- Research article
- Open Access
Genetic and phenotypic variation along an ecological gradient in lake trout Salvelinus namaycush
© The Author(s). 2016
Received: 11 May 2016
Accepted: 4 October 2016
Published: 19 October 2016
Adaptive radiation involving a colonizing phenotype that rapidly evolves into at least one other ecological variant, or ecotype, has been observed in a variety of freshwater fishes in post-glacial environments. However, few studies consider how phenotypic traits vary with regard to neutral genetic partitioning along ecological gradients. Here, we present the first detailed investigation of lake trout Salvelinus namaycush that considers variation as a cline rather than discriminatory among ecotypes. Genetic and phenotypic traits organized along common ecological gradients of water depth and geographic distance provide important insights into diversification processes in a lake with high levels of human disturbance from over-fishing.
Four putative lake trout ecotypes could not be distinguished using population genetic methods, despite morphological differences. Neutral genetic partitioning in lake trout was stronger along a gradient of water depth, than by locality or ecotype. Contemporary genetic migration patterns were consistent with isolation-by-depth. Historical gene flow patterns indicated colonization from shallow to deep water. Comparison of phenotypic (Pst) and neutral genetic variation (Fst) revealed that morphological traits related to swimming performance (e.g., buoyancy, pelvic fin length) departed more strongly from neutral expectations along a depth gradient than craniofacial feeding traits. Elevated phenotypic variance with increasing water depth in pelvic fin length indicated possible ongoing character release and diversification. Finally, differences in early growth rate and asymptotic fish length across depth strata may be associated with limiting factors attributable to cold deep-water environments.
We provide evidence of reductions in gene flow and divergent natural selection associated with water depth in Lake Superior. Such information is relevant for documenting intraspecific biodiversity in the largest freshwater lake in the world for a species that recently lost considerable genetic diversity and is now in recovery. Unknown is whether observed patterns are a result of an early stage of incipient speciation, gene flow-selection equilibrium, or reverse speciation causing formerly divergent ecotypes to collapse into a single gene pool.
Sympatric ecological speciation is a process by which a segment of a population develops some level of reproductive incompatibility with other population members in the absence of geographic barriers and complete cessation of gene flow . Today, advances in genomics  and epigenetics [3, 4] have enabled testing of predictions generated by novel hypotheses on mechanisms underlying this evolutionary process. Sympatric ecological speciation has been summarized into four main steps from an evolutionary genomics perspective: first, an initially panmictic population experiences a new environment; second, disruptive natural selection causes divergence of functional phenotypic traits and adaptive loci while gene flow at neutral loci continues; third, gene flow at neutral loci becomes partially restricted; and fourth, complete reproductive isolation and speciation . The first stage involves character release where individuals of a species are exposed to new ecological opportunities . This character release stage typically shows high levels of within-group phenotypic variance where phenotypic responses to the environment may be mainly epigenetic (i.e., due to gene expression). The second stage describes how ecological opportunity can give rise to resource polymorphism where functional traits may become genetically accommodated, or hard-wired in the genome [6–8]. In the third stage, given a degree of environmental stability, ecologically and phenotypically divergent intraspecific forms, or ecotypes, with partially restricted gene flow may arise. In the fourth and final stage, reproductive isolation evolves among morphs leading to speciation. At each stage, a collapse into panmixis can occur when divergent selection is relaxed . This relaxation can be brought about by environmental change in which a heterogeneous environment (e.g., Enos Lake sticklebacks ) or diverse food sources are homogenized, as Darwin’s finches experienced after seed type homogenization . Alternatively, changes in species abundance resulting from local extirpations or release from predation pressure or both (e.g., Lake Huron coregonids Coregonus spp.  or shifts in community structure (e.g., Scandinavian coregonids ) could also lead to relaxed divergent selection. Additionally, hybridization of weakly diverged conspecifics can result in high within-group phenotypic variability . Therefore, although the stage of speciation may be assessed, the direction of evolutionary change cannot be discerned unless time-series data are available to assess directionality of selection and divergence.
Understanding whether gene flow is partially restricted along an ecological gradient can provide evidence of the prime drivers of selective divergence and maintenance of ecotypes [13–16]. Thus, studies of niche divergence along environmental gradients can be important to species conservation and understanding of the interaction between human-mediated disturbance and evolution of wild populations. In most aquatic environments, water depth is an especially important gradient that correlates with many environmental variables (e.g., hydrostatic pressure, light intensity, light quality, temperature, pH, or oxygen concentration). Phenotypic and reproductive divergence along water depth gradients has been described in Lake Victoria cichlids [17, 18] and coregonids in Europe and North America [19–22]. Thus, studies that compare neutral genetic and phenotypic divergence, Pst (term coined by Leinonen et al. ), can help ascertain the relative influence of selection on genes and random genetic drift on population differentiation . Pst and neutral genetic divergence (as measured using Fst) can be compared among and within groups to assess the stage of ecological speciation. Theoretically, if Pst is greater than Fst, then the phenotypic trait in question is interpreted to exceed levels of divergence based on neutral expectations, and is therefore under selection .
Here, we focus on neutral genetic and phenotypic variation in lake trout Salvelinus namaycush, a species believed to have developed partially reproductively isolated ecotypes in sympatry (e.g., ). Differentiation among lake trout ecotypes has been associated primarily with differences in traits related to trophic resource partitioning (e.g., cranioskeletal features) and locomotion (e.g., fin length) [25–30]. In Lake Superior, four ecotypes have been described: lean, humper, siscowet, and redfin . The lean ecotype is considered the ancestral form, in part because it is the most widely distributed across North America . Relative to other ecotypes, lean lake trout typically use relatively shallow water (<80 m depth) and have adaptations for sustained swimming similar to that required in fluvial environments. Siscowet are the most abundant ecotype [32, 33], typically occupy deep water (>50 m), and have much higher fat content than leans. Historically, lean and siscowet would have fed differentially on a suite of cisco species (Coregonus spp.; ). Since the introduction of non-native forage fishes and shifts in the native forage base in Lake Superior, adult lean and siscowet lake trout now prey on similar shallow water diet items, but in differing proportions [26, 32, 33]. Humper lake trout live on isolated offshore reefs (or “humps”) or on steep sloping banks surrounded by deep water , and are intermediate in fat content between lean and siscowet lake trout . The diet of humpers has not been described in detail; however, a humper-like ecotype from Lake Mistassini, Quebec had a diet rich in pelagic opossum shrimp Mysis relicta , which are abundant in Lake Superior. The redfin only recently has been described as a distinct ecotype in Lake Superior and is the largest bodied of the ecotypes . Little is known about the diet of redfins, but they occupy deep water (>80 m) and likely have diets similar to that of siscowet.
Our main objectives were two-fold: 1) determine if contemporary lake trout genetic, morphological, and life history trait variation are divergent along a water depth gradient; and 2) determine the stage of ecological divergence (e.g., panmixis, restricted gene flow) at which lake trout exist today in Lake Superior. To accomplish this, we directly compared genotype to phenotype, assessed gene flow, and quantified divergence of phenotypic traits of lake trout among water depth strata. Historically, water depth was considered a primary axis for lake trout ecotype divergence (e.g., ). This hypothesis was supported by breeding experiments on physiological traits related to maintaining their position in the water column and capability to move among depths [35, 37–39], and associations of ecotypes with depth of capture . Due to the recently documented overlap in morphology  and genetic diversity  among putative ecotypes, lake trout may have collapsed into a single gene pool and ecological axes of divergence for lake trout may have diminished. If phenotypic variation is divergent along an axis in which gene flow is partially restricted, then we confirm the persistence of ecological opportunities that can maintain or promote divergence. Such information will be relevant for documenting intraspecific biodiversity  in the largest freshwater lake in the world, and for the re-establishment in other Laurentian Great Lakes of a native fish that has lost considerable genetic diversity due to human disturbance [36, 43, 44].
