# Disentangling key drivers for counteracting growth loss in stone pine under climate change: results based on multisite provenance tests

Carlos Guadaño-Peyrot, Natalia Vizcaíno-Palomar, Sven Mutke, Ricardo Alía, Delphine Grivet, Sondes Fkiri, Marta Benito Garzón

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Carlos Guadaño-Peyrot, Natalia Vizcaíno-Palomar, Sven Mutke, Ricardo Alía, Delphine Grivet, et al. Disentangling key drivers for counteracting growth loss in stone pine under climate change: results based on multisite provenance tests. New Forests, 2025, 56 (6), pp.59. 10.1007/s11056-025-10130-9. hal-05502531

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# Disentangling key drivers for counteracting growth loss in stone pine under climate change: results based on multisite provenance tests

Carlos Guadaño-Peyrot¹,³ ID · Natalia Vizcaíno-Palomar¹ ID · Sven Mutke¹ ID · Ricardo Alía¹ ID · Delphine Grivet¹ ID · Sondes Fkiri⁴,⁵ ID · Marta Benito Garzón² ID

Received: 5 June 2025 / Accepted: 10 September 2025
© The Author(s) 2026

## Abstract

Increasing forest productivity through afforestation under climate change is challenging, as maladaptation to current conditions may reduce trees’ growth capacity. Hence, understanding adaptation mechanisms can boost new tree plantations success, particularly in drought-prone environments such as the Mediterranean Basin. Here, we provide an example on how tree height measured in common gardens can be used to generate growth models and management scenarios for stone pine, an emblematic conifer widely used for restoration, landscaping, and nut production in Mediterranean countries. We used a multisite international network with nine common gardens testing 56 range-wide provenances and fitted Linear Mixed-Effects models of tree height at age 10, accounting for mortality, the climate of both the provenance and the common garden, tree density and microenvironment. Aridity at the common garden and tree density were the main drivers of tree height. The best-fitted model was used to project management scenarios under the current climate and for the period 2041–2070 for the Shared Socioeconomic Pathway 3-7.0. Taller trees resulted when using provenances from colder origins growing in high densities across the aridity range covered by the common gardens. Therefore, establishing new plantations with high densities during the juvenile stage might, albeit reducing nut production, enhances protective reforestation and carbon assimilation, provided that site-specific constraints, such as fire risk, are not limiting. Our results highlight the value of networks of common gardens to support reforestation programs and identify populations with high growth potential for protective and restorative afforestation under adverse climatic conditions.

**Keywords** Mediterranean stone pine · Provenances trials · Planting density · Climate change · Management scenarios

Extended author information available on the last page of the article
Introduction

Tree growth is expected to decline under climate change due to rising temperatures, with critical consequences for forest productivity, and other ecosystem services such as carbon sequestration and soil protection. These effects are expected to be especially severe in regions facing reduced precipitation or more frequent and intense rainfall events (Lindner et al. 2010; Hogan et al. 2024). To mitigate these potential impacts, forest management plays an important role through strategies aimed at enhancing carbon sequestration and/or hydrological services (Ameray et al. 2021; FAO 2021), as for instance by afforestation of low-productivity lands (Paquette and Messier 2010; Yosef et al. 2018; Verkerk et al. 2020) or measures to reduce to water-related hazards (Carvalho-Santos et al. 2014).

These strategies must be used to carefully select the planting material origin, as local populations might be maladapted to their environmental conditions, potentially reducing tree growth (Pedlar and McKenney 2017; Fréjaville et al. 2020). Hence, afforestation efforts should ensure that planting material is well-suited to the climate conditions expected in the near future (Verkerk et al. 2020). As such, assisted migration programs can alleviate populations’ maladaptation by introducing reproductive material adapted to the warming conditions expected under climate change, potentially enhancing forest carbon sink (Chakraborty et al. 2024). These approaches generally rely on trial experiments, such as common gardens, where populations’ performance is assessed across large environmental gradients (Leites and Benito Garzón 2023). Common gardens can be used to investigate phenotypic plasticity and potential trade-offs among fitness-related traits, which may constrain life history strategies (Siefert and Laughlin 2023; Climent et al. 2024). For instance, increases in tree growth can lead to higher mortality rates in harsh environments, such as those occurring at marginal populations. This relationship may be due to hydraulic limitations or an imbalance between photosynthesis and respiration (Bigler 2016; Stovall et al. 2019), leading to negative feedback on growth and carbon storage (Brienen et al. 2020). Under optimal growing conditions, such as those experimented at the climatic core of the populations, faster growth does not involve higher mortality (Cailleret et al. 2017), although this response can vary depending on the populations’ sensitivity to climate and their adaptive capacity.

Besides planting material selection, forest management can influence tree growth and other traits - depending on the purpose of the stand - by controlling tree density through thinning intensity (Jandl et al. 2007). However, setting the optimal stand density for maximizing forest productivity and other services (e.g. carbon sequestration) under climate change remains a topic of debate, extending beyond uncertainties related to future climate conditions (Muys and Messier 2023). In temperate climates, lower stand densities generally involve greater water availability for trees, particularly under drought stress (Jandl et al. 2007; Navarro-Cerrillo et al. 2019; del Campo et al. 2022). Thinning can reduce water loss from precipitation caused by canopy interception of rainfall (Mazza et al. 2011; del Campo et al. 2022), and the resulting reduction of competition can benefit remaining trees under drought periods (del Río et al. 2017). However, in Mediterranean climates, thinning practices may impair soil quality by reducing nutrient return through litter fall (Lado-Monserrat et al. 2015) and increase soil erosion caused by water or by wind (Whicker et al. 2008).

Selecting the appropriate stand density is therefore a crucial decision, especially in Mediterranean forests due to their high exposition to global warming consequences, such as rising temperatures, prolonged droughts, large wildfires, and extreme climatic events (Stocker
2014; Ali and El-Magd 2016). The stand density selection depends on many factors, such as the species used or the plantation's purposes. For instance, stone pine (Pinus pinea L.) protective and wood productive reforestations were traditionally established with stand densities higher than 2500 seedlings/ha (Romero and Gilsanz 1886; Djaziri 1971; Montero and Candela 1998; Khaldi 2009). Over the past decades, this approach has shifted toward lower density in mature stands (<250 stem/ha) to boost cone production (Calama et al. 2019; Correia and Oliveira 2002; Loewe et al. 2020). Nevertheless, the question of the optimal planting density for the juvenile stage remains unresolved for this species when referring to afforestation with different purposes than pine nut agroforestry exploitation.

