# Macro- and micro-geographical genetic variation in early-fitness traits in populations of maritime pine
(Pinus pinaster)

Aida Solé-Medina, Agathe Hurel, Camilla Avanzi, Santiago C.

Gonzalez-Martinez, Giovanni G Vendramin, Francesca Bagnoli, Andrea Piotti, Maurizio Marchi, Ilaria Spanu, Juan José Robledo-Arnuncio, et al.

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► To cite this version:

Aida Solé-Medina, Agathe Hurel, Camilla Avanzi, Santiago C. Gonzalez-Martinez, Giovanni G Vendramin, et al.. Macro- and micro-geographical genetic variation in early-fitness traits in populations of maritime pine (Pinus pinaster). Annals of Botany, 2024, 10.1093/aob/mcae190. hal-04860048

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ORIGINAL ARTICLE

# Macro- and micro-geographical genetic variation in early-fitness traits in populations of maritime pine (*Pinus pinaster*)

Aida Solé-Medina¹,*, Agathe Hurel², Camilla Avanzi³, Santiago C. González-Martinez⁴,
Giovanni G. Vendramin³, Francesca Bagnoli³, Andrea Piotti³, Maurizio Marchi³, Ilaria Spanu³,
Juan José Robledo-Arnuncio¹,† and José Alberto Ramírez-Valiente¹,†

¹Institute of Forest Sciences (ICIFOR-INIA), CSIC, Madrid, Spain, ²Federal Research and Training Centre for Forests, Natural Hazards and Landscape, Vienna, Austria, ³Institute of Biosciences and BioResources, National Research Council, Sesto Fiorentino, Italy, and ⁴National Research Institute for Agriculture, Food and the Environment (INRAE); University of Bordeaux; BIOGECO; Cestas, France

†Joint last authors.

*For correspondence. E-mail aida.sole@inia.csic.es

Received: 15 August 2024 Editorial decision: 22 October 2024 Accepted: 4 November 2024

* **Background and Aims** Assessing adaptive genetic variation and its spatial distribution is crucial to conserve forest genetic resources and manage species' adaptive potential. Macro-environmental gradients commonly exert divergent selective pressures that enhance adaptive genetic divergence among populations. Steep micro-environmental variation might also result in adaptive divergence at finer spatial scales, even under high gene flow, but it is unclear how often this is the case. Here, we assess genetic variation in early-fitness traits among distant and nearby maritime pine (*Pinus pinaster*) populations, to investigate climatic factors associated with trait divergence, and to examine trait integration during seedling establishment.

* **Methods** Open pollinated seeds were collected from seven population pairs across the European species distribution, with paired populations spatially close (between <1 and 21 km) but environmentally divergent. Seeds were sown in semi-natural conditions at three environmentally contrasting sites, where we monitored seedling emergence, growth and survival.

* **Key Results** At large spatial scales, we found significant genetic divergence among populations in all studied traits, with certain traits exhibiting an association with temperature and precipitation gradients. Significant trait divergence was also detected between pairs of nearby populations. In addition, we found consistent trait correlations across experimental sites; notably, heavier seeds and earlier seedling emergence were both associated with higher seedling survival and fitness over two years in all experimental conditions.

* **Conclusions** We identified mean annual temperature and precipitation seasonality as potential drivers of *P. pinaster* population divergence in the studied early-life traits. Populations genetically diverge also at local spatial scales, potentially suggesting that divergent natural selection can override gene flow along local-scale ecological gradients. These results suggest the species exhibits substantial adaptive potential that has allowed it to survive and evolve under contrasting environmental conditions.

**Key words:** Adaptive divergence, climate adaptation, common garden, emergence, intraspecific genetic variation, *Pinus pinaster*, micro-geographical variation, regeneration.

INTRODUCTION

Estimated climate change risks for forest tree species are growing across Europe, especially at lower latitudes (IPCC, 2014). Temperature rise and annual rainfall reduction, particularly during summer months, are already increasing aridification in this region (Vicente-Serrano et al., 2014). Climate projections indicate that this trend will be exacerbated in the next decades (Giorgi and Lionello, 2008; Somot et al., 2008), with droughts of increasing intensity, frequency and duration, coupled with a higher frequency of extreme climatic events such as heatwaves (Sheffield and Wood, 2008; Polade et

al., 2014). Even though tree species inhabiting drier European areas possess traits that allow them to cope with water stress (e.g. deep rooting, sclerophylly, resistance to cavitation; see Larcher, 2000; Nardini et al., 2014; Moran et al., 2017 and references therein), large diebacks have been documented in the region as a result of extreme drought events (Carnicer et al., 2011), which may affect the long-term population persistence and alter species distributions (EEA, 2017; Sánchez-Salguero et al., 2018). Consequently, a deeper knowledge of tree genetic variation in ecologically important traits and its interaction with environmental factors is becoming of primary importance for adaptive forest management, as well as for the conservation
of forest genetic resources potentially valuable in current and future environments (Aitken et al., 2008; Lefèvre et al., 2013; Razgour et al., 2019).

Differential selective pressures across species distribution ranges can lead to the evolutionary divergence of populations (Lynch and Walsh, 1998; White et al., 2007), and the climate itself has been identified as a major driver of adaptive population genetic variation in forest tree species (see Alberto et al., 2013; Ramírez-Valiente et al., 2022 and references therein). Predictions of the impacts of future climatic conditions on forest tree populations and species will therefore benefit from a better understanding of how the historical climate has shaped standing genetic variation (Chevin et al., 2010; Moran et al., 2017). For this purpose, in addition to investigating macro-climatic determinants of adaptive population genetic differentiation, it is important to consider smaller spatial scales of analysis. In fact, micro-geographical adaptation along steep local environmental gradients under gene flow might be more prevalent than commonly thought and it remains understudied (Richardson et al., 2014; Gauzere et al., 2020; Scotti et al., 2022).

Common garden and reciprocal transplant experiments are needed for disentangling the effects of genetic and environmental factors on phenotypic variation (White et al., 2007; Li et al., 2017) and have been widely used to assess forest trees' climatic adaptation and potential responses to climate change (e.g. Ramirez-Valiente et al., 2010; Wilczek et al., 2014; Sáenz-Romero et al., 2019; Fréjaville et al., 2019; Patsiou et al., 2020). These experiments also allow the exploration of genetic and environmental effects on trait covariation and life history strategies. In particular, when replicated across multiple sites, common garden experiments may provide insights into the environmental factors that affect trait integration and trade-offs, which are key to forecast the evolutionary potential of populations and species under variable environmental conditions (Sgrò and Hoffmann, 2004; Matesanz et al., 2021). A potential caveat is that experimental conditions during plant development may impact trait expression and trait covariation (Solé-Medina et al., 2022), and therefore it may be risky to extrapolate inferences under controlled conditions to natural environments, highlighting the need to establish the common gardens under conditions that mimic natural environments as much as possible.

Common garden experiments provide knowledge of tree phenotypic variation that is essential to improve predictions of species distribution models under climate change (Valladares et al., 2014; Benito Garzón et al., 2019; Liepe et al., 2022). However, much of the available information on intraspecific genetic variation is based on adult tree traits (Gibson et al., 2016; Benito Garzón et al., 2019), overlooking intraspecific variation in early life stages. The transition from seed to established seedling is a critical period in the life cycle of a plant, subjected to strong selective pressures (Verdú and Traveset, 2005; Petit and Hampe, 2006). In fact, less than 10% of viable seeds usually reach the seedling stage and survive the first year in natural conditions, or even less than 1% in harsh environments (e.g. Castro et al., 2005; Vizcaíno-Palomar et al., 2014). Seedling emergence success and its timing may strongly determine survival rates at later ages (Baskin and Baskin, 1998; Donohue et al., 2010) and overall recruitment success Manso et al., 2013a, b). Moreover, because seed-to-seedling stages are

highly sensitive to environmental cues, the regeneration niche of a species is often narrower than the adult survival niche (Baskin and Baskin, 1998; Walck et al., 2011; Solé-Medina et al., 2020), and it may exhibit genetically determined differences among populations adapted to different environments (Baskin and Baskin, 1998; Correia et al., 2014). Thus, conducting common-garden experiments to assess intraspecific variation in seedling emergence and survival during early life stages represents a necessary addition to build more comprehensive predictions of forest tree species adaptation to future climates (Climent et al., 2021).

