RESEARCHPAPER

Assessing global biome exposureto climate change through theHolocene–Anthropocene transitionMarta Benito-Garzón1,2*, Paul W. Leadley3 and

Juan F. Fernández-Manjarrés1,3

1CNRS, Laboratoire d’Ecologie, Systématique

et Evolution, Université Paris-Sud, CNRS,

UMR 8079, F-91405 Orsay Cedex, France,2CNRS, Centre International de Recherche sur

l’Environnement et le Développement

(CIRED), 94736, Nogent-sur-Marne Cedex,

France, 3Laboratoire d’Ecologie, Systématique

et Evolution, Université Paris-Sud, CNRS,

UMR 8079, F-91405 Orsay Cedex, France

ABSTRACT

Aim To analyse global patterns of climate during the mid-Holocene and conductcomparisons with pre-industrial and projected future climates. In particular, toassess the exposure of terrestrial biomes and ecoregions to climate-related risksduring the Holocene–Anthropocene transition starting at the pre-industrialperiod.

Location Terrestrial ecosystems of the Earth.

Methods We calculated long-term climate differences (anomalies) between themid-Holocene (6 ka cal bp, mH), pre-industrial conditions and projections for2100 (middle-strength A1B scenario) using six global circulation models availablefor all periods. Climate differences were synthesized with multivariate statistics andaverage principal component loadings of temperature and precipitation differences(an estimate of climate-related risks) were calculated on 14 biomes and 766ecoregions.

Results Our results suggest that most of the Earth’s biomes will probably undergochanges beyond the mH recorded levels of community turnover and range shiftsbecause the magnitude of climate anomalies expected in the future are greater thanobserved during the mH. A few biomes, like the remnants of North American andEuro-Asian prairies, may experience only slightly greater degrees of climate changein the future as compared with the mH. In addition to recent studies that haveidentified equatorial regions as the most sensitive to future climate change, we findthat boreal forest, tundra and vegetation of the Equatorial Andes could be atgreatest risk, since these regions will be exposed to future climates that are welloutside natural climate variation during the Holocene.

Conclusions The Holocene–Anthropocene climate transition, even for a middle-strength future climate change scenario, appears to be of greater magnitude anddifferent from that between the mH and the pre-industrial period. As a conse-quence, community- and biome-level changes due to of expected climate changemay be different in the future from those observed during the mH.

KeywordsAnthropocene, biodiversity, biome refugia, climate change, global circulationmodels, mid-Holocene, no-analogue, resilience.

*Correspondence: Marta Benito-Garzón, CNRS,Laboratoire d’Ecologie, Systématique etEvolution, UMR 8079 Université Paris-Sud,CNRS, F-91405 Orsay Cedex, France.E-mail: marta.benito@gmail.com

INTRODUCTION

The cumulative human modification of landscapes, ecosystems

and biomes since the settlement of people and the invention of

agriculture has pushed the Earth outside the conditions of the

relatively stable Holocene period into what has been termed the

Anthropocene (Steffen et al., 2011; Vince, 2011). However,

targets like the 2 °C global warming limit that has been the focus

of recent UNFCCC (United Nations Framework Convention on

Climate Change) negotiations may be insufficient to maintain

the Earth in a state that is reasonably close to that of the last

10,000 years (Ellis et al., 2012). The tight links between climate

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and species distributions have spawned a wealth of research that

aims to understand and predict the impacts of future climate

change on the biota of the Earth (Pereira et al., 2010; Beaumont

et al., 2011; Bellard et al., 2012; Ellis et al., 2012). Substantial

efforts are currently being devoted to understanding the differ-

ences between the current or the pre-industrial climates and

projections for the end of the 21st century (Williams et al.,

2007), together with the probable consequences of climate

change for flora and fauna (Pereira et al., 2010; Beaumont et al.,

2011; Bellard et al., 2012). Despite substantial inter-model

uncertainty (Rogelj et al., 2012), great emphasis has been placed

on detecting novel climates relative to current conditions that

might pose substantial challenges for species and ecosystem

adaptation (Williams et al., 2007; Beaumont et al., 2011). Simi-

larly, several efforts have been undertaken to understand differ-

ences between the early 20th-century climate and the climates

of the Quaternary period in general (Pickett et al., 2004;

MacDonald et al., 2008a; Willis et al., 2010; Zhang et al., 2010).

