An Update of Recent Developments and Applications of the Seapodym Model Wcpfc-sc5-2009/eb-wp-10

Foreword SEAPODYM is a model developed initially for investigating spatial tuna population dynamics, under the influence of both fishing and environmental effects. The model development also benefited of a grant from the PFRP (Pelagic Fisheries Research Program) of the University of Hawaii, allowing the implementation of irregular grids and initiating the work for parameter optimization (2004-05). Since 2006, the development has continued within the MEMMS section (Marine Ecosystem Modeling and Monitoring by Satellites) of the Spatial Oceanography Division of CLS, a subsidiary of the French CNES and IFREMER Institutes. An enhanced version of the model (SEAPODYM.v2.0) has been achieved last year (Lehodey et al 2008; Senina et al. 2008; Lehodey et al. 2008b) and the user's manual updated (Lehody and Senina 2009). Collaboration with SPC and PFRP continues, with funding support from the EU-funded SPC project SCIFISH, and a second PFRP grant (Climate and Fishing Impacts on the Spatial Population Dynamics of Tunas: project no. 657425). Another project to apply SEAPODYM to Pacific swordfish has been initiated recently at the NOAA-NMFS Pacific Islands Fisheries Science Center, in Hawaii. The model is also used to investigate the movement and habitats of Atlantic bluefin tuna under a one-year project funded by the Large Pelagic Research Center of the University of New Hampshire, with the objective of using archival tagging data assimilation for parameter optimization (Lehodey et al., 2009). Introduction Using the enhanced version of the model SEAPODYM.v2.0, our first key objective is to produce an envelope of prediction of biomass for the main Pacific tuna species (skipjack, yellowfin, bigeye and albacore) for the last 50 years, based on different environmental forcing data sets. The approach is using data assimilation techniques (Fig. 1), like in other models used for stock assessment studies (e.g., MULTIFAN-CL, A-SCALA, …), however, it is different as it is an explicit spatial model driven by oceanic environmental variables. Therefore, fishing data (catch, effort and size frequencies) are spatially-3 disaggregated, typically at monthly 1 to 5 degree square resolution for catch and effort, and quarterly 5 to 20 degree square resolution or more for size frequency data (Senina et al 2008). In SEAPODYM, the dynamics of tuna populations are driven by environmental forcing (temperature, currents, dissolved oxygen, euphotic depth, and primary production) and prey (micronekton) distribution that can be predicted from coupled physical-biogeochemical models. Because the model is driven by the bio-physical environment of the ecosystem, it was possible to …