In Mediterranean forests, pines species play a relevant role in afforestation programs due to their capacity to inhabit harsh environments and poor soils, as well as their ecological and socio-economic importance (Bravo-Oviedo et al. 2008). Among them, stone pine has been widely used in afforestation and dune fixation due to its drought tolerance, thermophilia, and frugality (Montero et al. 2004), as well as for ornamental planting, lumbering and nut production (the most relevant species product, Mutke et al. 2013a). The species exhibits large environmental tolerance to temperature (Vizcaíno-Palomar et al. 2020), low genome diversity (Vendramin et al. 2008; Pinzauti et al. 2012; Jaramillo-Correa et al. 2020) and limited genetic variation in phenotypic traits such as seedling mortality, height growth and nut production (Gordo et al. 2007; Mutke et al. 2008, 2013b). However, other studies suggest more pronounced genetic variation in survival and growth traits (Court-Picon et al. 2004; Carrasquinho and Gonçalves 2013). The environmental tolerance of stone pine growth and nut production is strongly influenced by micro-environmental conditions, such as soil chemical composition or groundwater depth. These micro-environmental effects result in a pronounced spatial autocorrelation in trait measurements that masks part of the species' population effect and environmental tolerance (Court-Picon et al. 2004; Mutke et al. 2013).

Here, we jointly analysed height growth and mortality in stone pine using data from a network of nine common gardens established in the 1990's under the *Silva Mediterranea* FAO's framework (Vizcaíno-Palomar et al. 2019a, b). We analysed 56 provenances, covering the entire distribution range of the species.

Our main goal was to identify the ecological constrains and propose management practices that optimize juvenile height growth in stone pine range-wide populations for enhancing climate resilience of future reforestations. To this aim, we fitted mixed-effects models to analyse the effects of tree density, mortality, climate at the growing environment, and climate of the provenance origin on tree height at 10-year-old. Tree height is commonly used as a proxy of fitness in forest trees and an indicator of forest productivity (Gellie et al. 2016; Warwell and Shaw 2017; Siefert and Laughlin 2023; Di Fabio et al. 2024). The age of 10 has often been indicated as appropriate for undertaking the first thinning within stands since plantation for afforestation purposes (Pavari 1955; Agrimi and Ciancio 1993; Montero et al. 2008; Jaouadi et al. 2020). Specifically, we assessed three objectives: (1) Analyze the potential relationship between tree height and mortality based on the hypothesis that taller trees may present higher mortality under harsher environments (Bennet et al. 2015; Bigler 2016; Stovall et al. 2019). (2) Investigate the combined effects of tree density, growing environment (i.e. common garden), and provenance to identify management options that maximize tree height. We expect that high stand densities would negatively affect tree height growth due to competition among neighbouring trees for nutrient and water resources rather than competition for light, as the species is considered moderately shade tolerant during its early
developmental stages (Montero et al. 2004). Besides, tree growth differences among provenances can be due to adaptation processes over time (O’Brien et al. 2007; Lu et al. 2014). (3) Predict management scenarios to maximize tree height under current climate and for the period 2041–2070 following the Shared Socioeconomic Pathway (SSP) 3–7.0.

# Materials and methods

## Plant material and common gardens

We used information from a multi-site international provenance test established under the *Silva Mediterranea Pinus pinea* network in the 1990s. Specifically, we used data from nine common gardens established in France (4), Spain (3), and Tunisia (2), including 56 provenances from nine countries covering the species distribution (Fig. 1; more details in Supplementary Table S1). Common gardens were planted with different number of provenances, seedling size, experimental design and spacing (see Table 1 for details).

## Phenotypic data

Phenotypic data included tree height and mortality records. Measurements were taken annually in Tunisian common gardens, whereas in the other common gardens they were taken at different ages without a consistent time pattern. Tree ages ranged from 0 to 20 years since planting. After the age of 10, available phenotypic data decreased and became more widely spaced over time. Therefore, the full range of tree ages was used to fit the models, but the timeline of the results was limited to the first 10 years of tree age.

The French and Spanish data collection was coordinated by INRAE and by the Spanish network of forest genetic trials (GENFORED), respectively, whereas Tunisian common

<figure><img src="image_4.png" /><figcaption>**Fig. 1** Location of provenances and common gardens of *Pinus pinea* L. used in the present study. Species natural distribution modified from Caudullo et al. (2017) (https://doi.org/10.1016/j.dib.2017.05.007 under CC-BY 4.0 license)</figcaption></figure>

<figure><img src="image_5.png" /></figure>
<table><caption>**Table 1** Main characteristics of the multi-site international provenance test of Pinus Pinea L.</caption><thead><tr><th>Common garden<fnref n="1" /></th><th>Nb² of Prov³.</th><th>Design</th><th>Nb blocks</th><th>Plots per block</th><th>Nb trees per plot</th><th>Nb repetitions</th><th>Nb of trees</th><th>Spacing (m)</th><th>Plantation year</th><th>Density (stem/ha)</th></tr></thead><tbody><tr><td>Ain Zana (TU)</td><td>23</td><td>RCB⁴</td><td>6</td><td>23</td><td>5</td><td>6</td><td>690</td><td>3.0 × 3.0</td><td>1996</td><td>1,110</td></tr><tr><td>Baume (FR)</td><td>26</td><td>RIB⁵</td><td>64</td><td>10</td><td>3</td><td>24.6</td><td>1920</td><td>2.0 × 5.0</td><td>1993</td><td>1,000</td></tr><tr><td>Cucalón (SP)</td><td>34</td><td>RIB</td><td>68</td><td>12</td><td>3</td><td>24</td><td>2448</td><td>2.5 × 4.0</td><td>1995</td><td>1,000</td></tr><tr><td>Gaillarde (FR)</td><td>37</td><td>RIB</td><td>59</td><td>10</td><td>3</td><td>15.9</td><td>1770</td><td>2.0 × 4.0</td><td>1995</td><td>1,250</td></tr><tr><td>Laquina (FR)</td><td>37</td><td>RIB</td><td>66</td><td>10</td><td>3</td><td>17.8</td><td>1920</td><td>1.0 × 4.0</td><td>1995</td><td>2,500</td></tr><tr><td>Loupian (FR)</td><td>26</td><td>RIB</td><td>80</td><td>9</td><td>3</td><td>27.7</td><td>2400</td><td>2.0 × 4.0</td><td>1993</td><td>1,250</td></tr><tr><td>Oued El Bir (TU)</td><td>23</td><td>RCB</td><td>6</td><td>23</td><td>5</td><td>6</td><td>690</td><td>3.0 × 3.0</td><td>1997</td><td>1,110</td></tr><tr><td>Quintos (SP)</td><td>29</td><td>RIB</td><td>43.5</td><td>12</td><td>3</td><td>18</td><td>1566</td><td>2.5 × 4.0</td><td>1996</td><td>1,100</td></tr><tr><td>Tordesilas (SP)</td><td>34</td><td>RIB</td><td>51</td><td>12</td><td>3</td><td>18</td><td>1836</td><td>2.5 × 4.0</td><td>1996</td><td>1,100</td></tr></tbody></table>

