Accounting for preferential sampling in species distribution models
Accounting for preferential sampling in species distribution models
复制标题
考虑物种分布模型中的优先抽样
作者：
M. Pennino;I. Paradinas;J. Illian;F. Muñoz;J. Bellido;A. López;D. Conesa
物种分布模型(SDMS)目前在生态学中被广泛应用于陆地、淡水和海洋领域的管理和保护目的。对SDMS日益增长的兴趣引起了生态学家对空间模型的注意，特别是地统计模型，这些模型用于将物种出现或丰度的观测与有限数量的地点的环境协变量联系起来，以预测一个物种可能出现在哪里(以及有多少)出现在未抽样的地点。标准地统计学方法假定抽样地点的选择与感兴趣变量的值无关。然而，在自然环境中，由于与时间和资金限制相关的实践限制，这一理论假设经常被违反。事实上，数据通常来自机会主义抽样(例如，观鲸或观鸟)，在这种抽样中，观察者倾向于在他们希望找到的地区寻找特定的物种。这些都是所谓的优先抽样的例子，这可能会导致对物种分布的有偏见的预测。这项研究的目的是讨论一种解决这个问题的SDM，它在计算上比现有的MCMC方法更有效。从统计学的角度来看，我们将数据解释为一个标记点模式，其中采样位置形成一个点模式，在这些位置进行的测量(即物种丰富度或出现次数)是相关的标记。利用贝叶斯方法进行物种分布的推断和预测，并使用集成嵌套拉普拉斯近似(INLA)方法和软件进行模型拟合，以最大限度地减少计算负担。无论是在模拟实例中，还是在使用渔业数据的实际应用中，我们都表明，当不考虑优先采样效应时，在低丰度位置的丰度被高度高估。这突出表明，当一项调查是基于非随机和/或非系统抽样时，生态学家应该意识到优先抽样造成的潜在偏差，并在模型中对其进行解释。
Species distribution models (SDMs) are now being widely used in ecology for management and conservation purposes across terrestrial, freshwater, and marine realms. The increasing interest in SDMs has drawn the attention of ecologists to spatial models and, in particular, to geostatistical models, which are used to associate observations of species occurrence or abundance with environmental covariates in a finite number of locations in order to predict where (and how much of) a species is likely to be present in unsampled locations. Standard geostatistical methodology assumes that the choice of sampling locations is independent of the values of the variable of interest. However, in natural environments, due to practical limitations related to time and financial constraints, this theoretical assumption is often violated. In fact, data commonly derive from opportunistic sampling (e.g., whale or bird watching), in which observers tend to look for a specific species in areas where they expect to find it. These are examples of what is referred to as preferential sampling, which can lead to biased predictions of the distribution of the species. The aim of this study is to discuss a SDM that addresses this problem and that it is more computationally efficient than existing MCMC methods. From a statistical point of view, we interpret the data as a marked point pattern, where the sampling locations form a point pattern and the measurements taken in those locations (i.e., species abundance or occurrence) are the associated marks. Inference and prediction of species distribution is performed using a Bayesian approach, and integrated nested Laplace approximation (INLA) methodology and software are used for model fitting to minimize the computational burden. We show that abundance is highly overestimated at low abundance locations when preferential sampling effects not accounted for, in both a simulated example and a practical application using fishery data. This highlights that ecologists should be aware of the potential bias resulting from preferential sampling and account for it in a model when a survey is based on non-randomized and/or non-systematic sampling.