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Evaluating Bayesian spatial methods for modelling species distributions with clumped and restricted occurrence data

机译：评估贝叶斯空间方法，用成簇和受限发生数据建模物种分布

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Statistical approaches for inferring the spatial distribution of taxa (Species Distribution Models, SDMs) commonly rely on available occurrence data, which is often clumped and geographically restricted. Although available SDM methods address some of these factors, they could be more directly and accurately modelled using a spatially-explicit approach. Software to fit models with spatial autocorrelation parameters in SDMs are now widely available, but whether such approaches for inferring SDMs aid predictions compared to other methodologies is unknown. Here, within a simulated environment using 1000 generated species’ ranges, we compared the performance of two commonly used non-spatial SDM methods (Maximum Entropy Modelling, MAXENT and boosted regression trees, BRT), to a spatial Bayesian SDM method (fitted using R-INLA), when the underlying data exhibit varying combinations of clumping and geographic restriction. Finally, we tested how any recommended methodological settings designed to account for spatially non-random patterns in the data impact inference. Spatial Bayesian SDM method was the most consistently accurate method, being in the top 2 most accurate methods in 7 out of 8 data sampling scenarios. Within high-coverage sample datasets, all methods performed fairly similarly. When sampling points were randomly spread, BRT had a 1–3% greater accuracy over the other methods and when samples were clumped, the spatial Bayesian SDM method had a 4%-8% better AUC score. Alternatively, when sampling points were restricted to a small section of the true range all methods were on average 10–12% less accurate, with greater variation among the methods. Model inference under the recommended settings to account for autocorrelation was not impacted by clumping or restriction of data, except for the complexity of the spatial regression term in the spatial Bayesian model. Methods, such as those made available by R-INLA, can be successfully used to account for spatial autocorrelation in an SDM context and, by taking account of random effects, produce outputs that can better elucidate the role of covariates in predicting species occurrence. Given that it is often unclear what the drivers are behind data clumping in an empirical occurrence dataset, or indeed how geographically restricted these data are, spatially-explicit Bayesian SDMs may be the better choice when modelling the spatial distribution of target species.
机译：推断分类单元空间分布的统计方法（物种分布模型，SDM）通常依赖于可用的发生数据，这些数据通常是成团的并且在地理上受到限制。尽管可用的SDM方法解决了其中一些因素，但可以使用空间明确的方法对它们进行更直接和准确的建模。现在可以广泛使用适合SDM中具有空间自相关参数的模型的软件，但是与其他方法相比，这种推断SDM的方法是否有助于预测尚不清楚。在这里，在使用1000个生成物种范围的模拟环境中，我们比较了两种常用的非空间SDM方法（最大熵建模，MAXENT和增强回归树，BRT）与空间贝叶斯SDM方法（使用R拟合）的性能。 -INLA），则基础数据表现出不同的聚集和地理限制组合。最后，我们测试了旨在解决数据中空间非随机模式的任何推荐方法设置如何影响推理。空间贝叶斯SDM方法是最一致的方法，在8种数据采样方案中的7种中，它是前2种最准确的方法。在高覆盖率的样本数据集中，所有方法的执行情况都非常相似。当采样点随机分布时，BRT的精度比其他方法高1–3％，而当样品成团时，空间贝叶斯SDM方法的AUC得分则高4％-8％。另外，当采样点被限制在真实范围的一小部分时，所有方法的准确度平均降低10-12％，方法之间的差异更大。除了空间贝叶斯模型中空间回归项的复杂性之外，在建议设置下考虑自相关的模型推断不受数据集或约束的影响。诸如R-INLA提供的方法等方法可以成功地用于解释SDM上下文中的空间自相关，并且通过考虑随机效应，可以产生可以更好地阐明协变量在预测物种发生中的作用的输出。鉴于通常不清楚经验驱动数据集中的数据背后的驱动因素是什么，或者这些数据的地理位置实际上受到多大限制，因此在对目标物种的空间分布进行建模时，空间明确的贝叶斯SDM可能是更好的选择。

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