Adaptation to fluctuations in temperature by nine species of bacteria

This is an electronic reprint of the original article. This reprint may differ from the original in pagination and typographic detail. Author(s): Title: Year: Version: Please cite the original version: All material supplied via JYX is protected by copyright and other intellectual property rights, and duplication or sale of all or part of any of the repository collections is not permitted, except that material may be duplicated by you for your research use or educational purposes in electronic or print form. You must obtain permission for any other use. Electronic or print copies may not be offered, whether for sale or otherwise to anyone who is not an authorised user. Adaptation to fluctuations in temperature by nine species of bacteria Saarinen, Kati; Laakso, Jouni; Lindström, Leena; Ketola, Tarmo Saarinen, K., Laakso, J., Lindström, L., & Ketola, T. (2018). Adaptation to fluctuations in temperature by nine species of bacteria. Ecology and Evolution, 8(5), 2901-2910. https://doi.org/10.1002/ece3.3823 2018 Ecology and Evolution. 2018;8:2901–2910.   | 2901 www.ecolevol.org 1 | INTRODUCTION Among the physical environmental variables, temperature has been called “a major driving force in evolution” (Hochachka & Somero, 2002). Evolution of temperature tolerance in general, and adaptation to high or increasing temperatures in particular, has been studied rather widely (reviewed in: Araújo et al., 2013; Hoffmann & Sgro, 2011). The demand for understanding consequences of especially fluctuating environments has grown bigger as climate change scenarios predict increased fluctuations in temperature and other environmental conditions (Stocker et al., 2014). The most traditional way of testing the tolerance of species or genotypes to environmental variation, like temperature, is by depicting species performance across different constant environments using tolerance curves (Huey & Kingsolver, 1989, 1993). For example, broad/flat tolerance curves (superior tolerance of extreme temperatures at both ends of the curve) and high elevation of the tolerance curve (superior tolerance of all the experienced temperatures) could be predictors of good tolerance to temperature fluctuations (Scheiner & Yampolsky, 1998). There is experimental evidence to show that constant environments favor specialism, and fluctuating or heterogeneous environments select for genotypes that are capable of tolerating a wide range of conditions (Condon, Cooper, Yeaman, & Angilletta, Received:13November2017 | Revised:8December2017 | Accepted:17December2017 DOI:10.1002/ece3.3823 ORIGINAL RESEARCH Adaptation to fluctuations in temperature by nine species of bacteria Kati Saarinen1 | Jouni Laakso2 | Leena Lindström1 | Tarmo Ketola1 This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2018 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. 1DepartmentofBiologicalandEnvironmental Science,CentreofExcellenceinBiological Interactions,UniversityofJyväskylä, Jyväskylä,Finland 2DepartmentofBiologicalandEnvironmental Science,CentreofExcellenceinBiological Interactions,UniversityofHelsinki,Helsinki, Finland Correspondence TarmoKetola,DepartmentofBiologicaland Environmental Science, Centre of Excellence inBiologicalInteractions,Universityof Jyväskylä,Jyväskylä,Finland. Email: tarmo.t.k[email protected] Funding information BiotieteidenjaYmpäristönTutkimuksen Toimikunta, Grant/Award Number: 1255572, 250248 and 278751; Centre of Excellence in BiologicalInteractions Abstract Rapid environmental fluctuations are ubiquitous in the wild, yet majority of experimental studies mostly consider effects of slow fluctuations on organism. To test the evolutionary consequences of fast fluctuations, we conducted nine independent experimental evolution experiments with bacteria. Experimental conditions were same for all species, and we allowed them to evolve either in fluctuating temperature alternating rapidly between 20°C and 40°C or at constant 30°C temperature. After experimental evolution, we tested the performance of the clones in both rapid fluctuation and in constant environments (20°C, 30°C and 40°C). Results from experiments on these nine species were combined metaanalytically. We found that overall the clones evolved in the fluctuating environment had evolved better efficiency in tolerating fluctuations (i.e., they had higher yield in fluctuating conditions) than the clones evolved in the constant environment. However, we did not find any evidence that fluctuationadapted clones would have evolved