Trait-based predictions of ecosystem properties in coastal forests

Abstract

Abstract Understanding and predicting ecological processes from species’ traits has been considered an essential issue in ecology. However, whether traits can reliably predict ecosystem properties and how trait variations affect the accuracy of these predictions remain debatable. Using data from coastal forests and controlled experiments simulating coastal environmental stresses, we conducted a meta-analysis of 396 data points from global studies to identify key traits for predicting ecosystem properties, including species diversity, biomass production, and soil nutrient dynamics. We tested the reliability of using these traits to predict ecosystem properties based on field data from coastal dwarf forests and non-coastal dwarf forests in eastern China (32 site). Results showed that plant height and basal diameter had greater intraspecific variation and effectively predicted ecosystem properties. These trait variations captured biotic and abiotic processes involved in resource capture, utilization, and stress tolerance, demonstrating that key traits, when selected based on mechanistic understanding, can predict ecosystem properties without being hindered by trait variations. Based on our findings, we propose a simplified framework for predicting ecosystem properties by integrating community cluster traits and environmental proxies for ecosystem function traits contained in key traits.

Affiliated Institutions

• Arizona State University US
• Chinese Academy of Sciences CN
• Tsinghua University CN
• Sun Yat-sen University CN
• Beijing Normal University CN
• Institute of Tibetan Plateau Research CN

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