population structure in northern pike (Esox lucius)
explained by landscape features
Geneviève Ouellet-Cauchon1*, Marc Mingelbier2, Frédéric Lecomte2 and Louis Bernatchez1
1 Université Laval, Institut de Biologie Intégrative et des Systèmes (IBIS), 1030 Avenue de la Médecine,
Québec (Québec), G1V 0A6, Canada
2 Ministère du Développement durable, de l’Environnement, de la Faune et des Parcs du Québec (MDDEFP),
Service de la Faune Aquatique, 880 chemin Sainte-Foy, Québec (Québec), G1S 4X4, Canada
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Résumé
Un nombre croissant d’études ont investigué l’influence de facteurs environnementaux contemporains sur la structure génétique de populations, mais un facteur qui jusqu’à maintenant est demeuré peu étudié est la stabilité de l’habitat. A cette fin, un modèle d’aire d’étude approprié est le système lac Ontario – fleuve Saint-Laurent qui présente localement divers degrés de variation interannuelle du niveau d’eau. Dans cet article, nous documentons la génétique du paysage du grand brochet (Esox lucius), un poisson fortement exploité qui fraie dans les plaines d’inondation, en se basant sur l’analyse de près de 3000 individus provenant de 40 sites d’échantillonnage en utilisant 22 marqueurs microsatellites dans le système lac Ontario – fleuve Saint-Laurent. La structure génétique sur l’ensemble de l’aire d’étude était globalement très faible (Fst = 0.0208) mais spatialement variable avec un niveau moyen de différenciation dans la section amont (Ontario) de l’aire d’étude étant trois fois plus importante (Fst = 0.0297) que dans le secteur aval (Québec, Fst = 0.0100). Vingt variables environnementales furent considérées et un modèle de régression multiple sur matrices de distances (R2 = 0.6397, P < 0.001) a révélé que les masses d’eau (b =
0.3617, P < 0.001) et la présence de barrages (b = 0.4852, P < 0.005) ont réduit la connectivité et conséquemment augmenté la structure génétique de populations. De plus, la stabilité interannuelle du niveau d’eau était positivement associée à l’intensité de la différenciation génétique (b = 0.3499, P < 0.05). Comme une importante variation du niveau d’eau influe sur la qualité et la localisation annuelle des habitats de reproduction de grand brochet, l’instabilité locale de l’habitat sous la forme de la variation interannuelle du niveau d’eau semble localement empêcher la structure génétique de populations, probablement en empêchant le comportement de philopatrie. La gestion du grand brochet devrait être conséquemment reconsidérée dans une perspective de métapopulation, particulièrement dans les régions qui présentent une importante variation du niveau d’eau.
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Mots-clés : structure génétique de populations, Esox lucius, variation environnementale,
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Abstract
A growing number of studies have been investigating the influence of contemporary environmental factors on population genetic structure, yet one factor that has been poorly studied is habitat stability. To this end, an appropriate model study area is Lake Ontario – St. Lawrence River system that locally presents diverse degrees of inter-annual water level variation. In this paper, we document the landscape genetics of northern pike (Esox lucius), a heavily exploited fish that spawns in flood plains, based on the analysis of nearly 3000 individuals from 40 sampling sites using 22 microsatellite markers in the Lake Ontario – St. Lawrence River system. Genetic structure over whole study area was globally very weak (Fst = 0.0208) but spatially variable with mean level of differentiation in the upstream (Ontario) section of the studied area being three-fold higher (Fst = 0.0297) than observed in the downstream (Québec) sector (Fst = 0.0100). Twenty environmental variables were considered and a multiple regression on distance matrices model (R2 = 0.6397, P < 0.001)
revealed that water masses (b = 0.3617, P < 0.001) and the presence of man-made dams (b = 0.4852, P < 0.005) reduced connectivity, thus enhancing population genetic structure. Moreover, inter-annual water level stability was positively associated to the extent of genetic differentiation (b = 0.3499, P < 0.05). Since high water level variation impacts on yearly quality and localization of northern pike spawning habitats, local habitat instability which is under the form of inter-annual water level variation seems to locally impede population genetic structure, perhaps by inhibiting philopatry behavior. Thus, northern pike management should be carefully reconsidered in a metapopulation perspective, especially within sensitive areas that present high water level variation.
