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Genetic analysis of Atlantic salmon captured in a netting station reveals multiple escapement events from commercial fish farms


Correspondence: Kevin Glover, Institute of Marine Research. P.O. Box 1870, Nordnes. N- 5817 Bergen, Norway (e-mail: kevin.glover@imr.no)Re-use of this article is permitted in accordance with the Terms and Conditions set out at http://wileyonlinelibrary.com/onlineopen#OnlineOpen_Terms


Microsatellite DNA analysis and statistical assignment methods were implemented to identify the origin of 190 farmed escaped Atlantic salmon recaptured over a period of 2 months at a netting station located in Trondheim fjord, Norway. Samples were also collected from farms within the region. The escapees originated from a minimum of two sources, separated in time of capture at the netting station. The majority of the escapees captured in the early period probably originated from a single farm within the region, while escapees captured in later period probably originated from multiple farms, including from outside of the region. Biological data from the escapees supported these conclusions. This study serves to exemplify the use of genetic methods to assist fisheries management.


The implementation of DNA-based methods to address fisheries (Ogden 2008; Waples et al. 2008) and aquaculture (Glover 2010) related challenges is becoming increasingly widespread, mimicking advances in the application of such technologies for wildlife management in general (Manel et al. 2005; Schwartz et al. 2007; Ogden et al. 2009; Ogden 2010). While the implementation of DNA methods into fisheries-related problems has not been without its specific challenges (Waples et al. 2008), there are many diverse examples where such methods have provided both fisheries managers and law-enforcers with tangible information. For example, Primmer et al. (2000) were able to use individual genetic assignment techniques to identify an unusually large Atlantic salmon, Salmo salar L., supposedly captured during a Finnish lake fishing competition to be most likely of Norwegian origin, purchased at a fish market. Looking at more traditional applications, genetic-based methods are actively used to manage fisheries catch at the population level for several species of salmonids in the Pacific (Shaklee et al. 1999), control the commercial harvest of marine mammals (Glover et al. 2012) and even document the apparent failure of an extensive and long-term stocking campaign of Atlantic salmon in the River Thames, UK (Griffiths et al. 2011).

The Atlantic salmon fish-farming industry has grown rapidly in the past 40 years to represent an important industry in several countries. However, containment is a major issue, and each year, hundreds of thousands of farmed Atlantic salmon escape into the wild. This represents a potential environmental problem as many farmed escapees enter rivers (e.g. Fiske et al. 2006), display a range of interactions with wild salmon (Jonsson & Jonsson 2006) and may even spawn successfully with them (Lura & Saegrov 1991; Saegrov et al. 1997). As a consequence of successful reproduction, farmed fish have caused genetic changes in native populations (Crozier 1993; Clifford et al. 1998; Skaala et al. 2006; Bourret et al. 2011), and there are global concerns over the long-term fitness of wild populations that receive farmed escapees (e.g. Hindar et al. 1991; Naylor et al. 2005). In general, it is agreed that there needs to be both a reduction in the number of fish escaping from farms and better opportunities for monitoring and control of escapement.

Farmed salmon are not tagged as part of standard aquaculture production. Thus, while it is possible to differentiate between wild and farmed salmon based upon external morphology or scale characteristics (Lund & Hansen 1991), it is not possible to identify the farm of origin based upon external examination alone. This situation is complicated further given that farmed salmon can disperse over large distances once they have escaped (Hansen 2006; Skilbrei & Wennevik 2006; Skilbrei 2010). To address this fisheries management challenge, a DNA-based method for the identification of escaped salmon back to their farm of origin, using genetic assignment techniques, was developed in Norway (Glover et al. 2008). Since its first implementation in 2007, the method has been constantly under development including additional statistical approaches (Glover et al. 2009) and testing single nucleotide polymorphisms (SNPs) versus microsatellites (Glover et al. 2010b). Furthermore, the method has been successfully applied in a number of forensic cases for the legal authorities (Glover 2010) and adapted to permit the identification of farmed rainbow trout, Oncorhynchus mykiss (Walbaum) (Glover 2008) and farmed Atlantic cod, Gadus morhua L., (Glover et al. 2010a, 2011) back to their origin.