These climate reconstructions have been used to explain the

responses of biota to climate variation in the past (Benito

Garzón et al., 2007; Terry et al., 2011; Willis & MacDonald,

2011). Evidence that the biosphere may have been exposed to

warmer and colder climates in the past can provide insight into

how species, communities and biomes respond through extinc-

tions, range shifts and community turnover under changing

climate conditions (Jackson & Overpeck, 2000; Pickett et al.,

2004; Willis & MacDonald, 2011). We have combined climate

analyses of anomalies of the mid-Holocene and future climate

change expectations in order to examine the extent to which

biomes and ecoregions may be exposed to future climates that

differ from cooler (pre-industrial) and warmer (mid-Holocene)

periods that occurred naturally during the Holocene. By using

variation in climate over the Holocene as a benchmark for eco-

system sensitivity, our approach differs from recent studies that

have calculated climate exposure or climate sensitivity of biomes

and ecoregions based on ratios of projected future climate

change relative to current inter-annual climate variability

(Williams et al., 2007; Beaumont et al., 2011).

We have focused our analysis of palaeoclimate on the mid-

Holocene (mH) thermal maximum, a period of about 2000

years centred around 6 ka cal bp, because it was the warmest

period of the Holocene for much of the Northern Hemisphere.

Starting at the beginning of the Holocene about 11.5 ka cal bp

climate warmed – very rapidly in some regions – to close to

pre-industrial temperatures in the Northern Hemisphere during

the mH. The climate system then went through several smaller

periods of warming (most recently the Medieval Warm Period,

c. 1–0.7 ka cal bp) and cooling (most recently the Little Ice Age,

c. 0.45–0.15 ka cal bp). Climate change during the mH, which

was driven by changes in the Earth’s orbit, differed from future

projected climate change which is being driven by the anthro-

pogenic emission of greenhouse gases (Steig, 1999; MacDonald

et al., 2008b). The climate during the mH was characterized by

summer temperatures that were as much as 2.5 °C warmer in the

Northern Hemisphere and precipitation patterns different from

present (Davis et al., 2003), but winters were colder in temperate

areas (Kaufman et al., 2004). During the mH, biomes responded

to gradual warming with shifts in species ranges and community

reorganization, but significant extinctions did not occur

(Colinvaux et al., 2000; Jackson & Overpeck, 2000; Davis et al.,

2003; Bush et al., 2004; Thompson et al., 2006; Urrego et al.,

2010; Willis, 2010).

In addition to our analysis of the mH, we discuss other

periods of warming in the palaeoclimatic record to provide

perspectives on biological responses to climatic events that

appear to have been as fast or faster than projected future

climate change, such as subglobal events of rapid warming

during the Bølling and Allerød oscillations (14–13 ka cal bp) and

at the end of the Younger Dryas that led into the Holocene

period (11.5 ka cal bp). We also discuss periods that were

warmer than the mH, such as the mid-Pliocene (3.6–2.6 Myr cal

bp) and the Eemian Interglacial (130–116 ka cal bp) (Salzmann

et al., 2009; Haywood et al., 2011; Willis & MacDonald, 2011).

Recent work in palaeoclimate modelling has opened the pos-

sibility of using multimodel simulations of mH climate that

have been benchmarked with a wide variety of palaeoclimate

proxies (the PMIP project; Braconnot et al., 2007a,b). This

allowed us to analyse global patterns of climate during the mH

and to make coherent comparisons with pre-industrial and pro-

jected future climates using the same suite of climate models. To

explore the Holocene–Anthropocene transition, we combined

multimodel simulations of palaeo, modern and future climate to

quantify the magnitude and direction of climate change

between the mH, pre-industrial conditions and projected

climate for the end of the 21st century. We used multivariate

statistics (principal components analysis, PCA) of climate

anomalies that included maximum, mean and minimum annual

temperature as well as annual precipitation. We then mapped

this indicator onto the world’s biomes and ecoregions to assess

exposure of the terrestrial biosphere to climate-related risks.

METHODS

Climate models and data

To examine global differences between potential future climate

change and the climate of the mH, we used six models with

simulations available for 2100, the pre-industrial conditions and

the mH (CCSM3, ECHAM, FGOALS, IPSL, MIROC, and MRI).

