ele12551-sup-0001-SuppInfo
1 Supplementary Information
2 Differential selection and adaptive trait responses in fish lakes vs fishless lakes
3 The selection pressures differ strongly between fish lakes and fishless lakes and thereby
4 generate largely opposite changes in most traits studied for two major reasons. (i) In fishless
5
lakes, large invertebrate predators replace fish as the most important predators (Wellborn et
6
1996). The general pattern is that invertebrate predators have a disproportionally impact on
7 small-bodied prey because functional constraints often limit the ability of these predators to
8 consume large prey, resulting in selection for large prey body size in fishless lakes as opposed
9
to fish selecting for small prey body size (Wellborn et al.
Chaoborus
10 phantom midges can be the most important predators on Daphnia
11
Chaoborus densities correlate negatively with fish densities
12
2012). Compared to fish predators,
Chaoborus predators induce opposite
13 morphological and life history changes in Daphnia : a larger body size, a later maturation, a
14
reduced somatic growth rate and reduced fecundity (Luning 1992; Beckerman et al.
15
2011; Riessen 2012); and an opposite behavioural change: upward vertical
16
2006; Oram & Spitze 2013). (ii) In the absence of fish,
Daphnia
17 densities are typically higher and as a result competition becomes much more important.
18 Competition, especially at low food, selects for a larger body size in Daphnia
19
20 Some traits, however, may be selected for in the same direction by fish predators and
21 large invertebrate predators. Spine length may reduce both the vulnerability to juvenile fish
22
2014) and to invertebrate predators such as
Chaoborus
23
2014) and copepods (Caramujo & Boavida 2000). Furthermore,
Chaoborus predators that are
24 active in the water column induce, as fish predators, horizontal migration away from the
25
littoral vegetation (Van de Meutter et al.
2005). Yet, other important size-limited invertebrate
1
26 predators on Daphnia, Ischnura
damselfly larvae (Thompson 1978), induce horizontal
27
migration away from the littoral vegetation (Van de Meutter et al.
Daphnia
28 encounter combined cues from Chaoborus and damselfly predators they move toward the
29
vegetation (Van de Meutter et al.
30
31 Supplementary Methods
32 Experimental conditions
33 Medium consisted of aged (24h) tap water, which in case of fish-conditioned medium was
34 enriched with fish kairomones. Fish medium was prepared by adding water in which we kept
35 two ides ( Leuciscus idus ; size: 6-8cm) for 2h. After filtering this medium over 0.45µm, the
36 medium was diluted to a final concentration corresponding to 2.5 fish in 100L. This high level
37 of fish predation risk was simulated to ensure maximal responses. The fish were fed Daphnia
38 in a separate aquarium to avoid contamination of the medium with Daphnia alarm substances.
39 Prior to the start of the life table experiments we set up five independent lines per
40 clone, which were raised separately in optimal conditions for at least two generations to
41 minimize interference from maternal effects. Individual animals were raised under
42 standardized conditions until they produced second clutch offspring, which was taken as the
43 grandmother generation. The mother generation consisted of the second clutch offspring of
44 these grandmother animals. Within 24h after birth, these juveniles were transferred into new
45 culture jars and assigned to one of the two fish kairomone treatments (presence vs. absence).
46 An additional generation was cultured in the presence or absence of fish kairomones. As a
47 result, all experimental animals in the life history experiment were exposed to the
48 experimental conditions for two consecutive generations. For each trait we used the
49 independent lines per clone as the units of replication. For the assessment of behavioural traits
50 the same procedure was followed, except for the fact that cohorts of 25 individuals were
2
51 grown in 1L jars, that only three independent replicate cohorts were established for each
52 assay, and that only two generations of culture under standardized conditions were run.
53
54 Behavioural assays
55 We quantified three behavioural antipredator traits both in the absence and in the presence of
56 fish-conditioned medium. Phototactic behaviour was assessed as in Cousyn et al. (2001). In
57 short, a cohort of ten animals was placed in an experimental column (10cm height) divided in
58 four equal compartments with external marks and the position of the animals was monitored
59 every minute for a period of 10 minutes. The phototactic index was calculated as the average
60 over the last 5 minutes of the number of animals in the upper compartment minus the number
61 in the lower compartment, divided by the total number of animals. We assessed horizontal
62 migration based on Van de Meutter et al.
(2004). We quantified the distribution of a cohort of
63 20 animals in an aquarium (49 × 24 × 30cm) with a central open cylinder (r = 14cm)
64 surrounded by artificial macrophytes (density of 17% plant infested volume). The horizontal
65 migration index was calculated as the average number of Daphnia in the central macrophyte-
66 free zone during the second hour of observation based on counts at 10-min intervals. To
67 estimate alertness, we measured the time needed to transfer a cohort of ten Daphnia out of a
68
1L jar into a new jar using a pipette (De Meester & Pijanowska 1996). Students blind to the
69 experimental treatments picked out Daphnia at most five times, fully randomizing clone ×
70 fish kairomone treatment combinations. The time needed to pipet the ten Daphnia was used
71 as an estimate of alertness.
72
73 (M)ANOVA assumptions
74 To meet ANOVA assumptions we log-transformed all traits except phototactic behaviour and
75 horizontal migration. After doing so, four traits showed outliers based on visual inspection of
3
76 the data plots (neonate size: 2 outliers, somatic growth rate: 3 outliers, relative spine length at
77 maturity: 2 outliers, late fecundity: 1 outlier). These numbers of outliers are low taking into
78 account we had 360 values per trait. After removing outliers all traits showed normal
79 residuals (all Shapiro-Wilk W > 0.98). For the three traits where deviations from
80 homoscedasticity were detected by Levene’s tests (relative spine length at maturity, late
81 fecundity and horizontal migration) the ratio between the largest and smallest variance was
82 smaller than 3.3; hence smaller than the critical value of 4 indicated by Zuur et al.
83 Running the follow-up two-way ANOVAs after the MANOVA without these outliers (so in
84 the situation where assumptions were reasonably met) did not change which factors were
85 significant in the original two-way ANOVAs (including all data), indicating that the few
86 outliers had a negligible effect. Therefore, we kept all outliers in the analyses reported in the
87 manuscript.
88 Since it is nearly impossible to obtain multivariate trait normality, a MANOVA
89 assumption, we also carried out a permutation MANOVA (PERMANOVA), which makes no
90
assumptions about the (multivariate) normality of the error distributions (Anderson 2001). All
91 significance values remained highly significant in the PERMANOVA and we report the
92 regular MANOVA in the results section.
93
94 Phenotypic trajectory analysis
95 Significance of the difference in magnitude and direction of the multivariate plasticity
96 responses (multivariate reaction norms) between the different subpopulations were tested
97
using a residual randomization procedure as described in Adams & Collyer (2009). The full
98 model contained the 14 standardized log-transformed traits as response matrix, and as
99 independent variables subpopulation, treatment and their interaction as fixed factors and clone
100 as random factor. This model was used to generate the predicted values for each observation.
4
101 The residuals of a reduced model only containing subpopulation and fish kairomone treatment
102 as fixed effects and clone as random effect were used in the subsequent randomization
103 procedure. After randomizing the residuals of this reduced model, they were added to the
104 predicted values of the full model to produce random values, which were then used to
105 calculate random multivariate plasticity responses. Based on this residual randomization
106 procedure Pvalues were calculated as the proportion of values being equal or more extreme
107 than the observed value.
108 Testing for subpopulation convergence/divergence was done as in Dennis et al.
109
(2011), using the same models in the residual randomization procedure as described above.
110 However, instead of calculating multivariate reaction norms, Euclidean distances between
111 populations within environments were calculated (Fig. 2b). In addition we also tested if the
112 distance between two subpopulations within the same fish kairomone condition was different
113 from zero. Significance was tested separately for the conditions with and without fish
114 kairomones. The full model contained subpopulation as a fixed effect and clone as a random
115 effect and the reduced model only included clone as a random factor.
116 Genetic variation in the magnitude and orientation of the multivariate reaction norms
117 within a subpopulation was evaluated based on pairwise clonal comparisons of the magnitude
118 and orientation of clonal reaction norms as done in Dennis et al.
119 were conducted for each subpopulation separately, therefore, the full model did not contain
120 subpopulation as a fixed factor. As we wanted to assess differences between clones, clone was
121 included as a fixed factor into the model, and also the interaction of treatment and clone was
122 included in the full model. Again predicted values were determined from the full model, while
123 residuals from the reduced model (i.e. here the model without interaction term) were
124 randomized and added to the predicted values to create random values. Genetic variation in
5
125 the magnitude and direction within a subpopulation was then assessed using the summary
126 statistics as described in Adams & Collyer (2009).