better tolerance to any measured constant environments (20°C, 30°C, and 40°C). Our results back up recent empirical findings reporting that it is hard to predict adaptations to fast fluctuations using tolerance curves. KEYWORDS experimental evolution, reaction norm, temperature fluctuation, tolerance curve 2902 | SAARINEN Et Al. 2014;Duncan,Fellous,Quillery,&Kaltz,2011;Kassen,2002;Ketola etal.,2013;Venail,Kaltz,Olivieri,Pommier,&Mouquet,2011). However, not all mechanisms on adaptation to fluctuating temperatures might be captured in tolerance curves measured at constant temperatures.Forexample,reversiblephenotypicplasticity(Bennett & Hughes, 2009; Hughes, Cullum, & Bennett, 2007),via increased heat shock protein expression at extremes (Ketola, Laakso, Kaitala, & Airaksinen, 2004; Sørensen, Kristensen, & Loeschcke, 2003), or increasedabilitytoutilizetheshorttimewindowofoptimalconditions between the extremes (Gilchrist, 1995; New et al., 2014), can be difficult to observe from tolerance curves (Ketola, Kellermann, Loeschcke, Lopez-Sepulcre,&Kristensen,2014).Evolutioncouldalsoleadtobet- hedging, in which an individual expresses different phenotypes with a certain probability, in completely random environments (Arnoldini, Mostowy, Bonhoeffer, & Ackermann, 2012; King & Masel, 2007). Therefore, to test the level of adaptation to fluctuations, we should preferably estimate fitness in fluctuating environments, rather than deducing it via tolerance curves (Ketola & Kristensen, 2017; Ketola & Saarinen, 2015; Ketola et al., 2014; Schulte, Healy, & Fangue, 2011; Sinclair et al., 2016). Experimental evolution studies are efficient systems for testing emergence of adaptations to various kinds of selection pressures (reviewed by Buckling, Maclean, Brockhurst, & Colegrave, 2009; Kawecki et al., 2012) and not surprisingly there exists quite a large body of the literature on evolution in fluctuating environments. However, most of the studies concentrate on changes in tolerance curves as a response to fluctuations (reviewed by Kassen, 2002), rather than testing directly if tolerance to fluctuations has increased as a consequence of selection. So far, only a handful of studies have actually tested performance in fluctuating environments (Hughes etal.,2007;Kassen& Bell,1998; Ketola&Saarinen,2015;Leroi, Lenski,&Bennett,1994;Magalhaes,Cailleau,Blanchet,&Olivieri, 2014; this study). However, most of the experiments consider mostly very slow fluctuations and thus fresh work on faster frequencies of fluctuations is direly needed. We ran parallel experimental evolution studies with nine different species/subspecies of bacteria (instead of concentrating on one species Figure 1) to create clones adapted to either fluctuating (20°C, 30°C, 40°C, at 2- hr intervals) or constant (30°C) temperature. After the experimental evolution, these bacterial clones were first tested for their ability to tolerate fluctuating temperature in fluctuating conditions.Iffluctuatingadaptedclonesperformbetteratfluctuatingconditions, we can then suggest that these clones have indeed adapted to tolerate fluctuations better. Then, we measured temperature tolerance in a few constant temperatures, to reveal if evolution had led to changes in tolerance in constant environments (20°C; 30°C; 40°C). With these data, we tested the generality of the idea that fluctuations should select genotypes that are good at tolerating fluctuating environments and if adaptation to fluctuations could be predicted from someofthemeasurementstakeninconstantenvironments.Byreplicating whole experimental evolution experiment with nine species allowsustogeneralizeresultsmuchbetterthanresultsfromnormal single species experimental evolution study. 2 | MATERIALS AND METHODS 2.1 | Study species We used nine different, wellknown and easilyculturable bacteria (eight different species and one subspecies) in the experiment. All the species, except Serratia marcescens ssp. DB11 (Flyg, Kenne, & Boman, 1980), were originally obtained from ATCC® (American TypeCultureCollection)andstoredat−80°C:Enterobacter aerogenes ATCC®13048™,Leclercia adecarboxylataATCC®23216™,Serratia marcescens ssp. marcescensATCC®13880™,Escherichia coliATCC® 11775™,Pseudomonas putidaATCC®12633™,Pseudomonas fluorescensATCC®13525™,Pseudomonas chlororaphisATCC®17418™and Novosphingobium capsulatumATCC®14666™.Thespecieswerechosen based on their abilities to grow well in the same medium and to tolerate the rapidly fluctuating temperature range (20°C, 30°C, 40°C, temperature change at 2- hr intervals). All species had shorter minimal generation