Eco-evolutionary feedback of adaptive evolution to a pesticide worsens the impact of a pesticide switch in a pivotal freshwater non-target species

Eco-evolutionary feedback of adaptive evolution to a pesticide worsens the impact of a pesticide switch in a pivotal freshwater non-target species | 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 Eco-evolutionary feedback of adaptive evolution to a pesticide worsens the impact of a pesticide switch in a pivotal freshwater non-target species Rafaela A. Almeida , Maxime Fajgenblat , Pieter Lemmens , Kiani Cuypers , Jade Maes , Kristien I. Brans , Luc De Meester doi: https://doi.org/10.1101/2025.11.30.691396 Rafaela A. Almeida 1 Laboratory of Freshwater Ecology, Evolution and Conservation, KU Leuven , Ch. Deberiotstraat 32, B-3000 Leuven, Belgium Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: rafaela.almeida{at}kuleuven.be Maxime Fajgenblat 1 Laboratory of Freshwater Ecology, Evolution and Conservation, KU Leuven , Ch. Deberiotstraat 32, B-3000 Leuven, Belgium 2 Data Science Institute (DSI), Interuniversity Institute for Biostatistics and statistical Bioinformatics (I-BioStat), Hasselt University , Martelarenlaan 42, B-3500 Hasselt, Belgium Find this author on Google Scholar Find this author on PubMed Search for this author on this site Pieter Lemmens 1 Laboratory of Freshwater Ecology, Evolution and Conservation, KU Leuven , Ch. Deberiotstraat 32, B-3000 Leuven, Belgium 3 Research Institute of Nature and Forest (INBO) , Dwersbos 28, 1630 Linkebeek, Belgium Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kiani Cuypers 1 Laboratory of Freshwater Ecology, Evolution and Conservation, KU Leuven , Ch. Deberiotstraat 32, B-3000 Leuven, Belgium Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jade Maes 4 Department of Fisheries Biology, Institute for Agricultural, Fisheries and Food Research (ILVO) , Jacobsenstraat 1, 8400 Oostende, Belgium Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kristien I. Brans 1 Laboratory of Freshwater Ecology, Evolution and Conservation, KU Leuven , Ch. Deberiotstraat 32, B-3000 Leuven, Belgium 5 Ecology, Evolution & Genetics (bDIV) research group, Department of Biology, Vrije Universiteit Brussel (VUB) , Pleinlaan 2, 1050, Brussels, Belgium Find this author on Google Scholar Find this author on PubMed Search for this author on this site Luc De Meester 1 Laboratory of Freshwater Ecology, Evolution and Conservation, KU Leuven , Ch. Deberiotstraat 32, B-3000 Leuven, Belgium 6 Leibniz Institute für Gewasserökologie und Binnenfischerei (IGB) , Müggelseedamm 310, 12587 Berlin, Germany 7 Institute of Biology, Freie Universität Berlin , Königin-Luise-Strasse 1-3, 14195 Berlin, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Pest management often involves switches in the identity of the pesticides applied, sequentially exposing non-target populations to different pesticides. In a two-phased experiment, we assessed whether exposure to the pesticide chlorpyrifos induces rapid evolution in the non-target species Daphnia magna, and quantified the response of control and pre-exposed populations to a second exposure to the same pesticide, another pesticide with the same mode of action (malathion), or a pesticide with another mode of action (deltamethrin). Chlorpyrifos selection induced rapid shifts in genotype composition and reduced genotype richness, and strongly influenced population development following the second exposure. Chlorpyrifos-selected populations outperformed control populations when subsequently exposed to chlorpyrifos and malathion, but underperformed when exposed to deltamethrin. Our results highlight an eco-evolutionary feedback in which rapid adaptive evolution to a pesticide worsens the response when exposed to a pesticide with different mode of action in non-target species, increasing vulnerability to common agricultural practices. Introduction Pesticides are strong drivers of adaptation in natural populations ( Almeida et al., 2021 ; Hua et al., 2015 ; Siddique et al., 2020 ). Upon exposure to pesticides, populations can develop resistance through adaptive plastic and epigenetic responses ( Brevik et al., 2018 ; Hua et al., 2013 ; Margus et al., 2019 ), which allow for rapid acclimation of exposed individuals and have been widely reported in response to pesticides ( DiGiacopo & Hua, 2020 ). Populations may also genetically adapt to better cope with pesticides ( Almeida et al., 2021 ; Brans et al., 2021 ; Coors et al., 2009 ; Jansen et al., 2011b ). Such genetic adaptations can happen through selection from standing genetic variation within populations, or through the occurrence of de novo mutations that confer higher resistance to pesticides ( Hawkins et al., 2019 ). While the capacity to adapt to pesticides has been repeatedly reported, adaptation to stressors can imply costs ( Callahan et al., 2008 ; van Kleunen & Fischer, 2005 ; Baucom & Mauricio, 2004; Bourguet et al., 2004 ; Semlitsch et al., 2000). Specifically, while adaptation to a pesticide is beneficial in the presence of the pesticide itself, such adaptation may lead to individuals underperforming when this stressor is no longer present ( Jansen et al., 2011b ; Lenormand et al., 1999 ) or make the adapted populations more susceptible to other stressors ( Cuenca