Enhancing the predictability of ecology in a changing world: A call for an organism-based approach - Musters, C. J. M.; DeAngelis, Don L.; Harvey, Jeffrey A.; Mooij, Wolf M.; van Bodegom, Peter M.; de Snoo, Geert R. (Frontiers in Applied Mathematics and Statistics, 2023)
Ecologists are usually very good in explaining what they observe, but they are often unable to make predictions regarding ecosystems. 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 higher predictability may be reached if we focus on individual organisms instead of species populations, because organisms, that maximize their fitness, 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. We show that Trait-based Ecology, Agent Based Models, and Maximum Entropy Theory of Ecology allow organism-based studies of ecosystems. 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.