Adaptation and constraint in a stickleback ... - Wiley Online Library

doi: 10.1111/jeb.12240

Adaptation and constraint in a stickleback radiation K. L. VOJE*, A. B. MAZZARELLA*, T. F. HANSEN*, K. ØSTBYE*†, T. KLEPAKER‡, A. BASS*, A. HERLAND*, K. M. BÆRUM*†, F. GREGERSEN§ & L. A. VØLLESTAD* *Department of Biology, Centre for Ecological and Evolutionary Synthesis, University of Oslo, Blindern, Norway †Faculty of Applied Ecology and Agricultural Sciences, Campus Evenstad, Hedmark University College, Koppang, Norway ‡Department of Biology, University of Bergen, Bergen, Norway §Multiconsult, Oslo, Norway

Keywords:

Abstract

adaptation; adaptive radiation; allometry; evolutionary constraint; Gasterosteus aculeatus; morphometrics; phylogenetic comparative method.

The evolution of threespine sticklebacks in freshwater lakes constitutes a well-studied example of a phenotypic radiation that has produced numerous instances of parallel evolution, but the exact selective agents that drive these changes are not yet fully understood. We present a comparative study across 74 freshwater populations of threespine stickleback in Norway to test whether evolutionary changes in stickleback morphology are consistent with adaptations to physical parameters such as lake depth, lake area, lake perimeter and shoreline complexity, variables thought to reflect different habitats and feeding niches. Only weak indications of adaptation were found. Instead, populations seem to have diversified in phenotypic directions consistent with allometric scaling relationships. This indicates that evolutionary constraints may have played a role in structuring phenotypic variation across freshwater populations of stickleback. We also tested whether the number of lateral plates evolved in response to lake calcium levels, but found no evidence for this hypothesis.

Introduction Phenotypic radiations are useful for investigating the relative roles of selection and evolutionary constraints on the location of species in morphospace (Schluter, 2000). Marine populations of Holarctic threespine stickleback (Gasterosteus aculeatus) have repeatedly colonized freshwater habitats that became available at the end of the last glacial cycle about 15 000 years ago (Bell, 1994, 2001). These freshwater populations provide a well-known example of rapid phenotypic evolution independently giving rise to similar phenotypes in different populations (Bell & Foster, 1994; Hendry et al., 2008). Assuming that all freshwater lineages originated from the same ancestral oceanic meta-population (Bell, 1984; Bell & Foster, 1994), it can be hypothesized that descendant freshwater populations inherited similar genetic architectures and developmental constraints. Correspondence: Kjetil L. Voje, Department of Biology, Centre for Ecological and Evolutionary Synthesis, University of Oslo, P.O. Box 1066 Blindern, Norway. Tel.: (+47) 22 85 44 00; fax: (+47) 22 85 40 01; e-mail: [email protected]

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Shared phenotypic optima across freshwater populations may accordingly have driven trait adaptation to similar phenotypic states in the different lineages. Although variational patterns may have changed during freshwater radiations (Berner et al., 2010), there is also evidence that morphological changes have been influenced by ancestral patterns of variation (Schluter, 1996a; Hansen & Voje, 2011; Leinonen et al., 2011). Here, we present a historically informed comparative study of threespine stickleback sampled in a large number of Norwegian freshwater lakes and marine locations to test the relative influence of local adaptation and variational constraints in explaining phenotypic evolution. Much of the morphological variation in threespine sticklebacks across lakes is hypothesized to be due to variation in foraging opportunity (e.g. Walker, 1997). Lake populations are usually uniform, but a few lakes in North America possess two sympatric ecomorphs (or species): a limnetic form specialized for feeding on plankton and a benthic form specialized for feeding on bottom-dwelling invertebrates in the littoral zone (McPhail, 1984). Benthic fish have larger and deeper bodies, smaller eyes and mouths, and shorter and

ª 2013 THE AUTHORS. J. EVOL. BIOL. 26 (2013) 2396–2414 JOURNAL OF EVOLUTIONARY BIOLOGY ª 2013 EUROPEAN SOCIETY FOR EVOLUTIONARY BIOLOGY

