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Use of Neural Networks to Forecast the Abundance of Argentine Hake in the Southwest Atlantic

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Use of Neural Networks to Forecast the Abundance of Argentine Hake in the Southwest Atlantic Not to be cited without prior reference to the author ICES CM 2010/A:12 Use of neural networks to forecast the abundance of Argentine hake in the Southwest Atlantic Darriba, M., González, L., Martínez, G., Torres, J.M. Departamento de Física Aplicada. Facultad de Ciencias del Mar, Universidad de Vigo. Box 36310, Vigo, Spain, tel. +34 986812631, fax. +34 986812556. [email protected], [email protected], [email protected], [email protected]. Abstract The Argentine hake (Merluccius hubbsi) is one of the most important commercial species in the Patagonian-Malvinas shelf. In this work, we propose a predictive model of abundance for this species based on a Multilayer Perceptron (MLP) neural network. The network was developed using fishery data that were collected on board Spanish commercial vessels operating in the area between 1989 and 2006 and environmental data provided by the oceanographic model implemented by MERCATOR Ocean. The MLP output is the cpue (catch per unit effort; kg h- 1), which is used as an abundance index. As input variables we included latitude, longitude, Julian day, temperature and salinity at three depth levels, sea surface temperature gradient and moon phase. The whole dataset was split into two independent sets, one to train the network and the other one to validate it. GIS techniques and statistical methods were used to analyse and visualize the data and the results. Model results show a good fitting between the observed and estimated cpue (r=0.74 using the validation set). This tool might be useful to implement an operational forecasting system. Keywords: Argentine hake, MLP, neural networks, prediction model Contact author: J.M. Torres Palenzuela, Departamento de Física Aplicada, Universidad de Vigo. [email protected]. Introduction The Argentine hake (Merluccius hubbsi) is an important target species for the fisheries fleet operating in the southwest Atlantic. The main aim of this paper is the implementation of a catch per unit effort (cpue) predictive model for this species based on an artificial neural network, in particular a multilayer perceptron (MLP). Different statistical methods were applied to analyse the relationship between the different environmental variables and cpue in a previous step to the development of the MLP. Geographic Information Systems (GIS) were used to visualize and analyze the results. Fisheries data were collected by the Galician trawlers operating in the southwest Atlantic area between 1989 and 2006, although only data between 1993 and 2006 were used in this study. Artificial neural networks are computer algorithms that work in a similar way to the human brain (Sugiyama and Ogawa, 2001), and are a powerful tool for modelling multivariate, complex and non-linear data (Chen et al., 1992), so that it is the ideal technique to be applied in fisheries forecasting problems (Guegan et al., 1998). Artificial neural networks have been successfully applied to model fishery variables and Pacific herring (Clupea pallasi) recruitment (Chen and Ware, 1999), to predict the relative abundance of bigeye tuna (Thunus obesus) from catch and effort data (Maunder and Hinton, 2006) or to estimate fishing set positions from vessel tracks derived from vessel monitoring system (VMS) data (Bertrand et al., 2008). The study area lies in the Patagonian Shelf, between longitudes 64ºW and 54ºW and between latitudes 55ºS and 40ºS. This area is characterized by the presence of a permanent thermohaline front located at the border of the shelf. The exact geographical location and the density gradient across the front depend on the dynamic of the two currents dominating the ocean circulation in the area— the warm southward flowing Brazil current and the cold northward flowing Falkland (Malvinas) current—which converge at approximately 36–38ºS (Peterson, 1992). As a consequence of the active regional and local circulation with frequent coastal fronts and upwelling zones, the waters in the southwest Atlantic area are highly productive and sustain important pelagic and demersal fisheries (Wang et al., 2007). Figure 1: Location map of the study area, showing the Falkland Islands. The numbered rectangles (from 1 to 3) enclose the zones used in the statistical study. In order to facilitate the spatial analysis, the study area was divided into three sub-areas (Figure 1). These zones coincide with the areas where the Spanish fleet is operating. Area 1 (around 42ºS) and Area 2 (between 43º30'S and 48ºS) are the portions of the continental shelf and slope which fall outside the Argentinean Exclusive Economic Zone (EEZ). The Area 3 is located around the Falkland Islands and cover approximately