time than experimental fluctuation. Measured from ancestorsandinNB(valuesreflectminimumgenerationtimefound at optimal conditions and at 30°C (experimental mean temperature) for each of the species. Enterobacter aerogenes (optimum: 0.647 hr, at +30°C: 0.801 hr), Leclercia adecarboxylata (optimum: 0.762 hr, at +30°C: 0.811 hr), Serratia marcescens ssp. marcescens (optimum: 0.594 hr, at +30°C: 0.720 hr), Serratia marcescens db11 (optimum: 0.699 hr, at +30°C: 0.718 hr), Escherichia coli (optimum: 0.523 hr, at +30°C: 0.692 hr), Pseudomonas putida (optimum: 0.774 hr, at +30°C: 0.774 hr), Pseudomonas fluorescens (optimum: 1.468 hr, at +30°C: 1.652 hr), Pseudomonas chlororaphis (optimum: 0.910 hr, at +30°C: 0.910 hr), and Novosphingobium capsulatum (optimum: 0.984 hr, at +30°C: 1.131 hr). The species have different optimum temperatures and species broadly fall into two categories based on their FIGURE1 Phylogenyofthestudyspeciesbasedon16SrRNA. The scale bar represents the number of nucleotide substitutions per site. The tree includes the sequences FJ971882 (Enterobacter aerogenes),GQ856082(Leclercia adecarboxylata), NR_041980 (Serratia marcescens ssp. marcescens), HG326223 (Serratia marcescens ssp.DB11[wholegenome,16SrRNApartincluded]),NR_024570 (Escherichia coli), AF094736 (Pseudomonas putida), AF094725 (Pseudomonas fluorescens),AB680102(Pseudomonas chlororaphis), and NR_025838 (Novosophingobium capsulatum). The sequence accession numberswereobtainedfromtheNCBInucleotidesequences database E. aerogenes L. adecarboxylata S. marcescens marc. S. marcescens db11 E. coli P. putida P. fluorescens P. chlororaphis N. capsulatum | 2903 SAARINEN Et Al. performance at extreme conditions: Three Pseudomonas species and N. capsulatum can tolerate +40°C only short periods of time, where as other species can grow well at +40°C (Figure 2). 2.2 | Evolution treatment To create bacterial strains that were either adapted to constant (30°C) or fluctuating (20°C, 30°C, 40°C) temperatures, we performed a 79- daylong evolution treatment. We reared 10 populations of each study species both in constant and in rapidly fluctuating temperature regimes (90 populations in both treatments, 180 populations in total). We started the experimental populations from single bacterial colonies growing on nutrient agar. For each species, a single colony was transferred to separate 10 ml centrifuge tubes containing 1 ml of nutrient broth. The bacteria were propagated for 3 days at 30°C to obtain high density. After this, each culture was divided into 10 wells of a 100-well Bioscreen C® (Growth curves Ltd, Helsinki, Finland) spectrophotometer plate (40 μl of bacteria inoculum into 400 μl of nutrientbroth(Nutrientbroth:10gofnutrientbroth[Difco,Becton& Dickinson,Sparks,MD,USA]and1.25gofyeastextract[Difco]in1L of dH2O))perwell;eachspeciesinseparateplatesandagainpropagated for 3 days to high density. From these 10 replicates for each species,weinitiatedthetreatmentsintwothermalcabinets(ILP-12; Jeio Tech, Seoul, Korea): 10 populations of each nine bacterial species in each cabinet. From the same 10 replicates, we also stored the ancestorsincryotubesat−80°C.Asthepopulationswerefoundedfrom single colonies, the starting genetic variance among the populations withinspeciesisassumedtobeclosetozero.Thetwodifferenttemperature treatments were constant 30°C and fluctuating 2 hr 20°C, 2 hr 30°C, 2 hr 40°C. We chose this temperature range to induce as severe thermal stress as possible without causing extinctions. The bacterial populations were transferred into new wells of the spectrophotometer plates every third day. Three days correspond to a minimumof3.32generationsinallthespeciesandtreatments(Bennett, Lenski,&Mittler,1992)andarethesameforallspecies.Theevolution treatment was continued for 79 days (theoretically ca. 86 generations, Bennettetal.,1992).Twiceamonth,populations were transferred between the chambers in order to prevent cabin effects from interfering with the evolutionary treatment effects. Samples from each populationwerestoredat−80°C(1:1high-densitybacterialpopulationin nutrient broth and 80% glycerol) twice a month. 