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Global Ecology and Biogeography, (Global Ecol. Biogeogr.) (2013) ••, ••–••
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R E S E A RC H
PAPER
Assessing global biome exposure
to climate change through the
Holocene–Anthropocene transition
Marta Benito-Garzón1,2*, Paul W. Leadley3 and
Juan F. Fernández-Manjarrés1,3
1
CNRS, Laboratoire d’Ecologie, Systématique
et Evolution, Université Paris-Sud, CNRS,
UMR 8079, F-91405 Orsay Cedex, France,
2
CNRS, 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 conduct
comparisons with pre-industrial and projected future climates. In particular, to
assess the exposure of terrestrial biomes and ecoregions to climate-related risks
during the Holocene–Anthropocene transition starting at the pre-industrial
period.
Location Terrestrial ecosystems of the Earth.
Methods We calculated long-term climate differences (anomalies) between the
mid-Holocene (6 ka cal bp, mH), pre-industrial conditions and projections for
2100 (middle-strength A1B scenario) using six global circulation models available
for all periods. Climate differences were synthesized with multivariate statistics and
average principal component loadings of temperature and precipitation differences
(an estimate of climate-related risks) were calculated on 14 biomes and 766
ecoregions.
Results Our results suggest that most of the Earth’s biomes will probably undergo
changes beyond the mH recorded levels of community turnover and range shifts
because the magnitude of climate anomalies expected in the future are greater than
observed during the mH. A few biomes, like the remnants of North American and
Euro-Asian prairies, may experience only slightly greater degrees of climate change
in the future as compared with the mH. In addition to recent studies that have
identified equatorial regions as the most sensitive to future climate change, we find
that boreal forest, tundra and vegetation of the Equatorial Andes could be at
greatest risk, since these regions will be exposed to future climates that are well
outside natural climate variation during the Holocene.
*Correspondence: Marta Benito-Garzón, CNRS,
Laboratoire d’Ecologie, Systématique et
Evolution, UMR 8079 Université Paris-Sud,
CNRS, F-91405 Orsay Cedex, France.
E-mail: [email protected]
Conclusions The Holocene–Anthropocene climate transition, even for a middlestrength future climate change scenario, appears to be of greater magnitude and
different from that between the mH and the pre-industrial period. As a consequence, community- and biome-level changes due to of expected climate change
may be different in the future from those observed during the mH.
Keywords
Anthropocene, biodiversity, biome refugia, climate change, global circulation
models, mid-Holocene, no-analogue, resilience.
I N T RO D U C T I O N
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
© 2013 John Wiley & Sons Ltd
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
DOI: 10.1111/geb.12097
http://wileyonlinelibrary.com/journal/geb
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M. Benito-Garzón et al.
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 differences 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). Similarly, several efforts have been undertaken to understand differences 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 extinctions, 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 ecosystem 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 midHolocene (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 anthropogenic 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
2
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 possibility 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 projected 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 Intergovernmental Panel on Climate Change (IPCC) A1B emissions
scenario, which result in projected increases in mean temperature that are close to the changes predicted for the mH reconstructions for certain regions in the Northern Hemisphere. We
concentrated on four climate variables: (1) mean annual temperature, (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 preindustrial conditions, and mH and 2100 (Fig. 1).
Global Ecology and Biogeography, ••, ••–••, © 2013 John Wiley & Sons Ltd
Biodiversity and long-term climate change
Figure 1 Climatic differences between
the mid-Holocene and early
20th-century climate (6–0 ka cal bp, left
panel), between 2100 (emissions scenario
A1B, middle panel) and early
20th-century climate, and between 2100
A1B and the mH (right panel): (a)
annual precipitation (mm); (b) mean
annual temperature; (c) maximum
temperature; and (d) minimum
temperature. All temperature scales are
in °C.
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 estimated 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 statistical analyses and summary statistics were calculated with the R
software (http://www.r-project.org/).
ing the weighted average of the PCA scores for each pixel of all
principal components (Fig. S1) according to the following
formula:
4
weighted average =
∑C
i
PCA i
(1)
i =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 intermodel 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.
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-
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 boundaries, 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
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M. Benito-Garzón et al.
Figure 2 Climate anomalies for 2100
A1B scenario–0 cal bp versus 6–0 ka cal
bp for annual precipitation, mean
temperature, maximum temperature and
minimum temperature. The black dotted
lines represent the climatic boundaries
for each variable based on the maximum
anomalies simulated for the
mid-Holocene.
minimum, average, maximum and range of the Euclidean distances 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 emission 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 Hemisphere, 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 correspond 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
4
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 anomalies (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 Hemisphere (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 variables 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).
