Enhancing the predictability of ecology in a changing world: A call for an organism-based approach

C. J. M. Musters* (Corresponding author), Don L. DeAngelis, Jeffrey A. Harvey, Wolf M. Mooij, Peter M. van Bodegom, Geert R. de Snoo

*Corresponding author for this work

Research output: Contribution to journal/periodicalArticleScientificpeer-review

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Abstract

Ecology is usually very good in making descriptive explanations of what is observed, but is often unable to make predictions of the response of ecosystems to change. This has implications in a human-dominated world where a suite of anthropogenic stresses are threatening the resilience and functioning of ecosystems that sustain mankind through a range of critical regulating and supporting services. In ecosystems, cause-and-effect relationships are difficult to elucidate because of complex networks of negative and positive feedbacks. Therefore, being able to effectively predict when and where ecosystems could pass into different (and potentially unstable) new states is vitally important under rapid global change. Here, we argue that such better predictions may be reached if we focus on organisms instead of species, because organisms are the principal biotic agents in ecosystems that react directly on changes in their environment. Several studies show that changes in ecosystems may be accurately described as the result of changes in organisms and their interactions. Organism-based theories are available that are simple and derived from first principles, but allow many predictions. Of these we discuss Trait-based Ecology, Agent Based Models, and Maximum Entropy Theory of Ecology and show that together they form a logical sequence of approaches that allow organism-based studies of ecological communities. Combining and extending them makes it possible to predict the spatiotemporal distribution of groups of organisms in terms of how metabolic energy is distributed over areas, time, and resources. We expect that this “Organism-based Ecology” (OE) ultimately will improve our ability to predict ecosystem dynamics.
Original languageEnglish
Article number1046185
JournalFrontiers in Applied Mathematics and Statistics
Volume9
DOIs
Publication statusPublished - 24 Jan 2023

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