尼泊爾小貓熊（Ailurus fulgens）的保育研究：小貓熊的分佈預測，棲地使用偏好與人類對小貓熊保育之態度

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(1)國立臺灣師範大學生命科學系中央研究院生物多樣性國際研究生學程. Department of Life Science, National Taiwan Normal University Taiwan International Graduate Program on Biodiversity, Academia Sinica. Doctoral Dissertation 尼泊爾小貓熊（Ailurus fulgens）的保育研究：小貓熊的分佈預測，棲地使用偏好與人類對小貓熊保育之態度 . Conservation of the red panda (Ailurus fulgens) in Nepal: predicting red panda distribution, habitat use and assessing people’s attitudes towards red panda conservation. 學生: 夏翰立. Student: Hari Prasad Sharma 指導教授: 李佩珍. Advisor: Dr. Pei-Jen Lee Shaner January 2018.

(2) Acknowledgments I would like to express my greatest appreciation and thanks to my advisor Dr. Pei-Jen Lee Shaner, for her encouragement and guidance throughout the study period. I always admire the way she handles herself as an academic professional and deeply respect her incredible knowledge on niche modelling and mammal ecology. I would like to thank my thesis advisory committee members, Dr. Jerrold L. Belant, Dr. Shou-Hsien Li, Dr. Teng-Chiu Lin and Dr. Chih-Ming Hung for serving as my committee members inspite of their busy schedule and providing constructive comments and suggestions to guide my research. I thank Dr. Ryuji Machida and Ya-Ying Lin for providing lab space and mentoring me on molecular biology, which although is not part of my thesis work, is going to serve me well in the future as I continue my career as a conservation biologist. I express my sincere thanks to Department of National Parks and Wildlife Conservation, and Department of Forests, Ministry of Forests and Soil Conservation, Government of Nepal for permitting this research. I thank to Dr. Mahendra Maharjan, late Ashok Bahadur Bam, Ravi Sharma, Bishnu Achhami, Bishnu Bajhajain, Rudra Timilsina, Badri Panta, Khum Thapa and Gyazo Lama for their campaign in the field. I am shocked and saddened to hear the untimely demise of my colleague Ashok Bahadur Bam, who campaigned for me during data collection. I am grateful to the Tribhuvan University, Nepal, for granting me study leave. I sincerely thank Prof. Dr. Ranjana Gupta, Head of Central Department of Zoology, Tribhuvan University, for her support, encouragement and administrative help for granting and extending my study leave. I am grateful to Taiwan International Graduate Program, Academia Sinica, Taiwan for providing PhD scholarship and Rufford Small Grant Foundation for supporting my field expenses. Studying in a foreign country is difficult. I am very fortunate to have strong support from the academic as well as administrative staff at Academia Sinica in Taiwan. In particular, I would. I.

(3) like to thank Rainer Wunderlich and Jin Wong for their generosity in sharing technical expertise and academic ideas with me throughout my study period. Furthermore, I would like to thank all my colleagues at Biodiversity program, Academia Sinica because my scholarly exchanges with them had helped improve my research. I am thankful to sister Prabha Regmi and sister-in-law Zheng, Yen-Chang (Eric) for their kind cooperation, suggestion and support in every step of my study and stay in Taiwan. I am thankful to Kamal Adhikari, Hem Tamang and Raj Kumar Poudel for their support during my illness in Taiwan. I owed everything to my late maternal uncle, who provided me an academic environment that gave me the strength to pursue my dream of a career in science. I thank my grandmother Nanda Kala Sharma, father Jhalak Nath Sharma, my wife Jyoti Sapkota, son Pragalvha Sharma and brother Ram Prasad Sharma for their patience, love and good wishes that have kept me focused on my goals all these years.. II.

(4) Summary The red panda Ailurus fulgens is a small-sized (3.3–6.2 kg), largely nocturnal and arboreal species. It inhabits forests with dense understory bamboo. The red panda has a narrow geographic distribution, found only in the mountain regions of Myanmar, China, Bhutan, India and Nepal. Currently, the red panda is listed as Endangered on the IUCN Red List of Threatened Species, and is included in both CITES Appendix I and Schedule I of the Protected Wildlife Species in Nepal’s National Park and Wildlife Conservation Act of 1973. In this PhD thesis, I investigated three major issues related to red panda conservation in Nepal: people’s attitudes towards red panda conservation, red panda habitat selection at a local scale and red panda potential distribution across Nepal. The ultimate goal of this study is to provide useful scientific information for red panda conservation planning in Nepal and the Himalayas. The thesis is divided into four chapters. The first chapter focuses on peoples’ knowledge and attitudes towards red panda conservation. Specifically, I aim to provide empirical data on people’s attitudes towards red panda conservation through interviewing local people. The results indicated that more than 90 percent of the interviewees had positive attitudes towards red panda conservation, with people living away from protected areas showing more positive attitudes than those people living in or near protected areas. I found that the awareness of the legal protection of red pandas, most likely assimilated with the help of non-government organizations, could play a key role in determining people’s attitudes. Therefore, I recommend awareness programmes as effective tools to protect red pandas in Nepal. Regardless of the people’s attitudes, local villagers do need to rely on resource-collecting and livestock-grazing to sustain their livelihoods. It is important to understand red panda habitat use at local spatial scales in order to manage human activities in red panda habitats. Therefore, the. III.

(5) second chapter focuses on the habitat characteristics preferred by red pandas. Based on direct observations and faecal signs, I found that bamboos and trees are critical habitat requirements for red pandas during feeding and resting. Shrub and fallen logs are also important habitat characteristics likely because they provide red pandas easy access to bamboos and trees. Such information can be disseminated to the local people, who are collecting resources from red panda habitats. At a larger spatial scale, red panda habitats are prone to fragmentation due to their narrow niche. To ensure their long-term persistence, a landscape approach is required. Therefore, the third chapter focuses on building biologically informed niche models for predicting red panda distribution. Among the five models evaluated, the climate—forest model and climate—water— forest model generally performed better than the climate model, climate—water model and water—forest model. The climate—water—forest model generated unique prediction in the farwestern region that may contain suitable red panda habitats. Although red pandas are not known to occur in this region, it is likely due to the limited efforts in field surveys rather than habitat suitability. In the western region, the predicted suitable areas fall outside the protected areas. The current state of knowledge on red pandas stated that little information is available on population, and habitat suitability map of red pandas. I recommend the use of genetic tools for assessing population status such as demographic composition. In addition, I suggest to developing accurate map of red pandas’ current and available habitats to design management plan. It can be studied based on field studies of radio-telemetry on red pandas, which also help us to extending protected areas or utilizing community forests to connect suitable red panda habitats or provide more habitats, particularly in the western region. Other than ecological researches on red pandas, it is important to motivate people for behavioural changes, therefore I suggest Nepal Government. IV.

(6) and non-government organizations to focus their works on motivating people for behavioural changes by providing economic incentives, conducting more awareness programmes, and regular monitoring, updating and implementing legal protections practices across the countries. Key words: Ailurus fulgens, conservation, attitudes, Himalayas, habitat use, distribution. V.

(7) Table of contents. Pages. Acknowledgments. I. Summary. III. Table of contents. VI. Chapter 1: People’s attitudes towards conservation of the Endangered red panda Ailurus fulgens in Nepal 1.1 . Introduction. 1. 1.2 . Study area. 2. 1.3 . Methods. 4. 1.4 . Results. 5. 1.5 . Discussion. 9. Chapter 2: Habitat use of the Endangered red panda Ailurus fulgens in Nepal 2.1 . Introduction. 13. 2.2 . Study area. 14. 2.3 . Methods. 15. 2.4 . Results. 18. 2.5 . Discussion. 21. Chapter 3: Predicting geographic distribution of the Endangered red panda Ailurus fulgens in Nepal using biologically-informed variables 3.1 . Introduction. 23. 3.2 . Study area. 25. 3.3 . Methods. 26. 3.4 . Results. 29. VI.

(8) 3.5 . Discussion. 33. Chapter 4: Conservation of the red panda Ailurus fulgens: a review of the current state-ofknowledge 4.1 . Red panda ecology. 37. 4.2 . Red panda conservation. 38. 4.3 . Recommendations. 39. References. 43. Supplementary Tables. 59. Supplementary Figures. 65. VII.

(9) List of Tables Table 1.1. Demographic and socio-economic backgrounds of interview respondents from three protected areas. Table 1.2.. Logistic regression model of individual-level factors on conservation attitudes among interview respondents. Table 2.1. 19. Model averaged parameter estimates for red-panda habitat selection. The vegetation PC1 increases. Table 3.1. 18. Selected top 15 models for red panda habitat use. The vegetation PC1 increases with increasing shrub cover. Table 2.3. 9. Loadings of the principal components for vegetation covers. The vegetation covers include three original variables. Table 2.2. 6. 20. Model performance based on the area under the receiver operating characteristic curve (AUC), true skill statistics (TSS)_____________________________33. VIII.