the same area as that of the Falkland Islands Interim and Outer conservation zones (FICZ and FOCZ respectively), but it was redesigned as rectangles to facilitate the integration into the model. Hakes (Merlucciidae) are abundant nektonic fishes inhabiting shelf and continental-slope waters of the Atlantic Ocean, eastern Pacific, and south-western Pacific off New Zealand. The Argentine hake inhabits waters over the Argentine and Uruguayan continental shelves between 28ºS and 54ºS, and between 50 and 800 metres depth (Buratti and Santos, 2010). The species lives in temperate-cold waters associated to the Malvinas current system and to the upwelling of sub-Antarctic waters along the southern coast of Brazil (Bezzi et al., 1994). Two main stocks have been identified, the northern one, between 34ºS and 41ºS, and the southern one, located between 41ºS and 54ºS (Bezzi et al., 1995). Our data are mostly concentrated in Area 2 (43º30'S - 48ºS) and therefore they mainly correspond to the southern stock. Although the species was one of the most abundant fish resource during the study period, during the last years there was a drastic decrease in its total biomass due to an over-exploitation of both stocks (Renzi and Irusta,2006; Cordo, 2006). Data sources Fishery data were collected on board commercial vessels operating in the southwest Atlantic area between 1989 and 2006, although only data between 1993 and 2006 were used in this work due to the environmental variables were only available after 1993. These vessels are part of the Fishing Vessel Owners´ Cooperative of the Port of Vigo (ARVI) fleet. The use of commercial ships to evaluate fisheries status requires caution because factors such us license conditions, commercial priorities or the knowledge and experience of the crew can influence the characteristics of the recorded data. In spite of these inconveniences, data from commercial vessels provide the most extensive and representative datasets available for these fisheries. Physical and biological parameters were recorded on board by trained observers working for the Instituto Español de Oceanografía, Vigo, Spain (IEO) and Falkland Islands Government Fisheries Department, Stanley. In a later step all these data were integrated into a database, including the following variables for each haul: temporal parameters (date, year, month, week of the year and Julian day, defined as the number of days elapsed since the 1st of January of the corresponding year), the fishing location (in latitude and longitude) recorded during the shooting operation, the fishing hours, the total catch estimated from processed fish by applying conversion factors [in kilograms (kg)] for each species and cpue, computed as the total catch per fishing hours, which were adopted as an index of effort. Unfortunately, there was insufficient available information to apply any correction factor for the variable catching power of individual vessels or the increasing fishing power over time, introducing some additional noise into the data. Environmental variables were derived from the MERCATOR model (from 1993 to 2006) and associated with the fisheries database. Operational forecasting systems developed by MERCATOR Ocean are based on three-dimensional (3D) ocean models described by primitive equations obtained from applying the Navier–Stokes equations in a stratified fluid. The formulation requires some physical approximations, and variables such us diffusion or viscosity are included by parameterizations. The model runs using medium-range weather forecasting models to generate the atmospheric forcing, in addition to a constant climatology and bathymetry. Moreover, for operational requirements and also for validation and reanalysis it receives, in real time, ocean measurements from satellite and in-situ observation systems. As output, it computes several ocean parameters, including temperature, salinity or currents, inside a 3D grid so that it is possible to evaluate the longitudinal, latitudinal and by-depth parameter variations (Drévillon et al., 2008). The following seven variables were obtained from the MERCATOR data set: Sea Surface Temperature (SST); Sea Bottom Temperature (SBT); Sea Surface Salinity (SSS); Sea Bottom Salinity (SBS); Thermocline Temperature (TT); Thermocline Salinity (TS) and SST gradient (GSST). All these parameters were linked to the fisheries database using the date and the geographic location of the hauls, so that the values of the grid cell containing these locations were extracted and correctly associated with the fisheries data. Daily temperature (in Celsius) and salinity (in psu) data at surface and bottom were directly provided by MERCATOR. The thermocline temperature and salinity required a previous determination of the thermocline depth, which was obtained for each grid cell in the model from the vertical temperature profile, computing the
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