Spatial truncation in species distribution models (SDMs) might cause niche truncation and model transferability issues, particularly when extrapolating models to non‐analog environmental conditions. While broad calibration extents reduce truncation issues, they usually overlook local ecological factors driving species distributions at finer resolution. Spatially‐nested hierarchical SDMs (HSDMs) address truncation by merging (a) a global model calibrated with broadly extended, yet typically low‐resolution, basic, and imprecise data; and (b) a regional model calibrated with spatially restricted but more precise and reliable data. This study aimed to examine HSDMs' efficacy to overcome spatial truncation in national‐scale studies. We compared two hierarchical strategies (‘covariate', which uses the global model output as a covariate for the regional model, and ‘multiply', which calculates the geometric mean of the global and regional models) and a non‐hierarchical strategy. The three strategies were compared in terms of niche truncation, environmental extrapolation, model performance, species' predicted distributions and shifts, and trends in species richness. We examined the consistency of the results over two study areas (Spain and Switzerland), 108 tree species, and four future climate scenarios. Only the non‐hierarchical strategy was susceptible to niche truncation, and environmental extrapolation issues. Hierarchical strategies, particularly the ‘covariate' one, presented greater model accuracy than non‐hierarchical strategies. The non‐hierarchical strategy predicted the highest overall values and the lowest decreases over time in species distribution ranges and richness. Differences between strategies were more evident in Switzerland, which was more affected by niche truncation issues. Spain was more negatively affected by climate change and environmental extrapolation. The ‘covariate' strategy exhibited higher model performance than the ‘multiply' one. However, uncertainties regarding model temporal transferability advocate for adopting and further examining multiple hierarchical approaches. This research underscores the importance of adopting spatially‐nested hierarchical SDMs given the compromised reliability of non‐hierarchical approaches due to niche truncation and extrapolation issues.

中文翻译：

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空间嵌套分层物种分布模型克服国家规模研究中的生态位截断


物种分布模型（SDM）中的空间截断可能会导致生态位截断和模型可转移性问题，特别是在将模型外推到非模拟环境条件时。虽然广泛的校准范围减少了截断问题，但它们通常忽略了以更高分辨率驱动物种分布的当地生态因素。空间嵌套的分层 SDM (HSDM) 通过合并 (a) 使用广泛扩展但通常是低分辨率、基本和不精确的数据校准的全局模型来解决截断问题； (b) 使用空间有限但更精确和可靠的数据校准的区域模型。本研究旨在检验 HSDM 在全国范围的研究中克服空间截断的功效。我们比较了两种分层策略（“协变量”，使用全局模型输出作为区域模型的协变量，以及“乘法”，计算全局和区域模型的几何平均值）和非分层策略。从生态位截断、环境外推、模型性能、物种预测分布和变化以及物种丰富度趋势等方面对这三种策略进行了比较。我们检查了两个研究区域（西班牙和瑞士）、108 种树种和四种未来气候情景的结果的一致性。只有非等级策略容易受到利基截断和环境外推问题的影响。分层策略，尤其是“协变量”策略，比非分层策略具有更高的模型准确性。非等级策略预测了物种分布范围和丰富度随时间的最高总体值和最低下降。 在瑞士，战略之间的差异更为明显，该国更容易受到利基截断问题的影响。西班牙受到气候变化和环境外推的负面影响更大。 “协变量”策略比“乘法”策略表现出更高的模型性能。然而，模型时间可转移性的不确定性主张采用并进一步检查多种分层方法。鉴于利基截断和外推问题导致非分层方法的可靠性受到损害，这项研究强调了采用空间嵌套分层 SDM 的重要性。