Additionally, linear measurements for eight putatively functional traits linked to feeding and locomotion measured by Muir et al.  were used in this study to examine signatures of phenotypic selection across habitats: caudal peduncle depth (CPD), caudal peduncle length (CPL), head length (HLL), maxilla length (MXL), orbital length (OOL), pectoral fin length (PCL), pelvic fin length (PVL), and pre-orbital length (POL) (See Additional file 1). Buoyancy was measured as an adaptive trait, because it is positively correlated with body lipid content and capture depth . As previously described in Muir et al. , percent buoyancy was calculated as the difference of the weight-of-fish-measured-in-air minus the weight-of-fish-measured-in-water, divided by weight-of-fish-measured-in-air times 100 . Linear and buoyancy measurements for each fish were log10 transformed and then regressed against standard length (SL). The studentized residuals were used as size-adjusted variables in subsequent analyses.
Life history trait estimation
Five life history traits were calculated in a concurrent study by Hansen et al.  on the same lake trout samples represented in this study. Annuli from transverse thin sections of dried sagittal otoliths were counted to estimate the age for each specimen. Nonlinear mixed-effects models were used to estimate growth parameters for individual fish, based on biological intercept back-calculations of growth histories of individual fish : age-at-time-zero (t 0 = years; incubation time of embryos from fertilization to hatching); length-at-time-zero (L 0 = mm; length at emergence from the egg); instantaneous growth rate (K = 1/year) at which fish length approaches the theoretical maximum length (L max), herein referred to as asymptotic length; early annual growth rate (ω = L max × K = mm/year; ). The lake trout specific biological intercept was based on equations describing relationships between length, age in days, and sagittal otolith width of age-0 estimated by Bronte et al.  where age-0 lake trout sagittal otolith radius = 0.137 mm and length = 21.7 mm . Detailed methods for these life history analyses are given by Hansen et al. .
DNA extraction and microsatellite amplification
Total genomic DNA was extracted using a glass-milk binding protocol . Eighteen microsatellite loci were genotyped using a variety of previously published primer combinations (see Additional file 2). Polymerase chain reactions (PCR) were conducted using 0.5 μL of 10x Thermopol reaction buffer, 200 μmol/L of dNTPs, 2 pmol of the forward and reverse primer, 0.2 units of Taq DNA polymerase, and 1 μL of DNA in 5 μL volumes. PCR conditions consisted of 95 °C for 5 min, 25–30 cycles of 94 °C for 30 s, locus specific annealing temperature (50–62 °C) for 30 s, 72 °C for 1 min, and 72 °C for 3 min. Amplicons were visualized on LI-COR Biosciences (Lincoln, NE, USA) DNA Analyzers with 6 % denaturing polyacrylamide gels. Scoring was performed by hand using a standard ladder (100–350 bp) and scores verified using two positive controls on every gel.
Sources of genetic structure
Three potential sources of group structure were recorded for each fish: morphology (ecotype), geographic location around Isle Royale (zone), and water depth stratum. Total genetic variation was partitioned with a hierarchical analysis of molecular variance (AMOVA) based on 10,000 permutations in ARLEQUIN. Several AMOVAs were performed to determine the variable that most strongly explained genetic organization among groups using the Fct statistic for main effects of ecotype, zone, and water depth stratum, as well as all two-way combinations of those variables.
MICROCHECKER 2.2.3  assessed scoring errors and the presence of null alleles. CREATE 1.37  converted file formats from our original raw data file. Hardy Weinberg Equilibrium (HW) and linkage disequilibrium were tested using ARLEQUIN 3.5 . All multi-test adjustments [e.g., HW, linkage disequilibrium) were based on a sequential goodness of fit metatest using the program R  package ‘SGoF’ 3.8 , except where stated otherwise. FSTAT 18.104.22.168  and ARLEQUIN measured the number of alleles (A), allelic richness (Ar), the number of private alleles, and observed and expected heterozygosity (Ho and He , respectively). Private allelic richness (PAr), the number of private alleles standardized by sample size for each group, was calculated in HP-RARE , inbreeding coefficients, Fis , were calculated using FSTAT, and significance of Fis departure from HW was tested using 10,000 permutations at α = 0.05.
Genetic structure within and among groups
Principal coordinates analysis (PCOA) in GENALEX 6.5  was used to visualize genotypic distribution of individual fish in multivariate space. Several Bayesian clustering analyses with the admixture model with a priori assumptions based on ecotype, zone, and water depth stratum in STRUCTURE 2.3.4 [63–65] determined the number of genetic groups, k. For each analysis, 10 independent runs were conducted for each value of k from k = 1 to k = 10 with burn-in length of 2.5 × 105, followed by 1.0 × 106 randomization step. Both the Evanno et al.  and Pritchard et al.  methods, as well as an estimation of when posterior probabilities for k plotted against k reached a plateau , estimated the most probable k from the STRUCTURE results. Delta k (∆k) and the estimated natural log probability of k (ln P(k))  were generated in STRUCTURE HARVESTER Web 0.6.92 . As an indicator of differentiation among groups, 10,000 permutations in ARLEQUIN compared pairwise Fst . ‘DEMEtics’ 0.8–2  package was implemented in R to obtain Jost D  and its significance values (P)  using 10,000 bootstrap re-samplings. The Bonferroni correction for Jost D was performed using ‘DEMEtics’ . Mantel tests assessed correlations of genotype with water depth strata and geographic distance, as implemented in GENALEX with 9999 permutations at an alpha level of P <0.05 for significance tests. Note that the original study design involved fish sampled at three water depth strata (<50 m, 50–100 m, and >100 m). More than three points (water depth strata) are required to perform a Mantel test in GENALEX. Thus, for this analysis only, the dataset was further subdivided into six water depths (40, 50, 80, 90, 100, and >120 m) based on maximum capture depth of net sets of individual fish. These depth categories while uneven (lacking 60 and 70 m, reflect the distribution of net depths in the data set. To test for isolation-by-distance along a geographic distance axis, a second Mantel test was performed on genotype against geographic distance among the 20 sampling sites.
Historical gene flow among the three original water depth strata (<50 m, 50–100 m, >100 m) was estimated using the maximum-likelihood approach in MIGRATE 3.03 [72, 73]. MIGRATE analyses were performed assuming an infinite allele model (IAM) and the parameters that follow: 10 short chains with a sampling increment of 100 where 500 genealogies are sampled; three long chains with a sampling increment of 1000 where 5000 genealogies are sampled. We discarded 10,000 genealogies at the beginning of each chain as burn-in, and averaged maximum-likelihood estimates over five independent runs.
Phenotypic trait variation among groups
To assess clinal relationships in morphological and life history traits, data was plotted graphically with their standard deviations. One-way ANOVA was performed for each trait by the appropriate independent grouping variable, e.g., water depth, and pairwise post hoc Fisher’s least significant difference (LSD) tests were conducted using SPSS 20.0 (IBM Corp. Released 2011. IBM SPSS Statistics for Windows, Version 20.0. Armonk, NY: IBM Corp.).
