www.sciencemag.org/344/6183/1247579/suppl/DC1

Supplementary Materials for

Multiple Dimensions of Climate Change and Their Implications for Biodiversity

Raquel A. Garcia,* Mar Cabeza, Carsten Rahbek, Miguel B. Araújo*

*Corresponding author. E-mail: raquel.ag@gmail.com (R.A.G.); miguel.araujo@imperial.ac.uk (M.B.A.)

Published 2 May 2014, Science 344, 1247579 (2014)

DOI: 10.1126/science.1247579

This PDF file includes:

Materials and Methods Figs. S1 to S7 Tables S1 to S2 References

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Multiple dimensions of climate change and their implications for biodiversity

Raquel A. Garcia, Mar Cabeza, Carsten Rahbek, and Miguel B. Araújo

Supplementary Materials

Materials and Methods

1. Climatic data

We used 30-year averages of mean annual temperature and total annual precipitation at a spatial resolution of 10 minutes. Baseline (1961-1990) climate data were publicly available from the Climatic Research Unit (121). For 2081-2100, we sourced 15 downscaled General Circulation Models (GCM) (122) for the A1B greenhouse gas emissions scenarios from the World Climate Research Programme’s Coupled Model Intercomparison Project phase 3 multi-model dataset projections. We followed the methodology described by Garcia and colleagues (123) to build multi-model ensembles of similar GCMs. The main ensemble (GCM1) used in the study combined nine models and the alternative ensemble (GCM2) used for sensitivity analysis combined six models.

The nine models in the main ensemble used in the study (GCM1) were: BCCR-BCM2.0 from the Bjerknes Centre for Climate Research; CGCM3.1(T47) from the Canadian Centre for Climate Modelling and Analysis; CNRM-CM3 from the Centre National de Recherches Météorologiques, Météo-France; CSIRO-MK3.0 from the Commonwealth Scientific and Industrial Research Organisation (CSIRO) Atmospheric Research; GFDL-CM2.0 from the Geophysical Fluid Dynamics Laboratory (GFDL), National Oceanic and Atmospheric, Administration (NOAA), U.S. Department of Commerce; GFDL-CM2.1 from the Geophysical Fluid Dynamics Laboratory (GFDL), National Oceanic and Atmospheric, Administration (NOAA), U.S. Department of Commerce; INM-CM3.0 from the Institute for Numerical Mathematics; MRI-CGCM2.3.2 from the Meteorological Research Institute; and PCM from the National Center for Atmospheric Research.

The six GCMs in the alternative ensemble (GCM2) were: CCSM3 from the National Center for Atmospheric Research; ECHAM5/MPI-OM from the Max Planck Institute for Meteorology; ECHO-G from the Meteorological Institute of the University of Bonn, Meteorological Research Institute of the Korea Meteorological Administration (KMA), and Model and Data Group; MIROC3.2 (medium resolution) from the Center for Climate System Research (University of Tokyo), National Institute for Environmental Studies, and Frontier Research Center for Global

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Change; IPSL-CM4 from the Institut Pierre Simon Laplace; and UKMO-HadCM3 from the Hadley Centre for Climate Prediction.

Time-series data were composed of averages for each month in the 30-year baseline and future periods. They were sourced from the CRU (124) at 0.5° resolution for the baseline period (1961-1990), and from the World Data Center for Climate (http://cera-www.dkrz.de/) (125–138) for IPCC AR4 simulations for the same 15 General Circulation Models (2069-2098; run 1) at their native resolution. The same ensembles of nine (GCM1) and six (GCM2) models were derived after resampling the projections to the lowest common resolution (4° x 5°) using bilinear interpolation. The same resampling was applied to the baseline time-series. Calculations were performed in R version 12.2 and 12.5.1 (139) and mapping in ArcGIS 9.3 (140).

2. Computing climate change metrics for temperature and precipitation

Six climate change metrics were computed for temperature and precipitation. Whereas temperature and precipitation can each have direct physiological impacts or indirect impacts on habitat and resource requirements, for some species it is the interaction between the two that becomes critical (141). When different methods exist to compute a given metric (see Table S1), we selected methods most commonly used in biodiversity assessments. Different methodologies may provide different results. Un-projected latitude/longitude climate data were used (WGCS 84), but computations of the change in area of, and distance to, baseline-analogous climates were performed taking into account the curvature of the Earth.