Comparative study of a stickleback radiation

fewer gill rakers than limnetic fish (McPhail, 1984, 1992; Schluter & McPhail, 1992; Hart & Gill, 1994; Schluter, 1996b). It has been suggested that monomorphic populations often resemble the morphology of one of these two ecomorphs: populations in deep, oligotrophic lakes tend to be specialized for feeding on plankton in open water, whereas those in small, shallow lakes tend to be specialized for foraging on invertebrates in the littoral zone (e.g. McPhail, 1984; Lavin & McPhail, 1985). The two sympatric ecomorphs may therefore represent the phenotypic extremes along a limnetic–benthic continuum on which most allopatric populations of sticklebacks are found (Foster et al., 1998). Certain lake characteristics are therefore predicted to correlate with morphological traits relevant to foraging across populations. For example, lake depth affects prey abundance and diversity, shoreline development may play a role in habitat complexity, and lake perimeter relates to habitat size. The number of lateral bony plates is another trait that varies within and among populations. Marine populations consist almost exclusively of completely plated fish with about 30–36 plates on each side, whereas freshwater populations are predominantly low plated and rarely have more than seven plates per side restricted to the anterior part of the fish (Bell & Foster, 1994). Loss of lateral plates, and armour in general, is a rapid and repeatable feature of freshwater adaptation (Bell, 2001; Bell et al., 2004; Lucek et al., 2010). There is also direct experimental evidence that the low-plated morphs, or at least the genes coding for them, have a selective advantage in freshwater lakes (Barrett et al., 2008; Le Rouzic et al., 2011). The hypothesis that variation in plate numbers can be related to changes in predator regimes and predator intensity between habitats has received some support (e.g. Hagen & Gilbertson, 1972; Moodie & Reimchen, 1976; Reimchen, 1983; Kitano et al., 2008). It has also been hypothesized that the abundance of cover and shelter in littoral habitats might select for fewer plates, as acceleration and manoeuvrability may be adaptations to predator regimes where fast-start performance is important (Bergstr€ om, 2002; Walker et al., 2005). A hypothesis that has been less thoroughly investigated is Giles’ (1983) suggestion that the reduction in nonessential bony tissue in freshwater-resident sticklebacks is an adaptation to reduced calcium availability (but see Marchinko & Schluter, 2007). In the present study, we use a comparative approach to test whether eleven metric traits and gillraker number in 74 freshwater populations of stickleback in Norway show indications of being adaptations to lake depth, lake area, lake perimeter and shoreline complexity. We use a model of evolution that allows the traits to evolve towards niche-dependent optima (Hansen, 1997; Butler & King, 2004; Hansen et al., 2008; Labra et al., 2009; see also Hunt et al., 2008),

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and hypotheses of adaptation are evaluated based on whether the estimated optima are influenced by environmental factors such as lake characteristics. How fast and reliably the traits evolve towards the optimal states are also estimated. We also estimate evolutionary allometric trait relationships across the freshwater populations and compare them with the same relationships in 13 marine (ancestral) populations to investigate whether linear morphological traits follow different evolutionary trajectories dependent on habitat. Contrary to recent suggestions that allometry is not an important constraint on stickleback evolution (e.g. McGuigan et al., 2010), we show that most trait variation across populations can be explained by allometric scaling. Marine and lacustrine populations follow the same allometric model, which may indicate that developmental and genetic constraints are important contributors shaping current stickleback phenotypic variation. Only the traits that are the least bound by allometric scaling show (weak) indications of adapting to lake characteristics.

Materials and methods Stickleback samples Threespine sticklebacks (Gasterosteus aculeatus) were sampled from 74 freshwater lakes and 13 brackish/marine sites along the Norwegian coastline between 58 and 71 degrees north latitude between 1988 and 2011 (see Fig. 1). Lake samples were collected using minnow traps (Breder, 1960; chamber 30 cm long, 15 cm in diameter, with openings of 1 cm in diameter) unbaited or baited with cheese. Marine samples were collected with hand-held dip nets, large minnow traps (Breder, 1960; chamber 100 cm long, 40 cm in diameter, with 1 cm diameter openings), as well as fine-mesh seine nets (0.6–1.2 cm mesh size). Captured sticklebacks were stored directly in 96% ethanol. Fish analysed were 30 mm or larger to ensure fully developed armour (Hagen, 1973), and reproductive fish were not used to avoid the effects of sexual dimorphism, which is only found in reproductively mature stickleback (Kitano et al., 2007). Lakes were selected to achieve good geographical spread (latitude, longitude and altitude) and large variation in lake age. We chose about 10 fish from each location for analysis, aiming to get an even sex ratio and avoiding fish with parasite infestation (e.g. Schistocephalus sp.). Morphological measures of metric traits Each fish was photographed on a common background in right lateral view using a tripod-mounted CANON EOS 350D digital camera fitted with a 90-mm lens (Tamron macro) from the same distance. The fish were