garden data were provided by INRGREF. Tree height data from the French and Spanish common gardens were published by Vizcaíno-Palomar et al. (2019a, b).

## Mortality

We computed the provenance cumulative mortality (Mortality) at the different ages for each common garden and block (Eq. 1).

$$CM_{ijkl}^{t} = (PNA_{ik}^{t} - -PNA_{ik}^{(t-1)}) / PNP_{ijk} \quad (1)$$

where $CM_{ijkl}^t$ is the cumulative mortality of the ith provenance in the jth common garden and kth block at the age t; $PNA_{ik}^t$ is the number of alive trees from the ith provenance in the jth common garden and kth block at age t; $PNP_{ijkl}$ is the number of initial planted trees excluding replacement- from the ith provenance in the jth common garden and kth block. This estimate assumes that tree mortality follows a spatial pattern within a group of trees that share common attributes, such as location, age, provenance, or planting layout (Calama et al. 2024).

## Tree density

Tree density (stems/ha) was calculated for each individual tree, year of measurement and common garden as the ratio of alive stems within the area of influence defined by its eight

<footnote n="1">¹TU, FR, and SP mean Tunisia, France and Spain, respectively. ²Nb means number. ³Prov mean Provenances. ⁴RCB and ⁵RIB mean random complete blocks and random incomplete blocks, respectively. m means meter. h means hectares</footnote>
nearest neighbours (NN). This is a dynamic spatial-temporal estimate of the tree density affecting each tree, based on the initial planting density of the common garden (Table 1) and the overtime survival rate among the NN. Therefore, when mortality is zero, tree density coincides with the initial planting density. Including this variable in the models allowed for spatial standardization of stand density across common gardens, while also accounting for the effects of mortality over time. The correlation between this estimate and Mortality was low (Pearson's correlation −0.25; see Supplementary Fig. 1).

Microenvironment

A micro-environmental effect on individual tree height was assessed by modelling the spa-
tial autocorrelation of the NN (de la Mata and Zas 2023). This is a spatial correction that
measures the effect on the height of each tree of the microspatial variation underlying its
area of influence (see Supplementary Fig. 2). The range of this factor is intrinsic to each
common garden.

To select the best spatial autocorrelation tree height correction, we tested three approaches
following Cappa et al. (2019) plus one non-correction option in every common garden at
the age closest to 10 years old (which represents the mean of the measured ages in the dataset): (i) non-spatial correction; (ii) blocks model; (iii) bidimensional B-spline regression;
(iv) bidimensional first-order autoregressive process (AR). The best approach was selected
according to the lowest AIC value (Akaike 1992) (see Supplementary Table S2), and then it
was applied to all common gardens and ages to get the residuals of every tree measurement.
We used the BreedR package in R to compute this task (Muñoz and Sánchez 2020). The
effect of the micro-environment on each individual’s height was assessed by averaging the
residuals of its NN.

Climatic data

We characterized the climate of the common gardens and the provenance origins using the ClimateDT database (Marchi et al. 2020), gridded at 1 km resolution. To relate tree height to the common garden environment, we used the meteorological conditions of the year preceding each tree height measurement. When measurements were not taken in consecutive years, we used the average climate over the corresponding period. Specifically, we selected four climate indices indicative of aridity or thermicity, along with three climate variables (one precipitation and two temperature related variables, see Table 2) previously identified as good predictors of Mediterranean pines height (Balekoglu et al. 2020; Vizcaíno-Palomar et al. 2020).

To represent the environments to which the provenances were naturally adapted, we
computed the average climate for the reference period 1961–1991. To select the most sig-
nificant climate variables for provenances, we applied the All-Possible-Subsets method
(APS, *leaps* package in R) from an initial set of 41 climate variables, including annual
and seasonal climate descriptors (Supplementary Table S3). APS identified the best linear
regression models for tree height, including tree age as a covariate, using the smallest BIC
as the selection criterion and restricting the models to a maximum of four simultaneous cli-
mate variables (Supplementary Figure S3). This procedure selected six climate variables for
characterizing the provenances. The best tree height regressions were achieved with various
<table><caption>**Table 2** Climatic variables selected</caption><thead><tr><th>Group</th><th>Label</th><th>Description</th><th>Unit</th><th>CG</th><th>P</th></tr></thead><tbody><tr><td rowspan="4">Compound</td><td>DMA</td><td>De Martonne aridity index</td><td>index</td><td>*</td><td></td></tr><tr><td>EPQ</td><td>Emberger Pluvio-termic Quotient</td><td>index</td><td>*</td><td>*</td></tr><tr><td>AHM</td><td>Annual heat moisture index</td><td>°C/mm</td><td>*</td><td>*</td></tr><tr><td>SHM</td><td>Summer heat moisture index</td><td>°C/mm</td><td>*</td><td>*</td></tr><tr><td>Precipitation</td><td>MOP</td><td>Mean Growing Season (Apr-Oct) Precipitation</td><td>mm</td><td>*</td><td></td></tr><tr><td rowspan="6">Temperature</td><td>RMT</td><td>Rivas-Martinez Termic index</td><td>°C</td><td>*</td><td></td></tr><tr><td>TD</td><td>Temperature difference (Continentality)</td><td>°C</td><td>*</td><td></td></tr><tr><td>Bio10</td><td>Mean Temperature of Warmest Quarter</td><td>°C</td><td></td><td>*</td></tr><tr><td>GDD0</td><td>Degree-days above 0 °C, chilling degree-days</td><td>°C days</td><td></td><td>*</td></tr><tr><td>GDD5</td><td>Degree-days above 5 °C, growing degree-days</td><td>°C days</td><td></td><td>*</td></tr></tbody></table>

*CG, P mean common garden and provenance climate variable, respectively. °C and mm mean Celsius degrees and millimeters, respectively

combinations of seven climate variables for common gardens and six for provenances (see Supplementary Table S4).