2.3 | Extraction of bacterial clones after the experiment After the evolution treatments, bacterial clones were extracted from populations with dilution plate technique. Two dilution plates (106 dilution) for each population were first propagated for several days at +30°C depending on how long it took for the colonies to grow big enough for further sampling. From these plates, we randomly chose four clones from each experimental population and from each species. Note that within every species, the procedures were the same for both evolutionary treatments. The selected 720 clones were propagated in the medium (300 μl of nutrient broth in 1.5 ml Eppendorf tubes) for 24 hr at 30°C (except 3 days for N. capsulatum) to ensure they had reached high enough density. The clones, each mixed with 80% glycerol (1:1), were then pipetted to spectrophotometer plates in prerandomizedorder,andtheplateswerefrozento−80°Cforfurtheruse. Theuseofacryoreplicationsystem(Duetz etal.,2000)withclone libraries allows efficient workflow without thawing the strains, with randomizedandbalancedsettingsforeachspeciesthatareeasyto use for growth measurements numerous times. 2.4 | Growth measurements To evaluate whether the constant and fluctuating evolution treatments caused differences between the bacterial clones of each species, we measured growth of the clones at fluctuating (1 hr 20°C – 1 hr 40°C) and constant (20°C, 30°C, 40°C) temperatures. Each measurement wasinitiatedbycryoreplicatingclonesfromfrozen plates toplates containing fresh medium, with cryoreplicator system described in Duetzetal.(2000).Tostandardizegrowthconditionsandtogetridof glycerol residues, the clones grew 3 days at 30°C after which the 40 μl ofbacteriasolutionwaspipettedonnewBioscreenplate,filledwith freshNB.Thegrowthmeasurementswereperformedintemperature- controlled spectrophotometers (Bioscreen C®, Oy Growth Curves Ab, Ltd, Helsinki, Finland), where one can adjust temperatures while measuringgrowth.Utilizingthisproperty,wewereabletofluctuate temperatures and follow instant changes in biomass. The fluctuation in this part of the experiment was faster than what the strains experienced during the evolution treatment, to allow capture of evolutionary effects on maximal instantaneous growth rate under fluctuations. Theopticaldensities(OD)ofthecolonieswererecordedat600nm absorbance and 5- min intervals for 3–5 days, until the growth in all wells had ceased. The length of the measurement depended on the temperature used and the growth rate of the study species. The raw growth measurement data were first processed using scriptwritten in MATLAB (MATLAB R2010b,The MathWorks Inc., Natick,MA,USA).Thescriptwasusedtodeterminemaximumgrowth rate and biomass yield values for each bacterial clone. From logtransformed data, the script finds the time of fastest growth by fitting linearregressionsoftimeandlog(OD)ona25-timestep(125min) sliding window. The fastest growth rate equals steepest slope of linearregressionfoundinslidingwindows(logtransformationlinearizes the exponential growth). The yield corresponds to the largest average populationsize(OD)foundfromslidingwindows.Growthrateindicatesthespeedofresourceutilizationduringgrowthinthebatchculture and yield allows deduction in the amount of biomass a species or clonecanproducewithgivenresources.Bothofthetraitsarebeneficial for bacterial fitness (see: Ketola & Saarinen, 2015). 2.5 | Dataanalysis To explore whether fluctuations selected for tolerance to thermal fluctuations,wemodeledthedatawithlinearmixedmodel(REML)for each species separately. We used maximum growth rate and biomass 2904 | SAARINEN Et Al. yield as dependent variables with evolution treatment as a fixed effect(SPSSv.20,IBM).Themodelscontainedpopulationasarandom effect to control for the nonindependency of clones extracted from the same replicate populations. This effect was nested within the evolutionarytreatments.Inaddition,inoculumsize(ODoftheinoculum) was fitted as a covariate to control for the differences in starting cell densities. We ran these models for each species for all measurement temperatures (fluctuating, 20°C, 30°C, 40°C). These single species analyses were combined metaanalytically to handleandmeasureheterogeneityoftheeffectsizesandtoincorporate phylogenetic dependency of observations in a random effect metaanalysis with metaphor R- package (Viechtbauer, 2010). The effectsizesarebasedont test on estimated marginal means from species and temperature specific models, for testing whether two evolutionary treatments differ in their growth or yield (Table 1). The significance of phylogenetic effect using 16sRNA- based phylogeny (Figure 1) was assessed with likelihood ratio tests. Hence, none of the trait indicated improved model fit with phylogenetic information (LRT non significant), we conclude that our results are not sensitive to phylogenetic nonindependence, and we present data fromrandomeffectmodelswithoutphylogeneticeffects.Itisnoteworthy that our initial aim was not to test phylogenetic effects in the FIGURE2 Measuredthermaltolerance of the study species (°C) expressed as maximumgrowthrate(OD600nm/ hr)(Pinkline:measurements,blackline: third degree polynomial fitted to the measurement data) R2 R2 R2 R2 R2 R2 R2 R2 R2 | 2905 SAARINEN Et Al. first place, as that would require larger dataset. Similarly nine species isstillrathersmallsamplesizeinhandforfittingeffectsofspecies differences for explaining differences in evolution. Species causing heterogeneity in analysis were removed from the final analyses (Table2A,B).Moreover,Pseudomonas putida did not grow at constant 40°C (Figure 1). 