Not that clean: Aquaculture‐mediated translocation of cleaner fish has led to hybridization on the northern edge of the species' range

Abstract Translocation and introduction of non‐native organisms can have major impacts on local populations and ecosystems. Nevertheless, translocations are common practices in agri‐ and aquaculture. Each year, millions of wild‐caught wrasses are transported large distances to be used as cleaner fish for parasite control in marine salmon farms. Recently, it was documented that translocated cleaner fish are able to escape and reproduce with local wild populations. This is especially a challenge in Norway, which is the world's largest salmon producer. Here, a panel of 84 informative SNPs was developed to identify the presence of nonlocal corkwing wrasse (Symphodus melops) escapees and admixed individuals in wild populations in western Norway. Applying this panel to ~2000 individuals, escapees and hybrids were found to constitute up to 20% of the local population at the northern edge of the species’ distribution. The introduction of southern genetic material at the northern edge of the species distribution range has altered the local genetic composition and could obstruct local adaptation and further range expansion. Surprisingly, in other parts of the species distribution where salmon farming is also common, few escapees and hybrids were found. Why hybridization seems to be common only in the far north is discussed in the context of demographic and transport history. However, the current lack of reporting of escapes makes it difficult to evaluate possible causes for why some aquaculture‐dense areas have more escapees and hybrids than others. The results obtained in this study, and the observed high genomic divergence between the main export and import regions, puts the sustainability of mass translocation of nonlocal wild wrasse into question and suggests that the current management regime needs re‐evaluation.


