Scottish Oceans Institute, School of Biology, University of St. Andrews, East Sands, St. Andrews, Fife KY168LB Scotland, United Kingdom.
Ecology. 2011 Mar;92(3):583-9. doi: 10.1890/10-0751.1.
Researchers employing resource selection functions (RSFs) and other related methods aim to detect correlates of space-use and mitigate against detrimental environmental change. However, an empirical model fit to data from one place or time is unlikely to capture species responses under different conditions because organisms respond nonlinearly to changes in habitat availability. This phenomenon, known as a functional response in resource selection, has been debated extensively in the RSF literature but continues to be ignored by practitioners for lack of a practical treatment. We therefore extend the RSF approach to enable it to estimate generalized functional responses (GFRs) from spatial data. GFRs employ data from several sampling instances characterized by diverse profiles of habitat availability. By modeling the regression coefficients of the underlying RSF as functions of availability, GFRs can account for environmental change and thus predict population distributions in new environments. We formulate the approach as a mixed-effects model so that it is estimable by readily available statistical software. We illustrate its application using (1) simulation and (2) wolf home-range telemetry. Our results indicate that GFRs can offer considerable improvements in estimation speed and predictive ability over existing mixed-effects approaches.
研究人员采用资源选择函数（RSF）和其他相关方法，旨在检测空间利用的相关性，并减轻不利的环境变化。然而，适用于一个地点或时间的数据的经验模型不太可能捕捉到在不同条件下物种的反应，因为生物对栖息地可用性的变化呈非线性反应。这种现象，在资源选择的功能反应中被广泛讨论，但由于缺乏实用的处理方法，继续被实践者忽视。因此，我们扩展了 RSF 方法，使其能够从空间数据中估计广义功能反应（GFR）。GFR 利用具有不同栖息地可用性分布特征的多个采样实例的数据。通过将基础 RSF 的回归系数建模为可用性的函数，GFR 可以解释环境变化，从而预测新环境中的种群分布。我们将该方法表述为混合效应模型，以便可通过现成的统计软件进行估计。我们使用（1）模拟和（2）狼的活动范围遥测来演示其应用。我们的结果表明，GFR 在估计速度和预测能力方面可以提供比现有混合效应方法显著的改进。
