Rapid temporal adaptation structures tolerance to toxic cyanobacteria in a natural population of the water flea Daphnia

Rapid temporal adaptation structures tolerance to toxic cyanobacteria in a natural population of the water flea Daphnia | 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 Rapid temporal adaptation structures tolerance to toxic cyanobacteria in a natural population of the water flea Daphnia View ORCID Profile Maxime Fajgenblat , View ORCID Profile Emma Gouwy , View ORCID Profile Manon Coone , View ORCID Profile Rafaela Almeida , View ORCID Profile Alice Boudry , Kiani Cuypers , Edwin van den Berg , View ORCID Profile Isabel Vanoverberghe , View ORCID Profile Luc De Meester , View ORCID Profile Ellen Decaestecker doi: https://doi.org/10.1101/2025.02.28.640829 Maxime Fajgenblat 1 Laboratory of Freshwater Ecology, Evolution and Conservation , KU Leuven, Leuven, Belgium 2 Data Science Institute, I-BioStat, Hasselt University , Diepenbeek, Belgium Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Maxime Fajgenblat For correspondence: maxime.fajgenblat{at}gmail.com Emma Gouwy 3 Laboratory of Aquatic Biology, Interdisciplinary Research Facility Life Sciences, KU Leuven, KULAK , Campus Kortrijk, Kortrijk, Belgium Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Emma Gouwy Manon Coone 3 Laboratory of Aquatic Biology, Interdisciplinary Research Facility Life Sciences, KU Leuven, KULAK , Campus Kortrijk, Kortrijk, Belgium Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Manon Coone Rafaela Almeida 1 Laboratory of Freshwater Ecology, Evolution and Conservation , KU Leuven, Leuven, Belgium Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Rafaela Almeida Alice Boudry 1 Laboratory of Freshwater Ecology, Evolution and Conservation , KU Leuven, Leuven, Belgium 3 Laboratory of Aquatic Biology, Interdisciplinary Research Facility Life Sciences, KU Leuven, KULAK , Campus Kortrijk, Kortrijk, Belgium Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Alice Boudry Kiani Cuypers 1 Laboratory of Freshwater Ecology, Evolution and Conservation , KU Leuven, Leuven, Belgium Find this author on Google Scholar Find this author on PubMed Search for this author on this site Edwin van den Berg 1 Laboratory of Freshwater Ecology, Evolution and Conservation , KU Leuven, Leuven, Belgium Find this author on Google Scholar Find this author on PubMed Search for this author on this site Isabel Vanoverberghe 3 Laboratory of Aquatic Biology, Interdisciplinary Research Facility Life Sciences, KU Leuven, KULAK , Campus Kortrijk, Kortrijk, Belgium Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Isabel Vanoverberghe Luc De Meester 1 Laboratory of Freshwater Ecology, Evolution and Conservation , KU Leuven, Leuven, Belgium 4 Leibniz Institute für Gewasserökologie und Binnenfischerei (IGB) , Berlin, Germany 5 Institute of Biology, Freie Universität Berlin , Berlin, Germany 6 Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB) , Berlin, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Luc De Meester Ellen Decaestecker 3 Laboratory of Aquatic Biology, Interdisciplinary Research Facility Life Sciences, KU Leuven, KULAK , Campus Kortrijk, Kortrijk, Belgium Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ellen Decaestecker Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Cyanobacteria blooms pose a substantial threat to freshwater systems globally. While zooplankton grazers such as Daphnia can have an important role in suppressing cyanobacteria blooms, cyanobacteria can adversely impact Daphnia fitness and even kill them. Earlier work has shown an evolutionary increase in tolerance to cyanobacteria across years and strong genotype x genotype interactions determining the interaction between Daphnia and the cyanobacterium Microcystis . Here, we test the hypothesis that D. magna can adapt during one growing season to changes in dominant strains of Microcystis . Over two consecutive years, we collected D. magna clonal lineages and Microcystis strains from a single pond early and late in the growing season and we assessed whether Daphnia survival differed when exposed to Microcystis strains from either the same or a different time point within the growth season. Our findings reveal important Daphnia genotype x Microcystis genotype interactions, with Daphnia survival being higher when exposed to Microcystis from the same time point than when exposed to Microcystis of a different time point. Our results extend earlier findings to variation within one single natural system and growth season, and suggest an important impact of rapid (co)evolutionary dynamics shaping the tolerance of zooplankton grazers to cyanobacteria. Introduction Cyanobacteria blooms are increasingly common across the globe because of rising temperatures and nutrient levels ( Kosten et al., 2012 ; Meerhoff et al., 2022 ). Cyanobacteria blooms can exert substantial pressure on ecosystem functioning due to their effect on the fitness of many organisms ( Pimentel et al., 2001 ). They reduce nutrient and light availability for submerged macrophytes, can typically not be grazed because they form colonies, are generally a low-quality food source for grazers, and can produce toxins ( Carton, 2005 ; Gray et al., 2014 ; Paerl et al., 2011 ; Sommer et al., 2012 ). As such, they can shift shallow lakes to a turbid state dominated by cyanobacteria, that is difficult to control by grazers ( Hansson et al., 2007 ; Scheffer & Van Nes, 2007 ). In temperate regions, cyanobacteria blooms typically occur in summer (PEG model; Sommer & Maciej Gliwicz, 1986 ). Higher temperatures, along with nutrient release from the sediment ( Paerl & Huisman, 2008 ) and decreased zooplankton grazing caused by fish predation ( Lampert & Sommer, 2007 ) lead to an increased fraction of cyanobacteria in the phytoplankton community. The water flea Daphnia plays a pivotal role in many standing freshwaters, as they are key grazers on phytoplankton and the preferred prey of fish ( Miner et al., 2012 ). Daphnia can often exert a strong top-down control on phytoplankton ( Gianuca et al., 2016 ; Jeppesen et al., 1996 ), and can even make the difference between a cyanobacteria bloom and a clear-water state ( Sarnelle & Wilson, 2005 ; Scheffer et al., 2001 ). Yet, many studies have reported that Microcystis can kill Daphnia due to the production of toxins or feeding inhibition and starvation, for instance through its low nutritional quality, the production of digestive protease inhibitors and colony formation ( Agrawal et al., 2001 ; Ghadouani et al., 2004 ; Lürling, 2003 ; Macke et al., 2017 ; Trubetskova & Haney, 2006 ; von Elert et al., 2003 ). Consequently, while Daphnia can serve as an efficient grazer of Microcystis , it can also be vulnerable to it. This leads to a large variation in results on top-down control of cyanobacteria blooms by Daphnia , as reported in field-based and experimental work (e.g. Benndorf & Henning, 1989 ; de Bernardi & Guissani, 1990 ; Dejenie et al., 2009 ; Gulati et al., 2008 ; Sarnelle & Wilson, 2005 ; Søndergaard et al., 2008 ). Several studies indicate that Daphnia displays inducible responses (which can be transferred through maternal effects; Gustafson et al. 2005) and can even genetically adapt when exposed to cyanobacteria (e.g. through selection on digestive proteases; Schwarzenberger et al., 2020 ), which may contribute to the variation in empirical outcomes. Lemaire