Dab population structure
Given that dab is the third most common fish in the North Sea (Daan et al. 1990), the relatively recent colonization by North and Irish Sea biota after the last glacial maximum (Maggs et al. 2008), and potential for egg and larval dispersal (Henderson 1998), little genetic structuring may be expected. Contrary to such expectations, temporally stable patterns of significant differentiation were observed. Dab around the British Isles exhibit detectable genetic structure: individuals from the North Sea were consistently genetically distinct from those in the Irish Sea as revealed by classical (Fig. 3), multivariate (Fig. 2) and Bayesian methods (Tysklind 2009). Genetic structuring within basins, while sometimes significant, was more subtle. Once basins were taken into account, significant isolation-by-distance patterns disappeared (Tysklind 2009), suggesting that the distribution of genetic diversity in dab was more likely influenced by coastal, oceanographic and/or biological features than geographical distance per se. Although many studies have evaluated population structure of European flatfish in the North East Atlantic (Danancher and Garcia-Vazquez 2011), relatively few have included samples from both the Irish and North Sea. Among them, very subtle differentiation between North and Irish Seas has been detected in sole, Solea solea (Cuveliers et al. 2012), but not in other closely related species such as flounder, Platichthys flesus (Galleguillos and Ward 1982; Hemmer-Hansen et al. 2007a,b), and plaice, Pleuronectes platessa (Hoarau et al. 2002, 2004, 2005; Was et al. 2010). Compared to other flatfish, dab appear to be more genetically structured across the range studied here. Nevertheless, comparison among studies is difficult given the diversity of markers, spatial and temporal sampling intensity and analytical methods employed.
Several nonmutually exclusive hypotheses may lead to detectable structure in the marine environment: fidelity to spawning site (Thorrold et al. 2001), reduced migration combined with genetic drift (Borsa et al. 1997), and local adaptation (Carvalho 1993; Conover et al. 2006; Hemmer-Hansen et al. 2007a). The existence of genetic structure at a regional level in dab may thus be explained by a combination of dab biology and oceanographic currents. Flatfish have in general been shown to exhibit homing behaviour (de Veen 1978; Rijnsdorp et al. 1992b; Hunter et al. 2003), and dab has been shown to undergo within-basin migrations and return to capture location after a year (Rijnsdorp et al. 1992a), which is compatible with patterns of genetic structuring found here. Dab eggs and larvae are ubiquitous throughout the North (Rijnsdorp et al. 1992a) and the Irish Seas (Fox et al. 1997). Eggs can, however, hatch quickly (4.5 days at 14°C), and larvae seem capable of controlling, to some extent, their movements (Henderson 1998), which together with their ability to settle both inshore or offshore waters (Bolle et al. 1994) may reduce the dispersal of most individual eggs. By studying seven different species, Galarza et al. (2009) showed that marine currents and sea fronts can represent effective barriers to gene flow between populations, regardless of egg type or pelagic larval duration, resulting in detectable genetic differentiation at regional scales. The cyclonic gyre that forms within the Irish Sea in spring and summer has been suggested as a larval retention mechanism for Norway lobster, Nephrops norvegicus, (Hill et al. 1996), and could have a similar effect on dab eggs and larvae. Furthermore, a strong jet-like westward flow across the St. Georges Channel (between St. Davids Head of Wales and Carnsore Point in Ireland) prevents the mixing of Celtic and Irish Sea waters (Brown et al. 2003), further constraining the exchange of eggs and larvae between the English Channel and North Sea. An egg transport model incorporating oceanographic data of the Irish Sea (van der Molen et al. 2007), suggested that eggs and larvae dispersed an average of 80 km from point of release and largely remained within 160 km of point of release, with very few dispersing up to 300 km from point of release. Such distances and the oceanographic features of the Irish Sea are in accordance with the genetic structure observed here in dab between North and Irish Seas, for which a major genetic boundary exists between IS3 and EC2 dab, suggestive of few migrants during the early life-history stages of dab.
Temporal aspects of genetic structure
The stability of genetic structuring over time indicates that it is not the outcome of the random distribution of genetic diversity, but more likely the result of biological processes shaping such distribution and thus strengthening inferences on the biological significance of those differences found (Carvalho and Hauser 1998; Waples 1998; Waples et al. 2008). In dab, the temporal stability of genetic structure across consecutive sampling years was marked, a pattern shared with sole (Cuveliers et al. 2011, 2012), but not plaice (Hoarau et al. 2005). Temporal stability was evident even among age cohorts for those samples for which age information was available (NS1-07, EC1-07 and IS5-07). Such patterns indicate that the stability of genetic composition was inherent to the local dab population, as found in other species (Jorde et al. 2007; Cuveliers et al. 2011), and not a chance effect of repeatedly sampling individuals born in the same year across the four sampling years. Conversely, the instability of the genetic relationships between EC2 and proximate samples is suggestive of an increase in the proportion of North Sea dab into the western English Channel and highlights that the putative boundary between North Sea and Irish Sea dab is probably transient, necessitating regular resampling.
