Department of Forestry and Natural Resources, Purdue University, West Lafayette, Indiana, United States of America.
Indiana Department of Natural Resources, Bloomington, Indiana, United States of America.
PLoS One. 2022 Aug 1;17(8):e0269258. doi: 10.1371/journal.pone.0269258. eCollection 2022.
Understanding habitat selection of top predators is critical to predict their impacts on ecological communities and interactions with humans, particularly in recovering populations. We analyzed habitat selection in a recovering population of bobcats (Lynx rufus) in south-central Indiana using a Random Forest model. We predicted that bobcats would select forest habitat and forest edges but avoid agriculture to maximize encounters with prey species. We also predicted that bobcats would avoid developed areas and roads to minimize potential antagonistic interactions with humans. Results partially supported our predictions and were consistent with bobcats in the early stages of population expansion. Bobcats exhibited elevated use near forest edges, thresholds of avoidance near agriculture, and thresholds of selection for low and intermediate habitat heterogeneity. Bobcats exhibited peak probability of use 1-3 km from major roads, >800 m from minor roads, and <1km from developed areas, suggesting tradeoffs in reward for high-quality hunting areas and mortality risk. Our Random Forest model highlighted complex non-linear patterns and revealed that most shifts in habitat use occurred within 1 km of the edge of each habitat type. These results largely supported previous studies in the Midwest and across North America but also produced refinements of bobcat habitat use in our system, particularly at habitat boundaries. Refined models of habitat selection by carnivores enable improved prediction of the most suitable habitat for recovering populations and provides useful information for conservation.
了解顶级捕食者的栖息地选择对于预测它们对生态群落的影响以及与人类的相互作用至关重要，特别是在种群恢复的情况下。我们使用随机森林模型分析了印第安纳州中南部一个恢复中的山猫（Lynx rufus）种群的栖息地选择。我们预测山猫会选择森林栖息地和森林边缘，但避免农业用地，以最大限度地增加与猎物相遇的机会。我们还预测山猫会避开发达地区和道路，以最大程度地减少与人类潜在的对抗性互动。结果部分支持了我们的预测，与处于种群扩张早期阶段的山猫一致。山猫在森林边缘附近的使用频率较高，在农业用地附近的回避阈值，以及在低和中等生境异质性方面的选择阈值。山猫在距离主要道路 1-3 公里处、距离次要道路 800 米以上处和距离发达地区 1 公里以内处的使用概率最高，这表明在高狩猎质量区域的奖励和死亡率风险之间存在权衡。我们的随机森林模型突出了复杂的非线性模式，并表明大多数栖息地使用的变化发生在每种栖息地类型边缘的 1 公里范围内。这些结果在很大程度上支持了中西部和整个北美的先前研究，但也对我们系统中山猫栖息地使用的细化，特别是在栖息地边界处产生了影响。对食肉动物栖息地选择的精细化模型可以提高对恢复种群最适宜栖息地的预测能力，并为保护提供有用的信息。
