Simulating bottom-up effects on predator productivity and consequences for the rebuilding timeline of a depleted population

Andre Buchheister, Michael J. Wilberg, Thomas J. Miller and Robert J. Latour

Ecological Modelling, 2015, vol. 311, issue C, 48-62

Abstract: Bottom-up control within ecosystems is characterized, in part, by predator populations exhibiting growth and recruitment changes in response to variability in prey density or production. Annual prey availability can vary more than 10-fold in marine ecosystems, with prey experiencing a dramatic increase or pulse in production within some years. To assess the bottom-up effects of such pulses on predator growth, production, and fisheries management, we developed an age-specific, predator–prey simulation model (parameterized for summer flounder, Paralichthys dentatus) based on simple hypothesized mechanisms for consumption, growth, and population dynamics. Pulses in each of the three modeled prey groups (small crustaceans, forage fish, larger fish prey) generated different magnitudes of change in predator weight-at-age (w), spawning stock biomass (S), fishery yield (Y), and recruitment (R), due to ontogenetic differences in growth potential and dietary composition across predator age classes. Increases in productivity of small forage fishes generated the greatest gains in predator w, S, Y, and R, relative to pulses of the other prey groups. Median increases in R following a prey pulse were minimal (<4%) except under high fishing rates that stimulated a stronger compensatory response in the population (8–11% increase in R), demonstrating the interactive role of top-down and bottom-up effects on predator productivity. Seasonal migration patterns determined the degree of spatiotemporal overlap of predators with the spatially constrained pulses in prey production. Prey pulses reduced the median time required for depleted populations to be rebuilt by 0–5% following declines in fishing pressure. Reductions in time to recovery were highly variable due to recruitment stochasticity, but stock recovery was more sensitive to the severity of harvest control measures than to availability of the non-limiting prey. Understanding the relative magnitudes of such bottom-up processes, particularly in the presence of varied fishing pressure can aid in developing ecosystem approaches to fisheries management that account for such ecological interactions more explicitly.

Keywords: Simulation modeling; Bottom-up forcing; Predator–prey dynamics; Functional response; Reproductive numerical response (search for similar items in EconPapers)
Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:311:y:2015:i:c:p:48-62

DOI: 10.1016/j.ecolmodel.2015.05.002

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