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Peter Norway Trondheim, by NO-7491 Edited Technology, and Science of University Norwegian Mathematics, of Spain; Department Barcelona, E-08003 Barcelona, de Naturals Kingdom; United 3FL, y Kingdom; EH9 United Edinburgh 9TH, Edinburgh, of University Sciences, Biological Finland; Inari, 80309; FIN-999870, CO Boulder, Finland, Institute Resources Natural Dynamics, Kingdom; United 3PS, OX1 Oxford Oxford, of Canada; Netherlands; QC, The Australia; 0A6 Nijmegen, 2600 G1V GL Netherlands; ACT 6500 The Canberra, University, Wageningen, University, Radboud AB National Research, 6700 Australian Ecology, The of Biology, Institute of Netherlands School Research Evolution, and Kingdom; Ecology United Aberdeen, 2TZ AB24 Aberdeen, France; D Paris, le pour Hautes Recherche des de Pratique Institut Lettres, et Science Paris Røstad Wiggo Ole McFarlane Eryn S. Festa-Bianchet Marco Brouwer Lyanne vltoayBooyadEvrnetlSuis nvriyo uih H85 uih Switzerland; Zurich, CH-8057 Zurich, of University Studies, Environmental r Norway; and Trondheim, Biology 7491 Evolutionary Technology, and Science of University Universit biologie, de a Kempenaers Bart mammals and Villemereuil de birds Pierre in date breeding selection on variable temporally and optimum Fluctuating www.pnas.org/cgi/doi/10.1073/pnas.2009003117 Wheelwright T. Nathaniel eateto eaiua clg n vltoayGntc,MxPac nttt o rihlg,839Seisn Germany; Seewiesen, 82319 Ornithology, for Institute Planck Max Genetics, Evolutionary and Ecology Behavioural of Department eateto ieSine,Ipra olg odn L P so,Berks,; Ascot, 7PY SL5 London, College Imperial Sciences, Life of Department eted’ Centre eune o h clg n vlto fseisi the in species of evolution and ecology con- with the timescales, for multiple sequences on vary environments atural clgeFntonleet Fonctionnelle Ecologie ´ | utaigenvironment fluctuating c | eateto clg,SeihUiest fArclua cecs 50 psl,Sweden; Uppsala, 75007 Sciences, Agricultural of University Swedish Ecology, of Department hntpcplasticity phenotypic u Etudes eateto clg n eeis psl nvriy 53 psl,Sweden; Uppsala, 75236 University, Uppsala Genetics, and Ecology of Department ´ f,g,h eSebok,JK21Sebok,Qu Sherbrooke, 2R1 J1K Sherbrooke, de e ´ j r eateto ilgclSine,Abr nvriy uun L36849; AL Auburn, University, Auburn Sciences, Biological of Department x u,v x aut fEvrnetlSine n aua eoreMngmn,NreinUiest fLf cecs 1432 Sciences, Life of University Norwegian Management, Resource Natural and Sciences Environmental of Faculty ui Schroeder Julia , oseE .Kruuk B. E. Loeske , a,b,1 m,f nrwCockburn Andrew , | ihe .Morrissey B. Michael , ai cecse ete,Mus Lettres, et Sciences Paris aa Marl , neCharmantier Anne , al Tufto Jarle , vltv,CR,Universit CNRS, Evolutive, ´ | teslandscape fitness n Gamelon ene ` y l etefrEooyadCnevto,Uiest fEee,Pny R09E ntdKingdom; United 9FE, TR10 Penryn Exeter, of University Conservation, and Ecology for Centre unCro Senar Carlos Juan , aa e bb f nttt fZooy olgclSceyo odn W R odn ntdKingdom; United London, 4RY NW1 London, of Society Zoological Zoology, of Institute eateto ilg,BwonClee rnwc,M 41;and 04011; ME Brunswick, College, Bowdoin Biology, of Department ok Kumpula Jouko , f n usMge Chevin Luis-Miguel and , teeD C D. Steeve , vlpeet 40 otele,France; Montpellier, 34000 eveloppement, ´ w u ainldHsor auel,CR,Sron Universit Sorbonne CNRS, Naturelle, d’Histoire National eum n | ´ oa P Tomas , a adaHamel Sandra , eMnplir Universit Montpellier, de e ´ bc Canada; ebec, ´ eoaArlt Debora , o D preetd ilge Universit Biologie, de epartement ´ i D preetd ilgeadCnr d’ Centre and Biologie de epartement ot ´ ˆ art ¨ z e ´ t s h eateto clg n vltoayBooy nvriyo Colorado, of University Biology, Evolutionary and Ecology of Department c i hmsKvalnes Thomas , eateto nmlEooyadPyilg,IsiuefrWtradWetland and Water for Institute Physiology, and Ecology Animal of Department e .Sheldon C. Ben , .SehnDobson Stephen F. , oehn .Pemberton M. Josephine , n z etefrBoiest yais eateto ilg,Norwegian Biology, of Department Dynamics, Biodiversity for Centre eaiua n vltoayEooyRsac nt ue eCi de Museu Unit, Research Ecology Evolutionary and Behavioural en lee yhmnatvte 7 ) rma evolutionary currently an are From 8). which (7, of activities 6), human pattern (4, by noise temporal altered or being and variation magnitude random the exhibit variables ronmental is ulse oebr3,2020. 30, November published First doi:10.1073/pnas.2009003117/-/DCSupplemental at online information supporting contains article This Submission.y 1 Direct PNAS a is article This the under Published interest.y O.W.R., competing A.Q., no J.M.P., declare T.P., authors data.y M.B.M., The contributed S.E.M., J.T. and A.G.M., N.T.W., M.E.V., T.K., M.v.d.P., B.C.S., J.K., A. M.G., J.C.S., M.F.-B., L.E.B.K., and J.S., S.R.E., B.K., F.S.D., data.; S.D.C., K.J., the Cockburn, gathering J.H., A. in S.H., L.B., T.K., helped Brekke, P. Charmantier J.K., Bize, A. P. L.E.B.K., D.A., paper; Charmantier, B.K., the N.T.W., wrote M.E.V., K.J., M.v.d.P., L.-M.C. B.C.S., J.H., J.C.S., and J.S., J.T., S.H., O.W.R., A.Q., M.G., J.M.P., T.P., M.F.-B., M.B.M., L.B., S.E.M., S.R.E., Brekke, A.G.M., P. F.S.D., Bize, P. S.D.C., research; D.A., Cockburn, Charmantier, performed A. P.d.V., P.d.V. A. data; research; analyzed L.-M.C. designed and L.-M.C. J.T., P.d.V., and P.d.V. contributions: Author c owo orsodnemyb drse.Eal [email protected] Email: [email protected]. y addressed. or be may correspondence whom To o hntp hog niiulpeoyi plasticity. phenotypic optimum individual the of through in tracking phenotype variance by buffered are temporal partly fluctuations is on selection optimum natural variability influence that their the find However, on We prevalent. influence selection. the its natural in and fluctuations of mam- date and of bird breeding patterns wild optimum estimate of to database populations, Here, large empirically. mal a estimated assembled seldom have is phenotype we this optimum but moving time, that a through assumes tracking theory predictions a Most involves question: theoretical However, adaptation this connect time. on work to over empirical trying to varies when selection remains natural gap way depend the strongly processes on evolutionary and ecological Many Significance oanHegelbach Johann , w ireBize Pierre , colo ilg,Uiest fS.Ades t nrw,Ff KY16 Fife Andrews, St. Andrews, St. of