Changing environmental conditions cause changes in the distributions of phenotypic traits in natural populations. However, determining the mechanisms responsible for these changes—and, in particular, the relative contributions of phenotypic plasticity versus evolutionary responses—is difficult. To our knowledge, no study has yet reported evidence that evolutionary change underlies the most widely reported phenotypic response to climate change: the advancement of breeding times. In a wild population of red deer, average parturition date has advanced by nearly 2 weeks in 4 decades. Here, we quantify the contribution of plastic, demographic, and genetic components to this change. In particular, we quantify the role of direct phenotypic plasticity in response to increasing temperatures and the role of changes in the population structure. Importantly, we show that adaptive evolution likely played a role in the shift towards earlier parturition dates. The observed rate of evolution was consistent with a response to selection and was less likely to be due to genetic drift. Our study provides a rare example of observed rates of genetic change being consistent with theoretical predictions, although the consistency would not have been detected with a solely phenotypic analysis. It also provides, to our knowledge, the first evidence of both evolution and phenotypic plasticity contributing to advances in phenology in a changing climate.

In this study, we use quantitative genetic animal models [ 21 , 45 ] to estimate the rate of evolution in parturition date and the contribution of plastic and demographic processes to the observed shift in phenology in the Rum red deer study population. We start by considering the response to selection that might be expected from the observed strength of selection and (narrow-sense) heritability of parturition date, based on a simple “breeder’s equation” prediction [ 46 ]. One of the most striking conclusions from the recent application of quantitative genetic theory in evolutionary ecology has been the failure of univariate “breeder’s equation” predictions to capture trait dynamics in wild populations [ 47 , 48 ]. This may be for multiple reasons, foremost of which is likely to be the unrealistic assumption that only the focal trait is relevant. We therefore also consider a multivariate breeder’s equation [ 49 ] and ask how selection on offspring size and the genetic correlation between parturition date and size alters the expected evolutionary response. However, there is a second, less well-explored reason for the failure of the theory: predicted genetic responses to selection are often compared to observed rates of phenotypic change rather than of genetic change. Phenotypic changes are generally affected not only by genetic changes but also by numerous nongenetic processes and therefore may poorly reflect underlying genetic changes. As the central analysis of this work we use trends in breeding values and the secondary theorem of natural selection (STS) to estimate the rate of evolution in parturition date. We then test whether the estimated rate of evolution is compatible with the response to selection predicted by either the univariate or multivariate “breeder’s equation,” or with genetic drift. We also consider the effect of nongenetic processes contributing to phenotypic change and quantify the role of phenotypic plasticity in response to warming temperatures and of changes in population structure.

In a population of red deer (Cervus elaphus, Linnaeus 1758) on the Isle of Rum, Northwest Scotland, parturition date has advanced at a rate of 4.2 days per decade since 1980, a change that has been linked to temperatures and other weather conditions in the year preceding parturition, especially around the time of conception [ 40 , 41 ]. Previous studies of this population have shown that phenotypic plasticity in response to temperature and population structure explain a substantial proportion (23%) of the advance in parturition dates [ 41 ] and also that within-individual plasticity is sufficient to explain the population-level relationship between temperature and parturition date [ 42 ]. However, the documented plasticity does not explain the majority of the observed phenotypic change over time, leaving room for processes that have not been investigated as of yet. It is plausible that evolution plays a role because the observed phenotypic change is qualitatively consistent with a genetic response to selection: parturition date is heritable in this population [ 43 ] and also under selection for earlier dates [ 44 ].

Climate change may have impacts on numerous aspects of an organism’s biology, but phenology (i.e., the seasonal timing of life-history events) appears to be particularly affected [ 3 , 27 – 29 ]. Dramatic changes of phenologies in response to earlier onset of spring are particularly well documented in mid- and high-latitude passerines, where breeding times are occurring earlier in numerous populations and species [ 18 , 30 ]. The study of avian systems in particular has shown that a fine-tuning of phenology to the climate is crucial in determining individual fitness. Mistiming between mean breeding date and a fitness optimum that shifts with climate may re-shape selective pressures and hence potentially reduce population growth rate [ 31 , 32 ], although establishing the link between individual-level and population-level processes is challenging [ 33 , 34 ]. The effects of climate change on mammalian phenology are less well documented and less clear than those of birds [ 29 ] and may be more complex because mammals’ long gestation times likely make their breeding phenology sensitive to climate across a longer timeframe [ 17 ]. Furthermore, despite the extensive evidence for phenotypic shifts in phenology, the few studies that test for a genetic basis to changes in phenology in wild populations have not found evidence of genetic changes [ 35 – 38 ]. One possible exception is the change of egg hatching date in winter moths [ 39 ], for which a common garden experiment suggested a contribution of genetic change.