In Norway, fish farmers are legally obliged to report escapement to the Norwegian Directorate of Fisheries (NDF), who are responsible for aquaculture regulation and enforcement. To assist this regulatory authority, the Institute of Marine Research (IMR) conducts a genetic analysis service to help identify the farm of origin of escapees when escapees are observed in large numbers in the sea but no farm has reported losses (Glover 2010). Here, a recent case study is reported where the NDF wanted to know whether a large group of escapees, captured over a period of 2 months at a netting station in Norway, originated from a single escapement or multiple farms. Specifically, two questions were put forward by the NDF: (1) Are the escapees recaptured at the fishing station at Agdenes from one or more sources? (2) Do these fish originate from farms in the vicinity of the fishing location?

Materials and methods

A seasonally operated net-fishing station is located at Agdenes in the entrance to Trondheimfjord in mid-Norway (Fig. 1). This station is operated to capture wild salmon migrating back to this region in the summer months. When the station started its fishing season on 20 June 2011, fish were captured that appeared to be farmed escaped salmon together with wild salmon. This classification was based upon visual inspection of the fish, followed by scale-reading analysis that permits the differentiation of farmed and wild salmon (Lund & Hansen 1991). While the station frequently captures farmed escapees, the numbers of escapees were unusually high, which suggested a potential escapement of fish from a local farm.

Figure 1.

Map of study area, illustrating location of the stationary salmon netting station at Agdenes, the farm reporting loss of salmon and local farms not reporting escapement.

In the period 20 June 2011 to 31 August 2011, 190 farmed salmon were captured at this fishing station (Fig. 2). While the fish weighed from 1.3 to 11.6 kg, which could suggest escapement from more than one source (i.e. farms rearing different sized fish), the NDF were interested in documenting whether these fish came from one or multiple farms. None of the farms in the immediate area (Fig. 1) had reported losses of salmon to the NDF, although a farm located outside the area (farm 8A) had reported an escapement of fish that overlapped in size with the majority of the escapees captured at Agdenes. While the exact date of reported escapement from this farm was not known, it probably occurred in the period 1 June 2011–7 July 2011.

Figure 2.

Number of farmed escaped salmon recaptured at Agdenes presented by week.

The NDF collected samples of salmon from all farms in the region that had fish overlapping in size with the escapees captured at Agdenes, including the farm that had reported escapement (8A). A single cage representing each genetic group was sampled (approximately 47 fish sampled per cage) from each farm. These samples were collected on 12 July 2011 and 14 July 2011, although the farm sample 7A was collected on 12 September 2011. A genetic group is defined as a smolt delivery per farm. Thus, if the farm received smolt deliveries on three different occasions, samples from one cage representing each of these deliveries, was sampled. In total, the genetic material upon which this case study is based includes the 190 escapees recaptured at Agdenes, in addition to approximately 47 fish per cage from 12 cages located at eight farms (754 samples).

All samples were subject to analysis with 18 microsatellite loci. DNA extraction was performed in 96-well plates using a Qiagen DNeasy®96 Blood & Tissue Kit. Each plate contained two or more negative controls, and to control for genotyping quality, DNA was extracted twice for 61 of the escapees recaptured at Agdenes. Routine genotyping control has been recommended by several authors (Hoffman & Amos 2005; Pompanon et al. 2005) and plays a standard role in genotyping in the laboratory at IMR (e.g. Glover et al. 2010b; Haaland et al. 2011). Loci were amplified in three multiplexes (full genotyping conditions available from authors upon request): SSsp3016 (Genbank no. AY372820), SSsp2210, SSspG7, SSsp2201, SSsp1605, SSsp2216 (Paterson et al. 2004), Ssa197, Ssa171, Ssa202 (O′Reilly et al. 1996), SsaD157, SsaD486, SsaD144 (King et al. 2005), Ssa289, Ssa14 (McConnell et al. 1995), SsaF43 (Sanchez et al. 1996), SsaOsl85 (Slettan et al. 1995), MHC I (Grimholt et al. 2002) and MHC II (Stet et al. 2002). PCR products were analysed on an ABI 3730 Genetic Analyser and sized by a 500LIZ size standard. Two persons control read the raw data before export for statistical analysis. No genotyping errors were observed among these re-analysed samples.

