Transgenerational Adaptation to Pollution Changes Energy Allocation

Article pubs.acs.org/est

Transgenerational Adaptation to Pollution Changes Energy Allocation in Populations of Nematodes Benoit Goussen,*,†,‡,⊥ Alexandre R.R. Péry,†,§ Jean-Marc Bonzom,‡ and Rémy Beaudouin† Unité Modèles pour l’Écotoxicologie et la Toxicologie (METO), Institut National de l’Environnement Industriel et des Risques (INERIS), BP2, F-60550 Verneuil en Halatte, France ‡ Institut de Radioprotection et de Sûreté Nucléaire (IRSN), PRP-ENV, SERIS, Laboratoire d’ÉCOtoxicologie des radionucléides (LECO), BP3, F-13115 Cadarache, France †

S Supporting Information *

ABSTRACT: Assessing the evolutionary responses of long-term exposed populations requires multigeneration ecotoxicity tests. However, the analysis of the data from these tests is not straightforward. Mechanistic models allow the in-depth analysis of the variation of physiological traits over many generations, by quantifying the trend of the physiological and toxicological parameters of the model. In the present study, a bioenergetic mechanistic model has been used to assess the evolution of two populations of the nematode Caenorhabditis elegans in control conditions or exposed to uranium. This evolutionary pressure resulted in a brood size reduction of 60%. We showed an adaptation of individuals of both populations to experimental conditions (increase of maximal length, decrease of growth rate, decrease of brood size, and decrease of the elimination rate). In addition, differential evolution was also highlighted between the two populations once the maternal effects had been diminished after several generations. Thus, individuals that were greater in maximal length, but with apparently a greater sensitivity to uranium were selected in the uranium population. In this study, we showed that this bioenergetics mechanistic modeling approach provided a precise, certain, and powerful analysis of the life strategy of C. elegans populations exposed to heavy metals resulting in an evolutionary pressure across successive generations.

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According to Kawecki et al.,20 experimental evolution studies require a biological model organism with a short life cycle which could be maintained in laboratory, with a sufficient population size to avoid genetic drifts,21,22 and with a high genetic diversity. This last factor is the main limiting factor of microevolution processes.9,15,23−26 The nematode Caenorhabditis elegans fully complies to these requirements (short life span, short life cycle, high fecundity, short length, high population size, and ease to culture in laboratory conditions27) and is a useful organism for the assessment of evolutionary responses of populations submitted to stress. According to Klerks,28 the analysis of the evolution of the resistance to a toxic compound alone should not be considered as an accurate tool to assess ecological impacts. Indeed, evolution of the individuals of a population under stress conditions may involve several other mechanisms such as physiological modifications, population diversity reduction, etc., and lead the population into evolutionary trade-offs. For example, an increase in the apparent resistance of a population

INTRODUCTION

The assessment of pollutant perturbations is required at population and multigenerations scales, especially if the considered exposure time-scale is longer than the organisms life span.1−6 It is known that stressful environments may lead to evolutionary responses such as local adaptation of long-term exposed populations.7 Indeed, environmental stress may introduce new selective evolutionary forces, with their own intensities and directions, which may impact significantly evolutionary processes and therefore the genetic and structural shape of populations.7−11 While local adaptation occurs, a population will be characterized by genetic and phenotypic particularities (which can evolve over time) allowing the individuals in a population to present a better fitness to succeed in local environmental conditions.12,13 This local adaptation is predominantly driven by selection phenomena that can reduce the genetic diversity of a population and present difficulties for this population to cope with other stressors.14−18 Medina et al.19 indicate that knowledge on the phenotypic and genetic changes involved within a population submitted to stress over several generations could allow the risk assessment of biodiversity loss and the potential extinction of populations due to long-term exposure. © 2015 American Chemical Society

Received: Revised: Accepted: Published: 12500

July 14, 2015 September 29, 2015 September 29, 2015 September 29, 2015 DOI: 10.1021/acs.est.5b03405 Environ. Sci. Technol. 2015, 49, 12500−12508