(10) List of Figures. Fig. 1.1. Map of the study sites. N: the number of households at each site (CBS, 2011; for Ilam, Williams, 2004. Fig. 1.2. Frequency of responses among interview respondents from three protected areas (n = 88) and three non-protected areas. Fig. 1.3. 8. Frequency of responses among interview respondents from three protected areas (n = 88) and three non-protected areas. Fig. 2.1. 3. 11. The layout of transect lines and the locations of faecal signs of red pandas Ailurus fulgens in Rara National Park.. 16. Fig. 3.1. The study area and the red panda Ailurus fulgens distribution. 26. Fig. 3.2. Model comparisons on predicted potential distributions of red pandas Ailurus fulgens: (a) Climate vs climate—water model. Fig. 3.3. 31. Predicted potential distribution of red pandas Ailurus fulgens overlaying existing protected areas in Nepal. 32. IX.

(11) List of Supplementary Tables Table S1.1. Questionnaire used to survey 142 households from three protected and three nonprotected areas in Nepal. Table S3.1. The predictive variables considered in the niche models of the red panda Ailurus fulgens used in models. Table S3.2. 59. 60. Spearman pairwise correlation coefficients between predictive variables considered in the niche models. 63. X.

(12) List of Supplementary Figures Fig. S2.1. Plot design for the red panda Ailurus fulgens micro-habitat. 65. Fig. S3.1. Area under the receiver operating characteristic curve. 66. Fig. S3.2. Standard deviation predicted cell on 20 replications. 67. . XI.

(13) Chapter 1 People’s attitudes towards conservation of the Endangered red panda Ailurus fulgens in Nepal 1.1. Introduction. People’s attitudes towards conservation can be influenced by their socio-economic backgrounds (e.g. Bhattarai & Fischer, 2014; Mir et al., 2015), the perceived costs and benefits of conservation (e.g. Dewu & Røskaft, 2017), cultural and aesthetic values (e.g. Glatston & Gebauer, 2011; Gebresenbet et al., 2017), personal experiences and knowledge (e.g. Bhattarai & Fischer, 2014; Talukdar & Gupta, 2017), and management intervention such as economic incentives (e.g. Mishra et al., 2003; Baral & Heinen, 2007). Protected areas often have stronger management interventions than other areas, both in terms of restrictions on natural resource use and economic incentives (Baral & Heinen, 2007), and therefore people living in or near protected areas and extracting natural resources from protected areas may have different conservation attitudes than those living away from protected areas, and by comparing these attitudes we can tailor the design and implementation of conservation projects accordingly. The red panda Ailurus fulgens is categorized as Endangered on the IUCN Red List (Glatston et al., 2015). The species is distributed throughout the Himalaya region (Kandel et al., 2015), where it is vulnerable to anthropogenic threats such as livestock grazing (Sharma & Belant, 2010; Dorji et al., 2012; Sharma et al., 2014a). Red pandas do not cause harm to people or their property (e.g. livestock predation, crop losses, human injuries; Acharya et al., 2016), and cultural beliefs in some regions of Nepal can positively influence people’ s attitudes towards red pandas. In a 2008 survey in Rara National Park, I recorded this statement from Lal Bahadur Rokaya (former President of the Buffer Zone Management Committee, Rara National Park): ‘Only lucky people are able to see red pandas. If you see them alive you will receive good news very soon’.. 1.

(14) Furthermore, the ramma or shamans (Jhakri) of Mugar and Dalit communities in western Nepal previously used the skin or hair of red pandas in their dress during rituals to avoid being attacked by spirits (Glatston & Gebauer, 2011). Although, given their rarity, red pandas are no longer used in rituals (Dil Man Gharti Magar, Rukum district, pers. comm.), these examples suggest the red panda is perceived positively in local cultures. The aim of this study was to provide empirical data on people’ s attitudes towards red panda conservation in Nepal, to inform the development of effective and practical conservation action plans. I expected that (1) local people generally have positive attitudes towards red panda conservation, given their cultural beliefs and that red pandas do not come into conflict with people, and (2) people living in or near protected areas have more positive attitudes towards conservation than those in non-protected areas, given the participatory approaches to protected area management adopted by the Government of Nepal (e.g. sharing tourism revenue with local people living in or near protected areas; Budhathoki, 2004). In addition, I explored the associations between conservation attitudes and individual-level factors (i.e. demographic and socio-economic factors, factors related to individual experiences, and knowledge). To provide information for conservation planning, I surveyed people’ s opinions on the benefits of red panda presence, the tools used to protect red pandas, and the primary sources of information through which people gain knowledge about red pandas. 1.2. Study area. I chose six sites for this study (Fig. 1.1): Rara National Park (29°30′N, 82°03′E; Chhayanath-Rara Municipality, formerly Pina Village Development Committee (VDC)), Dhorpatan Hunting Reserve (28°27′N, 83°15′E; Dhorpatan Municipality, formerly Bowang VDC: Ward 1-2), Langtang National Park (28°10′N, 85°33′E; Goshainkunda Village Council, formerly Syafru. 2.

(15) VDC), Ilam (27°01′N, 88°00′E; Sandakpur Village Council, formerly Jamuna and Mabu (Dobate) VDC), Panchthar (27°06′N, 87°59′E; Phalelung Village Council, formerly Sidin VDC: Ward 1) and Jajarkot (29° 03′N, 82°20′E; Barekot Village Council, formerly Nayakwada VDC).. Fig. 1.1 Map of the study sites. N: the number of households at each site (CBS, 2011; for Ilam, Williams, 2004); n: the number of households interviewed for this study. Among these study areas, three are protected areas (Rara National Park, Dhorpatan Hunting Reserve and Langtang National Park), and three are non-protected areas (Ilam, Panchthar and Jajarkot). All six sites are rural and remote, and I selected these sites because red pandas are known to occur in these areas, and they are geographically representative of the spatial extent of suitable habitat for the red panda in Nepal (Kandel et al., 2015). The two National Parks are Category II protected areas, and Dhorpatan Hunting Reserve is a Category VI protected area 3.

(16) (IUCN, 2017). People are allowed to continue living in Langtang National Park and Dhorpatan Hunting Reserve if they had been living there prior to establishment but no new settlements are allowed. People are not permitted to live inside Rara National Park. According to Himalayan Park Regulations (HMG, 1979), people can extract natural resources from protected areas for personal use but not for commercial purposes. The three non-protected areas are 60–70 km distant from studied protected areas, and people there extract natural resources from locally managed community forests. 1.3. Methods. In 2015 between August and October, I interviewed 142 people, 88 from protected areas (Rara National Park: 32, Dhorpatan Hunting Reserve: 24, Langtang National Park: 32) and 54 from nonprotected areas (Ilam: 18, Panchthar: 11, Jajarkot: 25; Fig. 1.1), using a semi-structured questionnaire (Supplementary Table S1.1). All interviewees at Langtang National Park lived inside the Park, all interviewees at Rara National Park lived near the Park, and the interviewees at Dhorpatan Hunting Reserve were seasonal residents, living inside the Reserve during March– October. I interviewed one adult (>21 years old) from each household. Interviewees were encountered opportunistically while they were engaging in activities such as livestock grazing or natural resource collection in red panda habitat. I showed them photographs of red pandas to ensure that they recognized the species. Although young people’s attitudes are important to long-term conservation, I found that young people had no knowledge of red pandas (e.g. I spoke with an 8and a 10-year old child and neither of them could identify red pandas in the photographs). Therefore, I focused to interview with adults. I collected demographic and socio-economic data, including gender, age, education, occupation, family size, income, whether the interviewee owned. 4.

(17) livestock, the number of livestock owned, livestock diseases, veterinary services, and livestock grazing locations (which is verified by me as being either inside or outside red panda habitat). I also collected data on conservation attitudes, personal experiences, knowledge and opinions regarding red pandas. I used Fisher’ s exact test and a Kruskal–Wallis test to examine any differences between people living in or near protected and non-protected areas, for binary and numeric responses respectively. I then combined data from protected and non-protected areas to test the associations between conservation attitudes and 10 individual-level factors in a logistic regression. I performed all analyses in R 3.3.1 (R Development Core Team, 2016) using p = 0.05 as the significance level. 1.4. Results. The respondents from protected and non-protected areas had similar demographic and socioeconomic backgrounds (Table 1.1), with two exceptions. Firstly, a higher percentage of the respondents from non-protected areas had sufficient income from crops alone to support their livelihoods (Table 1.1), which suggests that people living in or near protected areas may rely more on extracting natural resources from nearby forests. Although cultivation of crops is permitted in two of the three protected area sites (Langtang National Park and Dhorpatan Hunting Reserve), crop production is low because of harsh weather and extensive damage to crops by wild boar Sus scrofa based on my personal observation. Secondly, a higher percentage of the respondents from non-protected areas reported livestock diseases (Table 1.1). As the majority of respondents from both protected and non-protected areas owned livestock, allowed their livestock to graze in red panda habitat, and did not seek veterinary services for diseased livestock (Table 1.1), the higher incidence of livestock disease reported in non-protected areas suggests a higher disease risk in. 5.