Relevance of population genetic structure to biomarker prevalence
The significant and temporally stable genetic differences between North and Irish Sea dab indicate that they have the potential to evolve (through drift and adaptation) independently. Under such a scenario, regional differences in dab morphological traits (Bakhsh 1982; Deniel 1990; Rijnsdorp et al. 1992a; Henderson 1998), and most importantly here, the stable variation in different biomarker responses between North and Irish Seas (Cefas 2003; Ward et al. 2006; Stentiford et al. 2009; Tysklind 2009), could be population-specific. It follows that such patterns may represent not only phenotypic plasticity to local conditions (and contaminants), but raises the possibility they may comprise a genetic component.
No measured variables (average age, population genetic structure, cadmium, lead, mercury and 7 PCBs) were associated with prevalence of three of the external biomarkers: LY, EP and U, indicating that either: (i) other contaminants not considered here may play a role in the prevalence of these diseases, (ii) studying their prevalence at sample level was not appropriate, and an individual model may be more adequate, (iii) they are not biomarkers of contaminant exposure. In accordance with the latter, Stentiford et al.(2009) found that LY, EP and U had relatively low discriminatory power at discerning among CSEMP sites. Causality of the fourth external biomarker, HYP, is still debated: no evidence of infectious processes has been found (Noguera et al. 2013), and therefore, other causes such as variance in UV radiation, developmental processes and environmental factors must be explored. Our results indicated significant correlations with several contaminants (Cd, Pb and CB118) and population genetic structure. Such findings allow the definition of population-specific baseline levels of HYP and potentially link changes in HYP prevalence to particular contaminants. For example, among the low-HYP-prevalence Irish Sea population, only sites with high Pb (IS3) and CB118 concentrations above environmental standards (IS5) have detectable incidences of HYP. If sensitivity to HYP is population-specific, then increase in HYP from 5% in 1988 to >50% in 2005 (Grütjen et al. 2013) in some sites of the North Sea and its association with changes in genetic population structure should be evaluated.
Age and contaminant exposure have recognized effects in dab liver health (Feist et al. 2004; Stentiford et al. 2010). However, the liver pathology model also revealed that population genetic structure had an important effect on how pathologies develop with individual age. It is important to remember that the liver pathology classification collapses an array of 35 indicators, thus representing a suite of metabolic responses. Old fish in the northern Irish Sea exhibited consistently lower prevalence of neoplastic processes than their North Sea counterparts. Although such differences could be due to other contaminants or environmental parameters not accounted for here, it would be worthwhile investigating if the Liverpool Bay population–specific lower propensity to develop neoplasms, or less likelihood of surviving with them, is genetically based. Furthermore, the lack of malignant neoplasms of above 5-year-old dab might indicate that dab either develop malignant neoplasms and are likely to disappear, or develop benign neoplasms and individuals survive to join the >5 year classes. Such a system might provide an informative model in neoplasm-gene association studies. Together, these observations indicate that inferences of contaminant exposure based on liver pathology of dab over 4 years of age may be biased by population-specific sensitivity. Liver pathology of dab under 4 years of age seemed to truly represent heavy metal exposure. Cadmium has carcinogenic properties (Waisberg et al. 2003), and high concentrations were associated with the early onset of neoplastic processes, typical of site NS1 (Cefas 2003; Stentiford et al. 2009), where higher concentrations of cadmium are found in sediments (Langston et al. 1999). The opposite relationship was found for zinc, where high concentrations were associated with healthy livers and lower prevalence of inflammatory diseases. Zinc has been found to have protective effects against cadmium toxicity in other fish species (Malekpouri et al. 2011) through normalization of cadmium-disrupted antioxidant enzymatic pathways (Banni et al. 2011). Such processes could prevent the development of cadmium-induced neoplastic processes in dab. However, the relationship between zinc and liver health may be spurious as liver zinc concentrations were negatively correlated with age, and hence, high zinc and healthy livers correspond to one-year-old dab. Whether such a pattern is due to older dab having greater demand for, and faster metabolization of zinc (Sun and Jeng 1999), or young dab with high zinc exposure suffer high zinc poisoning (Hattink et al. 2006) requires further testing.
Genetic markers not only assisted in the identification of populations, but also yielded insights about whether dab disease phenotypes were indicative of exposure at capture location. Within the North Sea, Dogger Bank samples (NS1) are characterized by a high prevalence of skin hyperpigmentation, ulcerations and frequent occurrence of neoplastic processes (Cefas 2003; Stentiford et al. 2009). The existence of at least weak differentiation (Fig. 3) between coastal and Dogger Bank dab suggests that the latter are unlikely to mix extensively with the former; in addition, the lack of significant genetic differentiation among cohorts (from juveniles to 5-year-old adults) within sites is compatible with life-long residency at sampling sites, a particularly important attribute, and underlying assumption, in biomonitoring programmes (Stentiford et al. 2010; Bergek et al. 2012). Combined, these two characteristics suggest that dab captured at Dogger Bank are representative of locally higher concentration of contaminants (Langston et al. 1999).
