Pangolins in global camera trap data: Implications for ecological monitoring

Despite being heavily exploited, pangolins (Pholidota: Manidae) have been subject to limited research, resulting in a lack of reliable population estimates and standardised survey methods for the eight extant species. Camera trapping represents a unique opportunity for broad-scale collaborative species monitoring due to its largely non-discriminatory nature, which creates considerable volumes of data on a relatively wide range of species. This has the potential to shed light on the ecology of rare, cryptic and understudied taxa, with implications for conservation decision-making. We undertook a global analysis of available pangolin data from camera trapping studies across their range in Africa and Asia. Our aims were (1) to assess the utility of existing camera trapping efforts as a method for monitoring pangolin populations, and (2) to gain insights into the distribution and ecology of pangolins. We analysed data collated from 103 camera trap surveys undertaken across 22 countries that fell within the range of seven of the eight pangolin species, which yielded more than half a million trap nights and 888 pangolin encounters. We ran occupancy analyses on three species (Sunda pangolin Manis javanica, white-bellied pangolin Phataginus tricuspis and giant pangolin Smutsia gigantea). Detection probabilities varied with forest cover and levels of human influence for P. tricuspis, but were low (<0.05) for all species. Occupancy was associated with distance from rivers for M. javanica and S. gigantea, elevation for P. tricuspis and S. gigantea, forest cover for P. tricuspis and protected area status for M. javanica and P. tricuspis. We conclude that camera traps are suitable for the detection of pangolins and large-scale assessment of their distributions. However, the trapping effort required to monitor populations at any given study site using existing methods appears prohibitively high. This may change in the future should anticipated technological and methodological advances in camera trapping facilitate greater sampling efforts and/or higher probabilities of detection. In particular, targeted camera placement for pangolins is likely to make pangolin monitoring more feasible with moderate sampling efforts

Pangolins in Global Camera Trap Data: Implications for Ecological Monitoring

Despite being heavily exploited, pangolins (Pholidota: Manidae) have been subject to limited research, resulting in a lack of reliable population estimates and standardised survey methods for the eight extant species. Camera trapping represents a unique opportunity for broad-scale collaborative species monitoring due to its largely non-discriminatory nature, which creates considerable volumes of data on a relatively wide range of species. This has the potential to shed light on the ecology of rare, cryptic and understudied taxa, with implications for conservation decision-making. We undertook a global analysis of available pangolin data from camera trapping studies across their range in Africa and Asia. Our aims were (1) to assess the utility of existing camera trapping efforts as a method for monitoring pangolin populations, and (2) to gain insights into the distribution and ecology of pangolins. We analysed data collated from 103 camera trap surveys undertaken across 22 countries that fell within the range of seven of the eight pangolin species, which yielded more than half a million trap nights and 888 pangolin encounters. We ran occupancy analyses on three species (Sunda pangolin Manis javanica, white-bellied pangolin Phataginus tricuspis and giant pangolin Smutsia gigantea). Detection probabilities varied with forest cover and levels of human influence for P. tricuspis, but were low (M. javanica and S. gigantea, elevation for P. tricuspis and S. gigantea, forest cover for P. tricuspis and protected area status for M. javanica and P. tricuspis. We conclude that camera traps are suitable for the detection of pangolins and large-scale assessment of their distributions. However, the trapping effort required to monitor populations at any given study site using existing methods appears prohibitively high. This may change in the future should anticipated technological and methodological advances in camera trapping facilitate greater sampling efforts and/or higher probabilities of detection. In particular, targeted camera placement for pangolins is likely to make pangolin monitoring more feasible with moderate sampling efforts

Identification of biodiversity hotspot in national level – Importance of unpublished data

High priority areas for conservation are typically identified based on ad hoc methods without supporting data due to a lack of scientific resources and staff. The objective of this study was to show how unpublished data in combination with citizen science can be useful for identifying biodiversity hotspot areas using Thailand as an example. Species records were aggregated from various sources both published (records permanently archived or temporarily available on the internet) and unpublished data (derived from interviews). Data from 2001 to 2016 were used to identify hotspot areas. In total, 15% of the data were obtained from personal interviews. Most Thai hotspot areas were already inside government protected areas. Amphibians and reptiles were the taxa that unpublished data had the greatest influence on hotspot identification, while this influence was least for birds. Spatial coverage of species records showed a bias toward particular regions and areas probably due to a number of factors including their international reputation and previous species records. Results from this project reveal the importance of unpublished records as a source for identifying hotspot locations especially for less studied species groups and indicates the continued need for encouraging scientists as well as amateur naturalists to report their observations to archived websites and/or publish in peer-reviewed journals. Keywords: Citizen science, Amphibians, Reptiles, Birds, Mammal

Appendix H. Bayesian network model structure of habitat suitability for Chitra.

Bayesian network model structure of habitat suitability for Chitra

Appendix B. Map of Thailand showing its six geographic regions and two biogeographical subregions.

Map of Thailand showing its six geographic regions and two biogeographical subregions

Appendix A. An example of a Bayesian network.

An example of a Bayesian network

Appendix F. List and description of Bayesian network nodes used in this publication.

List and description of Bayesian network nodes used in this publication

Appendix G. The conditional probability table of the "habitat suitability" node.

The conditional probability table of the "habitat suitability" node