Rev. biol. mar. oceanogr. 51(1): 137-145Articlehttp://dx.doi.org/10.4067/S0718-19572016000100013 |
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Incorporating sea surface temperature into the stock-recruitment relationship: Applications to jack mackerel (Trachurus murphyi) off Chile |
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This email address is being protected from spambots. You need JavaScript enabled to view it.1, Juan Carlos Quiroz2,1, Rodrigo Wiff3 and Eleuterio Yáñez4 |
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1División de Investigación Pesquera, Instituto de Fomento Pesquero, Casilla 8V, Valparaíso, Chile
2Institute for Marine and Antarctic Studies, University of Tasmania (UTAS), 49 Private Bag, Hobart 7001, Australia
3Center of Applied Ecology and Sustainability (CAPES), Pontificia Universidad Católica de Chile, Av. Alameda 340, Santiago, Chile
4Escuela de Ciencias del Mar, Facultad de Ciencias del Mar y Geografía, Pontificia Universidad Católica de Valparaíso, Casilla 1020, Valparaíso, Chile
This email address is being protected from spambots. You need JavaScript enabled to view it. |
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The recruitment rate was modeled in relation to spawning biomass and to sea surface temperature (SST) for the jack mackerel (Trachurus murphyi) population off the Chilean coast using the Ricker model. Data regarding recruitment and spawning biomass were obtained from indirect stock assessment models from 1975 to 2001, while annual time series of SST were collected from the meteorological stations placed along the Chilean coast by the National Center of Hydrographic and Oceanographic Data (CENDHOC). The standard Ricker model was thus modified as follows: (1) the SST temporal series was included as a linear predictor; (2) the SST temporal series was modeled through smoothing functions; and (3) spawning biomass and SST temporal series were both modeled using smoothing functions. The resulting models were compared with the standard Ricker model without SST. Model selection was carried out using automatic information criteria (AIC). Including SST improved the fit of the recruitment model, despite the penalty of an additional term and a possible additional source of variability. The best model resulting includes the SST temporal series with smoothing functions and the spawning biomass with parametric functions, with a goodness-of-fit of 90%. Incorporating an environmental variable into stock-recruitment relationships may be a promising method for simultaneously considering effects from fishing and the environment, and is particularly relevant for managing fisheries in light of climate change.
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Key words: Generalized additive models, stock-recruitment relationships, SST, non-parametric |
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