et al. (2011) experimentally showed both variation in tolerance to Microcystis among Daphnia genotypes and variation in overall toxicity of Microcystis strains, but also striking genotype x genotype interactions that make the observed survival of Daphnia when exposed to Microcystis dependent on the specific combination of Daphnia and Microcystis genotypes. Such genotype x genotype interactions are often observed in host-parasite interactions and can lead to rapid local adaptation and Red Queen dynamics ( Capaul & Ebert, 2003 ; Decaestecker et al., 2007 ; Lambrechts et al., 2006 ). A resurrection study has indeed shown that Daphnia in Lake Constance evolved higher tolerance to cyanobacteria as the lake eutrophied ( Hairston et al., 1999 , 2001) and subsequently lost this adaptation as nutrient concentrations in the lake became lower again ( Isanta-Navarro et al., 2021 ). In line with these results, Sarnelle and Wilson (2005) observed that D. pulicaria populations from eutrophied ponds are significantly more tolerant to cyanobacteria than D. pulicaria populations from less eutrophic ponds. Because of their role as key grazers of phytoplankton, the capacity of Daphnia populations to evolve enhanced tolerance to cyanobacteria can have important ecological consequences. By combining field monitoring with genotyping and laboratory experiments, Schaffner et al. (2019) showed that D. mendotae clones dominant in summer are better able to feed on low quality algae communities with a high proportion of cyanobacteria and that this evolutionary change increased their abundance and thus the potential for top-down control of phytoplankton. Hegg et al. (2022) did not find evidence for increased tolerance to cyanobacteria when comparing clones obtained before and after a bloom. In this experiment of Hegg et al. (2022) , however, standard laboratory strains were used rather than local strains of Microcystis . If genotype x genotype interactions are important and lead to patterns of local (co)adaptation, the use of laboratory strains of Microcystis might fail to reveal the resulting eco-(co)evolutionary dynamics. In the present study, we set out to test whether adaptation of Daphnia populations to changes in Microcystis strain composition within a single growing season can be an important aspect of eco-(co)evolutionary dynamics that determine the outcome of the interaction between Daphnia and Microcystis . More specifically, we tested whether a D. magna population inhabiting a shallow lake can genetically track changes in Microcystis strain composition through rapid evolution as a bloom develops in spring. Over two consecutive years, we harvested Daphnia and Microcystis from two time points in the growing season. We subsequently performed a reciprocal exposure experiment where Daphnia clonal lineages were exposed to Microcystis strains from either the same or the other time point. We tested the hypotheses that (i) Daphnia genotype x Microcystis genotype interactions occur with the capacity of Daphnia to cope with Microcystis within the set of genotypes and strains isolated from a single lake, and (ii) Daphnia clonal lineages are more tolerant to Microcystis strains isolated during the same moment in the growing season than to Microcystis strains isolated from a different moment in the growing season. Material and methods Isolation of Daphnia and Microcystis We isolated twelve clonal lineages of D. magna and twelve isolates of Microcystis sp. from Langerodevijver, a manmade pond located in a nature reserve in Belgium (50°49’42.9”N; 4°38’23.8”E). To capture the interannual and seasonal dynamics within Langerodevijver, isolation took place in two consecutive years (2018 and 2019) during two distinct time periods that we will further refer to as ″early″ and ″late″ in the growing season: end of April 2018, end of May 2018, end of April 2019 and mid to end of June 2019 (Table S1). These time points were chosen because of the temporal dynamics of phytoplankton, cyanobacteria and different Daphnia species in this pond (see Fig. S1), where Daphnia appears in samples around mid-March and disappears from the pond around June, presumably because of predation by young-of-the-year fish. Cyanobacteria are still at low densities, and generally start to bloom after the disappearance of D. magna ; as a result all isolations of Daphnia and Microcystis were carried out before the peak annual Microcystis sp. summer bloom. At each of the four sampling moments, three D. magna females were isolated to establish clonal lineages and a water sample was taken and stored at 4°C for isolation of Microcystis sp. strains. Daphnia magna cultures D. magna individuals were individually placed in 150 mL glass jars filled with filtered tap water (Greenline e1902 filter) to establish clonal lineages, which were maintained under standardized laboratory conditions prior to the experiment (16:8 h light–dark cycle, constant ambient temperature of 19 ± 1°C, fed three times a week 200×10 3 cells/mL of Chlorella vulgaris , and medium refreshed once a month). Before the start of the experiment, all clonal lines were kept under optimal conditions for at least two generations to exclude interference of maternal effects and to ensure that the observed responses were due to genetic effects. Animals were kept individually in 50 mL of aged tap water (16:8 h light–dark cycle, constant ambient temperature of 19 ± 1°C, fed three times a week 200×10 3 cells/mL of Chlorella vulgaris , and medium refreshed once a week). Both the maternal generation and the generation of experimental animals were initiated by inoculating one neonate <24 h old from the second or third brood. Microcystis sp. cultures For the isolation of Microcystis sp., three droplets of sampled water were placed on a glass microscope slide and placed under an SZX10 stereomicroscope (Olympus, Tokyo, Japan). Single colonies were then picked out using the following mouth-pipetting technique: the tip of a glass Pasteur pipette was heated using a Bunsen flame and then extended using forceps. This extended pipette was then connected to an autoclaved flexible, plastic tube. The other end of the tube was placed in the mouth of the person isolating the cells. Single colonies were identified under the microscope and carefully pipetted up by exerting gentle suction on the plastic tube end. The isolated colony was then washed three times in modified Wright’s Crytophyte (WC) medium (i.e. without Tris and a fivefold of NaNO 3 and K 2 HPO 4 , supplemented with vitamins), known to promote Microcystis sp. growth ( Guillard & Lorenzen, 1972 ). The single Microcystis sp. colony was then placed in 2 mL of this medium in a 24-well plate. Plates were covered with parafilm to avoid contamination and placed on a shaker under a 16:8 h light cycle at 21 ± 1°C. Additionally, plates were covered with a neutral density light filter to reduce light intensity and to stimulate Microcystis sp. growth. Further manipulation of the cyanobacterial cultures was performed under the flow hood. Once the 2 mL culture showed a healthy green colour, it was added to 5 mL of fresh medium in a 6-well plate under the flow hood. The empty well was rinsed thoroughly, and the new plate was covered with parafilm and placed on a shaker. Once the cultures were dense, three cultures per timepoint were selected and individually transferred to a 1 L glass culture bottle (Düran) under aeration, with the bottle caps fitted with 0.22 µm Millipore air filters. At the aeration system’s input and output to supply CO 2 and to remove oxygen, a second 0.22 μm filter was used to prevent (cyano)bacterial contamination. To maintain regular mixing and prevent algal precipitation, magnetic stirrers were used. The algal culture cell density was assessed using flow cytometry on the FACS Verse cell sorter (Biosciences). Prior to cell counting, colonies were separated into individual cells by vigorously shaking the vials using a vortex mixer. Experimental set-up The experiment was conducted in February 2021. D. magna clonal lineages were administered Microcystis sp. strains from either the same (further referred to as “contemporal”) or a different timepoint (further referred to as “allotemporal”) within the same year (Fig. S2). We did this for D. magna clonal lineages and Microcystis sp. strains from Langerodevijver isolated in two consecutive years, enabling us to assess the repeatability of the observed pattern over time. We considered 72 combinations of Daphnia clonal lineages and Microcystis strains (2 years x 2 Daphnia populations x 3 Daphnia clonal lineages per population exposed to 2 Microcystis populations x 3 Microcystis strains per population) as experimental units, each including six experimental animals. One Daphnia clonal lineage ( 4b ) was excluded because it did not produce sufficient brood. Two other clonal lineages ( 1c and 4a ) did not produce sufficient brood to use six experimental animals, leading to 11 experimental units that only contained four or five individuals. Consequently, 66 out of the 72 experimental units with a total of 380 experimental Daphnia individuals were effectively executed. The Daphnia individuals were cultured for a total of 12 days, since this duration has been shown to highlight clonal differences in sensitivity to cyanobacteria ( Macke et al., 2017 ). Starting from day one of the experiment, 30 × 10 3 cells/mL of the corresponding Microcystis sp. strain were administered every four days. Survival was monitored on days 5, 9 and 12 of the experiment (Fig. S3). Experimental units were kept at 19 ± 1°C, 16:8 h light–dark regime. Statistical analyses Throughout the following, we refer to ‘ Daphnia timing’ as the variable indicating whether early (0) or late (1) Daphnia are considered, to ‘ Microcystis timing’ as the variable indicating whether Daphnia are exposed to early (0) or late (1) Microcystis strains and to ‘contemporality’ as the variable indicating whether Daphnia was exposed to Microcystis with a different (0, allotemporal) or matching timing (1, contemporal). As our main analysis, we use a binomial generalized linear mixed model (GLMM) with a logistic link function to model survival at the twelfth day, i.e. the final experimental day. The number of surviving individuals y i out of the total number of individuals n i in experimental unit i is modelled as follows: where is the intercept, is the effect of Daphnia timing, is the effect of Microcystis timing, is the effect of contemporality, is a normally distributed random effect for the Daphnia clonal lineage clone( i )used in experimental unit i and is a normally distributed random effect of the Microcystis strain( i )used in experimental unit i . To allow model parameters to vary across the two years, two distinct sets of regression coefficients are used (i.e. one set for 2018 and one set for 2019), with the superscript j ( i ) referring to the year of experimental unit ii . In an alternative model specification, a third random effect is included at the level of the experimental unit, to account for any overdispersion which might be caused by, for instance, additional genotype x genotype interactions unrelated to contemporality left unexplained by additive genotype effects captured by the Daphnia clonal lineage and Microcystis strain random effects alone. We implemented this model using the probabilistic programming language Stan ( Carpenter et al., 2017 ) through the ‘cmdstanr’ package in R v.4.3.1 ( R Core Team, 2023 ). Stan performs Bayesian inference by means of a dynamic Hamiltonian Monte Carlo (HMC) algorithm, a gradient-based Markov chain Monte Carlo (MCMC) sampler ( Carpenter et al., 2017 ). We used zero-centred, weakly informative priors with scale 3 for the variables β 0 , β 1 , β 2 and β 3 . Similarly, we used weakly informative half-normal priors with scale 3 for the scales of the random clonal lineage and strain effects and . We ran eight chains with 2,000 iterations each, of which the first 1,000 were discarded as warm-up. Model convergence was confirmed both visually by means of traceplots and numerically by means of effective sample sizes, the absence of divergent transitions and the Potential Scale Reduction Factor, for which all parameters had ( Vehtari et al., 2021 ). A satisfactory goodness-of-fit was confirmed through posterior predictive checks ( Gelman et al., 1996 ). We performed prior sensitivity analyses to confirm that model results are robust against alternative prior specifications. Traceplots and visualizations of the posterior predictive checks and prior sensitivity analyses are provided in the Supplementary Information (Fig. S4-9). We used the ‘tidybayes’ v.2.3.1 package ( Kay, 2022 ) to visualize the posterior distributions of the inferred effects and their corresponding derived quantities. To conform readers who are more familiar with frequentist statistics, we also fitted the binomial GLMM using maximum likelihood with the ‘glmer’ function of the ‘lme4’ package v.1.1 ( Bates et al., 2015 ) in R v.4.3.1 ( R Core Team, 2023 ). Rather than estimating two sets of parameters for both years within the same model (which is not readily possible using the considered software) or including interactions between all regression parameters and the year, we estimate two separate models in a year-by-year fashion. To ensure that our results are insensitive to the choice of survival at day 12 as endpoint, we also performed a Bayesian interval-censored frailty survival analysis ( Bogaerts et al., 2017 ) to model individual survival over the entire experiment′s duration. We outline methodological details for this analysis in the Supplementary Information (Section S1). Results Daphnia survival after twelve days of exposure to Microcystis varied widely across experimental units ( Fig. 1 ; Fig. S3). The average observed survival rate across individuals was 37.2%. Download figure Open in new tab Figure 1. Observed Daphnia survival at day 12. (a) Survival status of each individual, represented by a point (dead: transparent; alive: opaque), organized per Daphnia clonal lineage and per Microcystis strain. Allotemporal combinations are shown in pink and contemporal combinations are shown in blue. (b) Averaged survival for each combination of Daphnia clonal lineage and Microcystis strain. Grand means and standard errors for each combination of early and late Daphnia and Microcystis clonal lineage are shown by means of big dots and horizontal lines. Allotemporal combinations are shown in pink and contemporal combinations are shown in blue, jittered horizontally and vertically to a small extent to avoid overlap. Download figure Open in new tab Figure 2. Posterior distributions of the model intercept and the effects of Daphnia timing, Microcystis timing and contemporality on Daphnia survival (logit scale) at the twelfth day, for 2018 (green) and 2019 (orange), as obtained from the binomial GLMM. The posterior medians and 95% credible intervals are represented by dots and vertical lines respectively. Using a binomial GLMM to analyse survival at day 12, we found no evidence for differential survival between early and late Daphnia clonal lineages (posterior probability of a positive difference, averaged across both years: 70.9%), neither did we find evidence for a difference between early and late Microcystis strains (av. post. prob.: 52.4%). We did, however, find very strong statistical support for a positive effect of contemporality (av. post. prob.: 99.6%), with contemporal combinations featuring a higher survival compared to allotemporal ones ( Figs. 1 - 2 ). Averaged across both years, the posterior mean temporality effect equals 0.95 (95% CrI [0.27,1.67]) on the logit scale. This corresponds to a 17.7 % decrease in posterior mean survival probability when Daphnia are exposed to Microcystis strains from another moment (Fig. S10). Moreover, these patterns are strongly replicated across both years, with only 54.7% posterior probability of a positive or negative difference in the effect of contemporality between both years. For reference, a year-by-year frequentist binomial GLMM analysis yields highly similar results, with p-values of 0.006 and 0.013 for the effect of contemporality in 2018 and 2019, respectively (Table S2). The interval-censored frailty survival analysis, in which data of all time points is integrated, yields qualitatively identical results as the binomial GLMM analysis performed on the twelfth day (Fig. S11). The survival functions generated through this interval-censored frailty survival analysis visually confirm that contemporal combinations host an increased survival compared to allotemporal combinations, while there is much more overlap in confidence intervals among survival curves of early and late Daphnia or of Daphnia exposed to early and late Microcystis (Fig. S12). Posterior distributions for the different Daphnia genotypes and Microcystis strains show that there is much variation in survival upon exposure to Microcystis among Daphnia genotypes ( Figs. 3 - 4 ). Similarly, there is variation in survival of Daphnia when exposed to different Microcystis strains, reflecting that Microcystis genotypes differ in toxicity ( Figs. 3 - 4 ). For instance, Daphnia clonal lineages 2a and 2b feature the highest intrinsic difference in tolerance ( Figs. 3 - 4 ; posterior probability of a difference: > 99.9%). On the other hand, Daphnia individuals exposed to Microcystis strain 114 have a distinctly lower survival probability at day 12 than individuals exposed to Microcystis strain 121 (posterior probability of a difference: > 99.9%). Download figure Open in new tab Figure 3. Posterior distributions of the Daphnia clonal lineage (left) and Microcystis strain (right) effects on Daphnia survival (logit scale) at the twelfth day, as obtained from the binomial GLMM. These effects represent the clone- and strain-specific variation, corrected for the effects of year, Daphnia timing, Microcystis timing, contemporality, Microcystis strain (for the Daphnia clonal lineage effects only) and Daphnia clone (for the Microcystis strain effects only). The posterior medians and 95% credible intervals are represented by dots and horizontal lines respectively. Download figure Open in new tab Figure 4. Heatmap showing the posterior mean modelled survival probability for each combination of Daphnia clonal lineages and Microcystis strains, as obtained from the binomial GLMM. White letters indicate whether the combination of Daphnia clonal lineage and Microcystis strain pertains to a contemporal (‘c’) or allotemporal (‘a’) condition. A comparison of the main binomial GLMM with a fully saturated binomial GLMM, in which an observation-level random effect (i.e. a Daphnia genotype x Microcystis genotype random effect) is added, indicates that our main model can explain a posterior mean fraction of 67.3% (95% CrI [53.4,79.2]) of the saturated linear predictor’s variation (Fig. S13). Hence, approximately two thirds of the variation in survival can be explained by contemporality, Daphnia genotype and Microcystis genotype alone. Approximately one third of the variation is left unexplained, suggesting that genotype x genotype effects unrelated to contemporality, or other sources of overdispersion, were also important in determining Daphnia survival in our experiment. While our experimental design allows to disentangle additive effects of Daphnia genotype and Microcystis genotype as well as the genotype x genotype interaction induced by contemporality, variation induced by additional genotype x genotype interactions might be confounded with observational noise because of the absence of replicates at the level of Daphnia genotype x Microcystis genotype combination. Discussion In this study, we exposed early and late season genotypes isolated from a natural D. magna population over two years to Microcystis strains isolated from the same two time periods of their respective year of isolation. Our results do not only show that Daphnia genotypes differ in sensitivity to Microcystis and Microcystis strains differ in toxicity to Daphnia , but also reveal a striking pattern of higher survival when Daphnia genotypes are exposed to Microcystis strains that were isolated during the same time period compared to Daphnia that were exposed to Microcystis strains that were isolated during a different period of the growing season. Daphnia populations have been shown to be genetically differentiated in their capacity to cope with Microcystis ( Chislock et al., 2019 ; Sarnelle & Wilson, 2005 ; Schwarzenberger et al., 2020 ) and that Daphnia can become more tolerant to Microcystis through acclimatization ( Chislock et al., 2019 ; Sarnelle & Wilson, 2005 ), epigenetic effects ( Asselman et al., 2017 ; Ger et al., 2016 ) or through microbiome-mediated acclimatization ( Macke et al., 2017 ). Influential studies have shown that Daphnia can genetically adapt to the presence of Microcysti s over the course of decades in resurrection ecology studies ( Hairston et al., 1999 ; Isanta-Navarro et al., 2021 ; Jiang et al., 2016 ). Our results suggest that genetic adaptation of Daphnia to Microcystis can occur over the course of one growing season and enables Daphnia to effectively cope with contemporal Microcystis populations. While adapting to changes in the Microcystis population, Daphnia lose some of their tolerance to past populations of Microcystis . This is reflected by the fact that late season Daphnia populations suffer from exposure to the early-season Microcystis strains. This rapid adaptation is expected to influence seasonal phytoplankton and Microcystis dynamics and might explain the genetic adaptation affecting D. mendotae seasonality and phytoplankton biomass in Oneida Lake, as observed by Schaffner et al. (2019) . The PEG model explaining seasonal changes in phytoplankton and zooplankton biomass in lakes might in part be driven by the eco-evolutionary interactions revealed for a deep lake by Schaffner at al. (2019) and the shallow pond in our study. We show strong Daphnia genotype x Microcystis genotype interactions. These G x G interactions are a prerequisite for the pattern of contemporal adaptation that we observe, as it allows