Diverging Responses of Tropical Andean Biomes under Future Climate Conditions

Abstract

Observations and projections for mountain regions show a strong tendency towards upslope displacement of their biomes under future climate conditions. Because of their climatic and topographic heterogeneity, a more complex response is expected for biodiversity hotspots such as tropical mountain regions. This study analyzes potential changes in the distribution of biomes in the Tropical Andes and identifies target areas for conservation. Biome distribution models were developed using logistic regressions. These models were then coupled to an ensemble of 8 global climate models to project future distribution of the Andean biomes and their uncertainties. We analysed projected changes in extent and elevational range and identified regions most prone to change. Our results show a heterogeneous response to climate change. Although the wetter biomes exhibit an upslope displacement of both the upper and the lower boundaries as expected, most dry biomes tend to show downslope expansion. Despite important losses being projected for several biomes, projections suggest that between 74.8% and 83.1% of the current total Tropical Andes will remain stable, depending on the emission scenario and time horizon. Between 3.3% and 7.6% of the study area is projected to change, mostly towards an increase in vertical structure. For the remaining area (13.1%-17.4%), there is no agreement between model projections. These results challenge the common believe that climate change will lead to an upslope displacement of biome boundaries in mountain regions. Instead, our models project diverging responses, including downslope expansion and large areas projected to remain stable. Lastly, a significant part of the area expected to change is already affected by land use changes, which has important implications for management. This, and the inclusion of a comprehensive uncertainty analysis, will help to inform conservation strategies in the Tropical Andes, and to guide similar assessments for other tropical mountains.

Full text

Diverging Responses of Tropical Andean Biomes under
Future Climate Conditions
Carolina Tovar
1,2
*, Carlos Alberto Arnillas
1
, Francisco Cuesta
3
, Wouter Buytaert
4,5
1Centro de Datos para la Conservacio
´n, Universidad Nacional Agraria La Molina, Lima, Peru
´,2Long-term Ecology Laboratory, Biodiversity Institute, Department of
Zoology, University of Oxford, Oxford, United Kingdom, 3Consorcio para el Desarrollo Sostenible de la Ecorregion Andina, Quito, Ecuador, 4Civil and Environmental
Engineering, Imperial College London, London, United Kingdom, 5Grantham Institute for Climate Change, Imperial College London, London, United Kingdom
Abstract
Observations and projections for mountain regions show a strong tendency towards upslope displacement of their biomes
under future climate conditions. Because of their climatic and topographic heterogeneity, a more complex response is
expected for biodiversity hotspots such as tropical mountain regions. This study analyzes potential changes in the
distribution of biomes in the Tropical Andes and identifies target areas for conservation. Biome distribution models were
developed using logistic regressions. These models were then coupled to an ensemble of 8 global climate models to project
future distribution of the Andean biomes and their uncertainties. We analysed projected changes in extent and elevational
range and identified regions most prone to change. Our results show a heterogeneous response to climate change.
Although the wetter biomes exhibit an upslope displacement of both the upper and the lower boundaries as expected,
most dry biomes tend to show downslope expansion. Despite important losses being projected for several biomes,
projections suggest that between 74.8% and 83.1% of the current total Tropical Andes will remain stable, depending on the
emission scenario and time horizon. Between 3.3% and 7.6% of the study area is projected to change, mostly towards an
increase in vertical structure. For the remaining area (13.1%–17.4%), there is no agreement between model projections.
These results challenge the common believe that climate change will lead to an upslope displacement of biome boundaries
in mountain regions. Instead, our models project diverging responses, including downslope expansion and large areas
projected to remain stable. Lastly, a significant part of the area expected to change is already affected by land use changes,
which has important implications for management. This, and the inclusion of a comprehensive uncertainty analysis, will help
to inform conservation strategies in the Tropical Andes, and to guide similar assessments for other tropical mountains.
Citation: Tovar C, Arnillas CA, Cuesta F, Buytaert W (2013) Diverging Responses of Tropical Andean Biomes under Future Climate Conditions. PLoS ONE 8(5):
e63634. doi:10.1371/journal.pone.0063634
Editor: Keith A. Crandall, George Washington University, United States of America
Received July 4, 2012; Accepted April 9, 2013; Published May 7, 2013
Copyright: ß2013 Tovar et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: The Project was funded by the World Bank, and the Andean Regional Program of the Spanish Agency for International Cooperation and Development.
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: ctovar@lamolina.edu.pe
Introduction
Over the last decade, many studies have analyzed climate
change impacts on biodiversity (e.g. [1,2]). In mountain areas, one
of the most important effects on biodiversity is the upslope
migration of species [3,4] or even entire biomes. The latter has
been observed in many mountain regions, including Spain [5,6],
Alaska [7], the Swedish Scandes [8] and the Alps [9]. It is expected
that these migrations will intensify in the future, highlighting the
vulnerability of mountain biomes to climate change [10].
The Tropical Andes are a global biodiversity hotspot [11], and
expected to be one of the most affected by climate change over the
next 100 years [10,12–14]. However, these projections have
modelled biomes at relatively coarse resolutions (.50 km), which
do not capture the heterogeneity of the Tropical Andes. Although
studies with high resolution (5 km) exist for parts of the Tropical
Andes, such as the Peruvian Yungas [15], no comprehensive study
of climate change impact on biomes encompassing the entire
Tropical Andes has been published. The Tropical Andes are not
only important for their high levels of biodiversity [11], they also
provide a wide range of ecosystem services, including water
supply, carbon sequestration and fuel production [16]. Over 100
million people live in the Tropical Andes or in regions that depend
directly on these natural resources [17]. Therefore, more detailed
research is needed to understand climate change and its effects in
this region.
Observations of historical climate trends [16,18] indicate
potentially very diverse changes in future climate. Some parts of
the Andes such as the Bolivian highlands are expected to
experience a reduced precipitation (210%, with uncertainties of
up to 50% point), and others such as the Ecuadorian and Peruvian
highlands may see increases in precipitation ranging between 5%
and over 60% [19]. The combination of a complex climate and
topography with a highly diverse patchwork of biomes highlights
the potential for very different and diverging responses to climate
change in the Andes and different levels of vulnerability [20].
