An integrated approach for predicting the occurrence probability of an elusive species: the Southwest China serow
Thuc D. Phan
A F , Greg S. Baxter B , Hao A. D. Phan C , Luan S. Mai D and Hoang D. Trinh E
A Institute of Research and Development, Duy Tan University, Thank Khe, Da Nang 550000, Vietnam.
B School of Agricultural and Food Sciences, The University of Queensland, St Lucia, Qld 4072, Australia.
C Hanoi University of Science and Technology, Hai Ba Trung, Hanoi 100000, Vietnam.
D Cat Ba Langur Conservation Project, Cat Hai, Hai Phong 180000, Vietnam.
E Fauna and Flora International, Tay Ho, Hanoi 100000, Vietnam.
F Corresponding author. Email: pduythuc@gmail.com
Wildlife Research 46(5) 386-397 https://doi.org/10.1071/WR18116
Submitted: 14 July 2018 Accepted: 31 March 2019 Published: 4 July 2019
Abstract
Context: Understanding the environmental factors influencing the occurrence of wildlife has become increasingly important, contributing to better conservation actions for threatened species.
Aims: We aimed to understand the factors that influence the occurrence probability of the Southwest China serow (Capricornis milneedwardsii), a threatened species, thereby developing conservation interventions to save the species from local extirpation on the Cat Ba Archipelago, Vietnam.
Methods: An integrated approach, including literature reviews, interviews, field surveys, logistic generalised linear models and Bayesian networks, was applied to identify environmental variables and species occurrence, and model the occurrence probability of the species. Sensitivity analysis and scenarios were also performed to identify the influence of environmental variables on the probability of the species occurrence.
Key results: Distance to ranger station was found to be the most influential factor on serow occurrence, followed by total forest, distance to village, steepness and elevation. Hunting pressure has probably forced serows to inhabit the areas where they are well protected, and this need probably over-rides the effect of ecological variables.
Conclusion: Through combing knowledge of forest rangers and members of forest-protection groups, field surveys and logistic generalised linear models, a Bayesian network was developed to predict the occurrence probability of the threatened Southwest China serow on the Cat Ba Archipelago for conservation actions. The modelling results and findings from the present study provided further understanding of the relationships between environmental factors and the probability of the species occurrence, which have been rarely studied throughout the range of this species.
Implications: The modelling predictions give managers basic information for conservation and recovery planning in situations where integrated conservation interventions should be urgently conducted to save the threatened species from local extirpation.
Additional keywords: Bayesian networks, decision-support tool, isolated population, karst landscape, poaching, threatened species.
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