(18) non-protected areas and/or a higher level of knowledge and concern for livestock health among people living in those areas. Table 1.1 Demographic and socio-economic backgrounds of interview respondents from three protected areas (n = 88) and three non-protected areas (n =54) in Nepal (Fig. 1.1). Significant differences between protected and non-protected areas are in bold. Variable. Protected areas. Non-protected areas. Statistics. Gender. 63% males. 56% males. Fisher’s exact test, p = 0.48. Age. Median = 35. Median = 37. Kruskal-Wallis test, p = 0.82. Occupation. 67% agriculture only. 72% agriculture only Fisher’s exact test, p = 0.58. Education. 55% some education. Religion. 33% Buddhism, 53% 44% Buddhism, 54% Fisher’s exact test, p = 0.47 Hindu, 14% Christian Hindu, 2% Christian (Christians excluded due to small sample sizes). Family size. 5. formal 48% with education. 5. formal Fisher’s exact test, p = 0.49. Kruskal-Wallis test, p = 0.20. Overall income 77% sufficient livelihoods. for 83% sufficient for Fisher’s exact test, p = 0.52 livelihoods. Agriculture income. 33% sufficient livelihoods. for 59% sufficient for Fisher’s exact test, p = 0.003 livelihoods. Livestock ownership. 94% owning livestock. 99% livestock. owning Fisher’s exact test, p = 0.41. 6.

(19) Livestock sizes Median = 10. Median = 11. Kruskal-Wallis test, p = 0.53. Livestock grazing1. 100% in red panda 79% in red panda NA habitat habitat. Livestock diseases1. 69% noticed diseases. 91% noticed diseases Fisher’s exact test, p = 0.003. Veterinary services2. 32% sought services. 38% sought services Fisher’s exact test, p = 0.54. 1. Includes only respondents who owned livestock (83 and 53 for protected and non-protected areas,. respectively). 2Includes only respondents who reported livestock diseases during interviews (57 and 48 for protected and non-protected areas, respectively). Ninety percent of all respondents had positive attitudes towards red panda conservation, which supports my first expectation and the findings of previous studies in Nepal (e.g. Shrestha & Alavalapati, 2006; Baral & Heinen, 2007; Bhattarai & Fischer, 2014). However, people living in non-protected areas had more positive attitudes than those living in or near protected areas (Fig. 1.2a), contradicting my second expectation. Although red pandas occur in all six sites (34 and 35 respondents from protected and non-protected areas, respectively, had seen red pandas), proportionally more respondents from non-protected than protected areas had seen them (Fig. 1.2b) and were more knowledgeable about the conservation status of the red panda (Fig. 1.2c). The respondents from non-protected areas were marginally more aware of the legal protection of red pandas than those from protected areas (Fig. 1.2d). People living in or near protected areas gained this knowledge of the red panda’s legal status primarily from government officials and family members, whereas those living in non-protected areas gained knowledge from NGOs, media and family members (Fig. 1.3a). Of the individual-level factors, only awareness of the legal protection. 7.

(20) of red pandas was associated with conservation attitudes (Table 1.2). Ninety-seven percent of the respondents who knew about the legal protection had positive attitudes, compared to 73% of those who did not know about the legal protection.. Fig. 1.2 Frequency of responses among interview respondents from three protected areas (n = 88) and three non-protected areas (n = 54) in Nepal (Fig. 1.1) regarding (a) attitudes towards conservation of the red panda (Fisher’s exact test, two-tailed, p = 0.02), (b) personal experiences of red panda sightings (Fisher’s exact test, two-tailed, p = 0.003), (c) knowledge of the conservation status of the species (Fisher’s exact test, two-tailed, p = 0.007), and (d) awareness of the legal protection of the species (Fisher’s exact test, two-tailed, p = 0.06). 8.

(21) Table 1.2. Logistic regression model of individual-level factors on conservation attitudes among interview respondents (n = 142) from three protected and three non-protected areas in Nepal (Fig. 1.1). The significant effect is in bold.. Effect. Estimate. SE. z. p. Intercept. -0.21. 1.79. -0.12. 0.91. Gender. 1.55. 0.98. 1.59. 0.11. Age. 0.04. 0.04. 1.01. 0.31. Education. -0.75. 0.79. -0.95. 0.34. Family size. -0.14. 0.21. -0.66. 0.51. Livestock sizes. 0.05. 0.05. 0.89. 0.37. Overall income sufficiency. -0.17. 0.79. -0.22. 0.83. Agriculture income sufficiency. 0.29. 0.85. 0.34. 0.73. Red panda sighting. 1.03. 1.19. 0.87. 0.38. Awareness of conservation status. -0.05. 1.30. -0.04. 0.97. Awareness of legal protection. 2.03. 0.87. 2.34. 0.02. 1.5. Discussion. The lower level of dependency on natural resources, as well as more experience with and knowledge of red pandas, might have contributed to the more positive attitudes towards red panda 9.

(22) conservation among people living in non-protected areas. The Red Panda Network, which has been working in eastern Nepal since 2007, could also have played a role in fostering the more positive attitudes in non-protected areas (two of our three non-protected area sites are in eastern Nepal). Nevertheless, two red pandas were found dead from unknown causes in the community forest of Ilam on 27 November 2015 (Bhattarai, 2015), where the Red Panda Network is working, suggesting there may still be a disconnect between conservation attitudes and behaviours (Waylen et al., 2009). I suggest that awareness programmes designed specifically for school children because they can translate their knowledge to their parents (Damerell et al., 2013), and community groups could be effective tools for improving conservation attitudes towards red pandas in Nepal. One of the limitations of this study is that I focused only on adults, and therefore I do not have information on the conservation attitudes of the younger generation. The result indicates that family members, such as parents or elderly relatives who often accompany children when they go to the forests, are a primary source of information regarding red pandas (Fig. 1.3c), suggesting that the elderly in the family may have some influence on young people’ s attitudes towards red pandas. However, the two children with whom I spoke could not recognize red pandas in the photographs shown, and I recommend the inclusion of wildlife conservation materials in the curriculum for grade 5 students (9–10 years old). Such materials are currently included in the grade 10 curriculum (for students 15–16 years old), but children in rural Nepal often leave school before they reach this grade. For community groups I recommend the government establishes regular dialogue among wildlife managers, community leaders and local people about the ecology (e.g. the impacts of livestock grazing and livestock diseases) and conservation status of red pandas. Considering that more than half of all respondents chose ‘establishing awareness programmes’ as a tool to protect red pandas (Fig. 1.3b), such efforts are likely to be welcomed by local people.. 10.

(23) Fig. 1.3 Frequency of responses among interview respondents from three protected areas (n = 88) and three non-protected areas (n = 54) in Nepal (Fig. 1.1) regarding (a) benefits of red panda presence, (b) tools to improve the protection of red pandas, and (c) primary sources of information about the legal protection of the species. For (a) and (b) multiple answers were allowed.. 11.

(24) Only a small percentage of respondents chose ‘limiting resource collection/grazing’ as a tool to protect red pandas, and people did recognize that red pandas could bring benefits through tourism (Fig. 1.3a). It means that people are concerned about the conservation of the red panda. However, still they have limited knowledge on habitat resource requirements to red panda because they are collecting resources/livestock grazing in red panda habitat. Therefore, it is also crucial to identify the evidence based micro-habitat selection by red panda to disseminate it to local people to prevent their activities in the red panda habitat. Natural resource management plans that promote new farming practices (e.g. stall-feeding of livestock) as well as ecotourism could help red panda conservation through enhancing the livelihoods of local people (Scherl et al., 2004; NaughtonTreves et al., 2005). Alternative farming practices, such as stallfeeding, could reduce competition between livestock and red pandas (Sharma & Belant, 2010; Dorji et al., 2012; Sharma et al., 2014a) and disease risks from livestock or guard dogs (e.g. canine distemper; Bush et al., 1976; Deem et al., 2000; Loeffler et al., 2007). Although such farming practices also reduce the opportunities for local people to encounter red pandas and to develop their personal experiences with the species, ecotourism programmes could compensate for this. I recommend promoting ecotourism activities, such as wildlife observation, and training local people as tour guides or hotel hosts. A combination of alternative farming practices and ecotourism may be the best approach to improve red panda conservation in Nepal.. 12.