Keywords: population genetic structure, Esox lucius, environmental variation, habitat
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Introduction
Landscape genetic studies have mostly focused on long-term processes that structure populations. Recently, a growing number of studies have been focusing on quantifying the influence of natural and anthropic contemporary environmental changes that may alter population genetic structure. Since landscapes are dynamic, environmental features may be continuously altered by different levels of temporal landscape fluctuations. Environment temporal instability has been previously suggested to impact on biological diversity (reviewed in Fjeldså & Lovett 1997), population size and persistence (Nelson et al. 1987; Ostergaard et al. 2003), reproductive success (Titus & Mosegaard 1992) and dispersal (Baggiano et al. 2011; Messier et al. 2012). Since habitat stability level influence various aspects of population dynamics, its impact should be integrated in conservation strategies and in management plans of exploited species (Ostergaard et al. 2003).
Temporal landscape unsteadiness has also been shown to affect the temporal stability of population genetic structure, namely in insects (Baggiano et al. 2011), amphibians (Fitzpatrick et al. 2009), mammals (Messier et al. 2012) and freshwater fishes (Garant et al. 2000; Ostergaard et al. 2003). Most of these studies assessed temporal stability of genetic structure in a uniformly unstable habitat over the studied range of the species. In contrast, to our knowledge, for one species and in a single connected system, no study has investigated the consequences on population genetic structure of a range of different levels of habitat stability in relation to other environmental factors.
Due to its two components that greatly differ from their stability dynamic, Lake Ontario – St. Lawrence River system from the Great Lakes basin in north eastern North America represent a particularly relevant study site to investigate the impact of spatially variable habitat stability on freshwater fish population genetic structure (Figure 2.1). More specifically, Lake Ontario, the easternmost and the smallest of the Great Lakes, with a length of 311 km and a width of 85 km and totalling 19 000 km2 (Rukavina & Boyce
2012), receive most of its water from Great Lakes via the Niagara River and discharges into the St. Lawrence River. The St. Lawrence River, with a mean stream flow of about 12 600 m3/s near Quebec city (Gingras 1997), stretches on 1197 km (Marsh 2012) comprising
27 The two major tributaries of the St. Lawrence River are the Ottawa River and Richelieu River whose inflow seasonally varies, with an annual average at their mouth of 1937 and 374 m3/s, respectively (Gingras 1997). Ottawa River discharges in Lake Des Deux
Montagnes, itself discharging into Lake St. Louis whereas the Richelieu River discharges about 20 km upstream Lake St. Pierre.
The Lake Ontario – St. Lawrence River system is also characterised by two main water masses with distinct physicochemical properties (Annexe 1) : the so-called “green waters” from Lake Ontario and the “brown waters” from Ottawa River (Centre Saint-Laurent 1996 ; Leclerc et al. 2008). They flow in parallel in St. Lawrence River corridor with limited mixing (Centre Saint-Laurent 1996). Moreover, several hydroelectric dams and water regulation dams were built between 1914 and 1980 (Gingras 1997) throughout the St. Lawrence River (Figure 2.1). Climatic conditions, by driving water flow fluctuations in the system, naturally cause inter-annual water level variations (Mingelbier et al. 2004). One major water regulation dam was built in 1960 in the upstream part of St. Lawrence River corridor (Moses-Saunders dam, Figure 2.1) to seasonally and inter-annually control water level, a role that is also completed by other hydroelectric dams (Gingras 1997). Consequently, water levels of the “Ontario sector”, comprising Lake Ontario and upper St. Lawrence River, remain considerably stable on an inter-annual basis, in contrast to downstream dam complex in the “Quebec sector”, represented by the lower St. Lawrence River, where water level increasingly inter-annually fluctuate towards Quebec city (Ministère du Développement durable, de l’Environnement, de la Faune et des Parcs du Québec, unpubl. data).
Such water level variation may considerably shape fish spawning habitat by influencing local water depth that in turn influences vegetation location and density, water temperature and water velocity (Mingelbier et al. 2008). Furthermore, for fish species spawning in flood plains, water level variation influences young-of-the-year survival and year-class strength since premature spring water retrieval can cause massive mortality of eggs and larvae (Dumont & Fortin 1977; Fortin et al. 1982; Mingelbier et al. 2008). However, the impacts of these events on fish species population structure have not been investigated.
