Because predation by drilling gastropods is uniquely preservable in the fossil record, it represents important evidence for the study of coevolution. Previous studies of drilling gastropod predation have been largely descriptive and sometimes contradictory. We formulate and test a model of prey selection by naticid drilling gastropods. The model adequately predicts both prey species selection and prey size selection. Prey preferences parallel prey profitabilities, determined by calculating prey species-specific and predator size-specific cost-benefit functions. The model also specifically suggests the evolution of potential refugia from predation and the evolution of potential predatory attributes. Application of the model to several Miocene and Pliocene assemblages studied by Thomas (1976) corroborates the feasibility and utility of this approach in examining the evolutionary record of naticid predation, which extends from the Late Mesozoic. Apparent evolutionary stasis and convergent morphological trends among prey species may be consistent with continuous selection pressures against predation.

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 the GLM are presented. For swordfish, both nominal and standardized time series indicate a decline in CPUE, whereas the opposite trend was seen for blue shark.

To learn more about the movement patterns of bigeye tuna (Thunnus obesus), we deployed archival tags on 87 fish ranging in fork length from 50 to 154 cm. Thirteen fish were recaptured, from which 11 archival tags were returned, representing in aggregate 943 days‐at‐liberty. We successfully retrieved data from 10 tags, representing 474 days in aggregate. The largest fish recaptured was 44.5 kg [131 cm fork length (FL)] and the smallest 2.8 kg (52 cm). The deepest descent recorded was 817 m, the coldest temperature visited 4.7°C, and minimum oxygen level reached ∼1 mL L−1. Fish spent little time at depths where water temperatures were below 7°C and oxygen levels less than ∼2 mL L−1. Five fish were recaptured near the offshore weather buoy where they were tagged. Based on vertical movement patterns, it appeared that all stayed immediately associated with the buoy for up to 34 days. During this time they remained primarily in the uniform temperature surface layer (i.e. above 100 m). In contrast, fish not associated with a floating object showed the W‐shaped vertical movement patterns during the day characteristic of bigeye tuna (i.e. descending to ∼300–500 m and then returning regularly to the surface layer). Four fish were tagged and subsequently recaptured near Cross Seamount up to 76 days later. These fish exhibited vertical movement patterns similar to, but less regular than, those of fish not associated with any structure. Bigeye tuna appear to follow the diel vertical movements of the deep sound scattering layer (SSL) organisms and thus to exploit them effectively as a prey resource. Average night‐time depth was correlated with lunar illumination, a behaviour which mimics movements of the SSL.

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