Ten bamboo species with more than 10 presence records were used for building species-climate envelope models (Table 1).

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1 Preliminary Report Bamboos and Climate Change in China Colin McClean & Jon Lovett Centre for Ecology, Law and Policy Environment Department University of York York YO10 5DD England September 2008 Introduction This report is the output from the modelling component of the pandas and climate change project. The work was initially carried out by a group of MRes students under the supervision of Colin McClean and Jon Lovett. The MRes dissertations contain much more background information on bamboo, pandas and climate change than is presented here. The analysis was subsequently repeated by Colin McClean when more data became available. The intention is that this report will provide the basis for a journal paper and grant applications to further the study. Methods Collected records of bamboo species for a total of 472 survey sites were used as the dataset on which to build the species-climate envelope models. These locations were represented within a 30 arc second by 30 arc second raster (approximately 1km x 1km). Sites were selected from observations that could be geo-referenced to an individual 30'' x 30'' cell from: Zoe Goodwin's 417 March 2008 field observations; 172 herbarium records collated by Zoe Goodwin in autumn/winter If a cell had a record it was assumed it had been surveyed for all species. In this way, for each species a presence/absence variable of species occurrence was created. The use of this assumption means that many of the absence records inferred from the herbarium records may be of questionable quality. Ten bamboo species with more than 10 presence records were used for building species-climate envelope models (Table 1). Table 1. Species modelled, their status as food species for pandas and the number of cells surveyed where the species was recorded as present. Species no. Species name Panda food status No. of presence cells 1 Arundinaria faberi Major 25 6 Chimonobambusa szechuanensis Major 13 7 Fargesia angustissima Major 22 8 Fargesia denudata Major 51

2 9 Fargesia ferax Minor Fargesia nitida Major Fargesia robusta Major Fargesia rufa Minor Fargesia scabrida Minor Yushania brevipaniculata Major 16 Three climate variables were used to develop the species-climate envelope models; absolute minimum temperature (amintemp), total annual precipitation (annprec) and a moisture index (moist). All variables were derived from the Worldclim climate surface interpolations of monthly minimum temperature, maximum temperature, and precipitation. The resolution of these data was also 30 x 30. (Hijmans et al., 2005). Details of the calculated variables can be found in Termansen et al. (2006). Four species-climate envelope modelling approaches were applied in order to assess the robustness of the results. Classification trees were applied using the R (Maindonald and Braun, 2003) package, rpart. Cost-complexity pruning was used to select the number of splits. These were used to allow an initial visualisation of the data used to model the species. Generalized additive models (GAMs) using the R package, mgcv, were calculated using the default cross-validation methods for assessing the degree of smoothing to apply (Wood and Augustin, 2002). Logistic regression models were also developed in R using the GLM function. Quadratic terms were included for each of the variables when calculating these models. The final method used is a Bayes-based genetic algorithm, Yoga (York genetic algorithm) that searches for climate thresholds in the data that match the species distribution (Termansen et al., 2006). To assess how well the species data could be modelled using these methods, 70% of presence and 70% of absence records, randomly selected, for each species were used as training datasets, while the remaining 30% of presence and 30% of absence records were used as a test dataset. Area under the curve (AUC) of the receiver operating characteristics (ROC) curve was used as the basis of model assessment. A value of 1 means that the model has perfect predictive power, whereas a value of 0.5 suggests the model is performing no better than a random prediction. AUC scores were, therefore, calculated for models predictions based on the test data set and for the training dataset. Models that yield AUC scores that are fair (> 0.7) or better (Swets, 1988) for the test dataset were accepted as providing adequate models of climate suitability for bamboo species. 20 training/test splits were randomly generated from the data and mean AUC scores for the 20 splits are used here. Models deemed satisfactory using this process were applied to the full set of 472 species presenceabsence records to calculate final models for use in assessment of future climate suitability for species. ROC curves and AUC scores were also calculated for these final models. These models were then projected onto the climate space of all areas in China within 3 degrees of an observation used to build the models, where that observation has a neighbouring observation less than 3 degrees distance. In this way an arbitrary extent of analysis was set to avoid predicting climate suitability too far from observed data. This helps to avoid the development of comparisons between models that are based on analogue climate space in areas historically or evolutionarily separate from the range of panda/bamboo. For each species, an average of the three main model methods used was calculated (GAM, GLM and Yoga). The range of values produced by the three different modelling approaches for each grid cell was also calculated. This allows some assessment to be made of the uncertainty associated