In wild populations, the respective contributions of plasticity versus evolution remain unknown for the vast majority of documented phenotypic changes [ 14 , 15 ] (note that by evolution we mean genetic change, here and in the rest of the manuscript). To date, most of the evidence for evolutionary responses to climate change comes from plants [ 16 ]. In contrast, despite numerous examples of phenotypic changes apparently related to climate, there have been surprisingly few examples demonstrating unambiguously that a vertebrate population is evolving in response to climate change (see discussions in [ 17 – 20 ]). This lack of evidence may, in part, be due to the question not being prioritized [ 14 , 15 ]. However, it probably also reflects the substantial challenges inherent in testing for adaptive evolution, in terms of requirements for appropriate data and statistical methods. For wild populations in which experimental manipulations are not feasible, the most plausible means of testing for the genetic basis of phenotypic changes is to use long-term pedigree data to test for changes in “breeding values,” the estimated genetic merit of individuals as ascertained from the phenotypes of their relatives [ 21 ]. This needs to be done with care, as trends in predicted breeding values can be confounded with environmental trends unless appropriately controlled for [ 22 ], and the precision of estimates of evolutionary rates can be inflated if the correlation structure of breeding value estimates is not properly handled [ 23 ]. To our knowledge, among the studies of wild vertebrate populations that properly account for uncertainty in breeding value predictions, only 3 have found evidence of genetic change underlying phenotypic change in line with selection pressures changing with climate: plumage colouration in collared flycatchers [ 20 ], and body size in Siberian Jays [ 24 ] and snow voles [ 25 ]. However, only with more empirical studies explicitly testing for evolution will it become possible to say whether the current lack of evidence also reflects a generally slow rate of adaptation to environmental change in natural populations [ 26 ].

Climate change affects various aspects of biodiversity across the planet (e.g., [ 1 , 2 ]). In particular, shifts in phenotypic distributions within populations are widely reported, for a variety of morphological, phenological, or life-history traits [ 2 – 4 ]. Surprisingly, however, little is still known about the relative contributions of mechanisms underlying these shifts [ 5 ]. Within a population, phenotypic distributions may change due to a change in population structure (e.g., age structure or sex ratio), due to phenotypic plasticity (within or between individuals), and due to genetic change [ 6 – 8 ]. The exact mixture of mechanisms driving phenotypic change will determine the future of a population facing a prolonged change in environmental conditions [ 9 ], for several reasons. First, the consequences of changing population structure are variable and may be idiosyncratic (e.g., [ 8 , 10 ]). Second, phenotypic plasticity can provide an efficient way to cope with a changing environment, but its effect may be short-lived and even maladaptive [ 11 – 13 ]. Third, genetic evolution, when driven by natural selection, can improve population growth rate, potentially contributing to long-term population persistence [ 12 ].

All estimates are derived from the univariate animal model described by Eq 1 (except for STS, derived form the bivariate animal model). Unlike other distributions, those in the row “Estimates of evolution” relate to a single component of change, estimated in 3 different ways: “BLUPs year” is the conservative BLUP regression with offspring birth year fitted as a fixed effect, “BLUPs no-year” is the BLUP regression without offspring birth year fitted as a fixed effect, and “STS” is the secondary theorem of natural selection using the additive genetic covariance between parturition date and fitness. To accommodate strong differences in the uncertainty around component estimates, the scale of the y-axis differs among rows, and the density of the component “Offspring sex” was truncated. The black arrow below the x-axis indicates the observed total change of −12.3 days. Code for this figure is on page 53 of S1 Code . BLUP, best linear unbiased predictor; STS, secondary theorem of natural selection.

Fig 5 summarises all the components of change described earlier. Altogether, these effects captured a predicted change of −7.98 days (95% CI −12.85 to −3.22) over the study period. This leaves an unexplained change of −4.99 days (95% CI −9.76 to −0.13). Given the model specification, the unexplained fraction must capture persistent changes in maternal effects, individual-level (referred to here as “permanent”) environment effects, and various other effects of phenotypic plasticity (other than that due to sex of the offspring and mean temperature during the rut period), which were not explicitly accounted for in the model.

