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Adaptive potential of maritime pine under contrasting environments



Predicting the adaptability of forest tree populations under future climates requires a better knowledge of both the adaptive significance and evolvability of measurable key traits. Phenotypic plasticity, standing genetic variation and degree of phenotypic integration shape the actual and future population genetic structure, but empirical estimations in forest tree species are still extremely scarce. We analysed 11 maritime pine populations covering the distribution range of the species (119 families and 8 trees/family, ca. 1300 trees) in a common garden experiment planted at two sites with contrasting productivity. We used plant height as a surrogate of fitness and measured five traits (mean and plasticity of carbon isotope discrimination, specific leaf area, needle biomass, Phenology growth index) related to four different strategies (acquisitive economics, photosynthetic organ size, growth allocation and avoidance of water stress).


Estimated values of additive genetic variation would allow adaptation of the populations to future environmental conditions. Overall phenotypic integration and selection gradients were higher at the high productivity site, while phenotypic integration within populations was higher at the low productivity site. Response to selection was related mainly to photosynthetic organ size and drought-avoidance mechanisms rather than to water use efficiency. Phenotypic plasticity of water use efficiency could be maladaptive, resulting from selection for height growth.


Contrary to the expectations in a drought tolerant species, our study suggests that variation in traits related to photosynthetic organ size and acquisitive investment of resources drive phenotypic selection across and within maritime pine populations. Both genetic variation and evolvability of key adaptive traits were considerably high, including plasticity of water use efficiency. These characteristics would enable a relatively fast micro-evolution of populations in response to the ongoing climate changes. Moreover, differentiation among populations in the studied traits would increase under the expected more productive future Atlantic conditions.

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Plant populations respond to local environmental changes following two different groups of strategies [1]. The first strategy is linked to species shifting their range which is a combination of migration (niche tracking) and demographic changes (extinction risk due to demographic decline). The second is aimed to persist in their local habitat, either by niche persistence based on phenotypic plasticity or by niche evolution based on in situ genetic adaptation. It is expected that most of the forest tree species will rely on standing genetic variation to adapt in situ as the rate of environmental change is too fast for the species to migrate [2].

Adaptive potential, i.e. the genetic variance needed to respond to selection, depends on the additive genetic variance of relevant traits, typically expressed as the heritability or evolvability of a population, and the strength of stabilizing selection [3, 4]. Other factors are related to the biological effect of the environment: phenotypic plasticity, and environmental sensitivity of selection, i.e. the change in the optimum phenotype with the environment [3], and to demographic characteristics of the species, specially census size and metapopulation structure [4].

However, traits do not vary independently. At the intra-specific level of variation, trait-covariation derived from functional, genetic or developmental disposition is also known as phenotypic integration [5]. Previous studies have shown the existence of covariation at intra and inter population levels of variation between growth, reproduction, drought tolerance, and tolerance to insect herbivory [6, 7]. Moreover, the degree of phenotypic integration may vary depending on the environment. Therefore, stressful environments may limit the possibilities of adaptive genetic change due to unfavorable correlations among key traits [8]. This is especially important in traits related to the main fitness components of the species, i.e. life cycle processes including growth, development, reproduction, and survival. Phenotypic integration of life history traits may affect evolutionary change as a result of the variation in fitness of an organism in response to a given increase or decrease in the value of a trait (i.e. a selection gradient). These selection gradients can occur both within populations [9] and among populations [10,11,12].

Common gardens are a useful tool to analyze the response of different genotypes to environmental changes [13,14,15], as they can provide performance estimates for different traits under semi-natural contrasting conditions. Replicated common gardens tests are experiments where the individuals (i.e. individual genotypes), families or populations under evaluation are planted together in different sites using a specific experimental design, thereby providing a setting to assess genetic and environmental effects for the traits under evaluation. These experiments allow the estimation of in situ adaptation to these environments [16] by using a space by time approach [1]. By combining different levels of variation in the same setting, they allow the estimation of inter and intra population levels of variation as in the case of provenance-progeny trials.

Maritime pine (Pinus pinaster Aiton), an ecologically and economically important species in SW Europe and NW Africa, is a good example for which common garden experiments have revealed a well-structured genetic variation for different traits, related to growth, reproduction and response to biotic and abiotic factors [17,18,19]. Likewise, genetic changes after artificial or natural selection have been detected within and among populations [9, 20]. These results, together with the long-term network of common gardens available for this species, provide an excellent setting to gain more insight into phenotypic plasticity, standing variation and correlations among functional traits in forest trees.

The main goal of this work was to experimentally test in a model species, maritime pine, whether tree fitness is related to differences in life-history and functional traits that affect ecological strategies [21]. We evaluated the responses of 11 populations and 119 families of maritime pine covering the western natural range of the species [22], from France to Morocco. We analyzed the standing variation, phenotypic and genetic selection gradients, in a multi-site common garden experiment established in two contrasting environments differing in productivity. We expected high values of intra-population variation, resulting in high evolvability. We also expected a trade-off between growth and other avoidance and resistance traits under more stressful conditions, and a high phenotypic integration in the low productivity site. These trade-offs would result in differences in selection gradients depending on the environment, influencing the differentiation and evolvability of the populations under future climatic conditions.


Standing genetic variation and quantitative differentiation

Most traits presented significant values of genetic variation, judged by their heritability (0.10 to 0.48) (Fig. 1, Table S1), with differences among sites depending of the trait. Height, mean δ13C and phenology growth index had similar heritability values at both sites. Leaf dry weight and specific leaf area had lower heritabilities at the low productivity site (LoProd) than at the high productivity site (HiProd), while the heritability for δ13C plasticity was higher in LoProd than in HiProd. Evolvability (CVa) differed greatly among traits, with δ13C plasticity showing the highest value (greater than 25%). The rest of the traits had values close to 10%, while specific leaf area and the mean δ13C presented values lower than 3% (Fig. 1, Table S1).