To address the two questions posed by the NDF, a mixture of population genetic statistical methods were applied to these samples. First, the program MSA (Dieringer & Schlotterer 2003) was used to compute summary statistics and FST values. Thereafter, the data were analysed in Genepop V3.3 (Raymond & Rousset 1995) to compute Hardy–Weinberg equilibrium, heterozygosities and linkage disequilibrium between pairs of loci within samples. The Fisher's exact test (demorization 10 000; 100 batches; 5000 iterations) was implemented to test for statistical significance.

The program structure 2.2 (Pritchard et al. 2000; Falush et al. 2003) was used to compute individual admixture (also referred to as Bayesian cluster analysis). Individual admixture permits the identification and assignment of individual fish to genetic clusters (i.e. populations or genetic groups) without taking into consideration which population or location each individual were sampled from. This is especially useful to identify fish of different backgrounds that are mixed in time and space, as was potentially the situation in the current investigation. Correlated allele frequencies, an admixture model and no prior data were assumed. Each run consisted of a burn-in of 50 000 MCMC steps, followed by 250 000 steps. The program was run with all samples included, with the number of populations set between = 1–7. While a number of computational methods are available to determine the most appopriate k to represent the data, such a method was not applied. This was because although such methods can estimate the number of populations cryptically embedded in a mixed sample, the present study deals with farmed samples that do not originate from biological populations, and as such, estimating this was not of primary importance. Thus, the results were presented for = 2 and 5 after extensive visual inspection of all data. This manual acceptance of k did not affect the results nor their interpretation, and assignment of the fish at = 2–7 are presented in a supplementary file for inspection (Figure S1). Assignment of fish to farm using structure was based upon results at = 5. Each individual was assigned to the genetic cluster that it was most closely related (based upon its admixture score).

In addition to admixture analysis, individual assignment of the 190 escapees at Agdenes to each of the potential source farm samples (which represent the genetic baseline) was conducted using the program GeneClass2 (Piry et al. 2004). Here, a specific method of computation implemented within the program was used (Rannala & Mountain 1997). First, direct genetic assignment was conducted. This method places each escapee (or unknown) into the baseline sample (i.e. farm sample in this study) that it genetically most resembles. This assignment is conducted irrespective of the absolute degree of similarity between the unknown individual and the most similar baseline sample. While this method is suitable in closed systems, in situations where not all of the potential sources for the escapees are included in the genetic baseline, as is the situation here, genetic exclusion can be conducted at a given level of statistical threshold. Using this complimentary approach, each individual escapee is rejected or assigned to all of the potential source samples at a pre-determined significance threshold (α = 0.01 in this study). Thus, if an individual salmon is excluded from a given farm sample or all of the farm samples constituting the genetic baseline, this is to be interpreted as a clear signal that that individual must have originated from another source or set of sources. In this specific study, it would be interpreted as the escapee had originated from a farm outside of the sampling region.

A Kruskal–Wallis test was used to investigate differences in weight, length and condition factor among the groups of fish identified through clustering analysis (i.e. structure). This test took into consideration the variance not being homogenous among the groups. Post hoc comparisons of mean ranks of all pairs of groups were also computed (two-sided with a Bonferroni's adjustment).


