Cue reliability and the evolution of adaptive plasticity in maternal traits

Cue reliability and the evolution of adaptive plasticity in maternal traits | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Cue reliability and the evolution of adaptive plasticity in maternal traits View ORCID Profile Henrique Teotónio , Stephen R. Proulx doi: https://doi.org/10.1101/2025.09.06.674614 Henrique Teotónio 1 Institut de Biologie, École Normale Supérieure, CNRS UMR 8197, Inserm U1024, PSL Research University , Paris, F-75005, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Henrique Teotónio For correspondence: teotonio{at}bio.ens.psl.eu Stephen R. Proulx 2 Department of Ecology, Evolution, and Marine Biology, University of California Santa Barbara , Santa Barbara, CA 93106, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract Natural populations face challenges from multiple environmental factors, and adaptive phenotypic plasticity is expected to evolve only when these factors are strongly correlated across space and/or time. In situations where individuals rely on signals from their mothers to inform them about selection, maternal trait responses to environmental factors that provide reliable cues may evolve to enable adaptation to selective but unreliable environments. We performed evolution experiments with the hermaphroditic nematode Caenorhabditis elegans in a temporally uncorrelated anoxic selective environment, where, prior to reproduction, maternal generations received innocuous blue-light pulses that either reliably or unreliably cued their progeny for anoxia. After 60 non-overlapping generations of experimental evolution, populations that were reliably exposed to blue light during their history evolved plasticity in two maternal effect traits: one involved in the sensing of blue light and signal transduction to the germline, which was expressed only when grandmothers and mothers developed under anoxia; the other in oocyte deposition and embryo provisioning of essential energetic resources for survival in anoxia. We further found that reliably cued populations adapted to their temporally uncorrelated anoxic selective environment, while there was evidence of maladaptation in populations that were unreliably exposed to blue light, partly because they did not evolve maternal bet-hedging strategies of embryo resource provisioning. These findings demonstrate that environmental cue reliability can determine adaptive phenotypic plasticity in maternal traits. Introduction Whether the evolution of phenotypic plasticity determines adaptation to spatially and temporally variable environments depends on the correlation between the environmental factor cueing developing individuals to express the appropriate trait values and the environmental factor causing selection ( Moran, 1992 ; DeWitt and Scheiner, 2004 ; Tufto, 2000 ; Chevin et al., 2010 ; McNamara et al., 2016 ). When individuals cannot directly detect the relevant environmental factor during development they must instead rely on their parents, usually their mothers ( Heard and Martienssen, 2014 ; Bell and Hellmann, 2019 ), to survive challenging conditions and express the optimal trait values in the environment they will experience at the time of reproduction. Many empirical and theoretical examples illustrate how the evolution of maternal effect traits, whose expression might be influenced by the environment, can underlie adaptation to variable environments ( Roach and Wulff, 1987 ; Lande and Kirkpatrick, 1990 ; Wolf and Brodie, 1998 ; Mosseau and Fox, 1998 ; Wolf and Wade, 2009 ; Day and Bonduriansky, 2011 ; Marshall and Uller, 2007 ; English et al., 2015 ). For instance, in the nematode Caenorhabditis elegans , anoxic conditions early in life leads to embryo mortality when mothers, having accumulated glycerol to withstand osmotic stress, are unable to provision their progeny with enough glycogen for completing development and hatching as free living larvae ( Frazier and Roth, 2009 ; LaMacchia and Roth, 2015 ). Evolution experiments have shown that adaptation to environments characterized by constant high osmotic stress and temporally alternating anoxia and normoxia, that is, where oxygen level is negatively correlated between non-overlapping maternal and offspring generations, depends on the evolution of adaptive plasticity for maternal glycogen provisioning ( Dey et al., 2016 ). If the environmental factor both cueing and causing selection is not temporally correlated across generations, such as under random fluctuating conditions between (non-overlapping) maternal and offspring generations, mothers cannot reliably signal the impending conditions to their offspring. Adaptation to these environments is expected to occur through selection on the variance of trait distributions ( Bull, 1987 ; Seger and Brockman, 1987 ; Tufto, 2015 ), as would occur when there is selection for risk-aversion or bet-hedging maternal strategies that protect offspring against pathogens and predators or that provision them with essential resources for survival ( Agrawal et al., 1999 ; Bonduriansky et al., 2016 ; Moran et al., 2010 ; Joschinski and Bonte, 2020 ). However, in the C. elegans evolution experiments conducted by Dey et al. (2016) , populations exposed to temporally uncorrelated anoxic conditions showed neither evolution of the variance in glycogen provisioning nor evidence of adaptation. Furthermore, risk-aversion and bet-hedging maternal strategies have been difficult to demonstrate in natural populations ( Simons, 2011 ; Childs et al., 2010 ). This might be because environmental correlations between grandmaternal and offspring generations are important, which could result in the evolution of plasticity in grandmaternal traits and more generally of traits that carryover the ‘memory’ of past environments ( Jablonka et al., 1995 ; Day and Bonduriansky, 2011 ; Donelson et al., 2018 ; Leimar and McNamara, 2015 ), because selection on the variance of maternal trait distributions is weak, particularly in the presence of genetic variation for within-generation plasticity ( Crean and Marshall, 2009 ; Furrow and Feldman, 2014 ; Kuijper and Hoyle, 2015 ; Tufto, 2015 ; Proulx and Teotónio, 2017 ; Kuijper and Johnstone, 2018 ), or because the correlations between the multiple environmental