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Exploring the drivers of wildlife population dynamics from insufficient data by Bayesian model averaging
Author(s) -
Osada Yutaka,
Kuriyama Takeo,
Asada Masahiko,
Yokomizo Hiroyuki,
Miyashita Tadashi
Publication year - 2015
Publication title -
population ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.819
H-Index - 59
eISSN - 1438-390X
pISSN - 1438-3896
DOI - 10.1007/s10144-015-0498-x
Subject(s) - wildlife , wildlife management , population , bayesian probability , bayesian inference , ecology , population model , population ecology , wildlife conservation , predictability , geography , environmental resource management , computer science , biology , environmental science , statistics , mathematics , demography , artificial intelligence , sociology
A long‐standing interest in ecology and wildlife management is to find drivers of wildlife population dynamics because it is crucial for implementing the effective wildlife management. Recent studies have demonstrated the usefulness of state‐space modeling for this purpose, but we often confront the lack of the necessary time‐series data. This is particularly common in wildlife management because of limited funds or early stage of data collection. In this study, we proposed a Bayesian model averaging technique in a state‐space modeling framework for identifying the drivers of wildlife population dynamics from limited data. To exemplify the utility of Bayesian model averaging for wildlife management, we illustrate here the population dynamics of wild boars Sus scrofa in Chiba prefecture, central Japan. Despite the fact that our data are limited in both temporal and spatial resolution, Bayesian model averaging revealed the potential influence of bamboo forests and abandoned agricultural fields on wild boar population dynamics, and largely enhanced model predictability compared to the full model. Although Bayesian model averaging is not commonly used in ecology and wildlife management, our case study demonstrated that it may help to find influential drivers of wildlife population dynamics and develop a better management plan even from limited time‐series data.