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Being one of the most widely distributed and a heavily exploited fish by recreational fishing in this system, northern pike (Esox lucius) is particularly appropriate for studying the impact of habitat stability in relation to other environmental features on population genetic structure. Northern pike is mainly sedentary (reviewed by Craig 1996; Rosell & MacOscar 2002; Koed et al. 2006; Vehanen et al. 2006; Bosworth & Farrell 2006). There is also some evidence of a certain level of natal-site spawning fidelity (Miller et al. 2001; reviewed by Craig 2008), but philopatry rate still remains undefined. In the Lake Ontario – St. Lawrence River system, northern pike spawns in early spring during spring flood in shallow and lentic warmer waters of wetlands where dense vegetation occurs (Jude & Pappas 1992; Mingelbier et al. 2004, 2008) and thus could be extensively influenced by water level variation. In this context, our general objective was to investigate the possible consequences of the extent of habitat temporal stability on the population genetic structure pattern of northern pike (Esox lucius), and relative to others landscape features that could putatively affect connectivity in the system. We thus first documented the number of genetically distinct populations in the system, as well as their spatial distribution and level of connectivity. Secondly, we statistically assessed which environmental factors best explain the observed pattern of population genetic structure by means of a landscape genetics approach.
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Material and methods
Sample collection
Between 20 and 162 individuals per locality were sampled at 40 sites in 2007, 2009 and 2010 (Figure 2.1 and Table 2.1), totalling 2913 northern pikes. The 750-km long study area was subdivided in two regions on the basis of the occurrence of a 150 km sampling gap in the middle St. Lawrence River due to lack of suitable northern pike habitat and thus low abundance of the species. These two regions (upstream Ontario sector including Lake Ontario and upper St. Lawrence River and downstream Quebec sector comprising lower St. Lawrence River) are also separated by the Moses-Saunders regulation dam. Four sampling sites were geographically partially isolated from the main study system as they were either tributaries or isolated basins and hence they were considered lake or fluvial “annexes” (Niagara River, site 1; Hamilton basin, site 2; and Richelieu and Ottawa Rivers, sites 22- 23; Table 2.1 and Figure 2.1). Sampling mainly occurred during the spawning period from mid-March to the end of May (Farrell et al. 2006), although several samples were collected slightly before or after spawning (Table 2.1). Angled northern pike were sampled during the ice fishing season before spawning time and trap nets were used to sample individuals during and after spawning.
Genetic data
Genomic DNA was extracted from fins clips preserved in 95% ethanol using a salt extraction method (Aljanabi & Martinez 1997). Twenty-two microsatellite markers (Table 2.2) were amplified with multiplex PCR reactions using Bio-Rad IQ Supermix. Twelve of these were previously published (Miller & Kapuscinski 1997; Genetic Identification Service Inc. 2000; Hansen & Nielsen 2000; Aguilar et al. 2005) whereas the other ten markers were developed by us (see Chapter 1 ; Ouellet-Cauchon et al. in prep.). PCR cycles started with a 3 min denaturation step at 95°C, followed by 35 cycles of 30s of denaturation at 95°C, 30s of hybridization at 56 °C or 60°C and 30s of elongation at 72°C, and completed with a 30 min final elongation at 60°C. PCR products were pooled with formamide and LIZ (multiplex 1) or ROX (multiplexes 2-3-4) size standards from Applied Biosystems. Electrophoresis of the amplified loci was completed using an Applied Biosystems 3130xl Genetic Analyzer and the raw data were treated with DATA
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COLLECTION v3.1.1. Genotypes were resolved using GENEMAPPER v.4.1 and automated scoring manual verification.
Intra-population diversity and structure
For each sampling site, Fis values were calculated and exact tests for deviations from Hardy-Weinberg proportions and linkage equilibrium were computed using GENEPOP (Raymond & Rousset 1995) and Bonferroni corrections were applied (α = 0.05 ; Rice 1989). We evaluated expected (HE) and observed (HO) heterozygosity with GENETIX v.4.05.2 (Belkhir et al. 2004), allelic richness (Ar) and private allelic richness (Apr) over all loci both standardized for smallest sample size (N = 20) with HP-RARE (Kalinowski 2005). Temporal stability of genetic structure between 2009 and 2010 samples was assessed with an analysis of molecular variance (AMOVA, ARLEQUIN v.3.1, Excoffier et al. 2005) with selection of sites of minimum 20 samples each year. This revealed a very weak, albeit significant variation between years which was 10 times smaller than the extent of spatial genetic structure (0.12% vs. 1.38%, P < 0.01 ; Annexe 2). In the same way, we also assessed the stability of genetic structure as a function of sampling period (prior and during spawning period), and again a significant but very weak variation was found between sampling periods relative to spatial structuring (0.13% vs. 1.22%, P < 0.05; Annexe 2). Percentage of spatial genetic variation differed in these two tests since two different sets of sampling sites were used for these analyses. Given that these analyses revealed no major effect of the sampling period, temporal samples were pooled for subsequent analyses.