Cambronero et al., 2018 ; Jansen et al., 2011c ). This reduced capacity of stressor-adapted populations to cope with new stressors might be particularly important in the light of agricultural management schemes. Pesticide rotation is a common practice in pest management, to counter adaptation of a pest species to one particular pesticide and ensuring their effectiveness ( Denholm & Devine, 2013 ). This, coupled with the increasing variety of available pesticides ( Bernhardt et al., 2017 ) and policies that ban specific pesticides because of their adverse effects ( Butler, 2018 ; Zwetsloot et al., 2018 ), results in increasing replacements of formerly common pesticides by new ones ( Finger, 2018 ; Möhring et al., 2020 ). Changes in pesticide use also follow from the growing popularity of organic farming, which is promoted as a more environmentally friendly alternative to conventional agriculture. However, a shift from conventional to organic agriculture also involves a shift in pesticide application, as the pesticides used in organic agriculture differ strongly from those in conventional agriculture ( Gomiero et al., 2008 ). As a consequence of these policies, natural populations that live near or in agricultural areas are regularly exposed to new pesticides within one growing season. Negative effects of evolutionary adaptation to pesticides might in this context add to the burden of pesticide pollution on non-target species. Pesticides impose strong threats to freshwater ecosystems ( Peters et al., 2013 ; Rumschlag et al., 2020 ; Tang et al., 2021 ) and studies have shown that even lower pesticide concentrations than those regarded as safe by regulatory legislation can impact freshwater natural populations ( Liess et al., 2013 ; Liess & von der Ohe, 2005 ; Schäfer et al., 2007 ; Siddique et al., 2020 ). Farmland ponds are especially susceptible to pesticide contamination due to their location and small size. This is concerning, as collectively pond ecosystems are important biodiversity hotspots ( Dudgeon et al., 2006 ) and provide key ecosystem services ( Biggs et al., 2017 ). Zooplankton can be highly affected by pesticides ( Hébert et al., 2021 ; López-Mancisidor et al., 2008 ; Relyea, 2005 ), and, specifically for species of the genus Daphnia , lethal and sublethal effects have been reported, including reduced reproduction ( Song et al., 2017 ), delayed development ( Toumi et al., 2013 ) and reduced grazing efficiency ( Bengtsson et al., 2004 ). Daphnia are key grazers and exert a strong top-down control on phytoplankton communities ( Gianuca et al., 2016 ; Scheffer et al., 1993 ) and can thus prevent (toxic) phytoplankton blooms in ponds and lakes. Given the observation that Daphnia populations can adapt to pesticide pollution, its potential costs in terms of susceptibility to other stressors, and the fact that pesticide switches are common agricultural practice, we designed an experiment to assess the effect of a pesticide application on Daphnia magna populations that have been previously exposed to another or the same pesticide. We test the hypotheses that (i) exposure to a model organophosphate insecticide, chlorpyrifos, affects genotype composition of Daphnia magna populations, (ii) such exposure affects population dynamics when the populations are subsequently exposed to a new pesticide, and that (iii) this impact is dependent on a match between mode of action of the second to the first pesticide exposure. More specifically, we expect that selection through exposure to chlorpyrifos improves performance upon a second exposure to chlorpyrifos or exposure to malathion, another organophosphate pesticide with an identical mode of action. In contrast, selection through exposure to chlorpyrifos is expected to result in a lower performance in response to exposure to a pesticide with a different mode of action, such as deltamethrin. Deltamethrin is a pyrethroid pesticide with a different mode of action from organophosphates and, importantly, chemically based on pyrethrins, which are used in organic agriculture. This switch scenario in the experiment was chosen to reflect a changing pesticide application strategy, as also seen in the current transition toward more organic agricultural practices. Material and Methods Clone isolation and rearing We isolated 20 genetically unique clonal linages of Daphnia magna by hatching resting eggs from the top sediment layer (upper 2 cm) of the pond Langerodevijver, located in a nature reserve in Flanders, Belgium (50°49’42.2” N – 4°38’23.7” E) (sediment collection April 2018). These linages were kept under standardized optimal laboratory conditions (20±1°C, 16:8 light:dark photoperiod, renewal of 75% of the medium and feeding with 1×10 5 Acutodesmus obliquus cells/mL every second day) for at least two generations to avoid the interference from (grand)maternal effects. Pesticide solutions We selected three pesticides for this study: chlorpyrifos, malathion and deltamethrin. Chlorpyrifos (CAS 2921-88-2, purity > 99%, Sigma-Aldrich) is a broad-spectrum organophosphorus insecticide that has been commonly used in agriculture ( Eaton et al., 2008 ; Racke, 1993 ) and acts as an acetylcholinesterase inhibitor ( Solomon et al., 2014 ). Chlorpyrifos was banned by the European Commission in 2020. Malathion (CAS number: 121-75-5, purity > 99%, Sigma-Aldrich), another organophosphate insecticide, and similarly to chlorpyrifos, is also an acetylcholinesterase inhibitor. Malathion has been extensively used in agriculture for the past half century ( Jensen & Whatling, 2010 ). Deltamethrin (CAS 52918-63-5, purity >98%, Sigma-Aldrich) is a synthetic pyrethroid insecticide ( Soderlund, 2010 ) that acts on the voltage-gated sodium channels of nervous cells membranes ( Field et al., 2017 ). Deltamethrin is widely used worldwide and, besides being allowed in organic agriculture in insect traps under the EU Commission Regulation (EC) No 889/2008 ( Commission of the European Union, 2008 ), it also shares the same mode of action and similar chemical structure with pyrethrins, the class of pesticides most commonly used in organic agriculture ( Isman, 2006 ; Jansen et al., 2010 ). Environmentally relevant concentrations of chlorpyrifos, malathion and deltamethrin (0.35µg/L, 1.4µg/L and 0.075µg/L, respectively, Marino & Ronco, 2005 ; Vasseghian et al., 2022 ; Mestres & Mestres, 1992 ) were chosen for this experiment based on prior pilot tests. Experimental design The experiment was divided into two phases: a selection phase involving an exposure to the pesticide chlorpyrifos for three weeks, followed by a second exposure phase in which an exposure to either no pesticide, chlorpyrifos, malathion, or deltamethrin was administered (Fig S1). The two phases of the experiment were separated by a ten-day recovery period to reduce the impact of acclimatization. The experiment was carried out between September and December 2020. Selection phase In the selection phase, five juveniles (three to four days old) of each of the 20 clonal lineages were inoculated in 15L aquaria that were filled with 10L of dechlorinated tap water, resulting in six starting populations, with 100 individuals each. These populations were kept under standardized laboratory conditions (20°C, photoperiod 16:8 light:dark) and, after an acclimation period of three days, were exposed for three weeks to either a control treatment or a chlorpyrifos treatment (weekly pulses of 0.35 µg/L), each replicated three times (2 treatments: control and chlorpyrifos x 3 replicates = 6 units). Populations were fed daily with 1×10 5 Acutodesmus obliquus cells/mL. The aquaria were cleaned twice a week (on the day of a new pulse and three days after the pulse) by removing dead individuals and leftover algae to prevent deterioration of water quality. To ensure a weekly refreshed controlled pulse of pesticide exposure, the total amount of medium was renewed once a week and a new pesticide pulse was given. After three weeks, a random 500mL sample was collected from each aquarium to isolate Daphnia , which were preserved in absolute ethanol (>99.8%, Fisher Chemical) for genetic analyses. After this, the populations were allowed a ten-day resting period, during which all aquaria were kept under control conditions (i.e. no pesticide was added), keeping the maintenance schedule identical to the experiment, to allow the populations to recover in densities and reduce interference from acclimatization. Second exposure phase In the second exposure phase, the experimental populations from the selection phase were each divided into four populations by randomly isolating four sets of 70 sub-adult individuals from each aquarium. These populations were subsequently inoculated in 10L aquaria filled with 5L of dechlorinated tap water, and exposed to one of the four different treatments in triplicate: a control treatment (no pesticide exposure), exposure to chlorpyrifos (i.e. exposure to the same pesticide as during the selection phase), exposure to malathion (i.e. exposure to a pesticide with the same mode of action as chlorpyrifos) and exposure to deltamethrin (i.e. exposure to a pesticide with a different mode of action from chlorpyrifos). This design generated 2 treatments in the selection phase x 4 treatments in the second exposure phase x 3 replicates = 24 experimental units in the second exposure phase. New pulses of each pesticide were given once per week (chlorpyrifos - 0.35µg/L, malathion — 1.4µg/L, and deltamethrin — 0.075µg/L), following the same procedure as during the selection phase, over three weeks. Laboratory conditions and feeding routine were identical to the selection phase. Population densities were determined twice a week by counting individuals based on video recordings of the Daphnia populations. The entire populations were transferred to shallow glass trays and placed in a dark enclosure over a LED light pad (Huion L4S) with an overhead camera set-up (Canon EOS 700D). The individuals were recorded for ten seconds, and the video recordings were analyzed using the Trackdem R package ( Bruijning et al., 2018 ; R Core Team, 2023 ). After three weeks, a random 500mL sample of medium and animals was collected from each aquarium and stored in absolute ethanol for genetic analyses. Genotyping To genotype the individuals sampled from all experimental populations at the end of both the selection and the second exposure phase, we used eight microsatellite markers structured in one microsatellite multiplex (MO1) following Orsini et al. (2012) and Jansen et al. (2011a) . Individuals were considered to be of