Quantitative differentiation (QST) was site- and trait-specific, and significantly lower than global genetic differentiation (FST = 0.13 with 95% confidence intervals: 0.128–0.132) for 2 out of 6 traits in the HiProd site (SLA and PGI) and in the LoProd site (HT and PGI) (Fig. 1).

Fig. 1
figure 1

a Coefficient of genetic differentiation (QST), heritability (h2), and b Evolvability (CVa) of traits evaluated in maritime pine families in two common gardens (HiProd site –filled bars-, and LoProd site –hatched bars). HT: height at age 7; M_D13 C: average value of carbon isotopic discrimination; Pl_D13C: plastic response of carbon isotopic discrimination; SLA: specific leaf area; DW: leaf dry weight; PGI: phenology growth index

Phenotypic integration of the traits

Phenotypic integration at the within population level of variation was higher at the LoProd site, but the difference to the HiProd site was not statistically significant (Table 1). However, when considering the total breeding values, phenotypic integration was significantly higher in the HiProd site. HT was significantly correlated (both within population and total) with leaf dry weight (positive) and with phenology growth index (negative) at both sites (Fig. 2). Moreover, there were notable differences between the two sites in the correlations between HT and δ13C mean and plasticity. These correlations were positive at the LoProd site, but were not significant at the within population level of variation and negative with δ13C plasticity (as SLA and RGH) at the HiProd site.

Table 1 Corrected and relative degree of phenotypic integration (PINT) among six traits in two common gardens in maritime pine
Fig. 2
figure 2

Pattern of phenotypic integration assessed in two common garden experiments differing in productivity (HiProd site and LoProd site). Within population and total correlations of estimated breeding values of the mother trees are represented. Solid lines represent significant positive correlations, and broken lines represent significant negative correlations (significance level α ≤ 0.05). [HT: height at age 7; M_D13 C: average value of carbon isotopic discrimination; Pl_D13C: plastic response of carbon isotopic discrimination; SLA: specific leaf area; DW: leaf dry weight; PGI: phenology growth index]

Selection gradients

Significant and positive genetic selection gradients (βG) were found for dry leaf weight at both sites, while a significant and negative genetic selection gradient for phenology growth index was observed at the HiProd site. At the within population level, linear phenotypic selection gradients were significant for most traits, except for δ13C plasticity, specific leaf area and phenology growth index at the LoProd site (Table 2). Significant quadratic phenotypic selection gradients were found for dry leaf weight and phenology growth index at both sites.

The inclusion of population effects in the estimates slightly modified the outcomes, with non-significant linear selection gradients for δ13C plasticity at any site, and for mean δ13C at the LoProd site, while SLA and phenology growth index showed significant linear selection gradients at LoProd site (Table 2). For PGI, mean δ13C and SLA, the quadratic term of the selection gradients was positive at HiProd site. It is interesting to notice that the sign of the linear selection gradient was the same as for the two levels of variation analyzed (within population and total), as well as for the genetic selection gradients.

Table 2 Genetic selection gradients (βG), linear and quadratic phenotypic selection gradients within populations (β, γ respectively), linear and quadratic total phenotypic selection gradients (βP,γP respectively), respect to height as a fitness proxy at the two sites (in bold, significance level α ≤ 0.05)


By assessing the response of range-wide populations of maritime pine in two contrasted sites, we detected high values of additive variance, different selection gradients for functional traits and degrees of phenotypic integration, characteristics needed to forecast the adaptive potential of populations under climate change [23].

Our two common garden sites are located within the natural distribution of the species in the Atlantic region and the populations span the whole geographic range of the species (See Methods S1). Despite their geographical proximity and similar mean temperature and rainfall, both environments differed greatly in productivity, displaying values at both the extremes of the entire range of the species in Spain [24]. These differences in productivity could be mainly related to soil water availability, according to a negative relationship between site index and δ13C in the same species and in the same region [25].

In this study, we used height as a surrogate of fitness (Methods S2). At the age of the experiments (7 years) we were also able to measure other fitness-related traits (diameter in the two experiments, and male and female flowering in the high productivity site -the only one reaching a reproductive stage). These fitness related traits displayed high values of variation across populations as previously shown [19]. The results obtained using diameter and flowering are concordant with those presented for height in the present study as well as in other trees species, reinforcing the use of height as a fitness surrogate.

Our study focuses on two different levels of genetic variation: within-population and total. These two levels of variation are expected to affect differently the phenotypic integration and the variation among sites [26], the plastic responses [27], and the adaptability of individuals and families to different environments. Next, we discuss the outcomes of our study at the two considered levels of variation.

Within-population patterns

We report high values of standing genetic variation and phenotypic variation within populations for the six traits analyzed (see Fig. 1), which would allow mid-term adaptation to future climatic conditions through genetic change [2, 16], as predicted from the breeder´s equation [28]. Moreover, within-population variation was very high compared to differentiation among populations, following a general pattern in forest trees [16, 29].

The degree of phenotypic integration should be determined by the genetic architecture of fitness-related traits that in maritime pine follows the polygenic adaptation model [30]. However, it is still unclear whether pleiotropy plays a major role in determining the multi-trait association as in other species [31].