Genetic associations with phenotypic trait variation
Phenotypic variance (Pst) estimates on morphological and life history traits among the three original water depth strata were compared to Fst  estimates. Pst is a distance matrix analogue to Fst . Phenotype-environment associations are assumed to evolve under divergent natural selection, while DNA sequences such as microsatellite loci evolve neutrally [23, 75], have an additive genetic basis (where genes contribute a ‘fixed’ phenotypic value), and presumably are under stabilizing selection . On the other hand, divergent or directional selection of a phenotypic trait is implied when Pst is greater or less than Fst.
Among- (Pst) and within-group (rii) phenotypic variance was estimated for morphological traits by calculating genetic relationship matrices (R-matrix estimates) in program RMET 5.0 [76–78]. Phenotypic distances were adjusted for sample size in RMET, and we used a heritability score of 1.0 as recommended by Leinonen et al. . The program output contains an R-matrix (a variance-covariance matrix), a D 2 -matrix (producing D 2 estimates based on a Mahalanobis distance), and a global Pst-value (global Fst-value analogue and measure of overall variance across all groups). For pairwise comparisons of phenotypic to genetic distances among different water depth strata, we used the D 2 estimates (and their standard error estimates as calculated in RMET) because no heritability estimation was required. Fst for microsatellite markers was calculated with bootstrapped 95 % confidence intervals using the ‘diveRsity’  package in program R . Subsequently, and for direct comparison with Pst-values, the upper and lower confidence limits around Fst were converted to standard error (SE) using the following equation: SE = (upper limit – lower limit)/3.92. If within-group phenotypic traits showed greater variance at intermediate water depths, hybridization was considered as an explanation . On the other hand, if variance increased with an increase in water depth, it was interpreted as divergent selection and character release in early stages of sympatric divergence .
Sources of genetic structure
Analyses of molecular variance (AMOVA) for Isle Royale lake trout
Source of variation (standard AMOVA)
Sum of squares
Percentage of variation
Ecotype and zone
Ecotype and stratum
Zone and stratum
Because depth stratum was the strongest variable associated with group genetic structure according to AMOVA, subsequent genetic analyses were performed with fish samples grouped by water depth, and not by ecotype or zone, except where stated otherwise. Also, Zone 3 was excluded from analyses due to the interaction between zone and depth stratum, except where stated otherwise.
The number of lake trout sampled and genetic diversity across water depth strata
Water depth stratum
A R (i = 88)
Genetic structure within and among groups
Genetic differentiation among lake trout at three water depth strata
50 m–100 m
Distance matrices between the neutral genetic and the six depth categories (N.B.: six depth categories used for this analysis only) were moderately positively associated (Mantel test: r m = 0.45, P = 0.047). However, this correlation was significant only after interpolating a single putative outlier (original and adjusted plots and P-values shown in Additional file 8). The correlation between genetic and geographic distance (by shortest distance) among 20 samples sites, however, was not significant (Mantel test: r m = −0.05, P = 0.7).
Phenotypic trait variation with water depth
Relationship between morphology and genotype
Despite concern raised from recently documented overlap in morphological  and genetic diversity  among putative lake trout ecotypes, our results confirmed water depth as an ecological axis of divergence at Isle Royale, Lake Superior that can maintain or promote lake trout diversity. Evidence presented herein supports the hypothesis of genetic and phenotypic divergence along a water depth axis, in which gene flow was partially restricted. This pattern of gene flow suggests that lake trout were not in a state of panmixis. However, high gene flow and lack of strong genetic discontinuities among ecotypes suggest that lake trout genetic diversity, as represented by our collections, was organised along a continuum, rather than among discrete ecotypes, as was historically reported [43, 44, 81]. The exact number of discrete ecotypes at Isle Royale either may be underestimated or possibly cannot be determined using conventional methods. Therefore, we believe the level of lake trout differentiation may have been disrupted from the past and may now have been reset to an early stage of ecotype formation. Reduced diversity may render contemporary lake trout more sensitive to environmental perturbations. Below, we discuss how adaptive differentiation in lake trout ecotypes currently exists along an ecological gradient, their presumed stage of divergence along an ecological speciation continuum, and implications of our findings for species conservation and adaptive diversity in lake trout.
Water depth as a driver of genetic and phenotypic differentiation
Our findings supported the hypothesis that lake trout genetic and phenotypic variation are divergent along a water depth gradient at Isle Royale, Lake Superior. Despite a lack of significant genetic structure among ecotypes, a consistent pattern in genetic variation among depth strata suggests restricted gene flow by water depth, rather than by ecotype or by geography. Based on our results, water depth appears to be a strong ecological axis of divergence important to maintenance of organization of lake trout genetic and morphological diversity. Vertical distribution of spawning depth often explains neutral genetic group structure (e.g., [21, 82]). Unfortunately, little is known about lake trout spawning areas below 30 m due to logistical difficulties of sampling at great depths  (but see ), and during inclement weather of late autumn. Lake trout are thought to spawn at depths from 3 to at least 80 m , yet most documented egg observations have been at depths <20 m [51, 83, 84, 86, 87]. Variation in local adaptation to water depth we observed, likely depends on selective mechanisms (e.g., survival and reproductive success) on adaptive genes; but also on trait plasticity due to gene expression [4, 8, 88], often tied closely to the environment during development . Therefore, depth at which eggs are spawned and larvae are reared probably influences genetic variation and phenotypic expression in lake trout.
Size-corrected morphological traits, buoyancy, body shape, and pelvic fin length, diverged along the depth axis and appeared to exceed neutral expectations. These traits are likely tied to overall fitness of lake trout and their ability to distribute themselves vertically in the water column. Increased lipid metabolism leading to higher buoyancy is important in deep, cold, dense waters to behaviorally thermoregulate body temperature, and to maintain or change position in the water column [89, 90]. Large pelvic fins function to stabilize body position, and may have a fitness advantage in shallow water considering variable water current speeds, tributary outflows, and periodic storm events . A deep caudal peduncle is critical to life in shallow and fluvial waters, especially for ecotypes that predate on fast moving prey [92–94]. Because the Pst-Fst method (see ) cannot distinguish between variation from epigenetic-based plasticity and directional selection on genes, the mechanistic basis of lake trout trait evolution remains unclear [21, 22]. Evidence for a degree of genetic accommodation, or hard-wiring, of lipid metabolism has been previously reported (e.g., [35, 38, 39]). On the other hand, buoyancy, body shape, and lipid metabolism are more responsive to environmental stimuli than cranioskeletal traits [95, 96]. Buoyancy in Isle Royale lake trout varied more widely with depth than any other trait we measured, perhaps indicating a higher degree of environmental responsiveness relative to other traits. Although pelvic fin length decreased from shallow to deep strata, measurements were similar between the two shallowest strata. Consequently, pelvic fin length decreased from shallow to deep water for all ecotypes, except for leans (not shown). Possibly, pelvic fin length may be under stronger selection in leans than deep water ecotypes, which show higher levels of plasticity related to pelvic fin length than leans.
Of the life history traits studied here, early growth rate and asymptotic length decreased significantly from shallow to deep strata. These differences in growth and length may be associated with limiting factors related to cold (e.g., nutrient availability) and deep (e.g., possible energetic trade-offs between length and buoyancy) environments. Deep water lake trout tend to mature at smaller sizes and grow slower than shallow water ecotypes [48, 52, 97]. In Flathead Lake, Montana, lake trout life history traits diverged between lake trout caught from shallow and deep depths after the introduction of Mysis diluviana ; a fatty crustacean that lives mainly below the thermocline . Therefore, the ultimate cause of adaptations to deep water were resource partitioning, while the proximate cause was a shift in spawning and rearing depth that followed the diet shift. The difference between shallow and deep water Lake Superior lake trout life history traits, rather than a gradual cline, could possibly imply a spawning or nursery depth threshold (e.g., thermocline), beyond which environmental variables (e.g., water temperature, dietary protein:lipid ratio) affect eggs and growing larvae differentially [99–101].