Indeed, for parts of the Andes a post-glacial upslope migration of
biomes such as montane forest has been observed in response to
warming [21,22]. For other areas such as the Altiplano, the
upslope migration of forest has stopped or even reversed due to a
local response, for instance under influence of a microclimate such
as that of the Titicaca Lake region [23].
This study analyses the potential impact of climate change in
the biomes of the Tropical Andes. We aim to respond to two main
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scientific questions: 1) How will climate change affect the extent
and elevational range of the Andean biomes? 2) Is it possible to
identify regions most prone to change? Apart from the scientific
insights, these results may help guide conservation strategies, by
allowing conservation NGOs and government agencies responsi-
ble for ecosystem conservation to target biome areas that are most
likely to persist under changing climate conditions.
Biome distribution models were developed to project the
distribution of biomes under two future emission scenarios (A1B
and A2) for two time slices (2010–2039 and 2040–2069). Given the
high levels of uncertainty in future climate projections for the
Andes [24] and the consequences of this for decision making [25],
we used an ensemble approach to model future distribution of
biomes.
Currently, land use already has a large effect on Andean
biodiversity, which may either be reinforced or counteracted by
climate change [26]. Therefore the outputs of the biome models
were interpreted both as potential distribution and as remnant
distribution, by disregarding for the latter any areas where
vegetation is affected by human activities (denoted as human-
modified areas).
Materials and Methods
2.1 Study area
The Tropical Andes encompasses the Northern and Central
Andes (Venezuela, Colombia, Ecuador, Peru and Bolivia) from
11uNto23uS. The lower elevation limit is typically put at 600 m
a.s.l. but this may vary according to the latitudinal location and
mountain range [27]. The total area is around 1.27 million km
2
(Table 1 and Figure 1A). Within the Tropical Andean region four
major habitat types or biomes are found [28]: tropical and
subtropical moist broadleaf forests; tropical and subtropical dry
broadleaf forests; deserts and xeric shrublands; and montane
grasslands and shrublands. However, given the importance of
grasslands and shrublands in the highest part of the Tropical
Andes, for example for conservation planning [29], vulnerability
assessment [20], and ecosystem services [16]; we subdivided this
biome into four categories (see Table 1). Therefore we defined
seven Tropical Andean biomes: 1) paramo (P), 2) humid puna
(HP), 3) xeric puna (XP), 4) evergreen montane forest (EMF), 5)
seasonally dry tropical montane forest (SDTF), 6) montane
shrubland (MS) and 7) xeric pre-puna (PP). Glaciers and
cryoturbated areas (GC) were classified as a separate, eighth
biome, to evaluate changes in the upper limit of the Andean
region. The Tropical Andean biomes were obtained by grouping
the ecological systems of the Andean Ecological Systems Map
[27]. We used this map as the observed map (30 arc-seconds pixel
size resolution, approximately 1 km in the equator) of the
distribution of biomes for the year 2000 (Figure 1A). At the base
of the Andes, the non-Andean biomes were defined as those that
will possibly invade the Andean biomes under future climate
change.
2.2 Modelling approach
We modelled the potential distribution of each biome by using
presence and absence points from the observed map as dependent
variable and climatic and topographic variables as explanatory
variables. Subsequently, we applied these models using future
climatic variables to project future distribution of biomes. The
outputs of eight climatic models were used to account for
uncertainty. In addition to the present and future potential
distributions we calculated remnant biome distributions which
included human modified areas. Our approach is based on the
following assumptions:
1) Current climatic conditions and the distribution of biomes
are representative of climatic equilibrium conditions for the
existing biomes. Every biome is modelled independently and
each model represents the likeliness of occurrence of the
existing biomes.
2) Future potential biome distributions should be interpreted as
projected stabilised future biomes (in equilibrium with
climate), and therefore conditional to the establishment of
emerging areas. This process can take decades to centuries
and is dependent, among other factors, on the rate of
migration and establishment of representative species of each
biome among other conditions, which are not studied here.
3) We used a static land use scenario for the distribution of
remnant biomes. Although this does not allow taking into
account future land-use dynamics, which would need
separate land-use dynamics projections, it provides insights
in the relative impact of respectively climate change and land
use changes on Tropical mountain biota. This approach
represents the lowest impact (optimistic) scenario due to
climate change.
2.2.1 Modelling potential biomes. Multiple backward
stepwise logistic regression models were used to define the
distribution for each Andean and non-Andean biome. The
dependent variable (presence or absence of the biome) was
obtained from the observed map. A subset of observations was
used to construct the models. Points were sampled with a
minimum distance of 4 pixels (approximately 4 km) in between
to reduce spatial autocorrelation. Climatic and topographic
characteristics were used as independent variables. We used
initially the 19 bioclimatic variables from Worldclim [30] at 30
arc-seconds resolution (period 1950–2000) and two ombrothermic
indexes [31] to represent the present conditions. A correlation
matrix was constructed, and explanatory variables were selected
such that a final set with minimal multicollinearity was obtained.
These final explanatory variables were annual mean temperature,
mean monthly temperature range, annual precipitation, precipi-
tation of the driest month, precipitation seasonality calculated by
the coefficient of variation, precipitation of the warmest quarter,
precipitation of the coldest quarter, ombrothermic index and
ombrothermic index of the driest bimonth. The latter two are
based on the ratio of precipitation and temperature only in months
with a positive temperature. We also included three topographic
variables (Convergence index TCI, Terrain ruggedness index TRI
and slope) as topography is an important factor influencing
distributional patterns in the Andes [32]. These were calculated
using a 30 arc-seconds resolution digital elevation model from the
SRTM mission [33]. Some of the variables were log-transformed
to obtain normality, and quadratic terms for all the variables were
included to account for non-linear relationships (Table S1).
In this approach we obtained a probability map for each biome.
