Environmental effects on swordfish and blue shark catch rates in the US North Pacific longline fishery

• Published in: Fisheries oceanography 1999-09, Vol.8 (3), p.178-198
• Main Authors: Bigelow, Keith A., Boggs, Christofer H., HE, XI
• Format: Article
• Language: English
• Subjects: blue shark; fishery oceanography; generalized additive models; longline fishery; Marine; North Pacific; Prionace glauca; remote sensing; swordfish; Xiphias gladius
• ISSN: 1054-6006; 1365-2419
• DOI: 10.1046/j.1365-2419.1999.00105.x

Generalized additive models (GAMs) were applied to examine the relative influence of various factors on fishery performance, defined as nominal catch‐ per‐unit‐effort (CPUE) of swordfish (Xiphias gladius) and blue shark (Prionace glauca) in the Hawaii‐based swordfish fishery. Commercial fisheries data for the analysis consisted of a 5 year (1991–1995) time series of 27 901 longline sets. Mesoscale relationships were analysed for seven physical variables (latitude, longitude, SST, SST frontal energy, temporal changes in SST (ΔSST), SST frontal energy (ΔSST frontal energy) and bathymetry), all of which may affect the availability of swordfish and blue shark to the fishery, and three variables (number of lightsticks per hook, lunar index, and wind velocity) which may relate to the effectiveness of the fishing gear. Longline CPUE data were analysed in relation to SST data on three spatiotemporal scales (18 km weekly, 1°‐weekly, 1°‐monthly). Depending on the scale of SST data, GAM analysis accounted for 39–42% and 44–45% of the variance in nominal CPUE for swordfish and blue shark, respectively. Stepwise GAM building revealed the relative importance of the variables in explaining the variance in CPUE. For swordfish, by decreasing importance, the variables ranked: (1) latitude, (2) time, (3) longitude, (4) lunar index, (5) lightsticks per hook, (6) SST, (7) ΔSST frontal energy, (8) wind velocity, (9) SST frontal energy, (10) bathymetry, and (11) ΔSST. For blue shark, the variables ranked: (1) latitude, (2) longitude, (3) time, (4) SST, (5) lightsticks per hook, (6) ΔSST, (7) ΔSST frontal energy, (8) SST frontal energy, (9) wind velocity, (10) lunar index, and (11) bathymetry. Swordfish CPUE increased with latitude to peak at 35–40°N and increased in the vicinity of temperature fronts and during the full moon. Shark CPUE also increased with latitude up to 40°N, and increased westward, but declined abruptly at SSTs colder than 16°C. As a comparison with modelling fishery performance in relation to specific environmental and fishery operational effects, fishery performance was also modelled as a function of categorical time (month) and area (2° squares) variables using a generalized linear model (GLM) approach. The variance accounted for by the GLMs was ≈ 1–3% lower than the variance explained by the GAMs. Time series of swordfish and blue shark CPUE standardized for the environmental and operational variables quantified in the GAM and for the time‐area effects in