Application of an evolutionary algorithm to the inverse parameter estimation of an individual-based model

Duboz, Raphaël; Versmisse, David; Travers, Morgane; Ramat, Éric; Shin, Yunne-Jaï

doi:10.1016/j.ecolmodel.2009.11.023

Cited by 37 publications

(22 citation statements)

References 28 publications

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“…Calibration was undertaken using a dedicated genetic algorithm [39], [32], [33]) where the unknown larval mortalities of fish species are estimated in order to fit fish biomasses observed in the 1990s [40]. The time step was set to 15 days, which allows representation of seasonal dynamics.…”

Section: Methodsmentioning

confidence: 99%

Combined Fishing and Climate Forcing in the Southern Benguela Upwelling Ecosystem: An End-to-End Modelling Approach Reveals Dampened Effects

et al. 2014

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The effects of climate and fishing on marine ecosystems have usually been studied separately, but their interactions make ecosystem dynamics difficult to understand and predict. Of particular interest to management, the potential synergism or antagonism between fishing pressure and climate forcing is analysed in this paper, using an end-to-end ecosystem model of the southern Benguela ecosystem, built from coupling hydrodynamic, biogeochemical and multispecies fish models (ROMS-N2P2Z2D2-OSMOSE). Scenarios of different intensities of upwelling-favourable wind stress combined with scenarios of fishing top-predator fish were tested. Analyses of isolated drivers show that the bottom-up effect of the climate forcing propagates up the food chain whereas the top-down effect of fishing cascades down to zooplankton in unfavourable environmental conditions but dampens before it reaches phytoplankton. When considering both climate and fishing drivers together, it appears that top-down control dominates the link between top-predator fish and forage fish, whereas interactions between the lower trophic levels are dominated by bottom-up control. The forage fish functional group appears to be a central component of this ecosystem, being the meeting point of two opposite trophic controls. The set of combined scenarios shows that fishing pressure and upwelling-favourable wind stress have mostly dampened effects on fish populations, compared to predictions from the separate effects of the stressors. Dampened effects result in biomass accumulation at the top predator fish level but a depletion of biomass at the forage fish level. This should draw our attention to the evolution of this functional group, which appears as both structurally important in the trophic functioning of the ecosystem, and very sensitive to climate and fishing pressures. In particular, diagnoses considering fishing pressure only might be more optimistic than those that consider combined effects of fishing and environmental variability.

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Section: Methodsmentioning

confidence: 99%

Combined Fishing and Climate Forcing in the Southern Benguela Upwelling Ecosystem: An End-to-End Modelling Approach Reveals Dampened Effects

et al. 2014

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“…2007; Winkler & Heinken 2007), but many other measures are possible (Smith et al. 2008; Zinck & Grimm 2008; Duboz et al. 2010, see also Schröder & Seppelt 2006, for objective functions used in landscape ecology).…”

Section: Likelihood Approximations For a Single Parameter Value – Thementioning

confidence: 99%

Statistical inference for stochastic simulation models - theory and application

et al. 2011

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Statistical models are the traditional choice to test scientific theories when observations, processes or boundary conditions are subject to stochasticity. Many important systems in ecology and biology, however, are difficult to capture with statistical models. Stochastic simulation models offer an alternative, but they were hitherto associated with a major disadvantage: their likelihood functions can usually not be calculated explicitly, and thus it is difficult to couple them to well-established statistical theory such as maximum likelihood and Bayesian statistics. A number of new methods, among them Approximate Bayesian Computing and Pattern-Oriented Modelling, bypass this limitation. These methods share three main principles: aggregation of simulated and observed data via summary statistics, likelihood approximation based on the summary statistics, and efficient sampling. We discuss principles as well as advantages and caveats of these methods, and demonstrate their potential for integrating stochastic simulation models into a unified framework for statistical modelling.

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“…Heuristic techniques, such as evolutionary algorithms, can reduce the computation time for parameter exploration (Fig. 3) (Duboz et al, 2010). Nevertheless, convergence is still an issue and IBMs calibration remains difficult when the number of parameters is large.…”

Section: Ibm Calibrationmentioning

confidence: 99%