Article

Environmental Science & Technology

generation exposure was extensively described by Goussen et al.,37 and a detailed description is provided in the Supporting Information. Experimental Test for Each Generation. Experiments were carried out on both parental (P0) and filial (FX with “X” representing the generation number) generations. At generations P0, F2, F3, F6, F10, F12, and F16, individuals from the MGC and the MGU populations were exposed to a range of 7 concentrations of uranium (nominal concentrations: 0, 0.1, 0.3, 0.5, 0.9, 1.1, and 1.2 mM U). Generation F6 has not been used in the present study due to reglementary constraints leading to the absence of reproduction data for this generation, and generation F10 has been added to the present study compared to the one described in Goussen et al.37 The exposure was realized in 12-well tissue-plates. Growth and egg laying were monitored individually for 6 days. The experimental exposure procedure was extensively described by Goussen et al.,37 and a detailed description is provided in the Supporting Information. Modeling. Model Description. The DEBtox models describe five modes of action of chemicals on physiological processes. Two of these have a direct effect on the reproduction (cost and hazard models). The three others have indirect effects on the reproduction in conjunction with effects on growth (assimilation, maintenance, and growth models). Our previous study demonstrated that the mode of action of the uranium on C. elegans is likely to be a decrease of energy assimilation from food36 and this has been confirmed by the calculation of the Deviance Information Criterion (DIC)39,40 on the last generations of the MGU population (no change in response were expected in the MGC population over time; see the Supporting Information). Therefore, data have been modeled using the assimilation mode of action equations’ as described in Goussen et al.:36

to a toxic compound can be driven by the elimination of sensitive individuals19 and, as a consequence, result in a reduction of the population genetic variability.14 The ability of the population to resist a subsequent stress could be reduced, and thus the population viability decreased. The analysis of ecotoxicological data based on mechanistic models that predict the toxicokinetics (TK) and toxicodynamics (TD) permits the inference of physiological and toxicological parameters. These parameters allow us to quantify and to model the evolution of the life-history traits of the individuals of a population. Such knowledge allows the analysis of population change over time and can highlight possible adaptations of organisms. Such information may help elucidate the life strategy selected by the individuals of a population to prevail long-term stressful environments or assess an increase of sensitivity throughout successive generations.29 The DEB theory30 is based on a mathematical description of the uptake and use of energy within an organism. According to this theory, energy is taken up from food, assimilated, and stored into reserves. This energy is then dispatched among three main processes: (i) maintenance, (ii) growth, and (iii) reproduction. DEBtox models based on the DEB theory were developed by Kooijman and Bedaux31−33 and corrected since.34,35 DEBtox models describe the perturbation of energy management when an organism is exposed to a pollutant. They assume that the effects on one of the parameters of the DEB model for the investigated species appear when the internal concentration exceeds a threshold called the no-effect concentration (NEC). It has been shown in a previous study, that a DEBtox model can handle C. elegans life cycle and can assess accurately the mode of action of a pollutant on this nematode.36 In a previous study, two populations of C. elegans have been derived from an ancestral population37 presenting a large standing genetic diversity.38 One was submitted to a strong selection pressure over 16 generations and the other was a control population. In the present study, the evolution of physiological and toxicological parameters of life history traits of organisms composing of the two populations has been assessed using a nematode DEBtox model in order to better understand physiological modifications that may appear under long-term uranium exposure.

−1⎤ ⎡ ⎛ lf 3 ⎞ ⎥ ⎢ s f (l ) = α 1 − ⎜1 + 3 ⎟ ⎢⎣ l ⎠ ⎥⎦ ⎝

(1a)

(1 − sf )f + g dl = rB dt g + [(1 − sf )f (1 − s(cq))]

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× [(1 − sf )f (1 − s(sq )) − l]

MATERIALS AND METHODS Experimental Data. C. elegans Population. The population used in this study is an androdioecious population created by Teotónio et al.38 through a funnel cross strategy. Briefly, two-isolate hybrids were obtained by crossing in a pairwise fashion 16 wild isolates. The two-isolate hybrids were then intercrossed in a pairwise fashion to create four-isolate hybrids. Hybridizations continued until the 16-isolate hybrids were created. The authors maintained the population over 140 generations and did not observe any significant loss of genetic diversity after the recombination-selection equilibrium was mostly reached. Adaptation processes may be expected to occur within this population, which contains around 30% of males, as it is genetically highly diverse. Multigenerations Exposure. Two populations derived from the same maintenance population were followed over 16 generations. The first population was the control population (thereafter called MGC standing for Multi-Generations Control), the second one was exposed to a nominal concentration of 1.1 mM of uranium (thereafter called MGU standing for Multi-Generations Uranium). The full multi-

