Supporting Information for: Effects of payments for ecosystem services on wildlife habitat recovery

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1 Supporting Information for: Effects of payments for ecosystem services on wildlife habitat recovery Appendix S1. Spatiotemporal dynamics of panda habitat To estimate panda habitat suitability across the reserve in 2001 (after the full implementation of the NFCP) and 2007 (before the devastating Wenchuan Earthquake in 2008), we used a habitat model which quantifies the habitat suitability based on information about the most important landscape determinants of panda habitat (i.e., forest cover and bamboo distribution) captured by remotely sensed vegetation phenology (Tuanmu et al. 2011). In a previous study, we found that seasonal variability in vegetation portrayed by a time series of a spectral vegetation index derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) imagery is useful for obtaining and separating understory bamboos from overstory tree canopies (Tuanmu et al. 2010). A habitat model was then trained with panda feces locations obtained during a field survey in 2001 and phenology metrics derived from MODIS images. By contrasting the remotely sensed phenology at the locations which are suitable for giant pandas (e.g., the locations where panda feces were found) with that at background locations (i.e., potentially forested areas), the habitat model estimated the habitat suitability in 2001 for every 250-by-250m pixel across the reserve. The model was then applied to the phenology metrics to estimate 2007 habitat suitability and validated with panda feces locations collected in The model exhibited high accuracy at estimating panda habitat suitability in the reserve, as the area under the receiver operating characteristic curve (AUC) was and for 2001 and 2007, respectively (Tuanmu et al. 2011). Details on the data and procedures for modeling and validation are reported in Tuanmu et al. (2011). To investigate panda habitat dynamics, we calculated the change in the habitat suitability index (HSI, ranging from 0 to 1 with higher values indicating higher suitability) obtained from the habitat model for the two years (i.e., 2007 value minus 2001 value) for every pixel across the study area. We also estimated the areal change in suitable habitat in the reserve by applying a threshold to convert the continuous HSI scale

2 into a binary outcome (i.e., habitat or non-habitat; Fig. S1). We used a threshold corresponding to a 10% omission error. While commission errors are, in general, negatively related to omission errors, we did not explicitly consider commission errors because only confirmed presence data (i.e., locations of panda feces) were available for model evaluation. To assess the effect of choosing different thresholds, we also calculated habitat areas using thresholds corresponding to 5 and 15% omission errors. Choosing different thresholds affected the areal magnitude but not the trend in panda habitat change, since the habitat area increased ca. 2.4% and 5.9% with thresholds corresponding to 5% and 15% omission errors, respectively. Therefore, the threshold selection did not affect the increase trend observed. It also did not affect our analysis of NFCP implementation on the spatiotemporal dynamics of panda habitat because the continuous HSI values were used. Literature Cited Tuanmu, M.-N., A. Viña, S. Bearer, W. Xu, Z. Ouyang, H. Zhang, and J. Liu Mapping understory vegetation using phenological characteristics derived from remotely sensed data. Remote Sensing of Environment 114: Tuanmu, M.-N., A. Viña, G. J. Roloff, W. Liu, Z. Ouyang, H. Zhang, and J. Liu Temporal transferability of wildlife habitat models: implications for habitat monitoring. Journal of Biogeography 38:

3 Figure S1. Spatiotemporal dynamics of the extent of giant panda habitat between 2001 and 2007 in Wolong Nature Reserve, China. Spatial extents of panda habitat in 2001 (A) and 2007 (B) were mapped as areas with the value of habitat suitability index above 0.318, a threshold corresponding to 10% omission error, based on a satellite-based panda habitat model. Spatial patterns of the change in habitat extents are shown in (C).