Variation in lake trout craniofacial traits (e.g., orbital, head, maxilla length) at Isle Royale did not exceed expectations based on the variation at neutral loci, and phenotypic variance was low within (Additional file 9) and among water depth strata (Fig. 6). We expected Isle Royale lake trout craniofacial features to vary with depth due to presumably different prey assemblages at different depths [102, 103]. Alternatively, apparent neutral evolution of craniofacial traits may suggest stabilizing selection, wherein intermediate variants are favoured among depths, genetic diversity decreases, and the group mean stabilizes on a particular trait. Our results are consistent with studies of Arctic charr Salvelinus alpinus, where cranioskeletal genes were expressed differentially when exposed to different food types and were less diverse, and craniofacial traits were less responsive to novel environments than those associated with lipid levels and body shape [3, 95]). However, phenotypic variance in head shape as a whole did exceed neutral expectations when compared to neutral expectations based on genetic divergence in microsatellite loci (as measured by Fst). Thus, perhaps prey- or water depth-based selection or phenotypic plasticity has occurred or is still occurring to a degree. Nonetheless, head shape varied less than body shape at Isle Royale. By contrast, at Great Bear Lake (66.0 ° N, 121.0 ° W), head shape varied more than body shape among piscivorous and invertivorous lake trout ecotypes collected from shallow water <30 m [29, 30, 104]. Clearly, lake trout organization is influenced by water depth at Isle Royale. Perhaps the apparent fixation of individual linear craniofacial traits (not head shape as a whole) in Isle Royale lake trout reflect a strong prey resource gradient that originally existed in the lake, and potentially may reduce ability to adapt to exotic introductions or fish community changes [105, 106].
Our results suggest that hard-wiring at adaptive genes in the presence of high gene flow occurred differentially among traits (e.g., craniofacial versus body shape). Similarly, genetically differentiated intraspecific groups vary in the relative proportions of genetic correlations with different traits in three-spine stickleback Gasterosteus aculeatus [107–109], whitefish (Coregonus sp.; ), rainbow trout Oncorhynchus mykiss , brown trout Salmo trutta , and Arctic charr Salvelinus alpinus . Each trait likely has a different balance of genetic control and environmental responsiveness that varies among lakes, depending on respective selective pressures and their duration and intensity over generations ; also see [8, 113, 114].
Stage of ecological speciation in Lake Superior lake trout
Consistent with a species at an early stage of speciation, we found that four visually distinct lake trout ecotypes could not be distinguished from each other genetically using various population genetic methods for neutral genetic variation. PCOA and Bayesian clustering results on neutral microsatellite loci implied a single panmictic lake trout population (Additional files 5 and 6). However, we found evidence for weak genetic isolation in the form of partially restricted gene flow associated with increasing depth of capture. Increasing phenotypic variation with depth possibly indicated diversifying selection on adaptive genes. Therefore, lake trout ecotypes are likely at an early evolutionary stage characterized by disruptive natural selection on functional phenotypic traits and high gene flow (i.e., between the second and third stages as outlined by Bird et al.  and likely reset from a more advanced stage from the past . Lake Superior lake trout are part of a large post-glacial adaptive radiation that spans boreal North America involving at least 20 extant ecotypes in several large (>2000 km2) deep cold water lakes [115, 116]. Sufficient generations have passed since the last glacial maximum in the presence of ecological niche diversification (or heterogeneity) to allow parallel development of resource polymorphism in this species in several lakes across North America [27–30, 116]. In Lake Superior, however, gene flow seems high among ecotypes and appears to have increased in recent decades (see ), which may have impeded or reversed the historical trajectory of ecological speciation . Although our findings imply that the predominant direction of historical gene flow was from shallow to deep water, evidence of bi-directional gene flow is consistent with phenotypic mixing between the two shallowest depth strata. Considering that low gene flow is expected in the later stages of ecological speciation, lake trout are not likely experiencing strong forward speciation (toward greater divergence) in Lake Superior .
Implications for species conservation and adaptive diversity in lake trout
Lake trout fisheries have targeted the shallow water lean ecotype, whereas humper, siscowet, and redfin typically located farther offshore were fished less. Intensive stocking after the lake trout fishery collapse of the 1950s focussed solely on re-introductions of the lean ecotype during the 1960s through 1990s [36, 119]. Likewise, invasive sea lamprey predation is thought to have had the most impact in shallow water strata [120–122]. The selective fishery and sea lamprey predation may have disproportionately elevated mortality of leans, thereby enhancing fitness of deep water ecotypes and altering the fitness landscape in the lake . Ecological divergence is thought to accelerate when two or more ecological gradients coincide [1, 5, 123]. If lake trout populations were historically divergent on multiple niche axes (e.g., depth and diet simultaneously) and in modern times the number of niche axes has been reduced (e.g., homogenization of diet diversity), then speciation possibly has been impeded, reversed, or reset back to an earlier stage of diversification [124, 125]. At present, whether lake trout diversity in Lake Superior is in the process of collapsing, expanding, or in a stable gene flow-diversification balance cannot be determined. However, based on available information about Lake Superior lake trout, a depth gradient persists and remains evolutionarily important, and ecotypes have at least a partial genetic basis for divergence (see ).
Lake trout reproductive boundaries are not completely isolated in Lake Superior, and their evolutionary potential may be especially vulnerable to future changing environmental conditions, and to expansions and reductions in ecological opportunity. Lake trout of Lake Superior appear to have collapsed genetically and morphologically, concurrent with dramatic overall depletions in allelic richness since the 1940s [25, 41]. To discern the processes potentially underlying these losses (e.g., random genetic drift due to population reduction), changes in the selective environment, the nature of gene flow between wild populations and hatchery strains, and adaptive genetic diversity must be better understood. Future genetic analyses of historical collections of lake trout scale samples dating back to before the fishery collapse could shed light on temporal trends in genetic structure and ecological diversity. Such research could permit identification of genetic predictors of human impacts on evolutionary processes in wild populations.
We provide evidence of reductions in gene flow and divergent natural selection associated with water depth in Lake Superior. These patterns in genetic variation among depth strata persist despite a lack of significant genetic structure when the data set is divided by ecotype. Thus, water depth appears to be a strong ecological axis of divergence important to the maintenance of organization of lake trout genetic and morphological diversity. Lake Superior is the largest freshwater lake in the world, and lake trout have recently lost considerable genetic diversity . Therefore, the results represented in this study are relevant for documenting intraspecific biodiversity for this species. However, it is still unclear as to whether the observed patterns are a result of an early stage of incipient speciation, gene flow-selection equilibrium, or reverse speciation causing formerly divergent ecotypes to collapse into a single gene pool.
The authors are grateful to anonymous reviewers for their insightful comments about this study. We also thank M McBride and L Anstey, who generated the microsatellite DNA data set in the Marine Gene Probe Laboratory, Dalhousie University. We thank S Sivertson, and Enar and Betty Strom, for their hospitality at Washington and Barnam Islands, adjacent to Isle Royale. J Pyatskowit, CR Bronte, MS Zimmerman, HR Quinlan, and JD Glase provided cheerful and able assistance with field work at Isle Royale. This paper is part of a research project on ‘Re-establishment of Native Deepwater Fishes’ funded by the Great Lakes Fishery Commission. Use of trade, product, or firm names is for descriptive purposes and does not imply endorsement by the U.S. Government. This article is Contribution 2060 of the U.S. Geological Survey, Great Lakes Science Center.
