http://eprints.whiterose.ac.uk/103695/
Version: Accepted Version
Article:
Lucek, K., Sivasundar, A., Kristjansson, B.K. et al. (2 more authors) (2014) Quick
divergence but slow convergence during ecotype formation in lake and stream stickleback
pairs of variable age. Journal of Evolutionary Biology, 27 (9). pp. 1878-1892. ISSN
1010-061X
https://doi.org/10.1111/jeb.12439
This is the peer reviewed version of the following article:Lucek, K., Sivasundar, A.,
Kristjánsson, B. K., Skúlason, S. and Seehausen, O. (2014), Quick divergence but slow
convergence during ecotype formation in lake and stream stickleback pairs of variable
age. Journal of Evolutionary Biology, 27: 1878–1892., which has been published in final
form at http://dx.doi.org/10.1111/jeb.12439. This article may be used for non-commercial
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Quick divergence but slow convergence during ecotype formation in lake
1
and stream stickleback pairs of variable age
2
3
Kay Lucek1,2, Arjun Sivasundar1,2, Bjarni K. Kristjánsson3, Skúli Skúlason3, Ole
4
Seehausen1,2
5
6
1 Aquatic Ecology and Evolution, Institute of Ecology & Evolution, University of
7
Bern, Baltzerstrasse 6, CH-3012 Bern, Switzerland 8
2 Department of Fish Ecology and Evolution, EAWAG Swiss Federal Institute of
9
Aquatic Science and Technology, Center for Ecology, Evolution and 10
Biogeochemistry, CH-6047 Kastanienbaum, Switzerland 11
3 Department of Aquaculture and Fish Biology, Hólar University College, 550
12
Saudarkrokur, Iceland 13
14
15
Keywords 16
Abstract
18
When genetic constraints restrict phenotypic evolution, diversification can be 19
predicted to evolve along so-called lines of least resistance. To address the 20
importance of such constraints and their resolution, studies of parallel 21
phenotypic divergence that differ in their age are valuable. Here we investigate 22
the parapatric evolution of six lake and stream threespine stickleback systems 23
from Iceland and Switzerland, ranging in age from a few decades to several 24
millennia. Using phenotypic data, we test for parallelism in ecotypic divergence 25
between parapatric lake and stream populations and compare the observed 26
patterns to an ancestral-like marine population. We find strong and consistent 27
phenotypic divergence, both among lake and stream populations and between 28
our freshwater populations and the marine population. Interestingly, ecotypic 29
divergence in low dimensional phenotype space (i.e. single traits) is rapid and 30
seems to be often completed within 100 years. Yet, the dimensionality of 31
ecotypic divergence was highest in our oldest systems and only there parallel 32
evolution of unrelated ecotypes was strong enough to overwrite phylogenetic 33
contingency. Moreover, the dimensionality of divergence in different systems 34
varies between trait complexes, suggesting different constraints and 35
Introduction
37
38
If natural selection is the principal force governing evolutionary change, 39
divergence among populations can be considered as the tracking of alternative 40
adaptive peaks on the underlying fitness landscape (Wright 1932; Lande & 41
Arnold 1983; Steppan et al. 2002; Arnold et al. 2008). The degree of divergence is 42
then expected to depend on the time that has been available for selection to act, 43
the strength of selection, the topology of the fitness landscape and the amount of 44
adaptive standing genetic variation. All of these factors may affect both the rate 45
and the direction of evolution. Additionally, the strength of selection and/or the 46
fitness landscape itself may fluctuate through time due to environmental 47
variation (Jones et al. 2004; Arnold et al. 2008). Genetic drift and selection can 48
reduce standing genetic variation, which may lead to different evolutionary 49
outcomes across replicated cases of population divergence, even when selection 50
is acting in a parallel manner across replicated systems (Barrett & Schluter 51
2008).Consequently, strong parallel evolution is only expected if the selective 52
regime, the relative level of standing genetic variation and the segregating alleles 53
themselves are similar (Langerhans & DeWitt 2004; Kaeuffer et al. 2012) and if 54
selection has enough time to overcome potential historical contingencies (Young 55
et al. 2009). 56
57
Evolution towards adaptive peaks can be influenced by genetic “lines of least 58
resistance” or gmax, which can be quantified as the leading eigenvector of the 59
genetic variance-covariance matrix G (Lande & Arnold 1983; Schluter 1996; 60
largest proportion of genetic variance and is shaped by selection and drift, which 62
then influence genetic constraints within a population (Lande & Arnold 1983; 63
Steppan et al. 2002; Marroig & Cheverud 2005). Different G matrices can be 64
compared by calculating the angle θ between different gmax (Lande & Arnold 65
1983; Schluter 1996; Steppan et al. 2002). Whereas genetic constraints may 66
initially bias evolution towards gmax (Schluter 1996), selection may alter the 67
direction of gmax towards an existing or a new optimum on the adaptive 68
landscape (Lande & Arnold 1983; Jones et al. 2004; Arnold et al. 2008), e.g. 69
during the colonization of new environments (Bacigalupe 2009; Eroukhmanoff & 70
Svensson 2011). Similarly, genetic drift, bottlenecks or mutations may alter the G
71
matrix and hence gmax (Chapuis et al. 2008). 72
73
In the absence of quantitative genetic data, the G matrix might be approximated 74
by the P matrix, which is based on phenotypic data from wild populations 75
(Cheverud 1988), especially when phenotypic traits are highly heritable (Lande 76
1979), as has been suggested for several taxa (Cheverud 1988; Leinonen et al. 77