To integrate all individual maps into a one final biome map for the
present we overlaid all biome probability maps and selected for
each pixel of the study area the biome with the highest probability
of occurrence. As this procedure also assigns biomes to areas
currently modified by human activities, it results in a potential
biome map used as a baseline for the year 2000. Lastly, we
calculated the 95% confidence interval of the probability of
occurrence to analyse potential overlap with other projected
biomes.
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2.2.2 Model validation. We used 4 indicators of model
performance. First, for each biome model, a split sample test was
applied, using 70% of the sampled points to calibrate the models
and 30% for validation. The Akaike information criterion (AIC)
was used as measure of fit. We evaluated each regression model
through the ROC curve where values of the area under the curve
(AUC) close to the unit indicate a good performance.
Next, using the 95% confidence interval for the predicted
probabilities, we calculated for each pixel the number of non-
selected biomes of which the confidence interval overlapped with
the selected biome as a measure of biome model uncertainty.
As an over-all accuracy assessment, we compared the modelled
baseline biome map (Figure 1B) with the observed biome map for
2000 (Figure 1A).
Lastly, to assess the risk of extrapolation beyond the model
calibration envelope, we identified non-analogue future climate
conditions, i.e., regions where values are outside the range of any
variable used for calibration [34].
2.2.3 Future potential biome maps. To obtain potential
biome maps for the future, we ran the fitted biome distribution
models using the future climatic conditions projected by the global
climate models (GCMs) from the World Climate Research
Programme’s (WCRP’s) Coupled Model Intercomparison Project
phase 3 (CMIP3) multi-model dataset [35]. Climatic conditions
were extracted for the periods 2010–2039 and 2040–2069, and for
emission scenarios A1B and A2 using 8 models (bccr_bcm2_0,
csiro_mk3_0, csiro_mk3_5, inmcm3_0, miroc3_2_medres,
ncar_ccsm3_0, gfdl_cm2_0 and gfdl_cm2_1, using CMIP3
notation). These are all the CMIP3 models for which the climatic
Figure 1. Biome maps.Current (observed) biome map (A) based on the Andean Ecological Systems Map [27], modelled potential biome map for
the present 2000 (B) and an example of future biome map (C) using climatic variables of model gfdl_cm2_0 for A1B 2040–2069 scenario.
doi:10.1371/journal.pone.0063634.g001
Table 1. Tropical Andean biomes, characteristic plant life-form and ordinal ranking based on humidity levels (from less humid to
more humid) for each biome.
Biomes by Olson
et al.
[28] Tropical Andean biomes Area (%) Plant Life-form Humidity level
glaciers and cryoturbated areas (GC) 1.5 desert 2
montane grasslands and shrublands paramo (P) 3.2 grassland 5
humid puna (HP) 18.6 grassland 4
xeric puna (XP) 15.1 grassland 3
montane shrubland (MS) 4.8 shrubland 6
tropical and subtropical
moist broadleaf forests
evergreen montane forest (EMF) 19.3 forest 8
tropical and subtropical
dry broadleaf forests
seasonally dry tropical montane forest (SDTF) 14.2 forest 7
deserts and xeric shrublands xeric pre-puna (PP) 2.9 desert 1
Human-modified areas (human intervention) 20.5
Total (1.27 million km
2
)100.0
doi:10.1371/journal.pone.0063634.t001
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variables required for the biome modelling are available, i.e.,
monthly precipitation, daily minimum, mean and maximum
temperature. Future climatic variables were obtained using the
delta method on a monthly basis. We calculated the differences
between future and present (anomalies or deltas) by subtracting the
modelled simulations for the present of each variable and each
month of the year from their correspondent values in the future.
Afterwards the derived deltas were applied to observed temper-
atures and precipitation maps from Worldclim to obtain future
climatologies at a finer resolution (30 arc-seconds resolution,
approximately 1 km in the equator). The relative anomaly was
used for precipitation, and the absolute anomalies for temperature
(mean, minimum and maximum) [36].
2.2.4 Remnant biomes. Areas with land cover affected by
humans (i.e., human-modified areas) were extracted from the
Andean Ecological Systems Map [27] and overlaid on to the
potential biome maps for the present and the future climatic
conditions to obtain the remnant biome maps.
2.3 Potential impact: Analysis of biome changes
2.3.1 Changes in elevational range. A cumulative curve of
the biome area as a function of elevation was plotted for each
biome, emission scenario and period. The cumulative curve for
present conditions and those for future climatic conditions were
plotted together to identify significant shifts in elevation for each
biome.
2.3.2 Future changes in biome extents. Potential changes
in biome extents were assessed using three measures: 1) areas that
remained unchanged (stable areas), 2) emerging areas, where a
biome is projected to occur in the future but not in the present
and, 3) lost areas where a biome is likely to be replaced by another
biome. For all three measures, the spread of the GCM model
ensemble is summarised by reporting the minimum, median and
maximum of the ensemble for each scenario and period. Both the
potential and remnant biomes were analysed using this approach.
To assess which biomes are projected to replace current biomes, a
conversion matrix representing the percentage of change between
the different biomes was calculated (only for the potential biome
map). Also here the minimum, median, and maximum values of
the GCM ensemble are reported.
2.3.3 Regions most prone to biome change. We identified
changes in areas between major physiognomy groups (desert,
herbs/grasslands, shrubland, forest) and within them (levels of
humidity, for example a projected change from xeric puna to
humid puna) (Table 1), taking into account the agreement between
the different models for each combination of scenario and period
of time. In this approach any change between physiognomy
groups will imply a change in vertical structure. If 80% of the
biome models (at least 7 out of 8, each one using the outputs of the
different climatic models) had a similar tendency, the area was
assigned with one of the following categories: 1) increasing vertical
structure, 2) either increasing vertical structure or increasing
humidity level, 3) increasing humidity, stable plant physiognomy,
4) no change, 5) decreasing humidity, stable physiognomy, 6)
either decreasing vertical structure or decreasing humidity level, 7)
decreasing vertical structure. An eighth category was defined as
inconsistency when less than 80% of the models agreed on the
tendency of change.
Results
3.1 Biome model validation and future climate
The AUC values of all regression models exceed 0.9, suggesting
good individual model performance (Table S1). For the integrated
biome map of the present, 90.3% of the study area shows no
overlap of confidence intervals between the selected and any other
biome (Figure S1A). Areas of overlap mostly occur for selected
biomes with low probabilities and high standard errors (Figure S2).