We used simulations from the PMIP2 working group for the

period of the mH and pre-industrial conditions (Braconnot

et al., 2007a,b). For 2100, we used models based on the Inter-

governmental Panel on Climate Change (IPCC) A1B emissions

scenario, which result in projected increases in mean tempera-

ture that are close to the changes predicted for the mH recon-

structions for certain regions in the Northern Hemisphere. We

concentrated on four climate variables: (1) mean annual tem-

perature, (2) maximum summer temperatures, (3) minimum

winter temperatures, and (4) annual precipitation. For each of

the four variables we calculated climate between the mH and

pre-industrial conditions, the projected climate in 2100 and pre-

industrial conditions, and mH and 2100 (Fig. 1).

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We processed the six global circulation models selected by

averaging the provided 50 or 100 years of the palaeo-simulations

and the pre-industrial conditions (climate system c. 1750 ce)

and the last 10 years for the global warming simulations

(2090–99 ce). Maximum and minimum temperatures were esti-

mated from the monthly mean temperatures available for each

year and then averaged across years (over 10, 50 or 100 years

depending on the model run). Monthly and yearly averages,

totals and anomalies were calculated with the Climate Data

Operators (CDO) directly on the netcdf files (U. Schulzweida,

Max-Planck-Institute for Meteorology, https://code.zmaw.de/

projects/cdo/). The resolution of all the models was set to T85

(~1.4°) with the CDO bicubic interpolation. Subsequent statis-

tical analyses and summary statistics were calculated with the R

software (http://www.r-project.org/).

Climate analysis

We applied standard multivariate techniques (PCA) to examine

the overall patterns of climate anomalies between 6–0 ka cal bp

and 2100 A1B scenario–0 cal for all variables resulting from

averaging the six climate simulation models in a unique analysis

(Fig. S1 in Supporting Information). We included in the dataset

an additional single reference row of zero anomalies (no climate

differences between periods) for centring the PCA scores results

around this point. We recentred each axis on zero by subtracting

the scores corresponding to the row of zeros introduced in the

dataset to each score column. In this way, PCA scores close to

zero do represent areas of low anomalies and not the average

anomaly between periods. This represents only a translocation

of axis, and the relative separation of scores in the multivariate

space remains the same. We then calculated an integrated

climate anomaly index (see conceptually similar approaches in

Williams et al. (2007) and Beaumont et al. (2011)) by comput-

ing the weighted average of the PCA scores for each pixel of all

principal components (Fig. S1) according to the following

formula:

weighted average PCA==∑Ci i

i 1

4

(1)

where Ci is the contribution to the variance or loading from each

principal component and PCAi is the score for each axis. Finally,

to verify that the calculated anomalies were not biased by inter-

model variability, we estimated the between-models coefficient

of variation for each variable (Fig. S2). We then applied this

integrated climate anomaly to define the climate boundary of

each biome and ecoregion, which we define as the maximum

anomaly between the mH and pre-industrial climates, across the

set of all grid cells in a biome or ecoregion.

Estimation of climate boundaries for biodiversity

To estimate whether biomes and ecoregions through the

Holocene–Anthropocene climate transition remain within the

mH limits, we applied the classification by Olson (Olson et al.,

2001) using two different approaches. First, we calculated the

mean value of synthetic climate anomaly index for the world’s

14 biomes for both transitions (2100 A1B scenario–0 cal bp and

6–0 ka cal bp). Second, to determine if the expected exposure in

2100 A1B scenario–0 cal bp would be within the mH bounda-

ries, we calculated the Euclidean distance for the 766 ecoregions

between the PCA scores of both transition periods. In this way,

we evaluated the degree of similarity between anomalies of both

transition periods in a single map. The Euclidean distances were

computed between the PCA values that correspond to the

anomalies between 6–0 ka cal bp and 2100 A1B scenario–0 cal bp

for the same geographical coordinate. The results for the

Figure 1 Climatic differences betweenthe mid-Holocene and early20th-century climate (6–0 ka cal bp, leftpanel), between 2100 (emissions scenarioA1B, middle panel) and early20th-century climate, and between 2100A1B and the mH (right panel): (a)annual precipitation (mm); (b) meanannual temperature; (c) maximumtemperature; and (d) minimumtemperature. All temperature scales arein °C.

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minimum, average, maximum and range of the Euclidean dis-

tances are provided in Table S1.