Availability of data and materials
The microsatellite DNA data set supporting the conclusions of this article are available in the FigShare repository. https://figshare.com/articles/LSIR_STRATA-3_genepop_txt/3969579.
SMB performed the statistical analyses and wrote the manuscript. AMM and MJH generated the geometric morphometric and life history data, respectively. Both AMM and CK were key to the inception, and funding and collection of samples for this project. PB oversaw the genetics portion of the project. All five authors contributed to the discussion, read multiple drafts, and approved the final draft.
The authors declare that they have no competing interests.
Consent for publication
Ethics approval and consent to participate
Sampling and handling of fish was in accordance with guidelines for the care and use of fishes by the American Fisheries Society (http://fisheries.org/docs/wp/Guidelines-for-Use-of-Fishes.pdf).
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- Bird CE, Fernandez-Silva I, Skillings DJ, Toonen RJ. Sympatric speciation in the Post “Modern Synthesis” Era of evolutionary biology. Evol Biol. 2012;39(2):158–80.View ArticleGoogle Scholar
- Kirk H, Freeland JR. Applications and implications of neutral versus non-neutral markers in molecular ecology. Int J Mol Sci. 2011;12(6):3966–88.PubMedPubMed CentralView ArticleGoogle Scholar
- Kuettner E, Parsons KJ, Easton AA, Skulason S, Danzmann RG, Ferguson MM. Hidden genetic variation evolves with ecological specialization: the genetic basis of phenotypic plasticity in Arctic charr ecomorphs. Evol Dev. 2014;16(4):247–57.View ArticleGoogle Scholar
- Skinner MK, Gurerrero-Bosagna C, Haque MM, Nilsson EE, Koop JAH, Knutie SA, et al. Epigenetics and the evolution of Darwin’s finches. Genome Biol Evol. 2014;6(8):1972–89.PubMedPubMed CentralView ArticleGoogle Scholar
- Wellborn GA, Langerhans RB. Ecological opportunity and the adaptive diversification of lineages. Ecol Evol. 2015;5(1):176–95.PubMedView ArticleGoogle Scholar
- West-Eberhard MJ. Developmental plasticity and the origin of species differences. Proc Natl Acad Sci U S A. 2005;102:6543–9.PubMedPubMed CentralView ArticleGoogle Scholar
- Braendle C, Flatt T. A role for genetic accommodation in evolution? Bioessays. 2006;28(9):868–73.PubMedView ArticleGoogle Scholar
- Pigliucci M, Murren CJ, Schlichting CD. Phenotypic plasticity and evolution by genetic assimilation. J Exp Biol. 2006;209(12):2362–7.PubMedView ArticleGoogle Scholar
- Taylor EB, Boughman JW, Groenenboom M, Sniatynski M, Schluter D, Gow JL. Speciation in reverse: morphological and genetic evidence of the collapse of a three-spined stickleback (Gasterosteus aculeatus) species pair. Mol Ecol. 2006;15(2):343–55.PubMedView ArticleGoogle Scholar
- Grant PR, Grant BR. Phenotypic and genetic-effects of hybridization in Darwins finches. Evolution. 1994;48(2):297–316.View ArticleGoogle Scholar
- Todd TN, Stedman RM. Hybridization of ciscoes (Coregonus Spp) in Lake Huron. Can J Zool. 1989;67(7):1679–85.View ArticleGoogle Scholar
- Bhat S, Amundsen P, Knudsen R, Gjelland KO, Fevolden S, Bernatchez L, et al. Speciation reversal in European whitefish (Coregonus lavaretus (L.)) caused by competitor invasion. PloS One. 2014;9(3):e91208.PubMedPubMed CentralView ArticleGoogle Scholar
- Wilson SD, Keddy PA. Species competitive ability and position along a natural stress disturbance gradient. Ecology. 1986;67(5):1236–42.View ArticleGoogle Scholar
- Richman AD, Price T. Evolution of ecological differences in the Old-World leaf warblers. Nature. 1992;355(6363):817–21.PubMedView ArticleGoogle Scholar
- Austin MP, Nicholls AO, Doherty MD, Meyers JA. Determining species response functions to an environmental gradient by means of a beta-function. J Veg Sci. 1994;5(2):215–28.View ArticleGoogle Scholar
- Schneider CJ, Smith TB, Larison B, Moritz C. A test of alternative models of diversification in tropical rainforests: Ecological gradients vs. rainforest refugia. Proc Natl Acad Sci U S A. 1999;96(24):13869–73.PubMedPubMed CentralView ArticleGoogle Scholar
- Seehausen O, van Alphen JJM, Witte F. Cichlid fish diversity threatened by eutrophication that curbs sexual selection. Science. 1997;277(5333):1808–11.View ArticleGoogle Scholar
- Magalhaes IS, Seehausen O. Genetics of male nuptial colour divergence between sympatric sister species of a Lake Victoria cichlid fish. J Evol Biol. 2010;23(5):914–24.PubMedView ArticleGoogle Scholar
- Turgeon J, Reid SM, Bourret A, Pratt TC, Reist JD, Muir AM, et al. Morphological and genetic variation in cisco (Coregonus artedi) and shortjaw cisco (C. zenithicus): multiple origins of shortjaw cisco in inland lakes require a lake-specific conservation approach. Conserv Genet. 2016;17(1):45–56.View ArticleGoogle Scholar
- Helland IP, Harrod C, Freyhof J, Mehner T. Co-existence of a pair of pelagic planktivorous coregonid fishes. Evol Ecol Res. 2008;10(3):373–90.Google Scholar
- Vonlanthen P, Roy D, Hudson AG, Largiader CR, Bittner D, Seehausen O. Divergence along a steep ecological gradient in lake whitefish (Coregonus sp.). J Evol Biol. 2009;22(3):498–514.PubMedView ArticleGoogle Scholar
- Praebel K, Knudsen R, Siwertsson A, Karhunen M, Kahilainen KK, Ovaskainen O, et al. Ecological speciation in postglacial European whitefish: rapid adaptive radiations into the littoral, pelagic, and profundal lake habitats. Ecol Evol. 2013;3(15):4970–86.PubMedPubMed CentralView ArticleGoogle Scholar
- Leinonen T, McCairns RJS, O’Hara RB, Merila J. Q ST-F ST comparisons: evolutionary and ecological insights from genomic heterogeneity. Nat Rev Genet. 2013;14(3):179–90.PubMedView ArticleGoogle Scholar
- Krueger CC, Ihssen PE. Review of genetics of lake trout in the great lakes: History, molecular genetics, physiology, strain comparisons, and restoration management. J Great Lakes Res. 1995;21:348–63.View ArticleGoogle Scholar
- Muir AM, Bronte CR, Zimmerman MS, Quinlan HR, Glase JD, Krueger CC. Ecomorphological diversity of lake trout at Isle Royale, Lake Superior. Trans Am Fish Soc. 2014;143(4):972–87.Google Scholar
- Harvey CJ, Schram ST, Kitchell JF. Trophic relationships among lean and siscowet lake trout in Lake Superior. Trans Am Fish Soc. 2003;132(2):219–28.View ArticleGoogle Scholar
