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This is a Preprint and has not been peer reviewed.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. You must log in to post a comment. There are no comments or no comments have been made public for this article. This is a Preprint and has not been peer reviewed. This is version 1 of this Preprint. Add a Comment You must log in to post a comment. Comments There are no comments or no comments have been made public for this article. 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. https://doi.org/10.32942/X2F948 Life Sciences population dynamics, consumer-resource models, sublinear growth, functional response Published: 2025-07-31 02:51 Last Updated: 2025-07-31 02:51 CC BY Attribution 4.0 International 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. Language: English