Summary statistics and genetic differentiation among the farm samples

Statistics, including the numbers of alleles per locus, total number of alleles, allelic richness, heterozygosities, deviations from Hardy–Weinberg equilibrium and linkage disequilibrium are presented for each sample (Table S1). This supplementary information also includes global FST values per locus over all samples, illustrating which loci are most statistically informative. The above-mentioned parameters have been well documented in previous investigations of farmed salmon in cages (e.g. Glover et al. 2009) and are not essential to address the questions raised by the NDF in this study (beyond standard control checks of the raw data and looking for potential data abnormalities). Thus, these data are not dealt with further with the exception of referring to specific test statistics where appropriate.

A mixture of pairwise FST values (Table S2), self-assignment tests that demonstrate the level of expected precision to identify the escapees (Table 1) and admixture analysis (Fig. 3) revealed variable but in some instances highly significant genetic differences between the samples collected in cages on farms. The relationship unveiled among these samples is typical for farmed salmon in Norway and demonstrates that the ability to identify a single farm as the potential source for escapees accurately will depend on which farm has lost fish. For example, the salmon from farm sample 3A overlapped genetically with the salmon from farm 6A and to some extent 7A (Table 1; Fig. 3). Thus, if any of these three farms were to lose fish, it would be difficult to exclude these three farms as the potential source. By contrast, farm sample 5A was genetically distinct to all other groups, which would make highly accurate assignment of escapees from this source possible (Table 1).

Figure 3.

Individual admixture analysis depicting the genetic relationship between samples collected on farms operating within the study area, and the 190 escapees captured at Agdenes in 2011. Data are presented when the number of populations is set at 2 and 5 (see 'Materials and methods'). Each individual's genetic assignment is represented by a vertical bar, and each colour represents a distinct genetic cluster. Individuals may be admixed. An extended figure with number of populations set to 2, 3, 4, 5, 6 and 7 is available online (Figure S1).

Table 1. Results of self-assignment tests among the 12 farm samples constituting the genetic baseline. Mean overall self-assignment success = 59%. Numbers in bold represent the number of individuals in a sample correctly assigned to itself, while other numbers represent the mis-assigned individuals
SampleFarm 1ABFarm 2AFarm 3AFarm 4AFarm 4BFarm 4CFarm 4DFarm 5AFarm 5BFarm 6AFarm 7AFarm 8A n % Correct
Farm 1AB 31 4   14 3  24569
Farm 2A4 32  2 2  7   4768
Farm 3A1  31    3  101 4667
Farm 4A    18 6161  1 54738
Farm 4B   5 22 76 1  64747
Farm 4C   1410 12 5    54626
Farm 4D3145106 7  25224715
Farm 5A  1   1 45     4796
Farm 5B46 122   24 1  4060
Farm 6A1111   3   28 214760
Farm 7A  2   1  1 41  4591
Farm 8A 1 5371 11  41 6068

Identification of the escapees captured at Agdenes

Inspection of the genetic relationship among the fish from samples on the farms and captured at Agdenes revealed several trends (Fig. 3). First, and most distinctly, the escapees recaptured in the period 20 June–17 July (hereon referred to as the early period = the first 136 fish captured at Agdenes) were almost exclusively depicted by either the pink or yellow genetic cluster at = 5, while in the period 17 July–31 August (hereon referred to as the late period = the last 54 fish captured at Agdenes), the escapees belonged to the green and some to the red cluster at = 5. Individual admixture analysis demonstrated that the escapees captured at Agdenes originated from a minimum of two separate escapement events, separated in time (this trend is most easily read for = 2 but is reflected up to and including = 7; Figure S1).

