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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.
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Diverging Responses of Tropical Andean Biomes under
Future Climate Conditions
Carolina Tovar
*, Carlos Alberto Arnillas
, Francisco Cuesta
, Wouter Buytaert
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
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:
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
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
(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
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
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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.
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
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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
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.
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.
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.
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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
(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
, 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
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.
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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).
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
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.
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.
Tropical Andean Biomes and Climate Change
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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’’.
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
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
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
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
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.
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.
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.
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.
Figure S4 Median change in the area of potential
biomes for each model under A1B scenario, 2010–2039.
Figure S5 Median change in the area of potential
biomes for each model under A1B scenario, 2040–2069.
Tropical Andean Biomes and Climate Change
PLOS ONE | 10 May 2013 | Volume 8 | Issue 5 | e63634
Figure S6 Median change in the area of remnant
biomes for each model under A1B scenario, 2010–2039.
Figure S7 Median change in the area of remnant
biomes for each model under A1B scenario, 2040–2069.
Table S1 Variables used for each biome model.
Table S2 Median relative area changes between future
and present for potential and remnant biomes (A1B
2010–2039 and A2 2040–2069).
Table S3 Conversion matrix from present biomes to
future projected biomes for scenario A2 2040–2069.
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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Tropical Andean Biomes and Climate Change
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... The uncertainty linked to model predictions is often ignored by scientists and decision makers, or interpreted as a mere disagreement between experts; although, it is an important criterion in decision making (Beven, 2007). With this in mind, this study endeavoured to reduce the uncertainty surrounding climate change projections available for the Andes (Buytaert et al., 2009;Tovar et al., 2013) by using an ensemble approach based on two time horizons (2050 & 2070), two GHG emission concentration scenarios (RCP 4.5 & RCP 8.5) and several Global Climate Models (GCMs). This decision allowed for the control of errors and uncertainties in the individual models (Araújo and New, 2007). ...
... Bioclimatic variables were used as predictor variables to capture annual climatic patterns, seasonality and extreme environmental conditions for each páramo remnant (O'Donnell and Ignizio, 2012;WorldClim, 2005). The set of variables was chosen based on previous literature on tropical ecosystem modelling (Tovar et al., 2013;Cuesta, 2007;Cuesta et al., 2008;Ramirez-Villegas et al., 2014) and due to their applicability for estimating potential effects of climate (Table 2.2). In Ecuador, the WorldClim database has been verified and used in several climatic studies (e.g. ...
... The evaluation of the impact of climate change on páramo niches distribution was based only on niche areas identified as high consensus among the six GCMs results (at least 5 of 6 GCMs in agreement). Based on similar studies (Tovar et al., 2013;Broennimann et al., 2006;Ramirez-Villegas et al., 2014;Loehle and LeBlanc, 1996;Peterson et al., 2001) , niche pixels were analysed and classified into four categories of impact. The first category was unchanged niche ...
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The Ecuadorian páramo ecosystems play an important role in providing the local population with drinking water, irrigation, hydropower generation, carbon storage, and agricultural production. In Ecuador, páramo vegetation has suffered significant degradation and loss due to land use change. This has had a major impact on the capability of the ecosystems to resist or adapt to external pressures such as climate change. This research aims to understand the effects of climate change on the Ecuadorian páramo ecosystems and the potential consequences on the ecosystem services they provide. This study applies state-of-the-art techniques to evaluate: a) the impact of climate change on the climatic niche distribution of the páramo ecosystems based on future greenhouse gas concentration scenarios; b) the amount of carbon stored in both soil and vegetation for key types of páramo ecosystems; and c) the future exposure of the Ecuadorian páramos to land use pressures, considering climate as a determining factor for increases or decreases in the farming frontier. The research show that in 30 (2050) to 50 (2070) years, páramo ecosystems with isolated or restricted distribution could suffer significant niche contraction (>60%) or niche extinction (100%), while ecosystems with a broad distribution seem less vulnerable (<60%). The carbon (C) estimates show that C in soils could vary from 87.7 to 278.9 ton C/ha, while in vegetation could range from 5.3 to 8.9 ton C/ha in grassland and shrubland vegetation, and 96.3±32.4 ton C/ha in forest. Soil C stock is influenced by altitude and climatic conditions such as precipitation and temperature. The farming frontier could increase in 23% (2050) to 35% (2070) towards and within the páramo areas, most of them occurring in areas without protection (16%-21%). This study reveals considerable challenges for the future of the Ecuadorian páramo, highlighting the need to implement adaptation strategies in these natural areas.
