Vaniscotte Amélie, Pleydell David, Raoul Francis, Quéré Jean Pierre, Jiamin Qiu, Wang Qian, Tiaoying Li, Bernard Nadine, Coeurdassier Michael, Delattre Pierre, Takahashi Kenichi, Weidmann Jean-Christophe, Giraudoux Patrick
Department of Chrono-environment, UMR UFC/CNRS 6249, USC INRA, Université de Franche-comté, 25030 Besançon cedex, France.
Ecol Modell. 2009 May 17;220(9-10):1218-1231. doi: 10.1016/j.ecolmodel.2009.02.019.
We investigate the relationship between landscape heterogeneity and the spatial distribution of small mammals in two areas of Western Sichuan, China. Given a large diversity of species trapped within a large number of habitats, we first classified small mammal assemblages and then modelled the habitat of each in the space of quantitative environmental descriptors. Our original two step "classify then model" procedure is appropriate for the frequently encountered study scenario: trapping data collected in remote areas with sampling guided by expert field knowledge.In the classification step, we defined assemblages by grouping sites of similar species composition and relative densities using an expert-class-merging procedure which reduced redundancy in the habitat factor used within a multinomial logistic regression predicting species trapping probabilities. Assemblages were thus defined as mixtures of small mammal frequency distributions in discrete groups of sampled sites.In the modelling step, assemblages' habitats and environments of the two sampled areas were discriminated in the space of remotely sensed environmental descriptors. First, we compared the discrimination of assemblage/study areas by linear and non-linear forms of Discriminant Analysis (Linear Discriminant Analysis versus Mixture Discriminant Analysis) and of Multiple Regression (Generalized Linear Models versus Multiple Adaptive Regression Splines). The "best" predictive modelling technique was then used to quantify the contribution of each environmental variable in discriminations of assemblages and areas.Mixtures of Gaussians provided a more efficient model of assemblage coverage in environmental space than a single Gaussian cluster model. However, non-linearity in assemblage response to environmental gradients was consistently predicted with lower deviance and misclassification error by Multiple Adaptive Regression Splines. The two study areas were mainly discriminated along vegetation indices. However, although the Normalized Difference Vegetation Index (NDVI) could discriminate forested from non-forested habitats, its power to discriminate assemblages in Maerkang, where a greater diversity of forest habitat was observed, was seen to be limited, and in this case NDVI was outperformed by the Enhanced Vegetation Index (EVI). Our analyses highlight previously unobserved differences between the environments and small mammal communities of two fringe areas of the Tibetan plateau and suggests that a biogeograph-ical approach is required to elucidate ecological processes in small mammal communities and to reduce extrapolation uncertainty in distribution mapping.
我们研究了中国川西两个地区景观异质性与小型哺乳动物空间分布之间的关系。鉴于在大量栖息地中捕获的物种具有高度多样性，我们首先对小型哺乳动物群落进行分类，然后在定量环境描述符空间中对每个群落的栖息地进行建模。我们最初的两步“先分类后建模”程序适用于常见的研究场景：在偏远地区收集诱捕数据，并由专家现场知识指导采样。在分类步骤中，我们使用专家类合并程序对具有相似物种组成和相对密度的地点进行分组，从而定义群落，该程序减少了在预测物种捕获概率的多项逻辑回归中使用的栖息地因子的冗余。因此，群落被定义为采样地点离散组中小型哺乳动物频率分布的混合。在建模步骤中，在遥感环境描述符空间中区分了两个采样区域的群落栖息地和环境。首先，我们比较了通过线性和非线性形式的判别分析（线性判别分析与混合判别分析）以及多元回归（广义线性模型与多元自适应回归样条）对群落/研究区域的判别。然后使用“最佳”预测建模技术来量化每个环境变量在群落和区域判别中的贡献。高斯混合模型在环境空间中提供了比单个高斯聚类模型更有效的群落覆盖模型。然而，通过多元自适应回归样条，始终以更低的偏差和误分类误差预测群落对环境梯度的非线性响应。两个研究区域主要沿植被指数进行区分。然而，尽管归一化植被指数（NDVI）可以区分森林和非森林栖息地，但在马尔康观察到森林栖息地多样性更大的情况下，其区分群落的能力有限，在这种情况下，增强植被指数（EVI）的表现优于NDVI。我们的分析突出了青藏高原两个边缘地区环境与小型哺乳动物群落之间以前未观察到的差异，并表明需要一种生物地理学方法来阐明小型哺乳动物群落中的生态过程，并减少分布图绘制中的外推不确定性。