(1b)

⎤ g+l RM ⎡ dR ⎢ = (1 − sf )f (1 − s(cq))l 2 − lp3⎥ dt ⎥⎦ 1 − lp3 ⎢⎣ g + (1 − sf )f (1 − s(cq)) −R R × max R max (1c)

where sf is the size-dependent ingestion stress factor, (1 − α) (−) the proportion of food available whatever the length of the organism (see the Supporting Information and Goussen et al.36 for more information on the assumptions), rB (h−1) the von Bertalanffy growth rate, f (−) the actual ingestion rate divided by the maximal ingestion rate for a body size, R (#) the cumulated reproduction, RM (# h−1) the maximum reproduction rate, Rmax (#) the maximal cumulated reproduction, and g (−) the investment ratio. L (μm) represents the body length at time t, L0 (μm) the body length at birth (i.e., the start of feeding in the DEB framework), Lf (μm) the body length at which the ingestion rate is half the maximum ingestion rate, and Lp (μm) the body length at puberty. All length data are scaled by the maximal length Linf (μm) resulting in parameters l, l0, lf, and lp (−). 12501

DOI: 10.1021/acs.est.5b03405 Environ. Sci. Technol. 2015, 49, 12500−12508

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concerning the fitting process are available in the Supporting Information. Convergence of MCMC chains was assessed using the Gelman and Rubin44 convergence diagnostic modified by Gelman.45 Model fit quality was visually checked by plotting the fits for both the growth and the reproduction data and by the inspection of the distribution of the residual errors. Posterior distributions were estimated using the 1000 last iterations of the estimation process. Data Analysis. In the present study, the experimental conditions were slightly different from the conditions in which the stock population were maintained. Thus, the life history traits of the stock population may have influenced the response of the individuals during the first generations of the experiment. Maternal effects are known to mitigate individual responses to stress due to cross-generation phenotypic plasticity3,6 and it is asumed that they are sufficiently diminished after three generation.20,46 For this reason, we decided to analyze our data relative to both generation P0 and F3. We highlighted parameters where strictly more than 80% of the posterior distribution at a given generation was either higher than the 97.5th percentiles or lower than the 2.5th percentiles of the posterior distribution of the parameter at the reference generation (either P0 or F3). This is our criterion for statistical significance.

The internal concentration of the pollutant scaled by the bioconcentration factor, cq, has been calculated as described by Kooijman and Bedaux31 and modified in Goussen et al.:36 dcq dt

=C

⎡ k (1 − sf )f ke(1 − sf )f dln l 3 ⎤ − cq ⎢ e + ⎥ l l dt ⎦ ⎣

(2)

where ke (h−1) is the elimination rate and C (mM) the actual exposure concentration (measured concentration were used). The toxic stress function, s, was calculated as s(cq) = max[0,b (cq − NEC)] with b (mM−1) the slope of the effect and NEC (mM) the no-effect concentration. More information about the scaling of eq 2 can be found in the Supporting Information. Parameters Inferences. Parameters inferences were performed using the statistical computing software R 3.0141 and the JAGS 3.3.0 (Just Another Gibbs Sampler) software. This software is a program for the statistical analysis of Bayesian hierarchical models by Markov Chain Monte Carlo (MCMC).42 For each selected generation, we performed an adaptive phase of 6000 iterations. We then performed another 54 000 iterations. Calibration was performed on three independent MCMC chains. Prior values were extracted from Goussen et al.36 and are presented in Table 1. Because the Table 1. Prior Parameters Values Used for the Model Calibrationa parameter α (−) f (−) L0 (μm) Lf (μm) Lp (μm)

0.86 0.97 5(165, 37)T ]0, +∞] 364 5(818, 57)T ]0, +∞]

Linf (μm)

5(1383, 41)T ]0, +∞]

rB (h−1)

5(0.031, 310−3)T ]0, +∞] 12 5(15, 1.90)T ]0, +∞]

g (−) RM (# h−1) ke (h−1)

5(249, 22)T ]0, +∞] 5(0.16, 0.11)T ]0, +∞]

b (mM−1)

5(0.31, 0.03)T ]0, +∞]

NEC (mM)

5(0.42, 0.06)T ]0, +∞]

Rmax (#)