Standardized local anomalies

To quantify local changes in magnitude, we used standardized local anomalies. For each cell, we computed the sum of the standardized Euclidean distances for temperature and precipitation between the baseline and future periods following Williams and colleagues (23). The temporal differences for each climate parameter were standardized using the local inter-annual (baseline) standard deviation for that parameter. Where the standard deviation was zero, we set the standardized anomaly to the value of the anomaly. The baseline standard deviation was computed using the time-series climatic data for 1961-90 at half-degree resolution and resampled to 10 minutes using the nearest neighbor method. High standardized local anomaly scores correspond to large changes in temperature and precipitation.

Change in probability of local climate extremes

As daily data were unavailable, the analysis of extreme climates (117) was based on the time-series monthly data (see "Climatic data" above). For each cell, we calculated the 5th and 95th percentiles of the distributions of the monthly precipitation and temperature, respectively, over

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the 30 years of the baseline period. For each cell, the percentiles of the future distributions over a 30-year period that correspond to the extreme baseline values were then computed. These percentiles correspond to the probability that the historical extremes will be exceeded (in the case of temperature, the probability that the baseline 95th percentile will be exceeded was given by one minus the probability calculated). To obtain a measure of the probability of occurrence of either of the two extreme events (temperature and precipitation), for each cell, we summed the two probabilities and subtracted the product of the two probabilities to avoid counting probabilities twice. The future probability of historical extreme climates was then subtracted from the probability of baseline extreme climates to obtain the changes in the probability of extreme warm and dry climates. Positive values indicated increased probability in the future, whereas negative values indicated a decrease. These calculations captured one single aspect of extreme climates (hot and dry), but other aspects could be calculated in a similar manner. The use of climate data at finer temporal resolution would provide more meaningful outputs, yet the monthly data used here can draw attention to the information nested within gradual trends such as annual anomalies.

Change in area of analogous climates

We used a modified version of the Köppen-Geiger climatic classification (Fig. S4) (142) to classify each cell in both the baseline and future time periods. Each cell was checked against the Köppen-Geiger rules for classification (142), based on temperature and precipitation, and assigned a climate class. To quantify the change in area of analogous climates (classes), we computed the change in area occupied by a given class between the baseline and future periods (34, 118). These calculations were performed using the raster package in R (143), with the area of cells quantified taking into account the curvature of the Earth. For a given cell with a given climate class, the change in area of analogous climates represented the ratio (in percentage) of the difference between future and baseline area of that class to the baseline area of the same class. Positive values indicated gains in area, negative values indicated losses, and null values reflected no change.

Novel climates

We quantified the dissimilarities between baseline and future climates following Williams and colleagues (23). As above for the local anomalies, we used the sum of the standardized Euclidean distances for temperature and precipitation, but this time between each cell in the future and all cells in the baseline. The inter-annual standard deviation for each parameter was used for the standardization. For each cell, we computed the standardized Euclidean distances between the future climate of that cell and the baseline climates of all cells, and retained the minimum of those distances. The larger the score, the more dissimilar the future climate is in relation to the global pool of potential climatic analogues. These calculations were performed using the analogues R package (144).

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Change in distance to analogous climates

For a given cell, we calculated the distances to all cells with analogous climates in the baseline period, i.e., belonging to the same climate class of the given cell as defined above (34, 118). We also calculated the distances to all cells that were projected to experience analogous climates in the future. Using the raster package in R (143) we computed, for each cell, the median of the great-circle distances (in km) below the 10th percentile of the distribution of all values, for both baseline and future periods, and mapped the change over time. Negative values indicated a temporal decrease in distance, whereas positive values indicated an increase.