University Biology, of School a,1 alVal Paul e ´ PNAS k k n atj a ePol de van Martijn , | dadGe nttt,Dprmn fZooy University Zoology, of Department Institute, Grey Edward NSlicense.y PNAS b r otele 3, Montpellier ery ´ ntttd Syst de Institut d q eebr1,2020 15, December esa itovlnn,41 adl Norway; Mandal, 4516 Viltforvaltning, Jerstad nrwG McAdam G. Andrew , aa,Qu Laval, e ´ arcaBrekke Patricia , j v nttt fEouinr ilg,Sho of School Biology, Evolutionary of Institute io .Evans R. Simon , d tdsNrius Universit Nordiques, Etudes p ´ colo ilgclSine,Uiest of University Sciences, Biological of School utJerstad Kurt , v bc 1 A C Canada; QC, 0A6 G1V ebec, ´ naQvarnstr Anna , ematique, ´ cl rtqedsHautes des Pratique Ecole ´ g eateto nmlEcology, Animal of Department ,Universit e, ´ | bb . y o.117 vol. etefrBoiest Dynamics, Biodiversity for Centre g e vlto,Biodiversit Evolution, ´ https://www.pnas.org/lookup/suppl/ aclE Visser E. Marcel , q , k,l , t , e nils 75005 Antilles, des e ´ s | ersra Population Terrestrial , om o 50 no. ¨ aa,Qu Laval, e ´ u f s Norway; As, iiinof Division ˚ p m | , nttt of Institute D Etudes 31969–31978 ´ epartement ´ encies ` e, ´ ebec, ´ Ecole g ´ | ,

EVOLUTION standpoint, these environmental fluctuations are important genic, quantitative traits, which form the bulk of ecologically because they can lead to temporal variation in natural selection. important traits such as body size, behavior, or phenology (see This can in turn maintain genetic polymorphism and pheno- ref. 6 for a review on fluctuating selection on discrete traits typic/genetic variance of quantitative traits (9–12); select for or major genes). Recent meta-analyses of temporal variation traits that enhance evolvability [including the properties of muta- in selection on quantitative traits (30, 31) have shown that— tions (13) or recombination (14, 15)]; and favor the evolution when carefully restricted to datasets for which measurement of specific mechanisms to cope with environmental fluctuations, error was reported (31)—the direction of selection was largely from (transgenerational) phenotypic plasticity to bet hedging consistent across years, despite evidence for some temporal (12, 16–18). A perpetually fluctuating environment also prevents variation in magnitude of the gradients (31). However, neither natural populations from being perfectly adapted to their current of these meta-analyses (30, 31) allowed direct connection with conditions at any time, resulting in a “lag load” (19) that may theory, because most theoretical predictions are expressed in impact population dynamics and extinction risk (20–23). Over terms of the variance and autocorrelation in the optimum (11, macroevolutionary time, temporal variation in selection is also 12, 16–18, 20–22, 25), which cannot be recovered directly from invoked to reconcile observations of rapid responses to selec- variation in selection gradients (as shown by ref. 29). In addi- tion with the relative paucity of long-term evolutionary change tion, these meta-analyses (30, 31) could not ascribe temporal (6, 24–26). variation in selection gradients to movements of the fitness func- Most theoretical work on adaptation to fluctuating environ- tion versus changes in the phenotype distribution (as illustrated ments rests on the classical framework of “moving optimum in Fig. 1). models” (27), illustrated in Fig. 1. In this model, directional Here, we investigate the extent of temporal variation in selec- selection on a quantitative trait is proportional to the deviation tion on breeding date. Breeding date can easily be compared of the mean phenotype from an environment-specific optimum across species and is likely to be under selection for an opti- phenotype (Fig. 1). Environmental fluctuations in the optimum mum phenotype, because reproducing either too early or too phenotype can thus lead to temporal variation in directional late should limit reproductive success (including offspring sur- selection, yet the two are not strictly equivalent, because changes vival) and possibly survival of the parents. Changes in phenology in the expressed mean phenotype also affect temporal variation (the seasonal timing of life history events) are a predominant in deviations from the optimum and thus in selection. A mean phenotypic response to climate change (32–35). Thus, under- phenotype that closely tracks movements of the optimum (via standing natural selection on phenology is crucial for many evolution or phenotypic plasticity) can thus buffer the influence eco-evolutionary projections of the effects of current anthro- of a fluctuating optimum on selection (28, 29). pogenic climate change on wild populations (36). In addition, The wealth of theoretical predictions on adaptation to fluc- most phenological traits (including breeding time) are plastic in tuating environments (11, 12, 16–18, 20–22, 25) has rarely been response to environmental variables such as temperature, and explicitly compared to empirical estimates, especially for poly- this plasticity is thought to have evolved to buffer the ecological

B

A

Fig. 1. Selection in the moving optimum model. (A) A fitness peak with an optimum (black curve) is modeled as a Gaussian fitness function following classical theory of adaptation. The maximum absolute fitness Wmax is reached at the optimal trait value θ, and the width of the fitness peak is parameterized by ω. A normal distribution of phenotypes is also shown underneath in green shading (note this distribution has its own scale of probability density, different from the fitness scale on the y axis, but we omit it for simplicity). The strength of directional selection is quantified by the linear selection gradient beta, which measures the mean local slope of the relative fitness function, and is proportional to the slope of the red straight line. In this model of Gaussian fitness peak, β is proportional to the deviation of the mean phenotype from the optimum and inversely proportional to ω2 + 1 (for SD-standardized traits), such that narrower fitness peaks cause stronger directional selection overall. (B) Temporal changes in the optimum θ and in the mean phenotype (mode of the green distribution) jointly translate into changes in selection gradients β. Note that while the maximum fitness Wmax remains constant in this plot, it is allowed to vary in our models.