2011; Kolbe et al. 2011). P is defined as the combination of the genetic and 78
environmental covariance matrices, i.e. G + E (Lande 1979; Arnold & Phillips 79
1999), where both effects could also interact (G x E; Falconer 1989). 80
Consequently, P matrices include phenotypically plastic effects, that are 81
differentially expressed in distinct environments (Pigliucci et al. 1999). The 82
leading eigenvector of a P matrix (pmax) therefore serves as an overall measure of 83
phenotypic variation observed in the wild, combining both genetic and 84
environmental effects. The changes of P and pmax towards novel adaptive peaks 85
Whitlock 2012) or adaptation from standing genetic variation (Lande & Shannon 87
1996; Barrett & Schluter 2008). In either case, pmax of different replicated 88
systems that vary in age should align, i.e. show a small or zero angle θ between 89
them (Figure S1). Alternatively, θ between mainly genetically determined pmax
90
may evolve over time through selection and drift (Lande & Arnold 1983; Jones et
91
al. 2004; Arnold et al. 2008). θ is thus expected to subsequently increase over 92
time between an ancestral pmax and the pmax of a derived population that evolves 93
towards a new adaptive peak (Lande & Arnold 1983; Jones et al. 2004; Arnold et
94
al. 2008). 95
96
In threespine stickleback (Gasterosteus aculeatus species complex), the ancestral 97
marine population repeatedly colonized freshwater throughout its distribution 98
mainly after the last glacial maximum and subsequently adapted to different 99
habitats such as streams and lakes. The result was a complex of phenotypically 100
and ecologically divergent populations and, in some cases, even sympatric or 101
parapatric species (Bell & Foster 1994; McKinnon & Rundle 2002). Alongside the 102
marine-freshwater transition, adaptive changes in both the G and the P matrix 103
have been recorded (Berner et al. 2010b; Leinonen et al. 2011), where gmax and 104
pmax are correlated (Leinonen et al. 2011). The parallel evolution of distinct 105
parapatric lake-stream pairs within freshwater has made this species complex an 106
excellent system to investigate the process of ecological speciation. However, on 107
an evolutionary timescale, most studies use relatively old systems and are often 108
limited one restricted geographical area (e.g. Reimchen et al. 1985; Hendry & 109
Taylor 2004; Berner et al. 2008; Kaeuffer et al. 2012; Ravinet et al. 2013, but see 110
contrast, some lake-stream systems became only recently available to 112
stickleback, e.g. due to contemporary translocations (Lucek et al. 2010; Berner et
113
al. 2010a; Moser et al. 2012; Lucek et al. 2012a) or the creation of artificial lakes 114
(Kristjánsson et al. 2002a; Hendry et al. 2013). Hence, stickleback provide a rare 115
opportunity to study the evolution of parapatric divergence along the lake-116
stream habitat axis and the corresponding changes in the P matrix and pmax over 117
a wide timescale, ranging from decades to millennia. 118
119
Here, we study replicated parapatric lake-stream stickleback from Switzerland 120
and Iceland that are between 50 and 10,000 years old in relation to their 121
putative ancestral marine population. Using this temporal gradient, we test if 122
phenotypic divergence emerges rapidly after the colonization of novel 123
environments and whether lines of least resistance (pmax) diverge over time as 124
suggested by several authors (Lande & Arnold 1983; Jones et al. 2004; Arnold et
125
al. 2008). Additionally, the large geographic scale coupled with the very different 126
colonization histories of Iceland and Switzerland (Ólafsdóttir et al. 2007a; Lucek 127
et al. 2010) allows us to test for parallel evolution. Specifically, we can test if 128
parapatric phenotypic divergence resulted in similar pmax and if the degree and 129
the direction of habitat dependent phenotypic divergence are similar among our 130
studied systems. We predict that habitat dependent phenotypic changes in 131
similar environments should result in similar pmax but that the degree of 132
phenotypic divergence may differ due to different historical contingencies, the 133
time for selection to act or differences in the selective regimes among our 134
studied systems. 135
Material and Methods
137
Sample collection
138
We studied three Swiss lake-stream systems in the invasive range of stickleback 139
that differ in their ages of stickleback colonization (Bern (Wohlen): ~50 yrs, 140
Constance: 140 yrs, Geneva: 140 yrs) and represent either independent 141
introductions from different freshwater lineages (Constance, Geneva) or a case of 142
recent admixture of these lineages (Bern; see Lucek et al. 2010 for details). In 143
addition, we studied three Icelandic lake-stream systems that differ in their 144
geological age (Mývatn: 2500 yrs, Thingvallavatn: 8000-10’000 yrs; 145
Saemundsson 1992; Einarsson et al. 2004) or are man-made (Hraunsfjördur: 50 146
yrs; Kristjánsson et al. 2002b) and have been separately colonized by stickleback 147
from ancestral marine populations. We also sampled two Icelandic marine 148
populations (Table S1), presumably resembling the phenotypic marine ancestral 149
state to most European freshwater stickleback (Mäkinen et al. 2006; Jones et al. 150
2012a). In Icelandic lakes, stickleback have been described to diverge in relation 151
to benthic substrate (Kristjánsson et al. 2002b). We thus sampled the largest 152
potential habitats in each lake (see Figure 1 and Table S1 for sampling locations). 153
154
Icelandic samples were obtained between August and September 2010 using 155
minnow traps and by hand netting. Samples from Switzerland were similarly 156