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Fig. 1 Geographical distribution of the 74 freshwater lakes (black dots) and 13 marine sites (white dots) from which threespine stickleback were sampled. Areas of increased site density are shown in inset maps.

photographed in random order. We placed 23 landmarks on each photograph (Fig. 2) and added a scale bar using tpsDig version 2.16 from the Thin Plate Spline (TPS) suite of software (Rohlf, 2005). Raw data from tpsDig were imported into MorphoJ 1.02j (Klingenberg, 2011), which we used to extract coordinates for each of the 23 landmarks. Landmark coordinates were used to extract 11 linear measures of morphological traits for each fish using R v.2.10.1 (R developmental Core Team, Vienna, Austria): body length, eye radius, mouth length, distance from snout to beginning of eye, distance from snout to centre of eye, head depth, head length, body depth, length of first and second dorsal spine, and caudal peduncle (see Fig. 2 for details). These traits are ecologically important

and might therefore show adaptation to lake types and habitats. We removed the within-population allometric effects of body size on each of the traits by regressing the log of each trait on lake-mean-centred log body length in an analysis of covariance (ANCOVA) with lake as a factor. We assumed a common within-species allometric coefficient for each trait to minimize the potential biasing effects that the modest sample sizes might have on the age and the size of the fish sampled within each population. We mean centred the log body length of fish from the same lake around zero to make the intercept in the regression model equal to the trait mean within each lake. We then used the intercepts estimated for each population as data for the comparative analyses.



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Fig. 2 The position of the 22 landmarks used to characterize body shape and linear morphological traits in threespine stickleback. Landmarks 1–20 were used to estimate body shape and the eleven linear characters are based on the following pairs of landmarks: 1–11 body length, 1–19 mouth (gape width), 1–4 snout to edge of eye, 1–3 snout to centre of eye, 3–4 eye radius, 5–17 head depth, 1–20 head length, 7–15 body depth, 6–21 length of first spine, 7–22 length of second spine, 10–12 caudal peduncle/tail width.

Morphological measures of meristic traits The number of right lateral plates and right upper and lower gill rakers was counted for all 870 fish in a randomized order to estimate the mean trait value for each population. Shape analysis Multivariate morphological shape analysis of the study populations was assessed using principal component analysis in MorphoJ 1.02j (Klingenberg, 2011) on the landmark coordinates extracted from tpsDig. Principal component analysis assumes independent and identically distributed data points, making it important to account for history when size-correcting traits (e.g. Revell, 2009). The assumptions of principal component analysis are close to being fulfilled by the majority of all linear morphological traits, as they seem independent of evolutionary history (see results). Procrustes superimposition was therefore created for the entire data set (870 fish) in MorphoJ to remove all variation due to size and orientation. One analysis was run on the full data set to check whether freshwater populations group together and orient differently on the shape axes than the populations from the marine environments. A second analysis was run on freshwater fish only to investigate shape variation within the freshwater environment. Lake data ArcGIS (version 10; Environmental Systems Research Institute; Redlands, CA, USA) was used to determine lake area in km2, perimeter (shoreline length) in km, distance to the ocean in metres, lake depth in metres and shoreline complexity (see equation below). The lakes of interest were extracted from a water-surface data set of all of Norway (Statens Kartverk 2009). Area and perimeter were determined using the Calculate Geometry tool within the attribute tables using the

