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SEPODYM Application to Skipjack Tuna (<I>Katsuwonus Pelamis</I>) in the Pacific Ocean: Impact of ENSO on Recruitment

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SEPODYM Application to Skipjack Tuna (<I>Katsuwonus Pelamis</I>) in the Pacific Ocean: Impact of ENSO on Recruitment SCTB16 Working Paper SKJ–5 SEPODYM application to skipjack tuna (Katsuwonus pelamis) in the Pacific Ocean: impact of ENSO on recruitment and population Patrick Lehodey Oceanic Fisheries Programme Secretariat of the Pacific Community Noumea, New Caledonia June 2003 1 Introduction Skipjack tuna are the most important tuna resource in terms of contribution by weight (~1.8 million t year-1) to the total world tuna catch. It is currently the 4th most productive and fished marine species in the World, after Peruvian anchoveta, Alaska pollock and Atlantic herring. Most of the skipjack catch comes from the warm waters of the western and central Pacific Ocean (WCPO). Skipjack tuna catches in the WCPO have increased steadily since 1970, more than doubling during the 1980s. The catch has been relatively stable during the 1990s. To explore the underlying mechanisms by which the environmental variability affects the pelagic ecosystem and tuna populations, a spatial environmental population dynamic model (SEPODYM) has been developed (Bertignac et al., 1998; Lehodey et al., 1998; Lehodey, 2001). SEPODYM is an Advection-Diffusion-Reaction Model (ADRM) at the ocean basin scale, combining a forage (prey) production model with an age structured population model of targeted (tuna predator) species. The model contains environmental and spatial components used to constrain the movement and the recruitment of tuna. All the spatial dynamics are described with an advection-diffusion equation (see previous references). Input data sets for the model are sea surface temperature, oceanic currents, primary production and dissolved oxygen. Preliminary simulations (Lehodey, 2001) were able to reproduce the observed ENSO-related spatio-temporal changes in the distribution of skipjack (Katsuwonus pelamis) population (Lehodey, 1997). However, the run represented a short time-series (1992-95) and was limited to the 20oN - 20oS equatorial region, whereas tuna stocks extend to sub-tropical and temperate oceanic regions. A new input data set simulated by a coupled physical-biogeochemical model (Chai et al., in press; Jiang et al., in press) is used to extend the analysis for skipjack to the Pacific basin over a longer time series (1960-1999). In addition, certain elements of the model parameterization have been improved. Results of simulations are evaluated by comparing predicted and observed catch by fishery (at the spatial level), predicted and observed length frequencies distribution of the catch, and predicted biomass and recruitment with independent estimates provided by the statistical population dynamics model MULTIFAN-CL (Fournier et al., 1998; Hampton and Fournier, 2001a). This paper summarises the last results concerning the application of SEPODYM to skipjack in the Pacific Ocean that have been described in a recent article (Lehodey, Chai and Hampton, in press). Environmental variables Temperature, currents and primary production are predicted from a coupled ocean-biogeochemical model. The biogeochemical model is driven by a physical model that is a full 3D ocean general circulation model (OGCM). The baseline of the physical model is the Modular Ocean Model (MOM)1. The model covers the entire Pacific (45oS-65oN, 100oE-70oW) except the Southern Ocean section (Li et al., 2001) for the period 1960-1999. The resolution is 2 deg. in longitude, 0.5 deg. in latitude near the equator (10oS to 10oN), and 2 deg. in latitude close to the northern and southern boundaries. There are 40 layers with 10 m resolution within the euphotic zone (120 m) and decreasing resolution with depth below. The surface forcing uses the Comprehensive Oceanic and 1 The Modular Ocean Model was developed by the Geophysical Fluid Dynamics Laboratory (GFDL)/ National Oceanic and Atmospheric Administration (NOAA), United States Department of Commerce 2 Atmospheric Data Set (COADS) monthly wind and heat flux. Details of the physical model and its surface forcing can be found in Li et al (2001). The biogeochemical model is a 10-component improved Nutrient-Phytoplankton-Zooplankton-Detritus (NPZD) ecosystem model designed originally for the equatorial Pacific (Chai et al., 2002; Dugdale et al., 2002). The model includes both nitrate and silicate as major potential nutrients, two sizes of phytoplankton and zooplankton, nonliving detrital particles, as well as total CO2. Currents are averaged over the 0-30 m surface layer, new primary production is integrated over the euphotic zone (0-120 m), and interpolated with the sea surface temperature (SST) on a grid of one-degree square resolution to be used with SEPODYM. SEPODYM (Spatial Environmental POpulations DYnamics Model) The structure of the model is summarized on Figure 1. Documentation of most of the model features is provided in Bertignac et al. (1998), Lehodey et al. (1998), and