Randomising spatial patterns supports the integration of intraspecific variation in ecological niche models

IF 5.4 1区 环境科学与生态学 Q1 BIODIVERSITY CONSERVATION Ecography Pub Date : 2024-11-12 DOI:10.1111/ecog.07289
Niels Preuk, Daniel Romero-Mujalli, Damaris Zurell, Manuel Steinbauer, and Juergen Kreyling
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Abstract

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.
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随机化空间模式有助于在生态位模型中整合种内变异
生态位模型(ENM)是生物多样性预测和保护中的一项重要建模技术,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,因为它们假定物种是同质的,忽略了种内变异。复合 ENM 可通过结合种内 ENM 来整合种内变异,捕捉物种地理范围内的个体环境响应。虽然最近的研究表明,考虑种内变异可以改善模型预测,但我们目前还缺乏方法来检验这种改善的显著性。在此,我们提出了一种空模型方法,将观测到的种内结构随机化,作为比较的适当基线。我们通过比较物种水平 ENM 与欧洲山毛榉复合 ENM 的预测性能来说明这种方法。为了研究空间世系结构的影响,我们用相同的保留数据对所有模型进行了测试,以便根据五个共同的性能指标对不同模型进行比较。我们发现,物种水平的 ENM 具有更高的总体性能(即 AUC、TSS 和 Boyce 指数)和特异性(预测缺失的能力),而复合 ENM 具有更高的灵敏度(预测存在的能力)。与此相应,与随机血系结构的空模型相比,复合 ENM 也显示出更高的灵敏度和更低的特异性。我们的研究表明,对模型性能的评估因所使用的测量方法而有很大不同,这就要求我们仔细研究多种评估方法。空模型的应用使我们能够区分观察到的 ENMs 种内变异模式的影响。此外,我们还强调了验证和使用基础良好的亚组进行建模。尽管种内变异提高了对欧洲山毛榉出现情况的预测能力,但它并不能完全取代经典的物种水平模型,因此应谨慎使用,以加深理解,并补充而非取代物种水平模型。
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来源期刊
Ecography
Ecography 环境科学-生态学
CiteScore
11.60
自引率
3.40%
发文量
122
审稿时长
8-16 weeks
期刊介绍: ECOGRAPHY publishes exciting, novel, and important articles that significantly advance understanding of ecological or biodiversity patterns in space or time. Papers focusing on conservation or restoration are welcomed, provided they are anchored in ecological theory and convey a general message that goes beyond a single case study. We encourage papers that seek advancing the field through the development and testing of theory or methodology, or by proposing new tools for analysis or interpretation of ecological phenomena. Manuscripts are expected to address general principles in ecology, though they may do so using a specific model system if they adequately frame the problem relative to a generalized ecological question or problem. Purely descriptive papers are considered only if breaking new ground and/or describing patterns seldom explored. Studies focused on a single species or single location are generally discouraged unless they make a significant contribution to advancing general theory or understanding of biodiversity patterns and processes. Manuscripts merely confirming or marginally extending results of previous work are unlikely to be considered in Ecography. Papers are judged by virtue of their originality, appeal to general interest, and their contribution to new developments in studies of spatial and temporal ecological patterns. There are no biases with regard to taxon, biome, or biogeographical area.
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