Xu Haigen, Cao Yun, Cao Mingchang, Wu Jun, Wu Yi, Le Zhifang, Cui Peng, Li Jiaqi, Ma Fangzhou, Liu Li, Hu Feilong, Chen Mengmeng, Tong Wenjun
Nanjing Institute of Environmental Sciences Ministry of Environmental Protection of China Nanjing China.
Department of Biology Nanjing University Nanjing China.
Ecol Evol. 2017 Sep 20;7(21):8829-8840. doi: 10.1002/ece3.3348. eCollection 2017 Nov.
Proxies are adopted to represent biodiversity patterns due to inadequate information for all taxa. Despite the wide use of proxies, their efficacy remains unclear. Previous analyses focused on overall species richness for fewer groups, affecting the generality and depth of inference. Biological taxa often exhibit very different habitat preferences. Habitat groupings may be an appropriate approach to advancing the study of richness patterns. Diverse geographical patterns of species richness and their potential mechanisms were then examined for habitat groups. We used a database of the spatial distribution of 32,824 species of mammals, birds, reptiles, amphibians and plants from 2,376 counties across China, divided the five taxa into 30 habitat groups, calculated Spearman correlations of species richness among taxa and habitat groups, and tested five hypotheses about richness patterns using multivariate models. We identified one major group [i.e., forest- and shrub-dependent (FS) groups], and some minor groups such as grassland-dependent vertebrates and desert-dependent vertebrates. There were mostly high or moderate correlations among FS groups, but mostly low or moderate correlations among other habitat groups. The prominent variables differed among habitat groups of the same taxon, such as birds and reptiles. The sets of predictors were also different within the same habitat, such as forests, grasslands, and deserts. Average correlations among the same habitat groups of vertebrates and among habitat groups of a single taxon were low or moderate, except correlations among FS groups. The sets of prominent variables of species richness differed strongly among habitat groups, although elevation range was the most important variable for most FS groups. The ecological and evolutionary processes that underpin richness patterns might be disparate among different habitat groups. Appropriate groupings based on habitats could reveal important patterns of richness gradients and valuable biodiversity components.
由于缺乏所有分类单元的充分信息，因此采用代理来代表生物多样性模式。尽管代理被广泛使用，但其有效性仍不明确。以往的分析集中在较少类群的总体物种丰富度上，影响了推断的普遍性和深度。生物分类单元通常表现出非常不同的栖息地偏好。栖息地分组可能是推进丰富度模式研究的一种合适方法。然后，我们研究了栖息地组的物种丰富度的不同地理模式及其潜在机制。我们使用了一个包含中国2376个县的32824种哺乳动物、鸟类、爬行动物、两栖动物和植物空间分布的数据库，将这五个分类单元划分为30个栖息地组，计算了分类单元和栖息地组之间物种丰富度的斯皮尔曼相关性，并使用多变量模型检验了关于丰富度模式的五个假设。我们确定了一个主要组[即依赖森林和灌木的（FS）组]，以及一些次要组，如依赖草原的脊椎动物和依赖沙漠的脊椎动物。FS组之间大多呈高或中度相关，而其他栖息地组之间大多呈低或中度相关。同一分类单元的栖息地组之间，如鸟类和爬行动物，突出变量有所不同。在同一栖息地内，如森林、草原和沙漠，预测变量集也不同。脊椎动物同一栖息地组之间以及单个分类单元的栖息地组之间的平均相关性较低或中等，FS组之间的相关性除外。物种丰富度的突出变量集在栖息地组之间差异很大，尽管海拔范围是大多数FS组最重要的变量。支撑丰富度模式的生态和进化过程在不同的栖息地组之间可能不同。基于栖息地的适当分组可以揭示丰富度梯度的重要模式和有价值的生物多样性组成部分。
