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Coll, M., Steenbeek, J., Sole, J., Palomera, I., & Christensen, V. (2016). Modelling the cumulative spatial-temporal effects of environmental drivers and fishing in a NW Mediterranean marine ecosystem. Ecol. Model., 331, 100–114.
Résumé: To realistically predict spatial-temporal dynamics of species in marine ecosystems it is essential to consider environmental conditions in conjunction with human activities and food web dynamics. In this study, we used Ecospace, the spatial-temporal dynamic module of Ecopath with Ecosim (EwE) food web model, to drive a spatially explicit marine food web model representing the Southern Catalan Sea (NW Mediterranean) with various environmental drivers and with fishing. We then evaluated the individual and joint effects of environmental conditions and fishing in various compartments of the food web. First we used a previously developed EwE model fitted to time series of data from 1978 to 2010 as a baseline configuration. The model included 40 functional groups and four fishing fleets. We first ran the original Ecospace spatial-temporal dynamic model using the original habitat configuration, in addition to fishing, and we predicted species distributions and abundances. Afterwards, we ran the new habitat foraging capacity model using the most important environmental drivers linked with the Ebro River delta dynamics (salinity, temperature, and primary production), in addition to depth, substrate and fishing, and we compared results with those from the original implementation of Ecospace. Three commercial species, European hake (Merluccius merluccius), anchovy (Engraulis encrasicolus) and sardine (Sardina pilchardus), were used to analyse results. Species distributions more closely matched the empirical information available from the study area when using the new habitat capacity model. Results suggested that the historical impacts of fishing and environmental conditions on the biomass and distributions of hake, anchovy and sardine were not additive, but mainly cumulative with a synergistic or antagonistic effect. Fishing had the highest impact on spatial modelling results while the spatial distribution of primary producers and depth followed in importance. This study contributes to the development of more reliable predictions of regional change in marine ecosystems of the Mediterranean Sea. (C) 2016 Elsevier B.V. All rights reserved.
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Heymans, J. J., Coll, M., Link, J. S., Mackinson, S., Steenbeek, J., Walters, C., et al. (2016). Best practice in Ecopath with Ecosim food-web models for ecosystem-based management. Ecol. Model., 331, 173–184.
Résumé: Ecopath with Ecosim (EwE) models are easier to construct and use compared to most other ecosystem modelling techniques and are therefore more widely used by more scientists and managers. This, however, creates a problem with quality assurance; to address this we provide an overview of best practices for creating Ecopath, models. We describe the diagnostics that can be used to check for thermodynamic and ecological principles, and highlight principles that should be used for balancing a model. We then highlight the pitfalls when comparing Ecopath models using Ecological Network Analysis indices. For dynamic simulations in Ecosim we show the state of the art in calibrating the model by fitting it to time series using a formal fitting procedure and statistical goodness of fit. Finally, we show how Monte Carlo simulations can be used to address uncertainty in input parameters, and we discuss the use of models in a management context, specifically using the concept of 'key runs' for ecosystem-based management. This novel list of best practices for EwE models will enable ecosystem managers to evaluate the goodness of fit of the given EwE model to the ecosystem management question. (C) 2016 The Authors. Published by Elsevier B.V.
Mots-Clés: benguela ecosystem; dynamics; Ecological network analysis; Ecopath with Ecosim; Ecosystem-based management; Ecosystem modelling; exploited ecosystems; impacts; indicators; marine ecosystems; Monte Carlo; network analysis; nw mediterranean sea; shelf ecosystem; southern benguela; Time series fitting
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