(25) Chapter 2 Habitat preferences of the Endangered red panda Ailurus fulgens in Nepal 2.1. Introduction. In Nepal, people are generally positive towards nature conservation (e.g. Sharma et al., 2017; also see Chapter 1), which presents great opportunities to protect the threatened wildlife. However, human attitudes need to be transformed into actions that actually benefit wildlife, particularly in rural areas where people still rely on extracting natural resources to sustain their livelihoods (Sharma et al., 2014a; Panthi et al., 2017; Sharma et al., 2017). Long-term survival of species depends on individuals’ daily resource requirements being satisfied at micro-habitat level (Johnson, 1980). Therefore, understanding habitat selection of wildlife species is a primary requisite for their effective management (Clark et al., 1993; Marzluff et al., 2004; Aarts et al., 2008). For cryptic species living in remote areas, it is often difficult to observe and quantify their habitat use behaviours (O'Shea & Bogan, 2003). As a result, physical signs such as faecal materials become very important to study a species’ habitat use and preference. The red panda Ailurus fulgens is a cryptic species living in remote areas at the heart of Himalayan Mountains (Roberts & Gittleman, 1984). Therefore, most of the field studies on red panda behaviours have relied on their faecal signs (Wei et al., 1999; Williams, 2006; Pradhan et al., 2001; Zaw et al., 2008; Sharma & Belant, 2009; Dorji et al., 2011, Sharma et al., 2014a; Kandel et al., 2015; Bista et al., 2017), with occasional radio-telemetry studies (Johnson et al., 1988; Reid et al., 1991b; Yonzon & Hunter, 1991). The red panda population in the wild is estimated to be <10000, and the species is currently listed as Endangered on the IUCN Red List (Glatston et al., 2015). It is commonly believed that habitat loss, landscape fragmentation, poaching and accidental killing, are the primary threats to red pandas (Yonzon & Hunter, 1991;. 13.

(26) Wei et al., 1999; Williams, 2006; Sharma & Belant, 2010; Sharma et al., 2014a; Panthi et al., 2017). The red panda is considered a habitat specialist (Glatston, 1994; Roberts & Gittleman, 1984), typically associated with forests with understory bamboos (Choudhury, 2001; Yonzon & Hunter, 1991; Pradhan et al., 2001; Sharma & Belant, 2009; Dorji et al., 2011). Several wildlife species that share similar habitat preferences with the red panda, such as the giant panda Ailuropoda melanoleuca (Schaller et al., 1985) and Asiatic black bear Ursus thibetanus (Reid et al., 1991a). However, very little is known about their interspecific competition for resources with other co-existing species. Furthermore, livestock that are grazing within red panda habitats may also compete for similar resources, creating additional pressure on red pandas (Yonzon et al., 1991; Sharma et al., 2014a). In Nepal, certain human activities, such as livestock-grazing, are still allowed in the protected areas according to Himalayan Park Regulations (HMG, 1979). Therefore, it is crucial to understand red panda habitat use at local spatial scale in order to better manage human activities and reduce human-wildlife conflicts. In this study, I aim to quantify habitat characteristics preferred by red pandas at microhabitat level. I focused on several characteristics that are likely to influence red panda habitat use, including aspect (north-facing has been suggested to be more supportive of red pandas; Yonzon et al., 1991), distance to water (proximity to water has been suggested to be a basic requirement for red pandas; Yonzon & Hunter, 1991), vegetation cover (tree canopy/shrub/herbaceous covers), and number of trees, bamboos, fallen logs and cut tree stumps. Furthermore, I also investigated their selection of substrate use (i.e., trees/fallen logs/ground). Such quantitative information could help improve the management of red panda habitats through preventing felling trees, fallen logs collection and reducing human and livestock disturbances. 2.2. Study area. 14.

(27) The study site was in Rara National Park, which comprises 106 km2 in the mid-western Nepal (Fig. 2.1; 29°30′N, 82°03′E) with elevations ranging from 2800 to 4090 m. The area is dominated by trees and shrubs of blue pine Pinus wallachiana, rhododendron Rhododendron arboreum, black juniper Juniperus indica, west Himalayan spruce Picea smithina, oak Quercus semecarpefolia, Himalayan cypress Cupressus torulosa, Indian horse-chestnut Aesculus indica, walnut Junglans regia and Himalayan poplar Populus ciliata. The fauna in the area include Himalayan black bear Ursus thibetanus, common leopard Panthera pardus, musk deer Moschus chrysogaster, goral Nemorhaedus goral, Himalayan tahr Hemitragus jemlahicus and wild dog Cuon alpinus. The park may support >50 individual red pandas (Jnawali et al., 2012). 2.3. Methods. I conducted the field study from May–July 2011–2012 at Rara National Park. I set up 25 transect lines and established 10 x 10 m plots along each transect line at every 100 m increase in elevation (a total of 106 plots). The transect length ranged from 600 m to 2500 m (1469 m ± 470 SD). The lowest plots along each transect were within 50 m of the park road. Each plot was sub-divided into 5 m x 5 m sub-plots, and each sub-plot was again sub-divided into 1 x 1 m sub-plots (four of the 1 x 1 m sub-plots were at the corners of a 10 x 10 m plot and the fifth sub-plot was at the centre of the 10 x 10 m plot) (see Fig. S2.1). I counted the numbers of trees (>2 m tall), fallen logs (circumference >30 cm, length >1.5 m) and cut tree stumps (>1 m height) within a 10 x 10 m plot. I determined tree canopy cover and shrub cover using Spherical Densitometer from the centre of each plot, whereas I visually estimated herbaceous cover based on the percentage of their occurrences. Finally, I counted the number of bamboos within one of the four 1 x 1 m sub-plots and the centre sub-plot. In addition, I recorded the aspect of each plot (North: 0–45° or 315–360°;. 15.

(28) East: 45–135°; South: 135–225°; West: 225–315°), and measured the distance from the plot centre to the nearest open water using a measuring tape.. Fig. 2.1 The layout of transect lines and the locations of faecal signs of red pandas Ailurus fulgens in Rara National Park. I recorded the presence or absence of red panda faeces for each plot. Red panda faeces are easily distinguished from other species’ faeces because they comprise large amount of bamboo fragments (Yonzon, 1989). Their faeces are deposited as pellet groups, with the number of pellets in each group ranging from 1 to many. Red pandas usually defecate at their feeding and resting sites (Yonzon, 1989; Reid et al., 1991b). In addition, I recorded the number of pellets and the number of pellet groups on different substrates (i.e. ground, fallen logs, trees) to explore microhabitat use related to two types of red panda activities (i.e. moving, resting/feeding). A higher 16.

(29) number of pellet groups with a lower number of pellets per pellet group (i.e. the number of pellets / the number of pellet groups) suggests a substrate used by red panda for moving, whereas a lower number of pellet groups with a higher number of pellets per pellet group a substrate for resting/feeding. I fitted the presence/absence data of red panda faeces to generalized linear models (binomial distribution; R Development Core Team, 2016) to evaluate their preferences for different habitat characteristics (i.e. aspect, distance to water, vegetation cover, and number of trees, bamboos, fallen logs and cut tree stumps). For vegetation cover, I first applied a principal component analysis to tree canopy cover, shrub cover and herbaceous cover, and then I used the first two PCs as two vegetation cover variables (the first two PCs explained 81% of total variance). The value of vegetation cover PC1 increases with increasing shrub cover whereas that of PC2 increases with increasing herbaceous cover (Table 2.1). I dredged all constructed 256 models (8 explanatory variables) and selected a subset of 15 candidate models based on the rule of delta AICc < 4 (AICc, Akaike Information Criterion adjusted for small samples; Burnham & Anderson, 2002). I estimated parameters using model averaging based on Akaike model weights across the 15 models (Table 2.2). I considered an explanatory variable to be statistically significant for red panda habitat use if its estimated 95% confidence intervals did not encompass 0. I fitted the model to the data on the number of pellets per pellet group to a general linear mixed model, with the substrate type as the fixed effect and sampling locations as the random effect. The number of pellets per pellet group was natural-log transformed. I fitted the model to the data on the number of pellet groups to a generalized linear mixed model (Poisson distribution), with the substrate type as the fixed effect and the sampling plot as the random effect. Post-hoc comparisons using Tukey tests were performed to explore differences between the three substrate types (i.e. ground/trees/fallen. 17.

