 4-2). The results from different models

often differ substantially both regarding forest productivity (e.g., Sitch

et al., 2008; Keenan et al., 2011) and potential species ranges (see

Section 4.3.3.1.2). Decreased forest production is expected in already

dry forest regions for which further drying is projected, such as the

southwestern USA (Williams, A.P. et al., 2010). Extreme drying may also

decrease yields in forests currently not water limited (e.g., Sitch et al.,

2008; see Section 4.3.3.1). Plantations in cold-limited areas could

benefit from global warming, provided that increased fires, storms,

pests, and pathogens do not outweigh the potential direct climate

effects on tree growth rates.

Low species diversity (and low genetic diversity within species where

clones or selected provenances are used) renders plantation forests less

resilient to climate change than natural forests (e.g., Hemery, 2008).

Choosing provenances that are well adapted to current climates but

pre-adapted to future climates is difficult because of uncertainties in

climate projections at the time scale of a plantation forest rotation

(Broadmeadow et al., 2005). How forest pests and pathogens will spread

as a result of climate change and other factors is highly uncertain. New

pathogen-tree interactions may arise (e.g., Brasier and Webber, 2010).

Adaptive management can decrease the vulnerability of plantation

forests to climate change (Hemery, 2008; Bolte et al., 2009; Seppälä,

2009; Dale et al., 2010). For example, risk spreading by promoting mixed

stands, containing multiple species or provenances, combined with

natural regeneration (Kramer et al., 2010), has been advocated as an

adaptation strategy for temperate forests (Hemery, 2008; Bolte et al.,

2010) and tropical forests (Erskine et al., 2006; Petit and Montagnini,

2006). Incomplete knowledge of the ecology of tropical tree species and

little experience in managing mixed tropical tree plantations remains a

problem (Hall et al., 2011). Especially at the equator-ward limits of cold-

adapted species, such as Norway spruce (Picea abies) in Europe, climate

change will very likely lead to a shift in the main tree species used for

forest plantations (Iverson et al., 2008; Bolte et al., 2010).

4.3.3.5.2. Bioenergy systems

The production of modern bioenergy is growing rapidly throughout the

world in response to climate mitigation and energy security policies

(Kirilenko and Sedjo, 2007). WGIII AR5 Chapter 7 addresses the potential

of bioenergy as a climate mitigation strategy. The vulnerability of

ioenergy systems to climate change is similar to that of plantation

forestry (Section 4.3.3.5.1) or food crops (Section 7.3): in summary, they

remain viable in the future in most but not all locations, but their

viability is increasingly uncertain for high levels of climate change

(Haberl et al., 2011). Oliver, R.J. et al. (2009) suggested that rising CO

might contribute to increased drought tolerance in bioenergy crops

(because it leads to improved plant water use efficiency).

The unintended consequences of large-scale land use changes driven

by increasing bioenergy demand are addressed in Section 4.4.4.

4.3.3.5.3. Cultural landscapes

Cultural landscapes are characterized by a long history of human-nature

interactions, which results in a particular configuration of species and

landscape pattern attaining high cultural significance (Rössler, 2006).

Examples are grassland or mixed agriculture landscapes in Europe, rice

landscapes in Asia (Kuldna et al., 2009), and many others across the globe

(e.g., Rössler, 2006; Heckenberger et al., 2007). Such landscapes are often

agricultural, but we deal with them here because their perceived value is

only partly in terms of their agricultural products.

It has been suggested that protected area networks (such as Natura

2000 in Europe, which includes many cultural landscape elements) be

adjusted to take into account climate change (Bertzky et al., 2010).

Conserving species in cultural landscapes (e.g., EU Council, 1992)

generally depends on maintaining certain types of land use. Doing so

under climate change requires profound knowledge of the systems and

species involved, and conservation success so far has been limited (see

Thomas et al., 2009, for a notable exception). Understanding the relative

importance of climate change and land management change is critical

(Settele and Kühn, 2009). To date land use changes have been the

most obvious driver of change (Nowicki et al., 2007); impacts have been

attributed to climate change (with low to medium confidence) in only

a few examples (Devictor et al., 2012). Even in these, combined land

use-climate effects explain the pattern of observed threats better than

either alone (Schweiger et al., 2008, 2012; Clavero et al., 2011).

There is very high confidence that species composition and landscape

structure are changing in cultural landscapes such as Satoyama

landscapes in Japan or mixed forest, agricultural landscapes in Europe.

Models and experiments suggest that climate change should be

contributing to these observed changes. The land use and land

management signal is so strong in these landscapes that there is very

low confidence that we can attribute these observations to climate

change (Figure 4-4).

4.3.3.5.4. Urban ecosystems

Although urban areas (for definition see Section 8.1.2) cover only 0.5%

of the Earth’s land surface (Schneider et al., 2009), more than half of

humanity lives there (increasing annually by 74 million people; UN DESA

Population Division, 2012) and they harbor a large variety of species

(McKinney, 2008). The frequency and magnitude of warm days and

nights (heat waves) is virtually certain to increase globally in the future

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Terrestrial and Inland Water Systems Chapter 4

(IPCC, 2012); this trend is higher in urban than in rural areas (McCarthy

et al., 2010). Heavy rainfall events are also projected to increase (IPCC,

2012), and although the hydrological conditions in urban areas make

them prone to flooding (medium confidence), there is limited evidence

that they will be over-proportionally affected. It is very likely that sea level

rise in the future will contribute to flooding, erosion, and salinization of

coastal urban ecosystems (IPCC, 2012). Climate change is projected to

increase the frequency of landslides (UN-HABITAT, 2011). Climate

change impacts on urban ecosystems and biodiversity have received

comparatively little attention, with water availability being an exception

(Hunt and Watkiss, 2011). Changes in water availability and quality due

to changes in precipitation, evaporation, or in salinity regimes will

especially affect urban freshwater ecosystems (Hunt and Watkiss, 2011).

As in other ecosystems, climate change will lead to a change in species

composition, the frequency of traits, and ecosystem services from urban

ecosystems. Knapp, S. et al. (2008) found that trait composition of plant

communities changes during urbanization toward adaptive characteristics

of dry and warm environments (see also Sections 4.2.4.6 and 4.3.2.5).

Urban areas are one of the main points of introduction of alien species

(e.g., for plants through urban gardening; Knapp, S. et al., 2012). Increased

damage by phytophagous insects to plants in urban environments is

anticipated (Kollár et al., 2009; Lopez-Vaamonde et al., 2010; Tubby and

Webber, 2010; see also Section 8.2.4.5).

4.3.4. Impacts on Key Ecosystem Services

Ecosystem services are the benefits that people derive from ecosystems

(see Glossary). Many ecosystem services are plausibly vulnerable to

climate change. The Millennium Ecosystem Assessment classification

(Millennium Ecosystem Assessment, 2003) recognizes provisioning

services such as food (Chapter 7), fiber (Section 4.3.4.2), bioenergy

(Section 4.3.4.3), and water (Chapter 3); regulating services such as

climate regulation (Section 4.3.4.5), pollination, pest and disease control

(Section 4.3.4.4), and flood control (Chapter 3); supporting services such

as primary production (Section 4.3.2.2) and nutrient cycling (Section

4.2.4.2, and indirectly Section 4.3.2.3); and cultural services, including

recreation and aesthetic and spiritual benefits (Section 10.6). Section

4.3.4.1 focuses on ecosystem services not already covered in the sections

referenced above.

