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Global vegetation and land-surface models embody interdisciplinary scientific understanding of the behaviour of plants and ecosystems, and are indispensable to project the impacts of environmental change on vegetation and the interactions between vegetation and climate. However, systematic errors and persistently large differences among carbon and water cycle projections by different models highlight the limitations of current process formulations. In this review, focusing on core plant functions in the terrestrial carbon and water cycles, we show how unifying hypotheses derived from eco-evolutionary optimality (EEO) principles can provide novel, parameter-sparse representations of plant and vegetation processes. We present case studies that demonstrate how EEO generates parsimonious representations of core, leaf-level processes that are individually testable and supported by evidence. EEO approaches to photosynthesis and primary production, dark respiration and stomatal behaviour are ripe for implementation in global models. EEO approaches to other important traits, including the leaf economics spectrum and applications of EEO at the community level are active research areas. Independently tested modules emerging from EEO studies could profitably be integrated into modelling frameworks that account for the multiple time scales on which plants and plant communities adjust to environmental change.
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http://dx.doi.org/10.1111/nph.17558 | DOI Listing |
Ageing Res Rev
August 2025
Department of Environmental Health Sciences, Butler Columbia Aging Center, Mailman School of Public Health, Columbia University, New York, NY, USA. Electronic address:
Inflamm-aging is widely considered a hallmark of aging, yet emerging evidence challenges its universality. Here, we re-examine inflamm-aging through an eco-evolutionary lens, underlining its context dependence across biological scales. Combining mechanistic, evolutionary, comparative, anthropological, genetic, and environmental evidence, we show how fundamental inflammatory mechanisms are integrated and regulated in diverse biological contexts, representing a suite of flexible stress responses.
View Article and Find Full Text PDFJ Exp Bot
July 2025
Division of Plant Sciences, Research School of Biology, The Australian National University, Canberra, ACT 2601, Australia.
This article comments on: 2025. Nitrogen demand, availability, and acquisition strategy control plant responses to elevated CO. Journal of Experimental Botany , 2908–2923 https://doi.
View Article and Find Full Text PDFGlob Chang Biol
June 2025
Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, Arizona, USA.
Warming alters soil microbial traits through ecological and evolutionary processes, directly influencing the decomposition of organic matter, which significantly affects global soil carbon emissions. Yet, soil carbon models largely ignore these processes and their implications for global responses to warming. Here, we incorporate eco-evolutionary theory into a mechanistic model describing microbial soil carbon decomposition to address the question of whether such processes could have consequential effects on climate carbon feedbacks globally.
View Article and Find Full Text PDFNat Plants
April 2025
Key Laboratory of Humid Subtropical Eco-Geographical Process (Ministry of Education), Fujian Normal University, Fuzhou, China.
Photosynthetic efficiency (PE) quantifies the fraction of absorbed light used in photochemistry to produce chemical energy during photosynthesis and is essential for understanding ecosystem productivity and the global carbon cycle, particularly under conditions of vegetation stress. However, nearly 60% of the global spatiotemporal variance in terrestrial PE remains unexplained. Here we integrate remote sensing and eco-evolutionary optimality theory to derive key plant traits, alongside explainable machine learning and global eddy covariance observations, to uncover the drivers of daily PE variations.
View Article and Find Full Text PDFAnn Bot
March 2025
Environmental Sciences, Copernicus Institute of Sustainable Development, Utrecht University, Utrecht, The Netherlands.
Background And Aims: Dynamic global vegetation models (DGVMs) are essential for quantifying the role of terrestrial ecosystems in the Earth's climate system, but struggle with uncertainty and complexity. Eco-evolutionary optimality (EEO) theory provides a promising approach to improve DGVMs based on the premise that leaf carbon gain is optimized with resource costs. However, the timescales at which plant traits can adjust to environmental changes are not yet systematically incorporated in EEO-based models.
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