(30) logs). For substrate preference analysis, I only used data from the sampling locations where faeces were found on all three types of substrates. This ensures that at any given location, red pandas had a chance to access to all types of substrates. 2.4. Results. The aspect was not included in any of the top 15 models. The number of models including each of the remaining 7 variables is: the number of bamboo individuals (15 models), followed by number of trees (14 models), vegetation cover PC1 (13 models), number of fallen logs (6 models), number of cut tree stumps (5 models), vegetation cover PC2 (5 models) and distance to water (4 models). The 95% confidence intervals for the number of bamboos and trees, as well as vegetation cover PC1 did not include zero (Table 2.3), suggesting that red pandas prefer shrubby habitats with more bamboos and trees.. Table 2.1 Loadings of the principal components for vegetation covers. The vegetation covers included three original variables, tree canopy cover, shrub cover and herbaceous cover. Variable. PC1. PC2. PC3. Tree canopy cover (%) -0.5566 -0.6051 -0.5693 Shrub cover (%). 0.7539. -0.0799 -0.6521. Herbaceous cover (%). -0.3491 0.7922. -0.5006. 18.

(31) Table 2.2 Selected top 15 models for red panda habitat use. The vegetation PC1 increases with increasing shrub cover whereas PC2 increases with increasing herbaceous cover. Model. df. LogLik. AICc. ΔAICc. Weight. No. bamboos + No. trees + Vegetation PC1. 4. -35.13. 78.66. 0. 0.2. No. bamboos + No. stumps + No. trees + Vegetation PC1. 5. -34.63. 79.87. 1.2. 0.11. No. bamboos + No. fallen logs + No. trees + Vegetation PC1. 5. -34.65. 79.89. 1.23. 0.11. No. bamboos + No. trees + Vegetation PC1 + Vegetation PC2. 5. -34.88. 80.36. 1.7. 0.09. No. bamboos + No. fallen logs + No. stumps + No. trees + Vegetation PC1. 6. -33.82. 80.48. 1.82. 0.08. No. bamboos + No. trees + Vegetation cover PC1 + Water distance. 5. -35.12. 80.84. 2.18. 0.07. No. bamboos + No. stumps + No. trees + Vegetation PC1 + Vegetation PC2. 6. -34.31. 81.46. 2.8. 0.05. No. bamboos + No. fallen logs + No. trees + Vegetation PC1 + Vegetation PC2. 6. -34.45. 81.74. 3.08. 0.04. No. bamboos + No. trees. 3. -37.76. 81.75. 3.09. 0.04. No. bamboos + No. fallen logs + No. trees + Vegetation PC1 + Water distance. 6. -34.6. 82.05. 3.39. 0.04. No. bamboos + No. stumps + No. trees + Vegetation PC1 + Water distance. 6. -34.63. 82.11. 3.44. 0.04. No. bamboos + No. fallen logs + No. stumps + No. trees + Vegetation PC1 + Vegetation 7. -33.57. 82.28. 3.61. 0.03. PC2 No. bamboos + No. fallen logs + No. trees. 4. -36.97. 82.34. 3.68. 0.03. No. bamboos + Vegetation PC1. 3. -38.17. 82.57. 3.91. 0.03. No. bamboos + No. trees + Vegetation PC1 + Vegetation PC2 + Water distance. 6. -34.87. 82.6. 3.93. 0.03.

(32) Table 2.3 Model averaged parameter estimates for red-panda habitat selection. The vegetation PC1 increases with increasing shrub cover whereas PC2 increases with increasing herbaceous cover. Significant variables are in bold. Variable. Estimate. Std.. Lower. Upper. Error. 95% CL. 95% CL. z. p. Intercept. -3.7853. 0.8730. -5.5161. -2.0544. 4.29. 0.00002. No. bamboos. 0.0374. 0.0090. 0.0195. 0.0553. 4.09. 0.00004. No. trees. 0.0495. 0.0212. 0.0074. 0.0916. 2.31. 0.02. Vegetation PC1. 0.6117. 0.2806. 0.0550. 1.1683. 2.15. 0.03. No. stumps. -0.3217. 0.3537. -1.0230. 0.3796. 0.90. 0.37. No. fallen logs. 0.1095. 0.0958. -0.0805. 0.2995. 1.13. 0.26. Vegetation PC2. 0.2410. 0.3300. -0.4137. 0.8957. 0.72. 0.47. Water distance. 0.0010. 0.0058. -0.0105. 0.0124. 0.17. 0.87. Based on faecal signs, the red pandas used ground, trees and fallen logs as substrates in different ways (number of pellet groups: χ2 = 30.0, p < 0.0001, df = 2; number of pellets per pellet group: χ2 = 16.7, p = 0.0002, df = 2; 66 substrate use data points from 22 sampling locations). Specifically, the red pandas had a larger pellet group size (i.e. a higher number of pellets per pellet group) on the trees than on fallen logs or ground (Tukey tests, trees vs. ground: p < 0.001; trees vs. fallen logs: p = 0.03; fallen logs vs. ground: p = 0.19). On the other hand, the red pandas had a lower number of pellet groups on the trees and fallen logs than on ground (Tukey tests, trees vs. ground: p < 0.001; trees vs. fallen logs: p = 0.90; fallen logs vs. ground: p < 0.001). Taken together,.

(33) the red pandas likely used trees primarily for resting and feeding, used ground primarily for travelling and used fallen logs for both purposes. 2.5. Discussion. I found that trees and bamboos are key characteristics of red panda habitats. A shrubby environment could be preferred by red pandas because such vegetation cover is likely to contains more trees and bamboos. Bamboo is the major food resource for red pandas (Yonzon & Hunter, 1991; Panthi et al., 2012; Sharma et al., 2014b) and trees and shrubs could provide red pandas easy access to bamboos (Johnson et al., 1988). On steep slopes, shrubs and fallen logs have also been found to be used by red pandas to climb for feeding where high-quality bamboo leaves existed (Wei et al., 2000; Wei & Zhang, 2011a). Faecal pellets on cut tree stumps and structural observations (HPS, per. obs.) suggesting cut tree stumps can be used by red pandas for eating bamboos and easy access to fallen logs. I have personally observed that red pandas bask in the sun on the upper tree branches (HPS, per. obs.), suggesting that trees may serve thermal regulation function in addition to providing feeding, resting or even denning sites (e.g. trees have been suggested to offer resting and denning sites with lower predation risks; Yonzon & Hunter, 1991; Glatston, 1994). Distance to water was not major influencing factor in my study, however water availability on !100 m distance also supports red panda occurrences (Sharma et al., 2014a), probably due to needing water after eating bamboos (Yonzon, 1989). The importance of trees to red pandas is also supported by the evidence of their substrate use. A larger pellet group size with fewer pellet groups on trees relative to other substrates indicates that red pandas defecate more at one spot when they are using trees. Assuming defecation occurs at a more or less constant rate while red pandas are active, we would expect that more time at a single spot on trees creates a larger pellet group size with fewer number of pellet groups (Johnson. 21.

(34) et al., 1988; Reid et al., 1991b). My findings are in agreement with Bista et al. (2017), which reported more faecal pellets on tree branches and fallen logs than other substrates. This study demonstrated that bamboos, trees, and shrubs are some of the most important habitat characteristics for red pandas at local spatial scale. However, people in rural Nepal still rely on collecting wood-related resources and livestock-grazing in red panda habitats for their livelihoods (Sharma et al., 2014a; Sharma et al., 2017; Panthi et al., 2017). Based on the findings, I recommend the Nepal Government to consider placing restrictions on specific types of natural resource use by local people in red panda habitats, such as cutting trees and bamboos, collecting fallen logs and removing shrubs. I also suggest the promotion of using gas and electricity for heating by local people in place of using firewood and fallen logs, which may be achieved through awareness programmes and financial incentive programmes. These local-scale ecological information should be continuously collected and disseminated to local people as one way to transfer their positive attitudes toward on red panda conservation to actual actions (Sharma et al., 2017). Local-scale habitat selection of red pandas can inform larger-scale distribution prediction. For example, consider that trees and bamboos are important to red pandas at local scale, we would expect that large-scaled distribution of forests, particularly those associated with bamboo understory, should play a role in red panda distribution. To date, the red panda’s potential distribution in the Hindu-Kush region has only been predicted using climatic variables (Kandel et al., 2015). In the next chapter, I will explore the red panda niche models using biologicallyinformed variables such as vegetation covers (Tuanmu et al., 2013; Li et al., 2015a,b), and to test whether the predicted distribution of the red panda is sensitive to the same habitat characteristics found at local scale.. 22.