| INTRODUC TI ON
Moving organisms outside their natural boundaries can cause diverse effects on the ecosystems (Atalah & Sanchez-Jerez, 2020).
Introductions can affect some species through ecological competition, either by becoming their prey or predator, or by competing for resources (Evangelista et al., 2019). Introduced individuals can also carry pathogens, that being unknown to the local population, can spread quickly into a novel environment, which has not been able to develop any form of resistance .
Furthermore, if the introduced populations are genetically distinct from the local ones, hybridization and admixture can lead to altered population structure , lower effective population size and reduced fitness through outbreeding depression Glover et al., 2017;Laikre et al., 2010). Donor populations and ecosystems can also be negatively affected if harvest leads to disruption in species interactions and ecosystem function (Halvorsen, Larsen, et al., 2017). Adverse genetic effects, such as loss of diversity due to dwindling population size or selective harvesting, can also be experienced (Allendorf et al., 2008). However, and despite their known adverse effects, the introduction of species into new areas and translocation of individuals from foreign populations are still common practice in aquaculture and fisheries management. Translocations aim to increase catches, mitigate loss of wild stocks and restore or even create new fisheries (Laikre et al., 2010). Likewise, many species are harvested in large numbers in the wild to provide food or other services to cultured species such as cleaner fish to delouse salmonids.
The use of cleaner fish for sea lice control in commercial aquaculture was first implemented in the late 1980s (Bjordal, 1988) and increased dramatically from 2008 onwards as a result of sea lice developing resistance to widely used pharmaceutical treatments (Besnier et al., 2014;Fjørtoft et al., 2020;Kaur et al., 2017).
In Norway alone, the number of cleaner fish used increased from 1.7 million in 2008 to 60 million in 2019 (Norwegian Directorate of Fisheries, 2019). Outside Norway, the use of cleaner fish in parasite control is still relatively limited but set to increase (VKM et al., 2019). Some countries, such as UK and Ireland, apply a similar system to Norway by deploying a mixture of farmed and wild-caught cleaner fish (Bolton-Warberg, 2018;Riley et al., 2017) while others, for example Canada, do not allow the use of wild-caught cleaner fish in open marine aquaculture (Boyce et al., 2018). The possibility to use cleaner fish for parasite control in aquaculture is currently being investigated in other salmon-producing countries as well (Sanchez et al., 2018).
At present, there are five different species used as cleaner fish in Norwegian aquaculture: lumpfish (Cyclopterus lumpus), ballan wrasse (Labrus bergylta), goldsinny wrasse (Ctenolabrus rupestris), corkwing wrasse (Symphodus melops) and rock cook (Centrolabrus exoletus), the latter in lower numbers. Lumpfish, whose potential use as a cleaner fish was discovered in 2014, has since become the most commonly used cleaner fish (Imsland et al., 2014). The majority of lumpfish are farmed while almost all wrasses are caught wild and transported to aquaculture facilities. Currently, ballan wrasse is the only commercially reared wrasse species, albeit still at a relatively small scale (Norwegian Directorate of Fisheries, 2019). Goldsinny and corkwing wrasse are, by far, the most commonly used wild-caught cleaner fish.
In 2019, 7.9 million goldsinny and 7.3 million corkwing wrasse, all captured in the wild, were deployed as cleaner fish in Norwegian aquaculture.
Although often considered as an environmental friendly form of parasite control (Liu & Bjelland, 2014), the increasing fishing pressure and large-scale translocation of cleaner fish raise concerns about potential overfishing and human-mediated gene flow from translocated individuals to wild populations. Animal welfare during transportation and in sea cages is also a matter of concern (Geitung et al., 2020). An estimated 1 million wrasse are harvested in southwestern England and transported to Scottish salmon farms (Davies & West, 2017;Riley et al., 2017). In Norway, millions of wrasses are utilized as cleaner fish and translocated hundreds of kilometres to be used in salmon farms (VKM et al., 2019). There are many examples of salmonids escaping open-pen aquaculture and hybridizing with local populations, leading to genetic swamping and reduced fitness (Bolstad et al., 2017;Glover et al., 2017). Recently, several studies have collectively demonstrated that also wrasses are able to escape from salmon farms and potentially hybridize and admix with local populations (Blanco Gonzalez et al., 2019;Faust et al., 2018;Jansson et al., 2017). However, the geographical extent, magnitude of genetic mixing and the ecological consequences are largely unknown.
In contrast to regulations for salmonid farming, there are currently no requirements for preventing escape of cleaner fish from sea cages, nor reporting escapes when they occur.
Wrasses (Labridae) are a large and diverse family of marine fish with over 600 described species worldwide. Many of these species show natural cleaning behaviour, that is they feed on ectoparasites from other fish species' skin. The wrasse species utilized as cleaner fish on Norwegian fish farms inhabit shallow rocky areas along the coast from the Mediterranean Sea in the south, to the Norwegian coast in the north. In recent years, their abundance has shifted northwards and diminished in the south, which has been suggested to be due to increased seawater temperatures (Knutsen et al., 2013).
These species differ in their ecology and life history characteristics in several ways, but they are all believed to be territorial and nonmigratory, thus almost exclusively dependent on the planktonic early life stages for dispersal (Darwall et al., 1992;Halvorsen et al., 2020;Skiftesvik et al., 2014). Depending on species and the set geographic scope, previous studies of wrasses have shown varying degree of genetic population sub-structuring (see D'Arcy et al., 2013;Jansson et al., 2017;Knutsen et al., 2013;Robalo et al., 2012;Seljestad et al., 2020). One striking feature is, however, the detected genetic break for corkwing, which is located at the south-western tip of Norway around sandy beaches in Jaeren and Lista (Blanco González et al., 2016). The break only spans <60 km and has been suggested to be a result of postglacial recolonization and founder events separating the populations for more than ~10 kya (Mattingsdal et al., 2020).
Corkwing wrasse is a nest-building species that spawns benthic eggs, which are dependent on paternal care until hatching. Nesting males are brightly coloured and significantly larger than females or sneaker males, which mimic the females' brown colour and smaller body size (Halvorsen et al., 2016). Currently, nesting males are disproportionately targeted by Norwegian fisheries, which are regulated by a minimum size limit . However, size, maturity and proportion of nesting males to sneaker males do not seem to be consistent across populations. Recent studies suggest that populations south of the genetic break in south-western Norway are growing faster, maturing earlier, having a shorter life span and a lower proportion of sneaker males to nesting males (Halvorsen et al., 2016).
The strong genetic differentiation found between corkwing populations located on the south vs. the west coast of Norway has allowed for the development of genomic tools to identify escapees as well as first-and second-generation hybrids between southern individuals and local populations (Faust et al., 2018). Faust and colleagues showed in their study (2018) that translocated corkwing wrasse can escape and hybridize with local populations at the northern edge of the species current distribution limit in Flatanger, Norway.
Of the 40 corkwing wrasse they collected, two were identified as southern escapees and 13 as potential first-or second-generation hybrids. However, that proof-of-concept study was geographically limited, and only based on a low number of individuals. Therefore, more extensive sampling is needed in order to quantify the extent and magnitude of escapees and hybrids of wrasse from southern regions. In the present study, we addressed this by first developing an informative panel of genome-wide SNPs, and then using this panel to analyse ~2000 corkwing wrasse collected from aquaculturedense regions in western Norway and potential source populations in Skagerrak-Kattegat.