Climate change velocity

We computed climate change velocity (km/year; 9) as the ratio of the temporal climate gradient (units of climate parameter/year) to the spatial climate gradient (units of climate parameter/km). The two climate parameters were rescaled (to range from zero to one) and averaged to obtain a multi-variate climate parameter. The temporal gradient was given by the local difference between baseline and future values of the multi-variate parameter. We followed Sandel and colleagues (16) to calculate the spatial gradient as the slope of the multi-variate parameter surface for the baseline period from a 3x3 grid cell neighborhood. To avoid dividing by zero or values close to zero, the distribution of the spatial gradient values was truncated at the lower end. All values below 0.00005 rescaled units/km were set to 0.00005 rescaled units/km.

3. Computing climate change metrics for each climate parameter individually

Different climate parameters, including temperature, precipitation and derived bioclimatic parameters, can be important in different situations and for different species. We thus also computed all metrics for each parameter – temperature and precipitation – individually. Comparing both sets of results provides a better understanding of which parameter drives the patterns of combined temperature and precipitation change.

Standardized local anomalies

The method described above for local changes in mean temperature and precipitation was also applied individually to each climate parameter.

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Change in probability of local climate extremes

We used the local probabilities of extreme climates calculated above for temperature and precipitation separately, and subtracted the future from the baseline probabilities.

Change in area of analogous climates

The Köppen-Geiger climate classes used above to define analogous climates are based on both temperature and precipitation, and thus cannot be applied to each climate parameter individually. For this reason, we followed instead the approach of Ackerly and colleagues (35) to compute the change in area of either analogous temperature or analogous precipitation. We constructed histograms of the baseline climate space for each parameter, setting the width of the bins to the lower quartile of the distribution of the inter-annual variability in the baseline period over the study area. Due to a highly skewed distribution for precipitation, we used logaritmised values for this parameter. This procedure resulted in 64 bins for mean annual temperature and 6 bins for total annual precipitation. For both observed and projected climate, each cell could thus be assigned to a specific bin of temperature and precipitation. The computation of the change in area of analogous climates was done as described above for combined temperature and precipitation changes.

Novel climates

The method described above was applied individually to each climate parameter.

Change in distance to analogous climates

For each climate parameter we used the same computation described above for the two parameters. However, similarly to the computation of changes in area of analogous temperature or precipitation individually, we used the climate classification based on histograms.

Climate change velocity

The same method described for the two parameters was applied to the (non-scaled) values of each climate parameter separately.

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4. Correlation among climate change metrics

To test for correlation among metrics, we computed pair-wise spatial correlation tests. We used the modified.ttest function in the SpatialPack package in R (145), but performed Spearman correlations instead of Pearson correlations. We performed tests based on corrections of the number of degrees of freedom to account for existing spatial auto-correlation, under the null hypothesis of no spatial correlation.

5. Overlap among climate change metrics

Areas of simultaneous change in multiple metrics were identified by overlaying three metrics selected to depict three main threats and opportunities for species: standardized local anomalies, change in area of analogous climates, and climate change velocity. Following Williams and colleagues (23), we recomputed the change in area of analogous climates using a pool of climatic analogues within a radius of 500 km around each cell. In this way, we accounted for potential dispersal limitations of species.

The values for each individual metric were reclassified on a two-class scale using as threshold either the median of the distribution of values worldwide, or zero for metrics with positive and negative values (change in area of baseline-analogous climates, change in distance to baseline-analogous climates, and change in probability of local climate extremes). This scale was selected for ease of interpretation, and because it avoids the use of arbitrary thresholds in the case of metrics with positive and negative values. Notwithstanding, the patterns obtained with different scales (e.g., a 16-class scale) were generally similar.

To examine alternative combinations of metrics which might be more relevant under different circumstances, we computed the same overlap but replacing climate change velocity with the change in distance to analogous climates, and replacing standardized local anomalies with the change in probability of local climate extremes (Fig. S7).

6. Analysis of sensitivity to alternative climate models

The climate change metrics were computed using a multi-model ensemble of nine GCMs that were considered to form a cluster of GCMs similar to the median of a total of 15 GCMs (123) (GCM1). We repeated the analyses for the alternative cluster of six GCMs for the same emissions scenario (GCM2). We computed the same six metrics for temperature and precipitation (Fig. S1 and S2), the comparison across climate regions of the proportion of area affected by large changes (Fig. S5), and the overlap of the three selected metrics (Fig. S6).

