State-space model combining local camera data and regional administration data reveals population dynamics of wild boar |
| |
Authors: | Minoru Kasada Yoshihiro Nakashima Keita Fukasawa Gota Yajima Hiroyuki Yokomizo Tadashi Miyashita |
| |
Institution: | 1. Graduate School of Life Sciences, Tohoku University, Sendai, Japan;2. College of Bioresource Science, Nihon University, Fujisawa, Kanagawa, Japan;3. Biodiversity Division, National Institute for Environmental Studies, Tsukuba, Ibaraki, Japan;4. Health and Environmental Risk Division, National Institute for Environmental Studies, Tsukuba, Ibaraki, Japan;5. Graduate School of Agriculture and Life Sciences, The University of Tokyo, Tokyo, Japan |
| |
Abstract: | Recent increases in wildlife cause negative impacts on humans through both economic and ecological damage, as well as the spread of pathogens. Understanding the population dynamics of wildlife is crucial to develop effective management strategies. However, it is difficult to estimate accurate and precise population size over large spatial and temporal scales because of the limited data availability. We addressed these issues by first fitting a random encounter and staying time (REST) model based on camera trap data to construct an informative prior distribution for a capture rate parameter in a harvest-based Bayesian state-space model. We constructed a Bayesian state-space model that integrated administration data on the number of captured wild boar with the prior distribution of capture efficiency estimated by camera trap data. The model with informative prior distribution from the REST model successfully estimated population dynamics, whereas the model using only the administration data did not, owing to a lack of parameter convergence. We identified areas where (1) wild boars exhibit a high potential population growth rate and a high carrying capacity, (2) current trapping efforts are effectively suppressing local populations, and (3) trapping reinforcement is required to control populations in the whole region. The model could be used to predict future trends in populations under the assumptions of ongoing trapping pressure. This will help identify spatially explicit trapping efforts to achieve target population levels. |
| |
Keywords: | Bayesian state-space model camera trap modeling REST model wildlife management |
|
|