Consistent with the life history theory, we found that height traded-off with avoidance/tolerance traits (isotopic discrimination, shoot growth phenology, needle biomass) under more stressful conditions. The mean and plasticity of carbon isotopic discrimination have also an adaptive value in the low productivity site. The sign of these correlations indicates an adaptive value of early growth phenology and higher needle biomass (see [32]) to avoid summer drought period and the production of more tolerant needles to physical hazards or herbivory [33, 34].

According to expectations, we found that phenotypic integration in general was higher at the low productivity site (Fig. 2). Noteworthy is the significance of within population selection gradients obtained by regression for most traits, probably as a result of the precision of the methods applied [35]. We found negative quadratic selection gradients both in dry leaf weight and phenology growth index in the two sites. The value of the selection gradients for mean δ13C was low at the two sites, despite the high heritability of the trait and the higher differentiation in the low production site(as in [17]). Despite the widely documented importance of drought stress as a selection driver in pines [36,37,38], mainly under more stressful conditions [17, 39], our results suggest that within population selection in maritime pine is not driven by water use efficiency but to a major investment of resources (needles biomass, SLA) and avoidance mechanisms of cold tolerance (e.g. late flushing). This interpretation is consistent with a low signal of selection for cavitation related traits [40] and the overrepresentation of SNP-climate correlations with winter temperatures [22]. From an ecological perspective, however, differences in precipitation determine large part of the variation within the range of the species (see Methods S1).

Total phenotypic patterns

As expected, these intra-population trade-offs will influence the differentiation and evolvability of the populations under future climatic conditions. To understand these interactions, we need to consider population-specific evolutionary characteristics, and the relationships among traits [12, 41]. Differentiation between populations was lower than the neutral expectation, but not statistically different for most of the traits at both sites. The pattern of three traits (PGI at both sites, HT at the low productive site, and DW at the high productive site) can be interpreted as the result of stabilizing selection, that have been reported in xylem susceptibility to cavitation [42]. Lower genetic differentiation for height in low productivity sites is in accordance with other results in maritime pine [30, 43].

According to the expectations based on a generalized response syndrome to ‘stressful’ environments [44], phenotypic integration within population was higher in the low productivity site, but this pattern is not observed when considering the total phenotypic pattern (Table 1). Phenotypic integration was higher at the more productive site, where δ13C plasticity and specific leaf area were negatively correlated with total height, and could be regarded as maladaptive (according to the interpretation in [45]) derived from the costs that vary in magnitude depending on environmental conditions [46].

Most of the examined traits showed significant selection gradients except plasticity and mean isotopic discrimination at the low production site (Table 2), in agreement with results in the species [22, 47, 48]. The consistency of the sign of the linear selection gradient at the two levels of variation analyzed (within population and total), and those of the genetic selection gradients suggest a similar adaptive pattern at different scales in the species.

Implications for the adaptability of the species under future climates

It has been shown that the evolutionary responses of perennial species can be constrained in unsuitable areas because adults produce maladapted offspring [49]. According to some expectations, the Atlantic area under study will increase its productivity [50] and future suitability, while in the southern range of the species the suitability will decrease [51]. Moreover, the predicted changes in the distribution of the species under future climatic conditions, indicate that the Atlantic area could be part of the ecological niche of southern genetic groups of the species [52, 53]. Under these Atlantic environmental conditions it can be expected a higher degree of phenotypic integration and differentiation between populations. These results have implications in the movement of seeds and plants such in assisted migration or climate-adjusted seed sourcing [54, 55] of southern material to favor natural selection at the within and among population levels. The lower importance of selection at the among-population level, would reduce the value of seed sourcing strategies based on the inclusion of different gene-pools to favor adaptation. Also for a large part of the actual range of the species, it would be difficult to find any southern or more xeric population from which to get migrants.

Globally, species niche models integrating infra-specific information suggest a lower reduction in the expected distribution of the species [52], advocating for the integration of information at the population/local/regional levels. It is necessary to consider the adaptive potential of the populations, which in maritime pine does not represent a limiting factor. These considerations are essential for the conservation and sustainable use of a species genetic resources that aimed to favor adaptation [56]. In this context, the importance of phenotypic plasticity and the differences among populations should be taken into consideration to forecast the effects of climate change on the future distribution of the species [14, 53, 57].


Contrary to the expectations in a drought tolerant species, our study suggests that variation in photosynthetic organ size and acquisitive economics drive phenotypic selection across and within maritime pine populations. The contrasting pattern detected for total and within-population phenotypic integration, allows to forecast future trends. In particular, between-population differentiation would increase under the more productive Atlantic conditions.


Experimental design

We used eleven populations sampled across the major gene pools of the species [22] and covering the western range of the species (Methods S1): French Atlantic (2 populations), Iberian Atlantic (3 populations), Central Spain (4 populations), Southern Spain (1 population) and Morocco (1 population). At each population, we collected seeds from individual mother trees which were distant more than 50 m from each other, resulting in half-sib families [58].

The seedlots (identified by the mother tree) were tested at two common-garden with contrasting productivity (Fig. 3, Methods S1, Table S1.1). The High productivity site (HiProd) had a three and a half fold productivity than the low productivity site (LoProd) estimated by their site index (i.e. mean tree height in the site at age 20 [59]). The common gardens were established by late 2004, with nursery-produced one-year-old seedlings. The field design followed a row-column design with four replicates (blocks), and 4-seedling plots. We sampled up to 8 trees per family for a total of 119 families and 947 trees in the HiProd site, and 49 families and 375 trees in the LoProd site (Table S2). Forty-four of these families were present at both sites.