4.3.4.1. Habitat for Biodiversity

Climate change can alter habitat for species by inducing (1) shifts in

habitat distribution that are not followed by species, (2) shifts in species

distributions that move them outside of their preferred habitats, and

(3) changes in habitat quality (Dullinger et al., 2012; Urban et al., 2012).

Climate change impacts on habitats for biodiversity are already occurring

(see the polar bear example in Section 28.2.2.1.3) but are not yet a

widespread phenomenon. Models of future climate change-induced

shifts in the distribution of ecosystems suggest that many species could

be outside of their preferred habitats within the next few decades

(Urban et al., 2012; see Sections 4.3.2.5, 4.3.3, and Figure 4-1).

Hole et al. (2009) report that the majority of African birds would have

to move large distances (up to several hundred kilometers) over the

next 60 years (under SRES B2a), resulting in substantial turnover of

species within protected areas (>50% turnover in more than 40% of

Important Bird Areas of Africa). To reach suitable climates they will have

to migrate across unfavorable habitats. Many may continue to find

suitable climate within the protected area network, but will be forced

to cope with new habitat constraints (Hole et al., 2009). Araujo et al.

(2011) estimate that by 2080 approximately 60% (58 ± 2.6%) of plants

and vertebrate species will no longer have favorable climates within

European protected areas, often pushing them into unsuitable or less

preferred habitats (based on SRES A1, A2, B1, and A1FI scenarios). Wiens

et al. (2011) project similar effects in the western USA (until the year

2069, based on SRES A2 scenarios), but also find that climate change

may open up new opportunities for protecting species in areas where

Frequently Asked Questions

FAQ 4.5 | Why does it matter if ecosystems are altered by climate change?

Ecosystems provide essential services for all life: food, life-supporting atmospheric conditions, drinkable water, as

well as raw materials for basic human needs such as clothing and housing. Ecosystems play a critical role in limiting

the spread of human and non-human diseases. They have a strong impact on the weather and climate itself, which

in turn impacts agriculture, food supplies, socioeconomic conditions, ﬂoods, and physical infrastructure. When

ecosystems change, their capacity to supply these services changes as well—for better or worse. Human well-being

is put at risk, along with the welfare of millions of other species. People have a strong emotional, spiritual, and

ethical attachment to the ecosystems they know, and the species they contain.

By “ecosystem change” we mean changes in some or all of the following: the number and types of organisms present;

the ecosystem’s physical appearance (e.g., tall or short, open or dense vegetation); and the functioning of the

system and all its interactive parts, including the cycling of nutrients and productivity. Though in the long term not

all ecosystem changes are detrimental to all people or to all species, the faster and further ecosystems change in

response to new climatic conditions, the more challenging it is for humans and other species to adapt to the new

conditions.

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Chapter 4 Terrestrial and Inland Water Systems

limate is currently unsuitable. In some cases climate change may allow

species to move into areas of lower current or future land use pressure

including protected areas (Bomhard et al., 2005). These studies strongly

argue for a rethinking of protected areas networks and of the

importance of the habitat matrix outside of protected areas as a key to

migration and long-term survival of species (see Sections 4.4.2.2,

4.4.2.3).

In the long term, some habitat types may disappear entirely due to

climate change (see Section 4.3.3 and Figure 4-1). Climates are projected

to occur in the future that at least in some features do not represent

climates that existed in the past (Williams, J.W. et al., 2007; Wiens et

al., 2011), and in the past climate shifts have resulted in vegetation

types that have no current analog (Section 4.2.3). The impacts of habitat

change on species abundance and extinction risk are difficult to evaluate

because at least some species are able to adapt to novel habitats (Prugh

et al., 2008; Oliver, T. et al., 2009). The uncertainty in habitat specificity

is one reason why quantitative projection of changes in extinction rates

is difficult (Malcolm et al., 2006).

The effects of climate change on habitat quality are less well studied

than shifts in species or habitat distributions. Several recent studies

indicate that climate change may have altered habitat quality already

and will continue to do so (Iverson et al., 2011; Matthews et al., 2011).

For example, decreasing snowfall in the southwestern USA has negatively

affected the habitat for songbirds (Martin and Maron, 2012).

4.3.4.2. Timber and Pulp Production

In most areas with forest plantations, forest growth rates have increased

during the last decades, but the variability is large, and in some areas

production has decreased (see Section 4.3.3.1). In forests that are not

highly water limited, these trends are consistent with higher temperatures

and extended growing seasons, but, as in the case of forests in general,

clear attribution is difficult because many environmental drivers and

changes in forest management interact (e.g., Boisvenue and Running,

2006; Ciais et al., 2008; Dale et al., 2010; see also Section 4.3.3.1). In

Europe a reduction in harvesting intensity has contributed (Ciais et al.,

2008; Lindner et al., 2010).

Forest yield models project future increases in forest production under

climate change, perhaps over optimistically (Kirilenko and Sedjo, 2007;

see Section 4.2.4.4). Using a model that accounts for fire effects and

insect damage, Kurz et al. (2008) showed that the Canadian forest sector

may have transitioned from a sink to a source of carbon.

4.3.4.3. Biomass-Derived Energy

Bioenergy sources include traditional forms such as wood and charcoal

from forests (see Section 4.3.3.1) and more modern forms such as the

industrial burning of biomass wastes, the production of ethanol and

biodiesel, and plantations of bioenergy crops. While traditional biofuels

have been in general decline as users switch to fossil fuels or electricity,

they remain dominant energy sources in many less developed parts of

the world, such as Africa, and retain a niche in developed countries.

enerally, potentials of bioenergy production under climate change may

be high, but are very uncertain (Haberl et al., 2011).

4.3.4.4. Pollination, Pest, and Disease Regulation

It can be inferred that global change will result in new communities

(Gilman et al., 2010; Schweiger et al., 2010). As these will have had

little opportunity for coevolution, changes in ecological interactions,

such as shifts in herbivore diets, the range of prey of predators, or in

pollination networks are to be expected (Tylianakis et al., 2008; Schweiger

et al., 2012). This may result in temporarily reduced effectiveness of the

“regulating services,” which generally depend on species interactions

(Montoya and Raffaelli, 2010). Burkle et al. (2013) show that the loss

of species reduces co-occurrence of interacting species and thus reduces

ecosystem functions based on them.

Climate change tends to increase the abundance of pest species,

particularly in previously cooler climates, but assessments of changes

in impacts are hard to make (Payette, 2007). Insect pests are directly

influenced by climate change, for example, through a longer warm

season during which to breed, and indirectly, for example, through the

quality of food plants (Jamieson et al., 2012) or via changes in their

natural enemies (predators and parasitoids). Insects have well-defined

temperature optima; warming toward the optimum leads to increased

vitality and reproduction (Allen, C.D. et al., 2010). Mild winters in

temperate areas promote pests formerly controlled by frost sensitivity.

For the vast majority of indirect effects, information is scarce. Further

assessments of climate change effects on pest and disease dynamics

are found in Sections 7.3.2.3 for agricultural pests and 11.5.1 for human

diseases.

Climate change has severe negative impacts on pollinators (including

honeybees) and pollination (Kjøhl et al., 2011) (medium confidence).

