Comparative interpretation of count, presence-absence and point methods for species distribution models
Comparative interpretation of count, presence-absence and point methods for species distribution models
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DOI：
10.1111/j.2041-210x.2011.00141.x
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发表时间：
2012-02-01
影响因子：
6.6
通讯作者：
Matthiopoulos, Jason
中科院分区：
文献类型：
--
作者：
Aarts, Geert;Fieberg, John;Matthiopoulos, Jason
1.需要了解的过程中塑造人口分布导致了空间野生动物数据的多样性大幅增加，导致许多新的分析技术的发展，是适合的目的。人们可以将位置数据聚合到空间单元（例如网格单元）中，并将所得到的计数或存在缺失作为环境协变量的函数进行建模。或者，点数据可以直接建模，通过将个体观测与一组反映栖息地可用性的随机或规则点相结合，这种方法称为使用-可用性设计（或者，存在伪缺失或病例对照设计）。2.虽然这些空间点，计数和存在-不存在的方法被广泛使用，生态学文献是不明确的，它们的连接，以及如何对它们的参数估计和预测应被解释。本研究的目的是概括一些最近的统计结果，并说明在某些假设下，每种方法都可以由相同的潜在空间非齐次泊松点过程（IPP）模型激励，其中强度函数被建模为协变量的对数线性函数.用于计数数据的泊松似然是IPP似然的离散近似。类似地，存在-不存在设计将近似IPP似然性，但仅当空间单位（即像素）非常小时（Electric Journal of Statistics，2010，4，1151-1201）。对于较大的像素尺寸，存在-不存在设计不区分每个像素内的一个或多个观测，因此导致信息丢失。逻辑回归通常用于使用点数据估计IPP模型的参数。虽然响应变量对于可用性点被定义为0，但是这些零并不像通常假设的那样用作真实的缺席;相反，它们的作用是近似IPP似然中的分母的积分（The Annals of Applied Statistics，2010，4，1383-1402）。由于这种常见的误解，线性预测器的估计指数函数（即资源选择函数）通常被假设为与占用率成比例。与IPP和计数模型一样，该函数与观测的预期密度成比例。了解不同物种分布建模技术之间的这些（差异）相似性，应改善空间模型的生物学解释，从而促进生态和方法学的交叉施肥。
1. The need to understand the processes shaping population distributions has resulted in a vast increase in the diversity of spatial wildlife data, leading to the development of many novel analytical techniques that are fit-for-purpose. One may aggregate location data into spatial units (e.g. grid cells) and model the resulting counts or presenceabsences as a function of environmental covariates. Alternatively, the point data may be modelled directly, by combining the individual observations with a set of random or regular points reflecting habitat availability, a method known as a use-availability design (or, alternatively a presence pseudo-absence or casecontrol design).2. Although these spatial point, count and presence-absence methods are widely used, the ecological literature is not explicit about their connections and how their parameter estimates and predictions should be interpreted. The objective of this study is to recapitulate some recent statistical results and illustrate that under certain assumptions, each method can be motivated by the same underlying spatial inhomogeneous Poisson point process (IPP) model in which the intensity function is modelled as a log-linear function of covariates.3. The Poisson likelihood used for count data is a discrete approximation of the IPP likelihood. Similarly, the presence-absence design will approximate the IPP likelihood, but only when spatial units (i.e. pixels) are extremely small (Electric Journal of Statistics, 2010, 4, 1151-1201). For larger pixel sizes, presence-absence designs do not differentiate between one or multiple observations within each pixel, hence leading to information loss.4. Logistic regression is often used to estimate the parameters of the IPP model using point data. Although the response variable is defined as 0 for the availability points, these zeros do not serve as true absences as is often assumed; rather, their role is to approximate the integral of the denominator in the IPP likelihood (The Annals of Applied Statistics, 2010, 4, 1383-1402). Because of this common misconception, the estimated exponential function of the linear predictor (i.e. the resource selection function) is often assumed to be proportional to occupancy. Like IPP and count models, this function is proportional to the expected density of observations.5. Understanding these (dis-)similarities between different species distribution modelling techniques should improve biological interpretation of spatial models and therefore advance ecological and methodological cross-fertilization.