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26 January 2023 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
Author Affiliations +
Abstract

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.

© The Ornithological Society of Japan 2023
Akira Sawada, Tetsuya Iwasaki, Chitose Inoue, Kana Nakaoka, Takumi Nakanishi, Junpei Sawada, Narumi Aso, Syuya Nagai, Haruka Ono, Ryota Murakami, and Masaoki Takagi "Estimation of Condition-Dependent Dispersal Kernel with Simple Bayesian Regression Analysis," Ornithological Science 22(1), 25-34, (26 January 2023). https://doi.org/10.2326/osj.22.25
Received: 11 November 2021; Accepted: 8 April 2022; Published: 26 January 2023
KEYWORDS
Bayesian estimation
dispersal kernel
island
Otus elegans
Ryukyu Scops Owl
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