ORNITHOLOGICAL SCIENCE
Online ISSN : 2759-5897
Print ISSN : 1347-0558
ORIGINAL ARTICLE
Estimation of condition-dependent dispersal kernel with simple Bayesian regression analysis
Akira SAWADA, Tetsuya IWASAKI, Chitose INOUE, Kana NAKAOKA, Takumi NAKANISHI, Junpei SAWADA, Narumi ASO, Syuya NAGAI, Haruka ONO, Ryota MURAKAMI, Masaoki TAKAGI
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2023 年 22 巻 1 号 p. 25-34

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Empirical ornithologists often analyse dispersal distance by histograms separately drawn for categories of individuals (e.g., sexes), and/or by linear models with normal distribution (e.g., ANOVA). However, theoreticians describe dispersal distance by dispersal kernels with various parametric distributions. Therefore, it is a helpful exercise for empiricists to estimate dispersal kernels from field data. As a model case for such an estimation, we analysed dispersal data of the Ryukyu Scops Owls Otus elegans using a Bayesian Weibull regression model. Estimated dispersal kernels showed that males and individuals fledged from late-breeding nests had short natal dispersal distances and that no factors affected breeding dispersal significantly.

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