After land use changes, climate change is regarded as the second most

relevant factor responsible for the decline of pollinators (Potts et al.,

2010; for other factors see Biesmeijer et al., 2006; Brittain et al.,

2010a,b). The potential influence of climate change on pollination can

be manifold (compare Hegland et al., 2009; Schweiger et al., 2010;

Roberts et al., 2011). There are a few observational studies, which mostly

relate to the phenological decoupling of plants and their pollinators

(Gordo and Sanz, 2005; Bartomeus et al., 2011). While Willmer (2012)

states, based on experimental studies, that phenological effects may be

less important than has been suggested, an analysis of phenological

observations in plants by Wolkovich et al. (2012) shows that experimental

data on phenology may grossly underestimate the actual phenological

shifts.

Le Conte and Navajas (2008) state that the generally observed decline

in honeybees is a clear indication of an increasing susceptibility to

global change phenomena, with pesticide application, new diseases,

and stress (and a combination of these) as the most relevant causes.

Climate change may contribute by modifying the balance between

honeybees and their environment (including exposure or susceptibility

to diseases). Honeybees show a high capacity to adjust to a variety of

environments; their high genetic diversity should allow them to also

cope with climatic change (Bartomeus et al., 2011). The preservation of

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Terrestrial and Inland Water Systems Chapter 4

enetic variability within honeybees is regarded as a key adaptation

strategy for pollination services (Le Conte and Navajas, 2008).

4.3.4.5. Moderation of Climate Change, Variability, and Extremes

The focus of this section is on processes operating at regional to global

scales, rather than the well-known microclimatic benefits of ecosystems

in smoothing day-night temperature variations and providing local

evaporative cooling. In the decade 2000–2009, the global net uptake of

by terrestrial ecosystems was a large fraction of the anthropogenic

emissions to the atmosphere from all sources, reducing the rate of

climate change proportionately (Section 4.3.2.3; WGI AR5 Section 6.3.2).

Afforestation or reforestation are potential climate mitigation options

(Van Minnen et al., 2008; Vaughan and Lenton, 2011; Fiorese and Guariso,

2013; Singh et al., 2013) but, as discussed in Section 4.2.4.1, the net

effect of afforestation on the global climate is mixed and context

dependent. Wickham et al. (2012) found significant positive correlations

between the average annual surface temperature and the proportion

of forest in the landscape and conclude that the climate benefit of

temperate afforestation is unclear. Where low-albedo forest canopies

replace higher-albedo surfaces such as soil, grassland, or snow, the

resultant increase in net radiative forcing counteracts the benefits of

carbon sequestration to some degree (Arora and Montenegro, 2011).

Where the cloud cover fraction is low and the albedo difference is large,

that is, outside the humid tropics, the long-term net result of afforestation

can be global warming (Bala et al., 2007; Bathiany et al., 2010; Schwaiger

and Bird, 2010). Accounting for changes in albedo and indirect greenhouse

effects are not currently required in the formal rules for quantifying for

the climate effects of land use activities (Schwaiger and Bird, 2010;

Kirschbaum et al., 2012). There are potential negative trade-offs between

afforestation for climate mitigation purposes and other ecosystem

services, such as water supply (Jackson et al., 2005) and biodiversity

maintenance (CBD, 2012; Russell et al., 2012).

It has been suggested (Ridgwell et al., 2009) that planting large areas

of crop varieties with highly reflective leaves could help mitigate global

change. Model analyses indicate this “geo-engineering” strategy would

be marginally effective at high latitudes, but have undesirable climate

consequences at low latitudes. Measurements of leaf albedo in major

crops show that the current range of variability is insufficient to make

a meaningful difference to the global climate (Doughty et al., 2011).

4.4. Adaptation and Its Limits

4.4.1. Autonomous Adaptation

by Ecosystems and Wild Organisms

Autonomous adaptation (see Glossary under adaptation) refers to the

adjustments made by ecosystems, including their human components,

without external intervention, in response to a changing environment

(Smit et al., 2000)—also called “spontaneous adaptation” (Smit et al.,

2007). In the context of human systems it is sometimes called “coping

capacity.” The capacity for autonomous adaptation is part of resilience

but is not exactly synonymous (Walker et al., 2004).

ll social and ecological systems have some capacity for autonomous

adaptation. Ecosystems that have persisted for a long time can

reasonably be inferred to have a high capacity for autonomous

adaptation, at least with respect to the variability that they have

experienced in the past. An environmental change that is more rapid

than in the past or is accompanied by other stresses may exceed the

previously demonstrated adaptive capacity of the system. Adaptation

at one level, for instance by organisms in a community, can confer

greater resilience at higher organization levels, such as the ecosystem

(Morecroft et al., 2012). The mechanisms of autonomous adaptation of

organisms and ecosystems consist of changes in the physiology, behavior,

phenology, or physical form of organisms, within the range permitted

by their genes and the variety of genes in the population; changes in the

genetic composition of the populations; and change in the composition

of the community, through in- or out-migration or local extinction.

The ability to project impacts of climate change on ecosystems is

complicated by the potential for species to adapt. Adaptation by

individual species increases their ability to survive and flourish under

different climatic conditions, possibly leading to lower risks of extinction

than predicted from statistical correlations between current distribution

and climate (Botkin et al., 2007). It may also affect their interactions

with other species, leading to disruption of the biotic community (Visser

and Both, 2005).

4.4.1.1. Phenological

Changes in phenology are occurring in many species and locations

(Section 4.3.2.1). Further evidence since AR4 shows how this can be

an adaptation to climate change, but also the limits to phenological

adaptation. An organism’s phenology is typically highly adapted to the

climate seasonality of the environment in which it evolved. Species

unable to adjust their phenological behavior will be negatively affected,

particularly in highly seasonal habitats (Both et al., 2010).

Moreover, the phenology of any species also needs to be keyed to the

phenology of other species with which it interacts, such as competitors,

food species, and pollinators. Systematic cross-taxa studies indicate

different rates of phenological change for different species and trophic

levels (Parmesan, 2007; Cook et al., 2008; Thackeray et al., 2010). If

adaptation is insufficiently rapid or coordinated between interdependent

species, disruption of ecological features such as trophic cascades,

competitive hierarchies, and species coexistence is inferred to result

(Nakazawa and Doi, 2012). Lack of coordination can occur if one of the

species is cued to environmental signals that are not affected by climate

change, such as day length (Parmesan, 2006). Increasing temperatures

may bring species either more into or out of synchrony, depending on

their respective starting positions (Singer and Parmesan, 2010), although

evidence is more toward a loss of synchrony (Thackeray et al., 2010).

Changes in interspecific interactions, such as predator-prey or

interspecific competition for food, stemming from changes in phenological

characteristics and breakdown in synchrony between species have been

observed. For example, bird breeding is most effective when synchronized

with the availability of food, so changes in the phenology of food

supplies can exert a selective pressure on birds. In a study of 100

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uropean migratory bird species, those that advanced their arrival date

showed stable or increasing populations between 1990 and 2000, while

those that did not adjust their arrival date on average showed declining

populations (Møller et al., 2008). In a comparison of nine Dutch

populations of the migratory pied flycatcher (Ficedula hypoleuca) between

1987 and 2003, populations declined by 90% in areas where food

peaked early in the season and the arrival of the birds was mis-timed,

but not in areas with a later food peak that could still be exploited by

early breeding birds (Both et al., 2006). However, compensating

processes can exist: for example, in a 4-decade study of great tits (Parus

major), breeding populations were buffered against phenological

mismatch due to relaxed competition between individual fledglings

(Reed et al., 2013). Between 1970 and 1990, changes in migration date

did not predict changes in population sizes (Møller et al., 2008).

