Open AccessSeasonal and interannual variations in the distributions of tuna-associated dolphins in the eastern tropical Pacific Ocean
Author(s) -
Paul C. Fielder,
Cleridy E. LennertCody
Publication year - 2019
Publication title -
the journal of cetacean research and management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.355
H-Index - 23
eISSN - 2312-2692
pISSN - 1561-0713
DOI - 10.47536/jcrm.v20i1.240
Subject(s) - thermocline , upwelling , oceanography , tropical eastern pacific , bay , geography , habitat , yellowfin tuna , generalized additive model , fishery , environmental science , climatology , tuna , pacific ocean , geology , ecology , biology , statistics , mathematics , fish <actinopterygii>
Seasonal and interannual (El Niño–La Niña) variations in dolphin distributions in the eastern tropical Pacific Ocean have not been quantified, inspite of an extensive research vessel database. Fisheries observer data from the yellowfin tuna purse-seine fishery, collected year-round from 1986through 2015, were used to construct a binned spatiotemporal dataset of the presence/absence of spotted, spinner and common dolphin schools bymonth and 1° area. Distribution patterns were predicted from generalised additive logistic regression models applied to the binned data, with dynamicpredictors of surface temperature and salinity, thermocline depth and a stratification index. The dolphin taxa, especially common dolphins, showsome niche separation in relation to these variables. Predicted distributions for each taxon showed seasonal and interannual differences. Spottedand spinner dolphins responded to changes in the position and size of the eastern Pacific warm pool and avoided the equatorial cold tongue inSeptember–October and during La Niña. Common dolphins responded to seasonal and interannual changes in the Costa Rica Dome, the cold tongue,and the coastal upwelling habitat along Baja California, Peru and Ecuador. These predicted temporal variations are consistent with changes inpreferred habitat driven by environmental variability.