... Some previous studies have also shown that changes in precipitation are driving directional changes in the community composition of tropical forests [27][28][29], but other studies have shown little or no relationship between changes in species composition and changes in precipitation [11], and it has even been suggested that observed changes in composition in relation to drought tolerances may be driven primarily by changes in temperature and the inherent relationships between species' heat and drought tolerances (especially when measured based on geographic occurrence locations) [30]. The hypothesized drivers of these heterogeneous responses are complex changes in the climatic water balance (e.g., cloud cover immersion, climatic water deficit, and seasonal precipitation), emphasizing the potential importance of altered water availability [31]. Given that climate change is altering tropical precipitation regimes [32,33], this incomplete knowledge about how important water availability is for determining species' current and future distributions greatly limits our ability to predict the fate of these tropical communities and ecosystems. ...
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Little is known about how differences in water availability within the “super humid” tropics can influence the physiology of understory plant species and the composition of understory plant communities. We investigated the variation in the physiological drought tolerances of hundreds of understory plants in dozens of plant communities across an extreme elevation and precipitation gradient. Specifically, we established 58 understory plots along a gradient of 400–3600 m asl elevation and 1000–6000 mm yr−1 rainfall in and around Manu National Park in southeastern Peru. Within the plots, we sampled all understory woody plants and measured three metrics of physiological leaf drought tolerance—turgor loss point (TLP), cuticular conductance (Gmin), and solute leakage (SL)—and assessed how the community-level means of these three traits related to the mean annual precipitation (MAP) and elevation (along the study gradient, the temperature decreases linearly, and the vapor pressure deficit increases monotonically with elevation). We did not find any correlations between the three metrics of leaf drought tolerance, suggesting that they represent independent strategies for coping with a low water availability. Despite being widely used metrics of leaf drought tolerance, neither the TLP nor Gmin showed any significant relationships with elevation or the MAP. In contrast, SL, which has only recently been developed for use in ecological field studies, increased significantly at higher precipitations and at lower elevations (i.e., plants in colder and drier habitats have a lower average SL, indicating greater drought tolerances). Our results illustrate that differences in water availability may affect the physiology of tropical montane plants and thus play a strong role in structuring plant communities even in the super humid tropics. Our results also highlight the potential for SL assays to be efficient and effective tools for measuring drought tolerances in the field.
... Entre más alto se encuentre un ecosistema, mayor será su afectación, porque el espacio geográfico a altitudes mayores es más limitado. Por esto, hay estudios de simulación que indican que el superpáramo es el bioma más vulnerable a este desplazamiento altitudinal, seguido por el páramo propiamente dicho (Tovar et al., 2013). ...
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Páramo es un concepto complejo: un ecosistema, un bioma, un paisaje, un área geográfica, una zona de vida, un espacio de producción e inclusive un estado del clima. También es un territorio en disputa y un elemento fundamental de la cultura y la historia. Los páramos ecuatorianos han experimentado un constante cambio durante las últimas décadas. Su paisaje, su extensión, su vegetación, su fauna y su población se han visto alterados y con ellos la percepción que se tiene de los páramos.Este libro es una exploración para entender cómo y por qué el páramo ha cambiado, y cuáles son las consecuencias de este cambio. Creemos que parte de la riqueza del libro está precisamente en presentar no solo conocimientos, sino posiciones, todo lo cual enriquece las discusiones y las perspectivas.
... In the last decades, they have experienced striking alterations leading to biodiversity loss, including glacier retreat, the disappearance of water bodies and less foggy days [28]. These Andean ecosystems could lose about 31% of their current extent by 2050 [29]. In fact, melting glaciers are already causing water scarcity problems in the northern tropical Andes, especially in the Ecuadorian Andes [30,31]. ...