collected in 2007 and 2008 (Lucek et al. 2010). In all cases, stream stickleback 157
were obtained from inflowing streams (Table S1). All fish were sacrificed with an 158
overdose of clove oil and stored in 70% ethanol. A fin clip was additionally taken 159
for genetic analysis and preserved in absolute ethanol. Sample size per site 160
sites (Figure 1, Table S1). Altitudinal difference and pairwise waterway distance 162
between each stream site and the inflow of the stream into the lake were 163
measured using GOOGLEEARTH (Google, USA). 164
165
Genetic analysis
166
We extracted DNA for individuals from the Marine 1 site and all freshwater sites, 167
except for Mývatn, where only one of the three stream sites (MS1) was available 168
for genetic analysis (NTotal = 727, Table S1). DNA was extracted using a 10%
169
Chelex solution, following the manufacturer’s protocol (Biorad, California, USA). 170
In some cases, additional individuals were included for which no phenotypic data 171
was collected (Table S1). We amplified ten microsatellite markers in one 172
multiplex set following the protocols of Raeymaekers et al. (2007). Three of 173
these markers (Stn26, Stn96 and Stn130) have been shown to be associated with 174
known QTLs for spine lengths (Peichel et al. 2001). Detailed information on 175
marker identity, the multiplexing setup, and the PCR protocol are provided as 176
supplementary methods. We visualized alleles on an ABI 3130XL and scored 177
them with GENEMAPPER 4.0 (Applied Biosystems, Switzerland). We generated a
178
genetic tree-like relationship among sampling sites based on their Cavalli-Sforza 179
distances of allelic frequencies using a neighbour-joining algorithm implemented 180
in PHYLIP 3.69 (Felsenstein 2012). Significance was estimated using 1000
181
bootstrapped resampling replicates. To test if our markers conformed to neutral 182
expectations, we conducted an FST based outlier test using the software LOSITAN
183
1.0(Antao et al. 2008) separately for each lake-stream system. Using GENODIVE
184
2.0 (Meirmans & Van Tienderen 2004), we calculated pairwise FST between 185
within a lake. We estimated significances using 1000 bootstrapped replicates as 187
implemented in GENODIVE. Finally, we tested for a correlation of the obtained
188
pairwise parapatric FST values with either the altitudinal difference or the 189
geographical distance between a parapatric stream site and the lake using linear 190
models. Models were compared using the Akaike information criterion corrected 191
for small sample sizes (AICc). 192
193
Morphological data collection & analysis
194
We measured sixteen linear morphological traits (see Figure S2 for details), 195
many of which are known to be associated with ecological diversification in 196
stickleback (see Kristjánsson et al. 2002a; Mori & Takamura 2004; Berner et al. 197
2008; Leinonen et al. 2011 and references therein), on the left side of each fish to 198
the nearest 0.01 mm using a digital caliper. These traits were related to either 199
anti-predator defense (FSL - length of the first dorsal spine; DSL - length of the 200
second dorsal spine; PSL - length of the pelvic spine; PGL - length of the pelvic 201
girdle), feeding (HL - head length; UJL - upper jaw length; SnL - snout length; 202
SnW - snout width; ED - eye diameter), or body shape and swimming 203
performance (SL - standard length; PGW - width of the pelvic girdle; BD1 -body 204
depth measured after the first dorsal spine; BD2 - body depth measured after the 205
second dorsal spine; CPL - caudal peduncle length; BLA - basal length of the anal 206
fin; BLD - basal length of the dorsal fin; TLP - total length of the pelvic fin). We 207
measured two additional feeding related traits: the length of the lower gill arch 208
(AL) and the length of the second gill raker (GRL2), as counted from the joint of 209
the dorsal arch bone on the first lower gill arch (Berner et al. 2008). Both 210
dissection microscope. Because all traits were significantly correlated with SL 212
(results not shown), we size-corrected the data by using the residuals from a 213
regression of each trait against SL. This regression was either performed pooling 214
all individuals for the overall comparison of populations or separately (i.e. for 215
each lake-stream system and for the marine population) for pairwise 216
comparisons. By pooling all systems and populations, allometric information in 217
some populations may be retained if the allometric trajectories differ among 218
them. This allows, however, to estimate system and population specific 219
components of phenotypic variation, which can be explained by different 220
historical contingencies or differences in the selective regimes. 221
222
To estimate the relative contributions of country (Iceland or Switzerland), lake-223
stream system (Bern, Constance, Geneva, Hraunsfjördur, Mývatn, 224
Thingvallavatn), habitat (lake or stream) and the interaction of system x habitat
225
on diversification within freshwater, we calculated the percentage of non-error 226
variance based on the respective sums of squares using a sequential ANOVA 227
model (Langerhans & DeWitt 2004; Eroukhmanoff et al. 2009; Lucek et al. 2013). 228
Here, country should reflect variation due to different historical contingencies, 229
which include differences in past selection regimes, or differences between the 230
current selective regimes between Switzerland and Iceland. Similarly, system
231
accounts for the variation among isolated lake-stream systems. The habitat term 232
reflects the component of parapatric phenotypic divergence that is replicated 233
among systems, i.e. parallel. Finally, the system x habitat interaction accounts for 234