For future climate predictions we averaged five global circulation models (GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, and UKESM1-0-LL) available in ClimateDT (Marchi et al. 2024) for the Shared Socioeconomic Pathway (SSP) 370 and the period 2041–2070.

## Linear Mixed-Effects models

We firstly selected the best-supported linear mixed-effects model of tree height explained as a function of tree age (Age), climate at the common gardens and provenances, tree density and microenvironment. Then, we added mortality as a covariable. All those variables were considered as fixed factors in the model. The random effects included the blocks nested into common gardens, and the provenances (Eq. 2). All variables were standardized before modelling, i.e., the mean was subtracted from each value and divided by the standard deviation. Although tree age was included as a fixed effect to account for differences in measurement timing across sites, irregular measurement intervals may still contribute to residual variance heterogeneity among common gardens. Specifically, variation in the age at measurement can be associated with different phases of growth dynamics and changes in variance over time (Tahar et al. 2012). While random effects related to site and block structure help to absorb part of this variability, this aspect remains a potential source of noise in the model and should be considered when interpreting between-site comparisons.
The modelling process followed four steps: First, we selected two climate variables to be included in the linear mixed-effects model of tree height, one related with the provenance origin and another related with the common garden. We fitted 42 generalized linear models (GLM) resulting from pairwise combinations between the climate variable of the common garden and the climate variable of the population origin from the set of previous selected climate variables (Table 2), while the remaining variables were kept constant. To choose the best-fitted GLMs we obtained for each model the AIC value, the coefficient of determination (R²), and the estimated K-fold cross-validation prediction error, using the “boot” R package with K = 10.

Second, we performed a set of six mixed-effects models with the pair wise of climatic variables included in the best-fitted GLMs and stipulated two conditions to select the best-supported one: having the minimum value of AIC with the most statistically significant climate variables (see Supplementary Table S4).

Third, to get the final model structure, we used a hierarchical backward selection procedure based on AIC criteria in which net variations of two units of AIC associated with the elimination of any parameter in the full model determined the exclusion of the parameter from the final model structure (Supplementary Table S5). For the fixed part, we followed three rounds using maximum likelihood (ML) to test: (round 1) two-variable interactions; (round 2) quadratic effects; (round 3) main effects of each variable. For the random part, we tested every effect separately, a full model with all effects included and a model without any random effects, using the restricted maximum-likelihood method (REML). We computed the percentage of variance explained by the fixed effects of the best-supported model, R²M, and the percentage of variance explained by the fixed and random effects together, R²C. The goodness-of-fit of the best model was assessed by examining the predicted vs. observed values. We used normal error distribution with an identity link. Models were performed in R software (version 4.3.1, 16th June 2023); “lme4”, “lmerTest”, “sjPlot”, and “MuMln” packages were employed).

Fourth, we included the mortality as a covariate, represented by both linear and quadratic terms, along with the interactions with the other variables. We followed the same procedure as before to get the final model structure of the model (Table 3).

$$ \log(H_{ijkl}) = \alpha_0 + \sum_{n=1}^{6} \alpha_n X_{ijkl}^{(n)} + \sum_{n=7}^{11} \alpha_n X_{ijkl}^{(n_1)} + \sum_{n=12}^{41} \alpha_n (X_{ijkl}^{(n_1)} \times X_{ijkl}^{(n_2)}) + \beta_1 (\text{Common garden/Block/tree}) + \beta_2 (\text{Provenance}) + \beta_3 (\text{Common garden/Age}) + \varepsilon_{ijkl} \quad (2) $$