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Selection Results environment. variable a in selection the from of parameters theory key of esti- models) overall “meta-analysis” enabled (robust from This mators meta-estimates systematically systems. popu- wild-population and report models of populations to such of us couple number estimated large a here a in we across 39), estimated phenotype, (38, been mean lations optimum previously the moving opti- have While by moving models plasticity? a tracking phenotypic of adaptive through effect the notably by of is buffered extent (autocorrelation) what predictability mum to magnitude the And and/or is 5) direction What selection? the 4) in selection? variation of trans- temporal function into fitness the opti- late of fluctuating an fluctuation temporally Does for a 3) support for function? support fitness there environment there mov- Is Is changing 1) 2) the a phenotype? following: mum of to the inquired elements adaptation we key of (27), theory on optimum Based ing components. traits of fitness measurements analyze individual and using to 1, frame- Fig. us common in the allowed illustrated using work has selection natural This published in variation investigators. been contact- temporal have directly principal by datasets the versions these ing up-to-date of obtained parts we Although previously, y. long- mammal 63 8 39 to and bird combining (13 database populations species; natural a from assembled datasets term we estimates, tion 37). environment 17, fluctuating 16, a in (12, optimum moving a of consequences nta fpromn eaaayi fpbihdselec- published of meta-analysis a performing of Instead W (z θ ) ,oe eid pnigfo 9 from spanning periods over S1), Table Appendix, SI steotmmbedn ae o hc h expected the which for date, breeding optimum the is h xetdfins opnn o nidvda with individual an for component fitness expected the z W etu have thus we , ossetwt oigotmmmdl (27), models optimum moving with Consistent (z ahtxnmclvl(id,3 aaes rmmas aaes raltx together, taxa all or datasets; for 8 support mammals, statistical or datasets) relative datasets; 39 and 31 characteristics, (birds, their level considered, taxonomic models each Statistical 1. Table vrg fteeiec egt cmue rmteLOC(7,floigrf 8 vrtettlnme of number Total total the over 1). 48] to Eq. up ref. sum and following supports (47), 1 statistical LOOIC relative Fig. Mammal the that in from (note [computed described models function, tested weights as (directional) evidence monotonic optimum a the allowed to an of was correspond average include intercept models “Dir” the models years, shape, Bird models, “Opt” the between all Regarding while year. correlation In to without year optimum. from optimum fluctuating vary fluctuating autocorrelated to have have models models “FluctCorr” “Fluct” Autocorrelation while selection, constant to leading Gaussian Gaussian Fluctuations Monotonic Monotonic FluctCorrOpt Flat Gaussian FluctCorrDir Shape Monotonic FluctOpt FluctDir ConstOpt ConstDir NoSel ID = ) W NSl orsod oafltfins ucin .. oslcin Cnt oeshv tesfunction fitness a have models “Const” selection. no i.e., function, fitness flat a to corresponds “NoSel” max W max and , exp ω  ecie h it ftefit- the of width the describes − (z 2ω − en udai on quadratic being 1, θ 2 ) 2  , [1] h ietoa eeto rdetmauigtesrnt of strength the measuring (44) gradient is selection directional selection for directional even the drift of effec- levels the comparing like populations). allows (just non-Wright–Fisher the size datasets match population specific perfectly for tive not does function it fitness if actual even species, across and selection populations in variation to temporal theo- benchmark about in meaningful generalizations biologically prevalence draw a its and 44) (hence (27, optimum work) an retical for with approximation peak reasonable a fitness it any makes multiplica- This negative 40). (38, precludes fitness it tive because notably realistic, more locniee tesfntoswtota piu,nml a namely of optimum, we an form models, without the functions alternative fitness in As considered process. optimum, also pre- autoregressive the temporal first-order in a the allowing autocorrelation optionally investigated by temporal optimum, also for the in We fluctuations 39). of dictability variance (38, actual we selection the aim, with in error this random measurement a To conflating via prevents 1). peak year fitness (Fig. for the phenotype effect of mean fluctuations estimated the directly in caused that from changes function fitness by the in fluctuation by caused tion be can trait selection. the directional mean that and the such stabilizing both 46), of under (45, deviation optimum the the from of phenotype regardless trait), to unstandardized gener- proportional any also in is variance ation phenotypic reducing selection of stabilizing values larger to devia- leading given a phenotype optimum, For the mean optimum. from the tion the in if changes selection, tracks to directional appropriately few in in result fluctuations might no optimum the in fluctuations Furthermore, directional (θ in phenotype Fluctuations 1. Fig. (β in selection illustrated as optimum, the by replaced be β should inten- 1 selection trait, as described (θ also sity (41), gradient SD dimensionless their by divided where spootoa otedvaino h enpeoyefrom phenotype mean the of deviation the to proportional is nsc oe,adasmn omlydsrbtdtrait, distributed normally a assuming and model, a such In eaeitrse ndsigihn eprlvraini selec- in variation temporal distinguishing in interested are We z ¯ and stema hntp.Nt httatvle r here are values trait that Note phenotype. mean the is ω a hsrsl rmflcutosi h optimum the in fluctuations from result thus can ) ,flcutosi h enpeoye( phenotype mean the in fluctuations ), r iial tnadzd o nonstandardized a for standardized; similarly are eaiesaitclspoti the is support statistical Relative 1. 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EVOLUTION monotonic fitness function where the direction of selection does tion (σθ < 0.5; Fig. 2) and five inferred to have a large SD not change with the mean phenotype in the population (but can (σθ > 3; Fig. 2). Note that the latter also had E(θ) not signif- still change with the environment), and a flat fitness function icantly different from zero, which could be linked to a greater causing no selection. The models are summarized in Table 1. uncertainty in the estimation of E(θ) in the context of high levels of fluctuations. The meta-estimate for σθ was higher Fluctuation of the Fitness Function Is Predominant. We first inves- for mammals (3.14, [0.34, 6.7]) than for birds (1.89, [0.33, 4.1]; tigated the support for fluctuating fitness functions, by using Fig. 2). Interestingly, there was no obvious link between sta- an information criterion akin to the Akaike Information Cri- tistical support for fluctuations and the inferred SD of the terion (AIC), the Bayesian leave-one-out information criterion optimum (orange scale in Fig. 2). Autocorrelation of the opti- (LOOIC) (47). More specifically, we computed “weights of evi- mum was difficult to estimate, resulting in large 95% credible dence” inspired by Akaike weights used in model averaging (48) intervals overlapping zero most of the time (ϕ in SI Appendix, (and summing to 1 across all compared models), which we used Figs. S1 and S2, Bottom Left). Still, six datasets had a signif- to compare the statistical support for different features of selec- icant estimate of temporal autocorrelation in the optimum, of tion across datasets (see Table 1). The results of model selection which five were positive (blue tits, Cca7, 0.59[0.31, 0.84], CCa9, for