RESULTS

Climatic transitions between periods

Mean, maximum and minimum temperatures are projected to

increase across the entire globe in the A1B greenhouse gas emis-

sion scenario with respect to 0 cal bp (Fig. 1). Modelled mH

maximum and mean temperatures are higher in the Northern

Hemisphere than 0 cal bp. Maximum and mean temperatures

are lower for much of the Southern Hemisphere, with notable

exceptions in the Amazon Basin and parts of southern Africa.

Modelled minimum annual temperatures are lower during the

mH than 0 cal bp for most of the globe. Precipitation patterns

are projected to be different in virtually all regions of the world

in 2100 compared with current conditions and the mH (Figs 1 &

2). Precipitation will probably increase in the Northern Hemi-

sphere, the Andes, the Parana Basin, eastern Africa and the

Pacific tropical islands whereas it will probably decrease in the

Mediterranean Basin, northern and Equatorial Africa and

Central America. The general patterns of mH climate corre-

spond to palaeoclimate reconstructions (see Introduction), even

if the model shows high variation in precipitation for some areas

(Fig. S2). However, we have to bear in mind that that 6 ka

models underestimate the expansion of the African monsoon in

this region (Braconnot et al., 2007a). Temperature anomalies

between 2100 and pre-industrial climate, and 6 and 0 ka cal bp

follow similar patterns in their geographical distribution for the

Northern Hemisphere, but the magnitude is much higher in the

2100 A1B scenario–0 ka cal bp than in the 6–0 ka cal bp anoma-

lies (Fig. 1). This difference in magnitude among 2100 A1B

scenario–0 cal bp and 6–0 ka cal bp anomalies is especially

strong for the minimum temperature in the Northern Hemi-

sphere (Fig. 1d). Overall, precipitation levels were lower during

the mH except for the Sahel region, which contrasts sharply with

the extreme spatial variation in precipitation changes expected

for 2100 (Fig. 1a).

When the climatic anomalies between the 6–0 ka cal bp and

2100 A1B scenario–0 cal bp periods are plotted together (Fig. 2),

the minimum and mean temperatures of the Earth are the vari-

ables that are clearly projected to change more in the future with

respect to their maximum values during the mH (Fig. 2, dotted

lines). On the other hand, the expected range of changes in

precipitation and mean temperatures for 2100 A1B scenario–0

cal bp are within the range of 6–0 ka cal bp differences, at least

globally (Fig. 2).

When both sets of anomalies (2100 A1B scenario–0 cal bp and

6–0 ka cal bp) are combined in a single PCA (Fig. S1e), the first

three components (which explain 99% of the data variance)

show two separate, well-defined clouds that share little of the

multidimensional space of the PCA (Fig. S1e). The magnitude

and direction of expected climate changes for 2100 are projected

to largely surpass the conditions simulated for the mH. The first

component of the PCA is strongly determined by temperature

(minimum, mean and maximum) whereas precipitation is

clearly the most important variable in the second axis (Table 1,

Fig. S1).

Figure 2 Climate anomalies for 2100A1B scenario–0 cal bp versus 6–0 ka calbp for annual precipitation, meantemperature, maximum temperature andminimum temperature. The black dottedlines represent the climatic boundariesfor each variable based on the maximumanomalies simulated for themid-Holocene.

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Anomalies in the synthetic climate index are always higher for

the 2100 A1B scenario–0 cal bp period than for the 6–0 ka cal bp

one (Fig. 3). The highest 6–0 ka cal bp anomalies are concen-

trated in eastern Europe and the Middle East, the Sahel and most

parts of India and the Himalayas, but they are low in magnitude

compared with projected future changes (Fig. 3a). In contrast,

high 2100 A1B scenario–0 cal bp anomalies are expected over

the entire globe (Fig. 3b). While the northern circumpolar areas

appear with high anomalies in both transitions, strong differ-

ences for the 2100 A1B scenario–0 cal bp transition are also

largely localized in the central Andes, southern and eastern

Africa, the Central Asian plateau and the tropical Pacific islands

(Fig. 3a, b).

High inter-model variation was observed for the climate tran-

sitions between periods for Greenland, the Himalayan Plateau,

the Sahara and Sahel, mostly for temperatures and for a lesser

extent for precipitation (Fig. S2). A southern subtropical belt

including the dry areas of South America in the Chile, Bolivia

and Argentina areas, the western coast of South Africa and

Australia all exhibit high inter-model variation for precipitation.

Finally, boreal and tundra areas have high inter-model variation

for minimum temperatures.

