Tardy Olivia, Massé Ariane, Pelletier Fanie, Fortin Daniel
Centre d'Étude de la Forêt and Département de biologie, Université Laval, Québec, Québec, Canada.
Direction de la biodiversité et des maladies de la faune, Direction générale de l'expertise sur la faune et ses habitats, Ministère des Forêts, de la Faune et des Parcs, Québec, Québec, Canada.
PLoS One. 2015 Jun 4;10(6):e0128238. doi: 10.1371/journal.pone.0128238. eCollection 2015.
Isodar theory can be used to evaluate fitness consequences of density-dependent habitat selection by animals. A typical habitat isodar is a regression curve plotting competitor densities in two adjacent habitats when individual fitness is equal. Despite the increasing use of habitat isodars, their application remains largely limited to areas composed of pairs of adjacent habitats that are defined a priori. We developed a resampling method that uses data from wildlife surveys to build isodars in heterogeneous landscapes without having to predefine habitat types. The method consists in randomly placing blocks over the survey area and dividing those blocks in two adjacent sub-blocks of the same size. Animal abundance is then estimated within the two sub-blocks. This process is done 100 times. Different functional forms of isodars can be investigated by relating animal abundance and differences in habitat features between sub-blocks. We applied this method to abundance data of raccoons and striped skunks, two of the main hosts of rabies virus in North America. Habitat selection by raccoons and striped skunks depended on both conspecific abundance and the difference in landscape composition and structure between sub-blocks. When conspecific abundance was low, raccoons and striped skunks favored areas with relatively high proportions of forests and anthropogenic features, respectively. Under high conspecific abundance, however, both species preferred areas with rather large corn-forest edge densities and corn field proportions. Based on random sampling techniques, we provide a robust method that is applicable to a broad range of species, including medium- to large-sized mammals with high mobility. The method is sufficiently flexible to incorporate multiple environmental covariates that can reflect key requirements of the focal species. We thus illustrate how isodar theory can be used with wildlife surveys to assess density-dependent habitat selection over large geographic extents.
等达线理论可用于评估动物密度依赖型栖息地选择的适合度后果。典型的栖息地等达线是一条回归曲线，绘制出个体适合度相等时两个相邻栖息地的竞争者密度。尽管栖息地等达线的应用越来越广泛，但其应用仍主要局限于事先定义的由成对相邻栖息地组成的区域。我们开发了一种重采样方法，该方法利用野生动物调查数据在异质景观中构建等达线，而无需预先定义栖息地类型。该方法包括在调查区域随机放置区块，并将这些区块划分为两个大小相同的相邻子区块。然后估计两个子区块内的动物数量。这个过程重复100次。通过关联动物数量和子区块之间栖息地特征的差异，可以研究不同功能形式的等达线。我们将此方法应用于浣熊和条纹臭鼬的数量数据，这两种动物是北美狂犬病病毒的主要宿主。浣熊和条纹臭鼬的栖息地选择既取决于同种个体数量，也取决于子区块之间景观组成和结构的差异。当同种个体数量较低时，浣熊和条纹臭鼬分别偏爱森林比例相对较高和人为特征比例相对较高的区域。然而，在同种个体数量较高的情况下，这两个物种都更喜欢玉米-森林边缘密度和玉米田比例相当大的区域。基于随机抽样技术，我们提供了一种适用于广泛物种的稳健方法，包括具有高移动性的中型到大型哺乳动物。该方法足够灵活，可以纳入多个环境协变量，这些协变量可以反映目标物种的关键需求。因此，我们说明了如何将等达线理论与野生动物调查结合使用，以评估大地理范围内的密度依赖型栖息地选择。