Bird breeding can also be affected by phenological shifts in competing

species and predators. Between 1953 and 2005 in southwestern

Finland, the onset of breeding of the resident great tit Parus major and

the migratory pied flycatcher (Ficedula hypoleuca) became closer to

each other, increasing competition between them (Ahola et al., 2007).

The edible dormouse (Glis glis), a nest predator, advanced its hibernation

termination by -8 days per decade in the Czech Republic between 1980

and 2005 due to increasing annual spring air temperatures, leading to

increased nest predation in three out of four surveyed bird species

(Adamik and Kral, 2008).

Plant-insect interactions have also been observed to change. In Illinois,

USA, the pattern of which plants were pollinated by which bees were

altered by differing rates of phenological shifts and landscape changes

over 120 years, with 50% of bee species becoming locally extinct (Burkle

et al., 2013). Increasing asynchrony of the winter moth (Operophtera

brumata) and its feeding host oak tree (Quercus robur) in the Netherlands

was linked to increasing spring temperatures but unchanging winter

temperatures (van Asch and Visser, 2007). Warmer temperatures shorten

the development period of European pine sawfly larvae (Neodiprion

sertifer), reducing the risk of predation and potentially increasing the

risk of insect outbreaks, but interactions with other factors including

day length and food quality may complicate this prediction (Kollberg et

al., 2013). In North America, the spruce budworm (Choristaneura

fumiferana) lays eggs with a wide range of emergence timings, so the

population as a whole is less sensitive to changing phenology of host

trees (Volney and Fleming, 2007).

The environmental cues for phenological events are complex and multi-

layered (Körner and Basler, 2010; Singer and Parmesan, 2010). For

instance, many late-succession temperate trees require a chilling period

in winter, followed by a threshold in day length, and only then are

sensitive to temperature. As a result, simple projections of current

phenological trends may be misleading, since the relative importance

of cues can change (Cook et al., 2012b). The effects are complex and

sometimes apparently counterintuitive, such as the increased sensitivity

of flowering in high-altitude perennial herbs in the Rocky Mountains

to frost because plants begin flowering earlier as a result of earlier

snowmelt (Inouye, 2008).

It has been suggested that shorter generation times give greater

opportunity for autonomous adaptation through natural selection

(

Rosenheim and Tabashnik, 1991; Bertaux et al., 2004), but a standardized

assessment of 25,532 rates of phenological change for 726 UK taxa

indicated that generation time had only limited influence on adaptation

rates (Thackeray et al., 2010).

There is high confidence (much evidence, medium agreement) that

climate change-induced phenological shifts will continue to alter the

interactions between species in regions with a marked seasonal cycle.

4.4.1.2. Evolutionary and Genetic

Since AR4 there has been substantial progress in defining the concepts

and tools necessary for documenting and predicting evolutionary and

genetic responses to recent and future climate change, often referred

to as “rapid evolution.” Evolution can occur through many mechanisms,

including selection of existing genes or genotypes within populations,

hybridization, mutation, and selection of new adaptive genes and perhaps

even through epigenetics (Chevin et al., 2010; Chown et al., 2010;

Lavergne et al., 2010; Paun et al., 2010; Hoffmann and Sgro, 2011;

Anderson et al., 2012a; Donnelly et al., 2012; Franks and Hoffmann, 2012;

Hegarty, 2012; Merilä, 2012; Bell, 2013; Zhang et al., 2013). Mechanisms

such as selection of existing genes and genotypes, hybridization, and

epigenetics can lead to adaptation in very few generations, while others,

notably mutation and selection of new genes, typically take many tens of

generations. This means that species with very fast life cycles, for example,

bacteria, should in general have greater capacity to respond to climate

change than species with long life cycles, such as large mammals and trees.

There is a paucity of observational or experimental data that can be used

for detection and attribution of recent climate effects on evolution.

4.4.1.2.1. Observed evolutionary and genetic responses

to rapid changes in climate

There is a small but growing body of observations supporting the AR4

assessment that some species may have adapted to recent climate

warming or to climatic extremes through genetic responses (e.g.,

plants: Franks and Weis, 2008; Hill et al., 2011; Anderson et al., 2012b;

vertebrates: Ozgul et al., 2010; Phillimore et al., 2010; Husby et al., 2011;

Karell et al., 2011; insects: Buckley et al., 2012; van Asch et al., 2012).

Karell et al. (2011) found increasing numbers of brown genotypes of

the tawny owl (Strix aluco) in Finland over the course of the last 28

years and attributed it to fewer snow-rich winters, which creates strong

selection pressure against the white genotype. Earlier spawning by the

common frog (Rana temporaria) in Britain could be attributed largely

to local genetic adaptation to increasing spring temperatures (Phillimore

et al., 2010). Using a combination of models and observations, Husby

et al. (2011) have built a case for detection and attribution of genetic

adaptation in an insectivorous bird and in an herbivorous insect that has

tracked warming-related changes in the budburst timing of its host tree

(van Asch et al., 2012). In contrast, many species appear to be maladapted

to changing climates, in part because factors such as limited existing

genetic variation, weak heritability of adaptive traits, or conflicting

constraints on adaptation create low potential for rapid evolution

(Knudsen et al., 2011; Ketola et al., 2012; Merilä, 2012; Mihoub et al.,

2012). Most studies of rapid evolution suffer from methodological

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eaknesses, making it difficult to demonstrate clearly a genetic basis

underlying observed phenotypic responses to environmental change

(Gienapp et al., 2008; Franks and Hoffmann, 2012; Hansen et al., 2012;

Merilä, 2012). Rapid advances in quantitative genetics, genomics, and

phylogenetics, combined with recent progress on conceptual frameworks,

will substantially improve the detection and attribution of genetic

responses to changing climate over the next few years (Davis, C.C. et

al., 2010; Salamin et al., 2010; Hoffmann and Sgro, 2011). In sum, there

are few observational studies of rapid evolution and difficulties in

detection and attribution, so there is only medium confidence that some

species have responded to recent changes in climate through genetic

adaptations, and insufficient evidence to determine if this is a widespread

phenomenon (thus low confidence for detection and attribution across

all species; Figure 4-4).

The ability of species to adapt to new environmental conditions through

rapid evolutionary processes can also be inferred from the degree to

which environmental niches are conserved when environment is

changed. There is evidence that environmental niches are conserved for

some species under some conditions (plants: Petitpierre et al., 2012;

birds: Monahan and Tingley, 2012; review: Peterson et al., 2011), but

also evidence suggesting that environmental niches can evolve over time

scales of several decades following changes in climate (Broennimann

et al., 2007; Angetter et al., 2011; Konarzewski et al., 2012; Leal and

Gunderson, 2012; Lavergne et al., 2013). The paleontological record

provides insight into evolutionary responses in the face of natural climate

variation. In general, environmental niches appear to be broadly

conserved through time although there are insufficient data to determine

the extent to which genetic adaptation has attenuated range shifts and

changes in population size (Peterson et al., 2011; Willis and MacDonald,

2011). Phylogeographic reconstructions of past species distributions

suggest that hybridization may have helped avoid extinctions during

cycles of glaciation and could also play a key role in future adaptation

(Hegarty, 2012; Soliani et al., 2012). There is new evidence that epigenetic

mechanisms, such as DNA methylation, could allow very rapid adaptation

to climate (Paun et al., 2010; Zhang et al., 2013).