3 | RESULTS The raw data for pairwise tests exploring whether clones adapted to fluctuating or constant temperature have higher yield or growth in different environments are shown in Table 1. These data were used for metaanalysis, which confirmed that overall the clones that had evolved TABLE1 Results of pairwise tests exploring if clones adapted to fluctuating or constant temperature have higher yield or growth in different environments Environment Species Yield SE p Growth rate SE p Fluctuating N. capsulatum 0.083 .042 .053 0.053 .026 .048 Fluctuating P. chlororaphis 0.024 .042 .569 −0.018 .026 .49 Fluctuating P. fluorescens 0.489 .043 .001 0.017 .027 .525 Fluctuating P. putida 0.114 .043 .009 0.082 .027 .003 Fluctuating S. marcescens marc. −0.011 .03 .699 0.034 .043 .426 Fluctuating S. marcescens db11 0.016 .03 .587 0.061 .043 .161 Fluctuating E. coli 0.052 .03 .085 0.075 .043 .088 Fluctuating E. aerogenes −0.018 .03 .559 −0.066 .043 .133 Fluctuating L. adecarboxylata 0.049 .03 .103 −0.043 .043 .32 20°C N. capsulatum 0.002 .043 .967 0.006 .021 .768 20°C P. chlororaphis −0.1 .042 .02 0.012 .021 .57 20°C P. fluorescens −0.013 .045 .769 −0.01 .022 .669 20°C P. putida −0.001 .043 .973 −0.129 .021 .001 20°C S. marcescens marc. −0.006 .054 .918 0.014 .02 .481 20°C S. marcescens db11 0.081 .054 .139 −0.007 .02 .732 20°C E. coli 0.067 .055 .225 0.004 .02 .831 20°C E. aerogenes −0.059 .056 .291 0 .021 .993 20°C L. adecarboxylata 0.02 .054 .714 0.007 .02 .727 30°C N. capsulatum 0.006 .056 .92 0.01 .034 .772 30°C P. chlororaphis −0.057 .055 .303 −0.034 .033 .311 30°C P. fluorescens 0.024 .056 .669 −0.073 .034 .036 30°C P. putida 0.065 .055 .241 −0.148 .033 .001 30°C S. marcescens marc. 0.067 .048 .17 −0.003 .026 .914 30°C S. marcescens db11 0.031 .049 .534 −0.004 .026 .884 30°C E. coli 0.007 .049 .895 −0.035 .027 .185 30°C E. aerogenes −0.027 .049 .579 −0.063 .026 .019 30°C L. adecarboxylata 0.015 .048 .755 −0.054 .026 .041 40°C N. capsulatum 0.07 .029 .022 0.023 .015 .131 40°C P. chlororaphis −0.006 .042 .887 −0.022 .02 .272 40°C P. fluorescens 0.021 .072 .775 0.031 .031 .328 40°C S. marcescens marc. 0.009 .049 .859 0.032 .037 .385 40°C S. marcescens db11 0.014 .047 .763 0.021 .035 .559 40°C E. coli 0.08 .048 .096 −0.001 .035 .984 40°C E. aerogenes −0.048 .048 .321 0.008 .036 .818 40°C L. adecarboxylata 0.027 .055 .622 0.005 .041 .911 Positiveestimatefordifferenceindicatesthatthefluctuation-adaptedcloneshaveahigheryieldorgrowthratethanconstant(30°C)-adaptedclones. Values indicate estimated marginal means from mixed models testing for the fixed effect of evolution, and random effect of population, nested within evolutionarytreatment.Allmodelsalsoincludedinoculumsizeasacontinuouscovariatetocontrolfordifferentstartingdensitiesingrowthmeasurements (not shown). These results were compiled in the metaanalysis. 2906 | SAARINEN Et Al. in fluctuating environment were able to produce higher biomass yield in fluctuating environment than clones that evolved in constant environment (Table 2A, Figure 3). However, there were no differences in the maximum growth rate between clones in fluctuating conditions. When clones were assessed in constant conditions, the only difference between the evolution treatments was that the clones that had evolved at constant 30°C had better growth rate at constant 30°C (Table 2A, Figure 4) than the clones evolved in fluctuating environment. Oneplausibleexplanationforourresultscouldbethatdifferenttemperatures could be more critical to different species due to their different thermal optima. We further tested this by dividing the data into coldadapted (three Pseudomonas species and N. capsulatum) and hotadapted (all the rest) species (Figure 1) and tested whether different traits or hot and cold adaptation regarding thermal optima would indicate evolution of thermal tolerance in constant temperatures. However, these analyses indicated no evidence for adaptation to fluctuating environment (Table 3). 