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The present study aims to elaborate a hydrogeological characterisation in the Water Sowing and Harvesting context. The study is focused on rural parishes in the Ecuadorian Andes that, despite their proximity to snow sources (Chimborazo glaciers), need more supply of this resource, to satisfy the demand of a population of 70,466 inhabitants. The study is based on hydrology and geomorphological analysis, a geophysical exploration, and a definition of water management strategies. The application of non-destructive geophysical methods and Geographic Information Systems support the hydrogeological study and the proposal of strategies for sustainable water management on the slopes of the Chimborazo volcano. An aquifer potential was identified (sand, gravel and fractured porphyritic andesites) with resistivity values between 51.3 and 157 Ω m at an approximate depth of 30 m from the geophysical characterisation addressed. This potential saturated zone is on the southern slope of the Chimborazo volcano within the hydrographic watershed, with favourable drainage networks for water accumulation. The aquifer shows a high-water saturation level but uncontrolled losses. As a consequence of these characteristics, alternatives for managing water resources are proposed, such as wells construction, using Water Sowing and Harvesting system methods (“camellones”) based on Nature-Based Solutions, dam construction and environmental education. The different proposals are associated with the four sustainability axes of Brundtland (economic, social, environmental and cultural axis) and contribute to the sixth objective of the Sustainable Development Goal 2030 Agenda.
... The results of this study show macroclimatic conditions might play an important role when it comes to the distribution of range-restricted species. While models of future climates are full of uncertainties and some projections show less detrimental scenarios for the Andes (e.g., Tovar et al., 2013) the results of this study underline the need to include climatic changes in conservation planning. Protecting areas that might as refugia for species with narrow climatic niches will be key to maintain ecosystem functions and preserve biodiversity in the future. ...
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Almost 40% of the world's plant species are restricted in their geographic range size. However, little is known about the mechanisms that shape range sizes, particularly in a tropical context. Because small geographic range size is known to increase extinction risk, there is a need to better understand range size in the face of the ongoing biodiversity and climate crisis. In this study, the patterns of range size rarity within the two megadiverse tropical genera Solanum and Begonia were explored and the range size-niche breadth hypothesis was tested. The results of this study show that range-restricted Solanum and Begonia species in Peru inhabit geographically and climatically rare habitats within the Andes. A positive correlation between climatic niche breadth and range size was observed, suggesting that range-restricted species are often climatic specialists. These findings underline the importance of mountainous regions and rare habitats for species with narrow ranges, a pattern observed at the global scale. The results also indicate that range-restricted species might be particularly susceptible to the effects of climate change. With 60% of the range-restricted species having no known populations inside of protected areas, the outcomes of this study underline the importance of additional conservation measures to protect range-restricted species within Peru and globally.
... Páramos are a collection of high-mountain ecosystems dominated by grasslands, wetlands and shrublands (Tovar, Arnillas, Cuesta, & Buytaert, 2013). Usually, the characteristic saturated soils originate a variety of peatlands and lakes (J. ...
One of the most essential ecosystem services provided by high-Andean páramos is streamflow buffering. A combination of soil, vegetation and climate characteristics provides páramos with an exceptional ability to store, regulate and supply water, particularly in their natural state. However, páramo catchments are seldom pristine. Agriculture is one of the most widespread human activities in páramos and considerably affects their soil hydrophysical properties. This research assesses how soil properties are affected by the conversion from natural páramo vegetation to fallow, onion, or potato crops. We measured Soil Organic Matter (SOM), Bulk density (Bd), pH and electric conductivity (EC) at three depths (0–5, 10–15 and 20–25 cm), in a stratified random survey of different land uses in the Eastern Cordillera of Colombia. Samples were collected in wet and dry seasons. Agricultural use affects all the studied properties, increasing Bd (+0.11 g cm-3), decreasing SOM (-5.5%), and increasing pH (+1.3) and EC (+187 µS cm-1). Seasonality did not have a significant effect on the studied properties under natural vegetation; however, there were significant differences between wet season and dry season in agricultural soils in SOM (-7.2% and +5.7% in fallow and potato crop, respectively) and Bd (-0.22 gr cm-3 in crops). These changes show that agriculture in páramo grasslands leads to a significant decrease in soil porosity and water-holding capacity, which affects adversely the ecosystem hydrological regulation capacity. This paper contributes to a better understanding of the complexity of Andean páramos and provide crucial information to improve soil management, a key aspect for ensuring the sustainable provision of hydrological ecosystem services offered by Andean and other mountain ecosystems.
... Referring to another school of thought, Berauer et al. (2021) noted that the coupled effects of climate change on temperature and precipitation are expected to increase the frequency and intensity of drought periods, thus impairing vegetation cover and subsequently enhancing land degradation (Zwicke et al., 2013). In a study by Mei et al. (2021), global climate change is perceived as rapidly and significantly changing habitats in higher-elevation areas, which will inevitably lead to the dwindling of the natural species and the shifting of vegetation boundaries (Tovar et al., 2013). Shifting of vegetation boundaries will trigger land degradation in the near future; therefore, it needs special attention now to prevent further damage. ...