In this study, we used a multi-site common garden experiment to assess genetic variation and differentiation in early-fitness traits across maritime pine (Pinus pinaster) populations at large and local scales, as well as to infer potential climatic factors associated with observed trait divergence and to identify trait associations during seedling establishment. Maritime pine is a thermophilous conifer distributed in the western Mediterranean Basin and southern European Atlantic coast, from sea level up to 1800 m a.s.l., occupying a broad range of environments including oceanic, Mediterranean and semi-arid climates and a high variety of soils (Barbero et al., 1998). Studies on this species have revealed substantial population genetic variation, phenotypic plasticity and genotype-by-environment interaction (i.e. differences in phenotypic plasticity among populations), in a variety of traits, particularly in adult trees (Alía et al., 1995, 1997; Alía and Moro, 1996; González-Martínez et al., 2002; Correia et al., 2010; Zas et al., 2020; Ramírez-Valiente et al., 2022 and references therein). In addition, some authors have reported significant associations between population trait divergence and differences in abiotic factors such as precipitation, temperature or altitude (Alía et al., 1997; Correia et al., 2008; Sánchez-Salguero et al., 2018), whereas others have found non-significant or weak correlations between traits and climate (Correia et al., 2010; Gaspar et al., 2013; Vizcaíno-Palomar et al., 2016). Species distribution models based on traits measured on adult trees in common garden experiments predict a reduction of the species range in the future as a result of more adverse climatic conditions (Serra Varela, 2017). In addition, evidence of regeneration failure has been recorded in populations from Spain and the French Atlantic coast (Ribeiro et al., 2022). Although the species regeneration capacity has also been the focus of several studies (Juez et al., 2014; Fernandes et al., 2017; Moreno-Fernández et al., 2018), especially in relation to disturbances such as fire (Fernandes and Rigolot, 2007; Vega et al., 2009, 2010; Maia et al., 2012; Sagra et al., 2018, 2019), there are comparatively fewer studies on trait population divergence at the seed-to-seedling stage in natural or semi-natural conditions (Correia et al., 2014; Vizcaíno-Palomar et al., 2014). All these characteristics make *P. pinaster* an interesting study system to investigate potential early fitness trait divergence associated with climatic variation. Moreover, despite evidence on range-wide genetic divergence in this wind-pollinated and wind-dispersed species, the spatial scale below which gene exchange may override divergent natural selection precluding population divergence is unclear. Recent studies based on molecular markers have suggested that selection can override gene flow in conifers, including *P. pinaster*, at local spatial scales (Scotti et al., 2022; Budde et al., 2023). However, whether local-scale molecular differentiation translates into divergent
phenotypic trait expression and the ecological drivers involved
have not been tested.

Aiming to increase the knowledge on how natural selec-
tion has shaped standing genetic variation and to identify
the potential climatic factors involved in the process, we as-
sessed quantitative trait variation among distant and nearby
*P. pinaster* populations under three contrasting semi-natural
common garden environments. We evaluated population differ-
entiation in early-fitness traits spanning from seedling emer-
gence to seedling establishment over two years. Specifically,
we addressed the following questions: (1) What is the extent
of intraspecific genetic variation in early-fitness traits among
*P. pinaster* populations and does it correlate with provenance
climate? (2) Do genetic differences in early-fitness traits exist
among nearby but ecologically contrasting populations? (3)
Does the growing environment alter patterns of population
differentiation (i.e. is there a genotype-by-environment inter-
action)? (4) Do different early-fitness traits covary across popu-
lations and environments?

MATERIALS AND METHODS

Population sampling and common garden experiments

In 2016, we collected seeds from 14 natural populations of *Pinus pinaster* Ait. across the European distribution of the species to establish a network of three common garden experiments (Fig. 1).

The sampling design was intended to test for trait genetic
divergence among populations along both macro- and micro-
environmental gradients. First, selected populations spanned
most of the climatic range and the genetically distinct groups
reported for the species (Jaramillo-Correa et al., 2015; Serra-
Varela et al., 2015). Second, the design included seven pairs
of populations, with paired populations being spatially close
(from <1 to 21 km) but ecologically differentiated in elevation,
climate and/or soil water availability (Fig. 1; Table 1). Within
each population, we sampled cones from 13–25 dominant or
co-dominant trees separated by at least 30 m to reduce related-
ness among maternal families. In the laboratory, we extracted
seeds from cones, removed the wings, and discarded empty and
undeveloped seeds by water flotation. Viable seeds from each
mother tree were stored separately in dry conditions at 4 °C
until sowing.

We installed the common garden experiments in three cli-
matically contrasting sites within the distribution range of
the species, in central Spain (Madrid; latitude 40.457°, lon-
gitude -3.752°), south-western France (Bordeaux; 44.786°,
-0.577°) and central Italy (Arezzo; 43.481°, 11.879°) (Fig.
1; Supplementary Data Table S1). Madrid was the driest and
hottest site, especially in summer months. Bordeaux presented
lower temperature oscillations throughout the year and higher
precipitation during summer, while Arezzo presented an inter-
mediate climate but the highest annual precipitation (Fig. 1).
The three gardens followed the same latinized row–column
design with seven columns and 42 rows organized in three
complete blocks. The experimental unit consisted of 16 seeds
individually sown in adjacent 30-cm² octagonal cells of plastic
grids opened in the bottom and lateral walls (Guttagarden,

Gutta, Italy). In three populations (ES5, FR9 and IT20), those
geographically closest to the experimental sites, family struc-
ture was maintained in the design for purposes that go beyond
the objectives of this study, with a total of 21, 24 and 20 fam-
ilies for ES5, FR9 and IT20, respectively, and one experimental
unit per maternal family per population within each block. For
each of the 11 remaining populations, we randomly sampled
144 seeds from a lot formed by pooling an equal number of
seeds from every maternal family, which were then sown in
three experimental units per block. In total, 4714 seeds were
sown in each common garden. The experimental design was
constructed using CYCDESIGN software (Whitaker et al., 2002).
The common gardens were installed in open areas with flat top-
ography and full sun exposure, where around 5 cm of soil was
removed to place the grids, subsequently filled with sieved local
soil.

Before sowing, we measured the individual mass of seeds
from the three populations with family structure (ES5, FR9 and
IT20), as well as mean population seed mass for the remaining
populations, based on the total weight of 50 randomly sampled
seeds. Seeds were sown in spring 2018 in Spain and France and
in spring 2019 in Italy. All experimental sites were watered for
1 month to ensure enough germination and avoid very small
sample sizes, which would have precluded further analyses (see
Vizcaíno-Palomar et al., 2014; Solé-Medina et al., 2020). All
experimental sites were regularly weeded to reduce vegetative
competition and were fenced to prevent herbivory.

Measured traits

The experiment lasted 2 years from seed sowing. We moni-
tored emergence, epicotyl elongation, dwarf shoot development
(marking an important ontogenetic phase change, Climent et
al., 2013) and survival every 2 or 3 d from sowing until the
first winter, when seedlings stopped growing. Then, we con-
tinued monitoring the same traits every 7–15 d until the end of
the experiment. Height, defined as the stem length above the
cotyledons (to account for differences in seed sowing depth),
was measured on all alive seedlings in winter of the two studied
years. Based on these measurements, we estimated the pheno-
typic and fitness variables described in Table 2.

We considered two different reference periods for the ana-
lysis of emergence: the whole duration of the experiment and
the first 100 d after sowing. Almost 90 % of overall emergence
occurred within the first 100 d of the experiment, virtually stop-
ping thereafter for 2 months, until small peaks of late emer-
gence occurred at the end of the first growing season and even
during the second year in one site (see Fig. 2). The two defined
reference periods allowed clearer quantification of the fate of
early-emerging seedlings (the great majority) during the first
growing season (spring–early summer) and of all seedlings,
including those with late emergence in the subsequent favour-
able periods (first-year late summer to autumn and second-year
spring).

Analysis of population differentiation and phenotypic plasticity

We tested for differences in binomial variables (emergence,
survival, fitness and dwarf shoot rate) among populations and
<figure><img src="image_3.png" /><figcaption>FIG. 1. (A) Location of the 14 sampled *Pinus pinaster* populations (the two populations of each local pair shown in black and grey, sharing the same symbol) and the three common garden experiments (red, green and blue diamonds). The green area indicates the current species distribution range (EUFORGEN, 2009). (B) Principal component analysis of scaled and centred climatic variables of *P. pinaster* populations. Population scores and common garden scores are represented by the same symbols used in the top panel. Loadings for the climatic variables are in green. Selected climatic variables for correlation analysis are in bold. The climatic variables were annual mean temperature (bio1), mean diurnal range (bio2), isothermality (bio3), temperature seasonality (bio4), maximum temperature of warmest month (bio5), minimum temperature of coldest month (bio6), temperature annual range (bio7), mean temperature of wettest quarter (bio8), mean temperature of driest quarter (bio9), mean temperature of warmest quarter (bio10), mean temperature of coldest quarter (bio11), annual precipitation (bio12), precipitation of wettest month (bio13), precipitation of driest month (bio14), precipitation seasonality (bio15), precipitation of wettest quarter (bio16), precipitation of driest quarter (bio17), precipitation of warmest quarter (bio18) and precipitation of coldest quarter (bio19), extracted from CHELSA (Karger et al., 2020) for the reference period 1970–2000.</figcaption></figure>

sites, and their interaction, using binomial mixed models with
logit link functions (R package glmmTMB; Brooks et al.,
2017). We considered population, experimental site and their
interaction as fixed-effect factors, and family nested within
population, as well as row, column and block nested within site
as random-effect factors. We implemented linear mixed models