factors that all natural populations experience, which could influence the expression of maternal traits, have not been identified ( Chevin and Lande, 2015 ; Bonamour et al., 2019 ; Draghi, 2023 ). Theoretical work has shown that with multiple environmental factors adaptive plasticity is expected to evolve when they are correlated across time or space, as only their combined variation can reliably cue individual development to align with changing selection pressures ( Gavrilets and Scheiner, 1993 ; Chevin and Lande, 2015 ; King and Hadfield, 2019 ; English et al., 2015 ; McNamara et al., 2016 ; Draghi, 2023 ; Hudak and Dybdahl, 2023 ). Hence, in temporally uncorrelated environments, the evolution of adaptive plasticity in general and of maternal traits in particular should depend on the presence of an environmental factor in maternal generations that reliably cues selection in offspring generations ( Kuijper et al., 2014 ; Proulx and Teotónio, 2017 ). Furthermore, maternal trait responses to environmental factors that function only as reliable cues of selection in offspring generations should enable adaptation to the selective but potentially unreliable environments that mothers cannot anticipate ( Kuijper et al., 2014 ; Chevin and Lande, 2015 ; Leimar and McNamara, 2015 ; Draghi, 2023 ). Here we test these these predictions using C. elegans evolution experiments in a temporally uncorrelated anoxia environment between non-overlapping maternal and offspring generations, and weakly correlated grandmaternal and offspring anoxia environment. The experiments are similar to those of Dey et al. (2016) but they were designed so that during gametogenesis and prior to reproduction mothers received blue-light pulses that either reliably or unreliably cued their progeny for anoxia. Absence of blue-light pulses reliably or unreliably cued normoxia to which the ancestral population was originally adapted. C. elegans are able to discriminate blue light ( Ward et al., 2008 ; Edwards et al., 2008 ), avoiding the light source without adverse effects on survival or reproduction when exposed for short periods during gametogenesis ( Proulx et al., 2019 ). Therefore, in these experiments, we isolate the effects of blue-light on maternal signaling of upcoming selective conditions for their offspring from the effects of randomly fluctuating normoxia and anoxia on survival and reproduction to find whether cue reliability determines the evolution of adaptive plasticity in maternal traits. Materials and Methods Ancestral population and standard culture conditions All populations employed for experimental evolution are derived from a single ancestral population (named GA250). This ancestral population was derived from the hybridization of 16 inbred founder strains ( Teotónio et al., 2012 ; Teotónio et al., 2017 ), followed by 140 generations of domestication to lab conditions ( Teotónio et al., 2012 ; Chelo and Teotónio, 2013 ; Carvalho et al., 2014 ) and futher 50 generations of gradual adaptation to high salt conditions (305 mM of NaCl in growth media, from L1 larval stage to reproduction) ( Theologidis et al., 2014 ; Chelo et al., 2019 ). Standard culture conditions during domestication and high salt evolution included maintaining populations at sizes of N = 10 4 , and effective population sizes of N e = 10 3 ( Chelo and Teotónio, 2013 ), under a 4 day life-cycle ( Teotónio et al., 2012 ). Each population was kept in ten Petri plates with solid NGM-lite agar media (Europe Bioproducts) covered by an overnight grown lawn of HT115 E. coli that served as ad libitum food. After growth to maturity for 66 hours at constant 20°C and 80% relative humidity, all ten plates were mixed, and worms harvested with M9 isotonic solution and exposed to 1 M KOH: 5% NaOCl bleach solution for 5 min, to which only embryos survive. These embryos were then kept in M9 without E. coli for 18 hours. After this period, live L1 stage larvae density was estimated (five 5 µ L drops multiplied by total M9 volume), with the appropriate number of L1s then seeded to initiate the next generation. Under these culture conditions generations are non-overlapping. C. elegans hermaphrodites are able of self-fertilization but of outcrossing only when mated with males ( Stewart and Phillips, 2002 ). During domestication, males were maintained at a frequency of 20%-40% and thus outcrossing rates were between 40%-80%. High salt greatly reduces the opportunity for mating such that the ancestral population used here (GA250) was almost devoid of males after high salt adaptation ( Theologidis et al., 2014 ). At the start of experimental evolution reported here, and despite predominant selfing, there was standing genetic variation in GA250 for responses to result from the sorting of selfing lineages ( Noble et al., 2017 ; Dey et al., 2016 ). Genome-wide SNP diversity estimates of the number of effective lineages segregating in GA250, as defined by the inverse of the sum of squared lineage frequencies, were of 30 (for total number of at least 250, see Guzella et al. (2018) for this analysis of the ancestral population). A subset of 4 unreliable populations (U3, U4, U7 and U8, see below) were previously reported and showed an effective number of three lineages segregating after the 60 generations of experimental evolution [see Figure S3 in Dey et al. (2016) ]. Oxygen and blue-light culture conditions The first environmental factor manipulated was oxygen level. The environment consisted of anoxia or normoxia conditions during embryogenesis, after adult bleach and until the larvae L1 stage ( Figure S1 ). In detail, after bleaching and repeated washes with M9, 200 µ L containing embryos were transferred to 25 mM NaCl NGM-lite plates, without E. coli . Petri plates were then placed inside 7 L polycarbonate boxes with rubber clamp-sealed lids (AnaeroPack, Mitsubishi Inc.). Within these boxes, anoxic conditions were imposed by placing two GasPak™ EZ sachets (Becton, Dickinson and Company, ref. no. 260678). Anoxic conditions, defined as < 1% O 2 or approximately 1 kPa O 2 at atmospheric pressure according to the manufacturer, were confirmed using BBL™ Dry Anaerobic Indicator Strips (Becton, Dickinson and Company, ref. no. 271051). To prevent desiccation, paper towels moistened with ddH 2 O were placed