polygon feature and choosing metres as unit and scale. Lake elevation was determined by sampling a 30-m Digital Elevation Model of Norway produced by the United States Geological Survey. Distance to the ocean was determined using the manual measure tool and information from Digital Elevation Models and modernday flowlines (Statens Kartverk 2007). The distance was calculated by summing drawn straight-line segments (unit = metres) from the lake mouth to the ocean following elevation contours (river valleys) and paths of least resistance (i.e. without passing through major elevation obstacles). Methods used to estimate lake depth were similar to those used by Hollister et al. (2011). These methods assume that the geophysical processes that shape a lake basin are the same as the geophysical processes that shape the topography directly surrounding that basin (Hutchinson, 1957). Consequently, we assume that the slope surrounding the lake will continue to the lake bottom. As a result, lake depth should be a function of the distance from shore and the median percentage slope of the surrounding area (Hollister et al., 2011). We created 100-m buffers around each lake using the Analysis toolpack, for which we calculated the median slope from the 30-m Digital Elevation Models. The Euclidian distance from the point in each lake farthest from the shoreline was determined using the Spatial Analyst toolpack. The maximum distance from the shore was multiplied by the median slope to give an estimate of maximum depth for each lake. Shoreline complexity was measured as L D ¼ pffiffiffiffiffiffiffi ; 2 pA where L is the length of the shoreline and A is the lake area (Wetzel, 1975), such that a perfect circle has the minimum value D = 1. A larger shoreline complexity indicates a higher potential for the development of littoral communities and production. The ages of the lakes were estimated using the program Sealevel32 (Møller, 2003), using info on land uplift (isobase) and the current altitude of each lake.


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Giles (1983) suggested that the reduction in nonessential bony tissue in freshwater relative to marine populations of sticklebacks could be an adaptation to reduced available calcium. Two-hundred millilitres of water was collected from 46 of the 74 lakes to measure the calcium content in these lakes. Water samples were held in the field at 15–25 °C and then refrigerated at about 4 °C upon return to the lab (1–4 weeks after sampling), until processing. A total of 100 mL of water was preserved and matrix matched by adding 1 mL HNO3. Standard solutions were produced using a certified 1000 ppm Ca standard to cover the expected concentrations. All samples were analysed on a Varian Vista ICP-OES, using Ar 5.0 (instrument Argon) at a speed of 16–17 L min1. All lake variables can be found in Table S1. Comparative method The different ages of the lakes necessitate a ‘phylogenetic’ comparative approach to deal with the correlations that arise from different times of split from the marine ancestors and the different lengths of exposure to the freshwater niches. Although river paths may change due to isostatic rebound, none of the lakes in our data set are currently connected by rivers. We accordingly made the assumptions that each lake was colonized quickly after the formation of the lake and that gene flow between freshwater populations can be ignored. In one set of analyses, we also assumed that the populations originated from the same ancestral oceanic meta-population (Bell, 1984; Bell & Foster, 1994). This assumption is commonly made in studies of threespine sticklebacks (e.g. Schluter, 1996a; Berner et al., 2010) and has some empirical support (Deagle et al., 2013). We represented this historical scenario with a nonultrametric star phylogeny (i.e. a completely unresolved multifurcating tree in which the distance from

(a)

root to tip may differ across branches) with branch lengths corresponding to lake ages (Fig. 3a). In another set of analyses, we allowed the freshwater populations to originate from an evolving marine lineage by analysing an ultrametric phylogeny in which the 13 marine populations together with the youngest extant freshwater population constitute a monophyletic star phylogeny as illustrated in Fig. 3b. This model was motivated by recent findings of genomic variability and population structure in marine threespine stickleback (DeFaveri & Meril€ a, 2013; DeFaveri et al., 2013). The comparative method we used is designed to study adaptive evolution in the presence of phylogenetic inertia (Hansen, 1997; Butler & King, 2004; Hansen et al., 2008; Labra et al., 2009; Bartoszek et al., 2012) and was fitted with R v2.10.1 (R Development Core Team, 2010) using the SLOUCH 1.2 program and some independent scripts that are to be incorporated into the SLOUCH package (scripts available in supplementary material). The model of evolution is based on an Ornstein–Uhlenbeck process and assumes that a trait (e.g. body depth) has a tendency to evolve towards a ‘primary’ optimum h, defined as the average optimal state populations will reach in the given environment after ancestral constraints have disappeared (Hansen, 1997), at a rate proportional to a parameter a. The model additionally includes a stochastic component with standard deviation r, which can be interpreted as evolutionary changes in the trait due to unmeasured selective forces, genetic drift, etc. We report this as vy=r2/2a, which can be interpreted as the expected residual variance when adaptation and stochastic changes have come to an equilibrium. The primary optimum is modelled as a linear function of one or more predictor variables based on lake characteristics (e.g. lake depth). We assumed that these predictor variables have not changed since the inception of the lake. This makes the model different from the

