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Issue:ISSN 1000-7083
          CN 51-1193/Q
Director:Sichuan Association for Science and Technology
Sponsored by:Sichuan Society of Zoologists; Chengdu Giant Panda Breeding Research Foundation; Sichuan Association of Wildlife Conservation; Sichuan University
Address:College of Life Sciences, Sichuan University, No.29, Wangjiang Road, Chengdu, Sichuan Province, 610064, China
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Your Position :Home->Past Journals Catalog->2012 Vol.31 No.4

Bayesian Binomial Mixture Model to Study of Larus brunnicephalus Population Sizes in Relation to Local Environmental Characteristics
Author of the article:JI Tuo1,2, YANG Min1,2, YANG Le3, CAO Sheng1, LI Lai-xing1*
Author's Workplace:(1. Key Laboratory of Adaptive and Evolution of Plateau Biology, Northwest Plateau Institute of Biology, Chinese Academy of Science, Xining 810001, China; 2. Graduate School of the Chinese Academy of Science, Beijing 100049, China; 3.Tibet Plateau Institute of Biology, Lhasa 850000, China)
Key Words:Qinghai Lake; detection probability; Bayesian statistic; binomial mixture model; DIC; Larus brunnicephalus
Abstract:Count-based indices are widely used to study the relationship of bird population and environmental characteristic. But indices are often confounded by variation in detection probability. To characterize environmental conditions that affect breeding distributions, we analyzed count data on brown-headed gulls that were collected around Qinghai Lake in 2010 and 2011. We modeled count data for brown-headed gulls using Bayesian hierarchical model including five local-scale habitat covariates (area of lake, distance to the nearest road, distance to Qinghai Lake, level of grazing, vegetation cover) and four variables for detection probability (wind, rain, month, experience). We used DIC to choose the best model. Our state model for abundance contained four independent log-linear Poisson regressions on area of lake, distance to the nearest road, level of grazing and vegetation cover. The observation model for detection of an individual brown-headed gull contained factors of month and observer experience. Result showed that brown-headed gulls’ populations increased with area of lake, distance to the nearest road, vegetation cover and decreased with level of grazing. Detectability increased with observer experience and decreased with month. The weather conditions didn’t significantly effect on the detection probability for brown-headed gulls, suggesting that the habitat condition has affect on detectability.
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