The comparison between the final integrated model and the
observed map gives an overall accuracy of 89% (Table 2),
suggesting a similarly good performance. Some biomes show
higher commission and omission errors than others. The montane
shrubland biome in particular appears mixed with the SDTF and
in a lower degree with the EMF. To a lesser degree, some SDTF
areas tend to be classified as EMF (Table 2).
The climate model ensemble projects, on average for the entire
region, an increase in temperature between 1 and 1.5uC for 2010–
2039 and between 2 and 2.5uC for 2040–2069 under the A1B
scenario. The A2 scenario projects a further increase of around
0.5uon top of the previous figures. These projections are spatially
homogeneous. On the contrary, precipitation predictions are
much more variable. Generally, less than 7 of 8 climatic models
agree on the direction of change. Since temperature patterns for
the Andes are much better characterised than precipitation
patterns [37], there may be an inherent bias in the biome models
to fit better to temperature maps than to precipitation maps. An
example of future biome distribution is shown in Figure 1C.
Lastly, non-analogue future climatic conditions (i.e., outside the
range of calibrated data for each variable) are observed mostly for
the non-Andean biomes, mainly in the north coast of Colombia for
all scenarios and periods (Figure S1B as an example). Non-
analogue climates are absent in the Andean region for the period
2010–2039, while for 2040–2069 they represent 0.02% (A1B) and
0.05% (A2) of the Andean region.
3.2 Changes in elevational range
The upper boundaries of almost all biomes show an upslope
displacement (Figure 2). The only exceptions are the biomes
restricted to the upper parts of the Andes, i.e. glaciers and
cryoturbated areas, and the paramo. The trends for the lower limit
of the distribution of each biome, however, are more variable. The
majority of biomes are also projected to experience an upslope
displacement of their lower limit (Figure 2). This shift is more
marked for glaciers and cryoturbated areas, paramo, humid puna
and the evergreen montane forest and to a lesser degree for the
xeric puna. Yet our model projects downslope expansion of the
lower boundary of several biomes: seasonally dry tropical montane
forest, xeric pre-puna and especially montane shrubland. The
puna biomes, and especially the xeric puna, show the least change
in their elevational range.
3.3 Projected impacts of climate change in the extent of
Andean biomes
Future climate change will lead to a small general decrease of
the area currently occupied by Andean biomes [sensu 27]
according to the majority of the models, for both periods 2010–
2039 (median of all models: A1B = 22.6%, A2 = 22.6%) and
2040–2069 (median of all models: A1B = 24.6%, A2 = 21.3%).
For each case, only 1 or 2 models out of 8 project a small increase
in the total area of Andean biomes. Despite the general decreasing
trend, the magnitude of the projected changes varies across
biomes. Our discussion concentrates on the minimum, median
and maximum values of projected stable, lost and emerging biome
areas (Figure 3 and Table S2) to characterize the uncertainty in
the GCM model ensemble. For the potential biome map, the
paramo glaciers and cryoturbated areas are expected to suffer the
largest relative area loss in both emission scenarios, both periods
and in all GCM models (Figure 3 and Figure S3). For example,
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for the scenario A1B period 2010–2039, the glaciers and
cryoturbated areas are projected to lose 57.7% of their current
extent (median of all models, Table S2), mostly in favour of the
expansion of xeric puna (Table 3). The lower end of the projection
range still amounts to a loss of 49% (Table S2). Similarly, the
projected median reduction in the extent of the high-altitudinal
Table 2. Accuracy assessment of the modelled potential biome map for the present (thousands of pixels).
Biome GC P HP XP EMF SDTF MS PP non-andean PC/Pred
GC 16.6 0 2.4 4.3 0 0 0 0 0 71%
P 0 40.5 0.5 0 6.0 0 0.1 0 0 86%
HP 1.0 0 263.5 11.7 5.0 2.0 0.7 0.5 0 93%
XP 2.5 0 20.5 209.1 0 1.6 2.4 0.6 0 88%
EMF 0 6.8 8.6 1.8 224.2 13.2 2.1 0.1 34.6 77%
SDTF 0 0.2 11.1 15.7 42.7 114.9 18.2 2.4 15.6 52%
MS 0 1.5 1.9 1.4 9.8 20.9 31.3 5.9 2.1 42%
PP 0 0 1.0 1.4 0 2.6 1.6 36.3 1.2 82%
non-andean 0 0 0 0 22.3 6.2 0.1 1.2 1596.2 98%
PC/Obs 82% 83% 85% 85% 72% 71% 55% 77% 97% 89%
Rows represent the observed map (see methods) while columns represent the predicted biome for the present 2000. The number of pixels correctly identified by the
model is shown in the diagonal values. PC/Obs: percentage of pixels correctly classified, PC/Pred: percentage of pixels correctly identified by the model. GC = glaciers
and cryoturbated areas, P = paramo, HP = humid puna, XP = xeric puna, EMF = evergreen montane forest, SDTF = seasonally dry tropical montane forest, MS = montane
shrubland, PP = xeric pre-puna.
doi:10.1371/journal.pone.0063634.t002
Figure 2. Elevational range changes for A1B 2040–2069. Glaciers and cryoturbated areas, paramo, humid puna and evergreen montane forest
show upward displacement of the lower boundary. This can be observed in the left hand side of the accumulation curves, where curves of all models
for the future (in grey) are higher than the curves for the present (dotted line). Seasonally dry tropical montane forest, montane shrubland and xeric
pre-puna show downslope expansion in the lower boundary where future curves are lower than the present one. Upper boundary show upward
displacement for almost all biomes, observed at the right hand side of the accumulation curves. The x-values were scaled from 0 to 1 to compare
landscapes of different size.
doi:10.1371/journal.pone.0063634.g002
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paramo grasslands is 31.4%, mostly to be replaced by EMF
(Table 3). All models consistently project a net loss of paramo area
(Figure S4, Figure S5, Figure S6 and Figure S7). Further in the
future and under more severe emission scenarios, projected
reductions are larger (Table S2).