the population of the one interacting species to not only adapt to Microcystis as a stressor, but to adapt to the specific genotype composition of Microcystis. Microcystis strains are known to strongly differ in their toxicity, both in terms of the capacity to produce microcystins and the amount they produce, but also in terms of the synthesis of other polypeptides with potentially toxic effects ( Xu et al., 2023 ) or secondary metabolites such as digestive protease inhibitors ( Schwarzenberger et al., 2020 ). In addition to variation in toxicity, some Microcystis strains also exhibit inducible colony formation in response to grazing, impeding ingestion. Considering this wide variety of toxicity profiles and defense strategies, our results suggest that Daphnia genotypes are differentially able to adapt to different Microcystis strains, enabling clonal selection. Lemaire et al. (2011) also showed important genotype x genotype interactions in determining the performance of Daphnia when exposed to Microcystis . They used Daphnia clonal lineages and Microcystis strains that were isolated from habitats that were geographically isolated, including Daphnia from both the European and African continent. The present study shows that the same pattern exists within a single habitat, and contributes to our understanding of seasonality of zooplankton-phytoplankton interactions. Our results also align with the seasonal dynamics in the clonal composition of D. magna observed in the field by Schwarzenberger et al. (2013) , which might be driven by corresponding dynamics in the cyanobacterial composition and the protease inhibitors they produce ( Schwarzenberger et al., 2013 ). The results of our experiments demonstrate that Daphnia is indeed able to seasonally track changes in the composition of cyanobacterial strains. Our results testify to the large intrapopulation genetic variation that is regularly observed in Daphnia ( Chaturvedi et al., 2021 ; De Coninck et al., 2013 ; Ilić et al., 2021) and that can explain why local adaptation has been reported in Daphnia populations in response to a wide range of environmental gradients and change such as temperature ( Brans & De Meester, 2018 ; Geerts et al., 2015 ), pollution ( Almeida et al., 2023 ; Cuenca-Cambronero et al., 2021 ; De Coninck et al., 2013 ), UV light ( Miner & Kerr, 2011 ), including interactions with antagonists such as predation ( Stoks et al., 2015 ), parasites ( Decaestecker et al., 2007 ) and competitors ( Steiner, 2004 ). Daphnia-Microcystis interactions are especially interesting because both interactors can kill each other if they are sufficiently adapted to their enemy. This may result in fast and reciprocal responses, similar to those reported for host-parasite interactions ( Decaestecker et al., 2007 , 2013; Gandon et al., 2008 ). Similar to the strong genotype x genotype interactions that are often observed in host-parasite coevolution ( Capaul & Ebert, 2003 ; Ebert, 2008 ), we also observe strong Daphnia genotype x Microcystis genotype interactions, both at larger spatial scales ( Lemaire et al., 2011 ) as well as within single habitats (present paper). The strong Daphnia genotype x Microcystis genotype interactions might not only enable Daphnia to adapt to Microcystis but may also allow Microcystis populations to adapt to the changes in the Daphnia population, leading to continuous dynamics of coevolution ( Anderson & May, 1982 ; Ehrlich & Raven, 1964 ; Ley et al., 2008 ). Our results show that Daphnia populations seem to be able to track changes in Microcystis populations during a growing season (i.e. late-season Daphnia genotypes are better adapted to late-season Microcysti s than early-season Daphnia genotypes) and show that this adaptation has a cost in terms of susceptibility to early-season Microcystis (i.e. late-season Daphnia genotypes are less well adapted to early-season Microcysti s than early-season Daphnia genotypes). The fact that early-season Daphnia are more susceptible to late-season Microcystis might reflect adaptation of the Microcystis population to Daphnia , driven by selection against grazing. Given that we did not carry out genomic analyses on the different Microcystis strains used in this experiment, we cannot be certain that evolution is involved rather than epigenetic changes or a shift in relative abundances of closely related Microcystis species. Recently, it has been shown that also the microbiome can structure such G x G interactions resulting in G x M x G interactions and modified phenotypes with increased Microcystis tolerance ( Macke et al., 2017 ). If, however, both Daphnia and Microcystis evolve in response to each other, the Daphnia-Microcystis interaction might be a nice example of a geographic mosaic of coevolution in which temporal adaptation result in spatial variation of coevolutionary hotspots and coldspots ( Thompson, 2005 ). Modified locally adapted Daphnia microbiomes have been shown to mediate tolerance to Microcystis ( Houwenhuyse et al., 2021 ). These aspects have high potential relevance with respect to the management of Microcystis blooms. Overall, our study demonstrates the importance of short-term eco-(co)evolutionary dynamics in determining interactions between cyanobacteria and zooplankton grazers. We show that the composition of cyanobacteria and their toxicity for zooplankton grazers varies over short periods of time, and that the zooplankton grazer studied, the water flea D. magna , is able to adapt to these changes in order to reduce mortality imposed by Microcystis . This mechanism likely contributes to the ability of Daphnia populations to delay or even prevent cyanobacteria blooms in freshwater ecosystems ( Benndorf & Henning, 1989 ; de Bernardi & Guissani, 1990 ; Jeppesen et al., 1996 ; Matveev et al., 1994 ; Søndergaard et al., 2008 ; Vanni, 1984 ). It may also explain why the literature on the toxicity of Microcystis and the capacity of Daphnia to graze on and control Microcystis is so divergent in its conclusions, as the outcome may depend on who of the two interacting partners in the particular setting studied is at the moment of sampling winning in the coadaptation, and because most studies combine Microcystis and Daphnia strains that do not co-occur. The eco-evolutionary dynamics that may result from the dynamics reported in our study might impact the observed seasonality and food-web interactions in lakes and ponds ( Schaffner et al., 2019 ). Data and code availability statement The data and full code for the analysis is available through the following GitHub repository: https://github.com/MFajgenblat/Daphnia_Microcystis_temporal_dynamics . Author contributions AB, LDM and ED conceived the study. AB, KC, EvdB and IV performed the experiment. MF and MC lead the statistical analysis of the data. MF, EG and LDM lead the writing of the manuscript. RA provided critical feedback during the writing process. All mentioned co-authors contributed substantially to the further refinements of the manuscript. Funding MF acknowledges funding by the Research Foundation Flanders through a FWO PhD FR fellowship (grant number 11E3222N). M.F. and L.D.M. acknowledge support from the PONDERFUL project, funded by the European Union’s Horizon 2020 research and innovation program under grant agreement No ID 869296. L.D.M. and E.D. acknowledge financial support from a KU Leuven research project (number