Separation of the farmed escapees captured at Agdenes into a minimum of two genetic groups, separated in time of capture, is strongly supported by several additional genetic and biological measurements. Both direct and exclusion-based assignment of the escapees to the surrounding farms differed greatly between these two periods (Fig. 4). For example, while almost all of the escapees captured in the early period assigned directly to farms 4 and 8, the escapees captured in the late period display a much more widespread assignment among the farms. Furthermore, while almost none of the escapees captured in the early period were excluded from all of the farm samples, approximately half of the escapees captured in the late period could be excluded from all of the farms sampled. The latter observation strongly suggests that many of the farmed salmon captured in the later period originate from a farm(s) outside the sampling area. No significant differences in mean weight = 0.2 nor length = 0.1 were observed between the fish split into the genetic groups pink and yellow (forming the majority of the escapees captured in the early period at Agdenes) and genetic groups red and green (forming the majority of the escapees captured in the late period at Agdenes) (Fig. 5). By contrast, highly significant different condition factors were revealed between the fish belonging to the early and later periods < 0.0001 (Fig. 5). Fish represented by the pink and yellow genetic groups, captured primarily in the early part of the fishing period at Agdenes, were much heavier for their length than fish mostly representing the late period at Agdenes.

Figure 4.

Assignment of farmed escapees captured at Agdenes to farms operating in the region. Assignment is presented for all 190 fish grouped (left), then split into the early (20.06–17.07) (middle) and late capture periods (17.07–31.08) (right). Data are presented as direct assignment to farm (a.), and exclusion from farm at α 0.01(b.).

Figure 5.

Biological characteristics of the escapees when split into the early and late groups of escapees in Figure 2. Dotted bars represent the early escapees while bars with diagonal lines represent the late escapees.

Close examination of the escapees captured in the early period, depicted by the genetic clusters yellow and pink at = 5 (Fig. 3), suggests that these fish may be further divided into more than one escapement event/source. While this cannot be excluded based upon genetic analyses alone, there are several observations that suggest that these fish have originated from a single cage or farm. First, samples from farm 4 and farm 8 were also depicted by a mixture of yellow and pink bars. Mixing of fish from genetically diverse lines or strains into one production unit, that is, a cage, has been observed previously (Glover et al. 2010b2010b) and is likely to be the explanation for the genetic pattern revealed on both farm 4 and 8. Thus, the escapees captured in the early period may simply have originated from one or more of these cages where genetic material from diverse lines has already been mixed. In support of this, almost all of the escapees in the early period were directly assigned to these two samples (Fig. 4) and very few of them could be statistically excluded from either of these farms. By contrast, all other potential sources were excluded for a minimum of two-thirds of these escapees. Finally, no significant differences in length = 1.0, weight = 1.0 or condition factor = 0.2 were observed among the escapees represented by the yellow and pink genetic clusters. It is therefore deemed highly likely that they originated from a single source. Nevertheless, owing to there being such a large genetic overlap between fish in farm 4 and farm 8 (Table 1; Fig. 3; Supplementary file 2), it was not possible to exclude either farm 4 or 8 as the source of the escapees based upon genetic analyses alone (Fig. 4).

It is highly likely that fish captured in the late period originated from more than one escapement event. This is supported by all of the genetic and biological data. Firstly, the direct assignment results implicated a number of farms, and approximately half of the escapees captured in this period could be excluded from all of the farms sampled (Fig. 4). Secondly, the number of alleles observed for the 54 fish captured in this late period was larger than the early period, which included 136 fish, and larger than any single cage examined (Supplementary file 1), strongly suggesting mixed origin. Finally, the biological data (i.e. weight, length and condition factor) were more variable among the 54 fish captured in this period than for the 136 salmon captured in the early period (Fig. 5).


The application of genetic data into the management of marine resources has not been without challenges (Waples et al. 2008). Nevertheless, genetic methods to address management questions within fisheries (Ogden 2008) and aquaculture (Glover 2010) have been established and continue to grow. The aim of developing such tools is to improve opportunities for authorities to exercise regulation and control. In turn, this has the general goal of increasing compliance among operators, and ultimately, the sustainability of such activities. Here, an application of molecular genetic tools to assist the regulatory authorities address important questions related to the interactions between aquaculture facilities and fisheries has been demonstrated. It was possible to demonstrate to the NDF, who are responsible for the development and implementation of aquaculture regulation in Norway, that the escapees captured at Agdenes originated from at least two sources, separated in time. The escapees captured in the late period probably originated from multiple farms, including farms from outside the sampling region. This is in contrast to fish captured in the early period where the majority probably originated from a single farm and from within the sampling area. These primary findings were supported by various methods of genetic analysis applied (i.e. clustering, genetic assignment, estimations of genetic variation within samples and sub-samples), in addition to the biological data for the escapees.