each dataset appear in SI Appendix, Table S2. We found lit- 0.42 [5.9 × 10−4, 0.80], Cca10, 0.94 [0.84, 0.99] and great tits, tle support for models without selection (flat fitness function, Parus major, Pma4, 0.74 [0.42, 0.97] and Pma8, 0.83 [0.64, 0.97], 3.4 and 8%, respectively, for birds and mammals). The statistical all from The Netherlands except Pma8). The only dataset with support for an optimum was dominant (optimum vs. directional a significantly negative temporal autocorrelation was the hihi models: 51.7 vs. 44.9% for birds and 62.4 vs. 29.6% for mam- (Nci, −0.59[−0.98, −0.097]). Overall, these differences between mals). Similarly, the support for fluctuating fitness functions was datasets resulted in a wide variation across datasets of the also dominant (fluctuating vs. constant models: 77.7 vs. 22.3% behavior of the fitness function over years (SI Appendix, Fig. S3). for birds and 65.6 vs. 34.4% for mammals). Those results were qualitatively unchanged when considering a completely balanced Selection Varies in Strength, but Not in Direction. The inferred setting using ConstDir/ConstOpt models as the sole contes- selection gradients βt were consistent between models with and tants for “no fluctuation” and FluctCorrDir/FluctCorrOpt as the without an optimum (computed following refs. 40 and 50) for the sole contestants for “fluctuating fitness functions.” For some same dataset (SI Appendix, Fig. S4), so we hereafter focus only datasets, especially the smaller ones and/or those where fitness on results from the model with an optimum to avoid overfitting was analyzed as a binary trait, there was considerable uncertainty resulting from model selection. regarding the best model(s), even when there was clear evidence The temporal mean of the standardized selection gradient for fluctuating fitness functions. For two datasets, the mountain E(β) was significantly negative (selection for earlier breeding) goat (Oreamnos americanus, Oam) and the red-winged fairy wren for most bird datasets (only three great tit datasets, Pma2, Pma3, (Malurus elegans, Mel), the support for an absence of selection and Pma5 were not significantly negative; and one, a blue tit was dominant (weight above 0.5), so we removed them from sub- dataset, Cca10, was significantly positive; Fig. 2). On the con- sequent analyses to avoid commenting on spurious signals. In the trary, the temporal mean gradients for mammals were mostly rest of this paper, and for the sake of simplicity, we focus on the not significant (with two exceptions, the reindeer, Rta and the (maximal) model with an autocorrelated fluctuating optimum, Columbian ground squirrel, Urocitellus columbianus, Uco; Fig. 2). unless otherwise noted. However, we also discuss the support for The meta-estimates for the temporal mean of standardized gradi- different aspects of the model when commenting on the results. ent reflected these individual results, being significantly negative for birds (−0.17, [−0.26, −0.077]) but not for mammals (−0.087, The Optimum Fluctuates Differently between Birds and Mammals. [−0.22, 0.032]; Fig. 2). Six datasets (the European oystercatcher, In datasets with predominant support for an optimum (relative Hos; eastern gray kangaroo, Mgi; hihi, Nci; the reindeer, Rta; support >0.5 among models with selection), the peak width ω and two Alpine swift datasets, Tme1 and Tme2) had stronger was typically large (SI Appendix, Figs. S1 and S2), with a meta- mean selection gradients than the others (Fig. 2). Interestingly, estimate of 6.22 (95% higher posterior density credible interval large mean selection gradients over years (large absolute val- [3.2, 9.4]) for birds and of 4.94 ([1.2, 9.2]) for mammals. Such val- ues of E(β)) were sometimes associated with predominant sup- ues (in units of within-year phenotypic SD) correspond to weak port for an optimum and were then attributable to a narrow stabilizing selection (fitness peak broader than phenotype distri- fitness peak (small ω) rather than to a large temporal mean bution), consistent with previous estimates from the literature deviation from the optimum (large E(θ); SI Appendix, Fig. S5). and with values commonly used in theory (42, 43, 49). A few The magnitude of variation in directional selection, as quanti- notable exceptions had a narrow fitness peak with a low value fied by σβ , was highly different between datasets, although less so of ω (e.g., an Alpine swift dataset, Tachymarptis melba, Tme1; than for σθ. Overall, variation in standardized gradients ranged the eastern gray kangaroo, Macropus giganteus, Mgi; the oyster- from very small to large (0.004 to 0.38 for the posterior medians catcher, Haematopus ostralegus, Hos; and the reindeer, Rangifer of σβ ), with meta-estimates at 0.047 ([0.018, 0.11]) for birds and tarandus, Rta). The lowest ω was found in the hihi (Notiomystis 0.15 ([0.056, 0.36]) for mammals (Fig. 2). Despite such possibly cincta, Nci, 1.77 [1.56, 2.03]). large variation, there was very little evidence for fluctuations in The mean location of the optimum θt was often inferred to be the sign of selection gradients (e.g., negative gradients becom- significantly negative, implying that the average optimal timing ing positive [SI Appendix, Fig. S6 ], and 49% of datasets with was usually earlier than the average mean breeding date across strong support for no change of sign at all), and such fluctuations years (Fig. 2). In the three cases when a point estimate was were more frequent (posterior median above 30%) for datasets inferred to be positive, the sign of the estimate was uncertain with an especially small average gradient (−0.04 < E(β) < 0.02). (i.e., 95% credible intervals overlap zero), despite strong sup- Again, there was no link between statistical support in favor port for a model with an optimum for one of them (a blue tit, of fluctuations and the inferred σβ (Fig. 2, levels of orange), Cyanistes caeruleus, Cca10). The meta-estimate for birds was dif- which suggests that moderate variation in selection could still be ferent from zero (−3.7, [−7.5, −0.7]), while that for mammals strongly supported by the data. was not (−1.75, [−6.4, 3.0]; Fig. 2). The magnitude of fluctuations in the optimum differed Plasticity Causes Adaptive Tracking of the Optimum Phenotype. To strongly between datasets, with 5 datasets (out of 20 with better understand the causes of variation in directional selection, predominant support for an optimum) displaying low varia- we disentangled the relative contributions of fluctuations in the

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EVOLUTION AB

Fig. 3. Phenotypic tracking of fluctuations in the optimum. (A) SD of the selection gradient βt (dots, actual values σβ ; crosses, computation assuming no tracking, i.e., ρ¯z,θ = 0 in Eq. 3) against the SD expected when using optimum fluctuations only (i.e., σ¯z = 0 in Eq. 3). Arrows show the direction of the change 2 when accounting for tracking, and the red scale indicates the actual value of ρ¯z,θ . Note that long arrows tend to be red, while short arrows tend to be gray. For datasets with minority support for an optimum compared to the directional models, only grayed-out dots are displayed. The identity line is depicted in gray. (B) For the 15 datasets with predominant support for an optimum and repeated measures, posterior distributions (coming from propagated Bayesian uncertainty) of the correlation coefficients between shifts in the optimum and shifts in the average phenology for individuals measured in 2 consecutive years. In light red: The distribution does not contain zero in the 95% highest density posterior interval. The dataset codes are explained in SI Appendix, Table S1.

