Many species distribution models (SDMs) are built with precise but geographically restricted presence–absence data sets (e.g., a country) where only a subset of the environmental conditions experienced by a species across its range is considered (i.e., spatial niche truncation). This type of truncation is worrisome because it can lead to incorrect predictions e.g., when projecting to future climatic conditions belonging to the species niche but unavailable in the calibration area. Data from citizen-science programs, species range maps or atlases covering the full species range can be used to capture those parts of the species’ niche that are missing regionally. However, these data usually are too coarse or too biased to support regional management. Here, we aim to (1) demonstrate how varying degrees of spatial niche truncation affect SDMs projections when calibrated with climatically truncated regional data sets and (2) test the performance of different methods to harness information from larger-scale data sets presenting different spatial resolutions to solve the spatial niche truncation problem. We used simulations to compare the performance of the different methods, and applied them to a real data set to predict the future distribution of a plant species (Potentilla aurea) in Switzerland. SDMs calibrated with geographically restricted data sets expectedly provided biased predictions when projected outside the calibration area or time period. Approaches integrating information from larger-scale data sets using hierarchical data integration methods usually reduced this bias. However, their performance varied depending on the level of spatial niche truncation and how data were combined. Interestingly, while some methods (e.g., data pooling, downscaling) performed well on both simulated and real data, others (e.g., those based on a Poisson point process) performed better on real data, indicating a dependency of model performance on the simulation process (e.g., shape of simulated response curves). Based on our results, we recommend to use different data integration methods and, whenever possible, to make a choice depending on model performance. In any case, an ensemble modeling approach can be used to account for uncertainty in how niche truncation is accounted for and identify areas where similarities/dissimilarities exist across methods.

中文翻译：

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在物种分布区域预测中考虑空间生态位截断效应的数据整合方法

许多物种分布模型 (SDM) 是使用精确但受地理限制的存在-不存在数据集（例如，一个国家）构建的，其中仅考虑物种在其范围内经历的环境条件的一个子集（即空间生态位截断）。这种类型的截断令人担忧，因为它可能导致错误的预测，例如，当预测属于物种生态位但在校准区域不可用的未来气候条件时。来自公民科学计划、物种范围图或覆盖整个物种范围的地图集的数据可用于捕获区域性缺失的物种生态位的那些部分。然而，这些数据通常过于粗糙或过于偏向，无法支持区域管理。这里，我们的目标是 (1) 展示在使用气候截断的区域数据集校准时，不同程度的空间生态位截断如何影响 SDM 预测；(2) 测试不同方法的性能，以利用来自呈现不同空间分辨率的更大规模数据集的信息来解决空间生态位截断问题。我们使用模拟来比较不同方法的性能，并将它们应用于真实数据集以预测植物物种的未来分布（金黄马铃薯） 在瑞士。当投影到校准区域或时间段之外时，使用受地理限制的数据集校准的 SDM 预计会提供有偏差的预测。使用分层数据集成方法从大规模数据集中集成信息的方法通常会减少这种偏差。然而，它们的性能取决于空间生态位截断的程度和数据的组合方式。有趣的是，虽然一些方法（例如，数据池、缩减）在模拟和真实数据上都表现良好，但其他方法（例如，基于泊松点过程的那些）在真实数据上表现更好，表明模型性能依赖于模拟过程（例如，模拟响应曲线的形状）。根据我们的结果，我们建议使用不同的数据集成方法，并在可能的情况下，根据模型性能做出选择。在任何情况下，都可以使用集成建模方法来解释如何解释利基截断的不确定性，并确定不同方法存在相似/不同之处的区域。