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RESULTS Fit Quality. The quality of the fit of the DEBtox model, accounting for the decrease of assimilation efficiency as the physiological mode of action, was adequate for both populations and all studied generations (See Supporting Information Figures S11−S32). Changes in Parameter Values from Generation P0 Onward in the Two Populations. Both the MGC and the MGU populations evolved during the experiment. Indeed, as presented in Table 2 and Figure 1, parameters Linf, rB, Rmax, and ke significantly changed, even if only slightly in some cases, compared to P0 in the same way for both MGC and MGU populations. Some parameters did not change compared to P0 in the same time-scale for the two populations (Table 2). Parameter Linf started to increase from F3 onward in MGC (increase of 7% for F3, increase of 4% for F12, and 3% for F16). Its modification only became significant from generation F12 onward for the MGU population (increase of 3% for F12 and 9% for F16). The same pattern could be observed with parameter ke, which started to decrease from F3 onward in MGC and from F12 onward in MGU. Indeed, parameter ke started to decrease from F3 onward in MGC by over 40% (60% at F10, 43% at F12, and 50% at F16). Decrease of ke only became significant from generation F12 onward for the MGU population (respectively 64% at F12 and 50% at F16). Maternal Effects: Changes in Parameter Values from Generation F3 Onward. To analyze our data set without maternal effects, the changes in parameter values were assessed relative to the values observed at the F3 generation (cf. Materials and Methods). When compared to the third generation (F3) of the control population (MGC) and of the uranium population (MGU) (cf. Table 3 and Figure 2), it appears that the initial length (L0) increased in MGU at F16 compared to F3 by over 20%. The same pattern could be observed for the ultimate length (Linf) of MGU which increased by 7% at F16 compared to F3. In contrast, the maximal length

prior

a

Physiological prior were extracted from Goussen et al.36 The normal distribution of mean μ and standard deviation σ is noted 5(μ , σ ), and the uniform distribution between a and b is noted U[a, b]. T ]c, d] denotes an interval truncation between c (excluded) and d.

experimental protocol (including food conditions) was the same as for the experiment presented in Goussen et al.,36 the parameters related to food availability were not expected to differ between generations. Therefore, parameters α, f, and Lf were fixed. The parameter g was also fixed as the model sensitivity analysis has showed that it does not influence the model output.36 All the length data for each data set were used to fit the parameter Linf: the scaled length l multiplied by Linf was fitted against the measured length. The data observation process followed a normal prior distribution with a standard error following a non informative uniform prior distribution of wide range as proposed by Gelman.43 The likelihoods were calculated on length and reproduction variables. As the prior values are based on the estimation of the F2MGC parameters, this particular generation has not been included in the parameter estimation of the present study. More information 12502


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Table 2. Parameters Values Relative to P0 for Each Generation of the Two Populations MGC (Control) and MGU (Uranium)a MGC

L0

Lp

Linf

rB

RM

Rmax

b

NEC

ke

MGU

generation

relative to P0

95% P0

relative to P0

95% P0

P0 F2 F3 F10 F12 F16 F2 F3 F10 F12 F16 F2 F3 F10 F12 F16 F2 F3 F10 F12 F16 F2 F3 F10 F12 F16 F2 F3 F10 F12 F16 F2 F3 F10 F12 F16 F2 F3 F10 F12 F16 F2 F3 F10 F12 F16

1.00 0.98 0.94 0.91 0.93 0.95 1.04 1.04 1.01 1.01 1.02 1.01 1.07 1.02 1.04 1.03 0.95 0.95 0.95 0.92 0.86 0.96 0.90 0.90 1.06 0.88 0.99 0.80 0.97 0.81 0.92 0.76 0.93 1.15 0.85 1.15 0.93 0.87 0.56 0.89 0.91 1.14 0.50 0.40 0.57 0.50

− 0.42 0.43 0.80 0.54 0.42 − − − − − − − − − − 0.53 0.43 0.39 0.81 0.99 0.17 0.33 0.26 − 0.38 0.24 0.98 0.34 1.00 0.60 0.93 0.23 − 0.84 − 0.22 0.27 1.00 0.23 0.15 0.21 0.99 1.00 0.84 0.95

− − − − − − 0.32 0.03 0.10 0.07 0.10 0.36 1.00 0.51 0.99 0.81 − − − − − − − − 0.13 − − − − − − − − 0.95 − 0.74 − − − − − − − − − −

