SUPPLEMENTARY Brief Communication INFORMATION DOI: 10.1038/s41559-017-0317-1 In the format provided by the authors and unedited. Reassessing the conservation status of the giant panda using remote sensing Weihua Xu 1, Andrés Viña 2,3, Lingqiao Kong 1,4, Stuart L. Pimm 5 *, Jingjing Zhang 1,4, Wu Yang 6, Yi Xiao 1, Lu Zhang 1, Xiaodong Chen 3, Jianguo Liu 2 and Zhiyun Ouyang 1 * 1 State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, 100085 Beijing, China. 2 Center for Systems Integration and Sustainability, Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI 48823-5243, USA. 3 Department of Geography, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA. 4 University of the Chinese Academy of Sciences, 100049 Beijing, China. 5 Nicholas School of the Environment, Duke University, Box 90328, Durham, NC 27708, USA. 6 College of Environmental and Resource Sciences, Zhejiang University, 310058 Hangzhou, China. *e-mail: stuartpimm@me.com; zyouyang@rcees.ac.cn Nature Ecology & Evolution www.nature.com/natecolevol 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.
Supplementary informaiton Weihua Xu 1, Andrés Viña 2,5, Lingqiao Kong 1, Stuart L. Pimm 3, Jingjing Zhang 1, Wu Yang 4, Yi Xiao 1, Lu Zhang 1, Xiaodong Chen 5, Jianguo Liu 2, Zhiyun Ouyang 1 1 State Key Laboratory of Urban and Regional Ecology, Research Center for Eco- Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China 2 Center for Systems Integration and Sustainability, Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI 48823-5243, USA 3 Nicholas School of the Environment, Box 90328, Duke University, Durham, NC, 27708, USA. 4 College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China 5 Department of Geography, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA 1. Remote sensing data acquisition and classification We used cloud-free Landsat MSS/TM images acquired in 1976, 1988, 2001 and 2013 by scientific database of the Chinese Academy of Sciences (http://www.csdb.cn/) and the China Remote Sensing Satellite Ground Station (Table 1) to map vegetation within the panda s range. Considering that the MSS and TM images have different spatial resolutions, we re-sampled the TM and MSS imagery to 90 m, thus matching the spatial resolution of the DEM dataset used for panda habitat evaluation. Supplementary table 1. information of satellite images used in the study. Path/row Period 1976 1988 2001 2013 127/36-1990.4.7 2001.6.24-127/37-1987.6.2 2000.5.20 2013.9.29 128/36-1988.9.15 2001.5.22 2014.5.18 128/37-1988.8.28 2001.5.22 2015.7.24 129/37-1988.5.1 2001.5.13 2015.5.12 129/38-1986.7.31 2001.5.13 2011.5.17 129/40-1988.6.2 2001.6.14 2011.5.17
129/41-1988.6.2 2001.6.14 2013.5.22 130/37-1987.7.25 2002.7.10 2014.6.1 130/38-1987.7.25 2002.7.10 2014.6.1 130/39-1987.7.25 2001.6.13 2013.8.17 130/40-1989.5.11 2001.6.13 2015.7.6 131/36-1990.7.8 2001.7.22 2013.10.11 131/39 - - - 2013.10.11 136/37 1976.4.15 - - - 137/36 1978.8.19 - - - 137/37 1978.8.19 - - - 138/37 1976.6.10 - - - 139/37 1975.6.17 - - - 139/38 1978.8.3 - - - 139/40 1975.11.26 - - - 139/41 1977.3.26 - - - 140/36 1977.3.9 - - - 140/37 1975.10.4 - - - 140/38 1975.10.4 - - - 140/39 1975.10.4 - - - 140/40 1976.3.14 - - - Image classification followed an object-oriented procedure using the software ecognition (http://www.ecognition.com). This procedure used a multiresolution segmentation method with a Nearest Neighbour classification strategy 1. Through this procedure, we classified the remote sensing data into forest and non-forest. Groundtruthing for calibration and validation of the 2013 imagery used high spatial resolution imagery accessible through Google Earth. The 2001 imagery was classified first and the segmentation coefficients were used in the other years, thus the patch sizes obtained in the four years analyzed are comparable. Ground-truthing for the calibration and validation of the 2001 imagery was obtained from the third national panda survey in 2001. Based on a validation dataset of 500 points, overall classification accuracies were 89% and 94% for the 2013 and 2001 imagery, respectively. Considering that there was no calibration and validation data available for the 1988 and 1976 imagery, we used the classification of the 2001 imagery as the main reference since most of the forest areas did not drastically change, while the logged areas were relatively easy to identify on the satellite images (Supplementary figure1). Since the bands are different for TM
and MSS images, we used different parameters. Due to the lack of appropriate groundtruthing data, evaluation of the accuracy of the classifications obtained from the image datasets in 1988 and 1976 was impossible. However, given that the procedures used are the same, and logged forest were relatively easily identified, it is assumed that they exhibit similar accuracies as those of 2001 and 2013. MSS image of 1976 TM image of 1988 Supplementary figure1. Examples of MSS and TM images used in this study Forests can become giant panda habitat when the trees are at least 20 years old 2. Thus, areas of new forest within one period will not be regarded as panda habitat, but are considered habitat in the next period if the forest is still present. For instance, areas considered as new panda habitat between 1988 and 2001 were new forest areas between 1976 and 1988 that remain as forest in 2001. This procedure may be underestimating both the habitat in 1976 and habitat recovery between 1976 and 1988, due the inaccessibility of remote sensing data before 1976 across the entire panda geographic range. Nevertheless, we believe this underestimation does not
