Evaluating Bayesian spatial methods for modelling species distributions with clumped and restricted occurrence data.
Evaluating Bayesian spatial methods for modelling species distributions with clumped and restricted occurrence data.
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DOI：
10.1371/journal.pone.0187602
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发表时间：
2017
期刊：
影响因子：
3.7
通讯作者：
Jones KE
中科院分区：
文献类型：
--
作者：
Redding DW;Lucas TCD;Blackburn TM;Jones KE
推断分类群空间分布的统计方法（物种分布模型，SDMs）通常依赖于现有的发生率数据，这些数据通常是聚集的和地理限制的。虽然现有的SDM方法可以解决其中的一些因素，但使用空间显式方法可以更直接和准确地对它们进行建模。在sdm中使用空间自相关参数拟合模型的软件现已广泛使用，但与其他方法相比，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-INLA拟合）的性能，当底层数据表现出不同的聚集和地理限制组合时。最后，我们测试了任何推荐的方法设置是如何设计的，以解释数据影响推理中的空间非随机模式。空间贝叶斯SDM方法是最准确的方法，在8个数据采样场景中有7个是最准确的前2种方法。在高覆盖率的样本数据集中，所有方法的执行都相当相似。当采样点随机分布时，BRT方法的准确率比其他方法高1-3%；当样本聚集时，空间贝叶斯SDM方法的AUC得分比其他方法高4%-8%。或者，当采样点被限制在真实范围的一小部分时，所有方法的准确率平均降低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外，在考虑自相关的推荐设置下的模型推理不受数据聚集或限制的影响。诸如R-INLA提供的方法可以成功地用于解释SDM背景下的空间自相关，并且通过考虑随机效应，产生的输出可以更好地阐明协变量在预测物种发生中的作用。考虑到在经验发生数据集中数据聚集背后的驱动因素通常是不清楚的，或者这些数据在地理上有多受限制，在模拟目标物种的空间分布时，空间显式贝叶斯sdm可能是更好的选择。
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.
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影响因子：
8.9
作者：
Huang, J;Ling, CX
通讯作者：
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影响因子：
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作者：
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DOI：
10.1098/rsif.2015.0086
发表时间：
2015-06-06
期刊：
Journal of the Royal Society, Interface
影响因子：
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通讯作者：
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