127 Phenotypic trajectory analysis and multivariate partitioning
128 The contribution of the multivariate components (i.e. ancestral plasticity, constitutive
129 evolution, and evolution of plasticity) to the multivariate observed trait change was assessed
130 by calculating the magnitudes (i.e. Euclidean norm) of their corresponding vectors. For
131
132 example, the magnitude of multivariate ancestral plasticity was calculated as ||ȳ f
(PF) - ȳ nf
(PF)|| = ([ȳ f
(PF) - ȳ nf
(PF)]·[ȳ f
(PF) - ȳ nf
(PF)]
T
)
1/2
where ȳ f
(PF) and ȳ nf
(PF) are 14-
133 dimensional row vectors of trait value means of the pre-fish subpopulation in the presence and
134 in the absence of fish kairomones, respectively and where T represents a vector transpose.
135 Significance of these components were assessed separately for each transition using a similar
136 residuals randomization approach as described in Adams & Collyer (2009), with small
137 modifications when testing for the significance of the ancestral plasticity and constitutive
138 evolution components. For each component a reduced model was solved, and predicted values
139 and residuals calculated. These residuals were then randomized and added to the predicted
140 values (calculated from the full model, similar as in the PTA) to produce random values.
141 Calculating the randomized residuals from a reduced model ensured that non-targeted effects
142 were held constant. For ancestral plasticity of the first transition this means that we use a
143 reduced model only incorporating the trait values in the presence and in the absence of fish
144 kairomones of the pre-fish subpopulation as dependent variables and clone as independent
145 random factor to construct the residuals used in the randomization process. Similarly, for
146 constitutive evolution of the first transition we used a reduced model only incorporating as
147 dependent variables the trait values measured in the absence of fish kairomones of the pre-fish
148 and high-fish subpopulations and with clone as independent random factor to construct the
149 residuals used in the randomization process. The reduced model for evolution of plasticity and
6
150 total observed trait change was an additive model including subpopulation and treatment as
151 fixed factors and clone as random factor. To obtain Pvalues testing whether a given
152 component differs from zero, a residual randomization procedure was run where in each
153 iteration the magnitude of a given component (being ancestral plasticity, constitutive
154 evolution, evolution of plasticity) or of total observed trait change was calculated and
155 compared to their observed magnitude. The P -values of the observed statistics are then
156 calculated as the probability of finding more extreme values from the randomly generated
157 distributions.
158
159 Statistical analyses of heritabilities
160 We estimated broad-sense heritabilities by running for each trait one-way ANOVAs per
161
combination of subpopulation and kairomone treatment with clone as a random factor (Lynch
162
& Walsh 1998). A significant effect of clone indicates a heritability estimate significantly
163 larger than zero. Heritability estimates were calculated as the ratio of the among-clone
164 component of variance to the total variance with the among-clone variances estimated as
165 [MS(among clones) – MS (within clones)] / n, with n the number of individuals scored per
166 clone.
167
168 Statistical analyses of genetic correlations
169 To evaluate constraints for evolution as well as the potential for indirect evolution we tested
170 for genetic correlations between (i) different traits, (ii) character states of the same trait in the
171
absence and in the presence of fish kairomones (Via 1984), (iii) trait plasticities (Schlichting
172
1986), (iv) the trait plasticity (slope) and the mean trait value across both environments
173
2005), and (v) the trait plasticity and the mean trait value in one environment
174
(Lande 2009; Robinson 2013). We estimated and tested the significance of the several types
7
175 of genetic correlations using Pearson’s product moment correlations calculated on the clonal
176
means (Via 1984). We also explicitly evaluated whether the genetic correlations were
177 different from +1 and -1, a more useful null hypothesis for testing evolutionary constraints
178
2014). Indeed, genetic correlations with an absolute value less
179 than one will allow some degree of independent evolution, and even small deviations from a
180
correlation of one can allow evolutionary change towards multiple optima (Lande 1980; Fry
181
1996). The 95% confidence intervals for genetic correlations were computed using the z-
182
transformation (Sokal & Rohlf 1995). We evaluated whether genetic correlations differed
183 from +1 and -1 based on whether these values were included in the 95% intervals. For genetic
184 correlations between traits we indicated whether these were (i) reinforcing, i.e. concordant
185 with the direction of selection on pairs of traits, and therefore may accelerate evolution and
186 drive the adaptive evolution of traits not under direct selection or (ii) antagonistic, i.e. not in
187 accord with the direction of selection, and therefore may constrain adaptive evolution. We
188 similarly estimated and tested for broad-sense genetic correlations between plasticities.
189 To evaluate whether the evolution of plasticity could be the result of direct selection
190 on plasticity rather than indirect selection on the mean trait across environments or the trait
191 mean in a single environment, we calculated the correlations between the slope and the mean
192 trait value in a single environment, and the correlations between the slope and the mean trait
193
value across both environments (Scheiner 1993). Both correlations are, however, inflated
194
because of spurious correlations (van Kleunen et al.
2000; Roff 2011). To correct for spurious
195 correlations we used a randomisation procedure based on van Kleunen et al. (2000). We
196 randomized mean trait values of genotypes in one environment with regard to mean trait
197 values in the other environment 1,000 times. For each run, we calculated trait plasticity, trait
198 averages in a single environment (or trait averages across both environments), and the
199 correlations between both parameters. The distribution of the resulting 1000 correlation
8
200 coefficients was used to test the significance of the observed correlation between both
201 parameters (the mean of this distribution served as an estimate of the spurious correlation).
202
203
204
205
Quantification of evolutionary rates
Evolutionary rates were expressed as haldanes, which were calculated as ( ȳ
2
/s p
– ȳ
1
/s p
)/g where ȳ
1
is the mean trait value at time 1, ȳ
2
is the mean trait value at time 2, s p
is the pooled
206 standard deviation of trait values across time and g
is the number generations (Kinnison &
207
Hendry 2001). We thereby assumed that one calendar year (one growing season) equals one
208 sexual generation in cyclic parthenogenetic Daphnia
209 were calculated for both successive transitions in fish predation pressure and this for each trait
210 in the absence and in the presence of fish kairomones as well as for the plasticity of the trait.
211
212 Multiplicative method to disentangle the relative contribution of plasticity and evolution to
213 total trait change
214 The multiplicative method to estimate the components of the total phenotypic trait changes is
215 illustrated in Fig. S3. Here we assume that the plastic trait change to the fish kairomone is
216 proportional to the trait value in the absence of the fish kairomone. For a trait y its plasticity
217 can then be characterized by the ratio a between the mean trait values of the pre-fish
218
219
220
221
222
223 subpopulation in the presence of fish kairomones and of the pre-fish subpopulation in the absence of fish kairomones, which can be written as a = ȳ f
(PF) / ȳ nf
(PF) . If we multiply this ratio with ȳ nf
(HF) we get a hypothetical average value for trait y of the high-fish subpopulation in the presence of the fish kairomones; ŷ f
(HF) . The difference between ŷ f
(HF) and ȳ f
(HF) is the amount of evolution of plasticity. For the remaining two components we get the following formulas: ȳ f
(PF) - ȳ nf
(PF) for plasticity, and ŷ f
(HF) –
9
224
225
226
227 ȳ f
(PF) for constitutive evolution. The calculations going from the high-fish to the reducedfish subpopulations can be derived in a similar way. We get the following formulas: ȳ nf
(HF) - ȳ f
(HF) for plasticity, ŷ f
(HF) - ȳ f
(HF) for constitutive evolution, and ȳ nf
(RF) - ŷ f
(HF) for the evolution of plasticity, with a= ȳ nf
(HF)/ ȳ f
(HF).
228
229 Relationships between plasticity and evolutionary changes across traits
230 To test for covariation between ancestral plasticity, constitutive evolution and the evolution of
231 plasticity across traits we ran type II regressions as these variables were not controlled by the
232 researcher (Legendre & Legendre 2012). Specifically, given that these are dimensionless
233 variables we used major axis regression. For each test between two components, one
234 component was randomly considered the independent continuous variable and the other the
235 dependent continuous variable. Note that the slope of type II regression is not dependent on
236
which variable is considered as independent versus dependent variable (Legendre & Legendre
237
2012). All variables were normally distributed except for the estimates of constitutive
238 evolution in the first transition, which were (log+1)-transformed. Major axis regression was
239 performed using the lmodel2() function of the lmodel2 R package.
240
241 Supplementary Results
242 Univariate reaction norms
243 Despite the absence of fish in the pond, the pre-fish Daphnia showed adaptive subpopulation
244 reaction norms to fish kairomones for seven traits (somatic growth rate, relative spine length
245 neonates, age at maturity, late fecundity and intrinsic growth rate, phototactic behaviour and
246 horizontal migration) (Figs 3 and S2, Table S3). Yet some traits also exhibited considerable
247 maladaptive plasticity at the subpopulation level (against the direction of selection toward the
248
2007)) for five traits: the production of larger offspring (size
10
249 neonates, early and late offspring size), maturation at a larger size, and a smaller spine length
250 at maturity.
