Eltonian Niche Modelling: Applying Joint Hierarchical Niche Models to Ecological Networks

IF 7.6 1区 环境科学与生态学 Q1 ECOLOGY
Ecology Letters Pub Date : 2025-05-29 DOI:10.1111/ele.70120
D. Matthias Dehling, Hao Ran Lai, Daniel B. Stouffer
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引用次数: 0

Abstract

There is currently a dichotomy in the modelling of Grinnellian and Eltonian niches. Despite similar underlying data, Grinnellian niches are modelled with species-distribution models (SDMs), whereas Eltonian niches are modelled with ecological-network analysis, mainly because the sparsity of species-interaction data prevents the application of SDMs to Eltonian-niche modelling. Here, we propose to adapt recently developed joint species distribution models (JSDMs) to data on ecological networks, functional traits, and phylogenies to model species' Eltonian niches. JSDMs overcome sparsity and improve predictions for individual species by considering non-independent relationships among co-occurring species; this unique ability makes them particularly suited for sparse datasets such as ecological networks. Our Eltonian JSDMs reveal strong relationships between species' Eltonian niches and their functional traits and phylogeny. Moreover, we demonstrate that JSDMs can accurately predict the interactions of species for which no empirical interaction data are available, based solely on their functional traits. This facilitates prediction of new interactions in communities with altered composition, for example, following climate-change induced local extinctions or species introductions. The high interpretability of Eltonian JSDMs will provide unique insights into mechanisms underlying species interactions and the potential impacts of environmental changes and invasive species on species interactions in ecological communities.

Abstract Image

Eltonian生态位模型:联合层次生态位模型在生态网络中的应用
目前在格林奈尔生态位和埃尔顿生态位的建模中存在一种二分法。尽管基础数据相似,但格林奈尔生态位是用物种分布模型(SDMs)建模的,而埃尔顿生态位是用生态网络分析建模的,主要是因为物种相互作用数据的稀疏性阻碍了SDMs在埃尔顿生态位建模中的应用。在此,我们建议将最近开发的联合物种分布模型(JSDMs)与生态网络、功能特征和系统发育数据相结合,以模拟物种的埃尔顿生态位。通过考虑共生物种之间的非独立关系,JSDMs克服了稀疏性,提高了对单个物种的预测;这种独特的能力使它们特别适合于稀疏数据集,如生态网络。我们的Eltonian JSDMs揭示了物种的Eltonian生态位与其功能特征和系统发育之间的密切关系。此外,我们证明了JSDMs可以准确地预测没有经验相互作用数据的物种之间的相互作用,仅基于它们的功能特征。这有助于预测组成改变的群落中新的相互作用,例如,在气候变化引起的局部灭绝或物种引入之后。Eltonian JSDMs具有较高的可解释性,将为物种相互作用的机制以及环境变化和入侵物种对生态群落物种相互作用的潜在影响提供独特的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Ecology Letters
Ecology Letters 环境科学-生态学
CiteScore
17.60
自引率
3.40%
发文量
201
审稿时长
1.8 months
期刊介绍: Ecology Letters serves as a platform for the rapid publication of innovative research in ecology. It considers manuscripts across all taxa, biomes, and geographic regions, prioritizing papers that investigate clearly stated hypotheses. The journal publishes concise papers of high originality and general interest, contributing to new developments in ecology. Purely descriptive papers and those that only confirm or extend previous results are discouraged.
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