inside each box. For normoxic conditions, the GasPak™ EZ sachets were not used. After 16 hours, boxes were opened and live L1 larvae were washed off the plates with M9 buffer, and their density was estimated to proceed with the next generation (see standard culture conditions above). The second environmental factor manipulated was blue-light exposure during gametogenesis and prior to reproduction. Adult C. elegans are known to avoid blue light ( Ward et al., 2008 ; Edwards et al., 2008 ), though in our setup, individuals could not escape the light source. Petri plate rack holders (Starsted) were fitted with strips of blue-light LEDs (Nichia NS6B083T and NSPB300B), positioning each plate to receive illumination from approximately 10 individual LEDs placed on opposing sides. According to the manufacturer, these LEDs have a peak relative intensity at 465 nm. Accounting for Petri plate geometry, as well as reflection and scattering from plastic surfaces, the resulting irradiance was approximately 0.07 mW mm − 2 and 21,000 lux per plate. Starting 48 h after L1 seeding and over a 12 h period, blue light was flashed for 0.5 seconds every 2 seconds to prevent heat accumulation. A custom Python script running on a dedicated computer synchronized light exposure across all plates, while unexposed plates were kept in a separate incubator. Experimental evolution design C. elegans can be cryopreserved ( Stiernagle, 1999 ), with stocks being frozen at − 80 °C and revived as needed for experimental evolution and for comparison of ancestral and derived populations in common garden assays ( Teotónio et al., 2017 ). The ancestral GA250 population was thawed from − 80 °C stocks with > 10 4 individuals and cultured for two generations for expansion of individual numbers ( > 10 5 ). GA250 was then divided intro several populations to undergo experimental evolution. The sequences of alternating normoxia and anoxia at each of the 60 generations have been described in Proulx et al. (2019) . Oxygen level sequences were designed so that there were 30 generations in normoxia and 30 generations in anoxia and a weak negative temporal autocorrelation of anoxic conditions between maternal and offspring generations ( ρ 1 =-0.065). We used three sequences so that the anoxia autocorrelation between grandmaternal and offspring generations were close to zero ( ρ 2 =-0.017), positive ( ρ 2 =0.247) or negative ( ρ 2 =-0.311), and named Seq19, Seq31 and Seq32, respectively ( Figure S2 ). For each oxygen sequence, hermaphrodites either reliably or unreliably received blue-light pulses depending on whether their progeny would be faced with anoxia ( Figure S2 ). For the unreliable regime, the same number of 30 generations were subject to blue-light, although there was only 50% chance of cueing anoxia in the following generation. The temporal cross-correlation between blue-light exposure in maternal generations and anoxia exposure in offspring generations was thus of one ( R 1 =1) in the reliable regime and of zero ( R 1 =0) in the unreliable regime, while keeping the same number of anoxia and blue-light generations across the 60 generations in both regimes. There were weak cross-correlations between blue light in grandmaternal generations and anoxia in offspring generations ( Figure S2 ): for unreliable regimes in Seq19, Seq31 and Seq32 they were of R 2 =0.153, R 2 =0.085, and R 2 =-0.05, respectively; for reliable regimes they were of R 2 =-0.085 for all anoxia sequences. For each oxygen and blue-light regime, we independently cultured 3 replicate populations during the 60 generations, following the standard laboratory conditions with high salt (305 mM NaCl) in the growth media and populations sizes of N=10 4 from the L1 larval stage until reproduction. A total of 18 populations were maintained: for Seq19 there were replicate populations U3, U4 and U10 (in the ‘ U nreliable’ blue-light regime populations), and replicate populations R4-R6 (in the ‘ R eliable’ blue-light regime populations); for sequence Seq33 U16-U18 and R16-R18; and for sequence Seq31 U7, U8, U12, and R10-R12. A larger set of populations underwent experimental evolution but they were not assayed for fitness, cf. ( Proulx et al., 2019 ). Assays of offspring fitness in anoxia The population growth multiplier, called R 0 , was used as a proxy of absolute offspring fitness in anoxia. It was measured by dividing the number of live offspring L1-staged larvae, after oxygen level manipulation in the grandmaternal and maternal generations, and blue-light manipulation in the maternal generation, by the fixed number of L1 seeded in the maternal generation ( Figure S1 ). We measured R 0 in ancestral and evolved populations in common garden assays, after the experiment was finished. Assays were done in 18 different blocks, each corresponding to the date of thawing population samples and assay culture. In each block, all oxygen and blue-light manipulations were done (8 conditions) for the ancestral GA250 population and one of the 18 evolved populations. All environmental conditions were the same as those during experimental evolution. From the grandmaternal generation, three technical replicates for each population run in parallel in each block. In detail, at least 10 3 L1 individuals were thawed from − 80 °C stocks to regular Petri plates (25 mM NaCl, with E. coli , normoxia) and surviving adults bleached to derive one culture per population sample. These were then maintained for one full generation alongside in standard conditions for individual number expansion. On the third generation, after bleach, each culture was split into three cultures (each with 5000 individuals, 5 Petri plates), with each culture being then in either normoxia or anoxia (starting the grandmaternal generation). Adults were bleached and individuals from the maternal generations were then exposed to varying oxygen during development and initial larval growth and varying blue-light presence during early adulthood (5000 individuals, 5 Petri plates). After bleaching the maternal generation, embryos and L1 larvae from the offspring generation were exposed to anoxia. The total number of surviving offspring L1 was measured by counting them in ten 5 µ L drops, multiplied to the total volume of the wash (2-3 mL, to the nearest µ L). This