A theoretical framework for scaling ecological niches from individuals to species

• 成果类型: Article
• 署名作者: Lu, Muyang; Yanco, Scott W.; Carlson, Ben S.; Winner, Kevin; Cohen, Jeremy M.; Soto, Diego; Sharma, Shubhi; Rogers, Will; Jetz, Walter
• 署名单位: Sichuan University; Yale University; Yale University; Smithsonian Institution; Smithsonian National Zoological Park & Conservation Biology Institute; Yale University; Rutgers University System; Rutgers University New Brunswick; University of California System; University of California Berkeley
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-11217
• DOI: 10.1073/pnas.2425582122
• 发表日期: 2025-09-02
• 关键词: climate-change; vulnerability; performance; population; EVOLUTION; inference; collapse; fitness; models

摘要

The niche is a key concept that unifies ecology and evolutionary biology. However, empirical and theoretical treatments of the niche are mostly performed at the species level, neglecting individuals as important units of ecological and evolutionary processes. So far, a formal mathematical link between individual-level niches and higher organismal-level niches has been lacking, hampering the unification of ecological theories and more accurate forecasts of biodiversity change. To fill in this gap, we propose a bottom-up approach to derive population and higher organismal-level niches from individual niches. We demonstrate the power of our framework by showing that 1) the statistical properties of higher organismal-level niches (e.g., niche breadth, skewness, etc.) can be partitioned into individual contributions and 2) the species-level niche shifts can be estimated by tracing the responses of individuals. By using individual-level GPS (Global Positioning System) tracking data from three different species, we show that climate change could have contrasting consequences on population-level niche shift depending on individual niche compositions. Our method paves the way for a unifying niche theory and enables mechanistic assessments of organism-environment relationships across organismal scales.