251 Instead, the high-fish subpopulation was much better adapted to the presence of fish in
252 terms of its univariate plasticity responses (Figs 3 and S2, Table S3). Indeed, evolution of
253 plasticity during the ca. 6.5 year transition period resulted in the high-fish Daphnia
254 subpopulation reversing its plastic responses to fish kairomones towards the expected size
255 decreases under size-selective fish predation and no longer decreasing relative spine length at
256 maturity when exposed to fish kairomones. In addition, the amplitude of the adaptive
257 ancestral plastic responses evolved to larger values than in the pre-fish subpopulation for
258 several traits: this was true for the increase in relative spine length of the neonates, the
259 reduction in age at maturity and the increase in intrinsic growth rate. Furthermore, high-fish
260 Daphnia were constitutively better defended as they expressed a longer relative spine length
261 at maturity than pre-fish Daphnia . For most traits, this resulted in the pre-fish Daphnia
262 showing trait values in the presence of fish kairomones that made them better in dealing with
263 fish predation compared to the pre-fish Daphnia (Figs 3 and S2, Table S3). High-fish
264 Daphnia did not evolve more adaptive trait values for three traits (somatic growth rate, late
265 fecundity and horizontal migration) that already showed adaptive plasticity in the pre-fish
266 subpopulation, and for two traits (early fecundity and alertness) that apparently were not able
267 to evolve.
268 Compared to the high-fish subpopulation, and in line with the reduced fish predation
269 pressure, the most recent subpopulation showed a reduction in adaptive plasticity to fish
270 kairomones for five traits (somatic growth rate, relative spine length of the neonates, intrinsic
271 growth rate, phototactic behaviour and horizontal migration). Moreover, it evolved plasticity
272 for neonate size that results in a maladapted response in the presence of fish. In addition,
273 reduced-fish Daphnia also showed constitutive evolution that made them less defended in the
11
274 presence of fish kairomones for not less than ten traits: they were larger for all size traits, had
275 a lower somatic growth rate, shorter spine lengths, matured later with a lower intrinsic growth
276 rate and migrated less toward the vegetation.
277
278 Evolutionary rates
279 Nearly all (82 out of 84) evolutionary changes can be considered “rapid” according to the
280 criteria outlined by Kinnison & Hendry (2001) (Fig. S4, Table S5). Evolutionary rates were
281 especially high for relative spine length at maturity going from the pre-fish to high-fish
282 periods and this both in the absence and in the presence of fish kairomones. Evolutionary
283 rates across traits did not correlate between both transitions neither for traits measured in the
284 absence or in the presence of fish kairomones, nor for trait plasticity (Pearson correlations, all
285 r < 0.47, P > 0.10).
286
287 Heritabilities
288 The majority of traits measured in the absence and in the presence of fish kairomones showed
289 broad-sense heritabilities statistically different from zero in each of the three subpopulations
290 and only these values are shown in Table S6, indicating the widespread presence of
291 evolutionary potential. Furthermore, there was genetic variation for phenotypic plasticity in
292 six to eight traits depending on the subpopulation, and for nine of the fourteen traits if we
293 consider all three subpopulations as one population.
294
295 Genetic correlations
296 Overall, 17% of the genetic correlations between traits significantly differed from zero (91 out
297 of 546 combinations of trait pair × subpopulation × fish kairomone treatment) and varied
298 between 5% and 24% per combination of subpopulation and kairomone treatment (Table S6).
12
299 Ca. 1 out of 8 genetic correlations were significantly different from zero in both the pre-fish
300 and high-fish subpopulations, while this was ca. 1 out of 4 in the reduced-fish subpopulation.
301 All these genetic correlations (except two between relative spine length at maturity and size at
302 maturity both in the absence and in the presence of fish kairomones in the reduced-fish
303 subpopulation) were significantly different from +1 and -1, indicating that some degree of
304 independent evolution between characters was still possible. A small majority (63%, 57 out of
305 91) of the significant genetic correlations were reinforcing. A large part of these reinforcing
306 genetic correlations consisted of positive correlations between size-related traits (size at
307 maturity, and early and late offspring size), and negative correlations between these size-
308 related traits and relative spine length at maturity. Furthermore, somatic growth rate was
309 consistently negatively correlated with age at maturity, and in the presence of fish kairomones
310 positively with intrinsic growth rate. This indicates that selection for any of these traits would
311 generate adaptive evolution in the other traits. This may, for example, have contributed
312 (together with its high heritability and direct selection on the trait itself) to the especially high
313 evolutionary rates for relative spine length at maturity during the first transition. Antagonistic
314 correlations occurred most frequently with somatic growth rate (n=14), age at maturity (n=9),
315 and size at maturity (n=8), suggesting these traits may be more likely to experience
316 evolutionary constraints. For example, the observation that somatic growth showed eight
317 antagonistic correlations with other traits in the reduced-fish period may be related to the fact
318 that its values were lower in the reduced-fish period than in the pre-fish and high-fish periods,
319 and that it did no longer increase in the presence of fish kairomones. Phototactic behaviour
320 did not show significant genetic correlations with any other trait, neither reinforcing nor
321 antagonistic, suggesting it can evolve fully independently from the other traits studied.
322 The overall percentage of positive genetic correlations between character states of the
323 same trait between environments larger than zero was overall 29% (12 out of 42 trait ×
13
324 subpopulation combinations): 4 in the pre-fish subpopulation, 2 in the high-fish subpopulation
325 and 6 in the reduced-fish subpopulation (Table S7). Most of these correlations (7 out of 12)
326 were between the character states of behavioural traits. Given that all these correlations were
327 significantly smaller than 1, some degree of independent evolution between character states,
328 hence the evolution of phenotypic plasticity, was still possible. Indeed, we observed the
329 evolution of plasticity in five cases where a significant positive genetic correlations between
330 character states was present.
331
332
A small subset of the genetic correlations between plasticities was significant (13%,
36 out of the 273 combinations of trait pair × subpopulation): 9 in the pre-fish subpopulation,
333 12 in the high-fish subpopulation, 15 in the reduced-fish subpopulation (Table S8). The
334 majority of these correlations (75%, 27/36) was reinforcing: selection on the plasticity in one
335 trait would generate adaptive evolution of plasticity in the other trait. Consistent reinforcing
336 correlations between plasticities in each subpopulation were the significantly positive
337 correlations between the plasticities of early and late offspring size and size at maturity, and
338 the significantly negative correlations between the plasticities of relative spine length at
339 maturity and each of these three size-related traits. Furthermore, the plasticity of somatic
340 growth rate was consistently negatively correlated with the plasticity of age at maturity.
341 Among the few antagonistic correlations, that may have constrained the evolution of
342 increased plasticity in these traits, three occurred for horizontal migration in the high-fish
343 period (with the plastic responses of early and late offspring size and of vertical migration).
344 As all genetic correlations between plasticities were different from +1 and -1, some degree of
345 independent evolution was still possible.
346 Of the few genetic correlations between the slope and the mean intercept across both
347 environments that were significantly positive without correction, only the genetic correlation
348 for relative spine length neonates in the pre-fish subpopulation remained after correction for
14
349 spurious correlations (Table S9). Furthermore, after correction for spurious correlations, only
350 for one trait was the genetic correlation between the slope and the intercept in a single
351 environment significant: for relative spine length neonates in the control condition in the pre-
352 fish subpopulation (Table S10).
353
354 Contributions of plasticity and evolution to total trait changes in time based on the additive
355 method
356 Except for early fecundity and alertness, all other 12 traits had at least one of the three
357 components (ancestral plasticity, constitutive evolution, evolution of plasticity) significantly
358 contributing to the total univariate trait changes (Fig. 5, Tables S3 and S4), and we further
359 discuss patterns based on these 12 traits. Plasticity (non-genetic changes) had the largest
360 contribution to total univariate trait changes for five traits during each transition. Four of these
361 traits were in common during both transitions (late fecundity, intrinsic growth rate,
362 phototactic behaviour and horizontal migration). Additionally, the plasticity contribution was
363 also the largest for somatic growth rate during the first transition and for relative spine length
364 of the neonates during the second transition. For the seven other traits, genetic changes
365 (constitutive evolution or the evolution of plasticity) contributed more strongly to the total
366 trait change than plasticity. There was a striking shift in the contribution of the two
367 evolutionary components between both transitions (Fisher Exact test, P = 0.015): during the
368 first transition, the evolution of plasticity was the largest component in 6 out of 7 traits (four
369 size-related traits, relative spine length neonates and age at maturity), while constitutive
370 evolution had the largest contribution for relative spine length at maturity only. During the
371 second transition, constitutive evolution was the largest component in 6 out of 7 traits (four
372 size-related traits, relative spine length at maturity, age at maturity), while constitutive
373 evolution and the evolution of plasticity had very similar contributions for neonate size.