for the rest of the variables (emergence time, developmental
time, growth and height), using the same factor structure de-
scribed above. We considered the inverse of population sample
sizes as sampling weights in the models to account for the
overrepresentation of populations with family structure (Hahs-
Vaughn, 2006). We also conducted Cox proportional hazard
<table><caption>TABLE 1. Geographical coordinates and main climatic features of the 14 studied Pinus pinaster populations. The distance and main local ecological differences between paired populations are shown.</caption><thead><tr><th>Population</th><th>Latitude</th><th>Longitude</th><th>Distance between paired populations</th><th>Ecological contrast between paired populations</th></tr></thead><tbody><tr><td>ES1</td><td>36.827°</td><td>-3.941°</td><td>1.7 km</td><td>Altitude</td><td>Low altitude: 434 m a.s.l.</td></tr><tr><td>ES2</td><td>36.835°</td><td>-3.924°</td><td></td><td>Altitude</td><td>High altitude: 721 m a.s.l.</td></tr><tr><td>ES3</td><td>40.245°</td><td>-5.122°</td><td>6.3 km</td><td>Altitude</td><td>High altitude: 1074 m a.s.l.</td></tr><tr><td>ES4</td><td>40.189°</td><td>-5.108°</td><td></td><td>Altitude</td><td>Low altitude: 650 m a.s.l.</td></tr><tr><td>ES5</td><td>41.336°</td><td>-4.246°</td><td>1 km</td><td>Water availability</td><td>Dry. Dune-top position, dry sandy soil</td></tr><tr><td>ES6</td><td>41.341°</td><td>-4.235°</td><td></td><td>Water availability</td><td>Wet. Dune-bottom position, aquifer discharge area</td></tr><tr><td>ES7</td><td>39.917°</td><td>-0.394°</td><td>0.7 km</td><td>Water availability</td><td>Dry. South-facing slope, aspect: 202. Intense solar radiation</td></tr><tr><td>ES8</td><td>39.912°</td><td>-0.389°</td><td></td><td>Water availability</td><td>Wet. North-west-facing slope, aspect: 298</td></tr><tr><td>FR9</td><td>44.968°</td><td>-1.164°</td><td>21.5 km</td><td>Water availability</td><td>Dry. Dune-top position, dry sandy soil</td></tr><tr><td>FR10</td><td>44.78°</td><td>-1.23°</td><td></td><td>Water availability</td><td>Wet. Dune-bottom position, aquifer discharge area</td></tr><tr><td>FR13</td><td>41.756°</td><td>9.212°</td><td>7.7 km</td><td>Altitude</td><td>High altitude: 924 m a.s.l.</td></tr><tr><td>FR14</td><td>41.816°</td><td>9.258°</td><td></td><td>Altitude</td><td>Low altitude: 425 m a.s.l.</td></tr><tr><td>IT19</td><td>44.418°</td><td>8.671°</td><td>14.8 km</td><td>Climate</td><td>Coastal. South-facing. More rain and milder temperatures in summer and winter</td></tr><tr><td>IT20</td><td>44.551</td><td>8.645</td><td></td><td>Climate</td><td>Interior. North-facing. Semi-continental climate with higher diurnal and yearly temperature range</td></tr></tbody></table>

<table><caption>TABLE 2. Early phenotypic and fitness traits in the Pinus pinaster common garden experiments.</caption><thead><tr><th>Abbreviation</th><th>Measured trait</th><th>Description</th></tr></thead><tbody><tr><td>Emergence</td><td>Emergence rate</td><td>Number of emerged seedlings/number of sown seeds (binomial)</td></tr><tr><td>Emergence<sub>100</sub></td><td>Emergence rate in the first 100 d after sowing</td><td>Number of emerged seedlings in the first 100 d/number of sown seeds (binomial)</td></tr><tr><td>Emerg. time</td><td>Emergence time</td><td>Time from sowing to emergence (d)</td></tr><tr><td>Emerg. time<sub>100</sub></td><td>Emergence time in the first 100 d after sowing</td><td>Time from sowing to emergence for seedlings that emerged in the first 100 d (d)</td></tr><tr><td>Survival<sub>1</sub></td><td>Survival at the end of the first growing season (GS) (for the seedlings emerged in spring-early summer of the first GS)</td><td>Number of seedlings alive at the end of the first GS/number of seedlings that emerged in the first 100 d (binomial)</td></tr><tr><td>Survival<sub>2</sub></td><td>Survival at the end of the second GS</td><td>Number of seedlings alive at the end of the second GS/number of seedlings alive at the end of the first GS (binomial)</td></tr><tr><td>Fitness<sub>1</sub></td><td>Fitness at the end of the first GS (for the seedlings emerged in spring-early summer of the first GS)</td><td>Number of seedlings alive at the end of the first GS that emerged in the first 100 d/number of seeds sown (binomial)</td></tr><tr><td>Fitness<sub>2</sub></td><td>Fitness at the end of the second GS</td><td>Number of seedlings alive at the end of the second GS/number of seeds sown (binomial)</td></tr><tr><td>Height<sub>1</sub></td><td>Height at the end of the first GS</td><td>Height of alive seedling (above the cotyledons) at the end of the first GS (cm)</td></tr><tr><td>Height<sub>2</sub></td><td>Height at the end of the second GS</td><td>Height of alive seedling (above the cotyledons) at the end of the second GS (cm)</td></tr><tr><td>Growth</td><td>Height growth</td><td>Height<sub>2</sub> - Height<sub>1</sub> (cm)</td></tr><tr><td>Develop. time</td><td>Developmental time</td><td>Time from emergence to epicotyl elongation (d)</td></tr><tr><td>Dwarf shoot</td><td>Dwarf shoot rate</td><td>Number of seedlings that developed dwarf shoots/number of seedlings that were alive when the first dwarf shoot appeared in the garden (binomial)</td></tr></tbody></table>

mixed-effect models (Cox, 1972) to explore temporal patterns of seedling emergence and mortality, based on the same model structure (R package coxme; Therneau, 2015). We applied Benjamini and Hochberg's (1995) false discovery rate (FDR)

correction for multiple testing as implemented in R's built-in function *p-adjust*. When significant genetic differences among populations were found in the mixed-effect models for a given trait (i.e. when the population or the population-by-site factors
<figure><img src="image_4.png" /><figcaption>FIG. 2. Kaplan-Meier curves for cumulative emergence (A) and survival probability of emerged *Pinus pinaster* seedlings (B) in the Spanish, French and Italian experimental sites. Time represents days since sowing. Dashed vertical lines indicate the first 100 d after sowing (A) and the end of the first and second year of the experiment (B). Shaded areas represent 95 % confidence intervals.</figcaption></figure>

were significant), we fitted new models to assess population
differences within sites and extracted population means.

Analysis of trait-climate and trait-trait associations

To explore the associations between trait genetic divergence and climatic variation among populations, we selected six climatic variables to conduct univariate analyses based on their ecological importance for seed germination and early-fitness traits in Mediterranean species (Céspedes et al., 2012; Barrio-Anta et al., 2020; Solé-Medina et al., 2022). The six climatic variables were: annual mean temperature (bio1), maximum temperature of warmest month (bio5), minimum temperature of coldest month (bio6), annual precipitation (bio12), precipitation of driest month (bio14) and precipitation seasonality (bio15). We performed Pearson correlations among population trait means and individual climatic variables and implemented FDR correction for multiple testing.

Finally, we explored trait–trait associations within sites using Pearson correlations on populations’ mean trait values, conducted FDR correction and plotted them with network graphs (R package igraph; Csardi and Nepusz, 2006).

Analysis of population differentiation at local scale

We examined trait divergence at local spatial scale between
paired populations with the same rationale explained for all
populations, but with a modified model structure. For this ana-
lysis, we considered region (categorical factor grouping paired
populations), experimental site and their interaction as fixed-
effect factors, and population nested within region, family
nested within population, as well as row, column and block
nested within site as random-effect factors. When the inclusion
of the random factor *population nested within region* signifi-
cantly improved the models after FDR correction, we re-ran the
models including it as a fixed-effect factor and extracted popu-
lation means. The same procedure was implemented within ex-
perimental sites. We then ran post-hoc Tukey tests (R package
multcomp, Hothorn et al., 2008), to test for differences among
paired populations.

RESULTS

Trait variation across experimental sites

After FDR correction, mixed models revealed significant
phenotypic plasticity (i.e. a significant site factor) for all the
considered seedling traits except second-year survival, as well
as significant among-population genetic variation in pheno-
typic plasticity (i.e. significant site × population factor) for
all study traits except first-year survival (Table 3). Most seed-
ling emergence (89.2 %) occurred within the first 100 d after
sowing in all three sites (in spring and early summer), but there
was a second emergence peak at the end of summer and the
beginning of autumn (between 161 and 267 d after sowing), as
well as a third peak in spring of the second year, especially in
France (Fig. 2). Emergence occurring after the first spring ac-
counted for around 2 % of total emergence in the Spanish and
Italian sites, versus 14 % in the French site. The emergence rate
was higher at the Spanish site during the first spring, becoming
larger at the French site when considering the entire experi-
mental period due to higher delayed emergence (Fig. 2).

Seedling survival showed marked differences across sites (Table 3; Fig. 2). Survival at the end of the first growing season was by far the lowest in Spain, with only 12 % of the emerged seedlings alive compared to 40 and 53 % in France and Italy, respectively (Fig. 2). The highest mortality rate over the study period occurred at the beginning of the first growing season, with three-quarters of the recorded deaths occurring within the first 127 d after sowing (i.e. in late spring and summer months) (Fig. 2). In the second growing season, survival was around 75 % in all sites with no significant differences across sites (Table 3). Overall, fitness, i.e. the proportion of seedlings alive relative to the number of seeds sown, was significantly lower in Spain than in Italy and France at the end of the first and second growing seasons (Table 3). Specifically, fitness after 2 years was 5.8, 21.8 and 24.1 %, in the Spanish, French and Italian sites, respectively.