Population structure
Pairwise Fst values were computed with GENETIX v.4.05.2 (Belkhir et al. 2004) and a False Discovery Rate (FDR) correction for multiple testing at P < 0.05 was applied (Benjamini & Hochberg 1995). Isolation by distance (IBD) was tested with Mantel tests (Mantel 1967) using ecodist R package (R Development Core Team 2007) and pairwise Fst /(1-pairwise Fst) were plotted as a function of the shortest waterway geographic distance between sites (Rousset 1997). Mantel’s tests were computed for the whole study area and for comparisons within the Ontario sector and within the Quebec sector excluding the four
31 annexes. Genetic population structure was unsuccessfully assessed with STRUCTURE software (Pritchard et al. 2000), that is incongruous clusters were obtained because of weak global level of genetic differentiation (see Results below ; Latch et al. 2006 ; Jones & Wang 2012). Consequently, we alternatively used the BARRIER software v.2.2 to identify putative barriers to dispersal between all sampling sites and the 20 strongest barriers in terms of relative strength were first retained (Manni et al. 2004). Barrier relative strength is an index of a given barrier’s relative force in the system and it was evaluated by calculating ratio of mean pairwise Fst value over all crossed edges from every pair of sites of a given barrier and maximum pairwise Fst value observed, and then converted into percentage (Manni et al. 2004). Barriers strength was plotted as a function of downstream waterway distance, the starting point being western Lake Ontario (site 2), and correlation was assessed using the Spearman’s correlation coefficient. Barriers isolating St. Lawrence River tributaries were not considered here. Barriers boundaries were used to delimit population groupings which were subsequently evaluated using AMOVA (ARLEQUIN v.3.1, Excoffier et al. 2005) and, in parallel, mean pairwise Fst were assessed between groupings. To assess the relative extent of genetic structure across the system, global Fst values were computed for the whole study area, for the Ontario and Quebec sectors separately, both with and without the four annexes, with GENETIX v.4.05.2 (Belkhir et al. 2004).
Landscape genetics
Landscape genetic analysis was performed on the Quebec sector only (lower St. Lawrence River) since available environmental data were not as detailed for Lake Ontario region, and also because of the large sampling gap of 150 km between the upper and lower St. Lawrence River which is inappropriate for landscape genetics analysis (Manel et al. 2003). Pairwise environmental distance matrices for values at sampling sites were built for 20 environmental variables (Table 2.3). To select variables most explicative of the observed pattern of genetic structuring and to avoid including too many variables in the same multivariate model, Mantel tests were done (Mantel 1967) for pairwise Fstmatrix coupled to each pairwise environmental distance matrices (ecodist R package, R Development Core Team 2007). Six environmental variables were thus selected (Table 2.4, five variables with Mantel’s r2 > 0.10 and P < 0.05, in addition to waterway geographic distance). Secondly,
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collinearity between environmental variables was assessed by Mantel tests (Mantel 1967; ecodist R package, R Development Core Team 2007), which led to removal of the pH variable which was highly correlated with spring water conductivity (Mantel’s r = 0.86, P < 0.05; e.g. Martiny et al. 2011). Thirdly, to assess the relative contribution of the retained variables to the model, a multiple regression on distance matrices model (MRM; Legendre et al. 1994) was computed on selected variables with the ecodist R package (R Development Core Team 2007). A full model was run with all six standardized environmental variables, and the following final model only included the three significant variables from full model. For each model, Pearson’s correlation coefficient was computed and 9999 permutations were used to assess significance.
