The Centre of Excellence for Environmental Decisions, School of Botany, University of Melbourne, Parkville, Melbourne, Vic., 3010, Australia.
Glob Chang Biol. 2014 Aug;20(8):2566-79. doi: 10.1111/gcb.12598. Epub 2014 May 21.
Species distribution models (SDMs) are widely used to forecast changes in the spatial distributions of species and communities in response to climate change. However, spatial autocorrelation (SA) is rarely accounted for in these models, despite its ubiquity in broad-scale ecological data. While spatial autocorrelation in model residuals is known to result in biased parameter estimates and the inflation of type I errors, the influence of unmodeled SA on species' range forecasts is poorly understood. Here we quantify how accounting for SA in SDMs influences the magnitude of range shift forecasts produced by SDMs for multiple climate change scenarios. SDMs were fitted to simulated data with a known autocorrelation structure, and to field observations of three mangrove communities from northern Australia displaying strong spatial autocorrelation. Three modeling approaches were implemented: environment-only models (most frequently applied in species' range forecasts), and two approaches that incorporate SA; autologistic models and residuals autocovariate (RAC) models. Differences in forecasts among modeling approaches and climate scenarios were quantified. While all model predictions at the current time closely matched that of the actual current distribution of the mangrove communities, under the climate change scenarios environment-only models forecast substantially greater range shifts than models incorporating SA. Furthermore, the magnitude of these differences intensified with increasing increments of climate change across the scenarios. When models do not account for SA, forecasts of species' range shifts indicate more extreme impacts of climate change, compared to models that explicitly account for SA. Therefore, where biological or population processes induce substantial autocorrelation in the distribution of organisms, and this is not modeled, model predictions will be inaccurate. These results have global importance for conservation efforts as inaccurate forecasts lead to ineffective prioritization of conservation activities and potentially to avoidable species extinctions.
物种分布模型 (SDM) 被广泛用于预测物种和群落的空间分布在气候变化下的变化。然而，尽管在广泛的生态数据中存在空间自相关 (SA)，但这些模型很少考虑到它。尽管模型残差中的空间自相关会导致有偏的参数估计和 I 型错误的膨胀，但未建模的 SA 对物种范围预测的影响还不太清楚。在这里，我们量化了在 SDM 中考虑 SA 如何影响 SDM 对多个气候变化情景下物种范围变化预测的幅度。将 SDM 拟合到具有已知自相关结构的模拟数据和来自澳大利亚北部的三个红树林群落的实地观测数据上，这些红树林群落显示出强烈的空间自相关。实施了三种建模方法：仅环境模型（最常用于物种范围预测），以及两种包含 SA 的方法；自回归模型和残差自协方差 (RAC) 模型。比较了不同建模方法和气候情景下的预测差异。虽然所有模型的预测在当前时间都与红树林群落的实际当前分布非常吻合，但在气候变化情景下，仅环境模型预测的范围变化比包含 SA 的模型大得多。此外，随着情景中气候变化增量的增加，这些差异的幅度加剧。当模型不考虑 SA 时，物种范围变化的预测表明气候变化的影响更加极端，与明确考虑 SA 的模型相比。因此，在生物或种群过程导致生物体分布存在大量自相关且未建模的情况下，模型预测将不准确。这些结果对于保护工作具有全球重要性，因为不准确的预测会导致保护活动的优先级无效，并可能导致可避免的物种灭绝。