(35) Chapter 3 Predicting geographic distribution of the Endangered red panda Ailurus fulgens in Nepal using biologically-informed variables 3.1 Introduction Terrestrial wildlife species have experienced a 38% decline from 1970 to 2012 (WWF, 2016), with 16–33% of all vertebrate species now being considered globally threatened (Hoffmann et al., 2010). Understanding the factors shaping species distributions and their habitat suitability is critical to ensure their long-term persistence (Thuiller et al., 2013; Gordon et al., 2017). Correlational ecological niche models (ENMs) predict species distributions based on the relationships between species occurrences and the underlying abiotic and biotic factors (Guisan & Zimmermann, 2000; Guisan & Thuiller, 2005). Maps of potential species distributions generated from ENMs can greatly contribute to conservation planning (Alexander et al., 2017). For example, maps of potential species distributions can help identify habitat patches that can be connected to create effective meta-populations (Guisan & Thuiller, 2005; Mikoláš et al., 2017). Identified suitable areas can also be used for translocation of threatened species (e.g. the greater one horned rhinoceros Rhinoceros unicornis in Nepal; Dinerstein & Price, 1991; Thapa et al., 2009), or for a more comprehensive reserve design (e.g. three new Important Bird Areas were proposed for the Iberian Peninsula to conserve golden eagles Aquila chrysaetos based on nesting habitat selection; López!López et al., 2007). On a long-term basis, ENMs can even be used to predict species range shifts under climate change (e.g. the vulnerable wild yak Bos mutus on the Tibetan Plateau; Liang et al., 2017). The red panda Ailurus fulgens is listed as Endangered on the IUCN Red List of Threatened Species, and is included in both CITES Appendix I and Schedule I of the Protected Wildlife. 23.

(36) Species in Nepal’s Wildlife Protection Act of 1973 (HMG, 1977; Jnawali et al., 2011; Glatston et al., 2015). The core distribution of the red panda is in the Himalayas, specifically the mountains of Nepal, India, Bhutan, Myanmar and southwestern China (Fig. 3.1), with potential peripheral distribution in central China, Laos and northwestern Vietnam (Roberts & Gittleman, 1984; Wei et al., 1999; Choudhury, 2001; Duckworth, 2011; Dorji et al., 2012). Red panda populations are declining throughout their range (Glatston et al., 2015). Anthropogenic impacts are believed to be the main drivers of their declines (Yonzon et al., 1991; Wei et al., 1999; Ghose & Dutta, 2011; Wei & Zhang, 2011b; Dorji et al., 2012; Sharma et al., 2014a; Dendup et al., 2017; Panthi et al., 2017). Nepal is located in the westernmost region of red panda distribution. Groves (2011) considered the populations in Nepal, along with those in the adjacent Bhutan and India, to be a separate subspecies (A.f. fulgens) from that in China (A.f. styani). Therefore, Nepal has strategic importance for red panda conservation in terms of hosting potentially unique evolutionary lineages and providing early signs in their range shifts. Despite the difficulties in studying red pandas, some aspects of their biology are known. This small-sized (3.3 to 4.8 kg in the wild and 3.7 to 6.2 kg in captive), largely nocturnal and arboreal species in the order Carnivora feeds primarily on bamboos (Roberts & Gittleman, 1984; Yonzon, 1989; Panthi et al., 2012; Sharma et al., 2014b; Glatston et al., 2015). They are associated with forest habitats containing dense shrubs and fallen logs and prefer to stay near water (see Chapter 2; Wei et al., 2000; Sharma et al., 2014a). They have an elevational range between 2400– 4000 m (Yonzon & Hunter, 1991; Williams, 2006). The red panda has a low reproduction rate (once a year, mostly singletons or doubletons) and late sexual maturity (c. 18 months) based on the studies of zoo populations (Roberts & Kessler, 1979). The unique ecological niche and slow life history make their survival highly dependent on spatial and temporal stability of suitable. 24.

(37) habitats. Ensuring red panda long-term survival in Nepal requires an understanding of their potential distribution. Once the potential distribution of the red panda is identified, priority areas can be chosen for additional surveys to confirm their presence or for conservation planning. Red panda distribution in the Hindu-Kush region has been predicted using climatic variables (Kandel et al., 2015). However, given their unique niche requirements, namely the dependency on bamboo understory and water sources (Yonzon et al., 1991; Sharma et al., 2014a), I hypothesize that red panda distribution would be sensitive to forest cover and water occurrences, which have not been previously considered in red pandas’ niche models. My aims thus are to build biologicallyinformed ENMs of red pandas with climatic variables, forest covers and water occurrences as environmental layers, and to assess how robust their potential distributions are to the three environmental factors. 3.2. Study area. The study area is the entire Nepal, buffered by 20 km. Nepal comprises 147,181 km2, bordering China to the north and India to the south, east and west (Fig. 3.1). The elevation ranges from 60 m to 8,848 m, driving altitudinal gradients in climate and vegetation. Nepal is divided into five climatic zones: Terai, Siwalik, mid Hill, Mountain, and High Himal (LRMP, 1986). Terai is tropical, Siwalik subtropical, Mid Hills and Mountains temperate, and High Himal subalpinealpine. The vegetation in the tropical zone (60–1000 m) is dominated by Shorea robusta, Dalbergia sissoo and Acacia catechu. In the subtropical zone (1000–2000 m) the vegetation is dominated by Schima wallichii, Castanopsis indica, Pinus roxburghii and Alnus nepalensis. The temperate zone (2000–3000 m) supports Quercus spp., Rhododendron spp. and Juglans regia. The vegetation in the subalpine zone (3000–4000 m) is dominated by Abies spectabilis, Pinus. 25.

(38) wallichiana, Betula utilis and Rhododendron spp., and the alpine zone (>4000 m) has Juniperus spp. and Rhododendron spp. (Dobremez, 1970). Nepal’s climate is dry in the winter and wet in the summer, and >80% rainfalls occur during the summer (June–September). Nepal’s maximum annual precipitation increases with increasing elevations below 2000 m but decreases for elevations above 2000 m, and the average annual precipitation in the central Nepal was 3000 mm/yr while <1000 mm/yr in the north-western mountains (Ichiyanagi et al., 2007). Mean annual temperature at 100 m elevation in Nepal is c. 25°C and decreases by 0.53°C (adiabatic lapse rate) per 100 m increasing elevation (Bhattarai & Vetaas, 2003). The complex climate and topography of Nepal contribute to its high biodiversity, including 118 ecosystems, 75 vegetation types, 35 forest. types,. and. four. biodiversity. hotspots. (Dobremez,. 1972,. 1976).. Fig. 3.1 The study area and the red panda Ailurus fulgens distribution. 3.3. Methods. 3.3.1 Environmental data layers. 26.

(39) I downloaded environmental data layers of 19 climatic variables at 2.5 arc-minute (~4 km) resolution from WorldClim V2 (http://www.worldclim.org; Fick & Hijmans, 2017), four forest covers (Evergreen/Deciduous Needleleaf Trees, Evergreen Broadleaf Trees, Deciduous Broadleaf Trees, and Mixed/Other Trees) at 30 arc-second (~1 km) resolution based on remote sensing during 1992–2006 from EarthEnv (http://www.earthenv.org/landcover; Tuanmu & Jetz, 2014), and water occurrences at 3 arc-second (~30 m) resolution based on satellite images during 1984–2015 from Global Surface Water Explorer (https://www.global-surface-water.appspot.com; Pekel et al., 2016) (Table S3.1). Red pandas perfer dense bamboo understory (Yonzon et al., 1991). However, a comprehensive data set on bamboo distribution in Nepal is not available. Considering that the bamboos species such as Arundinaria spp., Himalayacalamus aristata, Thamnocalamus spp. eaten by red pandas grow under forest canopies and most forests in the mountain regions of Nepal have bamboo understory (Yonzon, 1989; Yonzon et al., 1997; Panthi et al., 2012; Sharma et al., 2014b), I used the forest covers as proxies for bamboo cover. I upscaled all forest cover layers and water occurrences layer to 2.5 arc-minute (~4 km) resolution based on the mean values of raster cells. The choice of the 4 km resolution is based on the known red panda home range size (c. 0.94–11 km2; Yonzon & Hunter, 1991; Reid et al., 1991b). All data layers were projected to the Modified Universal Transverse Mercator for Nepal system. 3.3.2 Red panda occurrences I obtained 1371 red panda occurrences from our surveys conducted in 2007–2009, 2011–2012 (Sharma, 2008; Sharma, 2009; Sharma & Belant, 2009; Sharma et al., 2014a,b) and 2014–2015 (Sharma,. unpublished. data),. as. well. as. from. an. institutional. repository. (https://scholarworks.alaska.edu/handle/11122/1012) of Kandel et al. (2015). In 2007–2009, we surveyed Dhorpatan Hunting Reserve in 18 transects and the length ranged from 800 m to 2400 m. 27.