4.4.1.2.2. Mechanisms mediating rapid evolutionary response

to future climate change

Studies of genetic variability across species ranges, and models that

couple gene flow with spatially explicit population dynamics, suggest

counterintuitive responses to climate change. Too much or too little gene

flow to populations at range margins can create fragile, maladapted

populations, which is in contrast to the current wisdom that populations

at the range margins may be best adapted to global warming (Bridle et

al., 2010; Hill et al., 2011). Conversely, there is evidence from experiments,

models, and observations that populations in the center of species

ranges may in some cases be more sensitive to environmental change

than those at range boundaries (Bell and Gonzalez, 2009). Generalization

is complicated by the interactions between local adaptation, gene flow,

population dynamics, and species interactions (Bridle et al., 2010;

Norberg et al., 2012).

Substantial progress has been made since AR4 in developing models

for exploring whether genetic adaptation is fast enough to track climate

hange. Models of long-lived tree species suggest that existing genetic

variation may be sufficient to slightly attenuate negative impacts of

future climate change (Kuparinen et al., 2010; Kremer et al., 2012).

However, these studies also indicate that adaptive responses will lag

far behind even modest rates of projected climate change, owing to the

very long generation time of trees. In a species with much shorter

generation times, the great tit (Parus major), Gienapp et al. (2013) found

that modeled avian breeding times tracked climate change, only at low

to moderate rates of change. For a herbivorous insect with an even

faster life cycle, van Asch et al. (2007, 2012) predicted that rapid

evolution of the phenological response should have allowed it to track

recent warming, which it has.

More broadly, models suggest that species with short generation times

(1 year or less) potentially have the capacity to genetically adapt to

even the most rapid rates of projected climate change given large

enough present-day populations, but species with longer generation

times or small populations could be at risk of extinction at moderate to

high rates of climate change (Walters et al., 2012; Vedder et al., 2013).

Recent experimental and theoretical work on “evolutionary rescue”

shows that long-term avoidance of extinction through genetic adaptation

to hostile environments is possible, but requires large initial genetic

variation and population sizes and is accompanied by substantial loss

of genetic diversity, reductions in population size, and range contractions

over many generations before population recovery (Bell, 2013; Schiffers

et al., 2013).

Model-based projections must be viewed with considerable caution

because there are many evolutionary and ecological mechanisms not

accounted for in most models that can either speed up or inhibit

heritable adaptation to climate change (Cobben et al., 2012; Norberg et

al., 2012; Kovach-Orr and Fussmann, 2013). In some cases, accounting

for evolutionary processes in models even leads to predictions of greater

maladaptation to climate change, resulting in rapid population declines

(Hendry and Gonzalez, 2008; Ferriere and Legendre, 2013). Phenotypic

plasticity is thought to generally improve the odds of adaptation to

climate change. High plasticity in the face of climate change that has

low fitness costs can greatly improve the odds of adaptation; however,

plasticity with high costs leads to only modest amounts of adaptation

(Chevin et al., 2010).

AR4 concluded that “projected rates of climate change are very likely

to exceed rates of evolutionary adaptation in many species (high

confidence)” (Fischlin et al., 2007). Work since then provides a similar, but

more nuanced view of rapid evolution in the face of future climate change.

The lack of adaptation in some species to recent changes in climate, broad

support for niche conservatism, and models showing limited adaptive

capacity in species with long generation times all indicate that high

rates of climate change (RCP8.5) will exceed the adaptive capacities of

many species (high confidence). On the other hand, evidence from

observations and models also indicates that there is substantial capacity

for genetic adaptation to attenuate phenological shifts, population

declines, and local extinctions in many species, especially for low rates

of climate change (RCP2.6) (high confidence). Projected adaptation to

climate change is frequently characterized by population declines and

loss of genetic diversity for many generations (medium confidence),

thereby increasing species vulnerability to other pressures.

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4.4.1.3. Migration of Species

This mode of adaptation has been extensively dealt with in Section 4.3.2.5.

It is anticipated that the observed movement of species—individually

and collectively—will continue in response to shifting climate patterns.

Its effectiveness as an adaptation mechanism is constrained by three

factors. First, the rate of migration for many species, in many regions of

the world, is slower than the rate of movement of the climate envelope

(see Figure 4-5). Second, the ecosystem interactions can remain intact

only if all parts of the ecosystem migrate simultaneously and at the same

rate. Third, the contemporary landscape and inland water systems contain

many barriers to migration, in the form of habitat fragmentation, roads,

human settlements, and dams. Mountain ecosystems are less constrained

by these factors than flat-land ecosystems, but have additional

impediments for species already close to the top of the mountain.

4.4.2. Human-Assisted Adaptation

Human-assisted adaptation means a deliberate intervention with the

intent of increasing the capacity of the target organism, ecosystem, or

socio-ecological system to survive and function at an acceptable level in

the presence of climate change. It is also known as “planned adaptation”

(Smit et al., 2007). This chapter focuses less on the adaptation of people,

human communities, and infrastructure, as they are the topics of

Chapters 8 to 17, and more on non-human organisms and ecosystems,

while acknowledging the importance of the human elements within

the ecosystem. Intervention in this context means a range of actions,

including ensuring the presence of suitable habitat and dispersal pathways;

reducing non-climate stressors; and physically moving organisms and

storing and establishing them in new places. In addition to the other

approaches assessed in this section, “Ecosystem-Based Adaptation” (see

Box CC-EA) provides an option that integrates the use of biodiversity

and ecosystem services into climate change adaptation strategies in

ways that can optimize co-benefits for local communities and carbon

management, as well as reduce the risks associated with possible

maladaptation. Note that there are risks associated with all forms of

human-assisted adaptation (see Section 4.4.4), particularly in the presence

of far-from-perfect predictive capabilities (Willis and Bhagwat, 2009).

4.4.2.1. Reduction of Non-Climate Stresses and Restoration of

Degraded Ecosystems

The alleviation of other stresses acting on ecosystems is suggested to

increase the capacity of ecosystems to survive, and adapt to, climate

change, as the effects are generally either additive or compounding.

Ecosystem restoration is one way of alleviating such stresses while

increasing the area available for adaptation (Harris et al., 2006). Building

the resilience of at-risk ecosystems by identifying the full set of drivers

of change and most important areas and resources for protection is the

core of the adaptation strategy for the Arctic (Christie and Sommerkorn,

2012). Protective and restorative actions aimed at increasing resilience can

also be a cost-effective means as part of an overall adaptation strategy to

help people to adapt to the adverse effects of climate change and may

have other social, economic, and cultural benefits. This is part of

“ecosystem-based adaptation” (Colls et al., 2009; Box CC-EA).