(b)

Marine ancestor Marine ancestor

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Time

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Fig. 3 (a) shows a nonultrametric star phylogeny (i.e. an unresolved multifurcating tree) with unequal branch lengths. The symbols represent different freshwater populations, and the length of the branches portrays the time span each population has been separated from the ancestral marine population. (b) shows an ultrametric phylogeny where all extant taxa have the same distance from the root and where freshwater populations split of the marine lineage (stippled line) as they colonize available lakes. The important difference between the comparative analyses using the two phylogenies is that the marine population evolves in B, but not in A. ª 2013 THE AUTHORS. J. EVOL. BIOL. 26 (2013) 2396–2414 JOURNAL OF EVOLUTIONARY BIOLOGY ª 2013 EUROPEAN SOCIETY FOR EVOLUTIONARY BIOLOGY


Trait (e.g., log mouth length)

‘random-effect’ model of Hansen et al. (2008), which assumes that the predictor variables evolve stochastically; this necessitated the modifications of the SLOUCH code mentioned above. The method uses generalized least squares for the estimation of the regression parameters and maximum likelihood for estimation of a and r in an iterative procedure (as detailed in Hansen et al., 2008). The model estimates the regression of the primary optimum on various lake characteristics (Fig. 4). The coefficients of this ‘optimal regression’ measure the influence of the lake characteristics on the primary optimum, and the amount of variation explained by the regression indicates the relative importance of adaptation to the variable(s) in question. Note that this optimal regression may be steeper than an observed ‘evolutionary’ regression. This is because a lag in adaptation (i.e. a finite value of a) introduces a deviation from the predicted optimum resulting in a shallower slope (Fig. 4). We quantify the lag in adaptation by a

Optimum (linear function of environmental variable)

Fig. 4 The black line represents the ‘optimal’ regression line, which describes the relationship between a trait (e.g. log mouth length), and an optimum, which is a linear function of an environmental variable, for example log lake perimeter), when the evolution of the trait towards the optimum has had sufficient time to lose all ancestral constraints. The slope of the optimal regression tells us how a change in the optimum would lead to a change in the trait. Given fast adaptation, the ‘optimal’ slope and the amount of variance explained by this model are accordingly informative of how important a specific optimum is for the evolution of the trait. The dashed line represents the observed or evolutionary regression. The slope of the evolutionary regression equals the slope of the optimal regression slope times a phylogenetic correction factor (p(at) = 1 – (1– eat)/at, see Hansen et al., 2008 for more details). The correction factor is a function of the estimated rate of adaptation (a) in the comparative model. The evolutionary regression is identical to the optimal regression if adaptation is immediate and the trait tracks the optimum without delay. The evolutionary regression will be increasingly shallower as a function of a slower rate of adaptation towards the optimum. The difference in slopes between the optimal and the evolutionary regression exemplifies a situation where there are some constraints on the adaptation towards the optimum.

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half-life parameter, t1/2 = ln(2)/a, which is the expected time it takes for the trait mean to evolve halfway from the ancestral state towards the predicted optimal freshwater state. A half-life value of zero signifies immediate adaptation to the freshwater environment, and then the optimal and evolutionary regressions are identical. A half-life value above zero indicates that the marine ancestry has an influence on the current trait state in the extant freshwater populations. The general statistical effects of lake age on the traits can be quantified by estimating the phylogenetic halflife in an Ornstein–Uhlenbeck model including only one intercept for the primary optimum when using the nonultrametric phylogeny (Fig. 3a). Such a statistical influence from evolutionary history on the traits can have two different sources. Younger freshwater populations could be more similar due to the fact that it takes time for freshwater populations to adapt to their new environments (a lag in adaptation or ‘phylogenetic inertia’), but they could also be more similar if the lake characteristics were not randomly distributed with respect to history, that is, if the populations are adapted to lake characteristics that correlate with lake age. The difference between the half-life estimates with and without a predictor variable tells us how much of the lake history effect is due to inertia (resistance to adaptation) as opposed to an age effect in the predictor variables (see Labra et al., 2009 for more discussion on this point). Akaike’s information criterion (AIC) was used to compare different models as suggested by Butler & King (2004), see Burnham & Anderson (1998) or Lajeunesse (2009) for justification. We used this approach to estimate lake age effects in the eleven metric traits and in the two meristic traits (gill-raker number and lateral-plate number). We then tested whether these traits individually showed signs of being adaptations to lake depth, lake area, lake perimeter, lake shoreline complexity and distance from ocean. As proportional differences in lake depth, area and perimeter may be as relevant as their absolute differences, we also tested the logarithmic values of these three variables as predictors. If more than one of the models that included a lake characteristic did better according to AIC than a nonadaptive model (i.e. a model with only a single intercept with the nonultrametric phylogeny, and a marine and a freshwater intercept with the ultrametric phylogeny), we ran multiple regression models to test whether a combination of lake variables could outcompete the models with single explanatory variables. We also tested whether lateralplate number evolved towards an optimum defined by calcium concentrations in the lakes. Finally, we investigated whether body size had an effect on lateral-plate and gill-raker numbers across lakes by fitting two models for the relationship. One ‘adaptation model’ was based on evolution towards an optimal relationship as