15
374
375 Contributions of plasticity and evolution to total trait changes in time based on the
376 multiplicative method
377 The results obtained by the multiplicative method are very similar to the results obtained by
378 the additive method. The contributions of plasticity, constitutive evolution and evolution of
379 plasticity to the total change based on the multiplicative method are given in Fig. S5. For the
380 same set of 12 traits with at least one significant component, the multiplicative method
381 indicated that plasticity had the largest contribution to total trait change for four traits during
382 the first transition (somatic growth rate, late fecundity, intrinsic growth rate and horizontal
383 migration) and five traits during the second transition (relative spine length neonates, late
384 fecundity, intrinsic growth rate, phototactic behaviour and horizontal migration). There was a
385 shift in the contribution of the two evolutionary components between both transitions (Fisher
386 exact test, P = 0.032). During the first transition, the evolution of plasticity component was
387 largest for 6 out of 8 traits that showed a major evolutionary component (cf. four size-related
388 traits, relative spine length at maturity and age at maturity), while constitutive evolution had
389 the largest contribution for relative spine length of neonates and phototactic behaviour.
390 During the second transition, constitutive evolution was the largest component in 6 out of 7
391 traits that showed a major evolutionary component (four size-related traits, relative spine
392 length at maturity and age at maturity), while evolution of plasticity was the larger component
393 for neonate size.
394
395 Relationships between plasticity and evolutionary changes
396 Using the estimates obtained by the additive method, the ancestral plasticity did not covary
397 with the degree of constitutive evolution ( P = 0.275) nor with the degree of plasticity
398 evolution ( P = 0.340) when going from the pre-fish to high-fish periods (Fig. S6a).
16
399 Furthermore, for this transition the degree of constitutive evolution did not covary with the
400 degree of plasticity evolution ( P = 0.502, Fig. S6c). In contrast, the degree of ancestral
401 plasticity across traits in the high-fish period covaried positively with the evolutionary change
402 due to constitutive evolution (slope = 0.75, P = 0.001) and negatively with the evolutionary
403 change in plasticity (slope = -0.57, P = 0.001) (Fig. S6b). For this transition, the degree of
404 constitutive evolution covaried negatively with the degree of plasticity evolution (slope =
405 -0.70, P = 0.001, Fig. S6d).
406 Similar relationships between plasticity and evolutionary changes were found based on
407 the multiplicative method compared to the additive method. Ancestral plasticity did not
408 covary with the degree of constitutive evolution ( P = 0.456), nor with the degree of plasticity
409 evolution ( P = 0.467) when going from the pre-fish to the high-fish periods (Fig. S7a).
410 Furthermore, for this transition the degree of constitutive evolution did not covary with the
411 degree of plasticity evolution ( P = 0.407, Fig. S7c). In contrast, the degree of ancestral
412 plasticity across traits in the high-fish period covaried positively with the evolutionary change
413 due to constitutive evolution (slope = 0.47, P = 0.050) and negatively with the evolutionary
414 change due to plasticity (slope = -0.48, P = 0.001) (Fig. S7b). For this transition, the degree of
415 constitutive evolution covaried negatively with the degree of plasticity evolution (slope = -
416 0.61, P = 0.017, Fig. S7d).
417
418 Supplementary Discussion
419 Discussion of univariate reaction norms
420 The evolutionary changes during the pre-fish to high-fish transition and the resulting plastic
421 responses during the high-fish period matched the typical selection patterns imposed by size-
422
selective fish predation as predicted by theory (Abrams & Rowe 1996) and empirically
423 demonstrated in Daphnia
17
424
2002; Riessen 2012): body size decreased, spine length and investment in reproduction
425 increased, and animals migrated deeper in the water column and toward the littoral vegetation.
426 The two traits (early fecundity and alertness) that apparently were not able to evolve toward
427 the new optimum when fish was introduced, showed a low heritability. Previous studies
428 indeed suggested early fecundity may already have been under strong directional selection in
429 the pre-fish period reflecting the importance of early reproduction for the fitness of Daphnia
430
in natural populations (Fisk et al.
2007). Alertness also showed an antagonistic genetic
431 correlation with late fecundity in the pre-fish period in the presence of fish kairomones. The
432 rapid evolution when fish predation was relaxed again made reduced-fish clones less adapted
433 to deal with fish predation: they evolved a reduction in adaptive plasticity to fish kairomones
434 for five traits and a non-adaptive plasticity for one trait, and they showed constitutive
435 evolution that made them less defended in the presence of fish kairomones for not less than
436 ten traits. These rapid responses likely reflected costs to predator-avoidance. More
437 specifically, many trait changes (e.g. larger size at birth and at maturity) make the Daphnia
438 better adapted in an environment where invertebrate predators dominate and where
439 competition is important.
440
441 Discussion of genetic correlations
442 Most traits (being their means or their plasticities) showed several genetic correlations with
443 other traits indicating the evolution of most traits was not independent. Exception was the
444 phototactic behaviour, which did not show significant genetic correlations with any other trait.
445 Of the relative small subset (17%) of genetic correlations between traits that were
446 significantly different from zero, a small majority were reinforcing, indicating that selection
447 on any of these traits would generate adaptive evolution in the other traits. This was mainly
448 the case between the set of three size-related traits (size at maturity, and early and late
18
449 offspring size) and between these traits and relative spine length at maturity. Moreover, also
450 the plasticities of these traits were positively correlated. As these traits also experience direct
451 selection by fish predation (based on theory and empirical work), their evolutionary patterns
452 thus likely are the result of direct and indirect selection by fish predation operating in the
453 same direction. This may explain why these four traits showed largely the same evolutionary
454 patterns, except for the constitutive evolution of relative spine length at maturity, the only trait
455 that showed constitutive evolution during the first transition. Antagonistic correlations
456 between traits occurred most frequently with somatic growth rate suggesting that constitutive
457 evolution and evolution of reduced plasticity of this trait during the second transition may
458 have been influenced by genetic constraints.
459 Genetic correlations and constitutive evolution
460 The patterns of genetic correlations indicate that indirect selection may have contributed to all
461 these evolutionary changes in mean trait values. The single case of constitutive evolution
462 during the first transition (for relative spine length at maturity) may have been driven not only
463 by direct selection for a higher elevation of the reaction norm but also by indirect selection as
464 this trait showed six (four) reinforcing genetic correlations in the pre- (high-) fish period. The
465 widespread (10 traits) occurrence of rapid constitutive evolution during the second transition
466 that made reduced-fish Daphnia less defended in the presence of fish kairomones, was every
467 time accompanied by antagonistic correlations (here correlations so that selection on the other
468 trait would make the focal trait less adapted to fish predation), indicating that in many cases
469 besides direct selection on these traits also indirect selection may have played a role in driving
470 the constitutive evolution.
471 A second overall pattern is that the rapid constitutive evolution often occurred despite
472 the presence of constraining correlations (antagonistic correlations for spine length at maturity
473 during the first transition; reinforcing genetic correlations during the second transition for
19
474 each trait except age at maturity). While genetic constraints may have precluded some traits to
475 show constitutive evolution during the first transition, several traits lacking constitutive
476 evolution, however, did not show antagonistic correlations with other traits (neonate size, late
477 offspring size, relative spine length neonates, early fecundity and phototactic index during the
478 first transition).
479 Genetic correlations and evolution of plasticity
480 Positive genetic correlations between character states in the absence and in the presence of
481 fish kairomones were mainly present for the behavioural traits, indicating a constraint on the
482 evolution of their plasticity. Moreover, among the few antagonistic correlations between
483 plasticities that may have constrained the evolution of increased plasticity in these traits, three
484 occurred for horizontal migration in the high-fish period (with the plastic responses of early
485 and late offspring size and of vertical migration).
486 Based on the genetic correlations between the slope and the mean intercept (both
487 across both environments and in a single environment), during the first transition only the
488 evolution of increased plasticity for relative spine length neonates can be explained by
489 selection on the elevation of the reaction norm. Yet, this does not exclude that direct selection
490 on plasticity was also at work. The evolution of increased plasticity during the first transition
491 for eight other traits (four size-related traits, relative spine length at maturity, age at maturity,
492 late fecundity and intrinsic growth rate) cannot be explained by selection on the elevation of
493 the reaction norm. Moreover, for neonate size, and late fecundity no reinforcing genetic
494 correlations between the plasticities with other traits were observed, suggesting that at least
495 for these two traits, the evolution of plasticity was entirely due to direct selection on the
496 plasticity of these traits.
497 The absence of genetic correlations between the slope and the mean intercept across
498 both environments in the high-fish and reduced-fish periods, indicates that the six cases of
20
499 evolution of plasticity during the second transition (neonate size, somatic growth rate, relative
500 spine length neonates, intrinsic growth rate and horizontal migration; all cases of reduced
501 plasticity to fish kairomones) cannot be explained by selection on the mean elevation of the
502 reaction norm. For neonate size, relative spine length of the neonates and intrinsic growth rate
503 no antagonistic correlations between the plasticities with other traits were observed. This
504 suggests that at least for these traits, the evolution of plasticity was entirely due to direct
505 selection to reduce trait plasticity.