Height also showed significant differences across sites at the
end of the first growing season (mean ± s.e. 4.3 ± 0.4, 2.1 ± 0.4
and 0.8 ± 0.4 cm for Italian, Spanish and French sites, respect-
ively). At the end of the second growing season seedlings were
<table><caption>TABLE 3. Mixed models for Pinus pinaster early phenotypic and fitness traits. $\chi^2$ and P-values are shown for the fixed-effect factors Site (df = 2), Population (df = 13) and Site × Population (df = 26). Statistically significant results after FDR correction are shown in bold. See Table 2 for trait descriptions.</caption><thead><tr><th rowspan="2"></th><th colspan="2">Site</th><th colspan="2">Population</th><th colspan="2">Site × Population</th></tr><tr><th>$\chi^2$</th><th>P</th><th>$\chi^2$</th><th>P</th><th>$\chi^2$</th><th>P</th></tr></thead><tbody><tr><td>Emergence</td><td>16.5</td><td><strong>&lt;0.001</strong></td><td>86.9</td><td><strong>&lt;0.001</strong></td><td>147</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Emergence<sub>100</sub></td><td>18.6</td><td><strong>&lt;0.001</strong></td><td>124</td><td><strong>&lt;0.001</strong></td><td>193</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Emerg. time</td><td>35.2</td><td><strong>&lt;0.001</strong></td><td>11.3</td><td>0.601</td><td>854</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Emerg. time<sub>100</sub></td><td>886</td><td><strong>&lt;0.001</strong></td><td>41.2</td><td><strong>&lt;0.001</strong></td><td>224</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Survival<sub>1</sub></td><td>19.3</td><td><strong>&lt;0.001</strong></td><td>44</td><td><strong>&lt;0.001</strong></td><td>29.8</td><td>0.301</td></tr><tr><td>Survival<sub>2</sub></td><td>1.82</td><td>0.424</td><td>25.4</td><td><strong>0.023</strong></td><td>49</td><td><strong>0.005</strong></td></tr><tr><td>Fitness<sub>1</sub></td><td>23.4</td><td><strong>&lt;0.001</strong></td><td>51.5</td><td><strong>&lt;0.001</strong></td><td>75.9</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Fitness<sub>2</sub></td><td>15.5</td><td><strong>&lt;0.001</strong></td><td>52</td><td><strong>&lt;0.001</strong></td><td>57.2</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Height<sub>1</sub></td><td>689</td><td><strong>&lt;0.001</strong></td><td>59.6</td><td><strong>&lt;0.001</strong></td><td>220</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Height<sub>2</sub></td><td>997</td><td><strong>&lt;0.001</strong></td><td>81.4</td><td><strong>&lt;0.001</strong></td><td>323</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Growth</td><td>668</td><td><strong>&lt;0.001</strong></td><td>52.3</td><td><strong>&lt;0.001</strong></td><td>220</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Develop. time</td><td>51.9</td><td><strong>&lt;0.001</strong></td><td>4.28</td><td>0.988</td><td>360</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Dwarf shoot</td><td>6.84</td><td><strong>0.036</strong></td><td>32.5</td><td><strong>0.002</strong></td><td>70.2</td><td><strong>&lt;0.001</strong></td></tr></tbody></table>

significantly taller in the Italian site (25.4 ± 0.8 cm), while no significant differences were observed between the Spanish and French sites (10.1 ± 0.9 and 9.0 ± 0.8 cm, respectively).

### Trait variation among populations and its association with macro-climatic provenance variation

Most phenotypic and fitness traits showed strong genetic variation among populations (i.e. significant population effect; Table 3), with some of them showing significant association with climatic variation, particularly with mean annual temperature and precipitation seasonality (Fig. 3; Supplementary Data Fig. S1). After FDR correction, Pearson correlations showed that seedlings from provenances with higher mean annual temperature and higher precipitation seasonality tended to have higher emergence rate, shorter emergence time and higher fitness and grew taller throughout the entire experiment (Fig. 3; Fig. S1). In addition, we found a significant positive association between seed mass and precipitation seasonality (Fig. 3).

### Trait variation between local populations within regions

Despite their geographical proximity within their respective regions, paired populations also exhibited significant trait divergence between each other (Supplementary Data Table S2). In particular, post-hoc Tukey tests revealed that all traits except first-year survival and dwarf shoot rate exhibited significant divergence between local populations (Table S2). Trait differences between local populations within regions were highly pair-, site- and trait-dependent (Table S2), but there were some remarkable patterns. First, in pairs where populations differed in soil water availability, the drier population of the pair showed higher emergence rate and earlier emergence, and in two out of

three population pairs, the drier population also showed higher fitness at the end of the experiment (Fig. 4). This held true even for populations separated by only a few hundred meters. Second, in pairs where populations differed in elevation, the lower population tended to emerge in higher proportion and earlier and to grow taller and faster (Fig. 4).

### Trait coordination within experimental sites

Significant trait–trait correlations were common, showing strong phenotypic integration especially in the Italian and French sites (Fig. 5). Remarkably consistent patterns across sites were found regarding seed mass and emergence time (Fig. 5). Specifically, populations with heavier seeds had higher first-year survival and higher fitness at the end of the experiment in all sites. In addition, populations that emerged earlier in spring showed higher first-year survival and higher fitness in both growing seasons in all sites (Fig. 5). Fitness at the end of the first growing season was highly and positively correlated with first-year survival in all sites, and with emergence rate in the French and Italian sites ($r = 0.94$, $P < 0.001$ and $r = 0.96$, $P < 0.001$, respectively). Finally, height, proportion of dwarf shoots, survival and fitness were strongly correlated to one another in Italy and Spain. On average, populations that grew more had a higher proportion of seedlings developing dwarf shoots and higher survival and fitness rates (Fig. 5).

### DISCUSSION

Our study revealed that *P. pinaster* populations genetically differed in virtually all studied early-fitness traits, presumably because of divergent selective pressures associated with heterogeneous climatic conditions at different spatial scales across the species range. We found clinal seedling trait variation associated with annual mean temperature and precipitation seasonality of the population of origin. Overall, populations from warmer provenances with higher precipitation seasonality tended to emerge earlier, to grow taller and to show higher fitness over the two study years. We also found evidence of significant micro-geographical trait variation among populations located a few hundred metres apart along steep environmental gradients. Finally, different populations showed contrasting patterns of phenotypic plasticity for most seedling traits, but certain trait–trait correlations were largely consistent across experimental environments.

### Clinal variation of seedling traits along macroclimatic gradients

It is well established that maritime pine presents high levels of population differentiation in quantitative traits (Alía *et al.*, 1995, 1997; Chambel *et al.*, 2007; Aranda *et al.*, 2010; de Miguel *et al.*, 2022; Ramírez-Valiente *et al.*, 2022 and references therein). However, few studies have focused on early-fitness traits or addressed the potential environmental drivers of population genetic divergence at different spatial scales.

Our results revealed associations between among-population variation in early-fitness traits and the climate of the populations of origin, especially with annual temperature and precipitation seasonality. Temperature and precipitation are critical
<figure><img src="image_5.png" /><figcaption>FIG. 3. Correlations between selected climatic variables and *Pinus pinaster* seedling phenotypic traits (see Table 2 for definitions) in the Spanish, French and Italian experimental sites (red, green and blue, respectively). Significant Pearson correlation coefficients (*r*) after FDR correction are shown, with *P* values represented by \* *P* < 0.07, \*\* *P* < 0.05, \*\*\* *P* < 0.01 and \*\*\*\* *P* < 0.001. Shaded areas represent 95 % confidence intervals.</figcaption></figure>

environmental factors for successful recruitment, and they have
previously been identified as important drivers of genetic di-
vergence in early-fitness traits for many species (Baskin and
Baskin, 1998), including pines (Ramírez-Valiente et al., 2021).
Beyond annual precipitation accumulation, its seasonal dis-
tribution (and its effect on soil moisture) is considered an im-
portant selective agent for germination strategies (Cowling et
al., 2005; Urbieta et al., 2008; de Dios Miranda et al., 2009).
In particular, studies on *P. pinaster* natural regeneration have

found precipitation timing to be a major determinant of seed
germination, early seedling development and survival (Ruano
et al., 2009; Rodriguez-Garcia et al., 2011). Our results showed
that *P. pinaster* populations that have evolved under stronger
seasonal precipitation regimes (characterized also by lower
summer precipitation) and warmer climates had earlier seed-
ling emergence than populations from provenances with lower
annual temperature and a more homogeneous seasonal distribu-
tion of rainfall (and higher summer rainfall).
<figure><img src="image_6.png" /><figcaption>FIG. 4. Early-life trait differentiation between spatially close but ecologically contrasting *Pinus pinaster* population pairs. The two populations of each pair are shown in black and grey, sharing the same symbol. The contrasting ecological factors were water availability (upper panels) or altitude (lower panels). Observed population means ± s.e. are shown. Solid and dashed lines indicate significant and non-significant differences after FDR correction among local population pairs, respectively.</figcaption></figure>

Importantly, the fact that maritime pine seedlings from warmer provenances tend to have higher fitness and grow taller at the warmest experimental site (Spain), relative to seedlings from colder provenances, suggested that they are better adapted to higher temperatures and drier conditions during early recruitment stages. In the Italian site, populations with higher precipitation seasonality showed higher and earlier emergence, higher fitness and were taller after 2 years, while in the French site we observed mixed trends with both environmental variables. These results suggest higher survival of seedlings from harsher (warmer and high seasonality in precipitation) environments irrespective of the growing site, and are consistent with those found by Alía et al. (1997) and Ramírez-Valiente et al. (2022) for adult trees. However, previous results had reported higher growth for maritime pines from (more humid) Atlantic provenances when grown in milder environments (Kremer and Roussel, 1986; Alía et al., 1995, 1997; Archambeau et al., 2022), including during early life stages (Fernández et al., 1999). The fact that our experiments were conducted in the field under semi-natural conditions, in contrast to the chamber experiments by Fernández et al. (1999), illustrates the risks of generalizing results obtained under controlled conditions (see also Alía et al., 2014), as well as the potentially strong dependency of field experiments on the environmental conditions experienced during the study years.