where $H_{ijkl}$ is tree height of the ith individual of the kth provenance in the lth common garden at the jth age; $\alpha_s$ is the set of parameters associated with the fixed effects of the model; $X_{ijkl}^{(n)}$ represent fixed effects Age, Mortality, Tree density, Microenvironment, climate at the Common Garden, and climate at the Provenance; $X_{ijkl}^{(n_1)}$ are quadratic terms (except Age) to model non lineal responses; $X_{ijkl}^{(n_1)} \times X_{ijkl}^{(n_2)}$ represent interactions between fixed variables; $\beta_s$ are the random effects to capture the hierarchical structure of the common gardens; $\varepsilon_{ijkl}$ is the residual error of the model.
<table><caption>**Table 3** Fixed and random effects selection for the tree height-growth-mortality model, following a hierarchical backward procedure using AIC. d.f.: degrees of freedom, AIC: Akaike values, ΔAIC: difference in AIC values between alternative models</caption><thead><tr><th>Fixed Effect Selection</th><th>d.f.</th><th>AIC</th><th>ΔAIC</th></tr></thead><tbody><tr><td colspan="4">Round 1: Two-way interaction</td></tr><tr><td>Tree height mortality full model</td><td>26</td><td>22026.72</td><td>0.00</td></tr><tr><td>Full model - Age X Tree density</td><td>25</td><td>24253.48</td><td>2226.77</td></tr><tr><td>Full model - Age X Mortality</td><td>25</td><td>22088.26</td><td>61.54</td></tr><tr><td>Full model - Age X SHM common garden</td><td>25</td><td>24788.82</td><td>2762.10</td></tr><tr><td>Full model - Age X Bio10 provenance</td><td>25</td><td>22194.66</td><td>167.95</td></tr><tr><td>Full model - Tree density X Bio10 provenance</td><td>25</td><td>22068.32</td><td>41.61</td></tr><tr><td>Full model - Microenvironment X SHM common garden</td><td>25</td><td>22034.93</td><td>8.21</td></tr><tr><td>Full model - Mortality X Tree density</td><td>25</td><td>22028.95</td><td>2.24</td></tr><tr><td>Full model - Mortality X Microenvironment</td><td>25</td><td>22036.07</td><td>9.35</td></tr><tr><td>Full model - Mortality X SHM common garden</td><td>25</td><td>22065.22</td><td>38.50</td></tr><tr><td>Full model - Mortality X Bio10 provenance</td><td>25</td><td>22039.65</td><td>12.94</td></tr><tr><td colspan="4">Round 2: Quadratic effects</td></tr><tr><td>Tree height mortality full model</td><td>26</td><td>22026.72</td><td>0.00</td></tr><tr><td>Full model - quadratic effect Tree density</td><td>25</td><td>22359.12</td><td>332.41</td></tr><tr><td>Full model - quadratic effect SHM common garden</td><td>25</td><td>22065.32</td><td>38.60</td></tr><tr><td>Full model - quadratic effect Microenvironment</td><td>25</td><td>22114.36</td><td>87.64</td></tr><tr><td>Full model - quadratic effect Mortality</td><td>25</td><td>22051.95</td><td>25.23</td></tr><tr><td colspan="4">Round 3: Main effects</td></tr><tr><td>Tree height mortality full model</td><td>26</td><td>22026.72</td><td>0.00</td></tr><tr><td>Full model - linear term Age</td><td>21</td><td>106569.90</td><td>84543.18</td></tr><tr><td>Full model - linear term Tree density</td><td>21</td><td>26587.15</td><td>4560.43</td></tr><tr><td>Full model - linear term Microenvironment</td><td>22</td><td>22134.67</td><td>107.96</td></tr><tr><td>Full model - linear term SHM common garden</td><td>21</td><td>25125.59</td><td>3098.87</td></tr><tr><td>Full model - linear term Bio10 provenance</td><td>22</td><td>22222.72</td><td>196.00</td></tr><tr><td>Full model - linear term Mortality</td><td>19</td><td>22283.33</td><td>256.61</td></tr><tr><td colspan="4">Random Effect Selection</td></tr><tr><td>Only fixed effects</td><td>22</td><td>49818.24</td><td>0.00</td></tr><tr><td>Only Common garden/Block/tree</td><td>23</td><td>49030.95</td><td>-787.29</td></tr><tr><td>Only Provenance</td><td>25</td><td>22384.31</td><td>-27433.93</td></tr><tr><td>Full model</td><td>26</td><td>22250.43</td><td>-27567.80</td></tr></tbody></table>
## Analysis of tree height through fixing factors in the mixed-effects model

Based on the best-fitted model, we evaluated tree height at 10-year tree age by fixing differ-
ently the factors within the model. First, we focused on investigating a potential relationship
between tree height and mortality by setting tree density at its average value across all com-
mon gardens and varying the mortality rates based on observations from both the common
gardens and provenances. Second, we assessed the planting density that maximizes tree
height by setting the average mortality rate and varying tree density across its full range, as
measured in the common gardens.

In both analyses, we predicted tree height by testing different provenances using their maximum, minimum, and mean climate values observed at the extremes of the common gardens. The microenvironmental effect was set to the mean value (close to zero) in both analyses, as its threshold varies according to each common garden.

### Tree height-based on management scenarios projections for current and future conditions

To evaluate different management strategies, we generated six scenarios (MS1 to MS6) and
compared their predicted tree heights for each common garden at 10-year tree age for the
current climate conditions and for the period 2041–2070. Specifically, these scenarios com-
bined provenance climate data (maximum, mean and minimum climate values) with two
initial stand density options: 1000 and 1500 stem/ha. For all scenarios and periods, we set
the multisite mean mortality value at age 10 to 0.125 (12.5%) and compute the tree density
value according to that mortality rate and the initial stand density. The microenvironment
was set to zero. We selected the SSP 370 climate scenario for future projections, as it repre-
sents the upper-middle range of possible climate change scenarios.

## Results

### Linear mixed-effects models of tree height

The best-supported model included tree age, mortality, the Summer Heat Moisture index (SHM) at the common garden, the mean temperature of warmest quarter (Bio10) of the provenance, tree density and microenvironment (Table 3; see Supplementary Table S6 for more details). The fixed effects of the model explained the 79.39% of the variance (R2M) and the combined fixed and random effects explained 92.64% (R2C). The model’s predictive capability showed a Pearson’s product-moment correlation of 0.85. The residuals’ examination indicated that the main assumptions of linear mixed-effects models were achieved (see Supplementary Figure S4).

According to ΔAIC, tree age showed the strongest effect to explain tree height, followed by tree density, the climate at the common garden, mortality, the climate of the provenance, and microenvironment (Table 3, see Main effects). We found strong interactions between the tree age and the climate at the common garden, and with tree density (Table 3, see Two-way interaction). Mortality showed a significant interaction with all the factors tested, being
stronger with tree age and the climate of the common garden than the climate of the provenance, tree density and microenvironment (Table 3; Supplementary Table S6).

Rounds 2, 3 and 4 of the backward selection began with the best-supported model obtained in the previous round. Age is the measurement year since plantation, Tree density is the area without competition around each individual, Microenvironment is the micro-environmental effect, and SHM common garden is the Summer Heat Moisture index at the common garden. Bio10 provenance is the mean temperature of warmest quarter at the provenance, and Mortality is the mortality rate of each population for every common garden and year of measurement.

# Analysis of tree height through fixing factors in mixed-effects models

## Relationship between tree height and mortality

Overall, the tallest trees were observed at the least arid growing environment (lower values of SHM) for all the range of climate of the provenance (Bio10). Furthermore, trees from colder provenances (min Bio10) were taller than those from warmer provenances (max Bio10) at low mortality rates (Fig. 2a and b).

The relationship between tree height and mortality differed at the extreme values of the growing environments (SHM). At the most arid environment (Fig. 2b), all provenances exhibited a negative relationship between tree height and mortality, meaning taller trees had lower mortality than shorter ones. This result rejects our initial hypothesis. In the least arid common garden (Fig. 2a), provenances from warmer climates (max Bio10) exhibited higher mortality in taller trees, whereas those from mean and colder provenances exhibited little variation in tree height across the mortality range.

