TY - JOUR
T1 - Modelling climate change impacts on anchovy and sardine landings in northern Chile using ANNs
AU - Yáñez, Eleuterio
AU - Plaza, Francisco
AU - Sánchez, Felipe
AU - Silva, Claudio
AU - Barbieri, María Ángela
AU - Bohm, Gabriela
N1 - Publisher Copyright:
© 2017, Escuela de Ciencias del Mar. All rights reserved.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
KW - Artificial neural net works
KW - Climate change
KW - Forecast
KW - Northern Chile
KW - Pelagic landings
UR - http://www.scopus.com/inward/record.url?scp=85031281049&partnerID=8YFLogxK
U2 - 10.3856/vol45-issue4-fulltext-4
DO - 10.3856/vol45-issue4-fulltext-4
M3 - Article
AN - SCOPUS:85031281049
SN - 0718-560X
VL - 45
SP - 675
EP - 689
JO - Latin American Journal of Aquatic Research
JF - Latin American Journal of Aquatic Research
IS - 4
ER -