Table 1. Final model results.

1. Depth was also found to have a significant effect on catch rates, with catches being highest at depths between 50 and 120 m. The analysis also revealed significant spatiotemporal heterogeneity among Nephrops CPUE (Table 1). Twin trawls were found to have a CPUE approximately 25 per cent higher than single trawls. Our analysis also revealed year and individual vessel characteristics to be correlated with catch rates of Nephrops (Table 1). The final model explained 39 per cent of the deviance. Conclusions: Improving catch rates of Nephrops by utilizing the variability in catches that occurs due to environmental factors can not only improve the economic viability within the fishery but also the resource efficiency. An increase in catch rates has the potential to reduce the effort required to fish the quota, therefore reducing the impact trawling has on the seabed as well as the amount of bycatch and discards. The use of at-sea-sampling data to dissociate environmental variability in Norway lobster (Nephrops norvegicus) catches to improve resource efficiency Jordan Feekings1, Asbjørn Christensen2, Patrik Jonsson3, Rikke Frandsen1, Mats Ulmestrand3, Sten Munch-Petersen2 • Research into the influence of environmental variables on the behaviour of Norway Lobster (Nephrops norvegicus), and hence catch rates, is rather extensive, dating back to the 1960s (e.g. Höglund and Dybern, 1965; Simpson, 1965). However, the use of fishery dependent data to dissociate such variability is uncommon, and where it does exist, it consists of data collected over relatively short time periods and from few vessels (e.g. Redant and De Clark, 1984; Maynou and Sardà, 2001). The primary aim of this study was to determine whether the information collected as part of the at-sea-sampling program could be used to identify hydrographical and environmental variables that are influential on catch rates of Norway lobster. Ultimately, we wanted to know whether environmental variables’ influence on catches could be accounted for in order to improve resource efficiency and economic viability. influence Nephrops behaviour and catch rates in both laboratory and field experiments can be observed in commercial fishery dependent data. Catch rates were modelled using Generalised Additive Models (GAMs) which considered a range of response variables, including depth, temperature, current speed, season, oxygen, time of day, wind stress, salinity and oxygen content, year, slope and orientation of the bottom, atmospheric pressure, total cloud cover, moon phase, trawl type, and the spatial distribution of hauls. Results: A crepuscular rhythm in catch rates was observed (i.e. higher catches being recorded at dawn and dusk; Table 1). Catch rates also differed significantly across season, with catches being highest during quarters 2 and 3. An effect of temperature was also observed, with catch rates increasing as temperature increased (Figure 2). Figure 2. Model-predicted effects of significant smoothing functions (solid lines) on the CPUE of Nephrops. Dotted lines represent the 95% confidence limits.. Figure 1. Spatial distribution of at-sea-observer hauls from Denmark (red) and Sweden(green) Table 1. Final model results. Methods: Data from at-sea-sampling in the Skagerrak and Kattegat (Figure 1), together with hydrographical data from the Danish Meteorological Institute, are used to dissociate environmental variability in Nephrops catches to improve resource efficiency within the Danish and Swedish trawl fisheries. Data were collected over 16 years from 155 vessels and 995 hauls to determine whether the factors previously found to Note: “edf” are estimated degrees of freedom. Significance levels: 0.001 ‘**’ 0.05 ‘*’ 1Technical University of Denmark, National Institute of Aquatic Resources, North Sea Science park, PO Box 101, DK-9850 Hirtshals, Denmark 2Technical University of Denmark, National Institute of Aquatic Resources, Charlottenlund Slot – JægersborgAllé 1, DK-2920 Charlottenlund, Denmark 3Institute of Marine Research, Department of Aquatic Resources, Swedish University of Agricultural Sciences (SLU), Lysekil, Sweden