<figure><img src="image_6.png" /><figcaption>**Fig. 2** Tree height predictions based on the best tree height-mortality model. Predicted tree height (Y-axis) at age 10 along the range of the provenance's cumulative mortality (X-axis), with maximum, minimum, and mean values of the mean temperature of warmest quarter (Bio 10) at the provenance. Minimum and maximum values of the Summer Height Moisture index (SHM) at the common garden were used in a and b respectively</figcaption></figure>
## Tree density effect on tree height

The initial tree density measured in common gardens ranged from 1000 to 2500 stems/ha (Table 1). After 10 years, this range shifted to 110–2200 stems/ha due to mortality.

Tree height showed a positive, non-linear response to density. However, beyond a certain threshold, tree height began to decline as density increased (Fig. 3a and b). The density that maximized tree height varied according to the climate of the provenance, regardless the SHM index at the growing environment, rejecting our initial hypothesis.

Tree height difference between provenances increased with density, with colder provenances growing the most, especially in less arid environments (Fig. 3a and b; more details in Supplementary Table S7). For instance, in the least arid environment, tree height of the coldest provenance (min Bio10) increased with density to 2414 stems/ha, while tree height of the warmer provenance (max Bio10) reached its maximum value at 2085 stems/ha of density (Fig. 3a). The same pattern occurred for the most arid environment (SHM Max, Fig. 3b).

## Tree height-based management scenarios projections for current and future conditions

According to the predictions for the period 2041–2070 under the SSP370 scenario, the SHM is projected to increase by an average of 32.9 °C/mm across the common garden locations of the present study (see Supplementary Figure S5). The highest increase would take place at Loupian (France) with a rise of 80.0 °C/mm, while the lowest would occur in Cucalón (Spain), with a rise of 12.4 °C/mm.

As a result of the different tested MSPs (Table 4), the highest tree height was predicted for MSP4, combining the coldest provenance (18.98 °C) and the highest tree density (1500 stems/ha) for all common gardens under present and future climates. In particular, the highest tree height prediction was reached at Loupian (France), under present climates, and at Baume (France), under future climates. Furthermore, greater differences in tree height were

<figure><img src="image_7.png" /><figcaption>**Fig. 3** Tree height model: expected tree height (Y-axis) at age 10 along the range of tree density (X-axis) and maximum, minimum, and mean values of the mean temperature of warmest quarter (Bio 10) at the provenance. Minimum and maximum values of the Summer Height Moisture index (SHM) at the common garden were used in **a** and **b** respectively. Values in red are the highest predicted tree height achieved by the climate at the provenances. Vertical dashed lines show the minimum and maximum initial stand densities tested from the set of common gardens</figcaption></figure>
<table><caption>**Table 4** Management scenario projections (MSP): values of climate at the provenance (Bio10, °C) and tree density (in stems/ha) used for every MSP</caption><thead><tr><th>Management Scenario<br>Projection</th><th>Climate at the Provenance<br>Bio10 (°C)</th><th>Tree<br>density<br>(stems/<br>ha)</th></tr></thead><tbody><tr><td>MSP1</td><td>18.98</td><td>1000</td></tr><tr><td>MSP2</td><td>22.05</td><td>1000</td></tr><tr><td>MSP3</td><td>27.82</td><td>1000</td></tr><tr><td>MSP4</td><td>18.98</td><td>1500</td></tr><tr><td>MSP5</td><td>22.05</td><td>1500</td></tr><tr><td>MSP6</td><td>27.82</td><td>1500</td></tr></tbody></table>

predicted between provenances and tree density management than in responses to climate change (Fig. 4). Regarding the difference in tree height expected according to the different MSPs, the gain between MSP4 and the lowest projection (MSP3) was 68.8 cm (36.5%) for both current and future climatic scenarios (Fig. 4; more details in Supplementary Table S8).

## Discussion

Our main findings showed that the tallest trees were predicted when growing at high densities and with the use of colder provenances, highlighting the potential of combining planting density and provenance selection to enhance tree height growth.

### Main drivers of tree height

The main drivers affecting tree height (in terms of ΔAIC) besides tree age were tree density and the summer heat moisture index (SHM) at the growing sites (common gardens). SHM is an index commonly used for describing aridity in Mediterranean region (Vizcaíno-Palomar et al. 2019a, b; Krajnc et al. 2025). This index is calculated by dividing the Mean Warmest Month Temperature (MWMT) by the Mean Summer (May-Sep) Precipitation (MSP) divided by 1000. While increasing SHM tends to reduce tree height, higher tree density appears to promote it. The negative effect of aridity on tree height is consistent with previous studies which highlighted that water shortage is the main limiting factor for growth in stone pine (Natalini et al. 2016; Calama et al. 2019), and this effect is similar regardless the age (Natalini et al. 2015). Furthermore, a positive effect of stand density on tree height has been reported previously on other forest species monospecific plantations (Gabira et al. 2023; Pretzsch 2020), even during dry periods. This might be explained by several non-mutually exclusive factors. Interactions among young trees are generally minimal due to their small size, with growth reductions typically emerging when their crowns and root systems begin competing for water and nutrients. Even then, different mutualistic mechanisms can enhance tree growth to some extent. At belowground level, root fusion by anastomosis enables access to deeper soil layers and favours mycorrhizal activity (Mutke et al. 2011). On the contrary, dense canopies may limit radiation reaching the forest soil and therefore reduce evapotranspiration alleviating drought stress (Fajardo and McIntire 2011), which is particularly relevant in dry sites. It is important to note that the use of high densities increases rainfall interception at the canopy level, which can lead to negative effects, such as water losses (Mazza et al. 2011), and positive effects, such as regulating runoff (Carvalho-Santos
<figure><img src="image_8.png" /><figcaption>**Fig. 4** Stone pine tree height prediction in common garden sites under current climate conditions and the period 2041–2070 under the SSP370 scenario with different combinations (Management Scenario Projections, MSP) of model effects values (Table 4)</figcaption></figure>

et al. 2014). Specific and localized studies are essential for assessing the optimum tree cover to increase groundwater recharge (FAO 2021).