(40) by three people, and in Rara National Park (RNP) we did opportunistic observations inside the park. In 2011–2012, four people surveyed along 25 transects, whose lengths ranged from 600 m to 2500 m. We surveyed the same transects in 2014 in RNP. In 2014, we also surveyed 12 transects, whose lengths ranged from 500 m to 1300 m, in Langtang National Park (LNP). In 2015, we did opportunistic observations in RNP, LNP, and forests of Ilam, Panchthar and Taplejung by eight people. Most occurrences were based on faecal samples (n = 1332; 97%), with the remaining based on direct sightings (n = 39; 3%). After removing duplicate occurrences within the same cells, I retained 88 occurrences for building ENMs. 3.3.3 Ecological niche models I used the maximum entropy algorithm (MaxEnt 3.3.3 K; Phillips et al., 2006; Phillips & Dudík, 2008; Elith et al., 2011; Warren & Seifert, 2011) to construct red panda ENMs. In order to evaluate the importance of biological variables (i.e., forest cover and water occurrences), relative to climatic variables, for predicting red panda distribution, I developed five ENMs: (1) climate model, using only climatic variables; (2) climate—water model, using climatic variables and water occurrences; (3) climate—forest model, using climatic variables and forest covers; (4) climate—water—forest model, using climatic variables, water occurrences and forest covers; and (5) water—forest model, using water occurrences and forest covers. I compared the predicted distribution of climate model (model 1) to the remaining four models to assess how adding biologically-informed variables (i.e. forest covers, water occurrences) alters predictive red panda distributions. I retained only one of the predictive variables that are highly correlated in model building (|r| > 0.85; Table S3.2), which helps to reduce multi-collinearity among the variables in model building process (Graham, 2003). Among a set of highly correlated variables, I retained the one that is biologically important and/or has the highest contribution to model fit based on a jackknife. 28.

(41) analysis in MaxEnt. For example, mean temperature of the coldest quarter (BIO-11 in WorldClim) may be considered biologically important for red pandas, because their metabolic rates were found to increase in winter at environmental temperature between 5.3°C and 7.6°C, but not in summer at environmental temperature between 15.5°C and 20.2°C (Fei et al., 2017). I used linear and quadratic features to constrain the variance of the predictors (Elith et al., 2011; Merow et al., 2013). I divided the occurrence data into subsets of 75% and 25% records as the training and test data sets respectively. I compared the area under the receiver operating characteristic curve (AUC) at different numbers of background points (background points are used as pseudo-absence data points) and selected the lowest number (500) at which the AUC approaches asymptote (Fig. S3.1). I ran 20 replicates for each model (see Fig. S3.2 for standard deviation rasters of the replicates). I converted MaxEnt outputs (i.e., suitability scores) to binary values (i.e. suitable/unsuitable) using the threshold rule of ‘maximum training sensitivity plus specificity’, which minimizes the mean of the error rates for presences and pesudo-absences and is appropriate for presence-only data (Liu et al., 2016). For model validation, I calculated AUC (Hajian-Tilaki, 2013), true skill statistic (TSS), sensitivity and specificity (Allouche et al., 2007; Lobo et al., 2008). For model comparison, I quantified differences in the amount of predicted suitable area between climate model and each of the other four models (i.e. climate—water model, climate— forest model, climate—water—forest model, water—forest model). To identify gaps in protection, I overlaid the predicted red panda distributions of all five models with the protected areas in the mountain regions of Nepal (http://www.wdpa.org). These protected areas are composed of national parks, wildlife reserves, conservation areas and hunting reserves. 3.4. Results. 29.

(42) All models performed reasonably well (Table 3.1). Across the four metrics of model performance, the climate—forest and climate—water—forest model performed the best and the water—forest model the worst. The water—forest model predicted substantially larger suitable area than other models, c. 29,952 km2 (Fig. 3.2d). Other models predicted a similar size of suitable area from c. 13,088 km2 (climate—forest model; Fig. 3.2b), to c. 15,360 km2 (climate—water—forest; Fig. 3.2c), c. 17,168 km2 (climate model; Fig. 3.2), and c. 17,424 km2 (climate—water model; Fig. 3.2a). Using the climate model as a baseline, the addition of water occurrences alone did not substantially alter predicted suitable areas (Fig. 3.2a) whereas the addition of forest covers alone reduced the predicted suitable areas in the Annapurna region (western Nepal; Fig. 3.2b). Furthermore, the addition of both water occurrences and forest covers yielded new predicted suitable areas in the far-western region although the Annapurna region remained unsuitable (Fig. 3.2c). Finally, without the constrains of climate variables, the water—forest model produced a very liberal prediction of suitable areas (Fig. 3.2d). Overall, the climate—forest model provided the best performance with a better balance between sensitivity and specificity, and the climate— water—forest model uniquely revealed potential suitable areas for red pandas in the far-western region of Nepal.. 30.

(43) Fig. 3.2 Model comparisons on predicted potential distributions of red pandas Ailurus fulgens: (a) Climate vs climate—water model, (b) Climate vs climate—forest model, (c) Climate vs climate— water—forest model, (d) Climate vs water—forest model. Pink color indicates the suitable areas predicted only by the climate model, light blue the suitable areas predicted only by the other model being compared to the climate model (i.e. climate—water model, climate—forest model, climate—water—forest model, water—forest model), and dark blue the suitable areas predicted by both models. Map on upper right corner represents developmental regions of Nepal. Grey: eastern; brown: central; blue: western; yellow: mid-western and red: far-western, and pink overlaying on western Nepal is Annapurna Conservation Area.. 31.

(44) Fig. 3.3 Predicted potential distribution of red pandas Ailurus fulgens overlaying existing protected areas in Nepal. The predicted suitable areas for red pandas are in green; solid blue lines delineate the boundary of existing protected areas with buffer zone. (a) Climate model, (b) Climate—water model, (c) Climate—forest model, (d) Climate—water—forest model, (e) Water—forest model. Map on upper right corner represents developmental regions of Nepal. Grey: eastern; brown: central; blue: western; yellow: mid-western and red: far-western, and pink overlaying on western Nepal is Annapurna Conservation Area. Approximately 7,728 km2 (c. 44%) of the predicted area in the climate—water model are currently protected, followed by c. 7,520 km2 (c. 44%) in the climate model, c. 7,040 km2 (c. 24%) in the water—forest model, c. 6,208km2 (c. 40%) in the climate—water—forest model, and c. 5,808 km2 (c. 44%) in the climate—forest model. Across the five models, at least half of potential 32.

(45) red panda habitat is outside the protected area system of Nepal (Fig. 3.3). Furthermore, there are some isolated suitable areas for red pandas in the western and far-western regions predicted by the climate—water—forest model but are not currently protected (Fig. 3.3d). Table 3.1 Model performance based on the area under the receiver operating characteristic curve (AUC), true skill statistics (TSS), sensitivity and specificity. Model. AUC. TSS. Sensitivity. Specificity. Climate. 0.911. 0.862. 0.967. 0.895. Climate—water. 0.919. 0.870. 0.960. 0.910. Climate—forest. 0.928. 0.913. 0.964. 0.949. Climate—water—forest. 0.922. 0.895. 0.978. 0.917. Water—forest. 0.883. 0.728. 0.920. 0.809. 3.5. Discussion. This study demonstrated that adding biologically-important variables (e.g., water occurrences, forest covers) to red panda ENMs could reveal potential suitable habitats not predicted by the climate models. Specifically, the models predicted suitable red panda habitats in the far-western region of Nepal. Although red panda occurrences have not been recorded in the far-western region, Jnawali et al. (2012) suggested that this region could contain red panda habitats. Indeed, forest habitats with bamboo understory are available in this region (HPS, per. obs.). Therefore, the lack of red panda occurrences in this region may simply be the results of low survey efforts. Field surveys are urgently needed in the far-western region to confirm red panda occurrences. Another area needing attention is the Annapurna region, which is currently protected but the suitable red panda habitats might fall just outside the boundaries of the protected areas (Bista et al., 2017). 33.