Finally, because we were particularly interested in the relationship of inter-annual water level variation, the relationship of that variable with genetic structure was further evaluated over the whole study area. We divided the sampling area in windows of 50 km width a 25 km sliding. We then computed mean pairwise Fst values between sites within each window and mean of inter-annual water level variation coefficients calculated for each sampling site within each window. Mean pairwise Fst values and mean inter-annual water level variation coefficients were plotted as a function of downstream waterway distance (from site 2) for each window. Moreover, mean pairwise Fst values were plotted as a function of mean inter- annual water level variation coefficients for each window for both upstream (Ontario sector) and downstream (Quebec sector) of the Moses-Saunders regulation dam. Spearman correlation coefficients were assessed for each of the four plots. When not mentioned otherwise, analyses where computed with R software (R Development Core Team2012).
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Results
Intra-population diversity and structure
Genetic diversity was modest and comparable among sampling sites (mean HE = 0.63 [range = 0.57-0.66], mean HO = 0.63 [range = 0.54-0.68], mean Ar = 5.65 [range = 4.63- 5.99], mean Apr = 0.05 [range = 0.01-0.16]; Table 2.1). Still, the four annexes globally presented a slightly lower genetic diversity (HE ranged between 0.57 and 0.64; Ar ranged between 4.63 and 5.46; Table 2.1) than mean values for all sampling sites (Table 2.1). Similarly, Greenstone Island East (site 16, HE = 0.59, Ar = 5.15) in the Thousand Islands region within upper St. Lawrence River presented a lower genetic diversity than mean values. Mean Fis on all loci was low (0.01 [range = -0.05-0.07]; Table 2.1). Hardy- Weinberg equilibrium showed a random pattern of significant deviations among markers and among populations and linkage disequilibrium tests revealed that no loci were strongly linked since no consistent pattern was detected.
Population structure
Given its geographic scale, the global extent of genetic structure across the whole study area (more than 750 km) was weak (global Fst = 0.0208, P < 0.001). Pairwise Fst values ranged between -0.0037 to 0.1123 (Annexe 3). The four annexes were the most divergent sites with a mean pairwise Fst values with all sites of 0.064 for Niagara River (site 1), 0.091 for Hamilton basin (site 2), 0.028 for Richelieu River (site 22) and 0.028 for Ottawa River (site 23). IBD relationship was significant (Mantel’s r = 0.4812, P = 0.0001) and moderately pronounced for the whole study area (slope = 6.17X10-5, P < 0.00001 ; Figure
2.2). However, genetic divergence increased more steeply with waterway geographic distance within the Ontario sector (slope = 6.32X10-5, P < 0.00001) than within the Quebec
sector (slope = 2.06X10-5, P = 0.00308) and waterway distance explained a greater part of
genetic distance within the former sector (Mantel’s r = 0.4905, P = 0.0001) than the latter sector (Mantel’s r = 0.2454, P = 0.0038).
The spatial variation in patterns of IBD was also reflected by the geographic distribution of the first 20 barriers inferred by BARRIER (Figures 2.3 and 2.4). Thus, the majority of barriers were stronger in the Ontario (Figure 2.3A) than in the Quebec sector (Figure 2.3B),
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and the strength of barriers significantly decreased from upstream Lake Ontario to downstream St. Lawrence River (Spearman's ρ = -0.6963, P = 0.0013; Figure 2.4). Accordingly, global Fst values were three times higher in the Ontario sector with and without annexes (Fst = 0.0297, P < 0.001 and Fst = 0.0186, P < 0.001, respectively) than in Quebec sector with and without annexes (Fst = 0.0100, P < 0.001 and Fst = 0.0051, P < 0.001, respectively).
Barriers that were defined determined the occurrence of 12 putative populations within the Ontario sector (Figure 2.3A) and 8 putative populations within the Quebec sector (Figure 2.3B). For the Ontario sector, from upstream to downstream, these were 1) west Lake Ontario, 2) north-west Lake Ontario, 3) West Lake, 4) Bay of Quinte, 5) Cataraqui River, 6) west Thousand Islands, 7) center-south Thousand Islands and 8) center-north Thousand Islands, 9) French Creek and 10) Crooked Creek and finally 11) Hamilton basin and 12) Niagara River as lake annexes. For the Quebec sector, from upstream to downstream, these putative populations were 1) west Lake St. Francis, 2) east Lake St. Francis, 3) Lake des Deux Montagnes, 4) north Lake St. Louis, 5) south Lake St. Louis and St. Lawrence River