interactions between system differences (colonization history and 235
systems (Langerhans & DeWitt 2004; Eroukhmanoff et al. 2009). To further 237
compare the overall phenotypic divergence among all sampled sites, we 238
constructed a tree like relationship using pairwise Mahalanobis distances based 239
on the overall size-corrected phenotypic measurements. 240
241
Because local adaptation can lead to phenotypic differentiation between 242
populations of the same ecotype (i.e. within one habitat type; Hendry & Taylor 243
2004; Kaeuffer et al. 2012; Ravinet et al. 2013), all individuals from the same 244
habitat were pooled if more than one site was sampled in a given lake or in a 245
given stream system to estimate the overall degree of habitat dependent 246
phenotypic divergence. We estimated the parapatric phenotypic divergence 247
using PST, an analog to QST (Spitze 1993) based on phenotypic measurements 248
from wild individuals, which serves as a unit-less proportional measure of 249
pairwise phenotypic divergence and is analogous to our measure of pairwise 250
genetic divergence (FST). Following Leinonen et al. (2006), we estimated PST as 251
PST = δ2
GB/(δ2GB + 2*( h2*δ2GW)), where δ2GB and δ2GW are the between population
252
and within population variance components for a specific trait and h2 is
253
heritability. For stickleback, only few heritability estimates are available, which 254
differ among the studied populations (e.g. Baumgartner 1995; Leinonen et al. 255
2011). We thus assumed a full trait heritability (h2=1), which provides a
256
conservative estimate for PST (Leinonen et al. 2006). For each PST, the 95% 257
confidence interval was established using a resampling approach of 1000 258
replicates. PSTs were either based on the residuals of the leading principal 259
component (PC) axis, combining all traits or a combination of traits, related to 260
performance) as well as separately for each trait. For cases where the 95% 262
confidence interval exceeded zero, the directionality of a PST was further 263
assessed by comparing the mean trait values between the different ecotypes. 264
265
We calculated the leading eigenvector (pmax) of the phenotypic
variance-266
covariance matrix P based on a PC analysis for each habitat and freshwater 267
system using traits that were separately size corrected for each system and the 268
marine population. For the marine populations, we pooled both sites to obtain a 269
better estimate of the putative ancestral state of stickleback. In addition, we 270
calculated the overall pmax for each freshwater system, where we pooled fish
271
from lake and stream sites. By pooling individuals from distinct populations and 272
habitats, the calculated P matrix may differ from the P matrices of each habitat 273
population as traits may covary between populations from different habitats 274
even if they do not covary within either of the populations. pmax of such overall P
275
matrices may however be compared among each other to test if parapatric 276
phenotypic divergence results in similar and hence predictable patterns. We 277
compared pmax of two P matrices by calculating the angle θ between them
278
following Schluter (1996), where θ is the inversed cosine of the dot product of 279
two leading eigenvectors that is divided by the summed length of both 280
eigenvectors. We estimated θ between parapatric lake and stream ecotypes for 281
all ecotype pairs, between the freshwater ecotypes and our marine populations, 282
as well as between our lake-stream systems. In the latter case, we pooled all 283
populations (lake and stream) from each lake-stream system. The significance of 284
θ between pmax of all comparisons was estimated using a bootstrap procedure
285
then correlated with the time since stickleback colonized each system using 287
linear models. All statistical analyses were performed in R 2.15.1 (R Core Team, 288
2012). 289
290
Results
291
Genetic divergence
292
The genetic tree indicates differentiation among our studied freshwater systems, 293
where Iceland and Switzerland form distinct clades (Figure 2a). In the Icelandic 294
clade, the Marine 1 population falls next to the Hraunsfjördur branch, which is 295
consistent with the very recent origin of this system (Kristjánsson et al. 2002b; 296
Ólafsdóttir et al. 2007b), whereas both Mývatn and Thingvallavatn form distinct 297
branches. Genetic substructure among the different sampling sites was 298
furthermore indicated in all Icelandic systems. In the Swiss clade, the Bern 299
system falls next to the Constance system, which are both distinct from the 300
Geneva system, reflecting their introduction and admixture history (Lucek et al. 301
2010). 302
303
The outlier tests performed separately for each lake-stream system suggested 304
that none of the markers deviated from neutral expectations (results not shown). 305
Therefore all markers were retained for the population genetic analyses. Habitat 306
dependent parapatric genetic differentiation was highest in the Lake Geneva 307
system in Switzerland (FST = 0.053, p < 0.001), which also showed the greatest 308
differences in altitude (∆Altitude: 108 m) and distance to the lake (61 km). All
309
parapatric ecotypes except Bern (FST = 0.000, n.s.) showed genetic differentiation 310
FST = 0.028, p < 0.001; Thingvallavatn: FST = 0.018, p = 0.009). Pairwise FST
312
between parapatric lake and stream populations was significantly correlated 313
with both altitudinal differences between sites (R2 = 0.823, F1,4= 18.6, p = 0.013)
314
and distance to the lake (R2 = 0.784, F
1,4= 14.5, p = 0.019). These explanatory
315
factors were significantly correlated with each other (R2 = 0.922, F