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Supplementary Tables

Table S1 | Examples of climate change metrics used in published studies. Descriptions refer to changes in climate between two time steps, t1 and t2. The list is not meant to be exhaustive, but rather shows the diversity of metrics by covering a diversity of approaches and computation methods.

Dimension Metric Ref. Changes in local climate

Magnitude Changes in average climate: anomalies for a climate parameter between t1 and t2, sum of normalized anomalies for all

parameters, or weighted sum of anomalies for all parameters. (14, 27, 32, 146–149)

the Euclidean distance between the t1 and t2 values of a climate parameter, standardized by the standard deviation of t1 inter-annual variability; the squared sum of standardized Euclidean distances for multiple parameters; or the weighted sum of Euclidean distances measured along the axes of a Principal Component Analysis performed on climate.

(10, 23, 24)

Changes in climate extremes: the probability of occurrence in t2 of the most extreme event in t1 for a given parameter; or

weighted average of probabilities for multiple parameters. (26, 117, 150)

additional number of occurrences in t2 of the “1 in 20 years” extreme event of t1. (151)

the variation between t1 and t2 in the length of the period where a given climate parameter is above or below quantile or threshold.

(36)

the average distance of t2 values for a climate parameter from those of t1, where distance is measured as standard deviations from the mean of t1; extreme values are defined as exceeding two standard deviations of the baseline mean.

(10)

the number of sub-units of time in t2 with extreme patterns of a climate parameter, i.e. exceeding a pre-determined number of standard deviations departing from the mean of t1.

(10)

the change in inter- or intra-annual variability for a climate parameter between t1 and t2. (27, 32, 36) the inter-annual variation in maximum (minimum) values for a climate parameter,

calculated as the difference in the 95th (5th) quantiles of maximum (minimum) values between t1 and t2.

(17)

Timing Changes in climate seasonality: difference in units of time in the date of climatic events. (33) shifts in seasonal timing of a climate parameter, measured by the ratio of the long term

trend for that parameter to the seasonal rate of change in the same parameter. (13)

Changes in regional climate Availability Change in area of analogous climates: the change over time in area experiencing climates that differ less than set thresholds. (8, 21, 34) the change over time in area experiencing climates that belong to the same climate domain

in k-variate histograms of t1 climate. (35, 92)

the change over time in area experiencing climates with the same cluster membership in hierarchical clustering of t1 climates or multivariate geographic clustering of climates through time.

(31, 119, 152, 153)

the change over time in area experiencing climates that belong to the same t1 climate class defined according to classification rules (e.g. Köppen-Geiger climate classification).

(118, 149, 154–159)

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Dimension Metric Ref. Changes in local climate

Novel climates / disappearing climates / stable climates: the minimum of the Euclidean distances between a given t2 climate value and all t1 climate

values in a region; minimum distances above pre-defined thresholds represent novel climates. Conversely, disappearing climates are based on the distances between a given t1 climate value and all t2 climate values in a region.

(23, 76, 92, 146, 160)

similarity between a given t1 climate and all t2 climates, by using a box-like analysis to assess whether t2 climates are within the t1 climatic range, either for each predictor at a time or considering interactions among predictors.

(161–163)

calibrating a bioclimatic model on the entire study region (considering presences throughout) and projecting it to the future to find novel climates (projected absences).

(164)

calculating the proportional distance of each grid cell from the t1 climatic range with respect to each predictor.

(165)

areas of overlap and non-overlap between t1 and t2 climate profiles, where climate profiles are defined through Principal Components Analysis of climate parameters across the region; t2 climates inside (outside) the t1 climate profile are persisting (novel) climates, and t1 climates outside the t2 climate profile are disappearing climates.

(11, 28–30)

t2 climates that have no t1 analogue (or t1 climates that have no t2 analogue, in the case of disappearing climates), with analogous climates defined in one of the several ways described above for 'Availability' metrics.

