Bayesian Analysis of Tree Distributions Across Space and Time in Eastern North America 2010-2011

The distributions of many organisms are spatially autocorrelated, but it is unclear whether including spatial terms in species distribution models (SDMs) improves projections of future species distributions. We provide the first comparative test of a purely spatial SDM, a purely non-spatial SDM, and an SDM that combines spatial and environmental information. Spatial SDMs provided better fits to the calibration data, more accurate predictions of a hold-out validation data set of modern trees, and lower false positive rates at all time periods than non-spatial SDMs. Hindcasted projection of spatial SDMs had higher variance than those of non-spatial SDMs. Overall predictive performance of non-spatial and spatial SDMs varied temporally and as a function of niche overlap. Ecological modelers should include spatial terms in SDMs used for projecting future distributions of species.诸多生物的分布均具有空间自相关性，但目前尚不明确在物种分布模型（Species Distribution Models, SDMs）中加入空间项是否能够提升未来物种分布的预测效果。本研究首次针对纯空间物种分布模型、纯非空间物种分布模型，以及融合空间与环境信息的物种分布模型开展对比测试。相较于非空间物种分布模型，空间物种分布模型对校准数据的拟合效果更优，对现代树木的留出验证数据集的预测更为准确，且在所有时间区间内的假阳性率更低。空间物种分布模型的回溯预测结果的方差高于非空间物种分布模型。非空间与空间物种分布模型的整体预测性能随时间推移以及生态位重叠度的变化而存在差异。生态建模研究者在用于预测物种未来分布的物种分布模型中，应当纳入空间项。