| SNP selection and bioinformatics
In order to find discriminant and divergent SNPs for the identification of nonlocal corkwing wrasse, we used 2b-RAD sequence-data from western Norway (import region) and Skagerrak-Kattegat (export region). Western sample sequences were taken from Faust et al. (2018) and contained 40 individuals from Austevoll, the only region where the authors did not detect any escapees or potential hybrids. As a reference for the exported fish, we used 120 individuals from three locations in the Skagerrak-Kattegat (Risør, Sandefjord and Kungsbacka). All raw sequences are available on NCBIs Sequence Read Archive (BioProject PRJNA702627). The unpublished sequences were sampled and processed in the same way as the ones from Austevoll using a modified version of 2b-RAD (Wang et al., 2012) full procedure (Faust et al., 2018). All sequences were mapped using bowtie2 (Langmead & Salzberg, 2012) to the published Symphodus melops genome (Mattingsdal, 2017). Variant calling was done following the GATK pipeline (McKenna et al., 2010) using UnifiedGenotyper after realigning sequences around indels and recalibrating base quality (BQSR). Variant score quality was recalibrated (VQSR) using site identity across technical replicates as a training set. To ensure high confidence in genotypes and SNPs, we used vcftools (Danecek et al., 2011) filtering on quality by depth (QD < 2.0), strand bias (FS > 60, SOR > 2) and mapping quality (MQ < 40). Sites with more than 10% missing data and with a fraction of heterozygotes above 0.5 (possible lumped paralogs) were removed, leaving a total of 10 747 putative SNPs.
To select the most divergent SNPs between western and Skagerrak-Kattegat individuals, we conducted pairwise comparisons between Austevoll (western Norway) and each of the three locations in Skagerrak-Kattegat. A total of 387 SNPs, distributed over 270 contigs, were identified among the 500 highest F ST values in all three pairwise comparisons. Reading and converting between file formats was done using VcfR radiator (Knaus & Grünwald, 2016 and Radiator (Gosselin, 2019), and the package diveRsity (Keenan et al., 2013) was used to calculate pairwise F ST .
SNPs displaying FST values >0.4 (183 SNPs total) were used for SNP locus primer design and resulted in four assays with a total of 106 SNPs. Primer design, amplification and genotype calling were based on the Agena MassARRAY iPLEX Platform, as described by Gabriel et al. (2009). Selected 106 SNP loci were analysed in four assay groups (Table S1). Accuracy, efficiency and power of the four assays to correctly identify escaping individuals from the two populations and their potential offspring were estimated using the R package HYBRIDDETECTIVE (Wringe et al., 2017a). Genotype frequencies from the reference samples in Austevoll and Risør with 40 individuals each were used to simulate three replicates of three independent data sets with pure parents (Pure1 and Pure2), first-and second-generation hybrids (F1 and F2), and backcrosses between F1 and pure parents (BC1 and BC2). The simulated data sets contained 288 individuals and were analysed using the R package parallelnewhybrid (Wringe et al., 2017b) and NEWHYBRIDS v. 1.1 (Anderson & Thompson, 2002), which estimates the posterior probability of each individual to belong to one of the six hybrid classes. The analysis was done using default priors and genotype proportions, with a burn-in period of 50,000 iteration and 300,000 MCMC sweeps. In case of nonconvergent MCMC chains, simulations were re-analysed.
Power was estimated as the product of efficiency (correctly assigned individuals over the known individuals per class) and accuracy (correctly assigned individuals over individuals assigned to that class) as described in Wringe et al. (2017a). Simulations demonstrated a high efficiency (>94%), accuracy (>98%) and power (>94) to detect individuals from all of the six hybrid classes ( Figure S1).