1.00 0.89 0.82 0.91 0.92 0.98 1.09 1.04 0.99 1.00 1.07 1.02 1.01 1.01 1.03 1.09 0.95 1.01 0.91 0.86 0.86 0.96 0.82 0.97 1.05 0.85 0.97 0.74 1.00 0.76 0.92 0.78 0.95 0.73 0.85 1.07 0.87 1.11 0.56 0.87 0.80 0.71 0.86 2.07 0.36 0.50

− 0.75 1.00 0.74 0.65 0.21 − − 0.08 0.04 − − − − − − 0.68 − 0.89 1.00 0.99 0.13 0.62 0.07 − 0.54 0.26 1.00 0.24 1.00 0.57 0.99 0.17 1.00 0.77 − 0.32 − 1.00 0.35 0.72 0.35 0.10 − 1.00 0.94

− − − − − − 0.82 0.42 − − 0.66 0.66 0.24 0.32 0.92 1.00 − 0.15 − − − − − − 0.12 − − − − − − − − − − 0.28 − 0.48 − − − − − 0.71 − −

a Results relative to P0 represent the ratio FX/P0. The 95% values correspond respectively to the proportion of the posterior distribution of the parameter of FX that was below the 2.5th percentile or above the 97.5th percentile of the posterior distribution of P0. The table is presented as a single-sized table in order to facilitate reading (i.e., only the highest value between the 95% is presented).

the NEC tends to decrease in MGU whereas it tends to increase across generations un MGC.

decreased in the MGC from F10 onward by over 3%. The von Bertalanffy growth rate (rB) decreased in both populations by over 10% and 15% in respectively MGC (at F16) and MGU (from F10 onward). Regarding the toxicity parameters, the slope of the effect (b) increased in MGC at F10 and F16 by 24%, but decreased by over 23% at F10 in MGU. The no-effect concentration (NEC) decreased by over 28% at F10 and F16 in MGU and by 36% at F10 only in MGC. (cf. Figure S9). The trend of the NEC differs between the two populations. Indeed,

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DISCUSSION In the present study, an experimental evolution study has been performed to evaluate the changes of life-history traits of C. elegans individuals from populations submitted to a high evolutionary pressure via exposures to uranium. The results were assessed using a bioenergetic mechanistic model. This 12503


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Figure 1. Parameter distribution relative to P0 for the parameters Linf, rB, Rmax, and ke throughout the generations. The means of the parameter posterior distribution values for each generation have been scaled by the mean of the parameter posterior distribution value at P0. The horizontal line corresponds to 1 (i.e., the reference value of P0). Confidence intervals represent the 95th percentiles of the posterior distribution.

for other heavy metal compounds. Indeed, for example, Bryan and Hummerstone48 sampled Nereis diversicolor individuals from several areas with low or high copper concentrations and measured an internal concentration of copper 100 times higher in copper-adapted individuals than in nonadapted ones without adverse impact on the individuals’ survival. This could be due to metallothioneins that could bind with heavy metal compounds and thereby allow higher accumulation without an increase of toxic effects.49,50 We postulate that the estimation of toxicological parameters obtained at F10 may be the result of an artifact driven by the lower quality of the data at this particular generation. Indeed, the time-series of the reproduction data were not complete at F10 due to technical reasons (time-series only include the beginning of the reproduction). The evolution of the life-history traits of the individuals of the control population during evolutionary experiments has already been encountered by Beaudouin et al.51 who highlighted individuals’ evolution in a nonexposed Chironomus riparius population throughout generations. These authors attributed the evolution they observed in the control population to a nondesired artificial selection process and indicated that the egg selection for the subsequent generations may have been biased. Aspects of our protocol may have resulted in a similar artificial selection over the generations. For example the test media transfer protocol that involves a medium change: agar-medium to liquid-medium to agarmedium. Alternatively, the nondesired phenotypic evolution may be a result of genetic drift, due to the small size of the population. Indeed, the protocol transfer involves a transfer of 500 individuals every 3 days, which introduces an artificial strong reduction of the population at each transfer. According to Kawecki and Ebert,52 the local adaptation could be confounded by the genetic drift. Indeed, genetic drift could