4 Appendix S2. Effects of NFCP implementation Using multiple linear regression, we related HSI change with the variables listed in Table 1 to assess the partial effect of different NFCP monitoring types (i.e., household monitoring vs. government monitoring) on panda habitat change (OLS in Table S1). All independent variables, except dummy variables (i.e., monitoring type and payment level) were standardized prior to model generation. A score test for non-constant variance (χ 2 = 1.29, df = 1, p = 0.26) and an examination of variance inflation factors (< 3 for all independent variables) indicated no heteroscedasticity or multicollinearity problems. A correlation analysis also showed that Pearson s correlation coefficients between almost all pairs of the continuous independent variables were lower than 0.4 (Fig. S2). The significant Moran s I for model residuals (Table S1), however, indicated strong spatial autocorrelation. We, therefore, built both spatial autoregressive lag (SAR) and error models (SEM) to account for it. Both models allow the value of the response variable (i.e., HSI values) at a given location to be dependent on the values at nearby locations. While the SAR model assumes that the dependency exists at the level of the dependent variable and includes the spatially weighted values of the response variable at nearby locations in the regression, the SEM model assumes that the dependency exists in the model residuals and includes a spatially weighted error term in the regression (Dormann et al. 2007). We defined a spatial weights matrix, which determines the extent of the neighborhood for a location and the dependency of the value at that location on the pixels within the neighborhood, by considering a pixel as a neighbor of another pixel if the Euclidean distance between their centers is shorter than the range (2,550m) in the variogram of the residuals from the OLS model. The significant autoregressive term, the non-significant Moran s I for the residuals, and the lowest Akaike Information Criterion (AIC) value of the SEM model (Tables 2 and S1) indicate that the SEM model was the best for controlling the spatial autocorrelation in our dataset to improve models ability to explain the spatial pattern of panda habitat change. With the SEM model, we further examined the effect of monitoring types under different payment levels by correlating HSI changes to three different implementation methods (government monitoring, household monitoring with low payments, and household monitoring with high payments; SEM 2 in Table 2). We

5 randomly selected 1,000 pixels (ca. 5%) from the study area below the tree line for generating the regression models because the area above the tree line does not constitute giant panda habitat (Schaller et al. 1985). With the bootstrapping approach, we repeated the pixel selection and model generation 100 times for each model to estimate the mean and 95% confidence interval of the partial effect for each independent variable. Literature Cited Dormann, C. F., J. M. McPherson, M. B. Araujo, R. Bivand, J. Bolliger, G. Carl, R. G. Davies, A. Hirzel, W. Jetz, W. D. Kissling, I. Kuhn, R. Ohlemuller, P. R. Peres- Neto, B. Reineking, B. Schroder, F. M. Schurr, and R. Wilson Methods to account for spatial autocorrelation in the analysis of species distributional data: a review. Ecography 30: Schaller, G. B., J. Hu, W. Pan, and J. Zhu The Giant Pandas of Wolong. University of Chicago Press, Chicago, Illinois.

6 Table S1. Summary of ordinary least squares (OLS) and spatial simultaneous autoregressive lag (SAR) models that relate the changes in panda habitat suitability index (HSI) with a set of independent variables. Mean standardized coefficient (95% CI) a Variable OLS SAR Intercept (0.005; 0.027) * (-0.001; 0.015) HSI_ (-0.083; ) * (-0.077; ) * FC_ (0.009; 0.024) * (0.010; 0.023) * Elevation (-0.060; ) * (-0.051; ) * Roughness (-0.012; 0.005) (-0.011; 0.004) Aspect_north (-0.001; 0.014) (0.001; 0.015) * Aspect_east (-0.002; 0.013) (-0.003; 0.011) CTI (-0.014; 0.001) (-0.013; 0.002) Dist2Household (-0.018; 0.002) (-0.010; 0.004) Dist2Road (-0.014; 0.002) (-0.013; ) * Monitoring type household vs. government (0.007; 0.044) * (0.009; 0.040) * monitoring Auto-regressive term (0.276; 0.469) * Moran s I of residuals b (0.110; 0.187) * (0.034; 0.064) * Akaike information criterion (-1619; -1411) (-1661; -1459) a Values obtained from 100 replicates of each model with the bootstrapping approach. Asterisks indicate the mean values that are significantly different from 0 (p < 0.05). The response variable and dummy variables (i.e., monitoring type and payment level) were not standardized. b Tested against its expected value of , indicating no significant spatial autocorrelation problem.

7 Figure S2. Pearson s correlation coefficients between each pair of continuous independent variables mentioned in Table 1 in the Main Text.