C16/2023/003) and FWO G061824N. Conflict of interest statement The authors have no conflicts of interest to declare. Acknowledgements We gratefully acknowledge the NGO Vrienden van Heverleebos en Meerdaalwoud and its volunteers for providing access to Langerodevijver and granting permission for sampling, which was essential for this study. We thank Laura Herregodts and Cedric De Witte-Vroman for their valuable practical help in performing this experiment. Footnotes ↵ ○ Shared first authorship ↵ ¥ Shared senior authorship References ↵ Agrawal , M. K. , Bagchi , D. , & Bagchi , S. N. ( 2001 ). Acute inhibition of protease and suppression of growth in zooplankter, Moina macrocopa, by Microcystis blooms collected in Central India . In Hydrobiologia (Vol. 464 ). ↵ Almeida , R. A. , Fajgenblat , M. , Lemmens , P. , & De Meester , L. ( 2023 ). Pesticide exposure enhances dominance patterns in a zooplankton community . Ecological Applications , 33 ( 7 ). doi: 10.1002/eap.2900 OpenUrl CrossRef ↵ Anderson , R. M. , & May , R. M. ( 1982 ). Coevolution of Hosts and Parasites . Parasitology , 85 ( 2 ), 411 – 426 . doi: 10.1017/S0031182000055360 OpenUrl CrossRef PubMed Web of Science ↵ Asselman , J. , De Coninck , D. I. M. , Beert , E. , Janssen , C. R. , Orsini , L. , Pfrender , M. E. , Decaestecker , E. , & De Schamphelaere , K. A. C. ( 2017 ). Bisulfite sequencing with Daphnia highlights a role for epigenetics in regulating stress response to Microcystis through preferential differential methylation of serine and threonine amino acids . Environmental Science and Technology , 51 ( 2 ), 924 – 931 . doi: 10.1021/acs.est.6b03870 OpenUrl CrossRef PubMed ↵ Bates , D. , Mächler , M. , Bolker , B. M. , & Walker , S. C. ( 2015 ). Fitting linear mixed-effects models using lme4 . Journal of Statistical Software , 67 ( 1 ). doi: 10.18637/jss.v067.i01 OpenUrl CrossRef PubMed ↵ Benndorf , J. , & Henning , M. ( 1989 ). Daphnia and toxic blooms of Microcystis aeruginosa in Bautzen Reservoir (GDR) . Internationale Revue Der Gesamten Hydrobiologie Und Hydrographie , 74 , 233 – 248 . doi: 10.1002/iroh.19890740302 OpenUrl CrossRef ↵ Bogaerts , K. , Komárek , A. , & Lesaffre , E. ( 2017 ). Survival analysis with interval-censored data. A practical approach with examples in R, SAS, and BUGS (1st edition) . Chapman and Hall/CRC . doi: 10.1201/9781315116945 OpenUrl CrossRef ↵ Brans , K. I. , & De Meester , L. ( 2018 ). City life on fast lanes: Urbanization induces an evolutionary shift towards a faster lifestyle in the water flea Daphnia . Functional Ecology , 32 ( 9 ), 2225 – 2240 . doi: 10.1111/1365-2435.13184 OpenUrl CrossRef ↵ Capaul , M. , & Ebert , D. ( 2003 ). Parasite-mediated selection in experimental Daphnia magna populations . Evolution , 57 ( 2 ), 249 – 260 . doi: 10.1111/j.0014-3820.2003.tb00260.x OpenUrl CrossRef PubMed Web of Science ↵ Carpenter , B. , Gelman , A. , Hoffman , M. , Lee , D. , Goodrich , B. , Betancourt , M. , Brubaker , M. , Guo , J. , Li , P. , & Riddell , A. ( 2017 ). Stan: A Probabilistic Programming Language . Journal of Statistical Software , 76 ( 1 ), 1 – 32 . doi: 10.18637/jss.v076.i01 OpenUrl CrossRef PubMed ↵ Carton , A. G. ( 2005 ). The impact of light intensity and algal-induced turbidity on first-feeding Seriola lalandi larvae . Aquaculture Research , 36 ( 16 ), 1588 – 1594 . doi: 10.1111/j.1365-2109.2005.01383.x OpenUrl CrossRef ↵ Chaturvedi , A. , Zhou , J. , Raeymaekers , J. A. M. , Czypionka , T. , Orsini , L. , Jackson , C. E. , Spanier , K. I. , Shaw , J. R. , Colbourne , J. K. , & De Meester , L. ( 2021 ). Extensive standing genetic variation from a small number of founders enables rapid adaptation in Daphnia . Nature Communications , 12 ( 1 ). doi: 10.1038/s41467-021-24581-z OpenUrl CrossRef PubMed ↵ Chislock , M. F. , Kaul , R. R. B. , Durham , K. A. , Sarnelle , O. , & Wilson , A. E. ( 2019 ). Eutrophication mediates rapid clonal evolution in Daphnia pulicaria . Freshwater Biology , 64 ( 7 ), 1275 – 1283 . doi: 10.1111/fwb.13303 OpenUrl CrossRef ↵ Cuenca-Cambronero , M. , Pantel , J. H. , Marshall , H. , Nguyen , T. T. T. , Tomero-Sanz , H. , & Orsini , L. ( 2021 ). Evolutionary mechanisms underpinning fitness response to multiple stressors in Daphnia . Evolutionary Applications , 14 ( 10 ), 2457 – 2469 . doi: 10.1111/eva.13258 OpenUrl CrossRef PubMed ↵ de Bernardi , R. , & Guissani , G. ( 1990 ). Are blue-green algae a suitable food for zooplankton? An overview . Hydrobiologia, 200/201 , 29 – 41 . doi: 10.1007/BF02530326 OpenUrl CrossRef Web of Science ↵ De Coninck , D. I. M. , Janssen , C. R. , & De Schamphelaere , K. A. C. ( 2013 ). An investigation of the inter-clonal variation of the interactive effects of cadmium and Microcystis aeruginosa on the reproductive performance of Daphnia magna . Aquatic Toxicology , 140–141 , 425 – 431 . doi: 10.1016/j.aquatox.2013.07.005 OpenUrl CrossRef Decaestecker , E. , De Gersem , H. , Michalakis , Y. , & Raeymaekers , J. A. M. ( 2013 ). Damped long-term host-parasite Red Queen coevolutionary dynamics: A reflection of dilution effects? Ecology Letters , 16 ( 12 ), 1455 – 1462 . doi: 10.1111/ele.12186 OpenUrl CrossRef PubMed ↵ Decaestecker , E. , Gaba , S. , Raeymaekers , J. A. M. , Stoks , R. , Van Kerckhoven , L. , Ebert , D. , & De Meester , L. ( 2007 ). Host-parasite “Red Queen” dynamics archived in pond sediment . Nature , 450 ( 7171 ), 870 – 873 . doi: 10.1038/nature06291 OpenUrl CrossRef PubMed Web of Science ↵ Dejenie , T. , Asmelash , T. , Rousseaux , S. , Gebregiorgis , T. , Gebrekidan , A. , Teferi , M. , Nyssen , J. , Deckers , J. , Van Der Gucht , K. , Vyverman , W. , De Meester , L. , & Declerck , S. A. J. ( 2009 ). Impact of the fish Garra on the ecology of reservoirs and the occurrence of Microcystis blooms in semi-arid tropical highlands: An experimental assessment using enclosures . Freshwater Biology , 54 ( 8 ), 1605 – 1615 . doi: 10.1111/j.1365-2427.2009.02209.x OpenUrl CrossRef ↵ Ebert , D. ( 2008 ). Host-parasite coevolution: Insights from the Daphnia-parasite model system . In Current Opinion in Microbiology (Vol. 11 , Issue 3 , pp. 290 – 301 ). doi: 10.1016/j.mib.2008.05.012 OpenUrl CrossRef PubMed Web of Science ↵ Ehrlich , P. R. , & Raven , P. H. ( 1964 ). Butterflies and plants: a study in coevolution . Evolution , 18 ( 4 ), 586 – 608 . doi: 10.1111/j.1558-5646.1964.tb01674.x OpenUrl CrossRef Web of Science ↵ Gandon , S. , Buckling , A. , Decaestecker , E. , & Day , T. ( 2008 ). Host-parasite coevolution and patterns of adaptation across time and space . Journal of Evolutionary Biology , 21 ( 6 ), 1861 – 1866 . doi: 10.1111/j.1420-9101.2008.01598.x OpenUrl CrossRef PubMed Web of Science ↵ Geerts , A. N. , Vanoverbeke , J. , Vanschoenwinkel , B. , Van Doorslaer , W. , Feuchtmayr , H. , Atkinson , D. , Moss , B. , Davidson , T. A. , Sayer , C. D. , & De Meester , L. ( 2015 ). Rapid evolution of thermal tolerance in the water flea Daphnia . Nature Climate Change , 5 ( 7 ), 665 – 668 . doi: 10.1038/nclimate2628 OpenUrl CrossRef ↵ Gelman , A. , Meng , X.