Statistical methods to identify mechanisms in studies of eco-evolutionary dynamics

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.1016/j.tree.2023.03.011. This is version 2 of this Preprint.

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Authors

Jelena Holly Pantel, Lutz Becks

Abstract

While the reciprocal effects of ecological and evolutionary dynamics are increasingly recognized as an important driver for biodiversity, detection of such eco-evolutionary feedbacks, their underlying mechanisms, and their consequences remains challenging. Eco-evolutionary dynamics occur at different spatial and temporal scales and can leave signatures at different levels of organization (e.g., gene, protein, trait, community) that are often difficult to detect. Recent advances in statistical methods combined with alternative hypothesis testing provides a promising approach to identify potential eco-evolutionary drivers for observed data even in non-model systems that are not amenable to experimental manipulation. We discuss recent advances in eco-evolutionary modeling and statistical methods and discuss challenges for fitting mechanistic models to eco-evolutionary data.

DOI

https://doi.org/10.32942/X2KG60

Subjects

Ecology and Evolutionary Biology

Keywords

hypothesis testing, biodiversity, Bayesian statistics, eco-evolutionary dynamics, Mechanistic models

Dates

Published: 2023-02-07 07:40

Last Updated: 2023-07-17 07:52

Older Versions
License

CC-By Attribution-NonCommercial-NoDerivatives 4.0 International

Additional Metadata

Conflict of interest statement:
None

Data and Code Availability Statement:
https://github.com/jhpantel/ecoevoR

Language:
English