Global Ecology and Biogeography, ••, ••–••, © 2013 John Wiley & Sons Ltd
Biodiversity and long-term climate change
Table 1 Summary of the statistics for the principal components
analysis (PCA) on the anomalies of four climatic variables
between projected global warming for 2100 (A1B scenario) and
the mid-Holocene (6 ka cal bp) with respect to pre-industrial
conditions (0 cal bp). Only significant correlations are shown for
the four principal components noted, C1–C4.
C1
Standard deviation
% of variance
Cumulative variance
Annual precipitation
Maximum temperature
Mean temperature
Minimum temperature
C2
Component
1.691
0.954
0.715
0.228
0.715
0.942
Loadings
-0.281
0.951
-0.528
-0.255
-0.572
-0.153
-0.561
0.000
C3
C4
0.468
0.055
0.997
0.106
0.003
1
-0.125
-0.782
0.228
0.566
0.000
-0.211
0.773
-0.598
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 concentrated 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 differences 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 transitions 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
Figure 3 Global synthesis maps depicting the weighted average
principal components for the anomalies between: (a) 6 and 0 ka
cal bp and (b) 2100 A1B scenario and 0 cal bp. Both maps are
based on the same principal components analysis (PCA) so the
scale is identical. Colours denote the number of standard
deviations by which the scores differ from zero (no climate
variation). The principal components from which these maps
were calculated are shown in Table 1 and depicted in Fig. S1. (c)
Bean-plot figure of the values (average and density) of the
weighted average PCA scores calculated for the main biomes of
the world based on (a) and (b). Biomes are as follows: TSM,
tropical and subtropical moist broadleaf forests; TSD, tropical and
subtropical dry broadleaf forests; TSC, tropical and subtropical
coniferous 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, flooded
grasslands and savannas; MG, montane grasslands and
shrublands; T, tundra; Me, Mediterranean forests, woodlands and
scrub; DX, deserts and xeric shrublands; Ma, mangroves.
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 different 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 indicator 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 Mediterranean 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).
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5
M. Benito-Garzón et al.
Figure 4 Mean values of the Euclidean distance between the principal components analysis (PCA) scores of the 6–0 ka cal bp and 2100
A1B scenario–0 cal bp anomalies for the 766 terrestrial ecoregions (Olson et al., 2001). Equatorial and northern circumpolar areas appeared
equally exposed to climates beyond the mid-Holocene (mH) boundaries (red areas). Areas with lower exposure correspond to regions
where expected climate change will resemble, to a certain degree, the changes that occurred during the mH (green areas). Table S1 contains
the individual weighted PCA scores for all the 766 ecoregions for the 6–0 ka cal bp and 2100 A1B scenario–0 cal bp analyses.
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 magnitudes 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 future
climate 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 composition 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 Hemisphere 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 distribution in Siberia (MacDonald et al., 2000; Prentice & Jolly, 2000;
Bigelow, 2003; Patricola & Cook, 2007; Ivory et al., 2012). Tem6
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, equatorial 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 southwest in the ecotones towards the El Chaco region and the Atlantic 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-
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Biodiversity and long-term climate change
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 presentday 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 variables 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 attention. 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 and
future 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). Comparative 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 Quaternary 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 extinction, species shifts, and community reshuffling (Willis &
MacDonald, 2011). Similar conclusions for biodiversity can be
extracted from more recent warming periods like those happening 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 environmental 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 vegetation 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
Global Ecology and Biogeography, ••, ••–••, © 2013 John Wiley & Sons Ltd
7
M. Benito-Garzón et al.
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 compensate 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 modelling 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 partially 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 ANRAMTools, and by the CNRS INGEO-ECO and IngECOtech
CNRS-Cemagref grants.
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Figure S1 Principal components for anomalies between 6–0 ka
cal bp, and 2100 A1B scenario-0 cal bp. Figures in rows represent 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 anomalies (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 temperature and (D) minimum temperature.
Table S1 Minimum, average, maximum and range of the Euclidean distances of the expected exposure in 2100 A1B scenario–0
cal bp for the 766 ecoregions as displayed in Fig. 4.
SUPPORTING INFORMATION
Editor: Navin Ramankutty
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 contributed to the final preparation of the manuscript.
Additional supporting information may be found in the online
version of this article at the publisher’s web-site.
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