Applicability of a Bayesian state-space model for evaluating the effects of localized culling on subsequent density changes: sika deer as a case study
European Journal of Wildlife Research, 63(4), pp 71, 2017-8
At the landscape scale, localised culling is often conducted to achieve various deer management aims. However, few studies have assessed the effects of localised culling on deer population dynamics, owing to the spatially and temporally insufficient datasets of deer abundance that are derived from limited survey efforts. In this study, we estimated the population dynamics of a sika deer (Cervus nippon) population in the Tanzawa Mountains, central Japan, by Bayesian state-space modelling using spatiotemporally insufficient abundance indices and evaluated the effects of unit-specific culls on subsequent density changes in 56 units. The responses of deer density to unit-specific culls differed greatly among units, and a very weak correlation was observed between the intensities of unit-specific culls and the reduction in density. Deer populations in some units tended to resist density decreases despite high culling pressure, whereas those in other units were susceptible to density decreases with little to no culling pressure. Because the spatial scales of each unit were relatively small, annual density changes in each unit were largely influenced by deer movement in this estimation. The obscured effects of unit-specific culls, which were probably derived from deer movement among units in this case study, re-emphasized that deer migration should be incorporated into the planning of localised culling and that deer management should be coordinated over a wide area beyond landscape components and landownerships. Thus, we conclude that Bayesian state-space modelling is valuable for practical deer management programs at a large spatial scale even if different abundance indices are used.
Integrated population model; Landscape-scale management; Management unit; Migration; Spatial population structure; Tanzawa Mountains