Fig. 3
figure 3

Location of the populations and the two common gardens (HiProd and LoProd sites) of maritime pine (Distribution map: CC by 4.0,

Measured traits

We measured height at age 7 (HT, cm) as a fitness proxy. This trait is linked to competitive vigour [60], tree fecundity [19], and shows trade-offs at the interspecific level with tolerance or avoidance environmental stress [61] (Supplementary Methods S3 for more details).

We also recorded a set of 5 phenotypic traits informative about ecological strategies of plants [21, 62]. We measured carbon isotopic discrimination −δ13C− indicative of water use efficiency (see Supplementary Methods S2 for details), in the 5th and 6th growing season. We considered two different traits [63]: (i) the average value (M_D13C), and (ii) the plastic response (PI_D13C) between the two years. The plastic response was computed as the value of the year with higher water availability (5th growing season) minus the value of the year with lower water availability (6th growing season) [64].

We measured leaf traits related to trade-offs between conservative vs. acquisitive investment of resources and size [65]: (iii) Leaf dry weight (DW, mg) reflect photosynthetic organ size. It was averaged across 10 needles and two sampling years. (iv) Specific leaf area (SLA −mm2mg−1) is indicative of acquisitive investment of resources. It was estimated as the ratio between needle area and dry weight, averaged across 10 needles sampled on the 6th growing season. Needles were collected for isotopic discrimination analysis, SLA and DW estimation in mid-August in two consecutive years (during the 5th and 6th growing season of the trees).

(v) Phenology growth index (PGI) is indicative of avoidance mechanism to summer drought as it is related to the Julian day the tree reaches the maximum daily shoot growth rate (see Methods S2). This trait was estimated as the ratio of growth at date 15/04 to the total annual height growth during the 4th growing season [30], a year with average precipitation and temperature (see Methods S1).

Variation of traits between populations and families

We estimated population and quantitative genetic differences in HT and the other five variables (M_D13C, PI_D13C, DW, SLA, PGI) at the two sites. Given the field design the following mixed model was declared:

$$\varvec{y}={\varvec{X}}_{1}\varvec{b}+{\varvec{Z}}_{1}\varvec{p}+{\varvec{Z}}_{2}\varvec{f}+{\varvec{Z}}_{3}\varvec{r}+{\varvec{Z}}_{4}\varvec{c}+ \varvec{\varepsilon }$$

where y is the vector of observations for a given trait, b is the vector of fixed block effects, p is the vector of random population effects, f is the vector of random genetic effects of mother trees within population, r is the vector of rows, c is the vector of columns within blocks and ε is the vector of residuals. We estimated a variance for each random effect, where σ2p is the genetic variance between populations, σ2f is the genetic variance between mother trees nested within a population, σ2r and σ2c are the rows and column variance in the field design, and σ2e is the residual variance. Bivariate models were also used to estimate covariance components among each pair of traits.

Variance and covariance components were estimated by restricted maximum likelihood (REML) using the ASREML software [66]. Population was considered as a random effect to draw inferences at the species level and to obtain an unbiased estimate of additive genetic variance, heritability and genetic population differentiation [67]. Within population additive variation was calculated (\({\varvec{\sigma }}_{\varvec{a}}^{2}=4{\varvec{\sigma }}_{\varvec{f}}^{2}\)) assuming half sib families [58].

Genetic parameters

Heritability (h2) was estimated from the within population family variation assuming half-sib families, and evolvability (CVa)− i.e. the coefficient of additive genetic variation [68] - for each trait and site.

$${\varvec{h}}^{2}= \frac{{\varvec{\sigma }}_{\varvec{a}}^{2}}{{\varvec{\sigma }}_{\varvec{f}}^{2}+{\varvec{\sigma }}_{\varvec{e}}^{2}};{\varvec{C}\varvec{V}}_{\varvec{a}}= \frac{{\varvec{\sigma }}_{\varvec{a}}^{2}}{\stackrel{-}{\varvec{X}}}$$

Population effects were not included in the heritability calculation. Genetic differentiation between populations for quantitative traits (QST) was calculated from the ratios between the variances within and between populations [69,70,71].

$${\mathbf{Q}}_{\varvec{S}\varvec{T}}=\frac{{\varvec{\sigma }}_{\varvec{p}}^{2}}{{\varvec{\sigma }}_{\varvec{p}}^{2}+8{\varvec{\sigma }}_{\varvec{f}}^{2}}$$

The standard deviation of quantitative genetic differentiation coefficient, and heritability were calculated by the delta method [28]. A global FST estimate for the eleven populations was computed by bootstrapping across loci (1000 bootstrap iterations) based on single nucleotide polymorphism (SNPs) available from a previous study (see [30] for details).

Within population and total breeding values for the traits

Breeding values of the mother trees for height and the 5 phenotypic traits were obtained as the BLUPs (Best linear unbiased predictors of individuals and populations- [28]) from the mixed model described above (see 5.3). We obtained two estimates: firstly, a within population breeding value (i.e. value excluding the population effect as natural selection appears to occur within each population), secondly, the total breeding value (i.e. value including the population effect, in order to take selective effects between populations into account).

Variation in the magnitude and patterns of phenotypic integration

We estimated the correlation matrices among traits for the two estimated breeding values (within population and total, see 5.5), and for each of the two sites. From the correlation matrices the index of phenotypic integration (corrected PINT) was computed as the relative variance of eigenvalues [72, 73]. This value is corrected by subtracting the expected amount of integration produced by random covariation, determined by (N − 1) / n, where N is the number of traits, and n the number of individuals. A higher PINT indicates that variation is more concentrated among some traits, which means integration is higher. The relative index of phenotypic integration (Relative PINT) was computed as the magnitude of the phenotypic integration expressed as a percentage of the maximum possible integration value. The statistical significance of the integration indices among the two sites was assessed by randomization. Analysis were conducted in R using the PHENIX package [74].

