Population context matters: Predicting the effects of metabolic stress mediated by food availability and predation with an agent- and energy budget-based model

Maxime Vaugeois, Paul A. Venturelli, Stephanie L. Hummel, Chiara Accolla and Valery E. Forbes

Ecological Modelling, 2020, vol. 416, issue C

Abstract: Population effects of stressors, such as toxic chemicals or increased temperatures, affecting the energy budgets of organisms are mediated by predation pressure and food availability. However, these two population contexts have mostly been considered separately. Moreover, because the sensitivity of the different pathways of energy to stress may differ, it is difficult to predict combined stressor effects. We used an agent-based model of fathead minnows (Pimephales promelas) to infer the population-level impacts of a hypothetical, sublethal stressor that affects an individual's metabolism (growth, reproduction, maintenance, or assimilation) in systems in which population size is controlled by different combinations of food availability and predation. We found that population-level effects are rarely directly proportional to individual-level effects, and were greater when the stressor impacted assimilation and populations were predation-controlled. Our results suggest that individual-level measurements alone are insufficient for inferring population-level impacts of stressors and that accurate inference hinges on insight into how populations are regulated. We suggest incorporating individual-level data into mechanistic models that take into account both the energy budgets of individuals and the population-level context.

Keywords: Population-level effects of stressors; Individual-based model; Dynamic energy budget theory; Population regulating factors; Context-dependent stressor effects; Ecological risk assessment (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:416:y:2020:i:c:s0304380019304119

DOI: 10.1016/j.ecolmodel.2019.108903

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