4.1 Article

An end-to-end coupled model ROMS-N2P2Z2D2-OSMOSE of the southern Benguela foodweb: parameterisation, calibration and pattern-oriented validation

期刊

AFRICAN JOURNAL OF MARINE SCIENCE
卷 36, 期 1, 页码 11-29

出版社

NATL INQUIRY SERVICES CENTRE PTY LTD
DOI: 10.2989/1814232X.2014.883326

关键词

individual-based model; model validation; pattern-oriented modelling; trophic interactions; two-way coupling

资金

  1. European Commission [FP6-511106]
  2. EUR-OCEANS scholarship
  3. French project EMIBIOS (FRB) [APP-SCEN-2010-II]
  4. European collaborative project MEECE (FP7) [212085]

向作者/读者索取更多资源

In order to better understand ecosystem functioning under simultaneous pressures (e.g. both climate change and fishing pressures), integrated modelling approaches are advocated. We developed an end-to-end model of the southern Benguela ecosystem by coupling the high trophic level model OSMOSE with a biophysical model (ROMS-N(2)P(2)Z(2)D(2)). OSMOSE is a spatial, multispecies, individual-based model simulating the whole life cycle of fish with fish schools interacting through opportunistic and size-based predation. It is linked to the biogeochemical model through the predation process; plankton groups are food for fish and fish apply a predation mortality on plankton. Here we describe the two-way coupling between the models and follow a pattern-oriented modelling approach to validate the simulations. At the individual level, model outputs are consistent with observed diets for several species from small pelagic fish to top predatory fish, although biases emerge from underestimation of macrozooplankton and lack of vertical structure. At the population level, the seasonality of the size structure is similar between the model and data. At the community level, the modelled trophic structure is consistent with the knowledge available for this ecosystem. The structure of the foodweb is an emergent property of the model, showing trophic links between species, their strength and the strong connectivity observed. We also highlight the capacity of this model for tracking indicators at various hierarchical levels.

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