Abstract
Projecting ocean biogeochemistry and fisheries resources under climate change requires confidence in simulation models. Core to such models is the description of consumer dynamics relating prey abundance to capture, digestion efficiency and growth rate. Capture is most commonly described as a linear function of prey encounter or by rectangular hyperbola. Most models also describe consumers as eating machines which “live-to-eat,” where growth (μ) is limited by a maximum grazing rate (Gmax). Real consumers can feed much faster than needed to support their maximum growth rate (μmax); with feeding modulated by satiation, they “eat-to-live.” A set of strategic analyses were conducted of these alternative philosophies of prey consumption dynamics and testing of their effects in the StrathE2E end-to-end marine food web and fisheries model. In an experiment where assimilation efficiencies were decreased by 10%, such as might result from a change in temperature or ocean acidity, the different formulation resulted in up to 100% variation in the change in abundances of food web components, especially in the mid-trophic levels. Our analysis points to a need for re-evaluation of some long-accepted principles in consumer-resource modeling.
Highlights
Consumer dynamics are central to ecology (e.g., Cohen et al, 1993; Hanley and La Pierre, 2015; Schaffner et al, 2019)
We explore how alternative permutations of grazing kinetics (RHt2 vs. satiation controlled encounter based (SCEB)) and control of grazing potential (L2E vs. E2L) impact upon predator-prey dynamics that would affect biogeochemical cycling and in a complex food web simulation, upon fisheries simulations
The effect on growth rates was not the same; the traditional L2E formulations showed a greater difference in growth between low and high stress than did the E2L formulation for both rectangular hyperbolic type-2 (RHt2) and SCEB variants (Figure 3)
Summary
Consumer dynamics are central to ecology (e.g., Cohen et al, 1993; Hanley and La Pierre, 2015; Schaffner et al, 2019). The magnitude and direction of this sensitivity becomes important when models are used to simulate the effects of changing environmental conditions (e.g., different prey types or temperature) which may affect processes such as maximum feeding rates, assimilation efficiencies or respiration rates. It is not clear whether changes projected by simulations run under different environmental conditions do provide a robust reflection of reality, or whether at least in part projections are the consequences of decisions made. The behavior of the consumer model affects its usefulness both to support our fundamental understanding of ecology, and our views on climate-induced and allied changes to ecosystems and ecosystem services, and how best to manage them
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