study demonstrates a joint evolution of the individuals from both the control population (MGC) and the stressed population (MGU) on traits of the organisms, highlighted by the time trends of several physiological and toxicological DEB parameters. Indeed, along the experiment, the maximal length of individuals of both populations increased and their growth rate decreased compared to the ancestral population. It is also interesting to highlight that the no-effect concentration (NEC) parameter did not change (except at F10) compared to the ancestral population (P0) throughout the generations whatever the population. This result, combined with the response observed on the elimination rate (decrease of ke), suggests an increase of the capacity of individuals of both populations to accumulate the uranium without an increase in the toxic effect. Nevertheless, this interpretation must be taken carefully as it is important to be aware that scaled internal concentrations (scaled by the bioconcentration factor) are used in the DEBtox framework. Thus, variations of ke and NEC could be determined either by the model structure or by real physiological processes. Indeed, the actual structure of the model involves a trade-off between ke, the NEC, and the intensity of the effect. To avoid this trade-off, it would be necessary to use the actual internal concentrations instead of the scaled internal concentrations. This would allow a more accurate estimation of the ke and thus a more accurate estimation of the NEC. Unfortunately, the measurement and use of internal concentrations is not straightforward especially for small organisms and complex processes such as chemical biotransformation processes (which can strongly affect the internal concentration of parent and metabolites compounds47) must be taken into account. The accumulation of well-known toxic compounds without an increase of the toxic effect has been observed in individuals 12504


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Table 3. Parameters Values Relative to F3 for Each Generation of the Two Populations MGC (Control) and MGU (Uranium)a MGC

L0

Lp

Linf

rB

RM

Rmax

b

NEC

ke

MGU

generation

relative to F3

95% F3

relative to F3

95% F3

F3 F10 F12 F16 F10 F12 F16 F10 F12 F16 F10 F12 F16 F10 F12 F16 F10 F12 F16 F10 F12 F16 F10 F12 F16 F10 F12 F16

1.00 0.97 0.99 1.01 0.97 0.97 0.98 0.95 0.97 0.96 1.00 0.97 0.90 1.00 1.18 0.98 1.22 1.02 1.16 1.24 0.92 1.24 0.64 1.03 1.05 0.80 1.14 1.00

− 0.16 0.07 0.03 0.08 0.09 0.04 1.00 0.90 0.99 0.03 0.31 0.85 − − 0.02 − − − − 0.53 − 1.00 − − 0.69 − −

− − − − − − − − − − 0.03 − − 0.02 0.27 0.02 0.71 0.01 0.47 1.00 − 1.00 − 0.14 0.16 − 0.20 0.07

1.00 1.12 1.13 1.20 0.94 0.95 1.03 1.00 1.02 1.07 0.90 0.85 0.85 1.19 1.28 1.04 1.34 1.03 1.24 0.77 0.90 1.13 0.50 0.78 0.72 2.42 0.42 0.58

− − − − 0.23 0.14 − 0.10 − − 0.84 0.99 0.99 − − − − − − 1.00 0.30 − 1.00 0.76 0.96 − 0.99 0.70

− 0.66 0.75 0.98 − − 0.09 0.10 0.62 1.00 − − − 0.18 0.45 0.03 0.90 0.01 0.60 − − 0.49 − − − 0.91 − −

a Results relative to F3 represent the ratio FX/F3. The 95% values correspond respectively to the proportion of the posterior distribution of the parameter of FX that was below the 2.5th percentile or above the 97.5th percentile of the posterior distribution of F3. The table is presented as a single-sized table in order to facilitate reading (i.e., only the hights value between the 95% is presented).

the MGU may suggest that the energy allocation shifted in this population toward individuals with a longer body-size and a lower resistance to the uranium. The maximal length increase and the growth rate decrease in the MGU population suggests the selection of larger individuals that develop at a slower rate. Moreover, as no significant difference in length at puberty was observed, the onset of the reproduction ought to be delayed across the generations. A slow down in development and a delay in reproduction onset under uranium pressure is consistent with experiments conducted on C. riparius.51 However, the authors also demonstrated the selection of individuals with a smaller larvae length. Our experimental design did not allow us to conclude definitively if the changes in the individuals life-history traits were due to a phenotypic (often referred to as acclimation) or a genetic adaptation as it did not include common garden experiments designed to diminish the maternal effects. Even so, to our point of view, genetic adaptation seems more probable. Indeed, another experiment on C. elegans exposed during 22 generations to uranium also concluded, while performing common-garden experiments designed to diminish the maternal effects in a control environment, to the selection by the uranium evolutionary pressure of individuals with a higher maximal length.11 Moreover, these uranium tolerant individuals tended to be longer in the common-garden than the nonadapted individuals starting from the 15th generation. These results can be compared to those of the present study as

