Tagging-recovery studies have been used successfully on many occasions, and the data analysed in a variety of ways with respect to fish movement. Here we provide a synoptic account of useful statistical techniques for delimiting area utilization and movement patterns of a population of individuals. We focus specifically on kernel smoothing for population utilization distribution and generalized additive models for temporal movement patterns. Furthermore, we use these 2 techniques to re-analyse data from tagging studies on coastal Atlantic cod Gadus morhua, shedding new light on one of the underlying mechanisms determining the population structure of the coastal cod along the Norwegian Skagerrak coast. Cod were found to use small areas. On average 25% of a population used areas smaller than 10 km 2 , while 95% of a population used areas smaller than 160 km 2 . The movements displayed were fjord-limited and predominantly within the bounds of the population's home range. By re-analysing existing data on neutral genetic markers, we show that the crossdistance of the area utilized was smaller than the distance over which pair-wise genetic differences appear.KEY WORDS: Atlantic cod · Gadus morhua · Population home range · Population density distribution · Local populations · Stock structure · Kernel model · GAM
Resale or republication not permitted without written consent of the publisherMar Ecol Prog Ser 372: [231][232][233][234][235][236][237][238][239][240][241] 2008 fishing intensity and the rate at which tags are reported (Pollock et al. 2001). Report rates may vary in time and space depending on the motivation to return tags. The fishing intensity depends on efficiency and effort (Ellis & Wang 2007), both of which may vary over temporal and spatial scales. In well-managed fisheries with well-controlled quotas, these factors may be estimated and it would be possible to apply different weighting schemes to deal with regional differences in fishery intensity (Hunt et al. 1999, Neilson et al. 2006, Stensland et al. 2006. In complex fisheries with a mix of recreational and commercial fisheries, both efficiency and effort depend on several factors such as incentives to fish (economic and recreational), geographical knowledge of the area, economic opportunities to use different gears and weather (Arlinghaus 2006). These factors are functions of both time and space, and may generate heterogeneity in fishing intensity on a fjord/bay scale. Furthermore, the spatial and temporal precision of reported tags depends on the fishers' knowledge of the species, interest in the marine environment and familiarity with the area encompassed by the fishery. All this may add error to the precision in recovery datasets, and, thus, may be difficult to adjust for.Every tag sighting is one indicator of the density distribution of the tagged fish. Given that tagged fish are representative of the underlying population, the recovery will be a 'draw' from the population distribution at the time of recovery. When making inferences ...