number was then divided by 5000 to obtain the population sample R 0 in anoxia (see next, Figure S1 ). A random id was assigned to each technical replicate to diminish potential bias during handling. All assays were performed by the same experimenter. There were therefore 5 generations of common culture between samples, which precludes our estimates from being biased due to uncontrolled environmental effects up to 5 generations. Due to unavoidable adult (prior to bleaching) and live L1 (after bleaching) loss that occurred with our manipulations during the assays, the reported R 0 in anoxia is a lower bound measure of the true value. Overall, the data for analysis consists of 845 observations: 18 blocks each with one evolved population plus the ancestral population, times 4 oxygen conditions, times 2 blue-light conditions, times 3 technical replicates, minus accidental loss (e.g., because of insufficient L1 numbers for continued culture in grandmaternal or maternal generations). Data analysis The discrete-time population growth multiplier, R 0 , is the appropriate measure of fitness given our discrete non-overlapping generation experimental evolution protocol, with the natural log of the R 0 being roughly equivalent to the instantaneous population growth rate ( Otto and Day, 2007 ). Given the experimental evolution regime, changes in ln( R 0 ) represent changes in fitness, and we define w = ln R 0 and conduct our statistical analyses on this measure of fitness ( w ). We defined the parameter β for each of the 8 environmental assay conditions (normoxia-anoxia grandmaternal-maternal combinations with or without blue-light exposure), which we term the environmental state. By defining these as independent parameters to be fit this is equivalent to a model with the full set of interactions between maternal environment, grand-maternal environment, and light cue. We first fit the ancestral population to better understand the baseline the experimental populations were starting from, using the following Bayesian model: along with priors for the parameters and variance terms. In this model, each observation of fitness is assumed to follow a normal distribution which can be thought of as encompassing both random variation in birth rates and observation variance. The measurement block is also given a random effect, where the variance among blocks is determined by an adaptive prior on the variance between blocks. From this model we can estimate fitness under all 8 environmental states along with the uncertainty in our estimate ( McElreath, 2020 ). In order to estimate the effects of experimental evolution on fitness, and the evolutionary responses of fitness to the blue-light cue, we model fitness of the experimental evolution populations as deviations from the ancestral values, with additional additive random effects of replicate population and the specific sequence of environmental conditions. This gives us the model specification: where I R is an indicator function that evaluates to 1 if the observation is of the reliabl populations (i.e. the “ETreatment” code is from the reliable branch of the experimental evolution regimes) and evaluates to 0 otherwise, and I U is an indicator function for unreliable populations. Note that the ancestral population does not have either a replicate population or a normoxia-anoxia sequence, so the indicator function I E was used to indicate observations of experimentally evolved populations (either reliable or unrealiable). We used weakly informative priors with the β s normally distributed with mean 0 and σ of 1 and the variance terms exponentially distributed with the rate parameter set to 2. Our fitness assay design allows us to infer for the evolution of maternal traits, cf. ( Teotónio et al., 2017 ). We use the method of parameter contrasts ( McElreath, 2020 ) to assess the evolution of plasticity of maternal traits, and in particular of a maternal trait in response to the blue-light cue. Because the parameters for the reliable and unreliable experimental evolution regimes were coded as a difference from the ancestral, they already represent contrasts that are independent of the inferred block effects. To evaluate the degree to which fitness evolved during experimental evolution we also need to include the average effect of replicate population and environmental sequence, so these are added back to the β parameters. We assessed the evolution of maternal traits that involve a response to the blue-light cue. The ancestral population’s responses to blue-light cues did not evolve in response to the experimental evolution regime that we imposed, and were either correlated responses to past selection pressures in the laboratory, or perhaps represent adaptive responses to light exposure of natural populations. Because our reliable and unreliable populations experienced the same uncorrelated sequences of anoxia and normoxia, they have the same opportunities to adapt to the experimental anoxia environment. The reliable and unreliable populations differ, however, in the reliability of the blue-light cue. Therefore, it is differences between the blue-light response of the reliable and unreliable populations that indicate adaptive differentiation in the responses to the blue light cue. In order to assess this differentiation, we estimated the difference in the fitness response to blue light between the reliable and unreliable populations, for each of the assayed environmental sequences, accounting for the block, sequence, and replicate population effects. All models were coded using the rethinking package ( McElreath, 2020 ) and run using R (4.3.1) and Stan (2.32.2) ( R Core Team, 2021 ; Stan Development Team, 2020 ). The MCMC sampler (Hamiltonian Monte Carlo) was run for 5000 iterations in four independent chains. We assessed convergence by checking to see that was less than 1.01. The effective sample size was larger than 2,500 for all models’ parameters. Archiving Data, statistical analysis scripts and contrast results can be found in our GitHub repository. Results and Discussion Oxygen and blue-light fitness effects in anoxia The ancestral population used for experimental evolution had been previously adapted to standard laboratory conditions, including normoxia during embryogenesis and hatching at the first