Warmer temperatures during the previous rut season tended to advance parturition date, with an overall effect of −1.40 days (95% CI −3.05 to 0.50) over the study period. The effect is less clear than the −2.4 days reported by Froy and colleagues [ 42 ] most likely because our model contains a covariate for year while Froy and colleagues [ 42 ] did not. When we remove the year covariate, we obtained an estimate of −2.56 days (95% CI −5.23 to −0.69).

Predicted effect of (A) age structure and (B) female reproductive status on parturition date across years. The origin of the y-axis is arbitrarily set to the predicted effect in the first year. The red thick dashed lines represent the net effect of changes in age structure and female reproductive status on parturition date reported in the text. The thin dotted lines in (A) represent the effect of changes in age structure before, and after, 1981, respectively. Code for this figure is on page 27 of S1 Code .

As in previous work [ 41 ], we found that mature females tended to give birth earlier than younger females, but very old females gave birth the latest ( S2 Text ). The effects of changes in the age structure on mean parturition dates tended to be in the opposite direction to the observed phenotypic change: during the first 10 years of the study, the mean age of females in the study increased steadily, pushing towards earlier mean parturition dates (−3.68 days [95% CI −5.63 to −1.92] from 1972 to 1981). For the rest of the study, the change in age structure tended to delay mean parturition date slightly (0.57 days [95% CI 0.39 to 0.71] from 1982 to 2016). Over the study period, the change in age structure had a predicted net effect of −0.58 days (95% CI −1.67 to 0.40) ( Fig 4A ). Changes in female reproductive status had a fluctuating effect on parturition date ( Fig 4B ), with an uncertain total effect over the study period of −0.32 days (95% CI −0.87 to 0.17). Offspring sex had no clear effect on parturition date, and since sex ratio at birth remained relatively stable over the study period (despite an early decline in the proportion of males [ 57 ]), this parameter is predicted to have had a small overall effect (−0.04 days [95% CI −0.18 to 0.04).

Nine percent of the simulations of genetic drift generated an advance as large or larger than the change estimated from the conservative BLUP linear regression (using the posterior mode for the BLUPs trend as a point of comparison; see Fig 3 ). Inbreeding tended to delay parturition date ( S1 Table ), and given that the estimated pedigree inbreeding inevitably increased over time with increasing pedigree depth [ 54 ], there was marginal evidence of inbreeding postponing parturition date by 0.38 days (95% CI −0.04 to 1.01) over the study period, thus opposing the phenotypic trend. However, this prediction may be spurious, because the increase in inbreeding coefficient was an artifact of estimating inbreeding from a pedigree [ 54 ]. Re-running the model without inbreeding led to almost identical estimates for all other parameters. The effect of gene flow (the proportion of immigrant genotype) was uncertain ( S1 Table ), and its overall predicted effect over the study period was a change of 0.15 days (95% CI −0.34 to 0.72).

(1) Evolutionary change possible due to genetic drift. This distribution was generated by simulating random changes conditional on the estimated additive genetic variance and on the pedigree. (2) Predicted evolutionary response to selection from the univariate and bivariate breeder’s equations, respectively. The response to selection was estimated using univariate and bivariate breeder’s equations, where phenotypic multivariate models gave selection gradients and animal models gave additive genetic variance-covariances of parturition date and birth weight. (3) Estimated contribution of evolution estimated in 3 ways: "BLUPs year" is the conservative BLUP regression with offspring birth year fitted as a fixed effect, "BLUPs no-year" is the BLUP regression without offspring birth year fitted as a fixed effect, “STS” is the secondary theorem of natural selection using the additive genetic covariance between parturition date and fitness. Parturition date was modeled using a log-transformation, and all estimates were subsequently converted to change in days over the study period (see S1 Text ). Parameter estimates are summarized in S1 Table . The distributions all have the same area, but the y-axes scales vary to help visualization. Code for this figure is on page 55 of S1 Code . BE, breeder’s equation; BLUP, best linear unbiased predictor; STS, secondary theorem of natural selection.

Red lines represent the linear regression of predicted breeding values on year (posterior mode as a thick continuous line, 95% CI as thin dotted lines). Thin black lines represent time splines of the change in predicted breeding values, allowing nonlinear change. Each black line was obtained from a different MCMC posterior sample, by fitting a spline to the mean of estimated breeding values among individuals living in the same year. Some black lines are straight because the smoother function used penalizes complex polynoms. Code for this figure is on page 18 of S1 Code . MCMC, Markov Chain Monte Carlo.

