SPRAT: A spatially-explicit marine ecosystem model based on population balance equations
Authored by Arne N Johanson, Andreas Oschlies, Wilhelm Hasselbring, Boris Worm
Date Published: 2017
DOI: 10.1016/j.ecolmodel.2017.01.020
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Model Documentation:
ODD
Mathematical description
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Abstract
To successfully manage marine fisheries using an ecosystem-based
approach, long-term predictions of fish stock development considering
changing environmental conditions are necessary. Such predictions can be
provided by end-to-end ecosystem models, which couple existing physical
and biogeochemical ocean models with newly developed spatially-explicit
fish stock models. Typically, individual-based models (IBMs) and models
based on advection-diffusion-reaction (ADR) equations are employed for
the fish stock models. In this paper, we present a novel fish stock
model called SPRAT for end-to-end ecosystem modeling based on population
balance equations (PBEs) that combines the advantages of IBMs and ADR
models while avoiding their main drawbacks. SPRAT accomplishes this by
describing the modeled ecosystem processes from the perspective of
individuals while still being based on partial differential equations.
We apply the SPRAT model to explore a well-documented regime shift
observed on the eastern Scotian Shelf in the 1990s from a cod-dominated
to a herring-dominated ecosystem. Model simulations are able to
reconcile the observed multitrophic dynamics with documented changes in
both fishing pressure and water temperature, followed by a predator-prey
reversal that may have impeded recovery of depleted cod stocks.
We conclude that our model can be used to generate new hypotheses and
test ideas about spatially interacting fish populations, and their joint
responses to both environmental and fisheries forcing. (C) 2017 Elsevier
B.V. All rights reserved.
Tags
Dynamics
global change
fisheries
Climate-change
Food webs
Size-spectrum
End-to-end
Pelagic fish
Cod
Atlantic
End-to-end modeling
Population balance equation
Fish stock prediction
Ecosystem-based management