506 The widespread evolution of trait plasticities (mainly during the first transition) often
507 occurred despite constraining genetic correlations between character states (for four traits
508 during the first transition, and three traits during the second transition), and sometimes despite
509 opposing correlations between trait plasticities (one trait during the first transition, three traits
510 during the second transition).
511 We observed a widespread occurrence of the rapid evolution of univariate trait
512 plasticities, mainly during the first transition). Several cases of rapid evolution of plasticity
513 during both transitions (neonate size and late fecundity during the first transition; neonate
514 size, relative spine length of the neonates and intrinsic growth during the second transition)
515 could not be explained by indirect selection and likely reflect direct selection on plasticity. In
516 some cases rapid evolution of plasticity could, based on the genetic correlations, have resulted
517 from indirect selection on the elevation of the reaction norm (relative spine length neonates)
518 or selection on the plasticities of other traits (size at maturity, early and late offspring size,
519 spine length at maturity, age at maturity and intrinsic growth rate during the first transition;
520 somatic growth rate and horizontal migration during the second transition). However, also in
521 the cases where selection on the mean elevation of the reaction norm across environments, on
522 the mean elevation in a single environment or on correlated plasticities of other traits could
523 explain the evolution of plasticity of a given trait, direct selection on trait plasticity itself may
21
524 still have played a role. Also rapid evolution of plasticity itself often occurred despite the
525 presence of constraining correlations between character states or between trait plasticities,
526 suggesting that adaptive changes in plasticity could be achieved against antagonistic genetic
527 correlations.
528
529 Discussion of relationships between plasticity and evolutionary changes
530 When we look at the across-trait level for a guiding role of ancestral plasticity in shaping
531 evolution, during the first transition ancestral plasticity was no good predictor neither of
532 constitutive evolution, nor of the evolution of plasticity. While during the second transition
533 the level of ancestral plasticity was correlated to the extent of both evolutionary components,
534 it did so in opposite ways. Traits showing a higher ancestral plasticity showed a higher
535 evolutionary change for constitutive trait values but a lower evolutionary change in plasticity
536 under relaxed fish selection. While the positive correlation between phenotypic plasticity and
537 constitutive evolution may suggest that plasticity accelerated constitutive evolution, we
538 interpret both correlations as merely reflecting a consequence of adaptive evolution and
539 partial reversal after relaxation of the selection pressure. The observation that more plastic
540 traits in the high-fish period showed a stronger reduction in plasticity when fish predation was
541 relaxed can either be interpreted as a partial reversal of the responses to increased fish
542 predation pressure during the previous transition period or as a reflection of a cost of high
543 plasticity. The positive correlation between plasticity in the high-fish period and constitutive
544 evolution in the second transition likely reflects a partial reversal: if the different traits during
545 the first transition responded differently to the selection pressure, leading to the evolution of
546 different levels of plasticity, then it is not unexpected that relaxation from the selection
547 pressure led to a differential response in these same traits. As discussed earlier, the reversal
548 was partial and was mediated through the evolution of mean trait values rather than plasticity
22
549 in the second transition, hence the correlation between phenotypic plasticity and the amount
550 of constitutive evolution across traits.
551
23
552 Supplementary tables
553 Table S1 Comparisons of pairwise distances between subpopulation centroids in 14-
554 dimensional phenotypic space. Different letters indicate distances that were significantly
555 different in the absence of fish kairomones (small letters) or in the presence of fish
556 kairomones (capitals).
557
558
No fish kairomones
Distance Distance (Pre-fish, Distance (High-fish,
Reduced-fish)
∆Distance
P
Reduced-fish)
∆Distance
P
Distance (Pre-fish, High-fish) 2.17
a
Distance (Pre-fish, Reduced-fish) 2.54
a
Distance (High-fish, Reduced-fish) 1.64
b
0.37 0.063 0.53
0.90 0.001
0.008
With fish kairomones
Distance (Pre-fish, High-fish) 3.42
A
Distance (Pre-fish, Reduced-fish) 2.76
B
Distance (High-fish, Reduced-fish) 2.76
B
0.66 0.011 0.66
0.00
0.011
0.99
24
Table S2. Factor loadings for the PCA of 14 morphological, life history and behavioural traits in the set of 36 D. magna clones reared in the absence and in the presence of fish kairomones. Shown are the first three PC axes that each explained >10% of the variation in the total dataset. Loadings with an absolute value larger than 0.5 are indicated in bold.
Variables
Neonate size
Size at maturity
Early offspring size
Late offspring size
Somatic growth rate
Relative spine length neonates
Relative spine length at maturity
Age at maturity
Early fecundity
Late fecundity
Intrinsic growth rate
Phototactic behaviour
Horizontal migration
Alertness
%Variance explained
0.24
0.87
-0.05
-0.23
-0.88
0.06
0.55
0.23
20.1%
PC1
0.11
0.02
0.02
0.02
-0.84
-0.32
PC3
-0.47
-0.20
-0.09
-0.02
0.02
0.56
0.71
-0.12
-0.19
0.25
0.18
-0.67
0.10
0.37
13.0%
PC2
-0.02
-0.08
0.34
-0.06
0.00
0.00
-0.02
18.2%
0.79
0.91
0.90
0.20
-0.17
-0.28
0.14
25
Table S3 Results of the univariate ANOVAs testing for the effect of the fish kairomone treatment, hence the presence of significant phenotypic plasticity, per subpopulation for each of the 14 traits under study. Significant P -values are indicated in bold. Plastic responses were coded as adaptive (‘A’) and non-adaptive (‘NA’) based on a priori predictions that fish predation increases investment in reproduction and that visual predators select for smaller body sizes, and for increases in spine length, vertical migration and migration to the littoral (expected adaptive trait changes are indicated as arrows in Fig. 3).
Morphological and life history traits
Neonate size
Size at maturity
Early offspring size
Late offspring size
Somatic growth rate
Relative spine length neonates
Relative spine length at maturity
Age at maturity
Early fecundity
Late fecundity
Intrinsic growth rate
Behavioural traits
Phototactic behaviour
Horizontal migration
Alertness df
Pre-fish
F P df
1,107 12.3 < 0.001 NA 1,107
1,107 10.4 0.002 NA 1,107
1,107 4.1 0.045 NA 1,107
1,107 4.6 0.035 NA 1,107
1,107 20.5 < 0.001 A 1,107
1,107 137.3 < 0.001 A 1,107
1,107 12.8 < 0.001 NA 1,107
1,107 5.3 0.023 A 1,107
1,107 0.4 0.552 1,107
1,107 26.0 < 0.001 A 1,107
1,107 30.9 < 0.001 A 1,107
1,83 16.0 < 0.001 A
1,81 23.7 < 0.001 A
1,107 0.3 0.586
1,83
1,83
1,89
Subpopulation
High-fish
F P
35.6
35.5
1,107
1,107
9.7 0.002 A 1,107
5.8 0.017 A 1,107
15.9
< 0.001 A
< 0.001 A
< 0.001 A df
1,107
1.7 0.193 1,107
24.0 < 0.001 A 1,107
0.2 0.644 1,107
48.9 < 0.001 A 1,107
86.6 < 0.001 A 1,107
29.4 < 0.001 A 1,107
74.0 < 0.001 A 1,83
0.1 0.746 1,103
Reduced-fish
F P
3.9 0.050 NA
13.5 < 0.001 A
3.9
3.9
0.1 0.749
447.5 < 0.001 A 1,107 151.0 < 0.001 A
13.5
5.1
0.3
0.5
9.3
0.5
0.052
0.051
< 0.001 A
0.025 A
0.600
18.6 < 0.001 A
43.1 < 0.001 A
0.490
0.003 A
0.483
26
Table S4 Results of the univariate ANOVAs per transition testing for the effect of the fish kairomone treatment, subpopulation and their interaction for each of the 14 traits under study. A significant treatment × subpopulation interaction indicates the presence of significant evolution of plasticity between two successive periods. A significant contrast comparing the trait means between both subpopulations in the ancestral kairomone condition indicates the presence of significant constitutive evolution. Significant P -values are indicated in bold.