| Sampling
In total, 1954 corkwing wrasse were collected from 22 locations in western and mid-Norway, which represents the primary region where cleaner fish originating from southern Norway and Sweden are translocated to delouse salmon on commercial farms (Table 1; Figure 1). As the aim was to cover a wide area and as many locations as possible, an opportunistic sampling scheme was introduced leading to very uneven sample sizes per location (range 1-365) and a time span of six years (from 2013 to 2018). Collection emphasis was focussed in mid-Norway (counties of Trøndelag and Møre og Romsdal), which is the primary recipient area of translocated corkwing wrasses, and where the hybridization between local and translocated fish had already been reported (Faust et al., 2018).  Figure 1) to increase the sampling effort in mid-western Norway. Additional 126 corkwing wrasses from 8 locations from mid-Norway were obtained as by-catch from a research cruise conducted in 2017 (Table 1) and included. Dense sampling in mid-Norway was complemented with 83 fish collected in Sula in 2013 (SUL13 in Figure 1). A total of 974 fish from south-western and south-eastern parts of the study region were collected during summer months (June-September) in 2013-2018 ( Figure 1; Table 1).
All fish were caught by trained research personnel or professional fishermen using fyke nets and pots, killed upon catch, and samples were taken immediately. Alternatively, killed whole fish were stored frozen until sampling in laboratory facilities. From each fish, a fin clip sample was taken for genetic analysis. When possible, biological data (length, weight and sex) were collected. The fish caught in Flatanger 2017 were also aged by counting annual growth increments in otoliths, following the procedure described in detail in Halvorsen et al. (2016). Age data from Flatanger 2016 were available from Faust et al. (2018).

TA B L E 1
Corkwing wrasse sample information. Samples are arranged from north to south following the Scandinavian coastline a Given geographic location is an approximate midpoint for several sampling locations.
b Samples marked with "#" were received as bycatch during research cruise in South Trøndelag and Møre og Romsdal counties.
c Number in parenthesis is the number of samples genotyped successfully and used in analyses.

| Genotyping
Genomic DNA was extracted from fin clips using the Qiagen DNeasy Blood & Tissue Kit in 96-well plates following the manufacturer's instructions. A total of 1954 unique individuals and 105 technical replicates were genotyped in four multiplexes for 106 SNPs. Loci that did not produce reliable clustering patterns were removed (N = 17).
Loci and individuals with more than 20% missing data were removed, leaving 1766 individuals and 85 SNPs. Genotyping robustness was evaluated by calculating concordance between 79 successfully genotyped technical replicates, removing any locus with more than 2 discordant genotypes. One locus showed several discrepancies between genotypes ( Figure S2) and was removed. The final data set consisted of 1766 unique individuals genotyped for 84 loci with a total of 2.9% missing data.