of Eq. 3, while setting ρz¯,θ = 0. The arrows connecting crosses unique database, comprising 39 datasets of wild populations of to dots thus represent the influence of phenotypic tracking on birds and mammals, allowed for an unprecedented estimation variation in selection gradients: The longer the arrow, the more of parameters that appear in a wealth of theoretical predictions ρz¯,θ becomes important to understand σβ (Eq. 3). These arrows for adaptation to changing environments (11, 12, 16–18, 20–22, are pointing down in most cases, indicating that realized σβ 25), answering our key questions laid out in the Introduction. In were smaller than expected when assuming independent fluctu- summary, we found predominant support for 1) models with a ations in the optimum and mean phenotype. The length of the fitness peak against the alternatives and 2) fluctuations of the fit- downward-facing arrows can thus be interpreted as the degree ness function over time. This translated into 3) variation in the to which temporal variation in selection was reduced by pheno- strength but not direction of selection, with a strong dependence typic tracking of the optimum causing a positive ρz¯,θ (color of the on taxa (mammal/bird), species, and population. We found 4) arrows in Fig. 3A). uncertainty in the estimation of autocorrelation in the optimum An obvious candidate mechanism for phenotypic tracking of and directional selection, owing to the high data requirements the optimum is adaptive phenotypic plasticity (51, 52). Using of these estimates. But we showed 5) substantial plastic pheno- only individuals with repeated measures in subsequent years (on typic tracking of the optimum phenotype between years for bird a subset of 15 datasets with both predominant support for an species. Beyond our case study on reproductive phenology, the optimum and sufficient repeated-individual data), we were able range of parameters we estimated here can serve as a much- to distinguish plastic from genetic changes in mean breeding needed benchmark of biologically realistic values for theoretical date. We detected plastic phenotypic tracking of fluctuations in studies of adaptation to changing and fluctuating environments. the optimum (Fig. 3B), especially in four datasets for which the Our results corroborate a consensus in the bird literature that correlation between plastic phenotypic change and change in natural selection on phenology tends to favor earlier breeding the optimum was significantly positive (in red in Fig. 3B; note (35), with a significantly negative meta-estimate for the direc- that Cca7 and Pma6 are both located in Hoge Veluwe in The tional selection gradients (Fig. 2). This pattern, which has been Netherlands). The meta-estimate of the correlation across the documented before (35, 39, 51, 53–60), was, however, not found 11 bird datasets was relatively strong and significant for birds in mammals overall, despite two individually significant datasets (0.25 [0.072, 0.44]; P = 0.0095), contrary to the meta-estimate (Fig. 2), previously shown to be under such negative selection across the 4 mammal datasets (0.13 [−0.17, 0.43]; P = 0.35). (61, 62). We also found support for the presence of an opti- Note, however, that American red squirrel (Tamiasciurus hud- mum phenotype (total statistical support of 54% for models sonicus, Thu) had a large correlation (0.53), which, despite being with an optimum; Table 1), with slightly more support in mam- nonsignificant using a sample-based P-value (P = 0.0675), had mals, perhaps in relation to the difference in significance of the a 95% higher posterior density interval nonoverlapping zero selection gradient above. Support for an optimum is consistent ([0.056, 0.78]). These results suggest that phenotypic plastic- with the intuition that breeding too early or too late should be ity indeed plays an important role in tracking the optimum detrimental in the temperate locations constituting most of our phenotype, at least in bird species. database, characterized by marked seasonality with stressful con- ditions in winter and summer (61, 62). This raises the question, Discussion especially for birds: Why are breeding dates in these popu- We investigated fluctuations of fitness functions and temporal lations not closer to their expected evolutionary equilibrium, variation in selection, as estimated by the relationship between instead displaying consistent deviations from their optimum? individual breeding date and yearly reproductive output. Our Among several possible explanations for this “paradox of stasis”

31974 | www.pnas.org/cgi/doi/10.1073/pnas.2009003117 de Villemereuil et al. Downloaded by guest on September 27, 2021 Downloaded by guest on September 27, 2021 ti osbeta eas nmmasgsainprosare periods al. gestation et Villemereuil mammals datasets, de in four because only that on possible based ungulates. is mainly generalize were it to which difficult datasets, significant mammal Although not tested was meta-estimate the The for (67). found expected) as was be detected, 1—was would correlation of tracking—correlation pheno- this perfect mean (no of between birds the for meta-estimate in 0.78) significant change to plastic A and 0.36 type. optimum (from the in correlation changes significant 3 pheno- a (Fig. this showing years individuals consecutive of that in responses reproduce plastic demonstrated by that phenotypic caused We by largely is optimum. attenuated tracking typic considerably the 3A), was of (Fig. latter tracking gradients the strongly selection that optimum in but the well variation in was temporal fluctuations optimum influenced that an fluctuating which found their for we datasets track supported, In populations time. if over selection optimum natural in ation (respectively fitness. 18% mean an in decrease to 33%) equivalent mammals, for 1.6]) ([1.6 ([0.0067, 0.199 as estimate here we euto nlgma tes asdb utaigoptimum fluctuating to a a proportional by as caused is (expressed fitness) load mean demographic log These the in (20–22). reduction that environment shown changing extinction have or higher studies novel to phenotypic a leading of in turn variance probability in stochastic optimum, with and mismatches magnitude both increasing average trajectories, the evolutionary in stochasticity randomness environmental causes Indeed, fluctuate. of not direction does the selection when even have impacts, can selection eco-evolutionary varying substantial randomly that little the- shown Nevertheless, has found (31). work of meta-analysis oretical we a findings on with However, based consistent study previous 0.38. selection, a of to direction gradi- the up selection in large, standardized variation of relatively SD was taxa the ents (all and 75% 1), above Table support together; an statistical of fluctuations had and/or includ- phenotype selection Models optimum of 2). strength the (Fig. in mammals variation and ing birds investigated most of in populations cate- time wild in phenotypic varies best-studied ecology, the evolutionary of in gories one trait, phenological a on variation. its and selection also natural could of (66), inference performance selection, our future of affect or estimate survival our maternal in as included such other multivariate not with a fitness trade-offs of in speaking, components condition broadly be physiological More would analysis. for effect selection proxy this a out include partitioning to extent. for some approach to promising selection in A variation also rela- estimated could its the success, in to reproductive contribute or and condition, date breeding in the with feed variation tionship birds temporal when that how competition Note possi- in stronger chicks. with of differences time, periods from over shorter and bly happening stem gradients is could selection