(A) Pre-fish to high-fish periods
Treatment Subpopulation Treatment × Subpopulation
Morphological and life history traits
Neonate size
Size at maturity
Early offspring size
Late offspring size
Somatic growth rate
Relative spine length neonates
1,214
1,214
0.02
36
0.897
< 0.001
1,22
1,22
1,214 573.6 < 0.001 1,22
Relative spine length at maturity 1,214 0.4 0.522 1,22
0.8
0.2
7.7
425.9
0.393 1,214 10.3
0.640
0.011
< 0.001
1,214
1,214
1,214
1.9
97.4
8.5
0.002
0.174
< 0.001
0.004
Age at maturity
Early fecundity
1,214 26.4 < 0.001 1,22
1,214 0.6 0.453 1,22
9.0
1.4
0.007
0.247
1,214
1,214
4.1
0.01
0.045
0.912
Late fecundity
Intrinsic growth rate df
1,214
1,214
1,214
F
4.2
4.4
0.6
P df F P df F P
0.041
1,22 20.5 < 0.001 1,214 45.9 < 0.001
0.038
1,22
0.429 1,22
2.3
3.4
0.142
0.080
1,214
1,214
42.8
13.3
< 0.001
< 0.001
1,214 74.8 < 0.001 1,22
1,214 111.5 < 0.001 1,22
0.2
0.4
0.668 1,214
0.550 1,214
6.7
8.2
0.010
0.005
Behavioural traits
Phototactic behaviour
Horizontal migration
Alertness
1,166
1,164 85.8 < 0.001 1,21
1,196
45.0
0.4
<0.001
0.531
1,22
1,20
5.6
2.4
1.2
0.027
0.129
1,166
1,164
2.0
3.0
0.287 1,196 0.02
0.157
0.086
0.876
χ²
Contrast
1
0.5
0.8
1.3
1.5
2.9
4.4
0.7
P
1.3
0.8
0.2
0.9
0.246
0.367
0.629
0.356
1.6
0.2
0.419
0.642
316.6 < 0.001
0.491
0.715
0.523
0.228
0.087
0.070
0.631
27
(B) High-fish to reduced-fish periods
Treatment Subpopulation Treatment × Subpopulation Contrast
Morphological and life history traits
Neonate size df F P df
1,214 12.5 < 0.001
1,22
F
4.1
P df F P
χ²
1
P
0.056
1,214 35.3 < 0.001 16.6
< 0.001
Size at maturity
Early offspring size
1,214 45.6 < 0.001
1,22
1,214 12.9 < 0.001
1,22
1,214 9.6 0.002
1,22
5.1
8.9
0.034 1,214
0.007 1,214
0.012
1,214
1.9
0.7
0.174
0.4
6.6
8.8
0.021
0.006
Late offspring size 7.4 0.1 0.748
6.5
0.022
Somatic growth rate
Relative spine length neonates
1,214
1,214
Relative spine length at maturity 1,214
8.9
577.8
8.6
0.003 1,22 20.9 < 0.001
1,214
< 0.001
0.004
1,22
1,22
3.4
7.3
0.080
0.013
1,214
1,214
6.3
63.4
0.4
0.013
< 0.001
0.522
27.1
17.9
5.3
< 0.001
< 0.001
0.021
Age at maturity
Early fecundity
Late fecundity
Intrinsic growth rate
1,214 26.1 < 0.001 1,22 88.5 < 0.001 1,214
1,214 0.01 0.942 1,22 0.01 0.941 1,214
1,214 63.3 < 0.001 1,22 1.0 0.337 1,214
1,214 127.2 < 0.001 1,22 19.2 < 0.001
1,214
3.9
0.5
3.0
5.1
0.050
0.482
0.084
0.025
64.7
0.2
0.3
24.3
< 0.001
0.999
0.590
< 0.001
Behavioural traits
Phototactic behaviour 0.1 1.0 0.317
Horizontal migration
Alertness
1,166 18.6 <0.001 1,22
1,166 64.0 < 0.001 1,22
1,192 0.1 0.803 1,19
0.6
3.5
0.810
1,166 11.1 0.001
0.457
1,166 11.9 < 0.001
0.076 1,192 0.5 0.482
6.0
4.0
0.028
0.091
28
Table S5 Evolutionary rates of the 14 morphological, life history and behavioural traits analysed in the natural Daphnia magna population for the two transitions in fish predation pressure: from the pre-fish to the high-fish periods, and from the high-fish to the reduced-fish periods.
Evolutionary rates are expressed in haldanes and are calculated for the traits in the absence (control) and in the presence (fish) of fish kairomones, as well as for the change in trait values (plasticity) upon exposure to fish kairomones. Significant evolutionary rates (based on a main effect of subpopulation in univariate ANOVAs) are given in bold.
Pre-fish to High-fish
(6.5 generations)
Control Fish
High-fish to Reduced-fish
(11.5 generations)
Plast. Control Fish Plast.
Morphological and life history traits
Neonate size
Size at maturity
Early offspring size
Late offspring size
Somatic growth rate
Relative spine length neonates
Late fecundity
Intrinsic growth rate
Behavioural traits
-0.046
0.041
0.017
0.036
0.045
-0.022
Relative spine length at maturity 0.600
Age at maturity -0.016
Early Fecundity -0.027
-0.047
-0.039
Phototactic behaviour
Horizontal migration
Alertness
-0.091
0.111
0.029
-0.300
-0.177
-0.122
-0.084
-0.025
0.253
0.754
-0.101
-0.027
0.015
0.067
-0.120
0.038
0.039
-0.168
-0.164
0.054
-0.009
0.047
0.047
0.098
0.073
0.023
0.086
0.017
-0.098 0.045 0.060 0.012
-0.089 0.048 0.053 0.005
-0.048 -0.048 -0.100 -0.045
0.252 0.020 -0.121 -0.115
0.054 -0.068 -0.062 0.008
-0.061 -0.082 0.128 0.033
0.001 0.008 0.010 0.012
-0.023
0.084 -0.056 -0.098 -0.049
-0.041
-0.057
0.008
-0.038
-0.026
0.047
0.028
0.052
0.054
0.059
0.073
0.018
29
1 Table S6 Broad-sense heritabilities of the 14 morphological, life history and behavioural traits scored in 36 D. magna clones derived from three
2 subpopulations separated in time. Heritabilities are calculated based on one-way ANOVAs for the traits in the absence (control) and presence
3 (fish) of fish kairomones, as well as for the difference between trait values in the presence and absence of fish kairomones (plasticity = fish -
4 control). Heritabilities are reported for each of the subpopulations separately as well as for the three subpopulations combined. Only heritabilities
5 significantly different from zero are reported.
6
Pre-fish subpopulation High-fish subpopulation Reduced-fish subpopulation
Control Fish Plast. Control Fish Plast
.
All subpopulations
Control Fish Plast. Control Fish Plast.
Morphological and life history traits
Neonate size 0.41
Size at maturity
Early offspring size
0.46
Late offspring size
Somatic growth rate
0.26
0.20
0.18
Relative spine length neonates
Relative spine length at maturity
Age at maturity
0.49
0.48
Early fecundity
Late fecundity
Intrinsic growth rate
Behavioural traits
Phototactic behaviour
Horizontal migration
Alertness
-
-
0.54
-
0.37
0.32
-
0.62
0.26
-
-
-
0.69
-
-
0.19
0.22
0.61
0.47
-
0.46
0.18
-
-
-
-
0.17
-
-
-
0.28
-
0.17
0.73
0.62
0.36
0.43
-
0.54
0.61
-
-
0.75 0.48 0.67
-
0.53
0.19
-
0.44
0.72
0.61
0.52
0.30 0.33
0.28 0.31
-
0.71
0.72
-
-
-
0.60
0.62
-
-
0.52
0.64
-
-
0.23
0.75
0.66
-
-
0.85 0.66 0.78
-
0.61
0.24
0.33
-
0.50
-
-
0.23
0.32
-
0.46
0.67
0.78
0.29
0.21
0.82
0.79
-
-
0.86 0.57
0.27
0.28
-
0.57
0.52
0.60
-
-
0.27 0.30
0.59
0.63
-
-
-
-
-
-
0.56
0.63
0.19
0.62
0.91
0.10
-
0.72
0.73
0.68
0.14
0.62
0.54
0.27 0.28 0.22
0.28 0.17 -
-
0.17 0.33 0.11
0.47
0.25
0.29
0.82
0.95
0.24
-
-
0.54
0.32
0.67
0.59
-
-
0.58
0.28
-
0.49 0.10
30
Table S7 Broad-sense genetic correlations (based on clonal means) (i) between character states of the same trait in the absence and in the presence of fish kairomones (grey cells along diagonal), (ii) between different traits in the absence of fish kairomones (below diagonal), and (iii) between different traits in the presence of fish kairomones (above diagonal). Genetic correlations significantly different from zero ( P < 0.05) are reported. Genetic correlations are classified as antagonistic (a) or reinforcing (r) (see supplementary methods). * indicates those correlations that are significantly different from one.
