| dc.creator | Yáñez,Eleuterio | |
| dc.creator | Plaza,Francisco | |
| dc.creator | Sánchez,Felipe | |
| dc.creator | Silva,Claudio | |
| dc.creator | Barbieri,María Ángela | |
| dc.creator | Bohm,Gabriela | |
| dc.date | 2017-01-01 | |
| dc.date.accessioned | 2019-05-03T13:28:10Z | |
| dc.date.available | 2019-05-03T13:28:10Z | |
| dc.identifier | https://scielo.conicyt.cl/scielo.php?script=sci_arttext&pid=S0718-560X2017000400675 | |
| dc.identifier.uri | http://revistaschilenas.uchile.cl/handle/2250/88341 | |
| dc.description | ABSTRACT 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. | |
| dc.format | text/html | |
| dc.language | en | |
| dc.publisher | Pontificia Universidad Católica de Valparaíso. Facultad de Recursos Naturales. Escuela de Ciencias del Mar | |
| dc.relation | 10.3856/vol45-issue4-fulltext-4 | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.source | Latin american journal of aquatic research v.45 n.4 2017 | |
| dc.subject | forecast | |
| dc.subject | pelagic landings | |
| dc.subject | climate change | |
| dc.subject | artificial neural net works | |
| dc.subject | northern Chile | |
| dc.title | Modelling climate change impacts on anchovy and sardine landings in northern Chile using ANNs | |
