The population dynamics of clustered consumer–resource spatial patterns: Insights from the demographics of a Turing mechanism
Contributed by Ivette Perfecto; received April 24, 2024; accepted November 27, 2024; reviewed by David Alonso and Robert M. Pringle
Significance
Alan Turing’s activator–inhibitor mechanism provides a general theory to understand spatial pattern formation in ecosystems. Consumer–resource interactions, which qualitatively correspond to Turing’s theory, have been hypothesized to drive some observed spatial patterns but empirical evidence has been scant. Here, we develop a framework to study consumer–resource spatial patterns by highlighting how demographic spatial patterns in clustered resources can influence trends in their population dynamics in space through time. By combining analysis of field data with modeling, we apply our approach to an arboreal ant and its parasitoid on a coffee farm in Mexico and find support for the consumer–resource interaction driving the observed spatial patterns of the ant.
Abstract
In ecology, Alan Turing’s proposed activation–inhibition mechanism has been abstracted as corresponding to several ecological interaction types to explain pattern formation in ecosystems. Consumer–resource interactions have strong theoretical arguments linking them to both the Turing mechanism and pattern formation, but there is little empirical support to demonstrate these claims. Here, we connect several lines of evidence to support the proposition that consumer–resource interactions can create empirically observed spatial patterns through a mechanism similar to Turing’s theory. We propose the existence of a fine-scale demographic spatial pattern (DSP), in which the youngest resources are located at the periphery and oldest in the center of clusters. We find evidence of a DSP in the spatially clustered distribution of arboreal ant nests, whose large-scale spatial patterning has previously been hypothesized to be driven by ant parasitoids. Through a combination of field surveys and analysis of demographic trends, we demonstrate how the DSP structures the interactions between the ant and its parasitoid. To explore the implications of DSP for consumer–resource pattern forming systems generally, we use a spatially explicit consumer–resource model to show how relative diffusion rates of the system shape multiscale spatial patterns that structure the demographic trends of the resource population in predictable ways. This work provides both empirical support for consumer–resource spatial patterns as well as a multiscale approach to understand their spatially explicit population dynamics.
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Data, Materials, and Software Availability
All data and code are publicly archived and open here: https://figshare.com/projects/The_population_dynamics_of_clustered_consumer-resource_spatial_patterns_insights_from_the_demographics_of_a_Turing_mechanism/224382 (48, 49).
Acknowledgments
We would like to thank Gustavo López Bautista and Braulio E. Chilel for managing data collection in Mexico; the Peter’s family and Walter Peters for allowing us to conduct research on their coffee farm and; Chatura Vaidya, Kristel Sanchez, Tamara Milton, Paul Glaum, Douglas Jackson, David Allen, Jonno Morris, and Kevin Li for feedback on ideas and earlier versions of the manuscript. Ferdinand LaMothe, Earl Hines, and Sidney Bechet provided inspiration. Portions of the present manuscript were also part of the PhD dissertation of Z.H.-F. Z.H.-F. was supported by the Agriculture and Food Research Initiative (AFRI) Predoctoral Fellowship [Grant No.13374090/Project Accession No. 2022-6701136581] from the United States Department of Agriculture (USDA) National Institute of Food and Agriculture. Additional funding support came from USDA Grants NIFA/USDA 20172017-67019-26292326292 and NIFA/USDA 2018-67030-28239.
Author contributions
Z.H.-F., I.S.R.-S., I.P., and J.V. designed research; Z.H.-F. and I.S.R.-S. performed research; Z.H.-F. contributed analytic tools; Z.H.-F. analyzed data; and Z.H.-F. wrote the paper with feedback from all authors.
Competing interests
The authors declare no competing interest.
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Copyright © 2025 the Author(s). Published by PNAS. This article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).
Data, Materials, and Software Availability
All data and code are publicly archived and open here: https://figshare.com/projects/The_population_dynamics_of_clustered_consumer-resource_spatial_patterns_insights_from_the_demographics_of_a_Turing_mechanism/224382 (48, 49).
Submission history
Received: April 24, 2024
Accepted: November 27, 2024
Published online: January 17, 2025
Published in issue: January 21, 2025
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Acknowledgments
We would like to thank Gustavo López Bautista and Braulio E. Chilel for managing data collection in Mexico; the Peter’s family and Walter Peters for allowing us to conduct research on their coffee farm and; Chatura Vaidya, Kristel Sanchez, Tamara Milton, Paul Glaum, Douglas Jackson, David Allen, Jonno Morris, and Kevin Li for feedback on ideas and earlier versions of the manuscript. Ferdinand LaMothe, Earl Hines, and Sidney Bechet provided inspiration. Portions of the present manuscript were also part of the PhD dissertation of Z.H.-F. Z.H.-F. was supported by the Agriculture and Food Research Initiative (AFRI) Predoctoral Fellowship [Grant No.13374090/Project Accession No. 2022-6701136581] from the United States Department of Agriculture (USDA) National Institute of Food and Agriculture. Additional funding support came from USDA Grants NIFA/USDA 20172017-67019-26292326292 and NIFA/USDA 2018-67030-28239.
Author contributions
Z.H.-F., I.S.R.-S., I.P., and J.V. designed research; Z.H.-F. and I.S.R.-S. performed research; Z.H.-F. contributed analytic tools; Z.H.-F. analyzed data; and Z.H.-F. wrote the paper with feedback from all authors.
Competing interests
The authors declare no competing interest.
Notes
Reviewers: D.A., Consejo Superior de Investigaciones Cientificas; and R.M.P., Princeton University.
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The population dynamics of clustered consumer–resource spatial patterns: Insights from the demographics of a Turing mechanism, Proc. Natl. Acad. Sci. U.S.A.
122 (3) e2407991121,
https://doi.org/10.1073/pnas.2407991121
(2025).
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