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 - Funding Information:
The authors gratefully acknowledge the financial support of FONDEF projects D11I1137: “Chilean pelagic fishing forecast systems in the face of different climate change scenarios” and the FONDECYT project 1130782 “Climate change and small pelagic resource predictions in Chile” and Undersecretary of Fisheries and Aquaculture (grant FIP 2014-25).
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
VL - 45
SP - 675
EP - 689
JO - Latin American Journal of Aquatic Research
JF - Latin American Journal of Aquatic Research
SN - 0718-560X
IS - 4
ER -