Biological traits predict species’ time-varying responses to multiple global change drivers

Read full text
Authors: Takehiro Sasaki, Yuki Iwachido, Orlando Lam-Gordillo, Katie M. Cook, Emily J. Douglas, Rebecca V. Gladstone-Gallagher, Barry L. Greenfield, Sarah F. Hailes, Kamryn Carter, Naohiro I. Ishii, Yoshiki Takayama, Shinji Shimode
+4 more Maiko Kagami, Judi E. Hewitt, Simon F. Thrush, Andrew M. Lohrer
Cited by: 3
References: 89

Abstract

Multiple drivers of global change are causing rapid biodiversity loss worldwide. However, predicting species' trajectories remains challenging due to the dynamic and state-dependent nature of ecological responses in real-world ecosystems. Here, we leverage nonlinear time series analysis of a multi-decadal, high-resolution dataset encompassing climate, freshwater, and sediment variables, alongside estuarine macroinvertebrate abundance. Our analysis shows that key biological traits, including body size, mobility, and lifespan predict the mean and variability of the time-varying sensitivity of species to specific environmental drivers. Species with smaller body sizes or lower mobility exhibit consistently negative responses to warming. The temporal variability of species sensitivity, an aspect often overlooked in previous studies of species' environmental responses, is strongly associated with lifespan, with shorter-lived species showing greater fluctuations over time. These findings did not always align with results from controlled laboratory or short-term field experiments, highlighting the complex, state-dependent responses of species shaped by multiple drivers of global change. We introduce a framework that links biological traits to long-term environmental responses, providing a predictive basis for trait-sensitivity relationships.

Fields of Study & Subjects

Physiological and biochemical adaptations Freshwater macroinvertebrate diversity and ecology Species Distribution and Climate Change

Publication details

Journal / source
Nature Communications
Publisher
Springer Science and Business Media LLC
Volume
17
Issue
1
Publication date
14 March 2026
Language
EN