(A)Pre-fish subpopulation
Neonate size
Size at maturity
Early offspring size
Late offspring size
Somatic growth
Relative spine length neonates
Relative spine length at maturity
Age at maturity
Early fecundity
Late fecundity
Intrinsic growth rate
Phototactic index
Horizontal migration
Alertness
Neonate size
-
-
Size at maturity
-
-
Early offspring size
-
0.74* (r)
-
-
0.85* (r)
0.84* (r)
-
0.98* (r)
-0.68* (r) 0.71* (a) 0.64* (a)
-
-
-
-
-
-
-
-
-
-
-0.97* (r)
-
-
-
-
-
-
-
-
-0.81* (r)
-
-
-
-
-
-0.59* (a)
-
Late offspring size
Somatic growth
-
-
-
-
0.70* (r)
-
-
-
-
-
-
-0.81*
(r)
-
-
-
-
-
-
-
-
-0.66* (a)
-0.74* (a)
-
-
-
-
-
-
Relative spine length neonates
-
-
-
-
-
0.93*
-
-
-
-
-
-
-
-
Relative spine length at maturity
-
-0.95* (r)
Age at maturity
-
-
-0.74* (r)
-0.63* (r)
-
-
-
-0.87* (r)
Early fecundity
Late fecundity
-
-
-
-
-
-
-
-
-
-
Intrinsic growth rate
-
-
-
-
0.74* (r)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-0.80* (r)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
0.68*
-
-
-
-0.59* (a)
-
-
-0.90* (a)
-
-
-
-
-0.68* (r)
-
Phototactic index
-
-
-
-
-
-
-
-
-
-
-
0.64*
-
-
Horizontal migration Alertness
-
-
-
-
-
-
-
-
-
-
-
-
-
-0.64* (r)
-
-
-
0.80*
-
-
-
-
-
-
-
-
-
-
31
(B) High-fish subpopulation
Neonate size
Size at maturity
Early offspring size
Late offspring size
Somatic growth
Relative spine length neonates
Relative spine length at maturity
Age at maturity
Early fecundity
Late fecundity
Intrinsic growth rate
Phototactic index
Horizontal migration
Alertness
-
-
-
-
-
-
-
-
Neonate size
-
Size at maturity
-
-
-
-
0.80* (r)
-
-
Early offspring size
-
Late offspring size
Somatic growth
- -
0.73* (r) 0.85* (r) 0.70* (a)
-
0.65* (r) 0.93* (r)
0.58* (a) -
0.91* (r)
-
-
-
-
-
Relative spine length neonates
0.75* (a)
-
-
-
-
Relative spine length at maturity
-
-0.92* (r)
-0.65* (r)
Age at maturity
-
-
-
-0.72* (r)
-
-
-0.68* (r)
Early fecundity
-0.70* (r)
-
-
-
-
- - - - - - - -0.68* (r) -
Late fecundity
-
Intrinsic growth rate
-
Phototactic index
-
-
-
-
-
-
-
-
-
-
0.69* (r)
-
-
Horizontal migration Alertness
- -
-0.58* (a)
-0.73* (a)
-
-
-0.63* (a)
-
-
-
- - - - -
-0.82* (r)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-0.73* (r)
-
0.71* (r)
-
-
-
-
-
-
-
-
-
-
-
-
0.68* (a) -
-
-
-
-
-
-
-
-
- -
0.58* 0.71* (a)
- 0.75*
32
(C) Reduced-fish subpopulation
Neonate size
Size at maturity
Early offspring size
Late offspring size
Somatic growth
Relative spine length neonates
Relative spine length at maturity
Age at maturity
Early fecundity
Late fecundity
Intrinsic growth rate
Phototactic index
Horizontal migration
Alertness
Neonate size
-
Size at maturity
-
-0.61* (a) -
-0.79* (a) 0.77* (r)
Early offspring size
-
Late offspring size
-
-0.58* (a) 0.76* (r) 0.89* (r) 0.80*
-0.70* (r) 0.87* (a) 0.75* (a) 0.61* (a)
Somatic growth
-0.64* (r)
0.95* (r) 0.89* (r) 0.86* (a)
- 0.95* (r) 0.84* (a)
0.83* (a)
-
Relative spine length neonates
-
-
-
-
-
- - - -0.62* (r) - -
0.59* (a) -0.99 (r) -0.76* (r) -0.77* (r) -0.85* (a)
-
-
-
-
-
-
0.58* (a)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-0.66* (r) -0.63* (r)
-0.64* (r)
-
-
-
-
-
-
-
-
-
-0.71* (a)
-
-
-
-
-
-
-
-
-
-
-
-
Relative spine length at maturity
-
-0.99 (r)
-0.95* (r)
Age at maturity
-
-
-
-0.90* (r) -
-0.85* (a) -0.66* (r)
Early fecundity
-
-
-
-
-
- -
Late fecundity
-
0.61* (a)
-
Intrinsic growth rate
-
-
-
- -
0.63* (r) 0.64* (r)
-0.69* (a) -0.60* (a) -0.65* (a)
Phototactic index
-
-
-
-
-
-
Horizontal migration Alertness
- -
-
-
-
-
-
-
-
-
- -
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-0.61* (a) -
- -0.72* (r)
- -
0.68* 0.91* (r)
0.90* (r) 0.59*
-
-
-
-
-
-
-
-
-
-
-
0.92*
-
-
-
-
-
-
-
-
0.68*
-
-
-
-
-
-
-
-
0.85*
33
Table S8 Broad-sense genetic correlations (based on clonal means) between the plasticities of different traits for the set of 14 morphological, life history and behavioural traits scored in 36 D. magna clones derived from three subpopulations separated in time. Only genetic correlations significantly ( P < 0.05) different from zero are reported.
(A)Pre-fish subpopulation
Neonate size
Size at maturity
Early offspring size
Late offspring size
Somatic growth
Relative spine length neonates
Relative spine length at maturity
Age at maturity
Early fecundity
Late fecundity
Intrinsic growth rate
Phototactic index
Horizontal migration
Alertness
Neonate size
Size at maturity
-
Early offspring size
Late offspring size
- -
0.74* (r) 0.81* (r)
0.87* (r)
Somatic growth
-
-
-
-
Relative spine length neonates
-
-
-
-
-
Relative spine length at maturity
-
-0.88* (r)
Age at maturity
-
-
-0.77* (r)
-0.94* (r)
-
-
-
-0.84* (r)
Early fecundity
-
-
-
-
-
- - -
Late fecundity
-
-
-
-
-
Intrinsic growth rate
-
-
-
-
0.79* (r)
Phototactic index
-
Horizontal migration Alertness
- -
-
-
-
-
-
-
-
-
-
-
-
-
- - - - -
- -
-
- -
-
-0.73* (a)
-0.95* (r)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
34
(B)High-fish subpopulation
Neonate size
Size at maturity
Early offspring size
Late offspring size
Somatic growth
Relative spine length neonates
Relative spine length at maturity
Age at maturity
Early fecundity
Late fecundity
Intrinsic growth rate
Phototactic index
Horizontal migration
Alertness
Neonate size
Size at maturity
-
Early offspring size
Late offspring size
- -
0.79* (r) 0.70* (r)
0.96* (r)
Somatic growth
-
-
-
-
Relative spine length neonates
-
-
-
-
-
Relative spine length at maturity
-
-0.96* (r)
Age at maturity
-
-
-0.73* (r) 0.63* (r)
-0.66* (r) 0.60* (r)
- -0.62* (r)
Early fecundity
-
-
-
-
-
- - -
Late fecundity
-
-
-
-
-
Intrinsic growth rate
-
-
-
-
-
Phototactic index
-
-
-
-
-
Horizontal migration Alertness
-
-
-
-
-0.58* (a)
-0.69* (a)
-
-
-
-
- - - - -
- -
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-0.63* (a)
-
-
-
-
-
-
-
35
(C)Reduced-fish subpopulation
Neonate size
Size at maturity
Early offspring size
Late offspring size
Somatic growth
Relative spine length neonates
Relative spine length at maturity
Neonate size
Age at maturity
Size at maturity
-
Early fecundity
Late fecundity
Intrinsic growth rate
Phototactic index
Horizontal migration
Alertness
Early offspring size
Late offspring size
Somatic growth
- - -
0.73* (r) 0.76* (r) 0.80* (a)
0.83* (r) 0.66* (a)
0.70* (a)
Relative spine length neonates
-
-
-
-
-
Relative spine length at maturity
-
-0.99 (r)
Age at maturity
-
-
-0.73* (r) -
-0.78* (r) -
-0.81* (a) -0.83* (r)
Early fecundity
-
-
-
-
-
- - -
Late fecundity
-
-
-
-
-
Intrinsic growth rate
-
-
-
-
-
Phototactic index
-
-
-
-
-
Horizontal migration Alertness
-
-
-
-
0.64* (r)
-
-
-
-
0.64* (r)
- - - - -
- -
-
-
-
-
-
-
-
0.75* (r)
-
-
-
-
-
-
-
-
-
-
-
-
-0.60*
(a)
-
-
-
-
-
36
Table S9 Broad-sense genetic correlations (based on clonal means) between slope (trait plasticity) and the mean intercept across environments (the absence and the presence of fish kairomones) for the set of 14 morphological, life history and behavioural traits scored in 36 D. magna clones derived from three subpopulations separated in time. For each significant uncorrected Pvalue it is indicated whether it remained significant (*: P corrected
< 0.05) or not
(NS: P corrected
> 0.05) when correcting for the spurious correlation between slope and mean intercept.
