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Mechanistic prediction of community composition across resource conditions and species richness

Zhijie Zhang,Lutz Becks

Published 2025 in Nature Communications

ABSTRACT

Predicting species coexistence and community assembly is a central goal in ecology. Traditional methods, based on the effect of one species on another (e.g., Lotka-Volterra competition model), are sensitive to environmental context. This is because they ignore the fundamental processes that can be applied across environments. While mechanistic approaches offer promise, empirical tests remain rare. Here, we integrate a mechanistic consumer-resource model with the growth of 12 phytoplankton species in monoculture over a range of nitrate, ammonium or phosphorous concentrations. We find that the mechanistic approach accurately predicts the composition of 960 communities across species richness and resource conditions. We confirm by simulations, species competing for substitutable resources (nitrate vs. ammonium) exhibit greater diversity than those competing for essential resources (nitrate vs. phosphorus), especially when initial species richness is high. This is because, in competition for essential resources, each species is likely to consume less of the resource that is more limiting to its growth, which violates the mechanistic rule of coexistence that states that each species must consume more of the resource that more limit it. Our study highlights the power of the mechanistic approach in understanding and predicting species loss across environments and, ultimately, mitigating its pace. Predicting species coexistence across environments is challenging. This study accurately predicts community composition using a consumer-resource model and reveals higher diversity when species compete for substitutable versus essential resources.

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