State‐space model combining local camera data and regional administration data reveals population dynamics of wild boar

• Published in: Population ecology 2023-01, Vol.65 (1), p.80-92
• Main Authors: Kasada, Minoru, Nakashima, Yoshihiro, Fukasawa, Keita, Yajima, Gota, Yokomizo, Hiroyuki, Miyashita, Tadashi
• Format: Article
• Language: English
• Subjects: Bayesian analysis; Bayesian state‐space model; Bayesian theory; camera trap; Cameras; Carrying capacity; Demography; Distribution; Ecological effects; Growth rate; Impact damage; Local population; Mathematical models; modeling; Parameter identification; Pathogens; Population control; Population dynamics; Population growth; Population levels; Population number; Population statistics; Probability theory; REST model; Sus scrofa; Trapping; Wildlife; Wildlife management
• ISSN: 1438-3896; 1438-390X
• DOI: 10.1002/1438-390X.12138

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. We developed a method of integrating administration data with a camera trapping technique to estimate heterogeneous population dynamics. The results suggested (1) wild boars exhibit a high potential population growth rate and a high carrying capacity, (2) current hunting efforts are effectively suppressing local populations, and (3) hunting reinforcement is required to control populations in the whole region.