The other drivers tested in our model - mortality, mean temperature of warmest quarter (Bio10) of the provenance, and microenvironment - were also significant in the model although with less importance than the other variables. It is worth noting that, although the
correlation between provenance mortality (block level) and tree density (individual level) was moderately low (rho = 0.25) and the best-fitted model shows a weak interaction between both variables, the general mortality ratio in some common gardens significantly affected the initial planting density, decreasing tree density in some areas to values close to 100 stems/ha. Our results suggest that mortality is more related to specific site environmental factors than to density. It contradicts other studies that have highlighted differences in mortality rates among contrasting climatic scenarios which were strongly amplified by high competition levels for most adult tree species as measured in National Forest Inventories (Ruiz-Benito et al. 2013; Archambeau et al. 2020; Changenet et al. 2021). A feasible explanation may be that competition among neighbours was not yet resource limiting at the juvenile age range considered in our study. This theory is supported by the observation that in lower-density areas, surviving trees exhibited reduced height growth compared with those areas without mortality, despite potentially greater access to water and nutrients.

The effect of the interaction between the climate of the provenance and that of the common garden on tree height was not significant, according to the best-fitted model. Even though climate at origin explains only a part of the provenance on height growth, this result aligns with other studies that have underscored high plasticity in tree height and low population-level adaptation in stone pine (Mutke et al. 2010; Vizcaíno-Palomar et al. 2019a, b). Our results showed that colder provenances achieved higher height growth than warmer provenances, regardless of the environment and tree density. This is consistent with previous studies (Mutke et al. 2010; Mutke et al. 2013) that found more vigorous growth in cold and inland stone pine provenances due to an earlier spring shoot phenology compared to warmer or coastal provenances.

The fact that stone pine colder provenances grew more than warmer ones across the entire aridity range tested seems to contradict studies conducted with other species that recommend using reproductive material from more arid provenances at higher latitudes to offset or reduce the effects of climate change (Schreiber et al. 2013; Young et al. 2020; Palik et al. 2024). Our results seem to indicate a lack of local adaptation of this trait in the species, or at least in the warmest populations. The reason could lie in the species' demographic history, which possibly stems from the few southern Iberian populations that survived the last glacial maximum (Mutke et al. 2024). From then on, the species has had a historically anthropized and highly dispersed distribution, especially in Mediterranean coastal populations (Martínez and Montero 2004). This may have led to the naturalization of stands in environmentally suboptimal areas with very limited or no gene flow between populations.

The significant effect of the microenvironment, which reflects the sum of properties in close proximity individual tree, is supported by previous studies (Court-Picon et al. 2004; Mutke et al. 2013) and may be linked to soil characteristics (Prieto-Recio et al. 2015; Navarro-Cerrillo et al. 2018) or to groundwater depth.

## The growing environment affects differently tree height and mortality relationship

The analysis of tree height and mortality at the extremes of mean summer aridity (maximum and minimum SHM) in the common gardens showed different provenance responses. Under a higher aridity environment, the highest mortality occurred among those trees with the lowest height growth independently of the provenance. This could indicate that trees with reduced growth experience higher mortality rates. Trees growing in more arid environments
showed reduced growth compared to those growing in less arid sites, consistent with find-
ing from other studies (Galiano et al. 2011). Reduced water availability is associated with
increased physiological stress, leading to reduced leaf area production (Correia 2016), ear-
lier defoliation (Mencuccini and Grace 1995; Maherali and DeLucia 2001), and eventually
to drought-induced dieback (Martínez-Vilalta and Piñol 2002).

Under wetter environments, higher mortality occurred among those trees that grew higher from warmer provenances whereas mean and cold provenances followed a similar pattern than in more arid environments but less pronounced. This would indicate the presence of a trade-off between growth and mortality in warmer provenances under less arid environments. A feasible explanation may be that trees with higher growth rates may be more exposed to cavitation risk during drought events (Voltas et al. 2013), and the different behaviour showed between warmer and colder provenances may be caused by a distinct drought stress coping strategy. According to Balekoglu et al. (2023), stone pine drought stress response strategies ranged from isohydry (avoidance of dehydration) in the most northern provenance to anisohydry (tolerance of dehydration) in the most southern provenances. Possibly, our findings may reflect that warmer provenances performed worse when facing water stress in environments that, although less arid, are different from those to which they are adapted.

Finally, we found a small but significant interaction between mortality and the prov-
enance's climate on tree height. This result differs slightly from that obtained previously
(Mutke et al. 2013), probably due to the incorporation of Tunisian common gardens in our
study. Specifically, the common garden located in Oued El Bir exhibited the highest SHM,
with a significant difference compared to the other common gardens, thus expanding the
range of aridity tested here.

Tree density increases tree height regardless the planting environment

According to our model, at tree age of 10-years, the expected heights increase as density rises, until reaching a turning point near 2250 stem/ha for the mean temperature of warmest quarter (Bio 10) of the provenance. This value corresponds to the maximum actual density value recorded at that age and it is remarkably high for the species, as will be discussed below.

This result suggests that the densities tested here are not above the range in which com-
petition between trees impairs growth (Ruiz-Benito et al. 2013) by constraining water and
nutrient resources availability. Previous studies have pointed out that competition nega-
tively affects height growth when resources are more limited (Schmid 1989; Henskens et
al. 2001; Woodruff et al. 2002; Kholdaenko et al. 2022). In stone pine, the negative effect
of density on tree height has only been found to be evident in poor stands, characterized by
strong aridity and very poor sandy soils (Calama et al. 2019).

At this point, it is necessary to reflect on the purpose of the common gardens network
established for the species in the 1990s (Fady and Rihm 2022). The environmental condi-
tions of the selected sites and the planting densities were likely designed to test the prov-
enance effect on tree height as a proxy of productivity. Common gardens were generally
established in relative productive areas where climate did not compromise survival or
growth (Guries 1990; Leites et al. 2012). Therefore, the main goal of the common garden
network was not to test the species outside its environmental range or even at its margins.
This may explain why our model did not exceed the aridity threshold at which density compromises tree growth or survival. The need to establish common gardens outside the species' native climatic range has been previously highlighted in studies on phenotypic plasticity in Mediterranean pine species (Vizcaíno-Palomar et al. 2019a, b).

Moreover, greater height growth does not necessarily mean a balanced height to diameter ratio or good crown development. In stone pine, lower planting densities are recommended to promote larger crown sizes and improve cone production (Calama et al. 2019; Loewe et al. 2020). Additionally, poor stands have been reported to have a stronger negative impact on cone production than tree growth (Pardos et al. 2015; Calama et al. 2016).