1,4= 47.2, p =
316
0.002) and fitted the linear model equally well (∆AICc = 1.22). 317
318
Historical contingency and divergence in freshwater
319
The trait based ANOVA models all explained a significant amount of phenotypic 320
variation (all p < 0.001, results not shown; Table 1). The highest proportion of 321
non-error variation was explained by historical contingency or differences in the 322
current selective regimes between Iceland and Switzerland (country: 37.4% ± 323
23.0%; system: 35.1% ± 18.4; Table 1), where Swiss and Icelandic stickleback 324
differed most strongly in defense related traits and to a lesser extent in feeding 325
related traits. Variation explained by system was highest for body shape related 326
traits. System specific components of parapatric lake-stream divergence 327
occurred especially for feeding and to a lesser extent for body shape related 328
traits as indicated by the system x habitat interaction. Habitat alone explained 329
only a small fraction of the variance (4.4% ± 5.4%), where the traits TLP and BLA 330
had the largest amount of variance explained. 331
332
The occurrence of individual trait based parapatric phenotypic divergence (PST) 333
and the overall dimensionality of parapatric divergence, measured as the 334
number of traits with significant parapatric PST, differed among systems and 335
stream differed among Swiss and Icelandic ecotype pairs, and to a lesser extent 337
also between ecotype pairs in different lake-stream systems within either 338
country (Figure 3). In the two oldest Icelandic lakes, PST exceededzero for 14 339
(Thingvallavatn) and 16 (Mývatn) out of 18 traits. However, even in the 50 year 340
old Hraunsfjördur system, this was true for 6 traits. In Switzerland, significant 341
trait specific PSTs were observed only in the slightly older Constance and Geneva 342
systems, especially for defense traits (4 and 7 traits respectively), whereas PST
343
did not exceed zero for any of the traits in the Bern system which stickleback 344
colonized 50 years ago. The PC based PST combining either all traits or only 345
defense related traits, exceeded zero in all but the two youngest systems (Figure 346
3). In contrast, PST exceeded zero only in Mývatn for feeding related morphology 347
and in the Icelandic systems for body shape and swimming performance related 348
traits. The magnitude of PST among parapatric ecotypes was not statistically 349
associated with the altitudinal difference, with the waterway distance between 350
sites, or with the age of a system for any trait combinations (all p > 0.1, results 351
not shown). 352
353
The angle θ between pmax from parapatric lake and stream populations based on
354
all phenotypic traits differed from zero in all cases except Thingvallavatn (Figure 355
4, Table 2), whereas it was greatest in the two other Icelandic systems (Table 2), 356
whose pmax were significantly differentiated from all other freshwater systems in
357
the pairwise comparisons (Table 3). In the Swiss systems, the parapatric θs were 358
significantly different from zero (Table 2), whereas pmax did not differ among the
359
systems (Table 3). When traits were analyzed by functional categories, the angle 360
θ between parapatric lake-stream pmax differed across traits and systems (Figure
4, Table 2). θ between parapatric ecotypes differed especially for feeding related 362
traits, albeit to a small degree (average θ: 9.3° ± 2.3° SD), whereas parapatric pmax
363
differed less commonly for defense, body depth and swimming performance 364
related traits (Table 2). pmax were furthermore comparable among lake-stream
365
systems for feeding and defense related traits as suggested by the non-significant 366
angle θ between them (Table 3). None of the angles between parapatric ecotypes 367
were statistically correlated with the time since stickleback colonization, the 368
altitudinal difference or the geographical distance between the lake and the 369
stream populations (all p > 0.1, results not shown). 370
371
Parallel adaptation trumps historical contingency late but not early in ecotype
372
formation
373
374
Mahalanobis distances showed overall consistent morphological differentiation 375
between Swiss and Icelandic freshwater stickleback populations (Figure 2b). 376
Despite the aforementioned evidence for consistent parapatric divergence, all 377
populations of young lake-stream pairs, i.e. Hraunsfjördur in Iceland and all the 378
Swiss systems, clustered by historical lineage rather than by ecotype. In contrast, 379
the populations from the two oldest lake systems, i.e. Mývatn and Thingvallavatn, 380
clustered strongly by ecotype despite being genetically more strongly 381
differentiated than the lineages with young lake-stream pairs (Figure 2a). 382
383
Phenotypic divergence during the marine-freshwater transition
384
The angle θ between pmaxof the different freshwater lake populations differed
385
Figure 5, Table 2), which was not true for stream populations (average θ: 29.7° ± 387
21.0° SD). Using only feeding related traits, the freshwater pmax differs commonly
388
from the marine one with relatively low angles θ (lake vs. marine: average θ = 389
9.2° ± 3.3° SD; stream vs. marine: average θ = 7.8° ± 2.0° SD)). In contrast, the 390
freshwater pmax based on defense or body shape and swimming performance
391
related traits differed less commonly from the marine pmax (Figure 5, Table 2). In
392
all but one case (marine vs. lake populations using all traits combined: F1,4 = 15.9, 393
p = 0.016), the observed angle θ between a freshwater derived pmax and the
394
marine pmax were not statistically correlated with the relative age of each
395