4.4.2.2. The Size, Location, and Layout of Protected Areas

Additions to, or reconfigurations of, the protected area estate are

commonly suggested as pre-adaptations to projected climate changes

(Heller and Zavaleta, 2009). This is because for most protected areas,

under plausible scenarios of climate change, a significant fraction of

the biota will no longer have a viable population within the present

protected area footprint. It is noted that the extant geography of

protected areas is far from optimal for biodiversity protection even

under the current climate; that most biodiversity exists outside rather

than in protected areas and this between-protected area matrix is as

important; that it is usually cheaper to acquire land proactively in the

areas of projected future bioclimatic suitability than to correct the current

non-optimality and then later add on areas to deal with climate change

as it unfolds (Hannah et al., 2007); and that the existing protected area

network will still have utility in future climates, even though it may

contain different species (Thomas et al., 2012).

Hickler et al. (2012) analyzed the layout of protected areas in Europe

and concluded that under projected 21st century climate change a third

to a half of them would potentially be occupied by different vegetation

than they currently represent. The new areas that need to be added to

the existing protected area network to ensure future representativeness

is situation specific, but some general design rules apply: orientation

along climate gradients (e.g., altitudinal gradients) is more effective

than orientation across them (Roux et al., 2008); regional scale planning

is more effective than treating each local case independently because

it is the network of habitats and protected areas that confers resilience

rather than any single element (Heller and Zavaleta, 2009); and better

integration of protected areas with a biodiversity-hospitable landscape

outside is more effective than treating the protected areas as islands

(Willis and Bhagwat, 2009). Dunlop et al. (2012) assessed the implications

of climate change for biodiversity conservation in Australia and found

many opportunities to facilitate the natural adaptation of biodiversity,

including expanding the network of protected areas and restoring habitat

at a large scale.

4.4.2.3. Landscape and Watershed Management

The need to include climate change into the management of vulnerable

ecosystems is explicitly included in the strategic goals of the Convention

on Biological Diversity. Oliver et al. (2012b) developed decision trees

based on three scenarios: (1) adversely sensitive, where areas within the

species current geographical range will become climatically unsuitable

with a changing climate; (2) climate overlap, where there are areas that

should remain climatically suitable within the species’ range; and (3)

new climatic space, which refers to areas outside of the current range

that are projected to become suitable. Heller and Zavaleta (2009)

reviewed recommendations in the published literature and argue that

the majority of them, such as increase habitat heterogeneity of sites

and connectivity of habitats across landscapes, lack sufﬁcient speciﬁcity

to ensure the persistence of many species and related ecosystem

services to ongoing climate change. To date, recommendations are

overwhelmingly focused on ecological data, neglecting social science

insights. Few resources or capacity exist to guide adaptation planning

processes at any scale.

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Climate-induced impacts to hydrological and thermal regimes in

freshwater systems can be offset through improved management of

environmental flow releases from reservoirs (Arthington et al., 2006,

2010 and references therein; Poff et al., 2010). Protection and restoration

of riparian vegetation in small stream systems provide an effective strategy

to moderate temperature regimes and offset warming, and protect

water quality for downstream ecosystems and water supply areas

(Davies, 2010; Capon et al., 2013).

General principles for management adaptations were summarized from

a major literature review by West et al. (2009). They suggest that in the

context of climate change, successful management of natural resources

will require cycling between “managing for resilience” and “managing

for change.” This requires the anticipation of changes that can alter the

impacts of grazing, fire, logging, harvesting, recreation, and so on. At

the national level, principles to facilitate adaptation include (1)

management at appropriate scales, and not necessarily the scales of

convenience or tradition; (2) increased collaboration among agencies;

(3) rational approaches for establishing priorities and applying triage;

and (4) management with the expectation of ecosystem change, rather

than keeping them as they have been. Barriers and opportunities were

divided into four categories: (1) legislation and regulations, (2)

management policies and procedures, (3) human and financial capital,

and (4) information and science.

Steenberg et al. (2011) simulated the effect on adaptive capacity of

three variables related to timber harvesting: the canopy-opening size

of harvests, the age of harvested trees within a stand, and the species

composition of harvested trees within a stand. The combination of all

three adaptation treatments allowed target species and old forest to

remain reasonably well represented without diminishing the timber

supply. This minimized the trade-offs between management values and

climate adaptation objectives. Manipulation of vegetation composition

and stand structure has been proposed as a strategy for offsetting

climatic change impacts on wildfires in Canada. Large areas of boreal

forests are currently being harvested and there may be opportunities

for using planned manipulation of vegetation for management of future

wildfire risks. This management option could also provide an additional

benefit to the use of assisted species migration because the latter would

require introducing non-flammable broadleaves species into forests that

are otherwise highly flammable (Girardin et al., 2013b; Terrier et al.,

2013). Harvesting practices, such as partial cuts that limit the opening

of the forest cover created by harvest, will be a key element to maintain

diverse forest compositions and age class distributions in boreal forests.

Another sound option for decreasing the exposure of silvicultural

investments to an increasing ﬁre danger is to use tree species requiring

a shorter rotation (Girardin et al., 2013a).

4.4.2.4. Assisted Migration

Assisted migration has been proposed when fragmentation of habitats

limits migration potential or when natural migration rates are outstripped

by the pace of climate change (Hoegh-Guldberg et al., 2008; Vitt et al.,

2010; Chmura et al., 2011; Loss et al., 2011; Ste-Marie et al., 2011). The

options for management can be summarized as: (1) try to maintain or

improve existing habitat or environment so that species do not have to

move (e.g., Settele and Kühn, 2009); (2) maintain or improve migration

corridors, including active management to improve survival along the

moving margin of the distribution (Lawson et al., 2012); and (3) directly

translocate species or genetically distinct populations within a species

(Aitken et al., 2008; Hoegh-Guldberg et al., 2008; Rehfeldt and Jaquish,

2010; Loss et al., 2011; Pedlar et al., 2012). There is low agreement

whether it is better to increase the resilience to climate change of

ecosystems as they currently occur, or to enhance capacity of ecosystems

to transform in the face of climate change (Richardson et al., 2009).

There is high agreement that maintaining or improving migration

corridors or ecological networks is a low-regret strategy, partly because

it is also seen as useful in combatting the negative effects of habitat

fragmentation on population dynamics (Hole et al., 2011; Jongman et

al., 2011). This approach has the benefit of improving the migration

potential for large numbers of species and is therefore a more ecosystem-

wide approach than assisted migration for individual species. However,

observational and modeling studies show that increases in habitat

connectivity do not always improve the population dynamics of target

Frequently Asked Questions

FAQ 4.6 | Can ecosystems be managed to help them and people to adapt to climate change?

The ability of human societies to adapt to climate change will depend, in large measure, on the management of

terrestrial and inland freshwater ecosystems. A ﬁfth of global human-caused carbon emissions today are absorbed

by terrestrial ecosystems; this important carbon sink operates largely without human intervention, but could be

increased through a concerted effort to reduce forest loss and to restore damaged ecosystems, which also co-beneﬁts

the conservation of biodiversity.

The clearing and degradation of forests and peatlands represents a source of carbon emissions to the atmosphere

which can be reduced through management; for instance, there has been a three-quarters decline in the rate of

deforestation in the Brazilian Amazon in the last 2 decades. Adaptation is also helped through more proactive

detection and management of wildﬁre and pest outbreaks, reduced drainage of peatlands, the creation of species

migration corridors, and assisted migration.

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Chapter 4 Terrestrial and Inland Water Systems

pecies, may decrease species diversity, and may also facilitate the spread

of invasive species (Cadotte, 2006; Brisson et al., 2010; Matthiessen et

al., 2010).