| Statistical analysis
To ease analysing and discussion phases, samples were ordered from north to south along the coastline and grouped into larger geographic units defined as: "western" (Norwegian west coast), "southern" (Norwegian south coast and Swedish west coast) or as "mid-western" (>62°N), "south-western" (<62°N, <8°E) and "south-eastern" (<60°N, >8°E) (Table 1) For details see Table 1 between geographic regions, between samples within regions and run and the first 20,000 were discarded as burn-in. K was set from 1 to 6, and the number of iterations was set to 5. To determine the optimal solution for K, the StructureSelector software (Li & Liu, 2018) was utilized. The software summarizes results as the optimal Ln Pr(X|K) given by the STRUCTURE software and the ad hoc summary statistic ΔK by This method first builds a maximum likelihood (ML) phylogeny and subsequently models migration between branches to determine whether migration/admixture events improve the likelihood fit. Phylogenetic trees were calculated both with the default correction for sample size effects and without, as it in some cases can lead to overcorrection.
First, a ML tree without migration events was constructed. After this, five more trees were built that included one to five migration events.
Finally, variance explained by the different models, with different numbers of migration events, was calculated.
Cline analysis is used to estimate the shape, centre and width of the sigmoid curves generated by molecular, phenotypic or environmental markers, and to test for concordance and coincidence in these parameters between markers (Gay et al., 2008). Geographic cline analyses over a 1200 km transect between Flatanger (Norway) and Marstrand (Sweden) were conducted with the R package HZAR (Derryberry et al., 2014). The fifteen models implemented in HZAR were fitted to the allele frequency of every individual locus to determine the position, width and shape of clines over the geographic distance. A reference cline was built using STRUCTURE Q-score for the total data set, and the best cline model was decided upon AIC scores. Clines were considered significantly displaced if the two loglikelihood unit support limits of the cline centre did not overlap with the STRUCTURE Q-score (Qb = 1-Qs). Temporal replicates were pooled, and sampled populations with small sample size (<10) were removed.

| Hybridization
In order to ensure high efficiency, accuracy and consequently power to detect true escapees and hybrids with the filtered data set of 84 markers, a second round of simulations was performed. The same procedure was used for both simulation and analysis as described above for the full panel of 106 SNPs. After simulations, the occurrence of escapees and hybridization along the Norwegian coast were investigated with the software NEWHYBRIDS. Analyses were done using the uniform prior option, default genotype proportions, and the burn-in period was set to 50,000 and the number of MCMC sweeps after burn-in to 300,000. Map visualization was done using the R packages shapefiles (Stabler, 2013) and mapplots (Gerritsen, 2018). The Wilcoxon rank-sum test was used to compare mean age between hybrids and pure western genotypes, and between the years 2016 and 2017 (hybrids and pure western pooled in each year). power ( Figure S3).

| Genetic diversity
The overall diversity showed a similar pattern to what has been observed in previous studies, with much lower diversity south of the genetic break (Table S2)

| Population structure and individual assignment
34% of the total genetic variation was distributed between the three geographic groups, and less than one percent between samples within these groups (Table 2). Consistently, pairwise F ST estimates between sampled populations revealed an overall lower genetic differentiation within each of the three geographic groups than between them (Table S3). Within-group differentiation was lowest in south-eastern samples (mean F ST of 0.0005 ± 0.0011), followed by the mid-western samples (F ST = 0.0054 ± 0.0052) and highest in south-western samples (F ST = 0.0120 ± 0.0146). When comparing divergence within and between the three geographic areas, the genetic differentiation TA B L E 2 Analysis of molecular variance (AMOVA). Geographic groups and samples are shown in Table 1 F I G U R E 2 STRUCTURE cluster assignment of 1766 corkwing wrasse individuals based on 84 SNPs for K = 2 (a) and 3 (b) with sampling location given as a priori. Each vertical bar represents one individual and the colour the proportion of that individual assigned to the different genetic clusters. Individuals are sorted from North (left) to South (right) within the western samples were order of magnitude lower (mean In concordance with pairwise F ST measurements, individualbased clustering using STRUCTURE differentiated the south-eastern cluster (pink) from the western samples (blue) (K = 2 in Figure 2). K = 2 was clearly supported as the highest level of population hierarchy by the Evanno method ( Figure S5). Support for additional substructure was also evident: Adding one additional cluster (i.e.

