Estimating space-use and habitat preference from wildlife telemetry data
Estimating space-use and habitat preference from wildlife telemetry data
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DOI：
10.1111/j.2007.0906-7590.05236.x
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发表时间：
2008-02-01
期刊：
影响因子：
5.9
通讯作者：
Matthiopoulos, Jason
中科院分区：
文献类型：
--
作者：
Aarts, Geert;MacKenzie, Monique;Matthiopoulos, Jason
动物种群的管理和保护需要关于它们在哪里、为什么在那里以及它们可能在哪里的信息。这些目标通常通过收集关于动物对空间的使用的数据、将这些位置数据与主要环境条件相关联并且采用所得到的统计模型来预测在其他地理区域的使用来实现。野生动物遥测技术的进步已经在可用数据的数量和质量方面实现了多方面的增加，从而需要一个统计框架，可以使用它们来对栖息地偏好和空间使用进行人口一级的推断。这一直是缓慢的到来，因为野生动物遥测数据是时空自相关的，往往不平衡，存在只观察行为复杂的动物，响应于众多的交叉相关的环境variables.We回顾了回归模型的演变空间使用和栖息地偏好的分析，并概述了从这些基础上自然出现的框架的基本特征。这使我们能够推导出地理空间中的点的使用与环境空间中的栖息地偏好之间的关系。在这个框架内，我们讨论了八个挑战，固有的遥测数据的空间分析，并为每一个，我们提出的解决方案，可以串联工作。具体而言，我们提出了一个逻辑，混合效应的方法，使用广义添加剂的环境协变量的转换，并适合于响应数据集，包括遥测和模拟观测，病例对照design.We应用这个框架的一个非平凡的案例研究，使用卫星标记的灰海豹Halichoerus grypus从苏格兰东海岸。我们通过交叉验证进行模型选择，并将最终模型的预测与来自相同以及不同地理区域的遥测数据进行比较。我们的结论是，尽管研究物种的复杂行为，灵活的经验模型可以捕捉到的环境关系，形状人口分布。
Management and conservation of populations of animals requires information on where they are, why they are there, and where else they could be. These objectives are typically approached by collecting data on the animals' use of space, relating these positional data to prevailing environmental conditions and employing the resulting statistical models to predict usage at other geographical regions. Technical advances in wildlife telemetry have accomplished manifold increases in the amount and quality of available data, creating the need for a statistical framework that can use them to make population-level inferences for habitat preference and space-use. This has been slow-in-coming because wildlife telemetry data are spatio-temporally autocorrelated, often unbalanced, presence-only observations of behaviourally complex animals, responding to a multitude of cross-correlated environmental variables.We review the evolution of regression models for the analysis of space-use and habitat preference and outline the essential features of a framework that emerges naturally from these foundations. This allows us to derive a relationship between usage of points in geographical space and preference of habitats in environmental space. Within this framework, we discuss eight challenges, inherent in the spatial analysis of telemetry data and, for each, we propose solutions that can work in tandem. Specifically, we propose a logistic, mixed-effects approach that uses generalized additive transformations of the environmental covariates and is fitted to a response data-set comprising the telemetry and simulated observations, under a case-control design.We apply this framework to a non-trivial case-study using satellite-tagged grey seals Halichoerus grypus from the east coast of Scotland. We perform model selection by cross-validation and confront our final model's predictions with telemetry data from the same, as well as different, geographical regions. We conclude that, despite the complex behaviour of the study species, flexible empirical models can capture the environmental relationships that shape population distributions.