There is medium agreement that the practice of assisted migration of

targeted species is a useful adaptation option (Hoegh-Guldberg et al.,

2008; Vitt et al., 2009; Willis and Bhagwat, 2009; Loss et al., 2011;

Hewitt et al., 2011). The velocity of 21st century climate change and

substantial habitat fragmentation in large parts of the world means

that many species will be unable to migrate or adapt fast enough to

keep pace with climate change (Figure 4-5), posing problems for long-

term survival of the species. Some ecologists believe that careful selection

of species to be moved would minimize the risk of undesirable impacts on

existing communities or ecosystem function (Minteer and Collins, 2010),

but others argue that the history of intentional species introductions

shows that the outcomes are unpredictable and in many cases have

had disastrous impacts (Ricciardi and Simberloff, 2009). The number of

species that require assisted migration could easily overwhelm funding

capacity (Minteer and Collins, 2010). Decisions regarding which species

should be translocated are complex and debatable, given variability

among and within species and the ethical issues involved (Aubin et al.,

2011; Winder, R. et al., 2011).

4.4.2.5. Ex Situ Conservation

Conservation of plant and animal genetic resources outside of their

natural environment—in gardens, zoos, breeding programs, seed

banks, or gene banks—has been widely advocated as an “insurance”

against both climate change and other sources of biodiversity loss and

impoverishment (Khoury et al., 2010). There are many examples of

existing efforts of this type, some with global scope (e.g., Millennium

Seed Bank, Svalbard Vault, Frozen Ark, Global Genome Initiative, and

others; Lermen et al., 2009; Rawson et al., 2011). Knowledge of which

genetic variants within a species have more potential for adaptation to

climate change could help prioritize the material stored (Michalski et

al., 2010).

Several issues remain largely unresolved (Li and Pritchard, 2009). The

physiological, institutional, and economic sustainability of such efforts

into the indefinite future is unclear. The fraction of the intraspecific

variation that needs to be preserved for future viability and how much

enetic bias is introduced by collecting relatively small samples

from restricted locations, and then later by the selection pressures

inadvertently applied during ex situ maintenance are unknown. Despite

some documented successes, it remains uncertain whether it is always

possible to reintroduce species successfully into the wild after generations

of ex situ conservation.

4.4.3. Consequences and Costs of Inaction

and Benefits of Action

Failure to reduce the magnitude or rate of climate change will plausibly

lead to changes (often decreases) in the value of ecosystem services

provided, or incur costs in order to maintain or restore the services or adapt

to their decline. There are several sources of such costs: administration

and assessment, implementation, and opportunity costs, including

financial cost. Owing to the number of assumptions made, knowledge

gaps, and recognized uncertainties, such result should be employed with

caution. A systematic review of costs related to ecosystems and climate

change by Rodriguez-Labajos (2013) shows that the monetary and non-

monetary costs are distributed across all ecosystem service categories.

It also discusses the potential and limits of monetary cost calculations,

and issues of timing, trade-offs, and the unequal distribution of costs.

A comprehensive monetary estimate of the effects of climate change on

ecosystem service provision is not available. The Millennium Ecosystem

Assessment (2005c,d,e) included climate change among the direct

drivers of ecosystems change and devoted a chapter to the necessary

responses. Building on results of the IPCC, the Millennium Ecosystem

Assessment offered some estimated costs of action: complying with the

Kyoto protocol for industrial countries would range between 0.2 and 2%

of GDP; a modest stabilization target of 450 ppm CO

in the atmosphere

over the 21st century would range from 0.02 to 0.1% of global-average

GDP per year. TEEB (2009) underlined priorities in the ecosystem service-

climate change coupling (reduction targets in relation to coral reefs,

forest carbon markets and accounting, and ecosystem investment for

mitigation), without going in depth into analysis of the cost types

involved. The Cost of Policy Inaction (COPI) Project (ten Brink et al., 2008)

estimated the monetary costs of not meeting the 2010 biodiversity goals.

Their model incorporates climate change, among other pressures,

through an impaired quality of land, in terms of species abundance in

diverse land use categories. They conclude that the cumulative losses

Frequently Asked Questions

FAQ 4.7 | What are the economic costs of changes in ecosystems due to climate change?

Climate change will certainly alter the services provided by most ecosystems, and for high degrees of change, the

overall impacts are most likely to be negative. In standard economics, the value of services provided by ecosystems

are known as externalities, which are usually outside the market price system, difﬁcult to evaluate, and often ignored.

A good example is the pollination of plants by bees and birds and other species, a service that may be negatively

affected by climate change. Pollination is critical for the food supply as well as for overall environmental health. Its

value has been estimated globally at US$350 billion for the year 2010 (range of estimates of US$200 to 500 billion).

327

Terrestrial and Inland Water Systems Chapter 4

f welfare due to land use changes, in terms of loss of ecosystem

services, could reach an annual amount of EUR 14 trillion (based on

2007 values) in 2050, which may be equivalent to 7% of projected

global GDP for that year. Eliasch (2008) estimates the damage costs to

forests as reaching US$1 trillion a year by 2100. The study used the

probabilistic model employed by Stern (2006), which did not value

effects on biodiversity or water-related ecosystem services.

The studies to date agree on the following points. First, climate change

has already caused a reduction in ecosystem services that will become

more severe as climate change continues. Second, ecosystem-based

strategies to mitigate climate change are cost effective, although more

difficult to implement (i.e., more costly) in intensively managed ecosystems

such as farming lands. Third, accurately estimating the monetary costs

of reduction in ecosystem services that are not marketed is difficult. The

provision of monetized costs tends to sideline the non-monetized

political, social, and environmental costs relevant for decision making.

Finally, there is a large funding gap between the cost of actions necessary

to protect ecosystem services against climate change and the actual

resources available.

In addition to direct costs, further costs may result from trade-offs

between services: for example, afforestation for climate mitigation and

urban greening for climate adaptation may be costly in terms of water

provision (Chisholm, 2010; Jenerette et al., 2011; Pataki et al., 2011).

Traditional agriculture preserves soil carbon sinks, supports on-site

biodiversity, and uses less fossil fuel than high-input agriculture

(Martinez-Alier, 2011) but, due to the typically lower per hectare yields,

may require a larger area to be dedicated to cropland. Leaving aside

the contested (Searchinger et al., 2008; Plevin et al., 2010) effectiveness

of biofuels as a mitigation strategy, there is evidence of their disruptive

effect on food security, land tenure, labor rights, and biodiversity in

several parts of the world (Obersteiner et al., 2010; Tirado et al., 2010).

4.4.4. Unintended Consequences

of Adaptation and Mitigation

Actions taken within the terrestrial and freshwater system domain or in

other sectors to mitigate or adapt to climate change can have unintended

consequences. Some issues relevant to this section are also found in

Section 14.7 and the Working Group III contribution to the AR5.

Several of the alternatives to fossil fuel require extensive use of the land

surface and thus have a direct impact on terrestrial ecosystems and an

indirect impact on inland water systems (Paterson et al., 2008; Turner et

al., 2010). As an illustration, the RPC2.6 scenario involves both bioenergy

and renewables as major components of the energy mix (Box 4-1; van

Vuuren et al., 2011).

