The importance of fine-scale predictors of wild boar habitat use in an isolated population

Predicting the likelihood of wildlife presence at potential wildlife-livestock interfaces is challenging. These interfaces are usually relatively small geographical areas where landscapes show large variation over small distances. Models of wildlife distribution based on coarse data over wide geographical ranges may not be representative of these interfaces. High-resolution data can help identify fine scale predictors of wildlife habitat use at a local scale and provide more accurate predictions of species habitat use. These data may be used to inform knowledge of interface risks, such as disease transmission between wildlife and livestock, or human-wildlife conflict. This study uses fine-scale habitat use data from wild boar (Sus scrofa) based on activity signs and direct field observations in and around the Forest of Dean in Gloucestershire, England. Spatial logistic regression models fitted using a variant of penalized quasi‐likelihood were used to identify habitat-based and anthropo...预测野生动物-家畜接触带（wildlife-livestock interfaces）内野生动物出现的概率颇具挑战。这类接触带通常为地理范围相对狭小的区域，景观在极小尺度内便存在显著异质性。基于大范围粗分辨率数据构建的野生动物分布模型，往往无法反映这类接触带的实际特征。高分辨率数据则有助于在局地尺度上识别野生动物栖息地利用的精细尺度预测因子，并能更精准地预测物种的栖息地利用模式。此类数据可用于深化对接触带风险的认知，例如野生动物与家畜间的疾病传播，抑或是人兽冲突问题。本研究基于英国格洛斯特郡迪恩森林及其周边区域的活动痕迹与实地直接观测数据，获取了野猪（Sus scrofa）的局地尺度栖息地利用精细数据。本研究采用基于惩罚拟似然（penalized quasi-likelihood）变体构建的空间logistic回归模型，以识别基于栖息地与人类活动相关的……