Decoupling residents and dispersers from detection data improve habitat selection modelling: the case study of the wolf in a natural corridor

IF 16.4 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY Accounts of Chemical Research Pub Date : 2022-01-18 DOI:10.1080/03949370.2021.1988724
O. Dondina, A. Meriggi, L. Bani, V. Orioli
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引用次数: 5

Abstract

Resource selection analyses based on detection data are widely used to parametrize resistance surfaces used to identify ecological corridors. To successfully parametrize resistance, it is crucial to decouple resident and disperser behaviours yet to date connectivity studies using detection data have not addressed this issue. Here, we decoupled data of resident and dispersing wolves by analysing detection data collected within a natural corridor crossing a human dominated plain in Italy. To decouple residents and dispersers, we ran a Kernel Density analysis to investigate whether clusters of wolf detection points characterized by sharply higher points’ density exist and checked whether the areas outlined by these clusters (core areas) hold specific characteristics. Habitat selection analysis was then performed to compare the intensity of habitat selection carried out by putative residents and dispersers. We identified a high-density cluster of 30 detection points outlining a small core area stably located in the central part of the park. The dramatic differences of the R2 and the AUC of the habitat selection models performed inside (R2 = 0.506; AUC = 0.952) and outside (R2 = 0.037; AUC = 0.643) the core area corroborated the hypothesis that the core area effectively encloses detection points belonging to residents. Our results show that through simple space use analyses it is possible to roughly discriminate between detection points belonging to resident-behaving and disperser-behaving individuals and that habitat selection models separately performed on these data have extremely different results with strong possible effects on resistance surfaces parametrized from these models. Highlights We decoupled data of resident and dispersing wolves by analyzing detection data collected within a natural ecological corridor. Through space use analyses on detection data, it is possible to roughly discriminate between resident-behaving and disperser-behaving individuals. Habitat selection carried out by resident-behaving and disperser-behaving individuals is dramatically different.
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从探测数据中解耦居民和分散者改进栖息地选择模型:自然走廊中狼的案例研究
基于检测数据的资源选择分析被广泛用于参数化生态廊道的阻力面。为了成功地将阻力参数化,将驻留和分散行为解耦是至关重要的,但迄今为止,使用检测数据的连通性研究尚未解决这一问题。在这里,我们通过分析在穿越人类主导的意大利平原的自然走廊中收集的检测数据,将居住狼和分散狼的数据解耦。为了将居民和分散者分离开来,我们进行了核密度分析,以调查是否存在以密度急剧增加为特征的狼检测点集群,并检查这些集群(核心区)所勾勒的区域是否具有特定特征。然后进行生境选择分析,比较推定的居民和分散者进行的生境选择强度。我们确定了一个高密度的30个检测点集群,勾勒出一个稳定位于公园中心的小核心区。生境选择模型的R2和AUC之间存在显著差异(R2 = 0.506;AUC = 0.952)和外部(R2 = 0.037;AUC = 0.643),证实了核心区有效包围了居民监测点的假设。我们的研究结果表明,通过简单的空间利用分析,可以大致区分属于居居行为和分散行为的个体的检测点,并且在这些数据上分别执行的栖息地选择模型具有非常不同的结果,并且这些模型参数化的阻力面可能具有很强的影响。通过对自然生态廊道内狼的检测数据进行分析,解耦了狼的居住和散居数据。通过对探测数据的空间使用分析,可以大致区分居民行为和分散行为的个体。居住行为个体和分散行为个体的生境选择存在显著差异。
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来源期刊
Accounts of Chemical Research
Accounts of Chemical Research 化学-化学综合
CiteScore
31.40
自引率
1.10%
发文量
312
审稿时长
2 months
期刊介绍: Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.
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