Policy shifts in developed countries favor the expansion of large-scale

bioenergy production, which places new pressures on terrestrial and

freshwater ecosystems (Searchinger et al., 2008; Lapola et al., 2010),

either through direct use of land or water or indirectly by displacing

food crops, which must then be grown elsewhere. Over the past decade

there has been a global trend to reduced rates of forest loss; it is unclear

if this will continue in the face of simultaneously rising food and biofuel

emand (Wise et al., 2009; Meyfroidt and Lambin, 2011). The EU

Renewable Energy Sources Directive is estimated to have only a

moderate influence on European forests provided that the price paid

by the bioenergy producers remained below US$50 to 60 per cubic

meter of wood (Moiseyev et al., 2011). However, a doubled growth rate

for bioenergy until 2030 would have major consequences for the global

forest sector, including a reduction of forest stocks in Asia of 2 to 4%

(Buongiorno et al. 2011). By 2100 in RCP2.6, bioenergy crops are

projected to occupy approximately 4 million km

, about 7% of global

cultivated land projected at the time. Modification of the landscape and

the fragmentation of habitats are major influences on extinction risks

(Fischer and Lindenmayer, 2007), especially if native vegetation cover is

reduced or degraded, human land use is intensive, and “natural” areas

become disconnected. Hence, additional extensification of cultivated

areas for energy crops may contribute to extinction risks. Some bioenergy

crops may be invasive species (Raghu et al., 2006).

Abandoned former agricultural land could be used for biomass production

(McAlpine et al., 2009). However, such habitats may be core elements

in cultural landscapes of high conservation value, with European

species-rich grasslands often developed from abandoned croplands

(Hejcman et al., 2013).

Damming of river systems for hydropower can cause fragmentation of the

inland water habitat with implications for fish species, and monitoring

studies indicate that flooding of ecosystems behind the dams can lead

to declining populations, for example, of amphibians (Brandão and

Araújo, 2007). Reservoirs can be a sink of CO

but also a source of

biogenic CO

and CH

; this issue is discussed in WG III AR5 Section 7.8.1.

Wind turbines can kill birds and bats (e.g., Barclay et al., 2007), and

inappropriately sited wind farms can negatively impact on bird

populations (Drewitt and Langston, 2006). Effects can be reduced by

careful siting of turbines, for example by avoiding migration routes

(Drewitt and Langston, 2006). Estimating mortality rates is complex and

difficult (Smallwood, 2007) but techniques are being developed to

inform siting decisions and impact assessments (Péron et al., 2013).

Wind farms in Europe and the USA are estimated to cause between 0.3

and 0.4 wildlife fatalities per gigawatt-hour of electricity, compared to

approximately 5.2 wildlife fatalities per gigawatt-hour for nuclear and

fossil-fuel power stations (Sovacool, 2009; but see Willis, C.K.R. et al.,

2010). One study found on-site bird populations to be generally affected

more by windfarm construction than subsequent operation, with some

populations recovering after construction (Pearce-Higgins et al., 2012).

Large-scale solar farms could impact local biodiversity if poorly sited,

but the impact can be reduced with appropriate planning (Tsoutsos et

al., 2005). Solar photovoltaic installations can decrease local surface

albedo, giving a small positive radiative forcing. There are some plausible

local circumstances in which this may be a consideration, but in general

the climate effect is estimated to be 30 times smaller than the avoided

radiative forcing arising from substituting fossil fuels with PV (Nemet,

2009).

Relocation or expansion of agricultural areas and settlements as climate

change adaptation measures could pose risks of habitat fragmentation

and loss similar to those discussed above in the context of mitigation

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Chapter 4 Terrestrial and Inland Water Systems

hrough bio-energy. Assisted migration (see Section 4.4.2.4) may

directly conflict with other conservation priorities, for example by

facilitating the introduction of invasive species (Maclachlan et al., 2007).

4.5. Emerging Issues and Key Uncertainties

Detecting the presence and location of thresholds in ecosystem response

to climate change, specifically the type of thresholds characterized as

tipping points, remains a major source of uncertainty with high potential

consequences. In general (Field et al., 2007), negative feedbacks

currently dominate the climate-ecosystem interaction. For most ecological

processes, increasing magnitude of warming shifts the balance toward

positive rather than negative feedbacks (Field et al., 2007). In several

regions, such as the boreal ecosystems, positive feedbacks may become

dominant, under moderate warming. For positive feedbacks to propagate

into “runaway” processes leading to a new ecosystem state, the

strength of the feedback has to exceed that of the initial perturbation.

This has not as yet been demonstrated for any large-scale, plausible,

and immanent ecological process, but the risk is non-negligible and the

consequences if it did occur would be severe; thus further research is

needed.

The issue of biophysical interactions between ecosystem state and the

climate, over and above the effects mediated through GHGs, is emerging

as significant in many areas. Such effects include those caused by

changes in surface reflectivity (albedo) or the partitioning of energy

between latent energy and sensible heat.

Uncertainty in predicting the response of terrestrial and freshwater

ecosystems to climate and other perturbations, particularly at the local

scale, remains a major impediment to determining prudent levels of

permissible change. A significant source of this uncertainty stems from

the inherent complexity of ecosystems, especially where they are coupled

to equally complex social systems. The high number of interactions can

lead to cascading effects (Biggs et al., 2011). Some of this uncertainty

can be reduced by better systems understanding, but some will remain

irreducible because of the failure of predictive models when faced with

certain types of complexity (such as those which lead to mathematical

bifurcations, a problem that is well known in climate science). Probabilistic

statements about the range of outcomes are possible in this context,

but ecosystem science is as yet mostly unable to conduct such analyses

routinely and rigorously. One consequence is the ongoing difficulty in

attributing observed changes unequivocally to climate change. More

comprehensive monitoring is a key element of the solution.

The consequences for species interactions of differing phenological or

movement-based responses to climate change are insufficiently known

and may make projections based on individual species models unreliable.

Studies of the combined effects of multiple simultaneous elements of

global change, such as the effects of elevated CO

and rising tropospheric

ozone on plant productivity—which have critical consequences for the

future sink strength of the biosphere, as they are of similar magnitude

but opposite sign—are needed as a supplement to the single-factor

experiments. For example, uncertainty on the magnitude of CO

fertilization is key for forest responses to climate change, particularly in

ropical forests, woodlands, and savannas (Cox et al., 2013; Huntingford

et al., 2013).

The effects of changes in the frequency or intensity of climate-related

extreme events, such as floods, cyclones, heat waves, and exceptionally

large fires on ecosystem change are probably equal to or greater than

shifts in the mean values of climate variables. These effects are

insufficiently studied and, in particular, are seldom adequately represented

in ESMs.

Understanding of the rate of climate change that can be tracked or

adapted to by organisms is as important as understanding the

magnitude of change they can tolerate. Despite being explicitly required

under Article 2 of the UNFCCC, rate studies are currently less developed

and more uncertain than magnitude (equilibrium) studies. This includes

evidence for the achievable migration rates of a range of species as

well as the rate of micro-evolutionary change.

The capacity for, and limits to, ecological and evolutionary adaptive

processes are known only in a few cases. The development and testing

of human-assisted adaptation strategies for their cost-effectiveness in

reducing risk are prerequisites for their widespread adoption.

The costs of the loss of biodiversity and ecosystem services as a result

of climate change are known for only a few cases, or are associated

with large uncertainties, as are the costs and benefits of assisting

ecosystems and species to adapt to climate change.

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