Using the most conservative method (namely, the model including calf birth year as a covariate), the slopes of the linear regressions of best linear unbiased predictors (BLUPs) for parturition date breeding values on mean offspring birth year—integrated over the posterior distribution—suggests an advance in breeding values, with the slope estimated at −0.10 (95% CI −0.23 to 0.03) per year on the log-transformed scale. This result is uncertain: there is a probability of 7% that the change in BLUPs is null or positive. Time splines fitted on the posterior distribution of the BLUPs visually support a linear decrease in breeding values ( Fig 2 ). The estimated rate of evolution corresponds to a total change over the study period of −2.1 days (95% CI −4.5 to 0.7) due to genetic change (Figs 2 and 3 ), equivalent to −0.045 days per year (95% CI −0.100 to 0.018), −0.36 days per generation (95% CI −0.79 to 0.14), or −0.028 Haldanes (95% CI −0.062 to 0.01).

Conditional on the fixed effects affecting each trait, the phenotypic correlation between (log) parturition date and birth weight was positive but weak (correlation = 0.12; 95% CI 0.05 to 0.16). The gradient of direct selection on (log) parturition date was negative (mode β z = −0.0003; 95% CI −0.0004 to −0.0002), and that on birth weight was positive (β bw = 0.0138; 95% CI 0.009 to 0.017). There was also additive genetic variance in offspring birth weight (0.68; 95% CI 0.57 to 0.90), corresponding to a heritability of 0.46 (95% CI 0.37 to 0.62). The additive genetic covariance between (log) parturition date and offspring birth weight was −1.78 (95% CI −4.38 to 0.56), corresponding to a weak negative genetic correlation of −0.16 (95% CI −0.32 to 0.05). The multivariate breeder's equation predicts a rate of evolution of −1.41 days (95% CI −2.70 to 0.11) over the study period, which is actually similar to the univariate breeder’s equation prediction of −1.45 days (difference = −0.01 days; 95% CI −0.71 to 0.55).

Selection was weaker among females that were culled than among females that died of natural causes ( S2 Fig ). Considering the subset of females that died of natural causes, the univariate breeder’s equation predicts a response of −2.04 days (95% CI −3.37 to −0.95) over the study period. In contrast, using the subset of females that were culled, the univariate breeder’s equation predicts a response of 0.11 days (95% CI −0.64 to 0.93) over the study period.

Females with earlier parturition dates had, on average, higher lifetime breeding success (LBS): the selection differential of parturition dates estimated with LBS was −1.37 days of change within a generation (95% CI−2.43 to −0.65; see S2 Table for random effect and fixed effect estimates of the model described in Eq 2 ). Given the heritability of parturition date of 0.17, the univariate breeder’s equation predicts a response to selection of −0.25 days per generation (95% CI −0.55 to −0.11), that is, −1.45 days over the 45-year study period (95% CI −3.01 to −0.60). The predicted response also corresponds to −0.031 days per year (95% CI −0.068 to −0.014) or −0.019 Haldanes (95% CI −0.042 to −0.008).

Parturition date was influenced by a female’s reproductive status and age, but there was no clear evidence for effects of her inbreeding coefficient, of offspring sex, or of the proportion of immigrant genetic ancestry ( S1 Table ). Parturition date was heritable, with additive genetic variance accounting for 17% (95% CI 11% to 21%) of phenotypic variance. The individual-level repeatability of parturition date (estimated as the sum of proportions of all variance components except offspring birth year and residual) was 19%, of which additive genetic variance was most important, with permanent environment effects and maternal effects (i.e., effects related to grandmother identity) both accounting for less than 1% of total phenotypic variance in parturition date ( S1 Table ). The random effect for offspring birth year (which captures the variance among years over and above that corrected for the temporal linear trend) accounted for about 8% of the phenotypic variance ( S1 Table ). Note that proportions are essentially invariant under monotonic transformation and that these proportions of variances are equivalent on the transformed (i.e., log) and on the original data scale.

Large black dots represent annual means, small grey dots represent individual parturition dates, with the darker shades indicating more calves being born on a given day. About 4% of individual parturition dates fall outside the plotted region (10 May–12 July; note these are still included in the analyses). The red lines represent the slope and associated 95% confidence interval of a linear regression of all individual parturition dates on year of parturition. Note that the years 1972–1975 have very negative residuals and that the rate of change over 1980–2016 is slightly underestimated by the linear regression being fitted over all years. Code for this figure is on page 5 of S1 Code .