competition optimum, mean interindividual mam- an of and significance for actual birds the between support the difference both of the in regardless light, mals, that population, In the (65). in date breed others that than individuals produces earlier territories favoring breeding selection for frequency-dependent competition when is similar a outcome apparent with at mechanism that Another even (64). such equilibrium condition—persists environment evolutionary abundance), external by resource the selection—mediated to in by directional displaced peak set be of optimum to date the (e.g., date than breeding time optimal later offspring a the more individuals causes have and that tim- This earlier such the breed (64). to output, both tend condition reproductive influence better and in to breeding known of are ing condition status) physiological nutritional of body (e.g., involves aspects time Nonheritable breeding (64). for condition one relevant particularly a (63), niomna utain ih o euti eetbevari- detectable in result not might fluctuations Environmental u nlssidctsta h tegho aua selection natural of strength the that indicates analysis Our 2(ω σ 2 θ 2 +1) fraS-tnadzdtat,which trait), SD-standardized a (for × 10 −5 o id n 0.401 and birds for 0.99]) , ,wt ordatasets four with B), tesfnto eaigtepeooia ri ofins nec breeding each in fitness to trait phenological the relating function fitness a missing function. Fitness record each Analyses. Statistical for assigned was (ID) ID. removed. identification female were measure dummy fitness the or A trait con- phenological or the the weaning with either for using agreement in value in was shown decided dataset is was a and reference Whether tributors the survival birth. as the after threshold used y 1-y 1 we the to event, with alive or mammals breeding or weaning For per weaning, offspring. to offspring of at two) numbers offspring large (occasionally of with one mammals number for the breeding y, entire 1 and the after species over bird summed fledglings for breeding of season and number female the each used the for we for output event, reproductive also of SD measure phenotypic stan- a within-year and As dataset average dataset. the the for by years dividing across by phenological mean dardized this overall dataset, the each to the centered For used was event. we trait breeding otherwise, unique the breeding); first of of the date (onset of start trait date phenological the the used we as season, event breeding per events breeding multiple Formatting. Data and each per 353 in on between available information breeding of detailed is females More total dataset 1,880). a 9 of (average for between events number 64.8) breeding (average varied average 12,357 236.3 years the and 15.7 of and between number 33.2) year The (average database locations. 63 collected different final and The 32 y). and 9 suf- mals) a least with (at datasets years only includes of such retained the number also episode, quantifies large We selection which event. par-ficiently offspring, this reproductive or of for a survival lay of fitness or as output offspring of such viable measure phenology, of a number reproductive as 2) to and relating date, trait information turition include a to 1) had dataset both a were on database, the fluctu- phenology of enter To reproductive parameters selection. of on ating estimation allowing selection years, multiple fertility over of monitored episodes which for Collection. Data Methods and Materials investigated be to remains plas- (74), to organisms further. phenotypes by their used cause adjust cues to tically environmental predicted the is of which evolution the scenario, tracking This for phenotype. suitable less optimum become to adap- plasticity originally phenotypic causing tive under degradation, reduced habitat been and has change reliability climate cue that is possibility Another cases; two in (with- autocorrelation of years nonsignificant estimate over the trend on significant impact much a out by our the explained of example, be five For can in is datasets 73). observed plasticity signal (67, autocorrelation for optimum positive norm the significant for reaction that the than that shallower and climate caused optimum warming advancing an by the behind that somehow lagging be are is likely may phenology but reason mean is possible optimum, A the it phenology. later of 70–72), toward tracking biased (60, allow cues tracking such plastic that with for populations the support of pheno- all substantial in with detected been associated have populations. strongly plasticity logical these cues in environmental plasticity that Given of thus optimum, adaptiveness the the to relative questioning late consistently was time typically breeding are that selection (69)—provides species. natural density other to upcoming of available of not cues cues social to year-round hoard- access species—food and this of (68) history (23). natural ing species the partially that this was possible in is tracking findings It previous which with nonungulate for consistent only Thu), supported, the squirrel, was trend red this (American to for exception challenging An particularly mammals. be might tracking measured S1), plasticity often Table through Appendix, is selection (SI fitness recruitment annual offspring and on birds based for than longer often vnwe lsi hntpctakn a tog h mean the strong, was tracking phenotypic plastic when Even N d = 9dtst,wt 1dfeetseis(3brsad8mam- 8 and birds (13 species different 21 with datasets, 39 easmldacleto fsreso idpopulations wild of surveys of collection a assembled We PNAS xadn nrf 8 ecnrse he hpso the of shapes three contrasted we 38, ref. on Expanding l aaeswr omte ossety ntecs of case the In consistently. formatted were datasets All al S1. Table Appendix, SI | l eod ihamissing a with records All S1. Table Appendix, SI eebr1,2020 15, December σ θ o l v,btrsligin resulting but five, all for | o.117 vol. i.S7 ). Fig. Appendix, SI | o 50 no. | 31975

EVOLUTION season: 1) a flat function corresponding to no selection (“NoSel” model), informed prior for ω, because we do not expect the fitness peak to be 2) a monotonic function for which the direction of selection is indepen- narrow relative to the phenotypic SD since this would lead to extremely dent of the mean phenotype (“Dir” models), and 3) a Gaussian optimum strong stabilizing selection, with most phenotypes having a fitness near (“Opt” models). Denoting as W(z) the expected number of offspring of zero, except in the immediate vicinity of the optimal timing for reproduc- an individual with phenotype z, these fitness functions took the following tion. We thus used a Gamma distribution parameterized so that 95% of the mathematical forms when fitness consisted of a count of offspring: prior distribution lies between 1 and 10 SDs of the trait (standardized to 1), leading to a shape parameter of 3.36 and a rate parameter of 0.78. The vari- 1) W(z) = exp(a), [4a] ances of the random effects added to log(Wmax), a, and b were assigned a weakly informative standard normal distribution prior, while the prior vari- 2) W(z) = exp (a + bz), [4b] ance of σθ was specified indirectly via an independent exponential prior of ! (z − θ)2 rate 1 on c = σθ /ω. Finally, the zero-inflation probability pzi was assigned 3) W(z) = Wmax exp − . [4c] a uniform prior between 0 and 1 and the autoregressive coefficient ϕ a 2ω2 uniform prior between −1 and 1. Note that for the exponential fitness function in Eq. 4b, the directional Statistical implementation. We implemented the models using Hamiltonian selection gradient is the parameter b (40), regardless of the phenotype dis- Monte Carlo (HMC) as available in the Stan framework (75). We ran 10 tribution. For the Gaussian fitness peak in Eq. 4c, the parameter ω describes chains, each with 