The observed patterns of covariation between seedling fitness traits and climate of the population of origin are consistent with climate-driven divergent selection across the species distribution (Davis et al., 2005; Alberto et al., 2013), although the influence of epigenetic or environmental maternal effects cannot be fully discarded. Seed provisioning is a well-known

mechanism of transmission of environmental maternal effects in many plant species, including maritime pine (Cendán et al., 2013; Zas et al., 2013; Suárez-Vidal et al., 2017), and we found that seed mass, which was positively correlated with seedling fitness (discussed below), was higher for seeds collected from populations with more seasonal precipitation regimes (Fig. 3). Therefore, the higher performance observed for the latter populations might have been partly mediated by differences in the maternal environment experienced by collected seeds. In any case, mean seed mass has been found to be a genetically variable and highly heritable trait in *P. pinaster*, with a comparatively weaker influence of non-genetic effects (Zas and Sampedro, 2015), suggesting that adaptive genetic factors have probably influenced the observed trends in the results. Future studies should consider investigating the relative importance of genetic versus environmental effects in seed mass variation among maritime pine populations.

### *Seedling trait variation at local spatial scale*

Unlike the frequent evidence of quantitative genetic divergence among distant *P. pinaster* populations, little is known on the minimum scale over which genetic divergence occurs in this (Archambeau et al., 2022) or many other species (Richardson et al., 2014). Our sampling design allowed us to investigate trait genetic variation at local spatial scales (from <1 km up to 21 km), a range of distances rarely considered in evolutionary ecology research because it is commonly assumed that high gene flow invariably overcomes selection at fine spatial scale (Richardson et al., 2014). However, quantitative differentiation
<figure><img src="image_7.png" /><figcaption>FIG. 5. Network graphs showing statistically significant (*P* < 0.05) *Pinus pinaster* seedling trait–trait correlations in the Spanish (red), French (green) and Italian (blue) experimental sites after FDR correction. Line thickness represents the strength of the correlation, as measured by Pearson correlation coefficient. Solid and dashed lines correspond to positive and negative correlations, respectively.</figcaption></figure>

at local scale could be of the same order of magnitude as that
detected in range-wide studies (Eckert et al., 2015).

Our results revealed genetic variation among populations lo-
cated spatially close but in sharply contrasting environmental
conditions. The differences found depended on the experi-
mental site and phenotypic trait, but all paired (nearby) popula-
tions showed a certain level of significant trait divergence (Fig.
4). At this local spatial scale, with presumably high levels of
gene exchange via pollen and seed dispersal (Salvador et al.,
2000), it is unlikely that the observed variation is explained by
neutral genetic drift, but rather by locally divergent selection
overcoming the homogenizing effect of gene flow (Eveno et
al., 2008). This was tested by Eckert et al. (2015), who pro-
vided strong evidence of divergent selection as the main driver
of genetic differentiation among nearby populations of Pinus
lambertiana.

Our results complement recent findings of molecular gen-
etic differentiation at local scales in *P. pinaster* (Scotti *et al.*,

2022; Budde *et al.*, 2023), showing that genetic differentiation also occurs at the phenotypic level. Importantly, our findings suggest that selection operating at micro-geographical scales may promote genetic differentiation among highly connected populations, maintaining and enhancing the adaptive potential of the species across heterogeneous landscapes, crucial for their persistence under changing climatic conditions.

Early fitness effects of seed mass and emergence time

Our results revealed a consistent positive association between
seed mass and seedling fitness over 2 years in all studied sites
(Fig. 5). Positive associations between seed mass and survival
seem to be common in species inhabiting highly seasonal cli-
mates (e.g. Gómez, 2004; Moles and Westoby, 2004; Ramírez-
Valiente et al., 2009; Larson et al., 2014; Lebrija-Trejos et al.,
2016), including pine species (Parker et al., 2006; Cendán et
al., 2013; Zas et al., 2013). A number of studies in different
species have found that larger seeds have more reserves and produce seedlings with larger growth and/or deeper roots (Surles et al., 1993; Westoby et al., 1996; Wennström et al., 2002; Bladé and Vallejo, 2008; Leishman et al., 2009; Wahid and Bounoua, 2013). Moreover, in pine species, higher seed mass has been associated with higher root investment (Matías et al., 2014; Ramírez-Valiente and Robledo-Arnuncio, 2015), which is of primary importance to access deeper water sources and increase the probabilities of seedling establishment under drought conditions.

We also found evidence of higher survival and fitness of seedlings with earlier emergence, consistent with results obtained for other species inhabiting seasonal climates (Seiwa, 2000; Simons and Johnston, 2000; Donohue, 2002; Castro, 2006; Warwell and Shaw, 2019). Selection for early emergence in the growing season is expected if it provides advantages with respect to a predictable environmental cue (Verdú and Traveset, 2005; Donohue et al., 2010). Early emergence is considered of great importance in Mediterranean and arid ecosystems, because it provides seedlings with more time to grow and develop before the onset of the recurrent and critical summer drought (Vizcaíno-Palomar et al., 2014; Callejas-Díaz et al., 2022). In fact, in our study we found a clear and consistent relationship between early emergence time in spring and seedling fitness and survival, suggesting that early emergence had an adaptive role in response to summer drought across sites, irrespectively of their climate. Importantly, both macroclimatic clines and local-scale variation among populations that differ in water availability point to the same conclusion. More arid populations (warmer and with more seasonal precipitation) emerged earlier and, at a local scale, the drier population of the pair tended to show earlier emergence across sites (although differences were not always significant), suggesting that earlier emergence is favoured under water limitation at both spatial scales.

## CONCLUSIONS

Altogether, our results showed that *P. pinaster* populations genetically differed in early life stages, with mean annual temperature and precipitation seasonality as potential macroclimatic drivers of population divergence. Evidence of micro-geographical genetic divergence among nearby populations suggests that natural selection can override gene flow under strongly contrasting environments, generating adaptive genetic variation locally within regions. Finally, consistent patterns of trait correlation across experimental sites highlight that heavier seeds and early seedling emergence elicit higher survival rates and fitness in this species. Strong genetic variation and differences in plastic responses among populations suggest high adaptive potential in *P. pinaster* at early-life stages, provided that the increasing aridification predicted in southern European regions does not critically hinder seedling survival.

## SUPPLEMENTARY DATA

Supplementary data are available at *Annals of Botany* online and consist of the following.

Table S1. Geographical coordinates and main climatic features of the 14 *Pinus pinaster* populations and three common-garden sites under study. Table S2. Population mean and standard deviation of *Pinus pinaster* seedling traits that showed significant divergence between paired nearby populations. Figure S1. Correlations between selected climatic variables and *Pinus pinaster* seedling phenotypic traits in the experimental sites.

## FUNDING

This work was supported by the European Union's Horizon 2020 research and innovation programme under grant agreement no. 676876 (GenTree project). A.S.-M. was supported by a PhD grant from the Subdirección General de Investigación y Tecnología of the Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (FPI-SGIT2016-01) and a contract within the Project 'Adaptive BREEDING for productive, sustainable and resilient FORESTs under climate change', grant agreement no. 773383.

## ACKNOWLEDGEMENTS

We thank Mariaceleste Labriola, Marco Manzelli, Valter Cresti, Eduardo Ballesteros, Fernando del Caño and Sergio San Segundo as well as the 'Unité Expérimentale Forêt Pierroton' (UEFP, INRAE; https://doi.org/10.15454/1.5483264699193726E12) for field work assistance. Special thanks to Eduardo Notivol (CITA) for help with the experimental design software. We also thank the staff at the Centro Nacional de Recursos Genéticos Forestales, 'Puerta de Hierro', the UEFP (INRAE) and CREA 'Foreste e Legno' Arezzo, for authorization and assistance in establishing the Spanish, French and Italian experimental sites, respectively.