larval stage (L1), high salt concentration (305 mM NaCl) in the growth medium from L1 to reproduction, and absence of blue-light pulses throughout the life cycle ( Teotónio et al., 2012 ; Theologidis et al., 2014 ). This ancestral population was derived from a hybrid of 16 inbred founders maintained for almost 200 generations at high population sizes ( N = 10 4 , N e = 10 3 ) under non-overlapping generations and high outcrossing rates when in low salt conditions, ensuring that standing genetic variation was available for selection before the onset of experimental evolution ( Chelo and Teotónio, 2013 ; Noble et al., 2017 ; Guzella et al., 2018 ). C. elegans hermaphrodites are capable of self-fertilization and outcrossing only when mated with males ( Stewart and Phillips, 2002 ). High salt conditions greatly reduce mating opportunities, such that the ancestral population was mostly devoid of males and the experimental evolution reported here resulted from the sorting of selfing lineages ( Theologidis et al., 2014 ; Dey et al., 2016 ). The ancestral population was assayed for the population growth multiplier in anoxia, R 0 , as a proxy for absolute offspring fitness under anoxic conditions during embryogenesis to L1 hatching ( Figure S1 ). Assays followed grandmaternal and maternal exposure to variable oxygen levels during development and larval hatching, and maternal exposure to blue-light pulses during hermaphrodite gametogenesis prior to reproduction (Materials and Methods). Oxygen levels were manipulated in the grandmaternal generation as it was previously found that R 0 depends on a carryover ‘memory’ trait that can have a detectable effect up to five generations later ( Proulx et al., 2019 ). There were thus 8 environmental states to be compared, and although the ancestral population was assayed and fitness data analyzed alongside the derived populations after experimental evolution (see next section and Materials and Methods), we first present results for the ancestral population. We previously found that offspring fitness is lower when they are exposed to anoxic conditions as developing embryos ( Dey et al., 2016 ; Proulx et al., 2019 ). When we compare among environment and blue-light treatments, we find that exposure to the blue light increases offspring fitness in anoxia relative to the dark treatments, regardless of the prior generational environment ( Figure 1 ), which may represent a hormetic effect of blue light ( Bijlsma and Loeschcke, 1997 ; Ward et al., 2008 ). The maternal and grandmaternal environments had little effect on offspring fitness in anoxia, except for when two generations of anoxic conditions were experienced. In this case, when two generations of anoxia were experienced in the absence of blue light, R 0 approaches one, indicating that populations would barely be able to replace themselves under prolonged anoxia. A similar pattern was observed in Dey et al. (2016) , where multiple generations of anoxia led to severe reductions in population sizes before adaptation to constant anoxia. Download figure Open in new tab Figure 1: Ancestral offspring fitness under anoxic conditions. Absolute offspring fitness under anoxic conditions during embryogenesis to L1 hatching was measured as the population growth multiplier, R 0 , following grandmaternal and maternal exposure to normoxia (N) or anoxia (A), and maternal exposure to either blue-light pulses (blue) or no light (gray) during gametogenesis ( Figure S1 ). Here the posterior R 0 estimates are shown for the ancestral population prior to experimental evolution, with the mean and 95% Credible Intervals (CIs) being represented. The estimation procedure includes uncertainty due to 18 independent assay blocks, each with 3 technical replicates (Materials and Methods). Evolution of plasticity in maternal traits Experimental evolution proceeded for 60 generations under uncorrelated anoxia conditions between maternal and offspring generations (autocorrelation of the anoxia time series at lag 1: ρ 1 = − 0.065), with three variable grandmaternal–offspring anoxia auto-correlations (− 0.311 < ρ 2 < 0.247). Blue-light pulses during gametogenesis and before reproduction (or their absence) served as either a reliable or unreliable cue of anoxia (or normoxia) for the offspring generation ( Figure S2 ; see Materials and Methods for the temporal cross-correlations between environmental factors). For each combination of blue-light and temporally uncorrelated anoxia regimes, three replicate populations were derived from the lab-domesticated ancestral population, yielding 9 reliable and 9 unreliable populations with respect to blue-light regime (Materials and Methods). It is important to note that because both reliable and unreliable populations experienced the same number of generations with blue-light any difference between them in the response to blue-light can only be due to the association of maternal traits related to blue-light sensitivity improving offspring survival in anoxia. After 60 generations, reliable and unreliable populations were compared to the ancestral population for offspring fitness in anoxia ( Figure S1 , Materials and Methods). We specifically contrasted the ancestral population’s environment-specific fitness values (as shown in Figure 1 ) to those of the evolved reliable and unreliable populations to assess the evolution of plasticity in maternal traits (Materials and Methods). Reliable populations showed increased fitness, particularly when mothers developed under normoxia, and this effect was relatively constant regardless of the oxygen conditions experienced by grandmothers or maternal blue-light exposure ( Figure 2A ). For assay conditions where the mothers experienced anoxia, reliable populations showed no increases in fitness. Unreliable populations showed evolution of reduced fitness, especially when assayed under maternal anoxia generations combined with maternal blue-light exposure ( Figure 2B ). While the different environmental sequences of oxygen fluctuations during experimental evolution contributed to the variance in fitness responses, their effect was relatively smaller than the effects from other sources ( Figure S3 ). Replicate population effects, block effects and intrinsic noise, including technical assay error, were of