Comparison of nominal and standardized catch per unit effort data in quantifying habitat suitability of skipjack tuna in the equatorial Pacific Ocean
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Abstract: In the western and central Pacific Ocean, upper strata waters exhibit highly dynamic oceanographic features under ENSO variability. This has been proved to be responsible for the dynamic change of both abundance and zonal distribution of skipjack tuna (Katsuwonus pelamis). Although causality has been suggested by researchers using physical–biological interaction models, cumulative evidence needs to be obtained and the tenability of assertion needs to be tested from an ecological habitat perspective, based on fisheries data. For purse seine fishery, the use of catch per unit effort (CPUE) as an indication of the abundance is confusing because of technical improvements over the whole exploitation history and unbalanced individual fishing characteristic of vessels. It is particularly interesting to discriminate between habitat characteristics in comparative scenarios of CPUE application. This study identified habitat traits based on a series of oceanographic factors from a global ocean reanalysis model. A comparison was conducted between two habitat models based on unprocessed purse seine CPUE and standardized CPUE considering fishing characteristics. The results suggest that standardized CPUE could model the regular zonal shift of habitat compatible with the observed fishing efforts transfer, and achieved better prediction capacity than unprocessed CPUE. Furthermore, the habitat of skipjack tuna was also characterized and linked with surface and subsurface thermal environment, ocean current, dissolved oxygen, biotic environment, and ENSO variability. The monthly-averaged habitat suitable index, derived from the optimal habitat model prediction, showed a significant linear relationship with the southern oscillation index, which suggested that El Niño episodes eventually provide more preferable habitat for skipjack tuna under ENSO variability.
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Key words:
- skipjack tuna /
- free-swimming schools /
- habitat characteristics /
- ENSO events /
- CPUE standardization
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Figure 1. Major oceanographic features in the fishing area of Pacific Ocean. a. SEC represents South Equatorial Current; NECC, North Equatorial Counter Current; NEC, North Counter Current; ICZ, Intertropical Convergence Zone; SPCZ, South Pacific Convergence Zone; b. kernel density contour of fish school presence in the fishing region from 2013 to 2017. High density regions are indicated in red in b. At the bottom of b, the zonal intensity of fish schools is indicated by a violin plot, representing the persistent thermal region around the 160°E and 170°E meridian.
Figure 2. Linear estimates of GLMM for random effect terms year (Y) on intercept (a) and a range of fixed effects (the vessel date of build, D; the type of fishing gear, G; and the type of free swimming schools caught, S) (b, c and d). a. Positive coefficients (in red) indicate positive effects on CPUE, whereas negative effects indicate minus coefficients (in blue). In a−d, bars and shaded region indicate 95% confidence intervals. e. The random intercepts of spatial-grid are mapped onto the corresponding fishing cells (1 degree square). For the purpose of protecting the commercial confidentiality, position information is not labelled aside the axis.
Figure 3. HSI distribution between models using nominal CPUE (a) and calibrated CPUE (b) on January 1, 2013, January 1, 2017 and August 1, 2015. ENSO periods are denoted by “+”: La Niña; “−”: strong El Niño; “o”: neutral condition. White regions indicate the null HSI due to the environmental data beyond the range of model prediction or at incredible interval. Open circles indicate catches in the corresponding month rather than day when fishing efforts are scarce.
Figure 4. Partial effect estimates of GAM for significant variables. Blue-shaded regions are 95% confidence bands for smooths. Density of raw data is displayed as a rug at the foot of each plot.
Figure 5. Linear relationship between monthly averaged HSI and SOI. Red, gray, and blue dots indicate El Niño episode (defined as six-consecutive-month average sea surface temperature anomaly exceeding 0.5°C), neutral condition, and La Niña episode (defined as six-consecutive-month average sea surface temperature anomaly less than negative 0.5°C), respectively. Shaded regions indicates 95% confidence band.
Table 1. Summary for the significant covariates as fixed effect in the optimal model of CPUE standardization
Covariates as fixed effect Estimate value SD t statistics p AIC D 0.40 0.10 3.94 <0.05 33756 G (factor 2) 11.07 4.15 2.67 <0.05 S (factor 2) −4.79 2.18 −2.2 <0.05 Note: SD represents standard error; D, the vessel date ofbuild; G, the type of fishing gear; S, the type of free swimming schoolscaught; AIC, the Akaike Information Criterion. 
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Table 2. Summary for the significant covariates in the optimal habitat models under scenarios of using nominal CPUE and calibrated CPUE
Habitat models Based on nominal CPUE Based on calibrated CPUE AIC score 33738 18129 Adjusted R2 0.023 0.35 Deviance explained 0.026 0.35 Variable EDF F p Variable EDF F p T100 1.48 7.29 <0.05 SST 2.83 6.49 <0.05 S50 2.78 2.71 <0.05 T50 2.17 6.45 <0.05 S100 2.71 3.61 <0.05 T100 2.90 41.33 <0.05 SOI 1.00 9.67 <0.05 SSS 2.85 29.48 <0.05 S50 2.99 21.08 <0.05 SSH 2.84 29.53 <0.05 SSV 2.86 7.04 <0.05 SSC 2.84 5.38 <0.05 SSO 2.94 23.78 <0.05 SSS-grad 2.10 5.21 <0.05 SSC-grad 1.00 46.09 <0.05 SOI 2.84 96.09 <0.05 Note: EDF represents estimated degree of freedom; F represents F statistics.
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