The EMF will suffer the largest absolute area loss for both
scenarios and periods. The range of models projects higher areas
of biome loss than emerging areas (Figure 3and Figure S3).
Around 69000 km
2
(median, A1B, 2010–2039) of EMF is set to be
replaced, mostly by non-Andean biomes and SDTF (Table 3 and
Table S3). However, a significant part of this loss may be
compensated by the expansion of EMF into other biomes
(25400 km
2
, A1B, 2010–2039), mostly into areas that currently
host paramo (Table 3 and Table S3).
The xeric and humid punas are expected to undergo both small
losses and small gains, which offset each other largely and generate
only a small impact in the total area of the potential biome map.
Again, the projected area loss is slightly higher for 2040–2069 than
for 2010–2039 (Figure 3 and Figure S3).
Contrastingly, xeric biomes (xeric pre-puna and SDTF) may
show an increase of their total current area because of a larger
share of emerging areas compared to the losses (Figure 3 and
Figure S3). This is particularly conspicuous for the SDTF, which is
projected to replace areas of predominantly montane shrubland
and EMF (Table 3). During the period 2040–2069, this expansion
is more prominent (Figure S3).
With the exception of glaciers and cryoturbated areas, the
remnant area of all biomes is necessarily smaller than that of their
potential distribution (Figure 3). The stable area of EMF in
particular shows clearly that human-modified areas have already
encroached a large part of the potential distribution of this biome,
particularly in the Northern Andes (i.e. Colombia and Ecuador)
(Figure 1A). Similarly, human-modified areas currently already
occupy around half of the projected potential emerging areas of
EMF.
However, when future changes are expressed relative to the
current area, the differences between potential and remnant
biomes are small for all biomes except for the paramo and
montane shrubland (Table S2). For the paramo, a median loss of
31.4% is projected for the potential distribution, but this is only
25% for the remnant areas (A1B, 2010–2039). This pattern is
consistent for all GCM models, ranging from a potential (remnant)
loss of 38.6% (35.6%) for bccr_bcm2_0 to 17.3% (11.19%) for
miroc3_2_medres. This observation suggests that climate change
will mostly affect areas that are currently already affected by
human activities. On the contrary, for biomes where the
differences are small, it may suggest that climate change will have
an equal impact on the natural and perturbed areas.
3.4 Regions most prone to biome change
For the scenario A1B and period 2010–2039, in 83.1% of the
total area currently occupied by Andean biomes (potential
Figure 3. Median change in the area of potential biomes versus remnant biomes for A1B scenario period 2010–2039 and 2040–
2069. In dark grey the lost areas (the biome will be replaced by another biome), in grey stable areas (areas that remained unchanged) and in light
grey new or emerging areas (the biome is projected to occur in the future but not in the present). Black lines represent the minimum and maximum
values of all models. The sum of the stable and lost areas represent the present area, while the sum of the stable and emerging areas represent the
future projected area.
doi:10.1371/journal.pone.0063634.g003
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modelled map), 7 or more models project that it will remain stable
and no change in biome is projected (Table 4 and Figure 4). A
similar value is reported for the A2 2010–2039 scenario, though
these figures are lower for the period 2040–2069. In only 3.3%
(scenario A2, period 2010–2039) or 3.8% (scenario A1B, 2010–
2039) of the total study area, 7 or more models effectively project a
change in biome. These figures increase for period 2040–2069 to
7.6% and 7.9% for scenario A2 and A1B respectively. In the
remaining areas, less than 7 out of 8 models agree on the
occurrence of change (Table 4 and Figure 4).
Areas of major change are located mainly in the ecotones
(Figure 4). Such changes can be identified, for example, for the A2
scenario, for which 2.2% of the current Andean area is expected to
experience an increase in vertical structure for the period 2010–
2039 (Table 4) with higher values (5%) for 2040–2069. Areas
following this pattern are the Boyaca paramo (Colombia), the
paramo of Azuay and Loja (Ecuador), and the paramo of Piura
and south Cajamarca (Peru). New projected climatic conditions
are typically those of evergreen montane forest. Glaciers and
cryoturbated areas are expected to follow the same trend especially
in the region of Arequipa in South Peru and Central Ecuador.
These areas are projected to be colonized by xeric or humid puna.
Finally, 0.2% of the current Andean landscape would convert into
a simpler vertical structure or into a biome with less humidity for
scenario A2, period 2010–2039 (Table 4). This is most notable in
the montane forest of the Eastern Cordillera in the province of La
Paz (Bolivia), the montane forest of the department of San Martin
(Peru), and on the Western versant of northern Ecuador
(Pichincha and Cotopaxi provinces) (Figure 4).
Discussion
It is expected that Andean biomes have different degrees of
vulnerability to climate change (e.g. [20]). Our results indeed
confirm that specific biomes are projected to be more affected than
others in terms of reduction of their extent and shifts in elevational
range. Additionally, our method allowed identifying those regions
that are likely most prone to changes at a fine spatial resolution
(1 km), while accounting for the inevitable uncertainties of climate
projections. In the next sections, we discuss the projected changes
and the implications for conservation of the biomes and regions
most prone to change. Lastly, we briefly discuss the potential
caveats of our modelling approach and the potential for future
improvements.
4.1 Changes in Andean biomes
Our results project that most biomes will experience upslope
displacement of the upper boundary, which implies a gradual
replacement of one biome by another. However, the question
remains how likely such a replacement is within the velocity of
climate change in Andean biomes. Although upslope displacement
has been observed for forest, paramo and punas in post-glacial
times [21,22,38,39] it is uncertain whether the right conditions for
displacement are met under current climate change. For instance,
temperature is now increasing at a faster rate than in post-glacial
times [21,40], which implies that biome displacement will require
species to migrate faster. If this does not occur, many Andean
species populations are likely to decline [26] and novel species
assemblages likely to emerge. Nevertheless it is important to note
that our approach is based on biome modelling and not on species
distributions. Even though species composition might change, the
vegetation physiognomy is the main characteristic that defines a
Table 3. Conversion matrix of biomes from present to future.