identical genotype (i.e. belonging to the same clonal line) if they did not differ in their genotype across the eight studied microsatellite loci. Note that all clones we inoculated are genetically unique as they hatched from sexual (dormant) eggs. We performed genomic DNA extraction on 20 randomly picked adult D. magna individuals from each population, following a Proteinase K digestion method as described in Mergeay et al. (2008) , followed by a qualitative PCR (QIAGEN multiplex PCR kit; QIAGEN, Netherlands; protocol detailed in Supplementary Information). Microsatellite alleles were scored with an ABI PRISM 3031 automated sequencer (Applied Biosystems) and analyzed with the Gene Mapper software (size standard Liz 500, Applied Biosystems). Data analysis Genetic data Given the clonal structure of Daphnia populations, we combined the allelic variant at every locus into a multi-locus genotype for each individual, and then used these data to assess genotype richness and the relative abundance of the different genotypes in all populations. We used principal component analysis (PCA) to visualize differences in genotype composition among experimental populations. Additionally, we used Bayes factors to formally quantify clonal selection throughout the experiment and clonal differentiation among treatments, acknowledging sampling uncertainty (see Supporting Information for methodological details). Following Kass and Raftery (1995) , we interpret Bayes factors as follows: values between 1 and 3.2 indicate “evidence not worth more than a bare mention,” values between 3.2 and 10 “substantial evidence,” between 10 and 100 “strong evidence,” and values greater than 100 “decisive evidence”. Bayesian hierarchical modelling of population growth curves We used a hierarchical Gaussian process (HGP) regression approach (Rasmussen & Williams, 2006) to model population densities over time and to investigate the impact of an initial selection through exposure to chlorpyrifos and subsequent exposure to a particular pesticide on key population growth characteristics, including the maximal growth rate, average population density and maximum population density. Gaussian processes (GPs) are a non-parametric method to parsimoniously model time series (Rasmussen & Williams, 2006). They have been shown to strongly outperform conventional parametric growth models when dealing with nonstandard growth curves (Tonner et al. 2017). Let Y u,t be the population density of experimental unit u = 1, 2, …, 30 at day t = 0, 4, 7, 11, 14, 18, 21, with corresponding exposure during selection p ( u ) = control, chlorpyrifos and second exposure e ( u ) = control, chlorpyrifos, malathion, deltamethrin. We assume Y u,t follows a negative binomial distribution: where μ u,t is the expected density of experimental unit u at time t , and Φ is a dispersion parameter common to all experimental units and time points. We model the linear predictor for each experimental unit as follows: where f treat p(u),e(u) ( t ) is a smooth function of time representing the actual growth curve for each the selection p and the second exposure e of experimental unit u , and where f unit u ( t ) is a smooth function of time capturing the residual temporal pattern through time for each experimental unit u , not captured by the treatment-level function. Both groups of smooth functions are modelled by means of GPs: where β treat p,e and β unit u can be seen as treatment- and unit-level intercepts, and where K treat p,e (Δ t ) and K unit p,e (Δ t ) are covariance functions, dictating how fast similarity among any two pairs of measurements decays as a function of the time difference Δ t that separates them. We specifically consider an exponentiated covariance function, and we use weakly informative to informative priors on the model parameters (see Supporting Information for methodological details). The HGP model allows us to probabilistically infer the true population growth trajectory for each combination of selection and second exposure condition, while simultaneously taking into account the temporally structured residual variation among replicates. We also quantified the maximum growth rate, the maximum population density and the average population density at each posterior draw to fully propagate uncertainty (see Supporting Information for methodological details), and we used a least squares approach to disentangle the effects of selection by chlorpyrifos exposure, second exposure to any of the three pesticides, as well as their interactions, across the eight treatment combinations. We implemented the above model using the probabilistic programming language Stan and the rstan v.2.21.2 package ( Stan Development Team, 2020 ) in R v.4.0.3 ( R Development Core Team, 2020 ). Stan performs Bayesian inference by means of dynamic Hamiltonian Monte Carlo (HMC), a gradient-based Markov chain Monte Carlo (MCMC) sampler ( Carpenter et al., 2017 ). We ran four chains with 2,000 iterations each, of which the first 1,000 were discarded as warm-up. We assessed model convergence visually by means of traceplots and numerically by means of effective sample sizes, divergent transitions and the Potential Scale Reduction Factor, for which all parameters had R ^ < 1.01 (Vehtari et al., 2021). We used the tidybayes v.2.3.1 