(8, 24, 29, 31, 34, 35, 92, 119, 152, 157)

Position Distance between analogous climates: the change over time in the average, minimum, or a given percentile of the geographical

distances between a given climate and all climates that differ less than set thresholds. (34)

the geographical distance between a given t1 climate and the t2 climate with the highest similarity (minimum Euclidean distance) to t1 climate.

(92, 146)

Direction to analogous climates: the bearing between a given t1 climate and the t2 climate with the highest similarity

(minimum Euclidean distance) to t1 climate. (92, 146)

the average direction between a given climate in t1 and all locations that in t2 have climates that differ less than set thresholds from that climate.

(34)

Velocity of climate change: the ratio of the rate of temporal change in a given climate parameter to the spatial gradient

for the same parameter (km/year). (9, 19, 92, 146)

the ratio of the distance between analogous climates in t1 and t2 to the length of the time interval.

(20)

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Table S2 | Pair-wise spatial correlation between climate change metrics. The correlation statistics for each pair of metrics is given above the diagonal line; the p-value and degrees of freedom, corrected to take into account spatial auto-correlation, are given below the diagonal line.

anomalies extremes area Novel distance velocity

anomalies

0.31 0.22 0.08 -0.03 -0.13

extremes P=6.68E-30, dof=1296

0.31 0.21 0.08 -0.15

area P=5.67E-18, dof=1497

P=1.88E-04, dof=134

0.28 0.11 -0.03

novel P=1.41E-34, dof=26046

P=6.92E-27, dof=2634

P=7.55E-29, dof=1485

0.10 -0.06

distance P=2.41E-03, dof=13126

P=4.08E-03, dof=1404

P=1.56E-05, dof=1595

P=7.79E-30, dof=11966

0.07

velocity P=2.50E-12, dof=2742

P=1.15E-02. dof=273

P=6.09E-01, dof=373

P=3.66E-04, dof=3801

P=4.67E-05, dof=3118

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Supplementary Figures

Figure S1 | Projected global climate change according to six metrics for the alternative multi-model ensemble (GCM2). The maps show projections of change in mean annual temperature and total annual precipitation between the baseline and the end-of-century alternative multi-model ensemble GCM2 for the A1B emissions scenario. The classes were defined using quantiles and reflect a gradient from small changes (light brown shades) to large changes (dark brown shades), or from positive (blue shades) to negative (brown shades) changes. Local anomalies and novel climates values were logaritmized for visualization.

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Figure S2 | Comparison of climate change metrics for two multi-model climate ensembles. Boxplots are shown for six climate change metrics computed using two alternative multi-model climate ensembles (GCM1 and GCM2). Outliers are excluded.

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Figure S3 | Quantile distribution of climate change metrics. For each metric, the maps show the distribution of the three classes of change (below the 25th percentile, between the 25th and 75th percentiles, and above the 75th percentile of the global distribution of values of each metric), and the barplots the proportion of area occupied by the three classes within each broad Köppen-Geiger climate region. The results shown are for the main multi-model ensemble (GCM1).

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Figure S4 | Köppen-Geiger climatic classification for the baseline period. Each location is assigned to one of the Köppen-Geiger climate classes.

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Figure S5 | Comparison of the exposure of the world’s climatic regions to different climate change components for two multi-model climate ensembles. For each Köppen-Geiger broad climate class depicted in the map, the star plots show the percentage of area exposed to values above the 75th percentile of the distribution of values for six climate change metrics. The star plots for the main (GCM1) and alternative (GCM2) multi-model ensembles are superimposed.

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Figure S6 | Spatial overlap among climate change metrics for the alternative multi-model ensemble (GCM2). The three metrics used (standardized local anomalies, change in area of neighboring baseline-analogous climates and climate change velocity) capture potential threats and opportunities associated with three main dimensions of climate changes.

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Figure S7 | Comparison of the spatial overlap among climate change metrics using different combinations of metrics. The maps show the overlap among metrics capturing three components of change in local climate, regional availability of climates, and regional position of climates for GCM1. For comparison with the combination of metrics used in the text, the climate change velocity is replaced with the change in distance to neighboring analogous climates (A), and the standardized local anomalies are replaced with the change in probability of local climate extremes (B).

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