freshwater system (all p > 0.1, results not shown). 396
397
Discussion
398
399
The extent of parallel evolution of phenotypically similar ecotypes depends on 400
the genetic constraints, the selective environment and the time for evolution to 401
act (Schluter & Nagel 1995; Langerhans & DeWitt 2004; Nosil et al. 2009; 402
Kaeuffer et al. 2012; Nosil 2012). Nonparallel phenotypic features may thus 403
occur between independently evolved yet ecologically similar ecotypes. The 404
extent of convergent evolution and hence the degree to which two independent 405
populations become more similar may further depend on the dimensionality 406
with which ecotypic divergence is being measured. Rapid evolution may be 407
inferred to be parallel when only few traits are being measured (e.g. Schluter et
408
al. 2004), whereas parallel divergence in the multivariate phenotype, leading to 409
overall phenotypic convergence of parallel evolved ecotypes, may need much 410
412
Comparing the phenotypic variance-covariance (P) matrices of different 413
stickleback freshwater ecotypes and their marine ancestors, we find that 414
phenotypic divergence can result in parallel lines of least resistance (pmax) both 415
for the marine-freshwater transition (Figure 5, Table 2) and the subsequent 416
ecotype formation within freshwater (Figure 4, Table 3). The extent and 417
parallelism of parapatric ecotype formation within freshwaters seems to be 418
driven by historical contingency, potential differences in the divergent selective 419
regimes between lake and streams and the time available for evolution, where 420
much of the phenotypic variation is explained by differences between Icelandic 421
and Swiss sticklebacks (Figure 2, Table 1). Parallel ecotypic divergence may 422
trump historical contingency only in the oldest lakes, where the divergent 423
selective regimes may moreover be strongest, an increased dimensionality of 424
ecotypic differentiation and a clustering of ecotypes despite being genetically 425
very distinct (Figure 2 & 3). 426
427
The evolution of freshwater stickleback
428
The evolutionary transition between the marine and freshwater environment 429
has been repeatedly studied in stickleback (e.g. Kristjánsson 2005; Leinonen et
430
al. 2006; Wund et al. 2008; Berner et al. 2010b; Leinonen et al. 2011; Jones et al. 431
2012a; Voje et al. 2013). Colonizing freshwater habitats requires adaptation to 432
new selective regimes, which may differ between distinct freshwater habitats 433
(Gross 1978; Gross & Anderson 1984; Reimchen 1994; Berner et al. 2009; 2010b, 434
Lucek et al. 2014). The degree of phenotypic divergence from an ancestral-like 435
traits due to differences in the selective regime and the colonization history. In 437
concordance, we find that the degree of phenotypic and genetic differentiation 438
differs among systems (Figure 2). In the genetic tree, the two oldest lakes Mývatn 439
and Thingvallavatn form distinct genetic clusters with the longest branch 440
lengths, whereas the Swiss populations, albeit being genetically distinct from 441
each other, form a separate branch. Conversely, ecotype specific clusters occur 442
for the old lakes in the phenotypic tree, whereas all populations of young ecotype 443
pairs cluster in concordance with their genetic lineage. Lineage dependent 444
phenotypic constraints may have consequently been retained in Switzerland as 445
the Constance and Geneva systems were colonized about 140 years ago by 446
genetically distinct freshwater lineages and the Bern system lies in a hybrid zone 447
between different lineages (Lucek et al. 2010). In contrast, Icelandic freshwater 448
populations likely derive from a common marine population, where in some 449
cases gene flow from the ancestral marine population may still be possible 450
(Ólafsdóttir et al. 2007c, Figure 2a). 451
452
Historical contingency or potential differences in current selective regimes for 453
different trait categories are further indicated by the changes in the P matrices 454
among the different marine-freshwater comparisons. Here, patterns for the pmax
455
of freshwater stickleback vary generally among all comparisons (Table 2, Figure 456
5). Whereas pmax of freshwater populations differ commonly from the marine 457
pmax when all phenotypic traits were combined, they are similar to the marine 458
one in all comparisons for anti-predator related traits. Predator communities are 459
thought to differ though, where marine and freshwater lake populations 460
birds and piscivorous fish (Gross 1978; Reimchen 1992), which shifts to 462
increased insect predation in freshwater streams (Reimchen 1994; Marchinko 463
2009). Invertebrate predation may however be negligible in Iceland (Lucek et al. 464
2012b) and empirical evidence for the role of invertebrate predators as a source 465
of selection is mixed for Swiss populations (Zeller et al. 2012a,b). 466
467
For trophic morphology on the other hand, freshwater pmax differ commonly 468
from the marine one with small but significant angles θ, suggesting that the 469
marine-freshwater transition may be generally associated with a change in the P
470
matrix (Figure 5). Habitat dependent ecotypic differentiation in stickleback is 471
indeed thought to be coupled with a change in diet and trophic morphology, 472
where marine and some freshwater lake populations forage commonly on 473
zooplankton in contrast to stream fish and some lake populations that feed on 474
benthic prey (Gross & Anderson 1984; Berner et al. 2009; Kaeuffer et al. 2012; 475
Lucek et al. 2012a). The small but significant angles θ, which we observe 476
especially for lake populations are consistent with prior findings in Canadian 477