3 with the average model grid cell values. A simple cluster analysis was performed to assess whether the predictions for species fell into any obvious groupings. Projections were made for current climate using the Worldclim data (Hijmans et al., 2005) and for downscaled Hadley CM3 global circulation model climate projections for 2020, 2050 and 2080 under the SRES scenarios A2 (High greenhouse gas emissions) and B2 (business as usual emissions) (also from Worldclim). Change in climate suitability for the bamboo species modelled was then assessed both for the entire study area and for protected areas (IUCN, 2008) within the study area. Results AUC scores The AUC scores indicate that all of the species can be modelled using the three climate variables by at least three methods (Table 2). A number of species models produce poor predictions for the test data when using the recursive partitioning classification tree. Table 2. AUC scores. Training and test scores are averages from random 20 splits of the data. Training data Test data Full data Species name GAM GLM Tree Yoga GAM GLM Tree Yoga GA M GLM Tree Yoga Arundinaria faberi Chimonobambusa szechuanensis Fargesia angustissima Fargesia denudata Fargesia ferax Fargesia nitida Fargesia robusta Fargesia rufa Fargesia scabrida Yushania brevipaniculata Species groupings

4 The results from the cluster analysis are summarised in the dendrogram represented in Figure 1. Figure 1. Dendrogram from cluster analysis showing groupings of species on the basis of average probability of occurrence in grid cells across three methods (GAM, GLM, YOGA). There appear to be few distinct groups of species based on their predicted distribution patterns using the average of the three methods. This appears to represent a general north to south progression of species from s10 (Fargesia nitida) on the left of the diagram to s15 on the right of the dendrogram (Yushania brevipaniculata). The average of the three predicted climate suitability distributions (GAM, GLM and Yoga) are mapped in Figures 2 to 10 in the order given by the dendrogram (right to left/bottom to top). The colours used in these figures attempt to represent the uncertainty associated with the average values presented. The average value is represented by the degree of redness, from white through yellow to red. However, the intensity and saturation of the images is associated with the range of values obtained for a pixel: if the range is narrow, the colour is sharp and bright; if the range is wide the colour is dulled and greyed. Change over time The average predicted percentage change in total climate suitability (the sum of probability of occurrence across an area) for the three methods under HADCM3 A2 projections are given for each species in Table 3. These were calculated over the total study area and also for protected areas only. Changes from the current climate period ( ) to 2020 and 2080 were calculated. The minimum and maximum predicted percentage change values from the three modelling approaches are also given to indicated the uncertainty around the average values. Table 3. Average predicted percentage change in total climate suitability for the three methods under HADCM3 A2 projections for each species for the total study area and for protected areas only. Changes from the current climate period ( ) to 2020 and 2080 are given. The minimum and maximum predicted percentage change values from the three modelling approaches indicate uncertainty around the average values.

5 Total Area Protected Species mean min max mean min max mean min max mean min max Yushania brevipaniculata Fargesia ferax Fargesia angustissima Fargesia denudata Chimonobambusa szechuanensis Arundinaria faberi Fargesia scabrida Fargesia nitida Fargesia rufa Fargesia robusta Four of the species appear to lose climate space under this scenario (Yushania brevipaniculata, Fargesia ferax, Fargesia angustissima and Fargesia denudata), while the picture is less clear for others. Two species, Fargesia nitidia and Fargesia rufa, appear to potentially increase climate space under this scenario. Maps of the average change in climate suitability by 2080 are given in Figures 12 to 22. Once again, the colours used in these figures attempt to represent the uncertainty associated with the average values presented. The average value is represented by the degree of red (decease in climate space) to blue (increase in climate space), through white (zero change). However, the intensity and saturation of the images is associated with the range of values obtained for a pixel: if the range is narrow, the colour is sharp and bright; if the range is wide the colour is dulled and greyed. Discussion This preliminary analysis demonstrates the potential for reasonable models to be built using a limited set of records. Both herbarium and field data are required, herbarium records are useful but they need to be supplemented by targeted fieldwork. In particular, absence records can to provide the model with information on where a species is not present. This is a major assumption if herbarium data alone are used. Different modelling techniques result in different predictions. Here we use an average of three different models to generate maps of the envelopes under current and future climate conditions, but the variation given in Table 3 illustrates the range of output. In addition, it is important to remember that the results are models based entirely on climate variables. Plant distribution is a function of many different factors, particularly bamboos which respond to disturbance. Additional variables can be built into the models, but they have to be spatially quantified. Moreover, we are only using one future climate scenario (Hadley CM3) in this analysis. Different scenarios will produce different results. The model outputs provide both good and bad news. The bad news is that some major panda food sources reduce their area under future climate scenarios, the good news is that other species increase their area. Further work is needed to clarify these findings and the implications for climate change on panda habitat. References Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones P.G. and Jarvis A. (2005) Very high resolution

