ABSTRACT

The home range of an animal describes the geographic area where this individual spends most of the time while doing its usual activities. From a statistical viewpoint, the problem of home range estimation can be considered as a set estimation one. In the ecological literature, there are a variety of home range estimators. We address the open question of choosing the ‘best’ home range from a collection of them constructed on the same sample. We introduce the penalized overestimation ratio, a numerical index to rank the estimated home ranges. The key idea is to balance the excess area covered by the estimator (with respect to the sample) and a shape descriptor measuring the over-adjustment of the home range to the data. To our knowledge, apart from computing the home range area, our ranking procedure is the first one both applicable to real data and to any type of home range estimator. Further, optimization of the selection index provides a way to select the tuning parameters of nonparametric home ranges. For illustration purposes, we apply our selection proposal to a dataset of a Mongolian wolf and we carry out a simulation study.

 抽象的

动物的活动范围描述了该个体在进行日常活动时大部分时间所呆的地理区域。从统计学的角度来看，归属范围估计问题可以被认为是一个集合估计问题。在生态文献中，有多种家庭范围估计器。我们解决了从基于同一样本构建的集合中选择“最佳”家居系列的悬而未决的问题。我们引入了惩罚高估率，这是一个对估计的家庭范围进行排名的数字指数。关键思想是平衡估计器覆盖的多余区域（相对于样本）和测量数据范围的过度调整的形状描述符。据我们所知，除了计算主场范围面积之外，我们的排名程序是第一个既适用于实际数据又适用于任何类型的主场范围估计器的程序。此外，选择指数的优化提供了一种选择非参数起始范围的调整参数的方法。出于说明目的，我们将我们的选择建议应用于蒙古狼的数据集，并进行模拟研究。