Present/
Future GC P HP XP EMF SDTF MS PP NAB
GC 42.3 0.0 21.6 35.4 0.0 0.0 0.0 0.0 0.0
(19.8–51) (0–0) (11.2–28.7) (20.5–59.8) (0–0) (0–0) (0–0) (0–0) (0–0.9)
P 0.0 68.6 0.5 0.0 25.4 0.1 1.5 0.0 1.1
(0–0) (57.4–82.4) (0–4.7) (0–0) (12.5–39.4) (0–0.3) (1.1–1.9) (0–0) (0–7.9)
HP 0.0 0.1 93.4 1.7 3.4 1.3 0.4 0.1 0.0
(0–0) (0–0.2) (88.9–96.2) (0–4.2) (2–3.8) (0.3–3.5) (0.1–1.3) (0–0.4) (0–0)
XP 0.0 0.0 1.2 91.7 0.0 3.9 0.7 1.2 0.3
(0–0) (0–0) (0.1–8.5) (85.1–96.4) (0–0) (2.1–6.4) (0.2–2.3) (0.9–1.8) (0.2–0.6)
EMF 0.0 0.0 0.0 0.0 82.0 4.4 0.7 0.0 11.4
(0–0) (0–0.1) (0–0) (0–0) (73.2–87.2) (2.6–9.8) (0.3–1.1) (0–0) (7.5–21.7)
SDTF 0.0 0.0 0.0 0.9 0.7 85.3 1.3 0.5 10.9
(0–0) (0–0) (0–0.2) (0.1–1.5) (0.1–3.3) (77–92.4) (0.7–3.1) (0.2–1.1) (5.1–15.8)
MS 0.0 0.0 0.0 0.0 0.5 20.2 75.7 0.5 4.1
(0–0) (0–0) (0–0.3) (0–0) (0.1–2.1) (8.3–29.8) (61.3–86.6) (0–1.9) (2.6–6.6)
PP 0.0 0.0 0.0 0.6 0.0 1.6 1.0 92.9 2.7
(0–0) (0–0.1) (0–0.6) (0–1.2) (0–0) (0.5–3.5) (0–5) (88.8–96.7) (1.7–4.4)
NAB 0.0 0.0 0.0 0.0 0.1 1.4 0.2 0.2 98.1
(0–0) (0–0) (0–0) (0–0) (0–0.3) (0.7–2.2) (0–0.6) (0.1–0.4) (96.6–98.7)
Median change in area (%) of all models, for scenario A1B 2010–2039, between potential present biomes (rows) and potential future biomes (columns). Minimum and
maximum values of all models are shown in parentheses. GC = glaciers and cryoturbated areas, P = paramo, HP = humid puna, XP = xeric puna, EMF = evergreen
montane forest, SDTF = seasonally dry tropical montane forest , MS = montane shrubland, PP = xeric pre-puna, NAB = non-andean biomes.
doi:10.1371/journal.pone.0063634.t003
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biome. The establishment of the biome in potential emerging
areas is a process that can take decades. Not only representative
species of each biome will have to establish but also functional
species or nurse plants that may act as facilitators of the
colonization process [41]. Additionally, even though some
individuals might be able to migrate, the establishment as
stabilized biome (in equilibrium with climate) will require
populations to adequately develop pollination and dispersal
processes to assure reproduction. Migrating species will have also
to face competition with currently existent species. If new climatic
conditions are variable enough to encompass previous climatic
conditions, competition would be stronger and migrating species
would have more difficulties to establish [39].
Despite the abovementioned conditions, the upslope displace-
ment of some biomes as a response to climate change has been
observed in European mountains for the last 50 years [5,6,9]. This
supports our projections of upslope displacement of the upper
boundary of most biomes. For the Andean forest biomes, the
limited carbon assimilation rates at higher elevations due to low
night time temperatures [42] might be overcome by a temperature
increase induced by climate change. In fact, present-day climate-
driven migrations have already been recently reported for some
tree species in the Andean region [4]. However, the rate of
Figure 4. Agreement on the direction of the projected change between biome models using different climatic models. Calculations
were made for scenario A1B 2010–2039 (A), A2 2010–2039 (B), A1B 2040–2069 (C) and A2 2040–2069 (D) based on physiognomy (desert, grassland,
shrubland, forest) or humidity level. +++ Increasing vertical structure, ++ Either increasing vertical structure or humidity level, +Increasing humidity
level, stable physiognomy, - Decreasing humidity level, stable physiognomy, -- Either decreasing vertical structure or humidity level, --- Decreasing
vertical structure. Areas where less than 7 models agree on the direction of change are considered under the class ‘‘disagreement’’.
doi:10.1371/journal.pone.0063634.g004
Table 4. Percentage of the present Andean area where more than 80% of the models (at least 7) agree on the direction of the
change in physiognomy (desert, shrubland, grassland, forest) and/or humidity levels.
A1B A2
2010–2039 2040–2069 2010–2039 2040–2069
Decreasing vertical structure 0.1 0.3 0.2 0.3
Either decreasing vertical structure or humidity level 0.1 0.3 0.1 0.3
Decreasing humidity level, stable physiognomy 0.4 0.9 0.4 0.8
No change 83.1 74.8 83.1 75.0
Increasing humidity level, stable physiognomy 0.5 1.4 0.4 1.2
Either increasing vertical structure or humidity level 0.0 0.0 0.0 0.0
Increasing vertical structure 2.6 5.1 2.2 5.0
Inconsistency (areas with disagreement) 13.1 17.2 13.5 17.4
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migration is lees than expected from the observed changes in
temperature. In addition, limitations due to high radiation [43],
soil types and humidity [16] will still be present. On the other
hand, an elevation gradient in biotic interactions may act as
positive force allowing upslope migration of trees in the Andes. A
study suggests for instance that tree seed predation is lower at
higher elevation [44].
A second important difference between post-glacial times and
the present is that human influence is larger now than in the past.
Andean landscapes nowadays are heavily transformed, for
example the Central and Eastern mountain chain in Colombia
[45,46]. This affects potential emerging areas of some biomes,
such as EMF (Figure 3 and Figure S3), and reduces their resilience.