stickleback, where freshwater lake populations have been shown to have a pmax
478
that has diverged from the marine one, involving a shift in gill raker lengths 479
(Berner et al. 2010b). Lastly, differences in the selective regimes between our 480
studied systems and countries may account for the observed changes in pmax for 481
body shape and swimming related traits that are linked to different foraging 482
strategies in lakes and streams (Hendry & Taylor 2004; Reid & Peichel 2010; 483
Hendry et al. 2011). The wide range for θ for both the overall system and stream 484
to environmental differences such as differences in flow regimes (Steppan et al. 486
2002; Ravinet et al. 2013). 487
488
Contingency, selection and parallelism during lake-stream divergence
489
Both the occurrence and the extent of parapatric divergence depend mainly on 490
the underlying environmental and selective gradient and the time for evolution 491
to act (Endler 1977; Doebeli & Dieckmann 2003; Nosil etal. 2009). Parallel 492
parapatric divergence is consequently only expected under comparable selective 493
regimes (Kaeuffer et al. 2012) where selection is acting on a similar gene pool 494
(Barrett & Schluter 2008). The repeated formation of parapatric lake-stream 495
freshwater stickleback systems has been proposed to provide such a case 496
(Reimchen et al. 1985; Thompson et al. 1997; Hendry & Taylor 2004; Berner et
497
al. 2009; Lucek et al. 2013). However, recent studies find non-parallelisms in the 498
realized divergence that occur both on smaller geographical scales (Kaeuffer et
499
al. 2012; Hendry et al. 2013; Ravinet et al. 2013; Lucek et al. 2013) as well as 500
between continents (Berner et al. 2010a). In the latter case, the authors 501
suggested that genomic constraints could be responsible for the observed lower 502
degree of divergence among Swiss populations and the evolutionary younger 503
Atlantic stickleback lineage in general, where only the Constance system showed 504
a level of divergence that is comparable to older Canadian systems (Berner et al. 505
2010a; Ravinet et al. 2013, but see Lucek et al. 2013). However, the respective P
506
matrices have not been compared. 507
508
Our results suggest that the evolution of parapatric lake-stream populations in 509
age of a system as it is indicated by the non-significant angles between the 511
overall pmax of different lake-stream systems for defense and feeding related 512
traits (Table 3). Thus ecotype formation along parallel axes may start quickly. 513
However, although parapatric lake-stream systems share similar pmax, only a 514
relatively small fraction of the overall phenotypic variation can be attributed to 515
parallel habitat dependent differentiation (Table 1), where the directionality of 516
parapatric divergence often differs between ecotype pairs in Switzerland and 517
Iceland and sometimes also between pairs from different lake-stream systems 518
within each country (Figure 3). In contrast, a much larger fraction is explained by 519
the system and habitat interaction and thus the combined effect of system-520
related historical contingency and/or system-related selection with ecotypic 521
divergence (Langerhans & DeWitt 2004; Eroukhmanoff et al. 2009; Kaeuffer et al. 522
2012). The increased dimensionality of parapatric differentiation in the two 523
oldest lake systems, Mývatn and Thingvallavatn (Figure 3), may either reflect 524
stronger habitat dependent divergent selection or that longer time is needed for 525
a parallel evolutionary response to similar divergent selection during ecotype 526
formation to trump historical contingency (Nosil et al. 2009; Young et al. 2009; 527
Nosil 2012). The increase in dimensionality further suggests that parallel 528
phenotypic divergence is associated with increased phenotypic integration 529
(Figure 3), which is consistent with studies on older adaptive radiations that 530
show increased convergence in multivariate trait dimensions in comparison to 531
younger radiations (Young et al. 2009; Kolbe et al. 2011). 532
533
In contrast to the observed phenotypic divergence and convergence, the degree 534
environmental gradient rather than the evolutionary age of the system. 536
Altitudinal gradients have similarly been found to explain the degree of 537
parapatric genetic divergence in other freshwater systems (Caldera & Bolnick 538
2008; Ravinet et al. 2013) as well as during the marine and freshwater transition 539
(Deagle et al. 2013, Lucek et al. unpublished data) and may be linked to physical 540
barriers restricting the potential for gene flow. 541
542
Rapid evolution versus plasticity
543
Although phenotypic divergence was greatest in the oldest lakes, the observed 544
differentiation in pmax was not associated with our studied temporal gradient. 545
Hence, plasticity could have initially promoted the colonization of freshwater 546
habitats (Smith & Skúlason 1996) by rapidly shifting pmax (Lande 2009; Draghi & 547
Whitlock 2012). Marine stickleback are known to be phenotypically plastic, 548
allowing them to respond to different diets readily when colonizing new 549
freshwater environments (Wund et al. 2008). Plasticity can furthermore evolve 550
in freshwater to initially promote a generalist life style where divergent selection 551
may then lead to canalization and a reduction in plasticity (Svanbäck & Schluter 552
2012), matching theoretical predictions (Lande 2009; Thibert-Plante & Hendry 553
2011). In theory, however, phenotypic plasticity and thus pmax may evolve quite 554
fast, i.e. over fewer generations than those separating the lake and stream 555
populations in our youngest system (Lande 2009; Draghi & Whitlock 2012). pmax
556