2,000 iterations following a burn-in of 1,000 iterations. the width of the fitness function, with smaller ω causing stronger stabiliz- After a thinning every 5 iterations, we obtained a total of 4,000 iterations. ing selection, while θ is the optimal timing for reproduction, and directional Divergent transitions can happen during HMC and hamper safe interpreta- selection depends on the mean deviation from the optimum, as illustrated tion of the output. Given the high number of models to be analyzed, we in Fig. 1. Since the phenological traits were standardized, θ and ω are in kept models with divergent transitions, although only if at low rates (less units of within-year phenotypic SD. When fitness measures consisted of sur- than 2.5% of the iterations), increasing the adapt delta parameter in Stan vival of one offspring, we replaced the exponential in Eqs. 4a and 4b with as needed to reach this threshold. Convergence was checked graphically and an inverse-logit, while for Eq. 4c we retained the Gaussian fitness peak, using the potential scale reduction factor diagnostic (76). Effective sample but obtained Wmax ∈ [0, 1] from a continuous latent scale on real numbers size was kept above 200 for all parameters. via a logit link. The realized reproductive output was then obtained from Model selection. The models were compared using a cross-validation pro- this expected fitness using a Poisson or binomial distribution, depending cedure, namely approximate leave-one-out with Pareto smooth importance on whether the fitness measures were a number or individual survival of sampling (LOO-PSIS) (47). An information criterion can be derived from offspring, respectively. The Poisson distribution could further be zero trun- LOO-PSIS, named LOOIC, which was used to compare models. LOOIC is cated or zero inflated, if posterior predictive checks on a Poisson model were akin to the Widely Applicable Information Criterion (WAIC) (but does not showing a bad fit for the zero category. Furthermore, we included female rely on asymptotic assumptions) (47) and can be interpreted in a similar IDs as a random effect on the intercept (a in Eqs. 4a and 4b and Wmax in fashion to other information criteria such as AIC. To compute the overall Eq. 4c), to account for repeated measurements. statistical support, across datasets, for each model in Table 1, we derived Models of fluctuating selection. To investigate temporally variable selec- weights of evidence inspired by Akaike weights used in model averaging tion (“Fluct” models throughout, e.g., “FluctOpt” and “FluctDir”), we (48), but based on LOOIC. The relative support for model i across datasets allowed the fitness function to vary from year to year, using random effects was defined as for time in the relevant parameters, as in refs. 38 and 39. For models with an optimum, a random effect for year was included for both and θ Wmax Nd (on the log or logit scale for W ). We did not allow ω to vary between 1 X exp(−∆i,j/2) max wi = , [6] N P7 exp(−∆ /2) years, because it is a difficult parameter to infer, and within-year sample d j=1 k=1 k,j sizes were likely not enough to bear with its estimation for each year. We can thus think of our estimates as fluctuations of an effective optimum with where ∆ is the difference between the LOOIC of the best model and that constant width, even though the true optimum may vary in width to some i,j of the focal model i (k iterates over the seven models), both for dataset j, extent. For models without an optimum, we used random effects for years and N is the total number of datasets as defined above. We repeated the on the a and b parameters. The random effects (following a Gaussian distri- d same analysis using only birds and then only mammals datasets, adjusting bution) allowed us to infer the SD over years of θ and W (on the log or max N in Eq. 6 as needed. logit scale), σ and σ , and of a and b, σ and σ . Models with only vari- d θ Wmax a b This procedure of using weights of evidence was preferred over a simple ation in the intercept (W or a) are referred to as “Const” models, because max computation of the proportion of datasets for which each model was the although the function varies in intercept from year to year, the actual selec- best model because the latter would necessarily be less precise. For instance, tion process is assumed constant. Temporal autocorrelation, in the form of a when several models (say, all those with fluctuating selection) have very sim- first-order autoregressive process (AR1) with slope ϕ, was optionally intro- ilar LOOIC scores, but differ substantially from the remainder of the models duced in the random effects for the θ and b parameters (referred to as for a given dataset (e.g., Cca1 in SI Appendix, Table S2), it is not particularly “FluctCorr” models). meaningful to select only the slightly best model; instead we want to mea- The combination of fitness functions and patterns of fluctuations led sure how well each model is supported relative to all others. This is what w to seven alternative parameterizations, which are summarized in Table 1. i does: It attributes a score to each model, reflecting the relative support the To compare the magnitude of selection and its fluctuation across models model offers to the data, compared to other models. with alternative fitness functions, we computed the selection gradients β t Post hoc analysis. We computed the posterior distributions of the selec- (estimated for each year t if fluctuations are assumed) from both kinds tion gradients β using the HMC samples of all parameters involved, to of statistical models with selection. For models with monotonic directional t propagate uncertainty in these estimates toward the β estimates. To do selection (ConstDir, FluctDir, FluctCorrDir), the selection gradient is simply t that while accounting for uncertainty in estimating ¯z for models with an the slope of the linear model β = b when using the log-link and was t t t optimum (Eq. 2), we implemented a Monte Carlo sampling of the mean phe- computed for logit-link as notype in each year, assuming a normal sampling distribution of the mean. ¯ 2 2 ! We thus used the Monte Carlo and HMC samples of zt , θt , and ω to prop- Wt βt = bt 1 − , [5] agate uncertainty in estimates of βt . We then directly used estimates of βt Wt to compute the mean selection gradient E(β) and its SD over the years σβ . Note that this strategy will cause a slight regression toward the mean and 2 where Wt and Wt are, respectively, the population mean fitness and mean thus a slight underestimation of σβ in general, but this is conservative with squared fitness, computed over all available individuals each year, adapted respect to the estimation of the prevalence and magnitude of fluctuating from ref. 50. For models including an optimum, the directional selection selection. gradient in year t is as in Eq. 2. Note that with an optimum, variation in To obtain “meta-estimates” (i.e., robust overall estimates across all directional selection gradients must account for year-to-year variation in the datasets, accounting for different uncertainties between datasets), we gen- mean phenotype ¯zt (Fig. 1). erated 100 tables (each composed of one row for each dataset), drawing Prior distributions. Diffuse, zero-centered normal distributions (with vari- from the posterior samples of E(θ), σθ , E(β), σβ , and ω. We used the 6 ance 10 ) were chosen as priors for log(Wmax), θ, a, and b, while for multiple-imputation framework of the R package brms (77) to perform a logit(Wmax) in the binomial model, we used a weakly informative nor- mixed-model analysis of each of these parameters using the taxon (bird or mal distribution with mean 0 and SD of 1. In contrast, we used a slightly mammal) as a fixed effect and species and population as random effects.