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Human population is envisaged to continue to grow, with a tremendous contribution to land degradation and climate change. Climate change and land degradation are intertwined, thus tackling climate change means mitigating land degradation. Climate change is a worldwide problem that affects lives and livelihoods; henceforth, mitigating measures are urgently required. With their unique, rich biodiversity, mountain areas are severely sensitive to climate change and land degradation; therefore, a speedy need to curb land degradation in mountain areas is needed. The aim of this systematic review was to appraise different strategic methods used globally to minimise land degradation and sustain mountainous areas in a frequently changing climate. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) was utilised in this systematic review. The Scopus data base was utilised for document search, with a selection of articles limited between the years 2012 and 2021. Only articles written in English were considered. After assessing the abstracts, 703 articles were retained for a full review, leading to the final selection of 84 articles. The results show that soil erosion, overgrazing and construction of infrastructure are major causes of land degradation. The human population increase is also an enormous contributing factor to activities leading to land degradation and climate change. A conspicuous intensification of agricultural activities is expected to continue due to rising food demand. Curbing land degradation and climate change in mountain areas can be enforced by the government through stricter regulations. However, regulations and policies must be locally initiated, instead of globally initiated, with local communities being the main stakeholders. Hence, bottom-up rather than top-down policies would encourage local communities to embrace mitigation policy initiatives.
... The climate change studies in alpine areas also reported that plant diversity is vulnerable in such areas (Halloy & Mark, 2003;Rather et al., 2022;Sofi et al., 2022;Thuiller et al., 2005). For most of the species growing in alpine areas, their suitable habitats could be significantly reduced or disappeared by the end of the twenty-first century, especially if global warming and a decline in precipitation rates persisted (Engler et al., 2011;Tovar et al., 2013). ...
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In the current era of the anthropocene, climate change is one of the main determinants of species redistribution and biodiversity loss. Worryingly, the situation is alarming for endemic and medicinally important plant species with a narrow distributional range. Therefore, it is pivotal to inspect the influence of accelerated climate change on medicinally important threatened and endemic plant species. Using an ensemble approach, the current study aims at modelling the present distribution and predicting the future potential distribution coupled with the threat assessment of Swertia petiolata—a medicinally important endemic plant species in the Himalayan biodiversity hotspot. Our study revealed that under current climatic scenarios, the suitable habitats for the species occur across the western Himalayan region which includes the north-western Indian states (Jammu and Kashmir, Himachal Pradesh, and southern Uttarakhand), northern Pakistan, and north-western Nepal. Also, temperature seasonality (BIO4) and precipitation seasonality (BIO15) are the most significant bioclimatic variables determining the distribution of S. petiolata. Furthermore, the study projected a reduction in the suitable habitats for the species under future changing climatic scenarios with a reduction ranging from − 40.298% under RCP4.5 2050 to − 83.421% under RCP8.5 2070. Most of the habitat reduction will occur in the western Himalayan region. In contrast, some of the currently unsuitable Himalayan regions like northern Uttarakhand will show increasing suitability under climate change scenarios. The current study also revealed that S. petiolata is classified as Near Threatened (NT) following the IUCN criterion B. Hopefully, the present study will provide a robust tool for predicting the cultivation hotspots and devising scientifically effective conservation strategies for this medicinally important plant species in the Himalaya and similar environments elsewhere in the world.
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Study region: Humid puna of the Central Andes, Perú Study focus: Bofedales, or peat-forming wetlands, are a characteristic feature of the humid puna-a high elevation, seasonally dry grass-and shrub-land throughout the Central Andes. Despite the hydrologic importance of the humid puna for downstream communities, and the inference that bofedales play an important role, few studies have explored the hydrology of this ecosystem, and none have quantified bofedal water yield to streams. We designed a 3-year study in the Upper Ramuschaka Watershed (URW), a 2.12 km 2 humid puna catchment sustaining a perennial stream used for irrigation downstream. We monitored hydrologic fluxes through the URW, periodically measured discharge in 19 nested subbasins across wet and dry seasons, and characterized the structure, hydraulic properties, and storage capacity of four bofedales. New hydrological insights for the region: Unit runoff is consistently higher in subbasins with greater bofedal coverage. High porosity peat fills in the wet season via groundwater recharge and drains slowly through underlying layers with low hydraulic conductivity. Bofedales cover 11.6% of the URW and store 105,000 ± 10,000 m 3 of water seasonally. In the dry season, bofedales yield 49 ± 5 mm to streams, equivalent to 20-98% of the URW's dry season runoff. Bofedales regulate drainage from the humid puna to downstream communities and are therefore vital to local and regional water security.