Selection analysis and adaptive value of the traits

Genetic selection gradients (βG) were computed as the genetic covariance between fitness and each of the five traits, divided by the genetic variance of the trait. This method based on the bivariate mixed model described above (see 5.3), increases the precision of the estimates [35].

We also estimated linear (β, βP) and quadratic (γ, γP) selection gradients for the two sets of breeding values (within population and total, respectively, see 5.5) and for each of the two sites. The selection gradients are estimate as the vector of partial regression coefficients of relative individual fitness on the five traits (M_D13C, PI_D13C, DW, SLA, PGI). The following log-linear model was used [75]:

$$\varvec{L}\varvec{n} \left({\varvec{\lambda }}_{\varvec{k}}\right)=\sum _{1}^{5}({\varvec{\beta }}_{\varvec{i}}{\mathcal{Z}}_{\varvec{i}\varvec{k}}+{\varvec{\gamma }}_{\varvec{i}}{\mathcal{Z}}_{\varvec{i}\varvec{k}}^{2})$$

Where λk refers to the relative individual height, i.e. the absolute fitness divided by the mean fitness in each site. The subscripts (1 to 5) refer to each of the five traits. The coefficients βi are analogous to linear selection gradients, and the γi are analogous to quadratic selection gradients as defined by Lande and Arnold (1983). Statistical significance of the selection gradients was estimated using likelihood-ratio tests, by subtracting the log-likelihood for the model excluding each parameter, one at a time, from the log-likelihood of the full model, which is asymptotically chi-square distributed with one degree of freedom. Breeding values of the traits were standardized to facilitate interpretation of results [76].

Residuals of the models by means of the DHPlottinARMa R package are presented in Supplementary Methods S4.

Availability of data and materials

The datasets analyzed during the current study are available in the Zenodo repository (DOI:





Leaf dry weight


Coefficient of differentiation

h2 :



High productivity site


Height at age 7


Low productivity site


Average value of isotopic discrimination in two consecutive years


Phenology growth index


Plasticity index of isotopic discrimination between the two years


Coefficient of phenotypic integration


Coefficient of quantitative differentiation


Specific leaf area

β :

Linear phenotypic selection gradients within populations

β G :

Genetic selection gradients

β P :

Linear total phenotypic selection gradients (including population effect)

γ :

Quadratic phenotypic selection gradients within populations

γ P :

Quadratic total phenotypic selection gradients (including population effect)


Carbon isotopic discrimination


  1. Waldvogel AM, Feldmeyer B, Rolshausen G, Exposito-Alonso M, Rellstab C, Kofler R, et al. Evolutionary genomics can improve prediction of species’ responses to climate change. Evol Lett. 2020;4:4–18.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Aitken SN, Yeaman S, Holliday JA, Wang T, Curtis-McLane S. Adaptation, migration or extirpation: climate change outcomes for tree populations. Evol Appl. 2008;1:95–111.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Chevin L-M, Lande R, Mace GM. Adaptation, plasticity, and extinction in a changing environment: towards a predictive theory. PLoS Biol. 2010;8:e1000357.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Hoffmann AA, Sgrò CM, Sgro CM. Climate change and evolutionary adaptation. Nature. 2011;470:479–85.

    Article  PubMed  CAS  Google Scholar 

  5. Armbruster WS, Pélabon C, Bolstad GH, Hansen TF. Integrated phenotypes: understanding trait covariation in plants and animals. Philos Trans R Soc B Biol Sci. 2014;369:20130245.

    Article  Google Scholar 

  6. Santini F, Climent JM, Voltas J. Phenotypic integration and life history strategies among populations of Pinus halepensis: an insight through structural equation modelling. Ann Bot. 2019;124:1161–71.

    Article  PubMed Central  CAS  Google Scholar 

  7. Bontemps A, Davi H, Lefèvre F, Rozenberg P, Oddou-Muratorio S. How do functional traits syndromes covary with growth and reproductive performance in a water-stressed population of Fagus sylvatica? Oikos. 2017;126:1472–83.

    Article  CAS  Google Scholar 

  8. Benavides R, Carvalho B, Matesanz S, Bastias CC, Cavers S, Escudero A, et al. Phenotypes of Pinus sylvestris are more coordinated under local harsher conditions across Europe. J Ecol. 2021;109:2580–96.

    Article  Google Scholar 

  9. Alía R, Chambel R, Notivol E, Climent J, González-Martínez SCSC. Environment-dependent microevolution in a Mediterranean pine (Pinus pinaster Aiton). BMC Evol Biol. 2014;14:200.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Carlson JE, Adams CA, Holsinger KE. Intraspecific variation in stomatal traits, leaf traits and physiology reflects adaptation along aridity gradients in a South African shrub. Ann Bot. 2016;117:195–207.

    Article  PubMed  Google Scholar 

  11. Ramírez-Valiente JA, Lorenzo Z, Soto de Viana A, Valladares F, Gil LA, Aranda I. Elucidating the role of genetic drift and natural selection in cork oak differentiation regarding drought tolerance. Mol Ecol. 2009;18:3803–15.

    Article  PubMed  Google Scholar 

  12. Ramírez-Valiente JA, Etterson JR, Deacon NJ, Cavender-Bares J. Evolutionary potential varies across populations and traits in the neotropical oak Quercus oleoides. Tree Physiol. 2019;39:427–39.

