Modelling species diversity through species level hierarchical modelling
Modelling species diversity through species level hierarchical modelling
复制标题
DOI：
10.1111/j.1467-9876.2005.00466.x
复制
发表时间：
2005-01-01
影响因子：
1.6
通讯作者：
Rebelo, AG
中科院分区：
文献类型：
--
作者：
Gelfand, AE;Schmidt, AM;Rebelo, AG
了解物种多样性的空间格局和个体物种的分布是生物地理学和保护中的一个棘手的问题。南非的开普省植物区系是一个具有多样性和地方性的全球热点地区，ProTea地图集项目在该地区拥有约6万个地点记录，为模拟生物多样性的模式提供了极其丰富的数据集。模式的发展在空间上集中在1(‘)个网格单元的尺度上(该地区总共约37000个单元)。我们报告了一个确定的次区域的开花植物科的23种植物的结果(在开普省植物区约有330种)。使用贝叶斯框架，我们开发了一个两阶段的、空间显式的分层Logistic回归。阶段1模拟每个单元中每个物种存在或不存在的潜在概率、给定的物种属性、具有物种级别系数的网格单元(站点级别)环境数据以及空间随机效应。层次结构的第二层对在每个细胞中观察到每个物种的概率进行建模，假设每个细胞中存在每个物种。由于地图集数据并不均匀地分布在整个地貌中，网格单元包含不同数量的采样位置。因此，该模型通过假设某一特定物种在某一地点被观察到的总次数服从二项分布，来考虑每个地点的抽样强度。在对模型中的所有量分配先验分布后，通过马尔科夫链蒙特卡罗方法从后验分布中获得样本。结果被映射为模型估计的跨领域每个物种的存在概率。这提供了一种替代传统的经验式“占用范围”显示。总和得出了该地区物种丰富度的预测结果。每个环境系数的后验概率汇总显示了哪些变量在解释物种的存在时最重要。我们的初步结果很好地描述了模拟区域的生物地理模式。特别是，物种本地种群大小和扩散方式与年降雨量、降雨量和海拔高度的变异系数一起，对预测模式有很大贡献。
Understanding spatial patterns of species diversity and the distributions of individ-ual species is a consuming problem in biogeography and conservation. The Cape floristic region of South Africa is a global hot spot of diversity and endemism, and the Protea atlas project, with about 60 000 site records across the region, provides an extraordinarily rich data set to model patterns of biodiversity. Model development is focused spatially at the scale of 1(') grid cells (about 37 000 cells total for the region). We report on results for 23 species of a flowering plant family known as Proteaceae (of about 330 in the Cape floristic region) for a defined subregion. Using a Bayesian framework, we developed a two-stage, spatially explicit, hierarchical logistic regression. Stage 1 models the potential probability of presence or absence for each species at each cell, given species attributes, grid cell (site level) environmental data with species level coefficients, and a spatial random effect. The second level of the hierarchy models the probability of observing each species in each cell given that it is present. Because the atlas data are not evenly distributed across the landscape, grid cells contain variable numbers of sampling localities. Thus this model takes the sampling intensity at each site into account by assuming that the total number of times that a particular species was observed within a site follows a binomial distribution. After assigning prior distributions to all quantities in the model, samples from the posterior distribution were obtained via Markov chain Monte Carlo methods. Results are mapped as the model-estimated probability of presence for each species across the domain. This provides an alternative to customary empirical 'range-of-occupancy' displays. Summing yields the predicted richness of species over the region. Summaries of the posterior for each environmental coefficient show which variables are most important in explaining the presence of species. Our initial results describe biogeographical patterns over the modelled region remarkably well. In particular, species local population size and mode of dispersal contribute significantly to predicting patterns, along with annual precipitation, the coefficient of variation in rainfall and elevation.