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In spite of their small global area and restricted distributions, tropical montane forests (TMFs) are biodiversity hotspots and important ecosystem services providers, but are also highly vulnerable to climate change. To protect and preserve these ecosystems better, it is crucial to inform the design and implementation of conservation policies with the best available scientific evidence, and to identify knowledge gaps and future research needs. We conducted a systematic review and an appraisal of evidence quality to assess the impacts of climate change on TMFs. We identified several skews and shortcomings. Experimental study designs with controls and long-term (≥10 years) data sets provide the most reliable evidence, but were rare and gave an incomplete understanding of climate change impacts on TMFs. Most studies were based on predictive modelling approaches, short-term (<10 years) and cross-sectional study designs. Although these methods provide moderate to circumstantial evidence, they can advance our understanding on climate change effects. Current evidence suggests that increasing temperatures and rising cloud levels have caused distributional shifts (mainly upslope) of montane biota, leading to alterations in biodiversity and ecological functions. Neotropical TMFs were the best studied, thus the knowledge derived there can serve as a proxy for climate change responses in under-studied regions elsewhere. Most studies focused on vascular plants, birds, amphibians and insects, with other taxonomic groups poorly represented. Most ecological studies were conducted at species or community levels, with a marked paucity of genetic studies, limiting understanding of the adaptive capacity of TMF biota. We thus highlight the long-term need to widen the methodological, thematic and geographical scope of studies on TMFs under climate change to address these uncertainties. In the short term, however, in-depth research in well-studied regions and advances in computer modelling approaches offer the most reliable sources of information for expeditious conservation action for these threatened forests.
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Este documento es el resultado del trabajo interinstitucional de la Secretaría General de la Comunidad Andina, el Programa Regional ECOBONA de Intercooperation, el Proyecto Páramo Andino de CONDESAN, el Programa BioAndes, NatureServe, EcoCiencia, el Instituto de Investigación de Recursos Biológicos Alexander von Humboldt, el Instituto de Ciencias Ambientales y Ecológicas-Universidad de Los Andes (ICAE-ULA), el Laboratorio de Teledetección- Universidad Nacional Agraria La Molina (UNALM), el Centro de Datos para la Conservación- Universidad Nacional Agraria La Molina (CDC-UNALM), y RUMBOL SRL. La información contenida en este documento incorpora resultados de la discusión técnica de los autores y no representa necesariamente posiciones de la Secretaría General de la Comunidad Andina o los Países Miembros.
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High-elevation tropical mountain regions may be more strongly affected by future climate change than their surrounding lowlands. In the tropical Andes a significant increase in temperature and changes in precipitation patterns will likely affect size and distribution of glaciers and wetlands, ecosystem integrity, and water availability for human consumption, irrigation, and power production. However, detailed projections of future climate change in the tropical Andes are not yet available. Here we present first results for the end of the 21st century (2071–2100) using a regional climate model (RCM) based on two different emission scenarios (A2 and B2). The model adequately simulates the spatiotemporal variability of precipitation and temperature but displays a cool and wet bias, in particular along the eastern Andean slope during the wet season, December–February. Projections of changes in the 21st century indicate significant warming in the tropical Andes, which is enhanced at higher elevations and further amplified in the middle and upper troposphere. Temperature changes are spatially similar in both scenarios, but the amplitude is significantly higher in RCM-A2. The RCM-A2 scenario also shows a significant increase in interannual temperature variability, while it remains almost unchanged in RCM-B2 when compared to a 20th century control run. Changes in precipitation are spatially much less coherent, with both regions of increased and decreased precipitation across the Andes. These results provide a first attempt at quantifying future climate change in the tropical Andes and could serve as input for impact models to simulate anticipated changes in Andean glaciation, hydrology, and ecosystem integrity.