    Article  PubMed  Google Scholar 

  13. Mátyás C. Climatic adaptation of trees: rediscovering provenance tests. Euphytica. 1996;92:45–54.

    Article  Google Scholar 

  14. Leites L, Benito Garzón M. Forest tree species adaptation to climate across biomes: building on the legacy of ecological genetics to anticipate responses to climate change. Glob Chang Biol. 2023;29:4711–30.

    Article  PubMed  CAS  Google Scholar 

  15. Sampedro L, Alía R. A claim for a ‘next generation’ of multisite range- wide forest genetic trials built on the legacy of ecological genetics to anticipate responses to climate. Glob Chang Biol. 2023;29:4700–2.

    Article  PubMed  CAS  Google Scholar 

  16. Alberto FJ, Aitken SN, Alía R, González-Martínez SC, Hänninen H, Kremer A, et al. Potential for evolutionary responses to climate change - evidence from tree populations. Glob Chang Biol. 2013;19:1645–61.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Corcuera L, Gil-Pelegrin E, Notivol E. Phenotypic plasticity in Pinus pinaster δ13C: environment modulates genetic variation. Ann for Sci. 2010;67:812–2.

    Article  Google Scholar 

  18. Zas R, Moreira X, Ramos M, Lima MRM, Nunes da Silva M, Solla A, et al. Intraspecific variation of anatomical and chemical defensive traits in Maritime pine (Pinus pinaster) as factors in susceptibility to the pinewood nematode (Bursaphelenchus Xylophilus). Trees. 2014;29:663–73.

    Article  Google Scholar 

  19. Santos-Del-Blanco L, Climent J, González-Martínez SC, Pannell JR. Genetic differentiation for size at first reproduction through male versus female functions in the widespread Mediterranean tree Pinus pinaster. Ann Bot. 2012;110:1449–60.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. Santos-del-Blanco L, Alía R, González-Martínez SC, Sampedro L, Lario F, Climent J. Correlated genetic effects on reproduction define a domestication syndrome in a forest tree. Evol Appl. 2015;8:403–10.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Westoby M, Falster DS, Moles AT, Vesk PA, Wright IJ. Plant ecological strategies: some leading dimensions of variation between species. Annu Rev Ecol Syst. 2002;33:125–59.

    Article  Google Scholar 

  22. Jaramillo-Correa JP, Rodríguez-Quilón I, Grivet D, Lepoittevin C, Sebastiani F, Heuertz M, et al. Molecular proxies for climate maladaptation in a long-lived tree (Pinus pinaster Aiton, Pinaceae). Genetics. 2015;199:793–807.

    Article  PubMed  Google Scholar 

  23. Shaw RG, Etterson JR. Rapid climate change and the rate of adaptation: insight from experimental quantitative genetics. New Phytol. 2012;195:752–65.

    Article  PubMed  Google Scholar 

  24. Bravo-Oviedo A, Río M, Del, Montero G. Site index curves and growth model for Mediterranean maritime pine (Pinus pinaster Ait.) In Spain. For Ecol Manage. 2004;201:187–97.

    Article  Google Scholar 

  25. Fernandez I, González-Prieto SJ, Cabaneiro A. 13 C-isotopic fingerprint of Pinus pinaster Ait. And Pinus sylvestris L. wood related to the quality of standing tree mass in forests from NW Spain. Rapid Commun Mass Spectrom. 2005;19:3199–206.

    Article  PubMed  CAS  Google Scholar 

  26. Ramírez-Valiente JA, Santos del Blanco L, Alía R, Robledo-Arnuncio JJ, Climent J. Adaptation of Mediterranean forest species to climate: lessons from common garden experiments. J Ecol. 2021;110:1022–42.

  27. Matesanz S, Blanco-Sánchez M, Ramos-Muñoz M, de la Cruz M, Benavides R, Escudero A. Phenotypic integration does not constrain phenotypic plasticity: differential plasticity of traits is associated to their integration across environments. New Phytol. 2021;231:2359–70.

    Article  PubMed  Google Scholar 

  28. Lynch M, Walsh B. Genetics and Analysis of quantitative traits. Massachusetts: Sinauer Assoc; 1998.

    Google Scholar 

  29. Savolainen O, Pyhajarvi T, Knurr T, Pyhäjärvi T, Knürr T. Gene flow and local adaptation in tees. Annu Rev Ecol Evol Syst. 2007;38:595–619.

    Article  Google Scholar 

  30. de Miguel M, Rodríguez-Quilón I, Heuertz M, Hurel A, Grivet D, Jaramillo-Correa JP, et al. Polygenic adaptation and negative selection across traits, years and environments in a long-lived plant species (Pinus pinaster Ait., Pinaceae). Mol Ecol. 2022;773383:2089–105.

    Article  Google Scholar 

  31. Chhetri HB, Macaya-Sanz D, Kainer D, Biswal AK, Evans LM, Chen JG, et al. Multitrait genome-wide association analysis of Populus trichocarpa identifies key polymorphisms controlling morphological and physiological traits. New Phytol. 2019;223:293–309.

    Article  PubMed  CAS  Google Scholar 

  32. Nicotra AB, Davidson A. Adaptive phenotypic plasticity and plant water use. Funct Plant Biol. 2010;37:117–27.

    Article  Google Scholar 

  33. Wright IJ, Westoby M, Reich PB. Convergence towards higher leaf mass per area in dry and nutrient-poor habitats has different consequences for leaf life span. J Ecol. 2002;90:534–43.