based on phenotypically plastic traits may thus align if populations experience a 557
comparable selective regime as we observe for defense and feeding related traits. 558
Phenotypic shifts during the marine-freshwater transition as well as between 560
distinct freshwater habitats in stickleback have similarly been suggested to occur 561
through recurrent selection on standing genetic variation in the marine 562
population (Deagle et al. 2012; Jones et al. 2012b). This is especially true for anti-563
predator related phenotypic shifts, where selection drives phenotypic 564
divergence over only a few generations (Bell et al. 2004; Barrett et al. 2008; 565
Schluter & Conte 2009) and may similarly account for phenotypic divergence in 566
other genetically determined traits such as gill rakers (Hermida etal. 2002). Our 567
observed parapatric divergence as well as the changes in the P matrix for these 568
traits may therefore be a combined result of both plasticity and adaptation from 569
standing genetic variation (Wund et al. 2008; Eroukhmanoff & Svensson 2011, 570
Lucek et al. 2014). Indeed, empirical evidence suggests that although pmax and 571
gmax are correlated in stickleback, pmax can only approximate gmax and hence the 572
underlying evolutionary constraints (Leinonen et al. 2011). 573
574
Conclusions
575
Our results suggest that parapatric ecotype formation can result in parallel and 576
hence predictable pmax for some trait combinations, i.e. trophic morphology, but 577
that the directionality of change may differ for others due to historical 578
contingency or environmental effects. Whereas changes in the P matrix during 579
the marine-freshwater transition seem to evolve independently of our studied 580
temporal axis, both the extent and the dimensionality of parapatric ecotype 581
formation depend on the available time for evolution to occur. Thus evolutionary 582
formation and may be aided by phenotypic plasticity, yet convergent phenotypic 584
evolution needs time to overcome contingency. 585
586
Acknowledgment
587
This research was supported by an European Science Foundation Exchange 588
Grant in the FroSpects program (grant number 2912) to KL and a Swiss National 589
Science Foundation Grant 31003A-118293 to OS. We thank Mélissa Lemoine, 590
Fabrice Eroukhmanoff and Pamela Woods for constructive feedback and 591
suggestions. Andrew Hendry, Catherine E. Wagner, Julia Schwarzer and one 592
anonymous reviewer provided valuable comments on earlier versions of the 593
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Figure Legends: 828
Figure 1: Overview of the studied systems: a) Sampled lakes and corresponding 829
sampling sites (squares: lake populations; diamonds: stream populations; circles: 830
marine populations) for both Iceland (top) and Switzerland (bottom). b) 831
Representative examples of each sex for the different stickleback ecotypes of 832
each lake-stream system. 833
834
Figure 2: Genetic and phenotypic relationship among sampling sites. Shape of tip 835
labels indicates habitat (square: lake; diamond: stream; circle: marine) and 836
colors represent different lake-stream systems. a) Genetic differentiation among 837
populations based on a neighbour-joining tree using Cavalli-Sforza distances 838
amongst sampling sites included in this study (see Figure 1), calculated from 839
allele frequencies at 10 microsatellite loci. The tree is midpoint rooted. Numbers 840
beside nodes indicate percent bootstrap support based on 1000 resampling 841
replicates. Bootstrap values below 50% are not shown. b) Dendrogram of 842
phenotypic Mahalanobis distances among all sampling sites. 843
844
Figure 3: Pairwise phenotypic divergence between lake and stream ecotype (PST
845
± 95% CI) for each system, calculated for each trait separately, for all traits 846
combined and for functionally distinct trait groups. PST for trait groups is based 847
on scores of the first principal component axis for either all traits combined, 848
defense related traits, feeding related traits, body shape related traits (see text 849
for details). Asterisks indicate cases where the 95% confidence interval for PST
exceeds 0. For the latter traits, triangles indicate the directionality of the 851
pairwise divergence, where a trait is larger (pointing right) or smaller (pointing 852
left) in lake fish in comparison to stream fish. For a description of each trait and 853
its abbreviation see the main text and Figure S1. 854
855
Figure 4: Angles between the major axis of phenotypic variation (pmax) in 856
parapatric lake versus stream populations. Angles were calculated including 857
either all phenotypic traits (a) or a subset of defense (b), feeding (c) or body 858
shape / swimming performance (d) related traits. Letters indicate the respective 859
system: B – Bern (red), C – Constance (black), G – Geneva (green), H – 860
Hraunsfjördur (blue), M – Mývatn (dark blue), T – Thingvallavatn (pink). Dashed 861
lines denote lake-stream systems from Iceland, solid lines systems from 862
Switzerland. 863
864
Figure 5: Angles between the major axis of phenotypic variation (pmax) between 865
the marine population and either the freshwater stream populations or the lake 866
populations. Angles were calculated using (from top to bottom): all phenotypic 867
traits or a subset of defense, feeding or body shape / swimming performance 868
related traits. For the overall divergence, indicated vectors are scaled according 869
to the eigenvalue of the leading axis. Letters indicate the respective system: B – 870
Bern (red), C – Constance (black), G – Geneva (green), H – Hraunsfjördur (blue), 871
M – Mývatn (dark blue), T – Thingvallavatn (pink). Dashed lines denote lake-872