Open access to an unprecedented, comprehensive coordinated set of global coupled climate model experiments for twentieth and twenty-first century climate and other experiments is changing the way researchers and students analyze and learn about climate. The history of climate change modeling was first characterized in the 1980s by a number of distinct groups developing, running, and analyzing model output from their own models with little opportunity for anyone outside of those groups to have access to the model data. This was partly a consequence of relatively primitive computer networking and data transfer capabilities, along with the daunting task of collecting and storing such large amounts
Discusses present-day land-use patterns in the northern Andean paramos (natural high-altitude grasslands). The various agricultural and pastoral production systems of this high mountain region are presented systematically and their organization is explained. In most Indian paramo communities one does not encounter nomadic pastoralism; the objective is to complement pastoral activities with small-scale agriculture within a community's boundaries. Two contemporary land-use strategies form the focus of the discussion - the upward expansion of the agricultural frontier and the escalation of market-oriented animal-raising activities in the lower ecozones of paramo communities. Of particular interest are the historical and social causes that led to these recent trends, and their negative ecological consequences for the rest of the country. -from Author
Mountains are rich in biodiversity and provide ecosystem services for their inhabitants. These regions are currently threatened by land use and land cover changes (LUCC), therefore an efficient monitoring is required to capture such changes. The aim of this study is to test a landscape change analysis in a mountain region to guide landscape management by including: (1) LUCC trends, (2) LUCC trends across the elevation gradient and (3) changes in spatial configuration. This framework was applied to the Peruvian Jalca grasslands (>3000 m a.s.l.), located in the Tropical Andes for the period 1987–2007. We used object-based classification of Landsat TM and patch metrics for each land cover class. Our results show an overall loss of Jalca (−1.5%/yr) and montane forest and shrubland (−2.8%/yr) with higher rates than other Andean regions. Furthermore, fragmentation is observed for the Jalca while montane forest and shrubland class is not fragmenting but the largest patches are vanishing, potentially affecting the connectivity between natural areas. Agriculture has replaced the Jalca, especially in the upper zones of the Andes showing an upward expansion of crops. However tree plantation and mining had increased more dramatically than agriculture (>9%/yr). Upper and less fragmented Jalca areas may be suitable for conservation purposes while agriculture may better expand in already degraded natural areas. Records of changes across the elevation gradient and in spatial patterns result in useful information for decision makers and may improve ecosystem management not only in the Tropical Andes but also in other mountain regions.
Globally, water resources for cities are under increasing stress. Two main stressors are climate change and population growth, but evaluating their relative impact is difficult, especially because of the complex topology of water supply. This is especially true in the tropical Andes, which is a region with strong climatic gradients and topographical limits to water resources. This paper presents an evaluation of both stressors on water resources in a geospatial framework to identify gradients in water availability that may lead to conflicts over water use. We focus on four major cities in, or receiving water from, the tropical Andes. A multimodel data set of 19 climate models is used as input for a regional water balance model. Per capita water availability is evaluated along topographic gradients for the present and for future scenarios of population growth and climate change. In all cases, the median projection of climate change suggests a relatively limited impact on water availability, but uncertainties are large. Despite these uncertainties, we find that the expected demographic changes are very likely to outpace the impact of climate change on water availability and should therefore be the priority for local policy making. However, distinctive geospatial patterns characterize the supply systems of the studied cities, highlighting the need to analyze the topology of water supply within an ecosystem services context. Our approach is flexible enough to be extended to other regions, stressors and water resources topologies.
In high‐elevation communities of the southern Andes, plant cover is low due to severe environmental conditions and vegetation occurs mostly as isolated small (2) patches. Most patches are dominated by flat cushion plants. We evaluated patterns of plant species co‐occurrence and species affinity for patches with and without cushion plants and different species richness. We mapped and recorded species composition of patches occurring within two 20 m × 20 m plots at the NE slope of Cerro Chall‐Huaco, Nahuel Huapi National Park, Argentina. In these plots, we identified 32 and 24 plant species, and a maximum of 15 and 12 species per patch, respectively. The community was characterized by positive associations between species. Patches in which either of the common cushion plants Mulinum leptacanthum and Oreopolus glacialis occurred sustained richer communities than patches in which they were absent. Patches dominated by different cushion plants did not differ in species composition, but species differed in their affinities for patches with different numbers of species. Because richness increased with patch size and patch size with time, differential affinities of plant species suggest that successional changes take place in the patches. Some small herbaceous species appear to act as late colonizers, mostly restricted to species‐rich patches. Flat cushion plants are considered ‘nurse plants’; they strongly modify micro‐environmental conditions and allow establishment and survival of associated species.