package ( Kay, 2020 ) to visualize posterior distributions. Results Genetic changes following the selection experiment Chlorpyrifos selection induced repeatable shifts in genotypic composition compared to the starting population and the experimental populations in the control conditions ( Figure 1 , 2 and S2), with the first principal component of the PCA being able to clearly discriminate between genotype composition of control and chlorpyrifos-selected populations ( Figure 2 ). Exposure to chlorpyrifos in the selection phase led to a reduction of genotypes present in the populations (Figure S2), resulting in a strong genetic differentiation of chlorpyrifos-selected from the initial inoculation, as well as from control-selected populations at the end of the selection phase (Bayes factor 7.1×10 10 and 6.9×10 9 , respectively, pooled replicates, Figure 1 ). Relative frequencies of genotypes of control populations also differed from the initial inoculation, although to a lower extent (Bayes factor 4.6×10 10 , pooled replicates). More specifically, genotypes 2, 8, 13 and 19 have higher frequencies in the chlorpyrifos-selected populations than in the control populations, whereas genotypes 5, 9 and 16 have higher frequencies in the control-selected populations ( Figure 2 , Figure S2). Genotype composition at the end of the second phase of the experiment reveals strong differences ( Figure 1 and S2), generally reflecting the differences that built up mostly during the selection phase. All populations that were initially chlorpyrifos-selected strongly differed from control ones in the genotype composition at the end of the second exposure phase, regardless of the type of second exposure ( Figure 1 ). Exposure to pesticides during the second phase led to strong differentiation between population that received the control treatment during the selection phase, with control-chlorpyrifos and control-deltamethrin populations differing the most (Bayes factor 3.6×10 4 , pooled replicates). This genetic differentiation at the end of the second phase, however, was not observed in the populations with the chlorpyrifos selection. Download figure Open in new tab Figure 1. Heatmap showing the evidence for clonal differentiation among the genotypes of all pairs of experimental units in terms of Bayes factors, for the pooled replicates and for the individual replicates. Axis labels display the first and second exposure treatments, separated by a dash. The brighter the color, the more evidence for clonal differentiation. Pairs of experimental units that have at least substantial evidence for clonal differentiation (i.e. Bayes factor > 3.2) are highlighted by a white star. Download figure Open in new tab Figure 2. Principal component analysis (PCA) of the genotype composition of the populations of the different experimental populations among the two phases of the experiment. Biplot showing the ordination of populations along the first and second principal components, with colors depicting the selection phase type and symbols depicting the second exposure phase. Green arrows with numbered arrowheads depict the genotypes and their variable loadings. For clarity, only the 10 variables with the highest variable loadings are shown. The black point at the origin shows the ordination of the original community used for inoculation, with equal genotype frequencies. Population dynamics in the new pesticide phase Our analysis revealed that chlorpyrifos selection strongly influences the response of the populations in the second exposure phase, especially with respect to the average and maximum population densities ( Figure 3 and 4 , Figure S3). We found chlorpyrifos-selection to increase maximum density during the second phase of the experiment by 650.8 individuals (posterior mean, 95% CrI [178.0; 1241.7], 99.4% posterior probability of an increase), average density by 199.5 individuals (95% CrI [9.3; 391.4], 97.8% post. prob. of an increase) and growth rate by 10.3 individuals/day (95% CrI [-4.0;35.7], 88.6% post. prob. of an increase) when compared to control-selected populations ( Figure 3 ). Download figure Open in new tab Figure 3. Estimated population density patterns of Daphnia magna (number of individuals) that were either non-exposed (blue – control) or exposed to chlorpyrifos (pink) during the seelction phase, throughout the second exposure phase of the experiment, when exposed to new control conditions, chlorpyrifos, malathion and deltamethrin. The full lines indicate the posterior median population densities, while the colored bands represent the 50, 80, 95 and 99% credible intervals. Original data are shown as points. Download figure Open in new tab Figure 4. Posterior densities for the maximum growth rate, average population densities and maximum population density for each treatment during the second exposure phase of the experiment, for the populations that were non-exposed (blue – control) or exposed to chlorpyrifos (pink) during the selection phase. The horizontal bars represent 95% credible intervals, while the dots represent the posterior medians For populations exposed to control conditions in the second phase of the experiment, previous selection by chlorpyrifos increased maximum density by 46.6 percentage points (95% CrI [11.2;96.4]), average