cause deviation of the phenotype and lead to a decrease of the mean fitness of the population.21 Once the maternal effects (i.e., cross generation phenotypic plasticity induced by the mother’s environment6,53) of P0 generation have been diminished, some differential evolution aspects appeared between the two populations. This indicates that the changes in elimination rate were mainly due to the three first generations in the MGC population whereas it was due to a microevolution for the MGU population. Another differential evolution appears regarding the maximal length. Indeed, although this parameter increased in both populations compared to P0, it decreased in MGC compared to F3 and continued to increase in MGU. This could suggest that the initial increase of maximal length in MGC may be due to maternal effects which was reversed in subsequent generations. In contrast, individuals’ maximal length still increased in the MGU population, which indicates the selection of larger individuals in this population. The results also demonstrated that the no-effect concentration tends to decrease in MGU compared to F3 but tends to increase in MGC compared to F3. This may indicate an evolution toward a higher sensitivity to uranium of individuals repeatedly exposed to uranium over multiple generations. This observation may have initially been hidden by the P0 maternal effect. Such an increase in sensitivity to uranium has also been measured on Daphnia magna with an increase in DNA damages and in the severity of effects on life history traits across the generations.54,55 This general pattern in 12505


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Figure 2. Parameter distribution relative to F3 for the parameters Linf, rB, Rmax, and ke throughout the generations. Means of the parameters posterior distribution values for each generation have been scaled by the mean of the parameter posterior distribution value at F3MGC and F3MGU respectively for MGC and MGU populations. The horizontal line corresponds to 1 (i.e., the reference value of F3MGC and F3MGU). Confidence intervals represent the 95th percentiles of the posterior distribution.

the physiological parameters estimated here are control parameters. The results indicate that once the maternal effects have been diminished, the individuals of the MGU population evolves in a different manner than the individuals of the MGC population, which results in differing energy allocation strategies between the two populations. Here, data analysis was performed using a mechanistic model. The use of such models for the analysis of multigenerations experiments enables an in-depth analysis of the variations of life history traits of individuals. Such models can help ascertain the evolution strategy of the individuals of a population. The insights and conclusions drawn from the present study were not possible using our previous nonmechanistic analysis described in Goussen et al.37 For example, it was not possible to observe the change of the maximal length of the individuals of the uranium population (MGU). This can be accounted for by the fact that the mechanistic analysis of the present study was a more integrative analysis considering both the reproduction and the growth dynamics simultaneously. To conclude, this study allowed us to highlight the impacts of a pollutant on the population of an organism exposed over multiple generations. It confirmed the importance of evolutionary ecotoxicology studies in environmental risk assessment in order identify the evolutionary responses of stressed populations across multiple generations, e.g., an increase in sensitivity to a chemical stressor. This study demonstrates the utility of mechanistic modeling approaches in ascertaining these evolutionary responses.

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Detailed experimental procedures, model description and estimation processes (PDF).

AUTHOR INFORMATION

Corresponding Author

*B. Goussen. Phone: +44 (0)1234 264878. E-mail: benoit. [email protected]. Present Addresses ⊥

Environment Department, University of York, Heslington, York, United Kingdom & Safety and Environmental Assurance Centre, Colworth Science Park, Unilever, Sharnbrook, Bedfordshire, United Kingdom § AgroParisTech, UMR 1402 EcoSys, F-78850 ThivervalGrignon, France Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We are especially grateful to Florian Parisot for experimental help and discussion, to Roman Ashauer, Enrico Mombelli, Oliver Price, Cleo Tebby, and two anonymous reviewers for helpful comments on the manuscript. We also thank Henrique Teotónio for providing us with his base population. This work was part of the Envirhom-Eco research program supported by the French Institute for Radioprotection and Nuclear Safety (IRSN) and the 190 DRC-08-02 program supported by the French Ministry of Ecology.

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ASSOCIATED CONTENT

S Supporting Information *

REFERENCES

(1) Nisbet, R. M.; Gurney, W. S. C.; Murdoch, W. W.; Mccauley, E. Structured population models: a tool for linking effects at individual and population level. Biol. J. Linn. Soc. 1989, 37, 79−99.

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.est.5b03405. 12506


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