8 Appendix S3. Influences of assessment assumptions An assumption of our assessment of NFCP effects on panda habitat is that panda habitat suitability would have remained unchanged from 2001 to 2007 if NFCP had not been implemented in the study area. To evaluate the potential influence of this assumption on the results, we analyzed the change in forest cover from 1994 to 2001 (i.e., before the implementation of NFCP) within the reserve as a measure of before-policy panda habitat change (i.e., a baseline). Forest/non-forest maps in 1994 and 2001 were generated from 30-m Landsat TM imagery acquired on June 26, 1994 and June 13, 2001, respectively, using the ISODATA unsupervised classification algorithm. The overall accuracy of the two maps was 79.2 and 78.2%, respectively. Based on the 30-m forest/non-forest maps, the forest cover in the two years and its change were then calculated for each 250-by-250m pixel across the study area. Detailed procedures of the classification and validation were reported in Viña et al. (2011). Overall, the reserve experienced forest cover loss from 1994 to 2001 with a change of -6.97%. Because forest cover is an important component of the panda habitat, forest cover loss is a good indicator of panda habitat loss and degradation. The negative trend of before-policy forest cover and panda habitat, which is consistent with previous studies in the reserve (Liu et al. 2001; Viña et al. 2007), implies that panda habitat suitability would have likely reduce from 2001 to 2007 if NFCP had not been implemented. Therefore, by assuming no habitat change from 2001 to 2007 without NFCP implementation, the magnitude of the after-policy habitat improvement indicated by our assessment tends to be an underestimation of the actual NFCP effects. For analyzing the influence of different NFCP implementations, an additional assumption is needed: the changes in panda habitat suitability without NFCP are similar across the study area.. To evaluate the validity of this assumption and the potential influence of violation of this assumption on our assessment, we compared the beforepolicy forest cover change (see above) within the government monitored parcels vs. the household monitored parcels, and within the forest parcels monitored under the high payment level vs. within the parcels under the low payment level. Results showed that government monitored parcels experienced greater beforepolicy forest cover loss than household monitored parcels did (forest cover change: -

9 0.068 vs ; Wilcoxon rank sum test: W = , p-value <0.001). This implies that our assessment likely underestimated NFCP effects on panda habitat within government monitored parcels relative to the effects within household monitored areas. However, while the before-policy forest loss within government monitored parcels was about 1.5 times greater than that within household monitored parcels (forest cover change: vs ), the after-policy habitat improvement within government monitored parcels was 2.8 times (HSI change: vs ) lower than the habitat improvement within household monitored parcels. When confounding effects of the other independent variables were controlled (i.e., at their mean values) using SEMs, the beforepolicy forest loss within government monitored parcels was 1.6 times greater than that within household monitored parcels ( vs ; Table S2) and after-policy habitat improvement within government monitored parcels was 2.4 times lower than the habitat improvement within household monitored parcels (0.016 vs ; Table 2). Therefore, even if the potential underestimation of NFCP effects within government monitored parcels is accounted for, it is very likely that household monitoring still resulted in greater panda habitat improvement than the government monitoring did. The comparison between parcels under different payment levels showed that before-policy forest cover loss was greater within the high payment parcels than within the low payment parcels (forest cover change: vs ; Wilcoxon rank sum test: W = , p-value <0.001). This implies that our assessment tends to have a larger underestimation of NFCP effects under the high payment levels than under the low payment level. This suggests that the actual positive influence of the high payment level on NFCP effects is likely even greater than what our assessment on panda habitat indicated. Literature Cited Liu, J., M. Linderman, Z. Ouyang, L. An, J. Yang, and H. Zhang Ecological degradation in protected areas: the case of Wolong Nature Reserve for giant pandas. Science 292:

10 Viña, A., S. Bearer, X. Chen, G. He, M. Linderman, L. An, H. Zhang, Z. Ouyang, and J. Liu Temporal changes in giant panda habitat connectivity across boundaries of Wolong Nature Reserve, China. Ecological Applications 17: Viña, A., X. Chen, W. McConnell, W. Liu, W. Xu, Z. Ouyang, H. Zhang, and J. Liu Effects of natural disasters on conservation policies: the case of the 2008 Wenchuan Earthquake, China. Ambio 40:

11 Table S2. Summary of the spatial simultaneous autoregressive error model that relates the changes in forest cover between 1994 and 2001 (i.e., before NFCP implementation) with the same set of independent variables used in modeling panda habitat changes between 2001 and 2007 (Table 1). Variable Mean standardized coefficient (95% CI) a Intercept (-0.100; ) * HSI_ (-0.028; ) * FC_ (0.036; 0.057) * Elevation (-0.024; 0.006) Roughness (-0.007; 0.016) Aspect_north (-0.004; 0.016) Aspect_east (-0.044; ) * CTI (-0.011; 0.010) Dist2Household (-0.018; 0.020) Dist2Road (-0.020; 0.006) Monitoring type household vs. government monitoring (0.003; 0.054) * Auto-regressive term (-0.019; 0.282) a Values obtained from 100 replicates of each model with the bootstrapping approach. Asterisks indicate the mean values that are significantly different from 0 (p < 0.05). The response variable and dummy variables (i.e., monitoring type and payment level) were not standardized.