## AUTHOR CONTRIBUTIONS

A.S-M: methodology, investigation, data curation, formal analysis, writing-original draft. A.H: methodology, investigation, data curation, writing—review & editing. C.A: methodology, investigation, data curation, writing—review & editing. S.C.G-M: resources, funding acquisition, writing—review & editing. G.G.V: conceptualization, resources, funding acquisition, writing—review & editing. F.B: methodology, investigation, data curation, writing—review & editing. A.P: methodology, investigation, data curation, writing—review & editing. M.M: investigation. I.S: investigation. J.J.R-A: conceptualization, methodology, resources, funding acquisition, supervision, formal analysis, writing—original draft, project administration. J.A.R-V: supervision, methodology, investigation, data curation, formal analysis, writing—original draft.

## LITERATURE CITED

Aitken SN, Yeaman S, Holliday JA, Wang T, Curtis-McLane S. 2008. Adaptation, migration or extirpation: climate change outcomes for tree populations. *Evolutionary Applications* 1: 95–111.
Alberto FJ, Aitken SN, Alía R, et al. 2013. Potential for evolutionary responses to climate change - evidence from tree populations. *Global Change Biology* 19: 1645-1661.

Alía R, Moro J. 1996. Comportamiento de procedencias de Pinus pinaster Ait. en el centro de Espana. *Investigación Agraria Serie Sistemas y Recursos Forestales* 5: 57-75.

Alía R, Gil LA, Pardos JA. 1995. Performance of 43 *Pinus pinaster* Ait. provenances on 5 locations in Central Spain. *Silvae Genetica* 44: 75-81.

Alía R, Moro J, Denis JB. 1997. Performance of *Pinus pinaster* provenances in Spain: interpretation of the genotype by environment interaction. *Canadian Journal of Forest Research* 27: 1548-1559.

Alía R, Chambel R, Notivol E, Climent J, González-Martínez SC. 2014. Environment-dependent microevolution in a Mediterranean pine (*Pinus pinaster* Aiton). *BMC Evolutionary Biology* 14: 1-12.

Aranda I, Alía R, Ortega U, Dantas AK, Majada J. 2010. Intra-specific variability in biomass partitioning and carbon isotopic discrimination under moderate drought stress in seedlings from four *Pinus pinaster* populations. *Tree Genetics and Genomes* 6: 169-178.

Archambeau J, Garzón MB, Barraquand F, Miguel M de, Plomion C, González-Martínez SC. 2022. Combining climatic and genomic data improves range-wide tree height growth prediction in a forest tree. *American Naturalist* 200: E141-E159.

Barbero M, Loisel R, Quézel P, Romane F, Richardson DM. 1998. Pines of the Mediterranean basin. In: Richardson DM. ed. *Ecology and biogeography of pinus*. Cambridge: Cambridge University Press, 153-170.

Barrio-Anta M, Castedo-Dorado F, Cámara-Obregón A, López-Sánchez CA. 2020. Predicting current and future suitable habitat and productivity for Atlantic populations of maritime pine (*Pinus pinaster* Aiton) in Spain. *Annals of Forest Science* 77: 1-19.

Baskin CC, Baskin JM. 1998. Causes of within-species variations in seed dormancy and germination characteristics. In: *Seeds*. Amsterdam: Elsevier, 181-237.

Benito Garzón M, Robson TM, Hampe A. 2019. ΔTraitSDMs: species distribution models that account for local adaptation and phenotypic plasticity. *The New Phytologist* 222: 1757-1765.

Benjamini Y, Hochberg Y. 1995. Controlling the false discovery rate: a practical and powerful approach to multiple testing. *Journal of the Royal Statistical Society Series B: Statistical Methodology* B 57: 289-300.

Bladé C, Vallejo VR. 2008. Seed mass effects on performance of *Pinus halepensis* Mill. seedlings sown after fire. *Forest Ecology and Management* 255: 2362-2372.

Brooks ME, Kristensen K, van Benthem KJ, et al. 2017. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. *R Journal* 9: 378-400.

Budde KB, Rellstab C, Huertz M, et al. 2023. Divergent selection in a Mediterranean pine on local spatial scales. *Journal of Ecology* 112: 278-290.

Callejas-Díaz M, Chambel MR, San-Martín-Lorén J, et al. 2022. The role of maternal age, growth, and environment in shaping offspring performance in an aerial conifer seed bank. *American Journal of Botany* 109: 366-376.

Carnicer J, Coll M, Ninyerola M, Pons X, Sánchez G, Peñuelas J. 2011. Widespread crown condition decline, food web disruption, and amplified tree mortality with increased climate change-type drought. *Proceedings of the National Academy of Sciences of the United States of America* 108: 1474-1478.

Castro J. 2006. Short delay in timing of emergence determines establishment success in *Pinus sylvestris* across microhabitats. *Annals of Botany* 98: 1233-1240.

Castro J, Zamora R, Hódar JA, Gómez JM. 2005. Alleviation of summer drought boosts establishment success of *Pinus sylvestris* in a Mediterranean mountain: an experimental approach. *Plant Ecology* 181: 191-202.

Cendán C, Sampedro L, Zas R. 2013. The maternal environment determines the timing of germination in *Pinus pinaster*. *Environmental and Experimental Botany* 94: 66-72.

Céspedes B, Torres I, Urbieta IR, Moreno JM. 2012. Effects of changes in the timing and duration of the wet season on the germination of the soil seed bank of a seeder-dominated Mediterranean shrubland. *Plant Ecology* 213: 919-931.

Chambel MR, Climent J, Alía R. 2007. Divergence among species and populations of Mediterranean pines in biomass allocation of seedlings grown under two watering regimes. *Annals of Forest Science* 64: 87-97.

Chevin LM, Lande R, Mace GM. 2010. Adaptation, plasticity, and extinction in a changing environment: Towards a predictive theory. *PLoS Biology* 8: e1000357.

Climent J, Dantas AK, Alía R, Majada J. 2013. Clonal variation for shoot ontogenetic heteroblasty in maritime pine (*Pinus pinaster* Ait.). *Trees* 27: 1813-1819.

Climent J, Alizoti P, Rodriguez-Quilón I, Kurt Y, Ducci F, Fady B, Alía R. 2021. Conservation and breeding of Mediterranean pines. In: Ne'eman G, Osem Y. eds. *Pines and their mixed forest ecosystems in the Mediterranean basin*. Cham: Springer Nature, 33-70.

Correia I, Almeida MH, Aguiar A, Alía R, David TS, Pereira JS. 2008. Variations in growth, survival and carbon isotope composition (δ¹³C) among *Pinus pinaster* populations of different geographic origins. *Tree Physiology* 28: 1545-1552.

Correia I, Alía R, Yan W, David T, Aguiar A, Almeida MH. 2010. Genotype × environment interactions in *Pinus pinaster* at age 10 in a multi-environment trial in Portugal: A maximum likelihood approach. *Annals of Forest Science* 67: 612-612.

Correia I, Santos L, Faria C, Nóbrega C, Almeida H, David T. 2014. Cone to seedling—variation between *Pinus pinaster* provenances from contrasting altitudes. *Forest Science* 60: 724-732.

Cowling RM, Ojeda F, Lamont BB, Rundel PW, Lechmere-Oertel R. 2005. Rainfall reliability, a neglected factor in explaining convergence and divergence of plant traits in fire-prone mediterranean-climate ecosystems. *Global Ecology and Biogeography* 14: 509-519.

Cox DR. 1972. Regression models and life-tables. *Journal of the Royal Statistical Society Series B: Statistical Methodology* 34: 187-202.

Csardi G, Nepusz T. 2006. The igraph software package for complex network research. *InterJournal. Complex Systems* 1695: 1-9.

Davis MB, Shaw RG, Etterson JR. 2005. Evolutionary responses to climate change. *Ecology* 86: 1704-1714.

de Dios Miranda J, Padilla FM, Pugnaña FI. 2009. Response of a Mediterranean semiarid community to changing patterns of water supply. *Perspectives in Plant Ecology, Evolution and Systematics* 11: 255-266.

Donohue K. 2002. Germination timing influences natural selection on life-history characters in *Arabidopsis thaliana*. *Ecology* 83: 1006-1016.

Donohue K, Rubio De Casas R, Burghardt L, Kovach K, Willis CG. 2010. Germination, postgermination adaptation, and species ecological ranges. *Annual Review of Ecology, Evolution, and Systematics* 41: 293-319.

Eckert AJ, Maloney PE, Vogler DR, Jensen CE, Mix AD, Neale DB. 2015. Local adaptation at fine spatial scales: an example from sugar pine (*Pinus lambertiana*, Pinaceae). *Tree Genetics and Genomes* 11: 1-17.

EEA. 2017. Climate change, impacts and vulnerability in Europe 2016. An indicator-based report. In: EEA Report No 1/2017. Luxemburg: Publications Office of the European Union, 424.

EUFORGEN. 2009. Distribution map of maritime Pine (*Pinus pinaster*), 6. www.euforgen.org (2 October 2024).

Eveno E, Collada C, Guevara MA, et al. 2008. Contrasting patterns of selection at *Pinus pinaster* Ait. drought stress candidate genes as revealed by genetic differentiation analyses. *Molecular Biology and Evolution* 25: 417-437.

Fernandes PM, Rigolot E. 2007. The fire ecology and management of maritime pine (*Pinus pinaster* Ait.). *Forest Ecology and Management* 241: 1-13.