Lehodey (2001). Additional information on recent developments was provided at the last Standing Committee on Tuna and Billfish (Lehodey, 2002) and is detailed in Lehodey et al. (in press). Below are described the main components and structure of the model. Population structure The tuna population is age-structured to account for growth and gear selectivity. Skipjack population is described with 16 age-classes of 1 quarter, though the time step of computation has a higher time resolution (one month). The last age class is a “+ group” in which are accumulated the oldest individuals. Four components of the population are recorded: juveniles (1st cohort of age 1 quarter), young tuna (from age 2 quarter to age of 1st maturity), adults (all cohorts after age of maturity) and recruits (cohort at age of recruitment). For skipjack, age of first maturity is considered to be 9 months and age of recruitment is one quarter (juvenile group). Age and Growth Growth parameters are those estimated from MULTIFAN-CL analyses (Hampton, 2002a). Tuna Forage Given the large spectrum of prey organisms of tuna and the lack of information on their spatio- temporal dynamics, the tuna forage is modeled as a single population (Lehodey et al., 1998; Lehodey, 2001). With the extension of the model to the whole Pacific basin, the parameterization of the forage population has been revised to take into account regions as different as the warm pool (SST>28oC) and the subarctic gyre (Lehodey, 2002). The new parameterization gives a “mean age” or turnover time for F ranging from 4 months in the warmpool to 12-16 months in the subarctic region. The mortality of the forage population is spatially dependent of the density of tuna (Lehodey 2001). Adult habitat index Ha The adult habitat index Ha combines the spatial distribution of tuna forage biomass F with an oxygen function (Lehodey 2002b) and a temperature function (1) defined for each species (Lehodey, 2001). Ha is used to constraint the tuna movement and to modulate the natural mortality (see below). 3 -0.4 (SST- 22) θa = 1 / (1 + e ) (1) Tuna movement Tuna movement is described with an advection-diffusion equation. Tuna larvae are passively transported by surface currents during their first quarter of life (as the forage). Then, young and adult tuna movements are constrained by the adult habitat index Ha. The advection term is proportional to the gradient of the adult habitat and a coefficient of proportionality Χο. An additional advective term due to the current can be selected between 0 (no effect) and 100%. In absence of information for this parameter, a medium value of 50% is used. To consider changes in tuna behaviour according to the quality of habitat, i.e. rapidly leaving a poor habitat region or conversely staying in very favourable habitat, a function is used to increase the diffusion (D) and the advection (Χ) at low values of habitat index. In addition, both D and Χ are proportional to the size of the fish. A simple linear relationship is used (Eq. 3a and 3b). Da = D . La . [1 - (Ha / (g2 + Ha) ) ] (3a) Χ,a = Χο . La . [1 - (Ha / (g1 + Ha) )] (3b) with Da and Χ,a the diffusion and advection at age a, La the fish length (in m) at age a, Ha the adult habitat index, and g1 and g2, two coefficients constraining the shape of the curvature towards zero; Χο and D being maximal values of advection and diffusion coefficient respectively. The parameterization is scaled to be in agreement with the range of values estimated from tagging data by Sibert et al. (1999) and with realistic swimming behaviour (advection due to habitat gradient ranges between 0 and ~2 body lengths per second). Spawning habitat index Hs and Recruitment A spawning habitat index (Hs) is used to constrain the recruitment to environmental conditions. At each point of the grid, the number of recruits is given by the product of a recruitment scaling value (Rs) and the spawning habitat index. Rs is used to scale the total biomass to independent estimates from the MULTIFAN-CL model. The spawning temperature function (θs) is a Normal function o N(Top, σ) centered on an optimal value (Top= 30 C) with a variance σ = 2. Other environmental effects are investigated. Along with temperature and physical constraints (e.g. advection creating favourable zones of retention for larvae and juveniles), which are already considered in the model, effects of food availability (approximated by primary production P) and predation on larvae (approximated by the biomass of forage organism F). The equation (2) is used to calculate Hs: (α LnΠ ) Hs = θs e (2) with θs the temperature function, Π the product of all other effects and α a coefficient allowing to scale the amplitude of these effects. Different combinations of effects have been tested: the temperature alone (simulation S1: α = 0; Hs = θs), the food effect (simulation S2: α = 0.5, and Π = P), the predation alone (simulation S3: α = 0.5, and Π = 1/F) and both predation and food effects (simulation S4: α = 0.5, and Π = P/F). Mortality The total mortality rate (Z) is the sum of natural (M) and fishing mortality (f).
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