The average parturition dates for female red deer became later from 1972 to 1980 (probably reflecting increased population density [ 50 ]), after which they advanced at an apparently constant rate ( Fig 1 ). A linear regression estimates the change in parturition date to be a total of −12.3 (95% CI −14.5 to −10.1) days over the 45-year study period (from 1972 to 2016). The slope was identical (−12.3) when data were aggregated among females (i.e., estimated using mean parturition date over mean breeding year for each female), which may be more comparable to subsequent results.

Discussion

In the Isle of Rum red deer study population, average parturition dates have advanced 12.3 days over the last 4 decades. Previous research has identified the contribution of plastic changes in response to warming temperatures to this change [42]. Here we have shown that adaptive evolution likely played a role as well (Fig 3), and we have quantified the relative importance of demographic, plastic, and evolutionary changes (Fig 5). Subsequently, we discuss the significance of the results for the red deer population, as well as the strengths and challenges associated with the quantitative genetic study of evolution in wild populations.

Moyes and colleagues [40] identified the trend towards earlier parturition dates in the Isle of Rum red deer population and related a substantial component of the trend to local climate warming. In addition, within-individual plasticity is sufficient to explain the relationship between temperature and parturition date, and plasticity in response to increasing temperature explains some of the phenotypic change [42]. There is little evidence of variation among females in their plastic responses to temperature [42]. Therefore, the plastic response to temperature is unlikely to have changed (by genetic evolution or other means) over the study period, and a change in the strength of individuals’ plastic responses (reaction norm slopes) probably did not contribute to the change in mean parturition dates.

The present work reveals a major new aspect of the complex picture of the dynamics of parturition date in this population, by identifying a role for evolution concurrent with the previously identified plastic responses. We estimated that evolution for parturition date accounted for a total change of −2.1 days (95% CI −4.5 to 0.7) over the study period. This estimate relies on the modern and conservative version of BLUP regression, which accounts for criticisms made by Hadfield and colleagues [23] and Postma [22], in particular by including offspring birth year as a covariate. Taking this approach yielded a conservative estimate of genetic change that accounted for 15% of the observed phenotypic change. As expected, the less conservative alternative of not including year as a covariate gave a more rapid estimate of evolution: −2.4 days (95% CI −4.9 to −0.2 days). As a third method, the STS predicted an evolutionary change of −4.9 days (95% CI −10.6 to −0.7), which is more evolution than estimated by the 2 BLUP regressions. The STS may be more powerful than BLUP regression because the addition of a second trait (LBS) in the model provides more genetic information compared to a univariate approach. On the other hand, the STS, as applied here, has the disadvantage of assuming a log-normal distribution for fitness (LBS here) [62, 63]. Log normality is violated because LBS is zero-inflated, and the variance-covariance components may therefore be inaccurate. Thus, all 3 methods have possible weaknesses, and none is likely to perfectly estimate the true amount of evolution. Nevertheless, the 3 methods agree qualitatively, and the posterior distributions for the role of evolution largely overlap (Fig 3).

Our results suggest modest roles for changes in demographic structure. Shifting proportions of females of different reproductive status and ages had a predicted combined effect of −0.9 days (about 7% of the phenotypic change). These effects were also identified by Stopher and colleagues [41]. Changes among individuals, other than change in breeding values, therefore probably explain only a small (but non-negligible) fraction of the observed phenotypic change. However, summing all the effects estimated here still leaves a change of −4.99 days (95% CI −9.76 to −0.13) unexplained. Plastic responses to other environmental variables likely account for some of the remaining change, since we did not consider the response to any variables other than mean temperature during a 5-month period, and the sex of the offspring (which responds to the biotic and abiotic environment in a complex way [57]). In particular, other climatic variables such as temperatures during other times of the year, temperature variability, rainfall, and wind speeds probably affect reproductive traits in red deer [41]. In addition, the evolution of indirect genetic effects [69] may play a role. For instance, maternal genetic effects [70], which in this study would be genetic effects for how a mother influences the reproductive timing of her daughters, could evolve. However, maternal genetic effects and their possible contribution to phenotypic change are likely small in this system, because total maternal effects (which include both genetic and nongenetic maternal effects) account for less than 1% of phenotypic variance. Other types of social interactions that influence parturition may have a genetic basis that evolves, but such effects are difficult to study without a priori knowledge of the relevant individual interaction mechanisms [69].

The indication of evolution towards earlier parturition dates is consistent with previous work, which found the trait to be heritable [43] and under selection for earlier dates [44] in this population. Under ideal conditions, the product of heritability and strength of selection predicts the evolutionary response to selection [46, 71]. However, this “breeder’s equation” frequently fails to give reliable predictions in wild populations [47, 71]. Simultaneous selection on genetically correlated traits is likely to be a major cause of this failure, because fitness is generally causally affected by many traits, and genetic correlations are common [48].