Biome and ecoregion exposure to climate change

Biomes with similar magnitudes of climate change during the

6–0 ka cal bp and 2100 A1B scenario–0 cal bp transitions are

relatively rare. All biomes were found to be subject to very dif-

ferent climatic patterns under future climate change compared

with the mH except for grasslands and savannas that showed

some overlap between periods (Fig. 3c). The biome exposure to

climate change for the 6–0 ka cal bp comparison is much lower,

ranging from 0 to 1 standardized units as defined in the

Methods, than the 2100 A1B scenario–0 cal bp anomalies, which

varied between 2 and 4 units.

The Euclidean distances between both anomalies are an indi-

cator of the dissimilarity of climate change between periods

(Fig. 4). Zones where 6–0 ka cal bp anomalies are the most

similar to the 2100 A1B scenario–0 cal bp anomalies include

areas of continental North America, Greenland, the Mediterra-

nean Basin and the temperate areas of Europe, some parts of

central Asia, Japan, Patagonia in South America (green colours).

The highest Euclidean distances between periods, indicating

that expected climates are well beyond the mH envelope, were

found for the boreal–tundra areas of North America and

Eurasia, and the tropical equatorial zones all around the Earth

(red colours).

Table 1 Summary of the statistics for the principal componentsanalysis (PCA) on the anomalies of four climatic variablesbetween projected global warming for 2100 (A1B scenario) andthe mid-Holocene (6 ka cal bp) with respect to pre-industrialconditions (0 cal bp). Only significant correlations are shown forthe four principal components noted, C1–C4.

C1 C2 C3 C4

Component

Standard deviation 1.691 0.954 0.468 0.106

% of variance 0.715 0.228 0.055 0.003

Cumulative variance 0.715 0.942 0.997 1

Loadings

Annual precipitation -0.281 0.951 -0.125 0.000

Maximum temperature -0.528 -0.255 -0.782 -0.211

Mean temperature -0.572 -0.153 0.228 0.773

Minimum temperature -0.561 0.000 0.566 -0.598

Figure 3 Global synthesis maps depicting the weighted averageprincipal components for the anomalies between: (a) 6 and 0 kacal bp and (b) 2100 A1B scenario and 0 cal bp. Both maps arebased on the same principal components analysis (PCA) so thescale is identical. Colours denote the number of standarddeviations by which the scores differ from zero (no climatevariation). The principal components from which these mapswere calculated are shown in Table 1 and depicted in Fig. S1. (c)Bean-plot figure of the values (average and density) of theweighted average PCA scores calculated for the main biomes ofthe world based on (a) and (b). Biomes are as follows: TSM,tropical and subtropical moist broadleaf forests; TSD, tropical andsubtropical dry broadleaf forests; TSC, tropical and subtropicalconiferous forests; TeB, temperate broadleaf and mixed forests;TeC, temperate coniferous forests; BT, boreal forests/taiga; TSG,tropical and subtropical grasslands, savannas and shrublands;TeG, temperate grasslands, savannas and shrublands; FG, floodedgrasslands and savannas; MG, montane grasslands andshrublands; T, tundra; Me, Mediterranean forests, woodlands andscrub; DX, deserts and xeric shrublands; Ma, mangroves.

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DISCUSSION

Our analysis of the climate transitions between the mH, 0 cal bp

and 2100 A1B scenario shows that expected biome exposure to

future climate change is heterogeneous spatially and future

climate change would typically greatly exceed the climatic limits

observed for the mH (Figs 1 & 2). This is broadly coherent with

previous analyses of palaeo and future climates (Jackson &

Overpeck, 2000). In fact, many terrestrial ecosystems of the

world appear to be subject not only to new climates in 2100 with

respect to current conditions but also with respect to the mH

(Fig. 1). This implies that many biomes and ecoregions will need

to respond to future climate change in ways not observed during

the Holocene. We have identified a few areas with similar mag-

nitudes of climate change during the 6–0 ka cal bp and 2100 A1B

scenario–0 cal bp periods. Past exposure to climate similar to

projected future climate may reduce the vulnerability of these

areas (Jackson & Overpeck, 2000; Willis & MacDonald, 2011).

