MODELING LARGE SCALE SPECIES ABUNDANCE WITH LATENT SPATIAL PROCESSES
MODELING LARGE SCALE SPECIES ABUNDANCE WITH LATENT SPATIAL PROCESSES
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DOI：
10.1214/10-aoas335
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发表时间：
2010-09-01
影响因子：
1.8
通讯作者：
Silander, John A., Jr.
中科院分区：
文献类型：
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作者：
Chakraborty, Avishek;Gelfand, Alan E.;Silander, John A., Jr.
利用当地环境特征对物种丰度模式进行建模是生态学中当前的一个重要问题。南非开普植物区 (CFR) 是全球多样性和特有性热点地区，为此类建模提供了丰富的物种丰度数据。在这里，我们提出了一个多阶段贝叶斯分层模型来解释该地区的物种丰度。我们的模型是在区域级别指定的，其中 CFR 被分为大约 37,000 个一分钟网格单元；在某些细胞内的某些位置观察到物种丰度。丰度值按顺序分类。模型中包含了可能影响丰度模式的环境和土壤类型因素。我们将经验丰度模式表述为潜在模式的退化版本，退化效应分两个阶段完成。首先，我们调整土地利用转变，然后调整测量误差，从而调整错误分类误差，以产生观察到的丰度分类。此分析中的一个重要点是，仅对 28% 的网格单元进行了采样，并且对于采样的网格单元，采样位置的数量从 1 个到超过 100 个不等。尽管如此，我们仍然能够在整个区域开发潜在的和转化的丰度表面。在层次框架中，分类丰度分类是由连续的潜在表面诱导的。上面的退化模型是建立在潜在尺度上的。在这个尺度上，使用区域水平空间回归模型来模拟物种丰度对环境因素的依赖性。为了捕获相邻区域之间丰度模式的预期相似性，指定了具有条件自回归先验（CAR）的空间随机效应。模型拟合是通过熟悉的马尔可夫链蒙特卡罗方法进行的。虽然具有 CAR 先验的模型通常可以有效拟合，即使对于大型数据集，但由于我们的建模和大量单元格，运行时间变得非常长。因此，开发了一种新颖的并行计算策略来加速拟合。该模型针对六种不同的物种运行。对于分类数据，显示所得的丰度模式是一个挑战，我们提供了几种不同的视图。这些模式本身非常重要，相对于整个地区和物种来说，对物种竞争以及更广泛的规划和保护都有影响。
Modeling species abundance patterns using local environmental features is an important, current problem in ecology. The Cape Floristic Region (CFR) in South Africa is a global hot spot of diversity and endemism, and provides a rich class of species abundance data for such modeling. Here, we propose a multi-stage Bayesian hierarchical model for explaining species abundance over this region. Our model is specified at areal level, where the CFR is divided into roughly 37,000 one minute grid cells; species abundance is observed at some locations within some cells. The abundance values are ordinally categorized. Environmental and soil-type factors, likely to influence the abundance pattern, are included in the model. We formulate the empirical abundance pattern as a degraded version of the potential pattern, with the degradation effect accomplished in two stages. First, we adjust for land use transformation and then we adjust for measurement error, hence misclassification error, to yield the observed abundance classifications. An important point in this analysis is that only 28% of the grid cells have been sampled and that, for sampled grid cells, the number of sampled locations ranges from one to more than one hundred. Still, we are able to develop potential and transformed abundance surfaces over the entire region.In the hierarchical framework, categorical abundance classifications are induced by continuous latent surfaces. The degradation model above is built on the latent scale. On this scale, an areal level spatial regression model was used for modeling the dependence of species abundance on the environmental factors. To capture anticipated similarity in abundance pattern among neighboring regions, spatial random effects with a conditionally autoregressive prior (CAR) were specified. Model fitting is through familiar Markov chain Monte Carlo methods. While models with CAR priors are usually efficiently fitted, even with large data sets, with our modeling and the large number of cells, run times became very long. So a novel parallelized computing strategy was developed to expedite fitting. The model was run for six different species. With categorical data, display of the resultant abundance patterns is a challenge and we offer several different views. The patterns are of importance on their own, comparatively across the region and across species, with implications for species competition and, more generally, for planning and conservation.