density by 25.0 percentage points (95% CrI [1.0; 53.2]) and growth rate by 60.5 percentage points (95% CrI [-20.4; 217.7]) when compared to the control-selected populations ( Figure 4 ). Exposure to any of the three pesticides (chlorpyrifos, malathion and deltamethrin) during the final phase of the experiment tends to negatively affect populations, with consistently negative posterior median effects ( Figure 5 ). Of the three parameters, the average population density is affected most: while there is a moderate signal for chlorpyrifos (−124.7 individuals, 95% CrI [−324.9; 77.2], 91.2% post. prob. of a decrease) and for deltamethrin (−75.0 individuals, 95% CrI [−232.5; 85.0], 85.4% post. prob. of a decrease), there is a clear signal for malathion in particular (−220.7 individuals, 95% CrI [−370.7; −63.9], 99.3% post. prob. of a decrease). We also identified a negative effect of malathion exposure on the maximum growth rate with moderately high probability (−3.3 individuals/day, 95% CrI [−11.5; 12.957], 81.8% post. prob. of a decrease). Download figure Open in new tab Figure 5. Posterior densities of the intercept of the control pre-exposure and control exposure baseline, the effect of chlorpyrifos selection, the effect of second exposure to chlorpyrifos, deltamethrin and malathion, as well as their interactions on the maximum growth rate, average population density and maximum population density of D. magna in the second phase of the experiment. Horizontal lines represent 95% credible intervals, and the points represent posterior medians. Posterior densities that have over 95% probability of being either smaller or larger than 0, are shown in orange. The displayed effects in this figure correspond to the quantities shown in Figure 4 as follows: each quantity in Figure 4 can be decomposed into the baseline intercept (corresponding to the control-control condition), the main effect of pre-exposure (if different from control), the appropriate main effect of exposure (if different from control) as well as the appropriate interaction effect if applicable. For instance, the total quantity for the chlorpyrifos-deltamethrin condition can be obtained by summing the intercept, the pre-exposure to chlorpyrifos effect, the exposure to deltamethrin effect as well as the interaction for chlorpyrifos selection and second exposure to deltamethrin. There was a very strong interaction effect of chlorpyrifos selection followed by exposure to deltamethrin for all end points, namely growth rate (96.7% posterior probability of a negative effect), average density (99.5% posterior probability of a negative effect) and maximum density (99.8% posterior probability of a negative effect). More specifically, chlorpyrifos-selected Daphnia populations that were subsequently exposed to deltamethrin faced a 26.6 percentage point (95% CrI [-3.6; 49.6]), 15.7 percentage point (95% CrI [-1.6; 30.2]) and 15.5 percentage point (95% CrI [-7.3; 34.1]) reduction in maximum growth rate, average density and maximum density, respectively, compared to control-selected populations. In contrast, we did not find evidence for an important interaction of chlorpyrifos-selection followed by exposure to either chlorpyrifos or malathion. Discussion Overall, exposure to chlorpyrifos during the selection phase strongly affected genetic composition and diversity within a three-week period, and subsequently affected demographic responses of the experimental Daphnia populations, both in the presence and absence of a second pesticide exposure. Whether chlorpyrifos selection had a positive or negative effect on population development upon a second exposure depended on the identity of the pesticide. In line with our expectations, population densities increased in chlorpyrifos-selected populations when exposed for a second time to the same pesticide or a pesticide with the same action mode (malathion), compared to control-selected populations. Population densities of chlorpyrifos-selected populations were negatively affected when exposed to deltamethrin in the second phase, compared to control-selected populations in that phase. Surprisingly, chlorpyrifos-exposed populations in the selection phase also reached higher densities compared to populations that were not previously exposed when released from exposure to any pesticide in the second phase. Overall, our results thus indicate that Daphnia populations can rapidly evolve following exposure to a pesticide, and that this can lead to predictable cross-tolerance but also to costs of adaptation. This has important implications in the context of environmental policies that often lead to shifts in pesticide use. A three-week exposure to the pesticide chlorpyrifos resulted in strong shifts in genotype composition in our experimental populations. Such shifts in clonal frequencies could result both from strong selection or from drift. However, the repeatable shift in genotype composition that we observe across replicates (see Figure 1 and 2 ) strongly suggests a non-random response to selection. While we did not test tolerance of each of the experimental genotypes to chlorpyrifos, it is likely that exposure to chlorpyrifos lead to selection in favor of more tolerant genotypes. Several studies have reported that Daphnia populations can adapt to pesticides ( Bendis & Relyea, 2014 ; Jansen et al., 2011b , 2011c ). Adaptation to pesticides can be a result of emerging de novo mutations ( Chen et al., 2015 ; Gressel, 2011 ) or of selection from standing genetic variation within populations ( Barrett & Schluter, 2008 ; Kersten et al., 2023 ), the latter allowing for such adaptations to occur in shorter timeframes ( Hawkins et al., 2019 ). Given that we observe repeatable shifts in clonal frequencies, the response to selection in our experiment is mainly driven by standing genetic variation. As we used 20 genotypes hatched from a dormant egg bank of a single lake as the starting population for our experiments, our experiment shows that the studied Daphnia population harbors substantial genetic variation relevant to the used selection factor. This is in line with the high capacity for (adaptive) evolution to another stressor (fish predation) that was found in a genomic study in the context of resurrection ecology ( Chaturvedi et al., 2021 ). While our experimental design allowed for a ten-day period recovery from pesticide exposure and as such reduced impacts of acclimatization, we did not perform a strict common garden procedure involving multiple generations of purging from maternal effects. While we therefore cannot without reservation claim that the differential responses of chlorpyrifos-selected vs. control-selected populations to a second pesticide exposure reflect the impact of evolutionary changes rather than acclimatization, our observation of repeatable shifts in genotype frequencies depending on the first exposure provides confidence that evolution very likely played a role. The observation that there was a cost in response to a pesticide of different mode of action in the second phase might also point to an involvement of an evolutionary response rather than that our observations would reflect a pure acclimatization response. While our results thus suggest an involvement of a genetic component, we do not exclude that physiological acclimatization and epigenetic effects might have contributed to the observed responses. The response was adaptive: even though the time for adaptation, three weeks, was very short, first exposure to chlorpyrifos resulted in a considerable improvement of the performance of the Daphnia when exposed to chlorpyrifos in the second phase of the experiment. Similar to the pattern observed for chlorpyrifos, chlorpyrifos-selected populations performed better compared to control-selected ones when exposed to malathion in the second phase of the experiment. This indicates cross-tolerance of the studied populations for these two pesticides. Chlorpyrifos and malathion are both organophosphates that act as acetylcholinesterase inhibitors ( Jensen & Whatling, 2010 ). Cross-tolerance between pesticides with the same mode of actions has been shown several times for different organism groups ( Bendis & Relyea, 2016 ; van de Maele et al., 2021 ; Hua et al., 2013 ; Saddiq et al., 2016 ). Surprisingly, populations chlorpyrifos-selected populations reached higher population densities under control conditions compared to those that were never exposed to a pesticide. Even though growth rate did not increase, chlorpyrifos-selected populations reached higher maximum and average densities in control conditions. These results contradict the findings that acquired resistance to a stressor reduces fitness under ancestral conditions ( Kliot & Ghanim, 2012 ; Wang et al., 2010 ). One possible explanation for our observations might lie in a re-allocation of energy. Pesticide exposure triggers allocation of energy budgets to detoxification mechanisms ( Ferrario et al., 2018 ; Sokolova et al., 2012 ), and exposure to sub-lethal concentrations of chlorpyrifos has been shown to drive D. magna individuals to invest more energy in synthesizing antioxidants and detoxification enzymes ( Ferrario et al., 2018 ). If chlorpyrifos exposure shifts clonal composition towards genotypes that have differently allocate energy reserves so that they can invest in detoxification, then these animals may have more available energy under control conditions. While this suggests an absence of cost of adaptation, this energy re-allocation may have a cost under different environmental conditions, as less energy might be available to sustain other functions. One example may be a reduced capacity to cope with parasites. Jansen et al. (2011d) have indeed shown that selection for higher tolerance to carbaryl, another cholinesterase inhibitor, increases the vulnerability of D. magna to the parasite Pasteuria ramosa . Populations that were first exposed to chlorpyrifos suffered a reduction in growth rate, maximum density and average density when exposed to deltamethrin. While there is no cost in the absence of a stressor, there is a cost when exposed to a different type of pesticides. Several studies have shown that genetic adaptation to a given stressor can affect fitness when the population is exposed to a different stressor ( Cuenca Cambronero et al., 2018 ; Dong et al., 2024 ; Jansen et al., 2011d , 2011c ). While chlorpyrifos is an acetylcholinesterase inhibitor, deltamethrin is a pyrethroid insecticide that disrupts the functioning of voltage-gated sodium channels ( Field et al., 2017 ). Here, we observed