The Holocene–Anthropocene transition versus futureclimate change

Even though the anomalies for 6–0 ka cal bp were relatively

small compared with that for 2100 A1B scenario–0 cal bp, they

were sufficient to produce significant changes in the composi-

tion of the vegetation from the mH to the present. The highest

6–0 ka cal bp climatic anomalies in our analysis are those of the

northern circumpolar areas, eastern Europe and the Middle

East, the Sahel and the Indo-Himalayan region – all of which

had recorded high species turnover during the mH (Jolly et al.,

1998; Prentice & Jolly, 2000; Bigelow, 2003; Giannini et al.,

2008). Warmer maximum temperatures in the Northern Hemi-

sphere and parts of the Southern Hemisphere were associated

with poleward or upward movements in altitude range shifts of

biomes and species (Figs 1, 3 & S1). For example, the tundra

vegetation extended at least 200 km north of its present distri-

bution in Siberia (MacDonald et al., 2000; Prentice & Jolly, 2000;

Bigelow, 2003; Patricola & Cook, 2007; Ivory et al., 2012). Tem-

perate forests extended further north than today in the Eurasian

continent (Prentice et al., 1998). Tropical coniferous forests

covered a larger region in western North America during the

mH than nowadays (e.g. the Madrean mountains of north-west

Mexico), as shown by biome reconstruction (Ortega-Rosas

et al., 2008). Similarly, tropical areas like the high Andes páramo

vegetation in equatorial South America were at least 300 m

higher in altitude during the warm period of the mH than at

present (Niemann & Behling, 2008; Niemann et al., 2009).

Some of the large climate anomalies between the mH and 0

cal bp are associated with cooler temperatures during the mH

and/or large differences in precipitation (e.g. the Sahel, equato-

rial regions in general). Overall, precipitation regimes made a

larger contribution to climate change in the equatorial belt than

temperatures over the periods that we analysed. Reconstruction

of the patterns of vegetation in Africa has revealed ample

responses to climate change during the mH: the northern extent

of tropical rain forest was substantially greater, whereas that of

the Sahara Desert was smaller during the mH than at present

(Jolly et al., 1998). However, climate change models for the

future remain highly uncertain for this area with respect to

precipitation, and there is discussion whether some greening of

the Sahel may occur (Giannini et al., 2008). It is also important

to note that even when vegetation feedbacks are included in mH

global circulation models, they fail to adequately simulate the

greening of the Sahara during this period, as precipitation

remains too low (Braconnot et al., 2007a,b).

Even though our analysis shows that almost all terrestrial

regions of the Earth could be exposed to future climate regimes

not seen during the mH, some areas of high biodiversity may be

particularly exposed. There is great concern that the drier parts

of the Amazon Basin (mostly towards the south-east and south-

west in the ecotones towards the El Chaco region and the Atlan-

tic forest) may change permanently, first to dry seasonal forest

and then to a savanna-like vegetation type due to interactions

between climate change, deforestation and fire (Malhi et al.,

2008; Lenton, 2011). However, the middle-elevation areas of the

central Andes in the eastern slopes of the Amazon Basin drain-

Figure 4 Mean values of the Euclidean distance between the principal components analysis (PCA) scores of the 6–0 ka cal bp and 2100A1B scenario–0 cal bp anomalies for the 766 terrestrial ecoregions (Olson et al., 2001). Equatorial and northern circumpolar areas appearedequally exposed to climates beyond the mid-Holocene (mH) boundaries (red areas). Areas with lower exposure correspond to regionswhere expected climate change will resemble, to a certain degree, the changes that occurred during the mH (green areas). Table S1 containsthe individual weighted PCA scores for all the 766 ecoregions for the 6–0 ka cal bp and 2100 A1B scenario–0 cal bp analyses.

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age appear much more exposed to climate change than the

Amazon region itself. During the mH, the vegetation of the

Amazon lowlands adapted to slightly drier conditions than

those experienced nowadays (Behling, 1998, 2003; Whitney

et al., 2011), while the mountain Andean flora responded mostly

by altitudinal migrations that are seen in the pollen records

(Urrego et al., 2010). Hence, more adaptive variation may exist

in the larger populations of the Amazon lowlands that allow the

system to maintain a physiognomy close to that of the present-

day forest, which could explain a certain degree of resilience of

this biome during periods of climate variation (Colinvaux et al.,

2000; Mayle & Power, 2008) compared with Andean populations

that have necessarily smaller population sizes. Hence, the eastern

Andes may be more exposed and more constrained to respond

to climate change than the better-studied areas of the Amazon.