comparable magnitude. Download figure Open in new tab Figure 2: Difference of offspring fitness in anoxia from ancestral. Fitness differences from the ancestral population in blue-light reliable (a) and unreliable (b) populations after 60 generations of evolution under temporally uncorrelated anoxia conditions ( Figure S2 ). Fitness was measured in the offspring generation under anoxia, following grandmaternal and maternal exposure to normoxia (N) or anoxia (A), and maternal exposure to either blue-light pulses (blue) or no light (gray) during gametogenesis ( Figure S1 ). Posterior estimates of fitness are shown as median differences from the corresponding ancestral environmental states, with 50% (boxes) and 95% (whiskers) CIs (Materials and Methods). Each oxygen and blue-light treatment combination includes 3 replicate populations. Posterior distributions are based on 18 independent assay blocks, each with 3 technical replicates (see also Figure S3 for variance component results). The next analysis examined the difference in the response to blue-light (presence versus absence) for each grandmaternal and maternal oxygen level combination using the same model as before (Materials and Methods). The divergence of blue-light responses between the reliable and unreliable populations was calculated to infer the evolution of a distinct response to blue-light between evolution treatments. As predicted, we find that the reliable populations did diverge from the unreliable populations, albeit only under specific environmental sequences. In particular, when assayed under grandmaternal and maternal anoxia, reliable populations evolved a response significantly increasing offspring fitness as compared with the unreliable population ( Figure 3 ). Although the ancestral population showed an increase in fitness when exposed to blue-light under repeated anoxic conditions ( Figure 1 ), this response did not evolved under our experimental conditions. We had no expectation that unreliable populations would evolve plastic responses to blue light since there was no relationship between the blue-light cue and the oxygen environment. Following evolution, the blue-light fitness of the unreliable populations was more similar to the no-light response, as evidenced by a fitness decrease in blue-light relative to no-light. Download figure Open in new tab Figure 3: Evolution of blue-light detection and signal processing for provisioning. Evolution of the fitness difference between reliable and unreliable populations in blue-light sensitivity (presence versus absence), relative to the ancestral populations. Median estimates with 50% (boxes) and 95% (whiskers) CIs. Positive values indicate increased fitness divergence of reliable populations when compared to unreliable populations, for all combinations of grandmaternal and maternal oxygen environments (‘N’ for normoxia, ‘A’ for anoxia). We do see that the reliable populations had an increase in offspring fitness under repeated anoxia with a blue-light maternal cue, relative to the unreliable populations. We hypothesize that this involves two maternal traits, the first involving the sensing of the blue-light cue and processing the signal, and the second being the induction of a germline response, presumably through deposition of glycogen in oocytes as observed in Dey et al. (2016) . The findings in Figure 3 further show that the evolution of blue-light detection and signal transduction to the germline was dependent on the expression of a carryover trait over two generations, as it was only expressed when mothers and grandmothers experienced anoxia, a result in line with previous findings in these populations showing the expression of a trait carrying the ‘memory’ of anoxia exposure of up to five generations in the past ( Proulx et al., 2019 ). Hence, only in populations that faced an environmental factor that reliably cued selection did the evolution of a plastic maternal trait such as provisioning occur, with this evolution being conditional upon the evolution of another plastic maternal trait, the blue-light sensitivity and signal transduction to the germline. Adaptation to temporally uncorrelated anoxia environments We observed the evolution of plastic maternal traits and we further wanted to test whether this plasticity evolved as an adaptive repsonse to the temporally uncorrelated anoxia environment. The appropriate measure of adaptation in temporally variable environments is the geometric mean of fitness over all environmental sequences that a population faced during its unique history ( Cohen, 1966 ; Gillespie, 1974 ; Saether and Engen, 2015 ). Accordingly, we averaged w = ln ( R 0 ) across all combinations of grand-maternal and maternal environments, taking into account that both environmental factors (blue-light and oxygen) were equally frequent in the evolution experiments and that there was little correlation between maternal and offspring environment for anoxia ( Figure S2 ). For the reliable populations, adaptation was calculated only when blue light was given to mothers and their progeny experienced anoxia, because this is the correlation environment that they were evolved in. For the unreliable populations, we calculated the geometric mean fitness under the conditions that they were exposed to which included blue light half of the time. Given our design, any adaptation directly to the blue-light exposure would have been similar between reliability regimes. Finally, we assumed that fitness did not change when offspring were hatched under normoxia ( Figure S1 ), because not much evolution is expected in the more permissive environment to which the ancestral population was already adapted ( Teotónio et al., 2012 ; Theologidis et al., 2014 ). However, because we did not perform fitness assays under the normoxic hatching conditions, we cannot rule out that there was correlated evolution of offspring fitness in normoxia, or that the response to uncorrelated environments with an unreliable cue involved increasing fitness under normoxic hatching conditions at the expense of decreased fitness under anoxic hatching conditions, cf. ( Dey et al., 2016 ). With these caveats in mind, we find that reliable populations adapted to their specific sequence of fluctuating normoxia and anoxia, as indicated by