Suchergebnisse

Abstract
ContextEstimates of a species abundance and habitat preferences provide vital information on their status and the appropriate conservation management. For nocturnal arboreal primates, obtaining reliable estimates of these parameters is particularly challenging because of their cryptic behaviour, often resulting in a small number of detections. Although techniques are available for assessing the abundance of species with a low probability of detection, most require strict assumptions that are difficult to meet.
AimsHere, we aimed to explore the possibility of improving nocturnal-primate abundance estimates when detection probability is low and to determine the minimal effort required to calculate reasonable estimates of their overall abundance and the effect of habitat type on abundance estimates.
MethodsWe used count data obtained from spotlighting along line transects for estimating density of Bengal slow loris (Nycticebus bengalensis) in north-eastern Thailand with N-mixture hierarchical modelling, to run simulations of varying survey parameters and asses the effort needed to produce robust estimates based on the relative bias from each simulation.
Key resultsN-mixture analysis showed that the data obtained from our study were still biased (9%), with a lambda of 1.79 lorises, detection probability of 0.11, 50 survey sites and a maximum of 12 sample occasions. The simulation results found that increasing the number of sample occasions to 14 per transect would produce an acceptable bias (<5%).
ConclusionsWe recommend that future studies on nocturnal arboreal species should use preliminary surveys to gauge the specific lambda and probability of detection so as to establish the effort needed to produce reasonable estimates of abundance.
ImplicationsOur study showed that count data obtained from spotlighting can be used to produce robust abundance estimates of nocturnal arboreal species. Unlike simple encounter rate, this method incorporates detection probability and habitat preferences, yet does not require additional trained field technicians.

Context With the ongoing biodiversity crisis and the continued loss of species, it becomes crucial to find practical solutions to monitor threatened animal populations for wildlife conservation and management. However, in practice, monitoring is especially challenging for elusive, rare, and wide-ranging species, where estimating abundance is often expensive and time-consuming. Alternatively, estimating occupancy (i.e. detection/non-detection data) may be less resource-intensive, while still providing useful information for monitoring population trends. Aims We aimed to describe a new field method, the random walk grid survey, to conduct a habitat selection study on elusive diurnal forest-dwelling primates. We explored how to improve occupancy estimates when detection probability is low and determined the minimal effort needed for reasonable estimates on the species habitat selection by using site-occupancy models. Methods We collected data to assess the northern pigtailed macaques' (Macaca leonina) occupancy and detection probability using a random walk survey of degraded forest fragments in Sakaerat Biosphere Reserve in Northeast Thailand. We ran simulations to identify what is required for minimum survey efforts to obtain reasonable estimates of occupancy and detection probability on small or relatively large spatial scales, covering a small primate community in Southeast Asia. Key results Simulations showed that the probability of detecting macaques increased dramatically with an increased survey effort. However, compared with similar line-transect survey methods, the random walk grid survey was less time-consuming. Additionally, the occupancy and habitat selection estimates were similar to our knowledge of macaque distribution within the study area. Conclusions Our findings suggest that the new random walk grid survey method is effective to assess the elusive northern pigtailed macaques' occupancy, and to provide reliable data on habitat selection where there is low macaque abundance and detection probability in a degraded forest fragment. Implications Our survey protocol could be used as a starting point to target high location occupancy to start habituation processes, but also for further intensive studies on primate behaviour and habitat use of primate communities. Finally, combining the random walk grid survey with automated recording devices (e.g. camera traps or passive acoustic surveys) could help improve occupancy and detection probability estimates for long-term monitoring programs and over large spatial scales.

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 nondiscriminatory 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. (c) 2019 The Authors. Published by Elsevier B.V. ; Fondation Segr~e; Biodiversity Monitoring Centre (Centre de Surveillance de la Biodiversit ~e) at the Faculty of Sciences of the University of Kisangani; Centre for International Forestry Research (CIFOR); Department of Science and Technology, Government of India (DST)Department of Science & Technology (India) [SR/S0/AS-100/2007]; Ministry of Education MalaysiaMinistry of Education, Malaysia [NRGS 2013/1088/02]; U.S. National Science FoundationNational Science Foundation (NSF) [BCS 1266389]; AXA Research Fellowship; Gordon and Betty Moore FoundationGordon and Betty Moore Foundation ; Thank you to the many individuals and institutions who generously made their data available for this study, and to the Zoological Society of London and donors to the IUCN SSC Pangolin Specialist Group for supporting the time of HK and CB during their research internships. The authors are grateful to Fondation Segr~e for supporting this research. AL would like to thank the Biodiversity Monitoring Centre (Centre de Surveillance de la Biodiversit ~e) at the Faculty of Sciences of the University of Kisangani and the Centre for International Forestry Research (CIFOR) for financial, academic and logistical support. AM would like to thank Agence Nationale des Parcs Nationaux and Centre National de la Recherche Scientifique et Technologique for kindly granting permission to conduct research in Gabon. CKO and TB would like to thank the Nouabal~e-Ndoki Foundation and Ministry of Forest Economy, Republic of Congo for kindly providing research permissions. GVG would like to gratefully thank the Department of Science and Technology, Government of India for their funding (DST. No. SR/S0/AS-100/2007), Mr. K. M. Selvan and Mr. S. Lyngdoh for their support in field data collection, and the Department of Environment & Forest, Government of Arunachal Pradesh for permissions. JAMwas supported by Ministry of Education Malaysia (NRGS 2013/1088/02). LAI acknowledges support from the U.S. National Science Foundation (BCS 1266389). ORW was supported by an AXA Research Fellowship. SE would like to thank R. Mueller and R. Roder for their input into data processing. Some data in this publication was provided by the Tropical Ecology Assessment and Monitoring (TEAM) Network, a collaboration between Conservation International, the Smithsonian Institution, and the Wildlife Conservation Society, and partially funded by these institutions, the Gordon and Betty Moore Foundation, and other donors.