Our analysis based on average distance between climate vari-

ables highlights the existence of the highest climatic risks for

equatorial and circumpolar areas (Fig. 4). The warming-related

risk of circumpolar areas has been well indentified by other

analyses (e.g. Lunt et al., 2012). However, the evaluation of

climate-related risks in equatorial areas has received less atten-

tion. Other analyses based on scaling future expected changes

with current intra-annual climate variability also indicate that

equatorial areas may be at particularly high risk (Williams et al.,

2007; Beaumont et al., 2011). This occurs because inter-annual

variability in climate is generally low in equatorial regions, and

therefore future climates frequently exceed the extremes of

inter-annual variability (Williams et al., 2007; Beaumont et al.,

2011). It is unclear, however, to what extent exceeding extremes

in inter-annual variability in temperature over relatively short

periods is a good general indicator of the climate sensitivity of

species.

Other recorded periods of warm climate change andfuture climate change

Whether ecosystems can adjust to climates beyond the natural

variation during the Holocene can be examined partially using

palaeo-analogues of future climate change (Salzmann et al.,

2008; Haywood et al., 2011; Willis & MacDonald, 2011). Com-

parative 2100 A1B scenario–0 cal bp climate analyses (Williams

et al., 2007), which have been used broadly to assess the risk of

projected climate change to biodiversity (Beaumont et al.,

2011), show high climate-related risks, either because current

climates disappear or novel climates are created. These decadal

timeframe analyses are extremely relevant from a species or

population perspective, but they do not inform us about their

relative strength with respect to previous major climate change

events. In general, warm events that occurred before the Qua-

ternary are not considered good analogues of future climate

change because the location of the continents and the climate

sensitivity to CO2 were different from nowadays, and the

warming rate was slower (Hunter et al., 2008; Salzmann et al.,

2008, 2009; Haywood et al., 2011). Among them, the most likely

analogue of future climate change is the mid-Pliocene warm

period (3.6–2.6 Myr cal bp) when continents were already in

their current location, and the reconstructions of the vegetation

based on palaeodata show that similar northward shifts of

boreal forest and tundra would happen in the future (Salzmann

et al., 2008, 2009) if human transformation of the earth does not

impede it. Overall, what can be learned from pre-Quaternary

warm periods is that no massive plant extinction happened even

with warmer temperatures than those expected for the near

future, but biomes changed their composition by local extinc-

tion, species shifts, and community reshuffling (Willis &

MacDonald, 2011). Similar conclusions for biodiversity can be

extracted from more recent warming periods like those happen-

ing during the Pleistocene–Holocene transition that entailed

relatively rapid warming and large temperature variability

(Moberg et al., 2005; Finsinger et al., 2011). Among them, the

Bølling–Allerød (c. 14.7–12.9 ka cal bp) period, and the end of

the Younger Dryas (c. 11.5 ka cal bp) are examples of rapid

climate change when temperatures increased by about 3 °C in

less than 200 years (MacDonald et al., 2008a), but starting from

very cold temperatures. Whilst one can be tempted to conclude

that there is no risk for biodiversity in surpassing the mH envi-

ronmental conditions or any other warming event known from

the past, the human transformation is hampering range shifts

and migration of species necessary for ecosystems to adjust in

the the Holocene–Anthropocene transition (Loarie et al., 2009;

Bertrand et al., 2011).

Implications: refugia from climate change

Recent interest in identifying patterns of species survival during

different periods of climate change has led scientists to coin the

term ‘refugia from climate change’ to define areas where species

could persist despite the new climate conditions that are

expected in the future (Williams et al., 2008; Ashcroft, 2010). In

our analyses, however, areas sharing similar degrees of climate

change between the 6–0 ka cal bp and 2100 A1B scenario–0 cal

bp transition are negligible (Figs 2–4) and belong mainly to the

grassland and savanna biomes. In temperate regions of North

America the ecotone between prairie and forest has shifted from

its mH position, but most of the North American prairies were

already present by 6 ka cal bp (Williams et al., 2009). Likewise,

semi-arid and grassland vegetation in western China appeared

to display similar patterns during the mH as today (Ni et al.,

2010). Hence, it is not unlikely that temperate grassland vegeta-

tion will be a biome of high species turnover during ongoing

climate change but with sufficient resilience in the long term.

Whether they can act as climate change refugia remains less

clear, as these areas are heavily urbanized and cultivated, and

may became more populated if climate change in these areas is

effectively buffered to some extent.