a cost on all assessed demographic parameters: there was on average a 26.6 % decrease in maximal population growth rate, a 15.7% decrease in average and 15.5% decrease in maximal population density of populations that were previously exposed to chlorpyrifos compared to control populations. Pesticide switches can be part of a strategy by farmers to prevent adaptation in target species ( Denholm & Devine, 2013 ; Palumbi, 2001 ), or motivated by environmental policies that stimulate pesticide bans or switches ( Gensch et al., 2024 ). Our results show that previous exposure to an organophosphate makes Daphnia populations more vulnerable to a pyrethroid, resulting in a reduction of population densities. This indicates that, even though in the long-term ecosystems may benefit from ban of certain pesticides or from a transition to organic farming ( Bengtsson et al., 2005 ; Nascimbene et al., 2012 ; Rundlöf et al., 2010 ), the associated shift in the type of pesticides applied can be harmful for non-target populations in the short term. Here, using a non-target species, we show that rapid evolution in response to exposure to a pesticide reduces performance in response to another pesticide. Applied to target species, this increases the effectiveness in pesticide switches, as it does not only reduce the likelihood of resistance evolution but also creates a cost when such adaptation already occurred. Applied to non-target species, however, this increases the harm done by pesticide use. Given that shifting pesticide use is a common agricultural practice, either to enhance pest management or in response to pesticide use legislation, the here reported eco-evolutionary feedback might be an important driver of negative impacts of pesticide use on natural populations and ecosystems.. Our results show that both increased resistance and associated costs in the response of Daphnia to pesticides may be observed within a very short time frame, namely a few weeks. Previous studies have indicated that populations can develop resistance to pesticides quite rapidly, through selection from standing variation ( Jansen et al., 2011d ; Pélissié et al., 2018 ) and through heredity of epigenetic traits that confer higher resistance to pesticides ( Brevik et al., 2018 ; Castano-Sanz et al., 2022 ). In our study, we observed a strong clonal selection exerted by chlorpyrifos exposure on the initial populations. The rate at which species are capable of adapting depends on their generation time ( Whitehead et al., 2017 ) and on the presence of beneficial heritable trait variation within the populations ( Hawkins et al., 2019 ). Daphnia species have short life cycles and are capable of rapid adaptation ( Geerts et al., 2015 ; Hairston Jr et al., 1999; Hochmuth et al., 2015 ; Miner et al., 2012 ). In our study, earlier selection under chlorpyrifos exposure followed by an exposure to deltamethrin reduced genetic diversity and population densities of Daphnia magna , but we did not observe a full elimination of these populations. However, other organism groups with longer generation times and smaller population sizes, and therefore reduced genetic diversity, may not be capable of adapting rapidly enough, or this adaptation may come at a higher cost in terms of losing evolutionary potential to respond to future environmental changes ( Whitehead et al., 2017 ). Our results highlight the negative impacts of frequent switches between pesticides with different modes of action on non-target species, which has not been considered to date in environmental risk assessments of pesticide applications. To the extent that exposure to the two different pesticides happens in different generations, this cost would only hold for populations that genetically adapted or showed increased resistance due to transgenerational epigenetic effects, as pure acclimatization that is not inherited would be less costly under these conditions. These negative effects may not only occur through demographic effects but also through genetic erosion. The need of populations to continuously adapt to new pesticides may result in a narrowing of the genetic variation and hence evolutionary potential. The latter may be especially important in species that have relatively low genetic diversity. Our results may be considered an example of what is likely a rather general observation: that the most effective strategies to suppress harmful species will likely also have the most negative effects on non-target species. Acknowledgements R.A.A acknowledges a FWO postdoctoral fellowship (grant number 1228725N) and M.F. a FWO PhD FR fellowship (grand number 11E3222N). We thank Edwin van den Berg for his valuable help with the practical work. R.A.A. and L.D.M. conceived and designed the study. R.A.A., K.C. and J.M. executed the data collection, in consultation with P.L., K.I.B. and L.D.M.. M.F. performed the data analyses in interaction with R.A.A. and L.D.M.. R.A.A. led the writing of the manuscript, with input from L.D.M., M.F., P.L., K.I.B., K.C. and J.M. in different rounds of editing. Funder Information Declared Fonds Wetenschappelijk Onderzoek (FWO), BE , 1228725N , 11E3222N References ↵ Almeida , R. A. , Lemmens , P. , De Meester , L. , & Brans , K. I. ( 2021 ). 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