4 | DISCUSSION We exposed several species of bacteria to fluctuating or constant temperature for 2.5 months and found that overall fluctuationadapted bacterial clones were able to attain higher yield at fluctuating temperature than strains evolved in constant environment (Figure 3), indicating clear adaptation to fluctuations in temperature. These results indicate that the fluctuating conditions could select for efficient resourceuse.Interestingly,previousstudyperformedwithS. marcescens in even faster temperature fluctuation indicated an increased growth rate during fluctuations (Ketola & Saarinen, 2015). This suggests that the speed of fluctuations could select for different mechanisms such as efficient resource use or adapting to grow quickly to make most of the fast changes in environment. However, it is noteworthy that many other aspects of these experiments were different, such as renewal rate and the medium used, making the comparison of these studies problematic. Such comparison problems also pinpoint the reasoning why repeating studies with several species in similar settings is veryimportant.Multispeciesstudiesarealsoefficientofreducingfile drawer effects that hamper conventional research synthesis based on separate publications. Interestinglywe did not find any evidence that the measurable adaptation to fluctuating conditions could be deduced from the improved yield measurements in the constant temperatures (20°C; 30°C; 40°C). This is contrary to the theoretical expectations, as the tolerance measured in constant environments has been considered indicative TABLE2 Results of random effect metaanalysis testing whether the fluctuationadapted clones outperform (positive estimate) or underperform(negativeestimates)theclonesadaptedtoconstant30°C.PanelAdenotesanalyseswithoutspeciescausingheterogeneityin meta-analysis.PanelBcontainsanalysisresultswithallspecies.Inbothpanels,Q stands for heterogeneity statistics and probability associated with it denotes its significance Environment Trait Estimate SE z p Q p Notes (A) Fluctuating 20–40°C Yield 0.415 .1619 2.5635 .0104 8.1601 .3187 P. fluorescens omitteda Fluctuating 20–40°C Growth rate 0.1743 .1612 1.0815 .2795 10.9911 .139 P. putida omitteda Constant 20°C Yield −0.0377 .1964 −0.192 .8477 9.0592 .3373 Constant 20°C Growth rate 0.0694 .1584 0.4384 .6611 1.0737 .9935 P. putida omitteda Constant 30°C Yield 0.1224 .1501 0.8158 .4146 4.175 .841 Constant 30°C Growth rate −0.5802 .1551 −3.7416 .0002 13.3459 .1005 Constant 40°C Yield 0.2109 .1602 1.3164 .1881 6.7024 .4605 Constant 40°C Growth rate 0.1699 .1594 1.0659 .2864 3.9484 .7857 (B)Allspeciesinthemodel Fluctuating 20–40°C Yield 0.5575 .1593 3.5003 .0005 32.3929 <.0001 Fluctuating 20–40°C Growth rate 0.2835 .1532 1.8502 .0643 15.7208 .0466 Constant 20°C Yield −0.0377 .1964 −0.192 .8477 9.0592 .3373 Constant 20°C Growth rate −0.1008 .1534 −0.6572 .511 19.39 .0129 Constant 30°C Yield 0.1224 .1501 0.8158 .4146 4.175 .841 Constant 30°C Growth rate −0.5802 .1551 −3.7416 .0002 13.3459 .1005 Constant 40°C Yield 0.2109 .1602 1.3164 .1881 6.7024 .4605 Constant 40°C Growth rate 0.1699 .1594 1.0659 .2864 3.9484 .7857 aDuetoheterogeneity.boldvaluesindicatesignificanteffect | 2907 SAARINEN Et Al. ofthelevelofadaptationtofluctuatingenvironments(Dobzhansky &Spassky,1963;Duncanetal.,2011;Gilchrist,1995;Kassen,2002; Ketola et al., 2013; Levins, 1968; Venail et al., 2011). The fluctuationadapted clones had lower growth rate than the constantadapted FIGURE3 Forest plots of metaanalyses (corresponds to Table 2) of the biomass yield in the four different measurement temperatures (a) fluctuating (2 hr 20°C, 2 hr 30°C, 2 hr 40°C) (b) constant 20°C (c) constant30°C(d)constant40°Cforallstudiedspecies.Ifeffectsizes arehigherthanzero,itindicatesabetterperformanceofclonesadapted to fluctuating temperature than clones adapted to constant (30°C) temperature.Effectsizesandtheirconfidenceintervals(±95%)are denoted in the righthand side of the figure. RE model indicates estimate forrandomeffectmeta-analysismodel.Differentsizedsymbolsdenote the magnitude of weighing (larger more weight, smaller less) RE model −3 −1.5 0 1.5 3 Observed outcome L. adecarboxylata E. aerogenes E. coli S. marcescens db11 S. marcescens marc. P. putida P. chlororaphis N. capsulatum 0.70 [−0.20, 1.60] −0.26 [−1.14, 0.62] 0.74 [−0.16, 1.65] 0.23 [−0.65, 1.11] −0.16 [−1.03, 0.72] 1.14 [0.19, 2.08] 0.24 [−0.64, 1.12] 0.85 [−0.07, 1.76] 0.41 [0.10, 0.73] Yield at fluctuating environment RE model −3 −1.5 0 1.5 3 Observed outcome L. adecarboxylata E. aerogenes E. coli S. marcescens