Here, however, we obtained a reasonable match between the estimated rate of evolution (i.e., the estimated change in the additive genetic component of parturition date) and the response to selection predicted by the breeder’s equation, both in its univariate and in its bivariate forms. We cannot discard the possibility that this match might be in part a coincidence; for instance, if the indirect response to selection on a trait not included in the analysis pulled evolution in one direction but genetic drift pulled it back to match the observed rate of evolution. Other factors may have biased the prediction of evolution and made the match coincidental. In particular, although LBS is widely used as a measure of fitness in evolutionary ecology, it is generally not exactly the quantity maximized by natural selection when generations overlap and the environment and the population structure vary [72, 73]. Moreover, we cannot measure parturition date in females that died before reproducing, which creates a missing fraction in the estimation of selection [53]. It is possible that those females who died early are not “missing at random” with respect to genetic merit for parturition time, meaning that the true response to selection may differ from that predicted. This second problem is in part solved by the calculation of the STS (using the additive genetic covariance between parturition date and LBS) because all individuals have a breeding value for parturition date even if they never expressed the trait. The STS is therefore estimated with a much smaller missing fraction, consisting only of local individuals that do not have a LBS record (e.g., aborted embryos). The STS clearly predicted negative evolution of −4.9 days (95% CI−10.6 to −0.7), reinforcing the idea that the true selection on parturition date favours earlier dates. However, it is possible that the STS is not only related to the direct selection on parturition date but is also influenced by selection on other traits genetically correlated to parturition date. In summary, there are several potential factors that may bias the estimate of selection in one way or another, and so we interpret the estimates with caution. However, our different analyses all point towards a role of selection in advancing parturition date.

We estimated evolution and selection averaged over the study period to obtain the total evolution and response to selection expected over the period. However, if an increase in temperature explains selection for earlier parturition, it is possible that selection has intensified in more recent years and that selection was strongest in warmer years (e.g., [74]). The multivariate models we used to estimate selection allowed the estimation of selection by correcting for fixed and random effects in both parturition date and fitness traits but are not well suited to estimating changes in selection. Future work could investigate the selective scenario by estimating the interaction between parturition date and temperature in a generalized linear model of fitness, but care should then be taken to correct for the effect of time or other selectively irrelevant aspects of variation in parturition date.

A changing climate is probably not the only selective agent relevant to the evolution of parturition date in this red deer population. Indeed, selection was stronger among females that died of natural causes (with a predicted response to selection of −2.0 days) than among the shot females only (+0.10 days). Culling may alter selection on parturition date, possibly by removing females from the population at random with respect to their potential parturition dates, thus diluting natural selection. Alternatively, culling may not be random with respect to parturition date but somehow exert a type of artificial selection for later parturition dates that thus effectively opposes natural selection. Either way, culling may be slowing down the adaptive response to natural selection in the population. If confirmed, this result would add to the list of evolutionary consequences of culling [15, 66].

The response to selection predicted by the breeder’s equation corresponds to the rate of evolution estimated from the trend in breeding values as well as from the STS. However, this genetic change is much less than the observed phenotypic change. The mismatch is not surprising given that several mechanisms of phenotypic change, with a genetic basis or not, have been identified on the Rum red deer population (in our analyses presented here as well as in [41, 42]). More generally, our results illustrate how phenotypic change can be simultaneously due to both plastic and genetic changes [6, 8, 47]. Plastic changes in response to climate change appear common in natural populations, but that does not preclude concurrent evolutionary change in response to climate change [14]. Evolutionary changes are substantially more difficult to infer than plastic changes, and to date, few appropriate tests of evolution have been performed [14, 18, 39, 47]. Moreover, here as in other systems, large contributions of evolution may represent only part of the overall phenotypic trend [66, 75]. Thus, evidence for plastic responses should not be taken as reason to dismiss a role for genetic change [76, e.g.], nor the other way around. As another side of the same coin, our results highlight the insights that a quantitative genetic perspective brings to the study of phenotypic trait dynamics. As outlined earlier, the breeder’s equation often fails to predict phenotypic change in the wild. One possible explanation for this failure is “cryptic evolution,” in which genetic change is hidden by plastic changes [47]. Our results illustrate that a simple application of the breeder’s equation can work, but that it should be tested by comparison with estimates of genetic changes, not of phenotypic changes.