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Predictions of biodiversity are improved by integrating trait-based competition with abiotic filtering

Authors :
Purcell Ast
Daniel C. Laughlin
Loïc Chalmandrier
Daniel B. Stouffer
Andrew J. Tanentzap
William G. Lee
Chalmandrier, Loïc [0000-0002-2631-0432]
Stouffer, Daniel B [0000-0001-9436-9674]
Lee, William G [0000-0001-7717-0807]
Tanentzap, Andrew J [0000-0002-2883-1901]
Laughlin, Daniel C [0000-0002-9651-5732]
Apollo - University of Cambridge Repository
Source :
Ecology Letters, bioRxiv, BioRxiv (preprint)
Publication Year :
2022
Publisher :
Wiley, 2022.

Abstract

All organisms must simultaneously tolerate the environment and access limiting resources if they are to persist. Otherwise they go extinct. Approaches to understanding environmental tolerance and resource competition have generally been developed independently. Consequently, integrating the factors that determine abiotic tolerance with those that affect competitive interactions to model species abundances and community structure remains an unresolved challenge. This is likely the reason why current models of community assembly do not accurately predict species abundances and dynamics. Here, we introduce a new synthetic framework that models both abiotic tolerance and biotic competition by using functional traits, which are phenotypic attributes that influence organism fitness. First, our framework estimates species carrying capacities that vary along abiotic gradients based on whether the phenotype tolerates the local environment. Second, it estimates pairwise competitive interactions as a function of multidimensional trait differences between species and determines which trait combinations produce the most competitive phenotypes. We demonstrate that our combined approach more than doubles the explained variance of species covers in a wetland community compared to the model of abiotic tolerances alone. Trait-based integration of competitive interactions and abiotic filtering improves our ability to predict species abundances across space, bringing us closer to more accurate predictions of biodiversity structure in a changing world.

Details

Database :
OpenAIRE
Journal :
Ecology Letters, bioRxiv, BioRxiv (preprint)
Accession number :
edsair.doi.dedup.....e7c3083d73639bd4e96f4ed58f740f3e