6 interpolated climate surfaces for global land areas. International Journal of Climatology,. 25, Maindonald J. and Braun J. (2003) Data Analysis and Graphics Using. Cambridge University Press. IUCN (2008) World Database on Protected Areas Swets, K. (1988) Measuring the accuracy of diagnostic systems. Science. 240, Termansen M., McClean C.J. and Preston C.D. (2006) The use of genetic algorithms and Bayesian classification to model species distributions. Ecological Modelling, 192, Wood, S. and Augustin, N. (2002) GAMs with integrated model selection using penalized regression splines and applications to environmental modelling. Ecological Modelling, 157,

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8 Figure 2. Yushania brevipaniculata. Average probability of occurrence across three species-climate envelope modelling approaches. Black crosses represent survey sites. Blue squares represent observed species presence.

9 Figure 3. Chimonobambusa szechuanensis. Average probability of occurrence across three speciesclimate envelope modelling approaches. Black crosses represent survey sites. Blue squares represent observed species presence.

10 Figure 4. Arundinaria faberi. Average probability of occurrence across three species-climate envelope modelling approaches. Black crosses represent survey sites. Blue squares represent observed species presence.

11 Figure 5. Fargesia robusta. Average probability of occurrence across three species-climate envelope modelling approaches. Black crosses represent survey sites. Blue squares represent observed species presence.

12 Figure 6. Fargesia angustissima. Average probability of occurrence across three species-climate envelope modelling approaches. Black crosses represent survey sites. Blue squares represent observed species presence.

13 Figure 7. Fargesia ferax. Average probability of occurrence across three species-climate envelope modelling approaches. Black crosses represent survey sites. Blue squares represent observed species presence.

14 Figure 8. Fargesia rufa. Average probability of occurrence across three species-climate envelope modelling approaches. Black crosses represent survey sites. Blue squares represent observed species presence.

15 Figure 9. Fargesia scabrida. Average probability of occurrence across three species-climate envelope modelling approaches. Black crosses represent survey sites. Blue squares represent observed species presence.

16 Figure 10. Fargesia denudata. Average probability of occurrence across three species-climate envelope modelling approaches. Black crosses represent survey sites. Blue squares represent observed species presence.

17 Figure 11. Fargesia nitida. Average probability of occurrence across three species-climate envelope modelling approaches. Black crosses represent survey sites. Blue squares represent observed species presence.

18 Figure 12. Yushania brevipaniculata. Average proportional change in climate suitability by 2080 across three species-climate envelope modelling approaches. Black crosses represent survey sites. Blue squares represent observed species presence.

19 Figure 13. Chimonobambusa szechuanensis. Average proportional change in climate suitability by 2080 across three species-climate envelope modelling approaches. Black crosses represent survey sites. Blue squares represent observed species presence.

20 Figure 14. Arundinaria faberi. Average proportional change in climate suitability by 2080 across three species-climate envelope modelling approaches. Black crosses represent survey sites. Blue squares represent observed species presence.

21 Figure 15. Fargesia robusta. Average proportional change in climate suitability by 2080 across three species-climate envelope modelling approaches. Black crosses represent survey sites. Blue squares represent observed species presence.

22 Figure 16. Fargesia angustissima. Average proportional change in climate suitability by 2080 across three species-climate envelope modelling approaches. Black crosses represent survey sites. Blue squares represent observed species presence.

23 Figure 17. Fargesia ferax. Average proportional change in climate suitability by 2080 across three species-climate envelope modelling approaches. Black crosses represent survey sites. Blue squares represent observed species presence.

24 Figure 18. Fargesia rufa. Average proportional change in climate suitability by 2080 across three species-climate envelope modelling approaches. Black crosses represent survey sites. Blue squares represent observed species presence.

25 Figure 19. Fargesia scabrida. Average proportional change in climate suitability by 2080 across three species-climate envelope modelling approaches. Black crosses represent survey sites. Blue squares represent observed species presence.

26 Figure 20. Fargesia denudata. Average proportional change in climate suitability by 2080 across three species-climate envelope modelling approaches. Black crosses represent survey sites. Blue squares represent observed species presence.

27 Figure 21. Fargesia nitida. Average proportional change in climate suitability by 2080 across three species-climate envelope modelling approaches. Black crosses represent survey sites. Blue squares represent observed species presence.