31976 | www.pnas.org/cgi/doi/10.1073/pnas.2009003117 de Villemereuil et al. Downloaded by guest on September 27, 2021 Downloaded by guest on September 27, 2021 3 .G oe,S .Anl,R B R. Arnold, J. S. Jones, G. A. 13. tem- to responses adaptive as Tempo- bet-hedging and plasticity, I. evolution, Genetic variance. Tufto, environment. phenotypic J. of Evolution 12. heterogeneous Bull, J. J. a 11. in variation Genetic Hedrick, W. P. 10. 1 .Lne .Sann h oeo eei aito naatto n population and adaptation in variation genetic of role The Shannon, S. Lande, R. 21. environmental-change” to response in extinction and “Evolution evolution?. Lande, of R. rate Lynch, the M. determines What 20. Smith, points M. tipping J. Evolutionary 19. Rubenstein, R. D. Wright, J. Weissing, J. phenotypic F. of Botero, evolution A. Theoretical C. by environment VI. 18. extraordinary plasticity. an phenotypic to of Adaptation Lande, genetics R. The 17. Scheiner, M. S. Gavrilets, S. 16. recombination. and sex of evolution the and selection Directional Charlesworth, B. 14. 2 .M hvn .Cto .Ahne,Sohsi vltoaydmgah ne a under demography evolutionary Stochastic Ashander, J. Cotto, O. Chevin, M. L. 22. B R. 15. eVleeei tal. et Villemereuil de on nGtu at Github in fixed found a as Availability. taxon Data using correlation above, effect. the random described a of as as meta-estimate ID brms, study the in and inferred effect model then mixed and We a Monte dataset times. using each sampled of 100 we size sample so correlations, the did to these amounting in iterations HMC trend and the Carlo overall for an test To uncertainty. of for significance accounting opti- correlation still in the years, changes across computed and phenotype then phenotype mum We individual mean tits). evi- in blue changes some on plastic example is between wild an the there for in 79 plasticity though its ref. even and (see phenology plasticity, reproductive by on of senescence instance selection for for dence estimate used generally was to is and 78 traits assumption ref. phenological This for ontogeny, ignored. approximation accurately be as good inferred can such a senescence be processes or other can choice, that change response) habitat provided phenotypic plastic information the the this response, (i.e., without plastic plasticity the envi- by an induces Although caused y about optimum. that 10 data an cue requires least for plasticity ronmental support at phenotypic statistical for of majority measurement years, a proper consecutive with and between total, least common to in at with in simulations datasets Carlo individuals only Monte retained five using We phenol- thereafter). (again, average uncertainty subset in for this account difference in reproduced the years that computed between individuals and ogy selected years consecutive we two level, in individual the at responses models. directional For to compared interval. meta-estimates models, credible the 95% as and models median and such posterior of their report intercepts and taxon-level the used We .J esnti,Tetertclpplto eeiso aibeslcinand selection variable of genetics population theoretical predictability The potential decadal Felsenstein, and J. variability interannual 9. in Changes Boer, J. G. 8. .T .L ily .L mt,B .Sne,Atrpgnciflec nteauto- the on influence Anthropogenic Santer, D. variable B. in Smith, adaptation L. R. of Wigley, renewal L. perpetual M. T. The 7. selection: Fluctuating Bell, Kruuk, B. G. E. L. 6. Pemberton, M. J. Clutton-Brock, H. T. Pilkington, G. J. noise. Robinson, environmental R. M. of color 5. The Yodzis, P. Vasseur, A. D. Saether, 4. B.-E. Engen, S. 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Swedish flycatcher Research the the Australian from the and by wheatear supported been Northern Research Norway has 223257. of wrens Project Council scheme, fairy Research funding on National the Excellence of for by the Ministry Centers supported its the were and through M.G. from Canada and (CGL-2016-79568-C3- grant T.K., Council J.T., of a Research 3-P). Spanish by NSERC Competitivity, supported and by was Economy The funded J.C.S. Volkswagenstiftung. was Foundation. and Science project Action, sheep supported squirrel Soay Skłodowska-Curie were data and Marie red sparrow tit, a Lundy blue NERC, NERC. Silwood UK by logistic deer, by and red the supported assistance The were and datasets valuable collection. data their Excellence, Reindeer in for Reindeer the of support thanked in the Center Station are Research through Association Nordic Charitable Reindeer Herder’s available Experimental North Conservation Kutuharju made Globalizing at Hihi were crew a the reindeer in Trust and on Leverhulme husbandry Zealand, data The UK, New The NERC Trust. Fund via under Marsden support Ewen financial UK, additional John 44300-FAU and with by and 36186-FAU, and contracts AK-24128-FAU, collected/managed management AK/15073/RES, hihi were permits Conservation research hihi of Trait Department for DEB-0089473). Zealand (Grant New data was Foundation study fitness (NERC) Science squirrel National and Council from the ground Research grants by Columbian Environment supported by The Natural provided NE/S010335/1). UK (NE/K006274/1, been the ERC has and (BB/L006081/1), Council (AdG250164), Wytham Research Canada. Sciences of for Biological (NSERC) and Biotechnology collection Council Research data Engineering Recent and supported Sciences were studies Natural kangaroo by gray eastern and goat, Obser- mountain – bighorn, l’Univers de Montpelli Sciences REcherche des acknowledges de Observatoire vatoire group the tit of support Montpellier Coun- long-term The Research the 678140-FluctEvol). European (Grant the (ERC) from cil support acknowledge P.d.V. and L-M.C. Evolution U.S.A. Sci. Acad. Natl. Nat. Am. 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