Neonate size traits
Size at maturity
Early offspring
Pre-fish period High-fish period Reduced-fish r
Morphological and life history
-0.02
-0.15
-0.34
-0.39
P
0.94
0.65
0.28
0.21 r
-0.21
-0.17
-0.13
-0.20
P
0.51 0.31
0.59 r
0.13
0.68 0.48 period
P
0.32
0.68
0.12
0.54 0.67 0.017
NS
Late offspring size
Somatic growth size
Relative spine rate
Relative spine length neonates
Age at maturity length at maturity
Early fecundity
Late fecundity
Intrinsic growth
0.08
0.86
-0.41
0.80
0.0003*
0.19
-0.10
0.31
-0.06
0.36 0.25 0.028 0.93 -0.01
0.66 0.020
NS
-0.14 0.66 0.13
0.66 0.020
NS
0.19
0.63 0.028
NS
0.30
0.77
0.33
0.84
0.56
0.34
0.18
-0.07
0.23
0.11
0.32
0.58
0.83
0.48
0.98
0.70
0.75
0.31
Behavioural traits rate
Phototactic
Horizontal behaviour
Alertness migration
0.61 0.033
NS
0.03
0.44
0.06
0.15
0.85
0.22
0.41
0.93 -0.31 0.33
0.49 -0.32 0.31
0.24 0.60 0.041
NS
37
Table S10 Broad-sense genetic correlations (based on clonal means) between slope (trait plasticity) and intercept in a single environment (in the absence or in the presence of fish kairomones) for the set of 14 morphological, life history and behavioural traits scored in 36 D. magna clones derived from three subpopulations separated in time. For each significant uncorrected Pvalue it is indicated whether it remained significant (*:
P corrected
< 0.05) or not (NS: P corrected
> 0.05) when correcting for the spurious correlation between slope and intercept. r
Pre-fish period
Control
P
Fish kairomones r P r
High-fish period
Control
P
Fish kairomones r P r
Reduced-fish period
Control
P
Fish kairomones r P
Morphological and life history traits
Neonate size -0.68 0.015
NS 0.66 0.019
NS -0.79 0.0022
NS 0.65 0.021
NS -0.29
Size at maturity
Early offspring size
-0.62 0.033
NS 0.43 0.16 -0.68 0.015
NS 0.50 0.10 -0.48
Late offspring size
Somatic growth rate
0.37
0.11
0.69
0.63
0.013
0.028
NS
NS
-0.78 0.0028
NS 0.47
-0.75 0.0051
NS 0.27
0.13
0.39
-0.77
-0.71
0.0034
0.0091
NS
NS
0.68
0.53
0.014
NS
0.078
-0.10
0.32
0.75
0.30
0.76
0.82
0.0038
0.00096
NS
NS
-0.54 0.070 0.63 0.029
NS -0.64 0.024
NS 0.54 0.070 -0.54 0.070 0.71 0.010
NS
Rel. spine length neo.
Rel. spine length mat.
Age at maturity
Early fecundity
0.72
-0.77
-0.49
0.0087*
0.0036
0.10
NS
0.92 <0.0001
0.29 0.36
NS -0.54
-0.71
0.068
0.0094
NS
0.79
0.66
0.0022
0.019
NS
NS
-0.53
-0.41
0.073
0.19
0.44
0.67
0.15
0.017
NS
0.80 0.0017
NS -0.78 0.0026
NS 0.79 0.0020
NS -0.78 0.0025
NS 0.78 0.0027
NS
-0.55 0.063 0.92 <0.0001
NS -0.78 0.0025
NS 0.70 0.012
NS -0.82 0.0011
NS 0.86 0.00034
NS
0.18
-0.12
0.57
0.71
0.84 0.00056
NS -0.38
0.87 0.00026
NS -0.42
0.22 0.62 0.031
NS -0.32
0.17 0.74 0.0060
NS -0.21
0.31
0.51
0.48 0.11
0.67 0.018
NS
Late fecundity
Intrinsic growth rate
Behavioural traits
Phototactic behaviour
Horizontal migration
Alertness
0.10
0.09
0.76
0.79
0.83
0.67
0.00095
0.018
NS
NS
-0.59
-0.30
-0.67 0.018
NS 0.71 0.0093
NS 0.04
0.046
0.34
0.91
NS 0.62
0.61
0.65
0.033
0.036
0.041
NS
NS
NS
-0.49
-0.63
0.31
0.11
0.027
0.33
NS
-0.09
0.13
0.76
0.77
0.68
0.0042
NS
38
Supplementary figures
Figure S1 Temporal patterns in the density of Daphnia magna dormant eggs and in the density of stocked fish in the pond Oud-Heverlee Zuid during a period of 26 years after creation of the pond (1970-1996). Fish stock densities are aligned with dormant egg abundance in the sediment after Cousyn et al.
39
40
Figure S2 Clonal reaction norms to fish kairomones of a set of 36 Daphnia magna clones that were hatched from dormant eggs from three successive periods in time (pre-fish, high-fish and reduced-fish) from a natural pond (Oud-Heverlee Zuid) for 11 morphological and life history traits (neonate size, size at maturity, early and late offspring size, somatic growth rate, relative spine length of neonates and relative spine length at maturity, age at maturity, early and late fecundity and intrinsic growth rate), and three behavioural traits (phototactic behaviour, horizontal migration and alertness). Fish kairomone treatments are coded as ‘C’
(control) and ‘F’ (fish kairomones present). Means are given ± 1SE.
41
Figure S3 Distribution of evolutionary rates plotted against generation time for the traits measured (a) in the absence (control) and (b) in the presence of fish kairomones and (c) for trait plasticity upon exposure to fish kairomones. Each point represents the logarithm of the absolute value of a single evolutionary rate expressed in haldanes. The unfilled symbols represent the original data of figure 4 of Hendry & Kinnison
Evolutionary rates associated with the pre-fish to high-fish transition are presented by squares (■) and evolutionary rates associated with the high-fish to reduced-fish transition are presented by triangles (▲).
42
Figure S4 Univariate reaction norm presentation of the multiplicative methodology used to divide the total phenotypic change of a trait during a transition in fish predation pressure in its three components: ancestral phenotypic plasticity, constitutive evolution and evolution of plasticity. Shown is the situation for the trait change between the pre-fish (●) and high-fish
(■) periods. White (black) symbols indicate subpopulation means in the absence (presence) of fish kairomones; the grey square represents the hypothetical trait value for the high-fish subpopulation mean in the presence of fish kairomones in case only constitutive evolution and ancestral plasticity occur. The dashed line refers to the total trait change, i.e. the trait change one would observe in situ . Letter codes are explained in the text. Note that in this multiplicative methodology (and in contrast with the additive methodology) the subpopulation reaction norms of the ancestral pre-fish subpopulation (connecting the white and black circles) and the hypothetical subpopulation reaction norm of the derived high-fish subpopulation in the presence of only constitutive evolution and ancestral plasticity
(connecting the white and grey squares) are not parallel.
43
Figure S5 Univariate and multivariate relative contributions of plasticity (black), constitutive evolution (white) and evolution of plasticity (grey) to the standardized total phenotypic changes in the set of 14 morphological, life history and behavioural traits for both transitions in fish predation pressure in the natural Daphnia magna population from Oud-Heverlee Zuid:
(a) from the pre-fish to the high-fish periods and (b) from the high-fish to the reduced-fish periods. Shown are the results based on both the univariate and the multivariate partitioning with contributions estimated using the multiplicative method explained in Fig. S4.
44
Figure S6 Relationships (a, b) between the degree of plasticity in the initial subpopulation
(ancestral plasticity) and constitutive evolution (open symbols, dotted line) and evolution of plasticity (closed symbols, solid line) during the subsequent transition in fish predation pressure, and (c, d) between constitutive evolution and evolution of plasticity for the set of 14 morphological, life history and behavioural traits in the natural Daphnia magna population from Oud-Heverlee Zuid. Partitioning of the components was based on the additive method explained in Fig. 1. Panels (a) and (c) show relationships for the pre-fish to high-fish transition and panels (b) and (d) show relationships for the high-fish to reduced-fish transition. Only significant regression lines are shown.
45
Figure S7 Relationships (a, b) between the degree of plasticity in the initial subpopulation
(ancestral plasticity) and constitutive evolution (open symbols, dotted line) and evolution of plasticity (closed symbols, solid line) during the subsequent transition in fish predation pressure, and (c, d) between constitutive evolution and evolution of plasticity for the set of 14 morphological, life history and behavioural traits in the natural Daphnia magna population from Oud-Heverlee Zuid. Partitioning of the components was based on the multiplicative method explained in Fig. S3. Panels (a) and (c) show relationships for the pre-fish to high-fish transition and panels b and d show relationships for the high-fish to reduced-fish transition.
Only significant regression lines are shown.
46
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