db11 S. marcescens marc. P. putida P. fluorescens P. chlororaphis N. capsulatum 0.16 [−0.72, 1.04] −0.45 [−1.34, 0.44] 0.52 [−0.37, 1.41] 0.64 [−0.26, 1.54] −0.05 [−0.92, 0.83] −0.01 [−0.89, 0.87] −0.12 [−1.00, 0.75] −1.02 [−1.95, −0.09] 0.02 [−0.86, 0.90] −0.03 [−0.32, 0.27] Yield at constant 20°C RE model −3 −1.5 0 1.5 3 Observed outcome L. adecarboxylata E. aerogenes E. coli S. marcescens db11 S. marcescens marc. P. putida P. fluorescens P. chlororaphis N. capsulatum 0.13 [−0.74, 1.01] −0.24 [−1.12, 0.64] 0.06 [−0.82, 0.94] 0.27 [−0.61, 1.15] 0.60 [−0.30, 1.49] 0.51 [−0.38, 1.40] 0.18 [−0.69, 1.06] −0.44 [−1.33, 0.44] 0.05 [−0.83, 0.92] 0.12 [−0.17, 0.42] Yield at constant 30°C RE model −3 −1.5 0 1.5 3 Observed outcome L. adecarboxylata E. aerogenes E. coli S. marcescens db11 S. marcescens marc. P. fluorescens P. chlororaphis N. capsulatum 0.21 [−0.67, 1.09] −0.43 [−1.31, 0.46] 0.71 [−0.19, 1.62] 0.13 [−0.75, 1.00] 0.08 [−0.80, 0.96] 0.12 [−0.75, 1.00] −0.06 [−0.94, 0.82] 1.03 [ 0.10, 1.97] 0.21 [−0.10, 0.52] Yield at constant 40°C (a) (b) (c) (d) FIGURE4 Forest plots of metaanalyses (corresponds to Table 2) of the growth rate in different measurement temperatures (a) fluctuating (2 hr 20°C, 2 hr 30°C, 2 hr 40°C) (b) constant 20°C (c)constant30°C(d)constant40°Cforallstudiedspecies.Ifeffect sizesarehigherthanzero,itindicatesabetterperformanceofclones adapted to fluctuating temperature than clones adapted to constant (30°C)temperature.Effectsizesandtheirconfidenceintervals (±95%)aredenotedintheright-handsideofthefigure.REmodel indicatesestimateforrandomeffectmeta-analysismodel.Different sizedsymbolsdenoteweighing(largermoreweight,smallerless) RE model Observed outcome L. adecarboxylata E. aerogenes E. coli S. marcescens db11 S. marcescens marc. P. fluorescens P. chlororaphis N. capsulatum −0.43 [−1.31, 0.46] −0.66 [−1.56, 0.24] 0.75 [−0.16, 1.65] 0.61 [−0.29, 1.50] 0.34 [−0.54, 1.22] 0.27 [−0.61, 1.15] −0.30 [−1.18, 0.58] 0.87 [−0.04, 1.79] 0.17 [−0.14, 0.49] Growth rate at fluctuating environment RE model Observed outcome L. adecarboxylata E. aerogenes E. coli S. marcescens db11 S. marcescens marc. P. fluorescens P. chlororaphis N. capsulatum 0.15 [−0.73, 1.03] 0.00 [−0.88, 0.88] 0.09 [−0.79, 0.96] −0.15 [−1.03, 0.73] 0.30 [−0.58, 1.18] −0.19 [−1.07, 0.68] 0.24 [−0.64, 1.12] 0.12 [−0.75, 1.00] 0.07 [−0.24, 0.38] Growth rate at constant 20°C RE model Observed outcome L. adecarboxylata E. aerogenes E. coli S. marcescens db11 S. marcescens marc. P. putida P. fluorescens P. chlororaphis N. capsulatum −0.89 [−1.81, 0.03] −1.04 [−1.97, −0.10] −0.56 [−1.45, 0.34] −0.07 [−0.94, 0.81] −0.05 [−0.93, 0.83] −1.92 [−2.98, −0.86] −0.92 [−1.84, 0.00] −0.44 [−1.33, 0.45] 0.13 [−0.75, 1.00] −0.58 [−0.88, −0.28] Growth rate at constant 30°C RE model −3 −1.5 0 1.5 3 −3 −1.5 0 1.5 3 −3 −1.5 0 1.5 3 −3 −1.5 0 1.5 3 Observed outcome L. adecarboxylata E. aerogenes E. coli S. marcescens db11 S. marcescens marc. P. fluorescens P. chlororaphis N. capsulatum 0.05 [−0.82, 0.93] 0.10 [−0.78, 0.97] −0.01 [−0.89, 0.86] 0.26 [−0.62, 1.14] 0.37 [−0.51, 1.25] 0.43 [−0.46, 1.31] −0.47 [−1.36, 0.42] 0.66 [−0.24, 1.56] 0.17 [−0.14, 0.48] Growth rate at constant 40°C (a) (b) (c) (d) 2908 | SAARINEN Et Al. clones at constant 30°C which was the average temperature during the experiment, reflecting the results found in Ketola and Saarinen (2015).Itcouldbethatsamespeciesshowingimprovedtoleranceto fluctuations could tradeoff their evolved capability to stand fluctuations by having lower growth rate at constant 30°C. However, we did not find statistical support for this idea from analysis exploring yieldat30°Cwithgrowthrateat30°Casacovariate(est:−0.2717, SE = .2812, z=−0.9660,p = .3340). Naturally three assessed temperatures is a small number for fitting actual tolerance curves. However, if we would expect to see changes in growth and yield, these three temperatures (20°C, 30°C, or 40°C) should capture the difference as they match the temperatures experienced during the experimental evolution. Furthermore, finetuning the temperature curves with an addition of measurements in constant temperatures within the “transition phase” temperatures do not necessary reveal the adaptation to fluctuating conditions either (see results of Ketola & Saarinen, 2015 for S. marcescens). Oneplausibleexplanationforourresultscouldbethatdifferent temperatures could be more critical to different species due to their different thermal optima, complicating finding the universal evolutionary effects from constant measurement temperatures. To test this idea further, we classified