K = 3) splits western samples into two distinct clusters between Sula
and Måløy implying an additional genetic break (green and blue in Figure 2; note that these clusters correspond to our Mid-Western and South-Western geographic groups, Table 1). Sampling location given as a priori clearly increased resolution power between the two western groups on an individual level for K = 3 (Figure 2; Figure S6a), but had little to no effect on the estimated admixture proportions with K = 2. Despite STRUCTURE gave clear clustering solutions with these two levels (K = 2 and 3) of population division, additional methods that were utilized favoured solutions for even higher levels of Ks (4-5; Figure S5a The reference cline based on the STRUCTURE Q-scores fitted an optN model, with the centre situated at 799 km (787-1087) ( Figure S9a). All the 84 loci fitted cline models with centres ranging between 706 and 1062 km (Table S4), and none of them was significantly displaced from the STRUCTURE reference cline ( Figure S9b).
This means that all loci showed a similar pattern of divergence. The cline centre is located close to the habitat break on the southwest tip of Norway.

| Hybridization
Samples were screened for potential hybrids using the software NEWHYBRIDS, which estimates each individual's probability of belonging to predefined classes (pure western, pure south-eastern, F1, F2, western backcross and south-eastern backcross). Of the 1766 individuals analysed, all of them could be assigned with a probability >50% to be either pure western (blue) or pure southern (pink) or hybrid (green) (Figure 4a). When distinguishing between the different hybrid classes (F1, F2, backcross 1 and backcross 2), all but one individual could be assigned with a probability >50% (Figure 4b and Figure S10a). When increasing the probability threshold to >80%, 1715 individuals could still be assigned to the different hybrid classes. Among the western samples, seven individuals had a very high probability (>90%) to be of pure-eastern origin, six in Flatanger and one in Årdalsfjorden. The majority of all potential hybrids could also be found in Flatanger where 70 individuals had more than a 50% probability to be F1, F2, western backcross or southeastern backcrosses ( Figure S10b). In all other western samples, only nine individuals could be identified as potential hybrids, all of them as western backcrosses. The age distribution did not differ between hybrids and pure western genotypes in the Flatanger in either 2016 or 2017 ( Figure S11; Table 3; W 2016 = 314, p = 0.7; W 2017 = 5642, p = 1). The two oldest individuals were classified as hybrids (aged 8 and 9 years), while the oldest individual with pure western genotype was 7 years old. Of the six individuals classified as of pure southern origin, all were larger than 175 mm (minimum size limit in the fishery for corkwing is 120 mm) and between 2 and 4 years old (Table 3).

| D ISCUSS I ON
We ent study, all of them were clearly clustered within the south-western group, indicating that there could be a stronger genetic discontinuity in this region than previously indicated. However, the markers used here are not ideal to resolve genetic population structure in this region as they were selected to distinguish south-eastern from western samples. It is therefore not possible to disentangle the nature of this second break, that is whether selection or neutral processes are at play.