Fernandes P, Máguas C, Correia O. 2017. Combined effects of climate, habitat, and disturbance on seedling establishment of *Pinus pinaster* and *Eucalyptus globulus*. *Plant Ecology* 218: 501-515.

Fernández M, Gil LA, Pardos JA, Fernández M, Gil LA, Pardos JA. 1999. Response of *Pinus pinaster* Ait. provenances at early age to water supply. I. Water relation parameters. *Annals of Forest Science* 56: 179-187.

Fréjaville T, Vizcaíno-Palomar N, Fady B, Kremer A, Benito Garzón M. 2019. Range margin populations show high climate adaptation lags in European trees. *Global Change Biology* 26: 484-495. doi:10.1111/gcb.14881

Gaspar MJ, Velasco T, Feito I, Alía R, Majada J. 2013. Genetic variation of drought tolerance in *Pinus pinaster* at three hierarchical levels: a comparison of induced osmotic stress and field testing. *PLoS One* 8: e79094-e79010.

Gauzere J, Klein EK, Brendel O, Davi H, Oddou-Muratorio S. 2020. Microgeographic adaptation and the effect of pollen flow on the
adaptive potential of a temperate tree species. *The New Phytologist* **227**: 641–653.

Gibson AL, Espeland EK, Wagner V, Nelson CR. 2016. Can local adaptation research in plants inform selection of native plant materials? An analysis of experimental methodologies. *Evolutionary Applications* **9**: 1219–1228.

Giorgi F, Lionello P. 2008. Climate change projections for the Mediterranean region. *Global and Planetary Change* **63**: 90–104.

Gómez JM. 2004. Bigger is not always better: conflicting selective pressures on seed size in *Quercus ilex*. *Evolution* **58**: 71–80.

González-Martínez SC, Alía R, Gil L. 2002. Population genetic structure in a Mediterranean pine (*Pinus pinaster* Ait.): a comparison of allozyme markers and quantitative traits. *Heredity* **89**: 199–206.

Hahs-Vaughn DL. 2006. Analysis of data from complex samples. *International Journal of Research and Method in Education* **29**: 165–183.

Hothorn T, Bretz F, Westfall P. 2008. Simultaneous inference in general parametric models. *Biometrical Journal. Biometrische Zeitschrift* **50**: 346–363.

IPCC. 2014. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. In: Core Writing Team, *Pachauri RK, Meyer L. eds. Climate Change 2014: Synthesis Report*. Geneva: Gian-Kasper Plattner, 151.

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

Juez L, González-Martínez SC, Nanos N, *et al*. 2014. Can seed production and restricted dispersal limit recruitment in *Pinus pinaster* Aiton from the Spanish Northern Plateau? *Forest Ecology and Management* **313**: 329–339.

Karger DN, Schmatz DR, Dettling G, Zimmermann NE. 2020. High-resolution monthly precipitation and temperature time series from 2006 to 2100. *Scientific Data* **7**: 1–10.

Kremer A, Roussel G. 1986. Décomposition de la croissance en hauteur du pin maritime (*Pinus pinaster* Ait.) Variabilité géographique des composantes morphogénétiques et phénologiques. *Annales des Sciences Forestières* **43**: 15–34.

Larcher W. 2000. Temperature stress and survival ability of mediterranean sclerophyllous plants. *Plant Biosystems - An International Journal Dealing with all Aspects of Plant Biology* **134**: 279–295.

Lebrija-Trejos E, Reich PB, Hernández A, Wright SJ. 2016. Species with greater seed mass are more tolerant of conspecific neighbours: a key driver of early survival and future abundances in a tropical forest. *Ecology Letters* **19**: 1071–1080.

Lefèvre F, Koskela J, Hubert J, *et al*. 2013. Dynamic conservation of forest genetic resources in 33 European Countries. *Conservation Biology* **27**: 373–384.

Leishman MR, Wright LJ, Moles AT, Westoby M. 2009. The evolutionary ecology of seed size. In: Fenner M. ed. *Seeds: the ecology of regeneration in plant communities*. Wallingford: CAB International, 2000, 31–57.

Li Y, Suontama M, Burdon RD, Dungey HS. 2017. Genotype by environment interactions in forest tree breeding: review of methodology and perspectives on research and application. *Tree Genetics and Genomes* **13**: 1–18.

Liepe KJ, van der Maaten E, van der Maaten-Theunissen M, Liesebach M. 2022. High phenotypic plasticity, but low signals of local adaptation to climate in a large-scale transplant experiment of *Picea abies* (L.) Karst. in Europe. *Frontiers in Forests and Global Change* **5**: 804857.

Lynch M, Walsh B. 1998. *Genetics and analysis of quantitative traits*. Sunderland: Snauser.

Maia P, Pausas JG, Vasques A, Keizer JJ. 2012. Fire severity as a key factor in post-fire regeneration of *Pinus pinaster* (Ait.) in Central Portugal. *Annals of Forest Science* **69**: 489–498.

Manso R, Calama R, Madrigal G, Pardos M. 2013a. A silviculture-oriented spatio-temporal model for germination in *Pinus pinea* L. in the Spanish Northern Plateau based on a direct seeding experiment. *European Journal of Forest Research* **132**: 969–982.

Manso R, Fortin M, Calama R, Pardos M. 2013b. Modelling seed germination in forest tree species through survival analysis. The *Pinus pinea* L. case study. *Forest Ecology and Management* **289**: 515–524.

Matesanz S, Blanco-Sánchez M, Ramos-Muñoz M, de la Cruz M, Benavides R, Escudero A. 2021. Phenotypic integration does not constrain phenotypic plasticity: differential plasticity of traits is associated to their integration across environments. *The New Phytologist* **231**: 2359–2370.

Matías L, González-Díaz P, Jump AS. 2014. Larger investment in roots in southern range-edge populations of Scots pine is associated with

increased growth and seedling resistance to extreme drought in response to simulated climate change. *Environmental and Experimental Botany* **105**: 32–38.

de Miguel M, Rodríguez-Quilón I, Heuertz M, *et al*. 2022. Polygenic adaptation and negative selection across traits, years and environments in a long-lived plant species (*Pinus pinaster* Ait., Pinaceae). *Molecular Ecology* **31**: 2089–2105. doi:10.1111/mec.16367

Moles AT, Westoby M. 2004. Seedling survival and seed size: a synthesis of the literature. *Journal of Ecology* **92**: 372–383.

Moran E, Lauder J, Musser C, Stathos A, Shu M. 2017. The genetics of drought tolerance in conifers. *The New Phytologist* **216**: 1034–1048.

Moreno-Fernández D, Montes F, Sánchez-González M, Gordo FJ, Cañellas I. 2018. Regeneration dynamics of mixed stands of *Pinus pinaster* Ait. and *Pinus pinea* L. in Central Spain. *European Journal of Forest Research* **137**: 17–27.

Nardini A, Lo Gullo MA, Trifilò P, Salleo S. 2014. The challenge of the Mediterranean climate to plant hydraulics: responses and adaptations. *Environmental and Experimental Botany* **103**: 68–79.

Parker WC, Noland TL, Morneault AE. 2006. The effects of seed mass on germination, seedling emergence, and early seedling growth of eastern white pine (*Pinus strobus* L.). *New Forests* **32**: 33–49.

Patsiou TS, Shestakova TA, Klein T, *et al*. 2020. Intraspecific responses to climate reveal nonintuitive warming impacts on a widespread thermophilic conifer. *New Phytologist* **228**: 525–540. doi:10.1111/nph.16656

Petit RJ, Hampe A. 2006. Some evolutionary consequences of being a tree. *Annual Review of Ecology, Evolution, and Systematics* **37**: 187–214.

Polade SD, Pierce DW, Cayan DR, Gershunov A, Dettinger MD. 2014. The key role of dry days in changing regional climate and precipitation regimes. *Scientific Reports* **4**: 1–8.

Ramírez-Valiente JA, Sánchez-Gómez D, Aranda I, Valladares F. 2010. Phenotypic plasticity and local adaptation in leaf ecophysiological traits of 13 contrasting cork oak populations under different water availabilities. *Tree Physiology* **30**: 618–627.

Ramírez-Valiente JA, Robledo-Arnuncio JJ. 2015. Exotic gene flow affects fitness trait values but not levels of heritable trait variation in the southernmost population of Scots pine (*Pinus sylvestris* L. var. *nevadensis*). *Biological Conservation* **192**: 331–342.

Ramírez-Valiente JA, Valladares F, Gil L, Aranda I. 2009. Population differences in juvenile survival under increasing drought are mediated by seed size in cork oak (*Quercus suber* L.). *Forest Ecology and Management* **257**: 1676–1683.

Ramírez-Valiente JA, Solé-Medina A, Pyhäjärvi T, *et al*. 2021. Adaptive responses to temperature and precipitation variation at the early-life stages of *Pinus sylvestris*. *The New Phytologist* **232**: 1632–1647.

Ramírez-Valiente JA, Santos del Blanco L, Alía R, Robledo-Arnuncio JJ. Climent J. 2022. Adaptation of Mediterranean forest species to climate: Lessons from common garden experiments. *Journal of Ecology* **110**: 1022–1042.

