Marine Ecosystem Modelling and Monitoring by Satellite: Presentation of the SEAPODYM model and its application to marine resources assessment and management
vendredi 17 octobre 2014, 9h30 - 10h30
Salle de réunion, espace Turing
The Spatial Ecosystem And Population Dynamics Model (SEAPODYM) is developed for investigating spatial population dynamics of fish and tuna particularly, under the influence of both fishing and environmental effects. The model is based on advection-diffusion-reaction equations, and population dynamics (spawning, movement, mortality) are constrained by environmental data (temperature, currents, primary production and dissolved oxygen concentration) and simulated distribution of mid-trophic (micronektonic tuna forage) functional groups. Different life stages are considered: larvae, juveniles and (immature and mature) adults. After juvenile phase, fish become autonomous, i.e., they have their own movement (linked to their size and habitat) in addition to be transported by oceanic currents. The model includes a representation of fisheries and predicts total catch and size frequency of catch by fleet when fishing data (catch and effort) are available. A Maximum Likelihood Estimation (MLE) approach is used to optimize the model parameters. Conventional tagging larvae density and acoustic biomass estimates data have been added recently in the MLE approach.
I’ll present recent works on the applications of SEAPODYM to Atlantic tuna and Indian swordfish.
In order to cope with climate varibility impact on exploited fish stocks, an application of SEAPODYM to the North Atlantic albacore tuna population (Thunnus alalunga) has been developed. We investigated the spatio-temporal dynamics of this ecologically and economically important species under the influence of environment and international fishing. Simulations were driven by 1960-2008 historical catch data and by the bio-physical environment predicted from a coupled physical-biogeochemical ocean model (NEMO-PISCES) driven by an atmospheric reanalysis (NCEP). A maximum likelihood estimation approach incorporating fisheries data has allowed robust parameter estimation, hence enabling the use of the model as a predictive and quantitative management application tool. Indeed, the spatially explicit estimates of stock size, structure and biomass can be used for management policy and help in evaluating the impact of climate variability on this species.
Swordfish is one of the most widely distributed pelagic fish species and represents an important fishery resource in the entire Indian Ocean. A first Spatial Ecosystem And Population Dynamic Model (SEAPODYM) application to Indian swordfish (Xiphias gladius) was developed in collaboration with the Indian Ocean Commission for Tunas (IOTC). The objective is to investigate the impacts of both fishing and climate variability on this species. The oceanic environment used to force SEAPODYM is predicted from a coupled physical-biogeochemical ocean model (NEMO-PISCES) driven by an atmospheric reanalysis (NCEP) on a 2Â° x month resolution (ORCA2 grid) over the historical fishing period (1948-2003). Available spatially-disaggregated catch per unit of effort (CPUE) data from the fisheries operating in the Indian Ocean were assimilated into the model. These preliminary results indicate that the proposed solution is already coherent with many features that characterize the swordfish population dynamics and fisheries in the Indian Ocean. A fully optimized configuration of the model should be therefore achievable in a reasonable timeframe. Once the optimal solution will be completed, the model could be used to estimate an average maximum sustainable yield accounting for interannual and decadal variability. Given its spatial structure, it can serve to investigate the connectivity of the stock(s) between any oceanic regions or EEZs. Finally, projections of population trends under different IPCC scenarios of Climate Change could be tested to compute possible change in MSY for the coming decades, and a Indo-Pacific configuration envisaged to increase the fishing dataset and the diversity of environmental conditions used with the MLE framework.