the data into two groups: coldadapted (three Pseudomonas species and N. capsulatum) and hotadapted (all the rest) species (Figure 4). After this, we used data from cold temperatures and hot temperatures, and from growth rate and yield to test whether hotadapted species evolve better cold tolerance and coldadapted species evolve better hot tolerance. However, these analyses, where we “cherry pick” data from different parts of the tolerance range, and different traits (growth and yield), indicated no evidence for adaptation to fluctuatingenvironment(Table3).Moreover,whenindividualspecies results (Table 1) are followed, it is also evident that only one species (N. capsulatum) indicate significant improvement of yield at 40°C if clones had evolved in fluctuating conditions. All other significant tests from constant conditions indicate the opposite: Fluctuationadapted strains do worse in constant conditions (Table 1). Thus, it is clear that fast temperature fluctuations do not cause observable benefits when growth traits are measured in these constant conditions. Slow fluctuations have been found to select for faster growth and higher yield when measured in constant conditions (Ketola etal.,2013),whichcontraststoourfindings.Itcouldbethatadaptations to chronic, days long, exposures can be predicted from the tolerance curves, whereas fast, hourly and acute (as in Ketola & Saarinen, 2015; and here), fluctuations are more visible in traits that are linked with short exposures to extreme temperatures, likeexpressionofheatshockproteins(HSP’s)(Ketolaetal.,2004; Sørensen et al., 2003). Yet, experimental evolution studies on adaptation to fluctuating environments are numerous (Kassen, 2002), only a few experimental evolution studies have measured performance at both the constant and fluctuating environments and very fewhavestudiedfurtherthepossiblemechanisms.Onlyonestudy suggests a positive association between tolerating constant and fluctuating environments (Hughes et al., 2007), and the majority of studiesshoweithernoclearassociation(Bennett&Lenski,1993; Kassen&Bell,1998;Ketolaetal.,2004;Leroietal.,1994)orthat adaptations to tolerate fluctuating temperatures tradesoff with tolerating constant temperatures (Ketola & Saarinen, 2015; New et al., 2014). These few studies and our data presented here thus indicate that tolerating constant conditions might have little in common with tolerating fluctuating environments or even may be competing from shared resources. This warrants attention when reaction norms or tolerance curves are used to judge genotypes or species for their ability to tolerate fluctuations in animal and plant breeding as well as in conservation biology (see also Ketola & Kristensen, 2017; Schulte et al., 2011; Sinclair et al., 2016). Inourexperiment,whereninebacterialspeciesweregrownindependently in constant or rapidly fluctuating environments, we found thatfluctuationsincreasedspecies’tolerancetofastfluctuations.In addition, our results give support to the idea that tolerances measured in constant environments might fail to capture adaptations to fast fluctuations (Ketola & Saarinen, 2015; Ketola et al., 2014). Effects of adaptation mechanisms, some of which might not be captured in tolerance curves,areimportanttobetakenintoaccountinpredictingspecies’or genotypes’abilitytosurviveclimatechangeassociatedenvironmental fluctuations.Bythisexperiment,wearealsoabletoshowthatthe evolutionary effects were observable over several species, using metaanalysis. Something that is not possible with single species studies. ACKNOWLEDGMENTS We thank the Biological Interactions Doctoral Programme and the University of Jyväskylä Doctoral Programme in Biological and Environmental Science (KS), Academy of Finland Projects 278751 (TK),1255572(JL),250248(LL),andCentreofExcellenceinBiological Interactionsforfundingandfacilities.WethankalsoElinaAhoand TABLE3 Meta-analysisexploringifhot-andcold-adaptedspeciesexpresstheirevolutionarychangesindifferenttraitsmeasuredinhotor cold constant environments. Cold adapted species refers to N. capsulatum and Pseudomonas species. All the rest are considered hot adapted Cold adapted species Hot adapted species Estimate SE zp Q pEnvironment Trait Environment Trait Constant 40°C Yield Constant 20°C Yield 0.2267 .1604 1.4132 0.1576 7.2228 .4061 Constant 40°C Growth rate Constant 20°C Growth rate 0.1225 .1593 0.769 0.4419 4.136 .7639 Constant 40°C Yield Constant 20°C Growth rate 0.1727 .1595 1.0827 0.279 4.3442 .7394 Constant 40°C Growth rate Constant 20°C Yield 0.1758 .1602 1.0972 0.2725 7.2277 .4056