In an era of ongoing biodiversity, it is critical to map biodiversity patterns in space and time for better-informing conservation and management. Species distribution models (SDMs) are widely applied in various types of such biodiversity assessments. Cross-validation represents a prevalent approach to assess the discrimination capacity of a target SDM algorithm and determine its optimal parameters. Several alternative cross-validation methods exist; however, the influence of choosing a specific cross-validation method on SDM performance and predictions remains unresolved. Here, we tested the performance of random versus spatial cross-validation methods for SDM using goatfishes (Actinopteri: Syngnathiformes: Mullidae) as a case study, which are recognized as indicator species for coastal waters. Our results showed that the random versus spatial cross-validation methods resulted in different optimal model parameterizations in 57 out of 60 modeled species. Significant difference existed in predictive performance between the random and spatial cross-validation methods, and the two cross-validation methods yielded different projected present-day spatial distribution and future projection patterns of goatfishes under climate change exposure. Despite the disparity in species distributions, both approaches consistently suggested the Indo-Australian Archipelago as the hotspot of goatfish species richness and also as the most vulnerable area to climate change. Our findings highlight that the choice of cross-validation method is an overlooked source of uncertainty in SDM studies. Meanwhile, the consistency in richness predictions highlights the usefulness of SDMs in marine conservation. These findings emphasize that we should pay special attention to the selection of cross-validation methods in SDM studies.

中文翻译：

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物种分布模型中的交叉验证问题：山羊鱼物种的案例研究


在生物多样性持续存在的时代，绘制空间和时间上的生物多样性模式以更好地为保护和管理提供信息至关重要。物种分布模型 （SDM） 广泛应用于各种类型的此类生物多样性评估。交叉验证是评估目标 SDM 算法的鉴别能力并确定其最佳参数的常用方法。存在几种替代交叉验证方法;但是，选择特定交叉验证方法对 SDM 性能和预测的影响仍未解决。在这里，我们使用山羊鱼 （Actinopteri： Syngnathiformes： Mullidae） 作为案例研究测试了 SDM 的随机与空间交叉验证方法的性能，山羊鱼被公认为沿海水域的指示物种。我们的结果表明，随机与空间交叉验证方法导致 60 个建模物种中的 57 个出现不同的最优模型参数化。随机交叉验证方法和空间交叉验证方法之间的预测性能存在显著差异，两种交叉验证方法在气候变化暴露下得出的山羊鱼的预测当前空间分布和未来预测模式不同。尽管物种分布存在差异，但两种方法都一致认为印度-澳大利亚群岛是山羊鱼物种丰富的热点地区，也是最容易受到气候变化影响的地区。我们的研究结果强调，交叉验证方法的选择是 SDM 研究中被忽视的不确定性来源。同时，丰富度预测的一致性凸显了 SDM 在海洋保护中的有用性。 这些发现强调我们应该特别注意 SDM 研究中交叉验证方法的选择。