-L. , & Stern , H. ( 1996 ). Posterior predictive assessment of model fitness via realized discrepancies . In Statistica Sinica (Vol. 6 ). ↵ Ger , K. A. , Urrutia-Cordero , P. , Frost , P. C. , Hansson , L. A. , Sarnelle , O. , Wilson , A. E. , & Lürling , M. ( 2016 ). The interaction between cyanobacteria and zooplankton in a more eutrophic world . In Harmful Algae (Vol. 54 , pp. 128 – 144 ). Elsevier B.V . doi: 10.1016/j.hal.2015.12.005 OpenUrl CrossRef PubMed ↵ Ghadouani , A. , Pinel-Alloul , B. , Plath , K. , Codd , G. A. , & Lampert , W. ( 2004 ). Effects of Microcystis aeruginosa and purified microcystin-LR on the feeding behavior of Daphnia pulicaria . In Limnol. Oceanogr (Vol. 49 , Issue 3 ). doi: 10.4319/lo.2004.49.3.0666 OpenUrl CrossRef ↵ Gianuca , A. T. , Pantel , J. H. , & De Meester , L. ( 2016 ). Disentangling the effect of body size and phylogenetic distances on zooplankton top-down control of algae . In Proceedings of the Royal Society B: Biological Sciences (Vol. 283 , Issue 1828 ). Royal Society of London. doi: 10.1098/rspb.2016.0487 OpenUrl CrossRef PubMed ↵ Gray , S. M. , Bieber , F. M. E. , Mcdonnell , L. H. , Chapman , L. J. , & Mandrak , N. E. ( 2014 ). Experimental evidence for species-specific response to turbidity in imperilled fishes . Aquatic Conservation: Marine and Freshwater Ecosystems , 24 ( 4 ), 546 – 560 . doi: 10.1002/aqc.2436 OpenUrl CrossRef ↵ Guillard , R. R. L. , & Lorenzen , C. J. ( 1972 ). Yellow-green algae with chlorophyllide c . Journal of Phycology , 8 ( 1 ), 10 – 14 . doi: 10.1111/j.1529-8817.1972.tb03995.x OpenUrl CrossRef Web of Science ↵ Gulati , R. D. , Pires , L. M. D. , & Van Donk , E. ( 2008 ). Lake restoration studies: Failures, bottlenecks and prospects of new ecotechnological measures . Limnologica , 38 ( 3–4 ), 233 – 247 . doi: 10.1016/j.limno.2008.05.008 OpenUrl CrossRef Hairston , N. G. , Holtmeier , C. L. , Lampert , W. , Weider , L. J. , Post , D. M. , Fischer , J. M. , Cáceres , C. E. , Fox , J. A. , & Gaedke , U. ( 2001 ). Natural selection for grazer resistance to toxic cyanobacteria: Evolution of phenotypic plasticity? Evolution , 55 ( 11 ), 2203 – 2214 . doi: 10.1111/j.0014-3820.2001.tb00736.x OpenUrl CrossRef PubMed Web of Science ↵ Hairston , N. G. , Lampert , W. , Cáceres , C. E. , Holtmeier , C. L. , Weider , L. J. , Gaedkes , U. , Fisher , J. M. , Fox , J. A. , & Post , D. M. ( 1999 ). Rapid evolution revealed by dormant eggs . Nature . doi: 10.1038/46731 OpenUrl CrossRef ↵ Hansson , L. A. , Gustafsson , S. , Rengefors , K. , & Bomark , L. ( 2007 ). Cyanobacterial chemical warfare affects zooplankton community composition . Freshwater Biology , 52 ( 7 ), 1290 – 1301 . doi: 10.1111/j.1365-2427.2007.01765.x OpenUrl CrossRef ↵ Hegg , A. , Radersma , R. , & Uller , T. ( 2022 ). Seasonal variation in the response to a toxin-producing cyanobacteria in Daphnia . Freshwater Biology , 67 ( 6 ), 1035 – 1044 . doi: 10.1111/fwb.13899 OpenUrl CrossRef ↵ Houwenhuyse , S. , Stoks , R. , Mukherjee , S. , & Decaestecker , E. ( 2021 ). Locally adapted gut microbiomes mediate host stress tolerance . ISME Journal , 15 ( 8 ), 2401 – 2414 . doi: 10.1038/s41396-021-00940-y OpenUrl CrossRef PubMed Ilic , M. , Cordellier , M. , & Fink , P. ( 2021 ). Intrapopulation variability in a functional trait: Susceptibility of Daphnia to limitation by dietary fatty acids . Freshwater Biology , 66 ( 1 ), 130 – 141 . doi: 10.1111/fwb.13623 OpenUrl CrossRef ↵ Isanta-Navarro , J. , Hairston , N. G. , Beninde , J. , Meyer , A. , Straile , D. , Möst , M. , & Martin-Creuzburg , D. ( 2021 ). Reversed evolution of grazer resistance to cyanobacteria . Nature Communications , 12 ( 1 ). doi: 10.1038/s41467-021-22226-9 OpenUrl CrossRef ↵ Jeppesen , E. , Søndergaard , M. , Jensen , J. P. , Mortensen , E. , & Sortkjaer , O. ( 1996 ). Fish-induced changes in zooplankton grazing on phytoplankton and bacterioplankton: a long-term study in shallow hypertrophic Lake Sebygaard . In Journal of Plankton Research (Vol. 18 , Issue 9 ). doi: 10.1093/plankt/18.9.1605 OpenUrl CrossRef Web of Science ↵ Jiang , X. , Gao , H. , Zhang , L. , Liang , H. , & Zhu , X. ( 2016 ). Rapid evolution of tolerance to toxic Microcystis in two cladoceran grazers . Scientific Reports , 6 . doi: 10.1038/srep25319 OpenUrl CrossRef ↵ Kay , M. ( 2022 ). tidybayes: Tidy Data and Geoms for Bayesian Models (3.0.2) . http://mjskay.github.io/tidybayes/ ↵ Kosten , S. , Huszar , V. L. M. , Bécares , E. , Costa , L. S. , van Donk , E. , Hansson , L. A. , Jeppesen , E. , Kruk , C. , Lacerot , G. , Mazzeo , N. , De Meester , L. , Moss , B. , Lürling , M. , Nõges , T. , Romo , S. , & Scheffer , M. ( 2012 ). Warmer climates boost cyanobacterial dominance in shallow lakes . Global Change Biology , 18 ( 1 ), 118 – 126 . doi: 10.1111/j.1365-2486.2011.02488.x OpenUrl CrossRef Web of Science ↵ Lambrechts , L. , Fellous , S. , & Koella , J. C. ( 2006 ). Coevolutionary interactions between host and parasite genotypes . Trends in Parasitology , 22 ( 1 ), 12 – 16 . doi: 10.1016/j.pt.2005.11.008 OpenUrl CrossRef PubMed Web of Science ↵ Lampert , W. , & Sommer , U. ( 2007 ). Limnoecology: The Ecology of Lakes and Streams (2nd ed .). Oxford University Press . doi: 10.1093/plankt/fbn013 OpenUrl CrossRef ↵ Lemaire , V. , Brusciotti , S. , van Gremberghe , I. , Vyverman , W. , Vanoverbeke , J. , & De Meester , L. ( 2011 ). Genotype×genotype interactions between the toxic cyanobacterium Microcystis and its grazer, the waterflea Daphnia . Evolutionary Applications , 5 ( 2 ), 168 – 182 . doi: 10.1111/j.1752-4571.2011.00225.x OpenUrl CrossRef PubMed ↵ Ley , R. E. , Hamady , M. , Lozupone , C. , Turnbaugh , P. J. , Ramey , R. R. , Bircher , S. J. , Schlegel , M. L. , Tucker , T. A. , Schrenzel , M. D. , Knight , R. , & Gordon , J. I. ( 2008 ). Evolution of mammals and their gut microbes . Science , 320 ( 5883 ), 1647 – 1651 . doi: 10.1126/science.1155390 OpenUrl Abstract / FREE Full Text ↵ Lürling , M. ( 2003 ). Daphnia growth on microcystin-producing and microcystin-free Microcystis aeruginosa in different mixtures with the green alga Scenedesmus obliquus . Limnology and Oceanography , 48 ( 6 ), 2214 – 2220 . doi: 10.4319/lo.2003.48.6.2214 OpenUrl CrossRef ↵ Macke , E. , Callens , M. , De Meester , L. , & Decaestecker , E. ( 2017 ). Host-genotype dependent gut microbiota drives zooplankton tolerance to toxic cyanobacteria . Nature Communications , 8 ( 1 ). doi: 10.1038/s41467-017-01714-x OpenUrl CrossRef PubMed ↵ Matveev , V. , Matveeva , L. , & Jones , G. J. ( 1994 ). Study of the ability of Daphnia carinata King to control phytoplankton and resist cyanobacterial toxicity: Implications for biomanipulation in Australia . Marine and Freshwater Research , 45 ( 5 ), 889 – 904 . doi: 10.1071/MF9940889 OpenUrl CrossRef ↵ Meerhoff , M. , Audet , J. , Davidson , T. A. , De Meester , L. , Hilt , S. , Kosten , S. , Liu , Z. , Mazzeo , N. , Paerl , H. , Scheffer , M. , & Jeppesen , E. ( 2022 ). Feedback between climate change and eutrophication: revisiting the allied attack concept and how to strike back . Inland Waters , 12 ( 2 ), 187 – 204 . doi: 10.1080/20442041.2022.2029317 OpenUrl CrossRef ↵ Miner , B. E. , de Meester , L. , Pfrender , M. E. , Lampert , W. , & Hairston , N. G. ( 2012 ). Linking genes to communities and ecosystems: Daphnia as an ecogenomic model . In Proceedings of the Royal Society B: Biological Sciences (Vol. 279 , Issue 1735 , pp. 1873 – 1882 ). Royal Society. doi: 10.1098/rspb.2011.2404 OpenUrl CrossRef PubMed ↵ Miner , B. E. , & Kerr , B. ( 2011 ). Adaptation to local ultraviolet radiation conditions among neighbouring Daphnia populations . Proceedings of the Royal Society B: Biological Sciences , 278 ( 1710 ), 1306 – 1313 . doi: 10.1098/rspb.2010.1663 OpenUrl CrossRef PubMed ↵ Paerl , H. W. , Hall , N. S. , & Calandrino , E. S. ( 2011 ). Controlling harmful cyanobacterial blooms in a world experiencing anthropogenic and climatic-induced change . In Science of the Total Environment (Vol. 409 , Issue 10 , pp. 1739 – 1745 ). doi: 10.1016/j.scitotenv.2011.02.001 OpenUrl CrossRef PubMed Web of Science ↵ Paerl , H. W. , & Huisman , J. ( 2008 ). Blooms like it hot . In Science (Vol. 320 , Issue 5872 , pp. 56 – 57 ). doi: 10.1126/science.1156721 OpenUrl Abstract / FREE Full Text ↵ Pimentel , D. , Mcnair , S. , Janecka , J. , Wightman , J. , Simmonds , C. , O’connell , C. , Wong , E. , Russel , L. , Zern , J. , Aquino , T. , & Tsomondo , T. ( 2001 ). Economic and environmental threats of alien plant, animal, and microbe invasions . Ecosystems and Environment , 84 , 1 – 20 . doi: 10.1016/S0167-8809(00)00178-X OpenUrl CrossRef Web of Science ↵ R Core Team . ( 2023 ). R: A Language and Environment for Statistical Computing (4.3.1) . R Foundation for Statistical Computing . https://www.R-project.org/ ↵ Sarnelle , O. , & Wilson , A. E. ( 2005 ). Local adaptation of Daphnia pulicaria to toxic cyanobacteria . Limnology and Oceanography , 50 ( 5 ), 1565 – 1570 . doi: 10.4319/lo.2005.50.5.1565 OpenUrl CrossRef Web of Science ↵ Schaffner , L. R. , Govaert , L. , De Meester , L. , Ellner , S. P. , Fairchild , E. , Miner , B. E. , Rudstam , L. G. , Spaak , P. , & Hairston , N. G. ( 2019 ). Consumer-resource dynamics is an eco-evolutionary process in a natural plankton community . Nature Ecology and Evolution , 3 ( 9 ), 1351 – 1358 . doi: 10.1038/s41559-019-0960-9 OpenUrl CrossRef ↵ Scheffer , M. , Carpenter , S. , Foley , J. A. , Folke , C. , & Walkerk , B. ( 2001 ). Catastrophic shifts in ecosystems . Nature , 413 . doi: 10.1038/35098000 OpenUrl CrossRef PubMed Web of Science ↵ Scheffer , M. , & Van Nes , E. H. ( 2007 ). Shallow lakes theory revisited: Various alternative regimes driven by climate, nutrients, depth and lake size . Hydrobiologia , 584 ( 1 ), 455 – 466 . doi: 10.1007/s10750-007-0616-7 OpenUrl CrossRef Web of Science ↵ Schwarzenberger , A. , D’Hondt , S. , Vyverman , W. , & von Elert , E. ( 2013 ). Seasonal succession of cyanobacterial protease inhibitors and Daphnia magna genotypes in a eutrophic Swedish lake . Aquatic Sciences , 75 ( 3 ), 433 – 445 . doi: 10.1007/s00027-013-0290-y OpenUrl CrossRef ↵ Schwarzenberger , A. , Hasselmann , M. , & Von Elert , E. ( 2020 ). Positive selection of digestive proteases in Daphnia: A mechanism for local adaptation to cyanobacterial protease inhibitors . Molecular Ecology , 29 ( 5 ), 912 – 919 . doi: 10.1111/mec.15375 OpenUrl CrossRef ↵ Sommer , U. , Adrian , R. , De Senerpont Domis , L., Elser , J. J. , Gaedke , U. , Ibelings , B. , Jeppesen , E. , Lürling , M. , Molinero , J. C. , Mooij , W. M. , Van Donk , E. , & Winder , M. ( 2012 ). Beyond the plankton ecology group (PEG) model: Mechanisms driving plankton succession . In Annual Review of Ecology, Evolution, and Systematics (Vol. 43 , pp. 429 – 448 ). doi: 10.1146/annurev-ecolsys-110411-160251 OpenUrl CrossRef ↵ Sommer , U. , & Maciej Gliwicz , Z. ( 1986 ). The PEG-model of seasonal succession of planktonic events in fresh waters . Archiv Für Hydrobiologie , 106 ( 4 ), 433 – 471 . doi: 10.1127/archiv-hydrobiol/106/1986/433 OpenUrl CrossRef ↵ Søndergaard , M. , Liboriussen , L. , Pedersen , A. R. , & Jeppesen , E. ( 2008 ). Lake restoration by fish removal: Short-and long-term effects in 36 Danish lakes . Ecosystems , 11 ( 8 ), 1291 – 1305 . doi: 10.1007/s10021-008-9193-5 OpenUrl CrossRef ↵ Steiner , C. F. ( 2004 ). Daphnia dominance and zooplankton community structure in fishless ponds . Journal of Plankton Research , 26 ( 7 ), 799 – 810 . doi: 10.1093/plankt/fbh067 OpenUrl CrossRef Web of Science ↵ Stoks , R. , Debecker , S. , Dinh Van , K. , & Janssens , L. ( 2015 ). Integrating ecology and evolution in aquatic toxicology: Insights from damselflies . Freshwater Science , 34 ( 3 ), 1032 – 1039 . doi: 10.1086/682571 OpenUrl CrossRef ↵ Thompson , J. N. ( 2005 ). Coevolution: The Geographic Mosaic of Coevolutionary Arms Races . Current Biology , 15 ( 24 ), R992 – R994 . doi: 10.1016/j.cub.2005.11.046 OpenUrl CrossRef PubMed Web of Science ↵ Trubetskova , I. L. , & Haney , J. F. ( 2006 ). Effects of differing concentrations of microcystin-producing Microcystis aeruginosa on growth, reproduction, survivorship and offspring of Daphnia magna . Archiv Fur Hydrobiologie , 167 ( 1–4 ), 533 – 546 . doi: 10.1127/0003-9136/2006/0167-0533 OpenUrl CrossRef ↵ Vanni , M. J. ( 1984 ). Biological control of nuisance algae by Daphnia pulex: Experimental studies . Lake and Reservoir Management , 1 ( 1 ), 151 – 156 . doi: 10.1080/07438148409354502 OpenUrl CrossRef ↵ Vehtari , A. , Gelman , A. , Simpson , D. , Carpenter , B. , & Burkner , P. C. ( 2021 ). Rank-Normalization, Folding, and Localization: An Improved R-hat for Assessing Convergence of MCMC . Bayesian Analysis , 16 ( 2 ), 667 – 718 . doi: 10.1214/20-BA1221 OpenUrl CrossRef ↵ von Elert , E. , Martin-Creuzburg , D. , & Le Coz , J. R. ( 2003 ). Absence of sterols constrains carbon transfer between cyanobacteria and a freshwater herbivore (Daphnia galeata) . Proceedings of the Royal Society B: Biological Sciences , 270 ( 1520 ), 1209 – 1214 . doi: 10.1098/rspb.2003.2357 OpenUrl CrossRef PubMed Web of Science ↵ Xu , J. , Chang , X. , MacIsaac , H. J. , Zhou , Y. , Li , J. , Wang , T. , Zhang , J. , Wen , J. , Xu , D. , Zhang , H. , & Xu , R. ( 2023 ). Is a lower-toxicity strain of Microcystis aeruginosa really less toxic? Aquatic Toxicology , 263 . doi: 10.1016/j.aquatox.2023.106705 OpenUrl CrossRef View the discussion thread. Back to top Previous Next Posted March 06, 2025. Download PDF Supplementary Material Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Rapid temporal adaptation structures tolerance to toxic cyanobacteria in a natural population of the water flea Daphnia Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Rapid temporal adaptation structures tolerance to toxic cyanobacteria in a natural population of the water flea Daphnia Maxime Fajgenblat , Emma Gouwy , Manon Coone , Rafaela Almeida , Alice Boudry , Kiani Cuypers , Edwin van den Berg , Isabel Vanoverberghe , Luc De Meester , Ellen Decaestecker bioRxiv 2025.02.28.640829; doi: https://doi.org/10.1101/2025.02.28.640829 Share This Article: Copy Citation Tools Rapid temporal adaptation structures tolerance to toxic cyanobacteria in a natural population of the water flea Daphnia Maxime Fajgenblat , Emma Gouwy , Manon Coone , Rafaela Almeida , Alice Boudry , Kiani Cuypers , Edwin van den Berg , Isabel Vanoverberghe , Luc De Meester , Ellen Decaestecker bioRxiv 2025.02.28.640829; doi: https://doi.org/10.1101/2025.02.28.640829 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Evolutionary Biology Subject Areas All Articles Animal Behavior and Cognition (7642) Biochemistry (17715) Bioengineering (13907) Bioinformatics (42003) Biophysics (21470) Cancer Biology (18624) Cell Biology (25533) Clinical Trials (138) Developmental Biology (13390) Ecology (19935) Epidemiology (2067) Evolutionary Biology (24356) Genetics (15617) Genomics (22529) Immunology (17753) Microbiology (40432) Molecular Biology (17200) Neuroscience (88681) Paleontology (667) Pathology (2840) Pharmacology and Toxicology (4828) Physiology (7653) Plant Biology (15161) Scientific Communication and Education (2046) Synthetic Biology (4304) Systems Biology (9826) Zoology (2271)