Current agriculture, grazing and burning practices in the border
paramo/EMF and puna/EMF have already degraded many of
these natural areas [26,43,47]. These practices have a strong
influence on the present-day upper forest line on the Andes, and
are likely also to have a critical role in controlling EMF upslope
displacement under climate change scenarios [48,49]. Upslope
migration of the upper boundary is not only constrained by land
use change but also by habitat fragmentation [50,51], which might
especially affect microrefugial expansion. This process is suggested
as an important strategy in the Andes, based on the observation
that only some populations of each species migrate while others
collapse [39].
Dry biomes (SDTF, montane shrublands and xeric pre-puna)
are the only biomes which lower boundaries are projected to
expand downslope, suggesting a heterogeneous response within
the Andes under climate change conditions. Historical evidence
has started to appear showing such a downslope expansion for
some plants [52,53]. The main driver for this process seems to be a
change in the climatic water balance [52]. The impact of a change
in water availability may indeed supersede or interact with
changes in temperature, hence leading to a more complex and
strongly biome-specific response. In our case, the downslope
expanding biomes are all dry biomes. This suggests that the
temperature increase puts more pressure on the water availability
(through an increasing evapotranspiration), which favours the
downslope expansion of more drought-resistant biomes. However
further studies are needed to explain this pattern in the Tropical
Andes.
Interestingly, our projections at biome level indicate that most
of the Andean area will remain within the same biome in contrast
to what is predicted for species [4,26]. Since biome models are
mainly focused on physiognomic characteristics and not species
composition it is likely that a biome model can encompass wider
climatic characteristics than those for specific species. For
example, an herbaceous species typical of montane forest may
migrate to grassland biomes without causing a major change in
biome. Species and biome modelling are complementary ap-
proaches [54] and future research in the Andes should focus on the
integration of both. However, neither approach includes evolu-
tionary processes and species plasticity. Hence they do not account
for the possibility that species may adapt to new climatic
conditions rather than to migrate [55–57].
4.2 Most affected biomes and regions: implications for
conservation
While global projections suggest the Tropical Andes are among
the most vulnerable areas under climate change [10,12,14] we find
diverse responses among biomes and regions for the projected
scenarios. The paramo grasslands and the glaciers and cryotur-
bated areas, located at the highest elevation, are most at risk due to
the lack of upslope area for migration. They are projected to lose
more than 30% of their present day area. Biomes located at mid-
elevations have potentially more area to migrate towards. The
steeper elevational gradient may allow them to reach their optima
temperature at smaller distances than lowland biomes [58,59].
Indeed, both montane forest biomes (EMF and SDTF) show an
upslope displacement of both their upper and lower boundaries in
the future projections, but only EMF would suffer a reduction of its
total area. The projected replacement of EMF by lowland non-
Andean biomes is one reason for this behaviour, as has been
observed in the Holocene [21]. Another reason for the projected
reduction of EMF is its replacement by dry forest taxa (SDTF).
However this has not been observed during the Holocene. This is
probably due to an alternation between dry and wet events [39],
rather than a continuous dry period as modelled in our future
climate projections (interannual climate variability was not
included). It is uncertain whether such replacement by SDTF will
occur in the future and information is still scarce to elucidate the
ecological patterns of SDTF under climate change.
Land use changes complicate the situation for the most
threatened biomes. Under the potential distribution scenario, part
of the paramo grasslands is projected to be replaced by forest
biomes. This is compatible with projections for other alpine
grasslands in the world [7,60,61] and with paleo records of
historical temperature increase [22]. In reality however, agricul-
tural activities have already encroached parts of the paramo and
forest, including the potential emerging areas of EMF (Figure 3
and Figure S3). Socioeconomic factors may drive this encroach-
ment at present (e.g. [62]), but it is likely that climate change will
contribute to the current expansion of agricultural areas by
providing more suitable climates in upper areas [63]. Our
approach should be considered as the baseline scenario (i.e., most
optimistic) of climate change, where land use will stay the same.
Under this approach the paramo grassland seems to be more
affected by land use change than by climate change (Table S2),
though an overall loss is projected for both potential and remnant
scenarios (Figure 3 and Figure S3). Given that it is very likely land
use change will increase in the future, the threat posed to this
biome is even higher than to any other biome.
Potential changes into biomes with different physiognomy or
different degrees of humidity would not only have ecological
consequences but also would impact directly ecosystem services
provided by the original biomes. In the case of a reduction in
vertical structure, aboveground carbon storage will be reduced.
Nevertheless, non-forested biomes such as the paramo, which have
a simpler vertical structure, tend to have a larger belowground and
soil carbon stock. Hence, the impact of any replacement of the
paramo biome on the overall carbon storage may not be
straightforward. On the other hand, areas with increasing
humidity levels will be more susceptible, for example, to leaching
processes until the vegetation stabilizes.
Although the identification of areas where most of the models
agree in changes is useful for conservation management, the
uncertainty in these projections remains problematic. Therefore,
areas with no projected change or with a consistent change would
be obvious target for conservation compared to those with large
uncertainty. Additionally, fostering landscape networks (protected
areas, connecting zones and intermediate landscapes) would be a
more effective conservation strategy than isolated protected areas
[64].
From this perspective, conservation strategies should be
designed to fulfil at least three main criteria: (1) Conservation
areas should ideally cover a large vertical range to capture the
projected biome displacement as a way to maintain connectivity
and ensure the integrity of functional processes such as water and
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carbon cycle and species migration. (2) Conservation strategies
should include not just pristine habitats but also secondary forest
and abandoned agricultural areas in the paramos and punas to
promote restoration schemes to reduce land use change and
fragmentation of protected areas and increase connectivity among
the natural reserves. This will also stimulate these productive
systems to shift from a source of carbon to sink and thus help
mitigating climate change impacts. (3) Many sensitive areas are
located in the border between Andean countries. Therefore it is
important to include key binational reserves where climate change
impacts are likely to be severe. This consideration calls for a
regional conservation agenda were political platforms such as the
Andean Community (CAN) can be of much help to foster
conservation actions that are beyond country governance.