| Extent of escapees and hybridization
Hybrid analysis identified a total of 7 individuals as potential escapees and 79 as potential hybrids on the Norwegian west coast. The majority of these individuals were caught in Flatanger, Trøndelag (6 potential escapees and 70 hybrids; in total 17.6% of samples from Flatanger), which is also the northern range of the species current natural distribution. It is noteworthy that besides fish identified as escapees or hybrids, majority of samples from Flatanger had some genetic resemblance to south-eastern population (mean q score of 6.5%; Figure 2), while no such pattern was observed for fish sampled elsewhere in mid-western Norway (q-score mean <0.1%). The only other pure south-eastern individual was found in Årdalsfjorden, <60 km from the sandy beaches in Jaeren, where the main genetic break has been previously identified (Blanco González et al., 2016).
Out of the 10 individuals successfully genotyped in Årdalsfjorden, one was identified as of south-eastern origin and two as hybrids. In all other south-western samples, we found no more than one or two potential hybrids. Given the proximity to the genetic break (Mattingsdal et al., 2020), Trøndelag recently and the population size is therefore small (Maroni & Andersen, 1996), and thus, escapees and hybrids are easier to detect, (2) smaller native populations make it easier for escapees to establish due to less competition Heino et al., 2015;Rhymer & Simberloff, 1996) and (3)  shown that corkwing in western Norway can reach twice the age (8 years) of corkwing wrasse from Skagerrak (4 years) (Halvorsen et al., 2016;Uglem et al., 2000). The older hybrid found in this study suggests that the introduction of southern material may not impact the longevity of local western populations. However, this and other aspects of hybrid fitness still need to be investigated.
A recent study found potential fitness differences between corkwing wrasse in southern and western Norway ( Trøndelag. This is also corroborated by import from Sweden. Since reporting started in 2017, more than three times as many corkwing wrasse have been transported to Trøndelag compared to Møre og Romsdal ( Figure S13).

| Implications
The effects of translocation between genetically distinct populations are difficult to predict and depend on many factors. Direct escapees can cause ecological effects and transmit novel diseases and pathogens. If hybridization occurs, genetic effects can also be anticipated as has been observed for a wide variety of traits in the case of domesticated salmon escapees in wild populations . Several escapees and backcrossed individuals were identified in the northernmost locations sampled. In addition, the Structure analysis indicates that in Flatanger, the majority of the investigated individuals show admixture (Figure 2; Figure S6c).
This means that a notable fraction of the population gene pool has a southern origin and that more permanent genetic changes (i.e. introgression) could also have taken place. In contrast, we did not detect such hybridization in for example Smøla despite frequent and abundant translocation of fish from south to this region.
Studies of genetic effects of stocking have shown that large and/ or well-connected populations seem to be relatively resistant to introgression (Bruce et al., 2020;Rougemont et al., 2019), likely because of simple dilution effect and selection being more efficient in large populations. While environment (Bruce et al., 2020) and population dynamics (Meirmans et al., 2009) also play some role, the level of admixture and hybridization success seem to be mostly dependent on stocking intensity and timing (Harbicht et al., 2014;Létourneau et al., 2018;Wringe et al., 2018). Thus, if the relative proportion of escapees is large and is still ongoing, the impact is likely to be greater and last longer (Castellani et al., 2018;Wringe et al., 2018).
The consequences of hybridization in the northern edge population are hard to predict but given the considerable difference in important abiotic factors between this region and southern Norway F I G U R E 5 Development in raw catch-per-unit effort (CPUE) for corkwing caught in commercial trap fishery (one fisher per location). CPUE is calculated as the total N corkwing caught, divided by the total number of traps sampled in each year. Error bars show ±SE of the mean and Sweden, inadvertently translocated individuals are likely to be maladapted and have lower fitness in the recipient populations. For example, the onset of the reproduction is affected by photo-period and temperature (Stone, 1996). If a genetic component is involved as well, it is possible that hybrids may initiate spawning at an un- We thus argue that any evaluation of the risk of translocation should not only include wrasse imported from Sweden but also the existing knowledge of genetically distinct populations within Norway.
The lack of documentation regarding the source and destination of cleaner fish transported within Norway is a big obstacle to assess and address the challenge of escapees.

| CON CLUS ION
We developed a SNP panel with the ability to detect corkwing wrasse translocated from Skagerrak-Kattegat to the Norwegian west coast as well as first-and second-generation hybrids. Using and studies of other species of wrasse (Jansson et al., 2017;Seljestad et al., 2020) with similar challenges, we emphasize the need to reassess the current management practices involving massive translocation of nonlocal wild wrasse.

CO N FLI C T O F I NTE R E S T
None declared.

DATA AVA I L A B I L I T Y S TAT E M E N T
The data that support the findings of this study are openly avail-