- Zimmerman MS, Krueger CC, Eshenroder RL. Phenotypic diversity of lake trout in Great Slave Lake: Differences in morphology, buoyancy, and habitat depth. Trans Am Fish Soc. 2006;135(4):1056–67.View ArticleGoogle Scholar
- Zimmerman MS, Krueger CC, Eshenroder RL. Morphological and ecological differences between shallow- and deep-water lake trout in Lake Mistassini, Quebec. J Great Lakes Res. 2007;33(1):156–69.View ArticleGoogle Scholar
- Chavarie L, Howland KL, Tonn WM. Sympatric polymorphism in lake trout: The coexistence of multiple shallow-water morphotypes in Great Bear Lake. Trans Am Fish Soc. 2013;142(3):814–23.View ArticleGoogle Scholar
- Chavarie L, Howland K, Harris L, Tonn W. Polymorphism in lake trout in Great Bear Lake: intra-lake morphological diversification at two spatial scales. Biol J Linn Soc. 2015;114(1):109–25.View ArticleGoogle Scholar
- Behnke RJ. Systematics of salmonid fishes of recently glaciated lakes. J Fish Res Board Can. 1972;29(6):639.View ArticleGoogle Scholar
- Ray BA, Hrabik TR, Ebener MP, Gorman OT, Schreiner DR, Schram ST, et al. Diet and prey selection by Lake Superior lake trout during spring, 1986–2001. J Great Lakes Res. 2007;33(1):104–13.View ArticleGoogle Scholar
- Bronte C, Ebener M, Schreiner D, DeVault D, Petzold M, Jensen D, et al. Fish community change in Lake Superior, 1970–2000. Can J Fish Aquat Sci. 2003;60(12):1552–74.View ArticleGoogle Scholar
- Rahrer JF. Age growth maturity and fecundity of humper lake trout Isle Royale Lake Superior. Trans Am Fish Soc. 1965;94(1):75.View ArticleGoogle Scholar
- Eschmeyer PH, Phillips AM. Fat content of flesh of siscowets and lake trout from Lake Superior. Trans Am Fish Soc. 1965;94(1):62–74.View ArticleGoogle Scholar
- Hansen MJ, Peck JW, Schorfhaar RG, Selgeby JH, Schreiner DR, Schram ST, et al. Lake trout (Salvelinus namaycush) populations in Lake Superior and their restoration in 1959–1993. J Great Lakes Res. 1995;21:152–75.View ArticleGoogle Scholar
- Ihssen P, Tait JS. Genetic differences in retention of swimbladder gas between two populations of lake trout (Salvelinus namaycush). J Fish Res Board Can. 1974;31(8):1351–4.View ArticleGoogle Scholar
- Goetz F, Rosauer D, Sitar S, Goetz G, Simchick C, Roberts S, et al. A genetic basis for the phenotypic differentiation between siscowet and lean lake trout (Salvelinus namaycush). Mol Ecol. 2010;19:176–96.PubMedView ArticleGoogle Scholar
- Goetz F, Sitar S, Rosauer D, Swanson P, Bronte CR, Dickey J, et al. The reproductive biology of siscowet and lean lake trout in southern Lake Superior. Trans Am Fish Soc. 2011;140(6):1472–91.View ArticleGoogle Scholar
- Bronte CR, Sitar SP. Harvest and relative abundance of siscowet lake trout in Michigan waters of Lake Superior, 1929–1961. Trans Am Fish Soc. 2008;137(3):916–26.View ArticleGoogle Scholar
- Baillie, SM, Muir, AM, Krueger, CC, Scribner, K, Bentzen, P. Loss of genetic diversity and reduction of genetic distance among lake trout Salvelinus namaycush ecomorphs, Lake Superior 1959 to 2013. J Great Lakes Res. doi: 10.1016/j.jglr.2016.02.001
- Taylor EB. Changes in taxonomy and species distributions and their influence on estimates of faunal homogenization and differentiation in freshwater fishes. Divers Distrib. 2010;16(4):676–89.View ArticleGoogle Scholar
- Guinand B, Scribner KT, Page KS, Burnham-Curtis MK. Genetic variation over space and time: analyses of extinct and remnant lake trout populations in the Upper Great Lakes. Proc R Soc London, Ser B. 2003;270(1513):425–33.View ArticleGoogle Scholar
- Guinand B, Page KS, Burnham-Curtis MK, Scribner KT. Genetic signatures of historical bottlenecks in sympatric lake trout (Salvelinus namaycush) morphotypes in Lake Superior. Environ Biol Fishes. 2012;95(3):323–34.View ArticleGoogle Scholar
- Zelditch ML, Swiderski DL, Sheets DH, Fink WL. Geometric morphometrics for biologists: a primer. New York: Elsevier Academic Press; 2004.Google Scholar
- Zimmerman MS, Schmidt SN, Krueger CC, Vander Zanden MJ, Eshenroder RL. Ontogenetic niche shifts and resource partitioning of lake trout morphotypes. Can J Fish Aquat Sci. 2009;66(6):1007–18.View ArticleGoogle Scholar
- Fraley C, Raftery AE. MCLUST version 3 for R: normal mixuture modeling and model-based clustering. Seattle: University of Washington; 2009. Technical Report 504.Google Scholar
- Hansen. MJ, Nate, NA, Muir, AM, Bronte, CR, Zimmerman, MS, Krueger, CC. Life history variation among four lake trout morphs at Isle Royale, Lake Superior. J Great Lakes Res. doi:10.1016/j.jglr.2015.12.011.
- Campana SE. How reliable are growth back-calculations based on otoliths. Can J Fish Aquat Sci. 1990;47(11):2219–27.View ArticleGoogle Scholar
- Gallucci VF, Quinn TJ. Reparameterizing, fitting, and testing a simple growth-model. Trans Am Fish Soc. 1979;108(1):14–25.View ArticleGoogle Scholar
- Bronte CR, Selgeby JH, Saylor JH, Miller GS, Foster NR. Hatching, dispersal, and bathymetric distribution of age-0 wild lake trout at the Gull Island shoal complex, Lake Superior. J Great Lakes Res. 1995;21:233–45.View ArticleGoogle Scholar
- Hansen MJ, Nate NA, Krueger CC, Zimmerman MS, Kruckman HG, Taylor WW. Age, Growth, Survival, and Maturity of Lake Trout Morphotypes in Lake Mistassini, Quebec. Trans Am Fish Soc. 2012;141(6):1492–503.View ArticleGoogle Scholar
- Elphinstone MS, Hinten GN, Anderson MJ, Nock CJ. An inexpensive and high-throughput procedure to extract and purify total genomic DNA for population studies. Mol Ecol Notes. 2003;3(2):317–20.View ArticleGoogle Scholar
- Van Oosterhout C, Hutchinson W, Wills D, Shipley P. MICRO-CHECKER: software for identifying and correcting genotyping errors in microsatellite data. Mol Ecol Notes. 2004;4(3):535–8.View ArticleGoogle Scholar
- Coombs JA, Letcher BH, Nislow KH. CREATE: a software to create input files from diploid genotypic data for 52 genetic software programs. Mol Ecol Resour. 2008;8(3):578–80.PubMedView ArticleGoogle Scholar
- Excoffier L, Lischer HEL. Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. Mol Ecol Resour. 2010;10(3):564–7.PubMedView ArticleGoogle Scholar
- R Core Team. R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing. 2015. https://www.r-project.org.