Potential limitations of our approach

We developed a multivariate statistical method that does not

account for any compensation mechanisms or feedbacks on

biome function. For instance, the role of CO2 fertilization in

drought-prone areas is still unclear. Whereas some studies

Biodiversity and long-term climate change

Global Ecology and Biogeography, ••, ••–••, © 2013 John Wiley & Sons Ltd 7

suggest that the combination of carbon fertilization with warm

conditions can induce increased water-use efficiency by stomatal

closure (Keenan et al., 2011), other studies highlight that even if

the water-use efficiency increases, it will be not enough to com-

pensate for future drought conditions for some areas of the

planet (Peñuelas et al., 2011). Second, our approach does not

account in the analysis for shifts in the vegetation during the mH

that could change our conclusions of overall biome exposure to

climate change. Other statistical techniques such as niche mod-

elling could have been used to estimate the relationship between

climate and ecosystem distribution (Roberts & Hamann, 2012),

but our multivariate PCA allowed us to compare in one single

analysis several periods of time (6 ka, pre-industrial and 2100)

which is not possible with SDM analyses.

Finally, the coarse resolution of our analysis would not detect

many possible microrefugia from future climate change

(Ashcroft, 2010) for species within heterogeneous landscapes in

areas of high anomalies.

ACKNOWLEDGEMENTS

The authors wish to thank the PMIP2 consortium for providing

palaeoclimate reconstructions and the IPCC Data Distribution

Centre for climate change model simulations. M.B.G. was par-

tially supported by a Juan de la Cierva fellowship and a Marie

Curie FPT7-PEOPLE-2012 ‘AMECO’ individual post-doctoral

fellowship. This study was partially supported by the ANR-

AMTools, and by the CNRS INGEO-ECO and IngECOtech

CNRS-Cemagref grants.

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SUPPORTING INFORMATION

Additional supporting information may be found in the online

version of this article at the publisher’s web-site.

Figure S1 Principal components for anomalies between 6–0 ka

cal bp, and 2100 A1B scenario-0 cal bp. Figures in rows repre-

sent principal components 1 to 4 (A, B, C and D), which explain

100% of the variance in the data. (E) Direction and intensity of

the coefficients of the first three principal components of the

principal components analysis in relation to the 2100 A1B

scenario–0 cal bp anomalies (red) and the 6–0 ka cal bp anoma-

lies (green).

Figure S2 Coefficients of variation for the climate models used

in our analyses. Each map represents the coefficient of variation

for each variable averaged for the six models for each period

6 ka cal bp, 0 cal bp and 2100 A1B scenario. Rows correspond to

(A) precipitation; (B) maximum temperature; (C) mean tem-

perature and (D) minimum temperature.

Table S1 Minimum, average, maximum and range of the Eucli-

dean distances of the expected exposure in 2100 A1B scenario–0

cal bp for the 766 ecoregions as displayed in Fig. 4.

BIOSKETCHES

Marta Benito Garzón is a post-doc at Centre

National de la Recherche Scientifique (CNRS) in

France. Her research focuses on anthropic and climatic

changes controlling vegetation patterns at regional and

global scale, and forest adaptation strategies to climate

change.

Paul Leadley is a professor and director of the

Ecology, Systematics and Evolution laboratory at the

Université Paris-Sud. He is involved in global

assessments as a lead author on the IPCC Fifth

Assessment Report, as coordinator of the scenarios

syntheses for the Global Biodiversity Outlooks of the

Convention on Biological Diversity and as a member of

the Multidisciplinary Expert Panel of Intergovernmental

Platform on Biodiversity and Ecosystem Services

(IPBES). His research focuses on the impacts of global

change on biodiversity and ecosystem function in

terrestrial ecosystems.

Juan F. Fernandez-Manjarrés is a scientist at the

CNRS in France. His research focus on the ecology of

managed forest ecosystems using ecological, genetic and

interdisciplinary tools.

J.F.F.-M. and M.B.G. conceived the investigation and

prepared the climate and biodiversity databases for

processing. P.W.L. contributed to the design of the

analysis and writing of the manuscript. M.B.G. carried

out data analyses. All authors discussed results and con-

tributed to the final preparation of the manuscript.

Editor: Navin Ramankutty

M. Benito-Garzón et al.

Global Ecology and Biogeography, ••, ••–••, © 2013 John Wiley & Sons Ltd10