Linking functional traits and demography to model species-rich communities

Loïc Chalmandrier (), Florian Hartig, Daniel C. Laughlin, Heike Lischke, Maximilian Pichler, Daniel B. Stouffer and Loïc Pellissier ()
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Loïc Chalmandrier: ETH Zurich
Florian Hartig: University of Regensburg
Daniel C. Laughlin: University of Wyoming
Heike Lischke: Swiss Federal Research Institute WSL
Maximilian Pichler: University of Regensburg
Daniel B. Stouffer: University of Canterbury, School of Biological Sciences
Loïc Pellissier: ETH Zurich

Nature Communications, 2021, vol. 12, issue 1, 1-9

Abstract: Abstract It has long been anticipated that relating functional traits to species demography would be a cornerstone for achieving large-scale predictability of ecological systems. If such a relationship existed, species demography could be modeled only by measuring functional traits, transforming our ability to predict states and dynamics of species-rich communities with process-based community models. Here, we introduce a new method that links empirical functional traits with the demographic parameters of a process-based model by calibrating a transfer function through inverse modeling. As a case study, we parameterize a modified Lotka–Volterra model of a high-diversity mountain grassland with static plant community and functional trait data only. The calibrated trait–demography relationships are amenable to ecological interpretation, and lead to species abundances that fit well to the observed community structure. We conclude that our new method offers a general solution to bridge the divide between trait data and process-based models in species-rich ecosystems.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-22630-1

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DOI: 10.1038/s41467-021-22630-1

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