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for the quantitative traits above, and the other ‘constraint’ model was based on assuming an immediate response of the traits to changes in body size as expected from, for example, an allometric relation (see Hansen & Bartoszek, 2012 for justification). We still allowed an age or phylogenetic effect in the residual variance of this model. Evolutionary allometries The evolutionary allometry is the log-log regression of mean trait size on mean body length across populations. We first tested whether the evolutionary allometric slope of each trait differed between marine and freshwater populations by conducting an ANCOVA using marine or freshwater habitat as a factor and including an interaction between habitat and body length, but we did not detect any significant difference between the two habitats in any of the 10 linear traits. We accordingly estimated a common evolutionary allometry for each trait combining the 74 freshwater and 13 marine populations using the ultrametric phylogeny (Fig. 3b). As above, we fitted two models: one in which the evolutionary allometry arises through adaptation towards an optimal trait–body relationship and one in which it arises due to an underlying constraint (e.g. a static allometry) so that changes in body size lead to immediate correlated responses in the trait. Both models allow for and estimate phylogenetic correlations in the residuals. Accounting for measurement error The trait and variable means used in the comparative analyses are subject to sampling errors. We controlled for known estimation error variances in response and predictor variables in the comparative analyses as described by Hansen & Bartoszek (2012), except that regression slopes were not corrected for bias. As all trait means are based on rather small sample sizes (= 10), we assumed that the sampling distributions of the different populations were similar and averaged the sample variance for the different traits across populations. The measurement variances included for each population in the comparative method were then estimated by dividing the average sample variance with the sample size of each population.

Results Morphological variation Means and standard deviations of the morphological traits within each of the 74 freshwater populations are reported in Table B in the supplementary material. The among-population coefficients of variation (CV) for the linear morphological traits were similar across

freshwater populations and ranged from 8.9% for eye radius to 16.6% for the length of the first dorsal spine. The CV was 63.2% for the lateral plates, mainly due to seven populations having very high plate numbers. Gill-raker number shows the least interpopulation variation with a CV of 5.5%. All metric traits were larger in the marine than in the freshwater habitat (supplementary Table C). The among-population coefficients of variation for the metric traits in marine populations ranged from 9.1% (mouth length) to 17.7% (first spine) and were comparable to those of the freshwater populations except for gill-raker number (CV = 3.5%) and lateral-plate number (CV = 11.0%), having less variation than in the freshwater populations. The first three principal components explained 22.3%, 14.6% and 13.2% of the morphological variance across all marine and freshwater fish put together. The shape change described by the first principal component (PC1) appears to describe the way the individual fish curves post-mortem (See Fig. 5a) and was not analysed further. The other two components represent population-based variation in shape. Fish from marine environments have on average deeper bodies (PC2) and smaller heads (PC3) than fish from freshwater, but the two groups showed substantial overlap (Fig. 6). The same three axes of variation appeared in the analysis that was run on freshwater fish only and explained similar amounts of variation (Table 1). Correlations between lake age and traits across freshwater populations We used the nonultrametric phylogeny (Fig. 3a) to investigate the effects of lake age on the traits (Table 2A). The majority of the metric traits were only weakly affected by lake age. The best estimates indicate that it took on average