- Carvajal-Rodriguez A, de Una-Alvarez J. Assessing significance in high-throughput experiments by sequential goodness of fit and q-value estimation. PloS One. 2011;6(9):e24700.PubMedPubMed CentralView ArticleGoogle Scholar
- Goudet J. FSTAT (Version 1.2): A computer program to calculate F-statistics. J Hered. 1995;86(6):485–6.Google Scholar
- Kalinowski S. Counting alleles with rarefaction: Private alleles and hierarchical sampling designs. Conserv Genet. 2004;5(4):539–43.View ArticleGoogle Scholar
- Wright S. Systems of mating. II. The effects of inbreeding on the genetic composition of a population. Genetics. 1921;6(2):124–43.PubMedPubMed CentralGoogle Scholar
- Peakall R, Smouse PE. GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research-an update. Bioinformatics. 2012;28(19):2537–9.PubMedPubMed CentralView ArticleGoogle Scholar
- Pritchard J, Stephens M, Donnelly P. Inference of population structure using multilocus genotype data. Genetics. 2000;155(2):945–59.PubMedPubMed CentralGoogle Scholar
- Falush D, Stephens M, Pritchard JK. Inference of population structure using multilocus genotype data: dominant markers and null alleles. Mol Ecol Notes. 2007;7(4):574–8.PubMedPubMed CentralView ArticleGoogle Scholar
- Hubisz MJ, Falush D, Stephens M, Pritchard JK. Inferring weak population structure with the assistance of sample group information. Mol Ecol Resour. 2009;9(5):1322–32.PubMedPubMed CentralView ArticleGoogle Scholar
- Evanno G, Regnaut S, Goudet J. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol. 2005;14(8):2611–20.PubMedView ArticleGoogle Scholar
- Earl DA, von Holdt BM. STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv Genet Resour. 2012;4(2):359–61.View ArticleGoogle Scholar
- Wright S. The Interpretation of Population-Structure by F-Statistics with Special Regard to Systems of Mating. Evolution. 1965;19(3):395–420.View ArticleGoogle Scholar
- Gerlach G, Jueterbock A, Kraemer P, Deppermann J, Harmand P. Calculations of population differentiation based on GST and D: forget GST but not all of statistics! Mol Ecol. 2010;19(18):3845–52.PubMedView ArticleGoogle Scholar
- Jost L. G(ST) and its relatives do not measure differentiation. Mol Ecol. 2008;17(18):4015–26.PubMedView ArticleGoogle Scholar
- Manly BFJ. A method for the estimation of parameters for natural stage-structured populations. Res Popul Ecol. 1997;39(2):101–11.View ArticleGoogle Scholar
- Beerli P, Felsenstein J. Maximum likelihood estimation of a migration matrix and effective population sizes in n subpopulations by using a coalescent approach. Proc Natl Acad Sci U S A. 2001;98(8):4563–8.PubMedPubMed CentralView ArticleGoogle Scholar
- Beerli P, Palczewski M. Unified framework to evaluate panmixia and migration direction among multiple sampling locations. Genetics. 2010;185(1):313–U463.PubMedPubMed CentralView ArticleGoogle Scholar
- Weir BS, Cockerham CC. Estimating F-statistics for the analysis of population-structure. Evolution. 1984;38(6):1358–70.View ArticleGoogle Scholar
- Spitze K. Population structure in Daphnia obtusa - quantitative genetic and allozymic variation. Genetics. 1993;135(2):367–74.PubMedPubMed CentralGoogle Scholar
- Relethford JH, Blangero J. Detection of differential gene flow from patterns of quantitative variation. Hum Biol. 1990;62(1):5–25.PubMedGoogle Scholar
- Relethford JH, Crawford MH, Blangero J. Genetic drift and gene flow in post-famine Ireland. Hum Biol. 1997;69(4):443–65.PubMedGoogle Scholar
- Relethford JH. The human species: an introduction to biological anthropology. 5th ed. Mayfield: Mountain View, CA; 2003.Google Scholar
- Keenan K, McGinnity P, Cross TF, Crozier WW, Prodoehl PA. diveRsity: An R package for the estimation and exploration of population genetics parameters and their associated errors. Methods Ecol Evol. 2013;4(8):782–8.View ArticleGoogle Scholar
- Seehausen O. Hybridization and adaptive radiation. Trends Ecol Evol. 2004;19(4):198–207.PubMedView ArticleGoogle Scholar
- Page K, Scribner K, Burnham-Curtis M. Genetic diversity of wild and hatchery lake trout populations: Relevance for management and restoration in the Great Lakes. Trans Am Fish Soc. 2004;133(3):674–91.View ArticleGoogle Scholar
- Florin A, Hoglund J. Population structure of flounder (Platichthys flesus) in the Baltic Sea: differences among demersal and pelagic spawners. Heredity. 2008;101(1):27–38.PubMedView ArticleGoogle Scholar
- Marsden JE, Janssen J. Evidence of lake trout spawning on a deep reef in Lake Michigan using an ROV-based egg collector. J Great Lakes Res. 1997;23(4):450–7.View ArticleGoogle Scholar
- Janssen J, Jude DJ, Edsall TA, Paddock RW, Wattrus N, Toneys M, et al. Evidence of lake trout reproduction at Lake Michigan’s Mid-Lake Reef Complex. J Great Lakes Res. 2006;32(4):749–63.View ArticleGoogle Scholar
- Marsden JE, Casselman JM, Edsall TA, Elliott RF, Fitzsimons JD, Horns WH, et al. Lake trout spawning habitat in the Great Lakes - A review of current knowledge. J Great Lakes Res. 1995;21:487–97.View ArticleGoogle Scholar
- Janssen J, Marsden JE, Bronte CR, Jude DJ, Sitar SP, Goetz FW. Challenges to deep-water reproduction by lake trout: Pertinence to restoration in Lake Michigan. J Great Lakes Res. 2007;33:59–74.View ArticleGoogle Scholar
- Maclean JA, Evans DO, Martin NV, Desjardine RL. Survival, growth, spawning distribution, and movements of introduced and native lake trout (Salvelinus-namaycush) in two inland Ontario lakes. Can J Fish Aquat Sci. 1981;38(12):1685–700.View ArticleGoogle Scholar
- Renaut S, Nolte AW, Bernatchez L. Gene expression divergence and hybrid misexpression between lake whitefish species pairs (Coregonus spp. Salmonidae). Mol Biol Evol. 2009;26(4):925–36.PubMedView ArticleGoogle Scholar
- Phleger CF. Buoyancy in marine fishes: Direct and indirect role of lipids. Am Zool. 1998;38(2):321–30.View ArticleGoogle Scholar
- Henderson BA, Anderson DM. Phenotypic differences in buoyancy and energetics of lean and siscowet lake charr in Lake Superior. Environ Biol Fishes. 2002;64(1–3):203–9.Google Scholar
- Standen EM. Pelvic fin locomotor function in fishes: three-dimensional kinematics in rainbow trout (Oncorhynchus mykiss). J Exp Biol. 2008;211(18):2931–42.PubMedView ArticleGoogle Scholar
- Lauder GV, Drucker EG. Morphology and experimental hydrodynamics of fish fin control surfaces. IEEE J Ocean Eng. 2004;29(3):556–71.View ArticleGoogle Scholar
- Hall B. Fins into Limbs: Evolution, Development, and Transformation University of Chicago Press. Chicago: University of Chicago Press; 2007.Google Scholar
