Ecological niche models (ENMs) are an essential modelling technique in biodiversity prediction and conservation and are frequently used to forecast species responses to global changes. Classic species‐level models may show limitations as they assume species homogeneity, neglecting intraspecific variation. Composite ENMs allow the integration of intraspecific variation by combining intraspecific‐level ENMs, capturing individual environmental responses over the species' geographic range. While recent studies suggest that accounting for intraspecific variation improves model predictions, we currently lack methods to test the significance of the improvement. Here, we propose a null model approach that randomises observed intraspecific structures as an appropriate baseline for comparison. We illustrate this approach by comparing predictive performance of a species‐level ENM to composite ENMs for European beech Fagus sylvatica. To investigate the influence of spatial lineage structure, we tested all models against the same withheld data to allow comparison across models based on five common performance metrics. We found that the species‐level ENM expressed higher overall performance (i.e. AUC, TSS, and Boyce index) and specificity (ability to predict absences), while the composite ENMs achieved higher sensitivity (ability to predict presences). In line with this, the composite ENMs also showed increased sensitivity and decreased specificity compared to the null models that randomised lineage structure. We showed that the assessment of model performance strongly varies based on the used measures, emphasising a careful investigation of multiple measures for evaluation. The application of null models allowed us to disentangle the effect of observed patterns of intraspecific variation in ENMs. Further, we highlight the validation and use of well‐founded subgroups for modelling. Although intraspecific variation improves the prediction of occurrences of European beech, it did not fully outcompete the classic species‐level model and should be used with care and rather to improve understanding and to supplement, not replace, species‐level models.

中文翻译：

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随机化空间模式支持将种内变异整合到生态位模型中


生态位模型 （ENM） 是生物多样性预测和保护中必不可少的建模技术，经常用于预测物种对全球变化的反应。经典的物种水平模型可能会显示出局限性，因为它们假设物种同质性，而忽略了种内变异。复合 ENM 允许通过组合种内水平 ENM 来整合种内变异，捕获物种地理范围内的个体环境响应。虽然最近的研究表明，考虑种内变异可以提高模型预测，但我们目前缺乏测试改进意义的方法。在这里，我们提出了一种零模型方法，将观察到的种内结构随机化作为比较的合适基线。我们通过比较物种水平 ENM 与复合 ENM 对欧洲山毛榉 Fagus sylvatica 的预测性能来说明这种方法。为了研究空间谱系结构的影响，我们针对相同的保留数据测试了所有模型，以便根据五个常见的性能指标进行模型比较。我们发现物种水平的 ENM 表达更高的整体性能 （即 AUC 、 TSS 和 Boyce 指数） 和特异性 （预测缺失的能力），而复合 ENM 实现了更高的敏感性 （预测存在的能力）。与此一致，与随机谱系结构的零模型相比，复合 ENMs 也显示出更高的敏感性和更低的特异性。我们表明，模型性能的评估因所使用的措施而异，强调对多种评估措施的仔细调查。 零模型的应用使我们能够解开在 ENM 中观察到的种内变异模式的影响。此外，我们强调了有根据的子组的验证和使用进行建模。尽管种内变异改善了对欧洲山毛榉出现的预测，但它并没有完全胜过经典的物种水平模型，应谨慎使用，而是要提高理解并补充而不是替代物种水平模型。