Opportunistic records of animal occurrence may be problematic for inferring species distribution and habitat requirements because of unknown and uncontrolled sources of sampling variance. In this study, we used occurrence records for river otters (Lontra canadensis) derived from sign surveys, road kills, trapper bycatch, and opportunistic sightings (n = 185 records collected 2001–2012) to assess the potential distribution and habitat relationships of otters across central and western New York, USA. To mitigate for obvious observation biases, we standardized observation intensity across regions a priori and restricted inference to readily accessible areas (i.e., ≤700 m from the nearest road). Model selection, and the direction of covariate effects, proved robust to these sampling biases although effect sizes varied −7.1% to +48.0% after bias correction, with the coefficient for the proportion of available shoreline being the most unstable. Ultimately, the top bias‐corrected model proved a reliable index for otter probability of occurrence given a strong, positive, and linear relationship with a withheld set of standardized survey records for otters collected in winter 2016–2017 (n = 57; R² = 0.90). This model indicated that approximately 20% of the study area represented high probability of otter occurrence. We demonstrated that reliable inference on wildlife habitat requirements can be obtained from disparate records of animal occurrence provided that data biases are known and effectively mitigated. © 2020 The Wildlife Society.

中文翻译：

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一种从不同的发生记录确定水獭分布的实用方法

由于样本方差的来源未知且不受控制，因此动物发生的机会性记录可能难以推断物种分布和栖息地需求。在这项研究中，我们使用了从水獭调查，道路杀害，捕捞者兼捕和机会性目击（n  = 185条记录，2001- 2012年）中得出的水獭（Lontra canadensis）的发生记录来评估水獭在整个地区的潜在分布和栖息地关系美国中西部纽约。为了减轻明显的观测偏差，我们先验地标准化了各个区域的观测强度并仅对容易接近的区域（即距最近道路≤700m）进行推断。模型选择和协变量效应的方向证明对这些采样偏差具有鲁棒性，尽管在偏差校正后效应大小在−7.1％至+ 48.0％之间变化，可用海岸线比例的系数最不稳定。最终，顶级偏置校正模型证明了水獭发生概率的可靠指标，因为它具有牢固，正向和线性关系，并且保留了一组在2016-2017年冬季收集的水獭标准化调查记录（n  = 57；R ² = 0.90）。该模型表明，大约20％的研究区域代表了水獭发生的高可能性。我们证明，只要知道并有效缓解数据偏差，就可以从不同的动物发生记录中获得对野生动植物栖息地需求的可靠推断。©2020野生动物协会。