    Article  Google Scholar 

  34. Taeger S, Sparks TH, Menzel A. Effects of temperature and drought manipulations on seedlings of scots pine provenances. Plant Biol. 2015;17:361–72.

    Article  PubMed  CAS  Google Scholar 

  35. Rausher MD. The measurement of selection of quantitative traits: biases due to environmental covariances between traits and fitness. Evol (N Y). 1992;46:616–26.

    Google Scholar 

  36. Plomion C, Bartholomé J, Bouffier L, Brendel O, Cochard H, de Miguel M, et al. Understanding the genetic bases of adaptation to soil water deficit in trees through the examination of water use efficiency and cavitation resistance: Maritime pine as a case study. J Plant Hydraul. 2016;3 e–008:23.

    Google Scholar 

  37. Gonzalez-Benecke CA, Martin TA. Water availability and genetic effects on water relations of loblolly pine (Pinus taeda) stands. Tree Physiol. 2010;30:376–92.

    Article  PubMed  Google Scholar 

  38. Goodrich BA, Waring KM, Kolb TE. Genetic variation in Pinus strobiformis growth and drought tolerance from southwestern US populations. Tree Physiol. 2016;36:1219–35.

    Article  PubMed  CAS  Google Scholar 

  39. Aranda I, Alía R, Ortega U, Dantas ÂK, Majada J. Intra-specific variability in biomass partitioning and carbon isotopic discrimination under moderate drought stress in seedlings from four Pinus pinaster populations. Tree Genet Genomes. 2010;6:169–78.

    Article  Google Scholar 

  40. Lamy JB, Delzon S, Bouche PS, Alia R, Vendramin GG, Cochard H, et al. Limited genetic variability and phenotypic plasticity detected for cavitation resistance in a Mediterranean pine. New Phytol. 2014;201:874–86.

    Article  PubMed  Google Scholar 

  41. Cope OL, Lindroth RL, Helm A, Keefover-Ring K, Kruger EL. Trait plasticity and trade-offs shape intra-specific variation in competitive response in a foundation tree species. New Phytol. 2021;230:710–9.

    Article  PubMed  CAS  Google Scholar 

  42. Lamy J-BB, Bouffier L, Burlett R, Plomion C, Cochard H, Delzon S. Uniform selection as a primary force reducing population genetic differentiation of cavitation resistance across a species range. PLoS ONE. 2011;6:e23476.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. Rodriguez-Quilon I, Santos-del-Blanco L, Serra-Varela MJ, Koskela J, Gonzalez-Martinez SC, Alía R. Capturing Neutral and adaptive genetic diversity for conservation in a highly structured tree species. Ecol Appl. 2016;26:2254–66.

    Article  PubMed  Google Scholar 

  44. Chapin F. Integrated Responses of Plants to stress. A centralized system of physiological response. Bioscience. 1991;41:29–36.

    Article  Google Scholar 

  45. Nicotra AB, Atkin O, Bonser S, Davidson A, Finnegan E, Mathesius U, et al. Plant phenotypic plasticity in a changing climate. Trends Plant Sci. 2010;15:684–92.

    Article  PubMed  CAS  Google Scholar 

  46. Steinger T, Roy BA, Stanton ML. Evolution in stressful environments II: adaptive value and costs of plasticity in response to low light in Sinapis arvensis. J Evol Biol. 2003;16:313–23.

    Article  PubMed  CAS  Google Scholar 

  47. Hurel A, de Miguel M, Dutech C, Desprez-Loustau M, Plomion C, Rodríguez‐Quilón I, et al. Genetic basis of growth, spring phenology, and susceptibility to biotic stressors in maritime pine. Evol Appl. 2021;14:2750–72.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  48. Rodríguez-Quilón I, Santos-del-Blanco L, Grivet D, Jaramillo-Correa JP, Majada J, Vendramin GG, et al. Local effects drive heterozygosity–fitness correlations in an outcrossing long-lived tree. Proc R Soc B Biol Sci. 2015;282:20152230.

    Article  Google Scholar 

  49. Cotto O, Wessely J, Georges D, Klonner G, Schmid M, Dullinger S, et al. A dynamic eco-evolutionary model predicts slow response of alpine plants to climate warming. Nat Commun. 2017;8:15399.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  50. Barrio-Anta M, Castedo-Dorado F, Cámara-Obregón A, López-Sánchez CA. Predicting current and future suitable habitat and productivity for Atlantic populations of maritime pine (Pinus pinaster Aiton) in Spain. Ann for Sci. 2020;77:41.

    Article  Google Scholar 

  51. Vizcaíno-Palomar N, Fady B, Alía R, Raffin A, Mutke S, Benito Garzón M. The legacy of climate variability over the last century on populations’ phenotypic variation in tree height. Sci Total Environ. 2020;749:141454.

    Article  PubMed  Google Scholar 

  52. Serra-Varela MJ, Alía R, Daniels RR, Zimmermann NE, Gonzalo-Jiménez J, Grivet D. Assessing vulnerability of two Mediterranean conifers to support genetic conservation management in the face of climate change. Divers Distrib. 2017;23:507–16.

    Article  Google Scholar 

  53. Benito-Garzón M, Alía R, Robson TM, Zavala MA. Intra-specific variability and plasticity influence potential tree species. Glob Ecol Biogeogr. 2011;20:766–88.

    Article  Google Scholar 

  54. Breed MF, Stead MG, Ottewell KM, Gardner MG, Lowe AJ. Which provenance and where? Seed sourcing strategies for revegetation in a changing environment. Conserv Genet. 2013;14:1–10.

