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Linking individual bioenergetics to ecosystem dynamics with integral projection models

Willem Bonnaffé, Martina Muraro, William Goulding, Doug W. Smith, Dan R. Stahler, Peter Hudson, Hamish McCallum, Sonya Clegg, Dan R. MacNulty, Tim Coulson

q-bio.PEarXiv:2608.28215

Abstract

Linking individual-level processes, such as survival and reproduction, to ecosystem dynamics is challenging due to interactions among populations and species. These interactions depend on organism traits, such as age, body size, and mass, and are influenced by fluctuations in population structure. Integral projection models (IPMs) have advanced our understanding of dynamics arising from population structure in continuous traits. Yet, these models have only been applied to relatively simple systems, featuring one or two species and spanning a fraction of the trophic levels observed in natural food webs. We extend these models in four ways. First, we provide a general multi-species IPM where population structure can be included in primary producers and primary and secondary consumers. Second, we link individual-level processes to ecosystem functioning by mapping fluxes of biomass between species to fluxes within individuals, using bioenergetic principles for allocation to maintenance, reproduction, or growth. Third, we introduce a nutrient recycling loop through which decomposers break down organic matter into nutrients that enable plant growth. Finally, we provide an efficient fitting algorithm to calibrate the model with time series of population sizes. We showcase our approach by parameterising and fitting the model to counts of elk, bison, wolves, and cougars in northern Yellowstone National Park to study the impact of predator extirpation and recovery. Our model predicts that bison decrease following predator removal and recover following predator recovery, suggesting a positive contribution of wolves and cougars to the increase of bison in northern Yellowstone. It also reproduces indirect effects of predators on woody deciduous vegetation regeneration. Our approach is suitable for a broad range of ecosystems, with applications for population management and ecological forecasting.

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