4.3 Evaluating the biome modelling approach and
outlook
The uncertainty analysis of the biome modelling shows no
overlap in confidence intervals between probability of occurrence of
the most probable biome and another biome for more than 90% of
the modelled area. Together with an overall accuracy of 89%, this
suggests that our approach to model Tropical Andean biomes is
robust. Areas with overlaps between the confidence intervals may be
caused by the coarse resolution and interpretation errors of the map,
but they can also represent ecotones between biomes.
Another potential issue of climate change impact assessments is
the need to project outside the current climate envelope, which
poses fundamental issues of model reliability [65]. However, the
large variability of current climates in the tropical Andes and its
buffer zone results in only a very small fraction of non-analogue
climate combinations only for the 2040–2069 period, which again
should make the modelling exercise relatively robust. One
potential pathway for improvement is to account for the effect of
the combined variables in the analysis of the observed envelopes.
Our approach did not do this because of the large number of
variables included. This may result, for example, in small deviation
of the observed values being assigned as new climates, which could
overestimate the non-analogue climates. An alternative approach
may be to identify non-analogue climates by areas where the
model predicts low probabilities for all biomes, or where it is hard
to differentiate between the most probable and other biomes.
Further research is needed in this matter.
Finally, more solid scenarios should ideally incorporate a
dynamic model of land use change. The absence of good quality
data such as past land use trajectories for the whole region and
updated detailed land cover maps for some of the countries is
currently a major limiting factor. Additionally the interactions
between the vegetation and water cycle should also be taken into
account but this is currently limited not only by the lack of a
conceptual model for the Tropical Andes but for the absence of
higher resolution climatic layers or information on the climatic
interannual variability. A better understanding of biological
processes and limiting factors on the Andes such as dispersal
and seed establishment is also needed.
Conclusions
According to our projections, the Tropical Andes will not
respond homogeneously to climate change. Different conservation
and adaptation measures should therefore be designed according-
ly. Some biomes are projected to experience an upslope
displacement of both the upper and lower boundaries, while
others are projected to expand downslope. The projected upslope
displacement is supported by palaeoecological evidence from post-
glacial time; however, future temperature anomalies are projected
to be higher and result from faster rates of change than in the past.
Biome displacement will need species to have faster migration
rates than present. Additionally, human land use has already
transformed important areas of the Andean landscape, which has
a strong effect on biome resilience. However, the interaction
between climate change and land use change is further compli-
cated. Since we assumed a static land use scenario, this impact is
underestimated given that future land use change is expected to
increase. The downslope expansion projected for the dry biomes
(seasonally dry tropical montane forest, montane shrubland, xeric
pre-puna) may result from changes in the water balance but this
needs further study in the Andes.
In contrast with other studies at species level, large areas of the
Tropical Andes are projected to remain stable (from 74.8% to
83.1%). However, several biomes are projected to lose more than
30% of their current area. Vulnerable areas include the biomes
which are currently already most threatened (glaciers and
cryoturbated areas, paramo and evergreen montane forest) but
also specific areas under stress due to changes in physiognomy or
humidity levels. The identification of these areas including
different climatic models accounts for the uncertainty of future
climate projections. The inclusion of the uncertainty analysis by
means of a GCM model ensemble has also implications for
management decisions such the establishment of protected areas in
regions with less uncertainty.
Future work should focus on improving the biome modelling,
which is currently limited by data availability and lack of
knowledge of specific processes. Despite its simplifications, the
good overall adjustment of our model shows that it is possible to
assess biome distribution changes at fine resolution to inform
decision-making. Additionally, our methodology can be applied to
other tropical mountain ecosystems as well.
Supporting Information
Figure S1 Maps representing uncertainty analysis of
the biome model and non-analogue climates. A) Map
showing the number of overlaps between the confidence interval of
the most probable biome and other biomes for the present. B) Map
showing the richness of non-analogue climates for the future under
scenario A2 2040–2069 based on the summed occurrence of all
variables exceeding the range of calibrated data for all models.
(TIF)
Figure S2 Density functions of the selected biome
probability and standard deviation, according to the
number of overlaps between the confidence interval of
the selected biome and another biome or biomes.
(PNG)
Figure S3 Median change in the area of potential
biomes versus remnant biomes under A2 scenario. In
dark grey the lost areas (the biome will be replaced by another
biome), in grey stable areas (areas that remained unchanged) and
in light grey new or emerging areas (the biome is projected to
occur in the future but not in the present). Bars represent the
minimum and maximum values of all models.
(EPS)
Figure S4 Median change in the area of potential
biomes for each model under A1B scenario, 2010–2039.
(EPS)
Figure S5 Median change in the area of potential
biomes for each model under A1B scenario, 2040–2069.
(EPS)
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Figure S6 Median change in the area of remnant
biomes for each model under A1B scenario, 2010–2039.
(EPS)
Figure S7 Median change in the area of remnant
biomes for each model under A1B scenario, 2040–2069.
(EPS)
Table S1 Variables used for each biome model.
(DOC)
Table S2 Median relative area changes between future
and present for potential and remnant biomes (A1B
2010–2039 and A2 2040–2069).
(DOC)
Table S3 Conversion matrix from present biomes to
future projected biomes for scenario A2 2040–2069.
(DOC)
Acknowledgments
This study is part of the Project "Climate change in the Tropical Andes",
implemented by the Consortium for the Sustainable Development of the
Andean Ecoregion (CONDESAN) and the General Secretariat of the
Andean Community (SGCAN). We acknowledge the modelling groups,
the Program for Climate Model Diagnosis and Intercomparison (PCMDI)
and the WCRP’s Working Group on Coupled Modelling (WGCM) for
their roles in making available the WCRP CMIP3 multi-model dataset.
Support of this dataset is provided by the Office of Science, U.S.
Department of Energy.
Author Contributions
Conceived and designed the experiments: CT CAA FC. Performed the
experiments: CT CAA WB. Analyzed the data: CT CAA WB. Wrote the
paper: CT CAA FC WB.
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