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Density dependence revisited: strong evidence for superlinear population growth

Density dependence revisited: strong evidence for superlinear population growth

This is a Preprint and has not been peer reviewed. This is version 1 of this Preprint.

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Authors

James A Orr, Kaleigh E Davis, Alicia H Williams, Jan Engelstädter, Daniel B Stouffer, Andrew D Letten 

Abstract

Density dependence is a core principle in ecological and evolutionary theory, and yet the precise nature of the relationship between per capita growth and population size continues to ignite debate. While sublinear (convex/decelerating) density dependence is frequently observed in empirical studies, standard techniques for estimating density dependence are prone to unreliable inference. At the same time, the putative ubiquity of sublinearity in nature is at odds with the predictions of mechanistic models of resource competition. We used a continuous-culture approach, which bypasses the inferential challenges hindering conventional methods, in order to investigate the shape of density dependence in Escherichia coli. In agreement with the predictions of a model of resource competition empirically parameterised from independent growth assays, we found strong evidence for superlinear (concave/accelerating) density dependence. Despite the simplicity of our experimental system, we hypothesise that the evidence for sublinearity as a widespread phenomenon is less robust than widely assumed. Resolving this debate has significant implications for our fundamental understanding of ecosystem stability and the development of reliable models informing conservation and resource management.

DOI

https://doi.org/10.32942/X2F948

Subjects

Life Sciences

Keywords

population dynamics, consumer-resource models, sublinear growth, functional response

Dates

Published: 2025-07-30 19:51

Last Updated: 2025-07-30 19:51

License

CC BY Attribution 4.0 International

Additional Metadata

Language:
English

Data and Code Availability Statement:
All the data and code associated with this study are available on GitHub at https://github.com/jamesaorr/chemo-dd.