significantly positive changes in geometric mean fitness across the range of grandmaternal and maternal environments experienced over 60 the generations of experimental evolution ( Figure 4 ). Blue-light cue reliability therefore lead to the evolution of adaptive plasticity in maternal traits. In contrast, unreliable populations exhibited signs of maladaptation. However, this was not sufficient to drive extinction during the experiment, as geometric mean fitness remained consistently above zero during experimental evolution; cf. ( Proulx et al., 2019 ). Maladaptation of unreliable populations indicates that they did not evolve either grandmaternal or maternal bet-hedging strategies, aligning with the results of Dey et al. (2016) . This outcome is somewhat unexpected and it may be that adaptation to randomly fluctuating normoxia and anoxia in the absence of reliable environmental cues requires the evolution of a carryover trait persisting beyond two generations, which we did not test for. Download figure Open in new tab Figure 4: Adaptation to temporally uncorrelated anoxia environments. To measure adaptation, we averaged w over all combinations of grandmaternal and maternal environments the replicate populations experienced during experimental evolution. Shown are the median estimates with 50% (boxes) and 95% (whiskers) CIs for reliable and unreliable populations relative to the ancestor population (zero line). Adaptation to temporally uncorrelated anoxia environments is only observed for reliable populations. Conclusions The evolution experiments show that adaptive plasticity in maternal traits evolves when environmental factors in maternal generations provide reliable cues for the selection environment that offspring will face. These findings are consistent with the theoretical expectations of Chevin and Lande (2015) , who showed that the combination of environmental factors that cue development is the best predictor of changing selection pressures [see also Leimar and McNamara (2015) ; Draghi (2023) ]. By design, in our evolution experiments, blue light in maternal generations was the best predictor of anoxia selection in offspring generations. More specifically, our findings validate the theoretical expectations of Kuijper et al. (2014) , who showed that the evolution of maternal traits depends on changing selection pressures because of a given environmental factor containing more stochastic noise relative to selection caused by another environmental factor. Indeed, even when each of the environmental factors is temporally uncorrelated on its own, as long as there is a temporal cross-correlation between them, selection will favor the evolution of plastic maternal traits [Figures 5 and S5 in Kuijper et al. (2014) ]. In our evolution experiments, maternal traits were selected because the (cross-)correlation between blue light in maternal generations and anoxia in offspring generations was maximal in the reliable regime. In contrast, when our experimental populations experienced no temporal correlation between blue-light and anoxia there was no evolution of adaptive maternal plasticity, as shown by the lack of responses in the populations from the unreliable regime. C. elegans are soil-dwelling bacteriophagous nematodes ( Schulenburg and Félix, 2017 ) that may use light, particularly at UV to blue wavelengths, to regulate their circadian rhythm, detect reactive oxygen species, forage for food, find dispersal vectors, or avoid pathogens and predators ( Edwards et al., 2008 ; Ward et al., 2008 ; Bhatla and Horvitz, 2015 ; Ghosh et al., 2021 ; Migliori et al., 2023 ). Despite nearly 200 generations of evolution in a relatively homogeneous laboratory environment (including constant density, absence of blue-light pulses, normoxia, and a single food source), our findings indicate that the ancestral population retained genetic variation enabling responses to anoxia selection in the reliable populations ( Theologidis et al., 2014 ; Noble et al., 2017 ); and although the ancestral population perhaps lacked much genetic covariance between maternal traits, such genetic covariance must have evolved to determine adaptation in the reliable populations ( Lande and Kirkpatrick, 1990 ; Wolf and Brodie, 1998 ; Wolf, 2000 ; Wolf and Wade, 2009 ). Plastic responses in maternal traits are expected to be polygenic, as multiple processes influence survival at low oxygen levels, including lipid and sugar oocyte deposition and the ability to enter a suspended metabolic state during embryogenesis and larval hatching ( Mendenhall et al., 2006 ; Powell-Coffman, 2010 ; LaRue and Padilla, 2011 ; Frazier and Roth, 2009 ; LaMacchia and Roth, 2015 ). Although we have not tested for the evolution of glycogen deposition in oocytes and thus embryo glycogen provisioning, this maternal trait should at least partly explain offspring survival in anoxia ( Dey et al., 2016 ). As shown, individuals that survive anoxia express a ‘memory’ trait that carries over to their offspring when they themselves are again faced with anoxia, possibly due to germline regulation of small RNA pools, histone post-translational modifications and mitochondrial function ( Kishimoto et al., 2017 ; Baugh and Day, 2020 ; Wang et al., 2022 ). However, and despite the possibility of standing genetic variation for this carryover trait – in C. elegans , it has been shown that there is natural genetic variance for multigenerational carryover traits, e.g. ( Frézal et al., 2018 ) –, there was little opportunity in our experiments for this trait to be selected because there were only very weak anoxia autocorrelations or cross-correlations between blue-light and anoxia at higher generation lags than maternal-offspring lags. One can only speculate about how physiological changes in the germline (of grandmothers) in turn influenced the evolution of photosensation in their adult (maternal) offspring. Photosensation in C. elegans occurs via ciliated head neurons and unique photoreceptor proteins whose expression could be dependent on anoxia exposure during development ( Edwards et al., 2008 ; Ward et al., 2008 ; Liu et al., 2010 ). Several neuroendocrine mechanisms, including insulin-like, serotonin and small RNA modulation, were likely involved in the evolution of signal transduction to the germline ( Hubbard et al., 2013 ; Burton et al., 2017 ; Posner et al., 2019 ; Das et al., 2020 ; Mignerot et al., 2024 ). Similar mechanisms have been described whereby pheromone concentrations, serving as cues of individual density, are processed and signals transduced to the germline ( Perez et al., 2021 ; Wasson et al., 2021 ). In conclusion, natural populations are faced with multiple environmental factors, yet there is still limited understanding of how phenotypic plasticity influences adaptation to changing conditions. For instance, it is still unclear whether many organisms living in seasonal environments rely on changing photoperiod or temperature as a cue to adjust the allocation of relevant resources and maximize reproductive success when conditions are optimal ( Wong and Ackerly, 2005 ; Bradshaw and Holzapfel, 2007 ; Oravec and Greenham, 2022 ), or whether photoperiod or temperature also cause selection on their own. Our findings indicate that natural populations possibly evolve to respond to cues from one aspect of their environment in order to adaptively anticipate changes in another, particularly through the evolution of plastic maternal traits. Author Contributions SRP: Conceptualization, Software, Formal analysis, Validation, Investigation, Funding acquisition, Writing; HT: Conceptualization, Resources, Data curation, Supervision, Funding acquisition, Investigation, Writing, Project administration.0 Supplementary Figures Download figure Open in new tab Figure S1: Fitness assay design. Colored boxes indicate the environmental factors manipulated to assess their effects on offspring fitness in anoxia. Middle boxes represent the life-history stage during the grandmaternal, maternal, and early offspring generations. In the grandmaternal and maternal generations, embryos were exposed to either normoxic or anoxic hatching conditions (orange); in the offspring generation, only anoxic conditions were applied. Although we refer to anoxia as the absence of oxygen, we could only confirm that the oxygen concentration was below 1% in the atmosphere (see Materials and Methods). In the maternal generation, and prior to reproduction, adults were either exposed to blue-light or left unexposed (blue). The population growth multiplier is calculated as the number of live hatched larvae following embryogenesis in anoxia, divided by the fixed number of larvae that initiated the maternal generation ( R 0 ), which we consider to be a proxy for absolute offspring fitness in anoxia. The natural logarithm of R 0 is the instantaneous population growth rate, which we simply consider to be a proxy for offspring fitness in anoxia ( w ). All 18 populations of experimental evolution, and the ancestral population from which they were derived, were measured contemporaneously in several assay blocks after 2 generations of common garden. Download figure Open in new tab Figure S2: Experimental evolution design. Three environmental sequences with weak temporal autocorrelation of anoxic conditions between maternal and offspring generations ( ρ 1 =-0.065; orange), and variable autocorrelations between grandmaternal and offspring generations (− 0.311 < ρ 2 < 0.247), were imposed over 60 discrete non-overlapping generations. Maternal generations were exposed to blue-light pulses that either reliably ( R 1 =1) or unreliably ( R 1 =0) cued anoxia in the offspring generation (blue). There were weak cross-correlations between grandmaternal blue-light exposure and offspring anoxia (− 0.08 < R 2 < 0.15; see Materials and Methods). For each combination of oxygen-level and blue-light, we cultured three independent replicate populations, for a total of 18 populations. All populations were derived at generation −2 from a single ancestral population with standing genetic variation ( Theologidis et al., 2014 ; Dey et al., 2016 ), previously domesticated to standard culture conditions for 140 generations [ N = 10 4 , normoxia, no blue-light pulses; Teotónio et al. (2012) ; Noble et al. (2017) ], followed by 50 generations of gradual adaptation to high salt (305 mM NaCl) in the larval-to-adult nematode-growth medium ( Theologidis et al., 2014 ). Under high-salt conditions hermaphrodites are favored ( Theologidis et al., 2014 ), and selfing was the predominant mode of reproduction during experimental evolution. Download figure Open in new tab Figure S3: Marginal and pair posterior distributions of variance terms. The multilevel model involved three sources of process variability (often termed random effects): fitness assay statistical block, sequence of fluctuating normoxia-anoxia during experimental evolution, and replicate populations. Generically, these variance terms can trade-off of each other to produce parameter sets with high likelihood. Each of the terms has a similar magnitude, although we have strong evidence that between-block variance is away from zero. The pair plots show, as expected, a negative correlation between each of the pairs of variance terms. Acknowledgments We are indebted to Snigdhadip Dey, a talented researcher who conducted the evolution experiments and performed the fitness assays, and who passed away during the course of this work. We also thank H. Gendrot, V. Pereira, S. Santos, A. Larue, S. Hotte and D. Tran for their help with worm handling, and C. Braendle and F. Mallard for discussion. This project was partially funded by grants from the National Science Foundation (EF-1137835) to SRP, and the European Research Council (FP7/2007-2013/243285) and Agence Nationale de la Recherche (ANR-17-CE02-0017-01, ANR-24-CE12-0904) to HT. We have no competing interests to declare. Funder Information Declared U.S. National Science Foundation, https://ror.org/021nxhr62 , EF-1137835 Agence Nationale de la Recherche, https://ror.org/00rbzpz17 , ANR-17-CE02-0017-0 , ANR-24-CE12-090 European Research Council , FP7/2007-2013/24328 Footnotes https://github.com/ExpEvolWormLab/CueReliability/ References ↵ Agrawal , A.A. , C. Laforsch , and R. Tollrian . 1999 . Transgenerational induction of defences in animals and plants . Nature 401 ( 6748 ): 60 – 63 . doi: 10.1038/43425 . OpenUrl CrossRef ↵ Baugh , L.R. and T. Day . 2020 . Nongenetic inheritance and multigenerational plasticity in the nematode c. elegans . 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