Combining species distribution modeling and distance sampling to assess wildlife population size: A case study with the northern yellow‐cheeked gibbon (Nomascus annamensis)
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Medientyp:
E-Artikel
Titel:
Combining species distribution modeling and distance sampling to assess wildlife population size: A case study with the northern yellow‐cheeked gibbon (Nomascus annamensis)
Beteiligte:
Tran, Dung Van;
Vu, Thinh Tien
Erschienen:
Wiley, 2020
Erschienen in:
American Journal of Primatology, 82 (2020) 9
Sprache:
Englisch
DOI:
10.1002/ajp.23169
ISSN:
0275-2565;
1098-2345
Entstehung:
Anmerkungen:
Beschreibung:
AbstractPopulation size and distribution data for wildlife species play an important role in conservation and management, especially for endangered species. However, scientists seriously lack data on the population status of many species. The northern yellow‐cheeked gibbon (Nomascus annamensis) is found in southern Lao PDR, central Vietnam, and northeastern Cambodia. The population of the species has significantly declined due to hunting, habitat loss, and the wildlife trade. To examine the population size and distribution ofN. annamensis, we conducted a field survey in Song Thanh Nature Reserve, Quang Nam Province, central Vietnam from February to April 2019 using the audio point count method. We combined Distance Sampling and Ecological Niche Modeling to estimate the population of the gibbons. Results showed that the total suitable area for the gibbons was about 302.32 km2, with the two most important variables of the habitat model being the distance‐to‐villages and forest type. We detected 36 gibbon groups through field surveys and estimated 443 (95% CI, 278–707) gibbon groups in Song Thanh Nature Reserve. Our results indicate that the gibbon population in Song Thanh Nature Reserve is the largest known population ofN. annamensisin Vietnam. In addition, our study was the first to combine species distribution modeling with distance sampling to estimate gibbon density and population size. This approach might be useful in surveying and monitoring gibbon populations because it takes imperfect detection probability into account in estimating gibbon population density while estimating the area of potential habitat using environmental variables.