Historical analysis of blue marlin (Makaira nigricans Lacepède, 1802) catches by the pelagic longline fleet in the eastern Pacific Ocean

Abstract

We analysed a historical (1959–2017) database of blue marlin catches reported by the industrial pelagic longline fleet operating in the eastern Pacific Ocean. The time series of catch-per-unit-effort (CPUE) for the time period 1959–2015 was modelled as a function of temporal and environmental variables by using generalised additive mixed models (GAMM). CPUE predictions were made on an independent dataset (2016–2017). Results suggested that a higher nominal CPUE occurred near the equator during boreal winter, spring and autumn, with a shift towards the north during the boreal summer. Two high CPUE events were observed, one in the early 1960s and one in the early 1990s. The final GAMM explained 61% of the total variance of the CPUE time series. Only a small percentage of total deviance was explained by the environmental variables, so we suggest that changes in fleet-targeting practices are the main cause of the large variability in the time series. The final GAMM can be used to predict blue marlin CPUE with a prediction accuracy of 3–8 fish per 1000 hooks. We observed an important decline in CPUE from the early 2000s; the factors responsible for this decline should be further investigated.

Global habitat loss of a highly migratory predator, the blue marlin (Makaira nigricans)

“…Although direct comparison of model predictions and relative CPUE data has limited value due to the inherent differences between telemetry and fishery‐dependent data (Brill & Lutcavage, 2001; Campbell, 2015; Maunder et al, 2006; Maunder & Punt, 2004), a positive correlation provided additional validation of the model in this study. Consistent with previous smaller studies on this species, predicted suitable habitat for blue marlin was centred on the equator and spanned a broad latitudinal range (Goodyear, 2016; Marín‐Enríquez et al, 2020; Su et al, 2008). Over the course of the study, there was little variation in predicted habitat between years despite an overall decline in habitat suitability.…”

Analysis of the Swordfish Xiphias gladius Linnaeus, 1758 Catches by the Pelagic Longline Fleets in the Eastern Pacific Ocean

“…A Generalized Additive Model (GAM) was constructed using a Gamma distribution, using the "train" database. The Gamma distribution was chosen because it has proven to be efficient when modeling continuous variables and with some overdispersion [19]. Standard techniques were used to assure the assumptions of normality (normalized residual analysis and quantile-quantile Q-Q plot) and heteroscedasticity (normalized residuals versus fits scatter plots).…”

“…The "validation" database was used to assess the predictive capability and rule out the potential overfit of the final GAMM, using the residual mean squared error (RMSE) and Pearson's correlation coefficient (r) between the observed values and the model prediction. This approach has been successfully used for blue marlin Makaira nigricans Lacepède, 1802 bycatch by the pelagic longline fleet [19]. Once the modeling process was complete, we computed a standardized CPUE index using the "predict.gam" function of the mgcv package (Version 1.8-36); such function computes the predicted values using the parameters of the model, given a database that includes both temporal (month, year) and environmental (ONI, PDO) covariates.…”

“…The main countries that catch swordfish in the Eastern Pacific Ocean (EPO) are Japan, Taiwan, and South Korea, with 85% of the total fishing effort of the longline fishery; other countries that catch swordfish to a lesser extent are China, Spain, Mexico, Belize, French Polynesia, the United States, and Vanuatu, accumulating the remaining 15% of the effort. Mexico has only approximately 0.15% of the total fishing effort of the commercial swordfish longline fishery [19]. At present, there is little research aimed at catches of this species in the Pacific Ocean, but research in the Atlantic mentions that the history of the X. gladius fishery shows different phases of intensity.…”

Seasonally mediated niche partitioning in a vertically compressed pelagic predator guild

“…However, owing to their large size, inability to be held captive, and their elusive nature, information on billfish ecology and competitive interactions remains limited. One region where multiple billfishes co-occur is the Pacific coast of Central America, known for high catch rates of sailfish ( Istiophorus platypterus ), blue marlin ( Makaira nigricans ), striped marlin ( Kajikia audax ) and black marlin ( Istiompax indica ) [14,15]. In Panama, recreational billfish catch is high during the dry season (December–April), when the Intertropical Convergence Zone (ITCZ) is pushed south by northerly trade winds, resulting in upwelling of cool, nutrient-rich waters [16].…”