Abstract Species distribution models can be affected by overprediction when dispersal movement is not incorporated into the modelling process. We compared the efficiency of seven methods that take into account spatial constraints to reduce overprediction when using four algorithms for species distribution models. By using a virtual ecologist approach, we were able to measure the accuracy of each model in predicting actual species distributions. We built 40 virtual species distributions within the Neotropical realm. Then, we randomly sampled 50 occurrences that were used in seven spatially restricted species distribution models (hereafter called M-SDMs) and a non-spatially restricted ecological niche model (ENM). We used four algorithms; Maximum Entropy, Generalized Linear Models, Random Forest, and Support Vector Machine. M-SDM methods were divided into a priori methods, in which spatial restrictions were inserted with environmental variables in the modelling process, and a posteriori methods, in which reachable and suitable areas were overlapped. M-SDM efficiency was obtained by calculating the difference in commission and omission errors between M-SDMs and ENMs. We used linear mixed-effects models to test if differences in commission and omission errors varied among the M-SDMs and algorithms. Our results indicate that overall M-SDMs reduce overprediction with no increase in underprediction compared to ENMs with few exceptions, such as a priori methods combined with the Support Vector Machine algorithm. There is a high variation in modelling performance among species, but there were only a few cases in which overprediction or underprediction increased. We only compared methods that do not require species dispersal data, guaranteeing that they can be applied to less-studied species. We advocate that species distribution modellers should not ignore spatial constraints, especially because they can be included in models at low costs but high benefits in terms of overprediction reduction.

中文翻译：

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处理物种分布模型中的过度预测：添加距离约束如何提高模型精度

摘要 当扩散运动未纳入建模过程时，物种分布模型可能会受到过度预测的影响。我们比较了在使用四种算法进行物种分布模型时考虑空间约束以减少过度预测的七种方法的效率。通过使用虚拟生态学家的方法，我们能够测量每个模型在预测实际物种分布方面的准确性。我们在新热带地区建立了 40 个虚拟物种分布。然后，我们随机抽样了 50 个事件，用于七个空间受限的物种分布模型（以下称为 M-SDM）和一个非空间受限的生态位模型（ENM）。我们使用了四种算法；最大熵、广义线性模型、随机森林和支持向量机。M-SDM方法分为先验方法，其中空间限制在建模过程中插入环境变量，以及后验方法，其中可达和合适的区域重叠。M-SDM 效率是通过计算 M-SDM 和 ENM 之间的佣金和遗漏误差的差异获得的。我们使用线性混合效应模型来测试 M-SDM 和算法之间的佣金和遗漏错误的差异是否有所不同。我们的结果表明，与 ENM 相比，整体 M-SDM 减少了过度预测，而没有增加预测不足，但很少有例外，例如与支持向量机算法相结合的先验方法。物种之间的建模性能差异很大，但只有少数情况下高估或低估增加。我们只比较了不需要物种扩散数据的方法，保证它们可以应用于研究较少的物种。我们主张物种分布建模者不应忽视空间约束，特别是因为它们可以以低成本包含在模型中，但在减少过度预测方面具有很高的收益。