1
2
3
1
3
2
Research into the influence of environmental variables on the behaviour of Norway Lobster (Nephrops
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).
Figure 1. Spatial distribution of at-sea-observer hauls from Denmark (red) and Sweden (green)
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
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
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.
: 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).
Table 1. Final model results.
Categorical terms Estimate Lower
Trawl type - OTB
Trawl type - OTT
TOD - Dawn
TOD - Day
TOD - Dusk
TOD - Night
Quarter - 1
Quarter - 2
Quarter - 3
Quarter - 4
Smooth terms s (year) s (temperature) s (depth) s (long., lat)
Random term s (vessel)
Note: “edf” are estimated degrees of freedom.
Significance levels: 0.001 ‘**’ 0.05 ‘*’
1
1.246
1
0.770
0.913
0.784
1
1.310
1.387
0.976
edf
4.737
2.152
7.584
23.398
SD
36.909
0.786
1.026
0.786
0.642
0.758
0.625
0.786
1.068
1.117
0.765
Upper
1.273
1.514
1.273
0.924
1.100
0.983
1.273
1.606
1.722
1.306
P value
< 0.001**
0.026*
< 0.001**
0.005*
0.337
0.035*
< 0.001**
0.010*
0.003*
0.842
P value
< 0.001**
< 0.001**
< 0.001**
< 0.001**
P value
< 0.001**
Figure 2. Model-predicted effects of significant smoothing functions (solid lines) on the CPUE of
Nephrops. Dotted lines represent the 95% confidence limits..
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.
Improving catch rates of
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.
1 Technical University of Denmark, National Institute of Aquatic Resources, North Sea Science park, PO Box 101, DK-9850 Hirtshals, Denmark
2 Technical University of Denmark, National Institute of Aquatic Resources, Charlottenlund Slot – Jægersborg Allé 1, DK-2920 Charlottenlund, Denmark
3 Institute of Marine Research, Department of Aquatic Resources, Swedish University of Agricultural Sciences (SLU), Lysekil, Sweden