    Article  Google Scholar 

  55. Aitken SN, Whitlock MC. Assisted gene flow to facilitate local adaptation to climate change. Annu Rev Ecol Evol Syst. 2013;44:367–88.

    Article  Google Scholar 

  56. Koskela J, Lefèvre F, Schueler S, Kraigher H, Olrik DC, Hubert J, et al. Translating conservation genetics into management: pan-european minimum requirements for dynamic conservation units of forest tree genetic diversity. Biol Conserv. 2013;157:39–49.

    Article  Google Scholar 

  57. Valladares F, Matesanz S, Guilhaumon F, Araújo MB, Balaguer L, Benito-Garzón M et al. The effects of phenotypic plasticity and local adaptation on forecasts of species range shifts under climate change. Ecol Lett. 2014;n/a-n/a.

  58. Gaspar MJ, de-Lucas AI, Alía R, Almiro Pinto Paiva J, Hidalgo E, Louzada J, et al. Use of molecular markers for estimating breeding parameters: a case study in a Pinus pinaster Ait. Progeny trial. Tree Genet Genomes. 2009;5:609–16.

    Article  Google Scholar 

  59. Alvarez-Gonzalez JG, Ruiz-Gonzalez AD, Rodriguez-Soalleiro R, Barrio-Anta M. Ecoregional site index models for Pinus pinaster in Galicia (northwestern Spain). Ann for Sci. 2005;62:115–27.

    Article  Google Scholar 

  60. Dong L, Xie Y, Wu HX, Sun X. Spatial and competition models increase the progeny testing efficiency of Japanese larch. Can J for Res. 2020;50:1373–82.

    Article  Google Scholar 

  61. Cornelissen JHC, Lavorel S, Garnier E, Díaz S, Buchmann N, Gurvich DE, et al. A handbook of protocols for standardised and easy measurement of plants functional traits worldwide. Aust J Bot. 2003;51:335–80.

    Article  Google Scholar 

  62. Wilson PJ, Thompson K, Hodgson J. Specific leaf area and leaf dry matter content as alternative predictors of plant strategies. New Phytol. 1999;143:155–62.

    Article  Google Scholar 

  63. de Jong G. Phenotypic plasticity as a product of selection in a variable environment. Am Nat. 1995;145:493–512.

    Article  Google Scholar 

  64. Valladares F, Sanchez-Gomez D, Zavala MA. Quantitative estimation of phenotypic plasticity : bridging the gap between the evolutionary concept and its ecological applications. J Ecol. 2006;94:1103–16.

    Article  Google Scholar 

  65. Pierce S, Brusa G, Vagge I, Cerabolini BEL. Allocating CSR plant functional types: the use of leaf economics and size traits to classify woody and herbaceous vascular plants. Funct Ecol. 2013;27:1002–10.

    Article  Google Scholar 

  66. Gilmour AR, Gogel BJ, Cullis BR, Thompson R. ASReml user guide release 4.0. Hemel Hempstead, HP1. 1ES. UK: VSN International Ltd; 2012.

    Google Scholar 

  67. Wilson AJ. Why h2 does not always equal VA/VP? J Evol Biol. 2008;21:647–50.

    Article  PubMed  CAS  Google Scholar 

  68. Houle D. Comparing evolvability and variability of quantitative traits. Genetics. 1992;130:195–204.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  69. Spitze K. Population structure in Daphnia obtusa: quantitative genetic and allozymic variation. Genetics. 1993;135:367–74.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  70. Weir BS, Cockerham CC. Estimating F-statistics for the analysis of population structure. Evol (N Y). 1984;38:1358–70.

    CAS  Google Scholar 

  71. Wright S. The genetical structure of populations. Ann Eugen. 1951;1:323–34.

    Google Scholar 

  72. Wagner GP. On the eigenvalue distribution of genetic and phenotypic dispersion matrices: evidence for a nonrandom organization of quantitative character variation. J Math Biol. 1984;21:77–95.

    Article  Google Scholar 

  73. Pavlicev M, Cheverud JM, Wagner GP. Measuring morphological integration using eigenvalue variance. Evol Biol. 2009;36:157–70.

    Article  Google Scholar 

  74. Torices R, Muñoz-Pajares AJ. PHENIX: an R package to estimate a size-controlled phenotypic integration index. Appl Plant Sci. 2015;3:1400104.

    Article  Google Scholar 

  75. Wright JW, Meagher TR. Selection on floral characters in natural Spanish populations of Silene latifolia. J Evol Biol. 2003;17:382–95.

    Article  Google Scholar 

  76. Lande R, Arnold SJ. The measurement of selection on correlated characters. Evol (N Y). 1983;37:1210–26.

    Google Scholar 

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We thank Fernando del Caño and Regina Chambel from INIA for assistance in the field and lab work.


Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This study was funded by European Union’s Horizon 2020 research and innovation program (grant agreement no 773383 B4EST), MITECO2023-AF. 20234TE003, and grant RTI2018-094691-B-C32), and by Junta de Castilla y León (project “CLU-2019-01, co-financed by the European Union (ERDF).

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Conceptualization, RA, JM, DG and JC; Methodology, RA, JC, LS, DG, AA, IF and JM; Data analysis: RA, LS; Writing—original draft preparation, RA: — review and editing, RA, JC, LS, DG, AA, IF and JM; Funding acquisition, RA, JC, JM, DG.

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Correspondence to Ricardo Alía.

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Alía, R., Climent, J., Santos-del-Blanco, L. et al. Adaptive potential of maritime pine under contrasting environments. BMC Plant Biol 24, 37 (2024).

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