The frequency of farmed escaped salmon in angling catches in rivers during the summer months is generally lower than the frequency of farmed escaped salmon that are reported on the spawning grounds in the same rivers during the autumn (Fiske et al. 2006). This is because farmed salmon tend to enter fresh water later than wild salmon (Fiske et al. 2006) and is likely to be caused by several factors. For example, fish escaping from farms will in most circumstances not have been imprinted to any specific river. Furthermore, because day-length photoperiod is routinely manipulated on commercial farms to inhibit puberty during production (Taranger et al. 2010), escapees may also exhibit delayed maturation patterns, which influence timing of freshwater migration. In the present study, fish captured in the early period at Agdenes probably originated from a single farm within the region. In the late period, however, these data suggest that the escapees originated from multiple farms, including farms from outside the sampling area. The latter conclusion is based on approximately 50% of the escapees captured in the later period being excluded from all farms within the region. Therefore, it is suggested that many of the fish captured in the late period probably represent the start of the general yearly migration of farmed salmon that have escaped from a variety of cages and farms along the Norwegian coastline to fresh water. This is supported by the large genetic diversity observed among this group compared with any cage or the early group of escapees, in addition to the mean condition factor for the fish captured in the late period being significantly lower than the fish captured in the early period. This strongly suggests that the latter group have been without food over a longer period, and have probably migrated longer distances (once again, also supported by their exclusion from nearby farms).

The DNA-based method for tracing farmed escaped fish back to cage and farm of origin, that is sometimes referred to in Norway as the stand-by method, was established and first implemented in 2007 (Glover et al. 2008). This method is designed to be employed quickly in situations where a relatively homogenous group of escapees (i.e. fish of similar sizes) are captured in a fjord or coastal area in limited space and time (Glover 2010). These implementation guidelines are usually adhered to so as to avoid implementing the method in situations where escapees have likely originated from a variety of sources, potentially travelled long distances (thus making sampling all potential farms impossible), or on otherwise diffuse cases. Nevertheless, although the escapees on which the present study is based were captured over a longer period of time than is usually the case when the stand-by method is typically implemented, the analyses were able to provide the management authorities with information regarding the nature of the escapement detected at Agdenes and the potential sources of the escapees. Nevertheless, as concluded previously (Glover 2010), when investigating farmed escaped salmon, the closer in time to spawning samples are taken, the more likely that sampling is being conducted on fish accumulating from multiple escapement events as opposed to a distinct local unreported escapement event. This is supported by the results here given that the escapees captured in the late period at Agdenes most likely represented fish that had escaped from multiple farms also outside the sampling region.

As a consequence of founder effects and genetic drift, differences are often observed for molecular genetic markers among strains of fish used in aquaculture. For example, large and highly significant genetic differences have been observed in allele frequencies among Atlantic salmon (Norris et al. 1999; Skaala et al. 2004; Karlsson et al. 2010), rainbow trout (Glover 2008) and Atlantic cod strains reared in culture (Glover et al. 2010a). For other fish species, which are gradually incorporated into aquaculture activities, the implementation of similar methodological approaches described here, previous studies of escapees (Glover et al. 2008, 2009, 2011), and other wildlife and fisheries forensic applications (e.g. Withler et al. 2004; Ogden 2008, 2010) will permit fishery and aquaculture managers and regulators to enforce legislation. Thus, the analyses demonstrated that genetic-based methods have an increasing and diverse role to play in modern-day management of fisheries, aquaculture and their interactions.


Samples for this study were collected by the Norwegian Directorate of Fisheries in addition to the local fisherman operating the salmon netting station at Agdenes. This study was financed by the Norwegian Department for Coastal Affairs, while Dr Zhiwei Zhang was financed by the Norwegian Research Council.