Point Pattern Modeling for Degraded Presence-Only Data over Large Regions
Point Pattern Modeling for Degraded Presence-Only Data over Large Regions
复制标题
大区域降级仅存在数据的点模式建模
DOI：
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发表时间：
2010
期刊：
影响因子：
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通讯作者：
A. Silander
中科院分区：
文献类型：
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作者：
A. Chakraborty;A. Gelfand;A. Wilson;A. Latimer;John;A. Silander
利用当地环境特征来解释物种的分布是一个长期存在的生态问题。通常，可用的数据只是作为一组存在位置收集，因此排除了进行期望的存在-不存在分析的可能性。我们建议将仅存在的数据视为一个区域上的点模式，并使用当地环境特征来解释驱动这种点模式的强度，这是很自然的。我们使用层次模型将存在数据视为空间点过程的实现，其强度由一组环境协变量控制。强度水平的空间依赖性用涉及零均值高斯过程的随机效应建模。我们扩大模型以捕获高度可变和典型的稀疏采样努力以及土地转换，这两者都会降低点格局。南非开普植物区（Cape Floristic Region， CFR）提供了这类物种的大量数据。从保护和政策的角度来看，整个地区潜在的（即未退化的）存在表面是值得关注的。该区域被划分为约3.7万个网格单元。为了处理大量单元格上的高斯过程，我们使用预测空间过程近似。通过添加异方差误差分量的偏差校正也已实现。我们用六种不同物种的模型来说明。此外，还与现在流行的Maxent方法进行了比较，尽管后者在推理方面受到限制。结果模式本身很重要，但也可以进行比较，例如，调查一对物种是否有可能在同一地区竞争。我们的建模的另一个特点是有机会通过物种丰富度来推断生物多样性，即一个面积单位中不同物种的数量。这样的调查紧随我们的建模框架。
Explaining the distribution of a species using local environmental features is a long standing ecological problem. Often, available data is collected as a set of presence locations only thus precluding the possibility of a desired presence-absence analysis. We propose that it is natural to view presence-only data as a point pattern over a region and to use local environmental features to explain the intensity driving this point pattern. We use a hierarchical model to treat the presence data as a realization of a spatial point process, whose intensity is governed by the set of environmental covariates. Spatial dependence in the intensity levels is modeled with random effects involving a zero mean Gaussian process. We augment the model to capture highly variable and typically sparse sampling effort as well as land transformation, both of which degrade the point pattern. The Cape Floristic Region (CFR) in South Africa provides an extensive class of such species data. The potential (i.e., nondegraded) presence surfaces over the entire area are of interest from a conservation and policy perspective. The region is divided into ∼ 37, 000 grid cells. To work with a Gaussian process over a very large number of cells we use predictive spatial process approximation. Bias correction by adding a heteroscedastic error component has also been implemented. We illustrate with modeling for six of different species. Also, comparison is made with the now popular Maxent approach though the latter is limited with regard to inference. The resultant patterns are important on their own but also enable a comparative view, for example, to investigate whether a pair of species are potentially competing in the same area. An additional feature of our modeling is the opportunity to infer about biodiversity through species richness, i.e., the number of distinct species in an areal unit. such investigation immediately follows within our modeling framework.