Modelling climate change impacts on anchovy and sardine landings in northern Chile using ANNs

Eleuterio Yáñez, Francisco Plaza, Felipe Sánchez, Claudio Silva, María Ángela Barbieri, Gabriela Bohm

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5 Scopus citations


Artificial Neural Networks (ANN) are adjusted to predict monthly landings of anchovy (Engraulis ringens) and sardine (Sardinops sagax) in northern Chile (18°21’-24°00’S). Fishing effort (FE), landings and twelve environmental variables are considered from 1980 to 2012. External validation for the best models using all variables showed an R2 of 95% for anchovy and 99% for sardine, with an efficiency of 0.94 and 0.96, respectively. The models were simplified by considering only FE and sea surface temperature (SST) from NOAA satellites (SST-NOAA). Using these variables, very similar fits were achieved, comparing with the previous models, maintaining their predictive capacity. Downscaled SST for A2 climate change scenario (2015-2065) obtained by statistical regionalization from the Community Climate System Model (CCSM3) from National Center for Atmospheric Research (NCAR) and three FE scenarios (2010-2012 average, + 50% and -50%), were used as inputs for ANN simplified models. For A2 future climate change scenario (2015-2065) using 2010-2012 average FE as inputs, anchovy and sardine landings would increase 2.8% and 19.2% by 2065 respectively. With FE variations (-50%), sardine landings show the highest increase (22.6%) by 2065 when FE is decreased.

Original languageEnglish
Pages (from-to)675-689
Number of pages15
JournalLatin American Journal of Aquatic Research
Issue number4
StatePublished - 2017


  • Artificial neural net works
  • Climate change
  • Forecast
  • Northern Chile
  • Pelagic landings


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