Razgour O, Forester B, Taggart JB, *et al*. 2019. Considering adaptive genetic variation in climate change vulnerability assessment reduces species range loss projections. *Proceedings of the National Academy of Sciences of the United States of America* **116**: 10418–10423.

Ribeiro S, Cerveira A, Soares P, Fonseca T. 2022. Natural regeneration of maritime pine: a review of the influencing factors and proposals for management. *Forests* **13**: 386–316.

Richardson JL, Urban MC, Bolnick DI, Skelly DK. 2014. Microgeographic adaptation and the spatial scale of evolution. *Trends in Ecology and Evolution* **29**: 165–176.

Rodríguez-Garcia E, Gratzer G, Bravo F. 2011. Climatic variability and other site factor influences on natural regeneration of *Pinus pinaster* Ait. in Mediterranean forests. *Annals of Forest Science* **68**: 811–823. doi:10.1007/s13595-011-0078-y

Ruano I, Pando V, Bravo F. 2009. How do light and water influence *Pinus pinaster* Ait. germination and early seedling development?. *Forest Ecology and Management* **258**: 2647–2653. doi:10.1016/j.foreco.2009.09.027

Sáenz-Romero C, Kremer A, Nagy L, *et al*. 2019. Common garden comparisons confirm inherited differences in sensitivity to climate change between forest tree species. *PeerJ* **7**: e6213. doi:10.7717/peerj.6213

Sagra J, Ferrandis P, Plaza-Álvarez PA, *et al*. 2018. Regeneration of *Pinus pinaster* Aiton after prescribed fires: Response to burn timing and biogeographical seed provenance across a climatic gradient. *Science of the Total Environment* **637-638**: 1550–1558.
Sagra J, Moya D, Plaza-Álvarez PA, et al. 2019. Prescribed fire effects on early recruitment of Mediterranean pine species depend on fire exposure and seed provenance. *Forest Ecology and Management* **441**: 253-261.

Salvador L, Alía R, Agúndez D, Gil L. 2000. Genetic variation and migration pathways of maritime pine (*Pinus pinaster* Ait) in the Iberian peninsula. *Theoretical and Applied Genetics* **100**: 89-95.

Sánchez-Salguero R, Camarero JJ, Rozas V, et al. 2018. Resist, recover or both? Growth plasticity in response to drought is geographically struc- tured and linked to intraspecific variability in *Pinus pinaster*. *Journal of Biogeography* **45**: 1126-1139.

Scotti I, Lalagüe H, Oddou-Muratorio S, et al. 2022. Common microgeographical selection patterns revealed in four European conifers. *Molecular Ecology* **32**: 393-411.

Seiwa K. 2000. Effects of seed size and emergence time on tree seedling es- tablishment: importance of developmental constraints. *Oecologia* **123**: 208-215.

Serra Varela MJ. 2017. *Integrating infra-specific variation of Mediterranean conifers in species distributions models. Applications for vulnerability assessment and conservation*. PhD. Thesis, Universidad de Valladolid, Escuela Técnica Superior de Ingenierías Agrarias. http://uvadoc.uva.es/handle/10324/23048

Serra-Varela MJ, Grivet D, Vincenot L, Broennimann O, Gonzalo-Jiménez J, Zimmermann NE. 2015. Does phylogeographical structure relate to climatic niche divergence? A test using maritime pine (*Pinus pinaster* Ait.). *Global Ecology and Biogeography* **24**: 1302-1313.

Sgrò CM, Hoffmann AA. 2004. Genetic correlations, tradeoffs and environ- mental variation. *Heredity* **93**: 241-248.

Sheffield J, Wood EF. 2008. Projected changes in drought occurrence under future global warming from multi-model, multi-scenario, IPCC AR4 simulations. *Climate Dynamics* **31**: 79-105.

Simons AM, Johnston MO. 2000. Variation in seed traits of *Lobelia inflata* (Campanulaceae): sources and fitness consequences. *American Journal of Botany* **87**: 124-132.

Solé-Medina A, Heer K, Opgenoorth L, et al. 2020. Genetic variation in early fitness traits across European populations of silver birch (*Betula pendula*). *AoB Plants* **12**: 1-13.

Solé-Medina A, Robledo-Arnuncio JJ, Ramírez-Valiente JA. 2022. Multi-trait genetic variation in resource-use strategies and phenotypic plasticity correlates with local climate across the range of a Mediterranean oak (*Quercus faginea*). *The New Phytologist* **234**: 462-478.

Somot S, Sevault F, Déqué M, Crépon M. 2008. 21st century climate change scenario for the Mediterranean using a coupled atmosphere-ocean regional climate model. *Global and Planetary Change* **63**: 112-126.

Suárez-Vidal E, Sampedro L, Zas R. 2017. Is the benefit of larger seed provisioning on seedling performance greater under abiotic stress? *Environmental and Experimental Botany* **134**: 45-53.

Surles SE, White TL, Hodge GR, Duryea ML. 1993. Relationships among seed weight components, seedling growth traits, and predicted field breeding values in slash pine. *Canadian Journal of Forest Research* **23**: 1550-1556.

Therneau TM. 2015. coxme: mixed effects cox models. *CRAN Repository* **2**: 1-14.

Urbeta IR, Pérez-Ramos IM, Zavala MA, Marañón T, Kobe RK. 2008. Soil water content and emergence time control seedling establishment in three co-occurring Mediterranean oak species. *Canadian Journal of Forest Research* **38**: 2382-2393.

Valladares F, Matesanz S, Guilhaumon F, et al. 2014. The effects of pheno- typic plasticity and local adaptation on forecasts of species range shifts under climate change. *Ecology Letters* **17**: 1351-1364.

Vega JA, Fernández C, Pérez-Gorostiaga P, Fonturbel T. 2009. The influ- ence of fire severity, serotiny, and post-fire management on *Pinus pin- aster* Ait. recruitment in three burnt areas in Galicia (NW Spain). *Forest Ecology and Management* **256**: 1596-1603.

Vega JA, Fernández C, Pérez-Gorostiaga P, Fonturbel T. 2010. Response of maritime pine (*Pinus pinaster* Ait.) recruitment to fire severity and post- fire management in a coastal burned area in Galicia (NW Spain). *Plant Ecology* **206**: 297-308.

Verdú M, Traveset A. 2005. Early emergence enhances plant fitness: a phylo- genetically controlled meta-analysis. *Ecology* **86**: 1385-1394.

Vicente-Serrano SM, Lopez-Moreno JI, Beguería S, et al. 2014. Evidence of increasing drought severity caused by temperature rise in southern Europe. *Environmental Research Letters* **9**: 044001.

Vizcaíno-Palomar N, Revuelta-Eugercios B, Zavala MA, Alía R, Gonzalez- Martínez SC. 2014. The role of population origin and microenvironment in seedling emergence and early survival in mediterranean maritime pine (*Pinus pinaster* Aiton). *PLoS One* **9**: e109132.

Vizcaíno-Palomar N, Ibáñez I, González-Martínez SC, Zavala MA, Alía R. 2016. Adaptation and plasticity in aboveground allometry variation of four pine species along environmental gradients. *Ecology and Evolution* **6**: 7561-7573.

Wahid N, Bounoua L. 2013. The relationship between seed weight, germin- ation and biochemical reserves of maritime pine (*Pinus pinaster* Ait.) in Morocco. *New Forests* **44**: 385-397.

Walk JL, Hidayati SN, Dixon KW, Thompson K, Poschlod P. 2011. Climate change and plant regeneration from seed. *Global Change Biology* **17**: 2145-2161.

Warwell MV, Shaw RG. 2019. Phenotypic selection on ponderosa pine seed and seedling traits in the field under three experimentally manipulated drought treatments. *Evolutionary Applications* **12**: 159-174.

Wennström U, Bergsten U, Nilsson JE. 2002. Effects of seed weight and seed type on early seedling growth of *Pinus sylvestris* under harsh and optimal conditions. *Scandinavian Journal of Forest Research* **17**: 118-130.

Westoby M, Leishman M, Lord J. 1996. Comparative ecology of seed size and dispersal. *Philosophical Transactions of the Royal Society B: Biological Sciences* **351**: 1309-1318.

Whitaker D, William E, John J. 2002. *CycDesigN version 2.0: A package for the computer generation of experimental designs*. Hamilton: University of Waikato.

White T, Adams W, Neale D. 2007. *Forest genetics*. Oxfordshire: CABI.

Wilczek AM, Cooper MD, Korves TM, Schmitt J. 2014. Lagging adaptation to warming climate in Arabidopsis thaliana. *Proceedings of the National Academy of Sciences of the United States of America* **111**: 7906-7913.

Zas R, Sampedro L. 2015. Heritability of seed weight in Maritime pine, a rele- vant trait in the transmission of environmental maternal effects. *Heredity* **114**: 116-124.

Zas R, Cendán C, Sampedro L. 2013. Mediation of seed provisioning in the transmission of environmental maternal effects in Maritime pine (*Pinus pinaster* Aiton). *Heredity* **111**: 248-255.

Zas R, Sampedro L, Solla A, et al. 2020. Dendroecology in common gardens: Population differentiation and plasticity in resistance, recovery and resili- ence to extreme drought events in *Pinus pinaster*. *Agricultural and Forest Meteorology* **291**: 108060.