- Helfman G, Collette BB, Facey DE, and Bowen BW. Functional morphology of locomotion and feeding. Chapter 8. In: The Diversity of Fishes: Biology. Hoboken, New Jersey: John Wiley & Sons; 2009. pp. 101–116.Google Scholar
- Kuettner E, Parsons KJ, Robinson BW, Skulason S, Danzmann RG, Ferguson MM. Effects of population, family, and diet on craniofacial morphology of Icelandic Arctic charr (Salvelinus alpinus). Biol J Linn Soc. 2013;108(3):702–14.View ArticleGoogle Scholar
- Parsons KJ, Marquez E, Cooper WJ, Albertson RC. The genetic basis of modularity in the African cichlid mandible. Integr Comp Biol. 2010;50:E134.Google Scholar
- Stafford CP, McPhee MV, Eby LA, Allendorf FW. Introduced lake trout exhibit life history and morphological divergence with depth. Can J Fish Aquat Sci. 2014;71(1):10–20.View ArticleGoogle Scholar
- Audzijonyte A, Vainola R. Diversity and distributions of circumpolar fresh- and brackish-water Mysis (Crustacea: Mysida): descriptions of M. relicta Loven, 1862, M. salemaai n. sp., M. segerstralei n. sp and M. diluviana n. sp., based on molecular and morphological characters. Hydrobiologia. 2005;544:89–141.View ArticleGoogle Scholar
- Horns WH. Differences in early development among lake trout (Salvelinus namaycush) Populations. Can J Fish Aquat Sci. 1985;42(4):737–43.View ArticleGoogle Scholar
- Atse CB, Audet C, de la Noue J. Effects of temperature and salinity on the reproductive success of Arctic charr, Salvelinus alpinus (L.): egg composition, milt characteristics and fry survival. Aquac Res. 2002;33(4):299–309.View ArticleGoogle Scholar
- Bogevik AS, Henderson RJ, Mundheim H, Olsen RE, Tocher DR. The effect of temperature and dietary fat level on tissue lipid composition in Atlantic salmon (Salmo salar) fed wax ester-rich oil from Calanus finmarchicus. Aquacult Nutr. 2011;17(3):E781–8.View ArticleGoogle Scholar
- Sierszen ME, Peterson GS, Scharold JV. Depth-specific patterns in benthic-planktonic food web relationships in Lake Superior. Can J Fish Aquat Sci. 2006;63(7):1496–503.View ArticleGoogle Scholar
- Sierszen ME, Hrabik TR, Stockwell JD, Cotter AM, Hoffman JC, Yule DL. Depth gradients in food-web processes linking habitats in large lakes: Lake Superior as an exemplar ecosystem. Freshw Biol. 2014;59(10):2122–36.View ArticleGoogle Scholar
- Harris LN, Chavarie L, Bajno R, Howland KL, Wiley SH, Tonn WM, et al. Evolution and origin of sympatric shallow-water morphotypes of Lake Trout, Salvelinus namaycush, in Canada’s Great Bear Lake. Heredity. 2015;114(1):94–106.PubMedView ArticleGoogle Scholar
- Christie WJ. Changes in fish species composition of Great Lakes. J Fish Res Board Can. 1974;31(5):827–54.View ArticleGoogle Scholar
- Mills EL, Leach JH, Carlton JT, Secor CL. Exotic species in the Great-Lakes - a history of biotic crises and anthropogenic introductions. J Great Lakes Res. 1993;19(1):1–54.View ArticleGoogle Scholar
- Lucek K, Sivasundar A, Seehausen O. Disentangling the role of phenotypic plasticity and genetic divergence in contemporary ecotype formation during a biological invasion. Evolution. 2014;68(9):2619–32.PubMedView ArticleGoogle Scholar
- Leinonen T, McCairns RJS, Herczeg G, Merila J. Multiple evolutionary pathways to decreased lateral plate coverage in freshwater threespine sticklebacks. Evolution. 2012;66(12):3866–75.PubMedView ArticleGoogle Scholar
- Berner D, Kaeuffer R, Grandchamp A, Raeymaekers JAM, Raesaenen K, Hendry AP. Quantitative genetic inheritance of morphological divergence in a lake-stream stickleback ecotype pair: implications for reproductive isolation. J Evol Biol. 2011;24(9):1975–83.PubMedView ArticleGoogle Scholar
- Keeley ER, Parkinson EA, Taylor EB. The origins of ecotypic variation of rainbow trout: a test of environmental vs. genetically based differences in morphology. J Evol Biol. 2007;20(2):725–36.PubMedView ArticleGoogle Scholar
- Meier K, Hansen MM, Bekkevold D, Skaala O, Mensberg K-D. An assessment of the spatial scale of local adaptation in brown trout (Salmo trutta L.): footprints of selection at microsatellite DNA loci. Heredity. 2011;106(3):488–99.PubMedPubMed CentralView ArticleGoogle Scholar
- Kapralova KH, Franzdottir SR, Jonsson H, Snorrason SS, Jonsson ZO. Patterns of miRNA expression in arctic charr development. PloS One. 2014;9(8):e106084.PubMedPubMed CentralView ArticleGoogle Scholar
- Jablonka E. Epigenetic inheritance and plasticity: The responsive germline. Prog Biophys Mol Biol. 2013;111(2–3):99–107.PubMedView ArticleGoogle Scholar
- Robinson BW. Evolution of growth by genetic accommodation in Icelandic freshwater stickleback. Proc R Soc London, Ser B. 2013;280(1772):20132197.View ArticleGoogle Scholar
- Lawrie AH, Rahrer JF. Lake-Superior - Effects of exploitation and introductions on salmonid community. J Fish Res Board Can. 1972;29(6):765.View ArticleGoogle Scholar
- Muir AM, Hansen MJ, Bronte CR, Krueger CC. If Arctic charr Salvelinus alpinus is ‘the most diverse vertebrate’, what is the lake charr Salvelinus namaycush? Fish Fish. 2015; doi: 10.1111/faf.12114
- Bierne N, Gagnaire P, David P. The geography of introgression in a patchy environment and the thorn in the side of ecological speciation. Curr Zool. 2013;59(1):72–86.View ArticleGoogle Scholar
- Hendry AP. Ecological speciation! Or the lack thereof? Can J Fish Aquat Sci. 2009;66(8):1383–98.View ArticleGoogle Scholar
- Krueger CC, Ebener M. Rehabilitation of lake trout in the Great Lakes: past lessons and future challenges. In: Gunn JM, Stedman RJ, Ryder RA, editors. Boreal Shield watersheds: Lake trout ecosystems in a changing environment. Boca Raton: Lewis Publishers; 2004. p. 37–56.Google Scholar
- Kitchell JF. The scope for mortality caused by sea lamprey. Trans Am Fish Soc. 1990;119(4):642–8.View ArticleGoogle Scholar
- Jorgensen JC, Kitchell JF. Growth potential and host mortality of the parasitic phase of the sea lamprey (Petromyzon marinus) in Lake Superior. Can J Fish Aquat Sci. 2005;62(10):2343–53.View ArticleGoogle Scholar
- Moody EK, Weidel BC, Ahrenstorff TD, Mattes WP, Kitchell JF. Evaluating the growth potential of sea lampreys (Petromyzon marinus) feeding on siscowet lake trout (Salvelinus namaycush) in Lake Superior. J Great Lakes Res. 2011;37(2):343–8.View ArticleGoogle Scholar
- Seehausen O. Conservation: Losing biodiversity by reverse speciation. Curr Biol. 2006;16(9):R334–7.PubMedView ArticleGoogle Scholar
- Nosil P, Harmon LJ, Seehausen O. Ecological explanations for (incomplete) speciation. Trends Ecol Evol. 2009;24(3):145–56.PubMedView ArticleGoogle Scholar
- Ingram T, Hudson AG, Vonlanthen P, Seehausen O. Does water depth or diet divergence predict progress towards ecological speciation in whitefish radiations? Evol Ecol Res. 2012;14(4):487–502.Google Scholar