(46) Given that different models yielded different predictions, particularly at high elevation (Fig. 3.3; also see Kandel et al., 2015; Bista et al., 2017), red panda presence in the Annapurna region warrants additional surveys. " All of the red panda ENMs performed well except the water—forest model, suggesting that climates could be an essential factor determining red panda distribution. There are several different and non-mutually exclusive reasons why climates are important to red panda niche distribution. First, climates can directly impact red panda’s ecophysiology. Red pandas can tolerate temperature between 5.3°C—20.2°C (Fei et al., 2017). However, heat stress could play a role limiting their distribution, as in the case found for the giant panda (Zhang et al., 2017). Second, climatic conditions such as temperature and precipitation determine primary productivity (Nemani et al., 2003; Del Grosso et al., 2008), thereby influencing food availability for animals. Third, the red panda is a bamboo specialist, using Abis spectabilis and Betula utilis for sheltering (Sharma & Belant, 2009), and feeding on specific bamboo species, such as Arundinaria spp., H. aristata, and Thamnocalamus spp. (Yonzon, 1989; Panthi et al., 2012; Sharma et al., 2014b). Because bamboo distribution can be sensitive to climatic conditions (Caccia et al., 2009; Lin et al., 2018), red panda distribution may be tracking the distribution of bamboos, making climate conditions useful predictors for red panda niche models. It is well recognized that for the giant panda, it is important to understand bamboo distribution and its possible shifts under climate change are important factors of their predicted distribution (Tuanmu et al., 2013; Li et al., 2015a,b; Tang et al., 2018). Bamboo has synchronized mass flowering (Janzen, 1976), and climate change might have negative effects on bamboo seedlings (Lin et al., 2018). Climate change effects have been reported for tree species such as A. spectabilis and B. utilis (Tiwari et al., 2017a,b), which are also used for sheltering by red pandas. 34.

(47) (Yonzon et al., 1997; Sharma & Belant, 2009). Increased rainfall intensity due to climate change could lead to more landslides (Crozier, 2010), which may reduce bamboo distribution in the mountain regions (Wang et al., 2009). Any negative effects of climate change on bamboo distribution will likely influence the current and future red panda distribution. Red pandas are prone to habitat fragmentation due to their limited mobility (Yonzon & Hunter, 1991). In fact, even inside the protected areas, forest resource extraction and natural barriers such as high mountains and rivers can isolate their populations (Roberts & Gittleman, 1984; Yonzon & Hunter, 1991; Crooks et al., 2017; Panthi et al., 2017). Outside the protected areas, anthropogenic impacts such as forest resource extraction, grazing and land use change are more intense, further reducing available habitats for red pandas. Approximately 56%–76% of the predicted suitable areas for red pandas across the five models fall outside the current protected areas, which is in agreement with the findings of smaller-scaled studies (Yonzon et al., 1997; Bista et al., 2017). To connect all suitable habitats and improve connectivity among red panda populations, we recommend extending current protected areas to include adjacent suitable red panda habitats and/or utilizing existing community forests as corridors to connect suitable red panda habitats and current protected areas. Community forests in Nepal are forested lands in close proximity to villages. The Nepal government encourages local people to co-manage community forests such that natural resources in these forests can be utilized in a sustainable way (Bhattarai, 2016). A total of 18,135 km2 of forest is managed as community forests outside the protected areas of Nepal (MOFSC, 2017), and deforestation has been decreasing in the past 15 years (2005–2014) owing to effective community forest programmes (Reddy et al., 2017). Furthermore, people in Nepal generally have positive attitudes towards red panda conservation (Sharma et al., 2017). Therefore, using community forests as red panda habitats or corridors is a real possibility.. 35.

(48) Biological corridors are known to facilitate animal migration, recolonization, and dispersal (Tischendorf & Fahrig, 2000; DeCesare & Pletscher, 2006; Beier et al., 2011). If more community forests in Nepal can be managed for wildlife conservation through working with local people (e.g., awareness programmes, incentive schemes, ecotourism promotion), not only red pandas but other species such as Himalayan black bear Ursus thibetanus, musk deer Moschus chrysogaster, common leopard Panthera pardus, ghoral Naemorhedus goral, Himalayan tahr Hemitragus jemlahicus and barking deer Muntiacus vaginalis may also benefit (Jnawali et al., 2011). There are two limitations to this study. First, the number of red panda occurrences used in constructing the ENMs was relatively small considering the size of the study area. Fortunately, the model performances were acceptable, suggesting that a modest sample size might not be a critical problem in this case (Elith et al., 2006; Wisz et al., 2008). More field surveys, particularly in the areas that have been identified in this study as potential red panda habitats (e.g., far-western region), will add valuable occurrence data if able to confirm their presence and further improve red panda niche models. Another limitation is the incompleteness of biologically-relevant data layers. For example, even if obtaining a comprehensive bamboo distribution is difficult, the reliability of using forest covers as proxies for bamboo distribution needs to be more carefully examined in future studies. Furthermore, layers of other biological factors that might be also important to red panda survival, such as predation pressure from common leopard Panthera pardus or livestock-grazing intensity (Yonzon & Hunter, 1991), remain unavailable. Despite the data limitation, this study illustrates the importance of incorporating biologically-relevant data other than the basic climate data set in the niche models of threatened species such as the red panda.. 36.

(49) Chapter 4 Conservation of the red panda Ailurus fulgens: a review of the current state-of-knowledge 4.1. Red panda ecology. The red pandas Ailurus fulgens, is a bamboo specialist (Panthi et al., 2012; Sharma et al., 2014b), inhabiting sub-tropical and temperate montane forests in the Himalayas and southern China (Roberts & Gittleman, 1984; Yonzon & Hunter, 1991; Yonzon et al., 1991; Glatston et al., 2015). The red panda is separated into two sub-species A. f. fulgens and A. f. styani by Nujiang river in Yunnan, China (Wei et al., 1999; Groves, 2011), with A. f. fulgens distributing in Myanmar, Bhutan, India and Nepal and A. f. styani in Sichuan and a small portion of Yunnan. Due to their remote geographic locations, isolated populations across multiple countries, and arboreal and nocturnal life styles (Roberts & Gittleman, 1984; Johnson et al., 1988; Glatston et al., 2015), field studies on red pandas have been sporadic (Yonzon & Hunter, 1991; Glatston et al., 2015), and some aspects of their ecology is studied using zoo populations. In the 1980–90’s, three groups of researchers attempted radio telemetry on adult red pandas (3 males and 3 females in Langtang National Park, Nepal; Yonzon, 1989; 1 female in Wolong Reserve, China; Johnson et al., 1988; 1 male and 1 female in Wolong Reserve, China; Reid et al., 1991b), and they were able to provide some estimates on red panda’s home range (0.94–11 km2) and mobility (daily movement distance c. 100–540 m). Red pandas use elevated objects such as fallen logs, cut tree stumps, shrub branches, to access bamboos (see Chapter 2; Wei & Zhang, 2011a). Red pandas are less active during snow fall period while more active during the time of bamboo shoot production and fruiting seasons (May–June) (Reid et al., 1991b). Red pandas’ temperature tolerance is estimated to be between 5.3°C—20.2°C (Fei et al., 2017), which may restrict their distribution in the Himalayas. Red pandas give birth once a year, mostly singletons. 37.

(50) or doubletons (Yonzon & Hunter, 1991). They reach sexual maturity at c. 18 months old based on the studies of zoo populations (Roberts & Kessler, 1979). Captive red pandas breed during January–March, and both captive and wild red pandas are found giving birth during June–August (Yonzon, 1989; Northrop & Czekala, 2011). Red pandas use hollow trees as nests (Roberts & Gittleman, 1984; HPS, per. obs.). 4.2 Red panda conservation The red panda is an endangered species listed on IUCN Red List of Threatened Species since 1994 (Glatston et al., 2015). Anthropogenic impacts, such as habitat degradation (Williams, 2006; Dorji et al., 2011; Dendup et al., 2017; Panthi et al., 2017), disturbances from livestock and grazing activities (Yonzon & Hunter, 1991; Yonzon et al., 1991; Sharma & Belant, 2010; Dorji et al., 2011; Ghose & Dutta, 2011; Sharma et al., 2014a), and wildlife trade (Choudhury, 2001; Wei & Zhang, 2011b) are believed to be the major threats to red panda survival. Generally, Nepalese people have positive attitudes towards red panda conservation (see Chapter 1; Sharma et al., 2017). However, positive attitudes do not necessarily lead to sustainable resource-use behaviours. For example, my studies (Chapter 2) suggest that bamboos, trees and fallen logs are some of the most important habitat features that red pandas reply on. Yet I still observe high prevalence of livestock grazing and wood collecting in red panda habitats. Nepal’s Wildlife Protection Act of 1973 lists the red panda as endangered, which provides the legal ground for their protection (HMG, 1977; Jnawali et al., 2011). For example, any offenders involved in illegal trade of red pandas could be fined NRs. 50,000 to 1,00,000 (US$500–1000) and/or prisoned for 5–15 years. Nepal’s Forest Policy (GoN, 2015) and National Biodiversity Strategy and Action Plan (GoN, 2014) both declared that conservation of endangered species, including red pandas, is a policy priority for the time period of 2014–2020. Accordingly, Nepal. 38.