The low prevalence of diseases in the English Channel may be a direct result of the absence of older fish (>4 years) at these sites, which typically exhibit higher disease prevalence than younger fish elsewhere (Stentiford et al. 2010). Hence, although sites in the English Channel are genetically similar to the North Sea, they should not be used as reference for the North Sea in biomonitoring programmes unless age is accounted for. Temporal genetic changes, such as those reported in this study at the western English Channel (EC2; Fig. 2), may command special treatment in biomonitoring analysis, as the susceptibility of immigrant individuals may be different to that of resident individuals.
Although contaminants have been shown to be capable of impacting neutral genetic diversity (Bourret et al. 2008; Nowak et al. 2009), we found no correlation between any of the population or individual measures of genetic diversity and either the biomarkers or contaminants (data not shown). The absence of a relationship between neutral heterozygosity and biomarkers/contaminants is not necessarily indicative of a lack of impact of contaminants on genetic diversity: contaminant effects may be associated with the frequency of alleles at non-neutral genes (Maes et al. 2005), or the balance between selective pressures and immigration may not be sufficiently skewed to generate signatures of genome-wide inbreeding (Tsitrone et al. 2001).
Conclusion and relevance for biomonitoring programmes
Overall, our data reveal the existence of significant genetic structuring in dab at the regional scale of sea basins. The congruence among microsatellite markers and the observed temporal stability of genetic differentiation re-emphasizes the importance of temporal replicate sampling in elucidating genetic structuring among marine populations. The subtle differentiation found here may not necessarily imply high levels of gene flow among populations (Hauser and Carvalho 2008). On the contrary, the existence of low but significant differentiation values with neutral markers despite such large populations, and recent colonization history, suggests the existence of biologically relevant divergence associated with locally adapted populations. Furthermore, our study shows how genetically based structuring, together with other habitat variables, may be associated with population-specific heterogeneity in the nature and extent of response to contaminants and other environmental stresses between basins and, hence, is of utmost importance in the interpretation of biomonitoring data. Such genetic heterogeneity in marine fish across locales adds to increasing evidence that despite the traditional expectation of low divergence in potentially high gene flow marine taxa, that local adaptations exist across populations at remarkably small geographical scales (Larsen et al. 2007, 2012; Williams and Oleksiak 2008; Limborg et al. 2012; Hemmer-Hansen et al. 2013). It is important to emphasize that the population-specific variation in response found here does not necessarily emerge from adaptation to anthropogenic contaminant exposure, but may be the result of genetic drift, a by-product of adaptation to other selective pressures, or adaptation to naturally high levels of the elements involved in contamination. Although our results do not allow us to clearly disentangle the impacts of genes versus environment, they do provide a population framework that facilitates interpretation of biomonitoring results and the construction of testable hypothesis to further elucidate key drivers. To unveil the mechanisms behind the heterogeneity of population-specific responses would require the application of high-throughput population genomic approaches targeting either candidate genes of known function or genomic regions under selection combined with translocation/common garden and exposure experiments (Stinchcombe and Hoekstra 2008; Larsen et al. 2012). Potential for adaptation can be evaluated experimentally by analysing candidate genes on multigenerational selected lines (van Wijk et al. 2013). However, detection of unequivocal adaptation to contaminant exposure in the wild would require the assessment of multigenerational changes at known functional genes, pre- and postexposure, while ratifying no population replacement (Hansen et al. 2012).
Our study illustrates how genetic markers can elucidate population structure in a key biomonitoring species, delineating boundaries of comparable sampling sites and potential for differential response to environmental stress. It is recommended that biomonitoring programmes integrate the extent and spatiotemporal patterns of genetic structuring within assessments: individuals from target polluted sites should be compared to individuals from reference sites within the same population boundaries. In doing so, environmental managers would avoid confounding variance potentially due to population-specific inherent susceptibility, or alternatively, resistance to disease, and that which is truly symptomatic of exposure to contaminants. Additionally, population-specific disease-frequency baselines can then be established with evolutionary meaningful geographical boundaries, allowing detection of finer variation in spatial patterns of disease prevalence. Population genetics, biomonitoring and functional genomic analyses should be integrated to further understand the underlying role of genetic variation in biomarker prevalence and heterogeneity in measures of exposure (Cerdá et al. 2010). Ultimately, the incorporation routinely of population genetic approaches into biomonitoring and other programmes that assess ecosystem health enhances prospects for yielding ecologically meaningful predictive frameworks for assessing future response and options for remediation.