change our conclusions about panda habitat dynamics between 1976 and 2013, particularly because few conservation projects were carried out in China before 1976. In addition, even were the amount of habitat recovered in 1976-1988 to be similar to that between 1988 and 2001 (an unlikely situation), the total habitat trend would not be drastically modified. 2. Major data sources and statistics analysis of panda habitat change The wetness index was calculated based on the DEM data to indicate the soil moisture 3, mean elevation was also calculated from DEM data 4. We obtained population data from the national population censuses of the National Bureau of Statistics of the People s Republic of China. If the years of national census data did not match well with the years of panda habitat evaluation, we selected the closest year. The forestry administration in Sichuan, Shannxi, and Gansu provinces provided the nature reserve boundaries. We calculated the proportion of nature reserves area to total habitat distribution from them. Road density was calculated based on road maps in published atlases 5-8. Using these variables, we developed multiple general linear regression models, using panda habitat loss and habitat gain as the dependent variable. Since panda habitat showed a declining trend from 1976 to 2001, and an improving one from 2001 to 2013, we analysed the impact of these factors in declining panda habitat from 1976 to 2001, and in improving panda habitat from 2001 to 2013. Tables 2 and 3 show that nature reserves reduced habitat loss between 1976 and 2001 and promoted habitat restoration from 2001 to 2013 (p<0.05). The habitats and their changes in previous periods also affect the change in later periods. The habitat area in 1976 was positively and significantly related to the habitat loss between 1976 and 2001 (p<0.001). Habitat gain from 1988 to 2001 and habitat loss from 1974 to 1988 were positively and significantly related to the habitat restoration between 2001 and 2013 (p<0.001).
Supplementary table 2. Standardized coefficients of multiple linear regressions for factors associated with giant panda habitat loss between 1976 and 2001 Category Independent variable Habitat loss Policy Proportion of nature reserves area to total habitat distribution area in 1976 -.123 * Change in proportion of nature reserves area to total habitat distribution area from 1976 to 2001 0.085 Biophysical Slope (degree) -.133 Wetness index -.167 * Mean elevation (m) -.009 Distance to river (m) 0.081 Habitat area in 1976 (km 2 ) 0.854 *** Socioecono mic Road density in 1976 (km km -2 ) 0.042 Change in road density from 1976 to 2001 (km km -2 ) 0.118 Total population in 1982 (thousand individuals) -.0388 Change in total population from 1982 to 2000 (thousand individuals) 0.020 R-squared - 0.711 N - 55 Notes: Unit of analysis is the county. The dependent variable is the change in the proportion of habitat loss to total habitat distribution. Change in each variable was calculated using the amount in 2000 subtracting the amount in 1976 correspondingly. VIFs were tested to be * p < 0.05; *** p < 0.001.
Supplementary table 3. Standardized coefficients of multiple linear regressions for factors associated with giant panda habitat gain between 2001 and 2013 Category Independent variable Habitat gain Policy Proportion of nature reserves area to total habitat distribution area in 1988 -.020 Change in proportion of nature reserves area to total habitat distribution area from 1988 to 2001 0.148 * Biophysica l Wetness index -.095 Mean elevation (m) -.086 Habitat gain between 1988 and 2001 0.578 *** Habitat loss between 1974 and 1988.547 *** Socioecon omic Road density in 2001 (km km -2 ) 0.034 Change in road density from 2001 to 2013 (km km -2 ) -.031 Total population in 2001 (thousand individuals) 0.080 Change in total population from 1982 to 2000 (thousand individuals) 0.017 R-squared - 0.806 N - 55 Notes: Unit of analysis is the county. The dependent variable is the change in proportion of habitat gain to the total habitat distribution. Change in each variable was calculated using the amount in 2013 subtracting the amount in 2001 correspondingly. VIFs were tested to be * p < 0.05; *** p < 0.001.
References: 1 Baatz, M. & Schäpe, A. Multiresolution segmentation: an optimization approach for high quality multi-scale image segmentation. Angewandte Geographische Informationsverarbeitung XII, 12-23 (2000). 2 Bearer, S. et al. Effects of fuelwood collection and timber harvesting on giant panda habitat use. Biol. Conserv. 141, 385-393 (2008). 3 Buchanan, B. et al. Evaluating topographic wetness indices across central New York agricultural landscapes. Hydrology and Earth System Sciences 18, 3279 (2014). 4 Jarvis, A., et al. Hole-filled SRTM for the globe Version 4. available from the CGIAR-CSI SRTM 90m Database (http://srtm. csi. cgiar. org) (2008). 5 Highway Administration of Ministry of Transportation. China Highway Atlas. (China Communications Press, 2012). 6 Editorial Board of China Transportation Mileage Atlas. China Transportation Mileage Atlas. (China Communications Press, 2000). 7 Department of Transportation of Sichuan Province. Traffic Atlas of Sichuan Province. (Department of Transportation of Sichuan Province, 1979). 8 Editorial Board of China Cartographic Publishing House. Traffic Atlas of China's Provincial Highway. (China Cartographic Publishing House, 1989).