It is important to underscore that our results are not applicable to plantations intended primarily for fruit production. The provenance trials on which our study is based were not focused on agroforestry pine nut production. Furthermore, our study is limited to an age at which the trees are not yet sexually mature.

## Tree height-based management projections for the period 2041–2070 (SSP 370)

According to our projections, the rise of the aridity for the future period and SSP 370 would lead to a slight decline in tree height growth across all future MSP scenarios. This is consistent with previous studies which considered more severe climate change scenarios predicted a general decline in growth, cone production, and carbon sequestration—among other ecosystem services—for the species within its current distribution (Pardos et al. 2015; Calama et al. 2019; Calama et al. 2021; Natalini et al. 2024).

Our results indicate that the best management option strategy to partially compensate the expected decline in tree height during the juvenile stage would be the combined use of provenances from cold origins and high planting density. As observed in Sect. “Relation-ship between tree height and mortality”, the appearance of a trade-off between growth and mortality would be lower when using cold-origin provenances, especially under increasing environmental aridity and the subsequent growth limitation. Although tree height growth and mortality are related to fitness, the latter relies on complex multidimensional pheno-type-environment interactions that we can not test along the climatic gradient provided by our common gardens (Laughlin and Messier 2015). However, the relationship between tree height and mortality at early stages and their interactions with the climate are a valuable insight of the species performing under the aridity observed range. This relationship influ-ences water runoff, changes in understory vegetation, and cover for wildlife (Tague et al. 2019; Adhikari et al. 2021).

The decision to use high plantation densities is by far more controversial. Several stud-ies suggest that low densities are necessary to promote pine nut production, ensure a more efficient use of water resources (Loewe et al. 2020; Calama et al. 2019), and reduce dam-age caused by forest fires (Damianidis et al. 2021). However, cone production is much more sensitive to harsh environmental conditions than biomass production, as the species prioritizes vegetative growth over the development of reproductive tissues (Pardos et al. 2015; Calama et al. 2016). In turn, high densities can mitigate the negative effects of arid-ity to a certain extent. From a strictly economic point of view, a study carried out based on a medium density (600 stem/ha) and different management approaches concluded that the combined utilization of wood and cones was more profitable than the use focused solely on either of the two products (Pardos et al. 2015). Another study stressed that the only way
to make profitable the conversion of cereal cropland into stone pine plantations was to incorporate income derived from carbon sequestration (Ovando et al. 2010). Some comparative studies also ranked stone pine among the Mediterranean pine species with the greatest capacity for carbon absorption and biomass production (Enríquez-de-Salamanca 2024; Montero et al. 2006).

Based on our results, the use of high initial densities and cold provenances would be advisable for restorative or protective plantations, enhancing carbon sequestration during juvenile stages.

Our study is limited by the characteristics of the common garden network used: the climatic range represented, the recorded phenotypes (tree height and mortality), and the varying ages and uneven frequency of measurements. Notable, these common gardens were established as monospecific, even aged, and high-density plantations which do not represent the plantation conditions used in reforestation. Risks such as increased fire spread and intensity or greater exposure to phytosanitary threats would discourage the installation of large plantations with the characteristics described above.

## Conclusion

The greater growth in height exhibited by the colder provenances throughout the entire range of aridity studied may indicate maladaptation to their environment by the warmer populations. Furthermore, in less arid environments, colder provenances did not exhibit height-mortality negative relationships as those from warmer areas did. This insight should be considered in the selection of genetic material for future actions. Subsequent studies employing molecular markers, which offer enhanced insights into the provenance of contemporary populations deemed natural, may facilitate a more comprehensive understanding of this matter.

The use of high initial densities to facilitate height growth must be carefully evaluated. It can be useful in localized protective or restorative reforestation, improving soil characteristics, regulating runoff and water retention, and increasing carbon sequestration during early tree stages.

Other factors, such as the planting objective, the weight of subsequent thinnings, and the level of fire risk and other hazards, should also be considered when designing the appropriate planting layout. Establishing new common gardens at the limits of the species' environmental range will provide a better understanding of its behaviour under more severe conditions caused by climate change.

**Supplementary Information** The online version contains supplementary material available at https://doi.org/10.1007/s11056-025-10130-9.

**Acknowledgements** We are particularly grateful to Prof. Mohamed Larbi Khouja and Dr. Issam Touhami (INRGREF), Dr. Bruno Fadi (INRAE), and GENFORED for providing us with the raw data on common gardens in Tunisia, France and Spain, respectively.

**Author contributions** All authors contributed to the study conception and design. Data preparation was performed by C.GP., N.VP., S.M. and S.F., data analysis was performed by C.GP., N.VP. and M.BG. The first draft of the manuscript was written by C.GP., N.VP., M.BG. and R.A. All authors contributed to the revision and editing of the final version and gave their final approval for publication.
**Funding** Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. The position of CGP was partly funded by ICIFOR-INIA (CISC) through the projects: (1) I088/2022 ForesteCCo, supported by Fundación Biodiversidad del Ministerio para la Transición Ecológica y el Reto Demográfico (MITECO) in the framework of Plan de Recuperación, Transformación y Resiliencia (PRTR), funded by European Union—NextGenerationEU; and (2) PID2022-140521OB-C32 PLASTICPINE, supported by Ministerio de Ciencia e Innovación and Agencia Estatal de Investigación, funded by European Union - FEDER. The position of NVP was funded by the grant referenced IJC2020-044557-I/AEI/https://doi.org/10.13039/501100011033, supported by Ministerio de Ciencia e Innovación and Agencia Estatal de Investigación, in the framework of Plan de Recuperación, Transformación y Resiliencia (PRTR), funded by European Union - NextGenerationEU. The positions of SM, RA and DG were partly funded by MITECO (MITECO2023-AF. 20234TE003. "Conservación y uso sostenible de recursos genéticos forestales"). Views and opinions expressed are, however, those of the authors only and do not necessarily reflect those of the European Union or other entities financially supporting the project.

**Data availability** The data set analysed in the current study is available at Zenodo with https://doi.org/10.5281/zenodo.15599058

**Declarations**

**Competing interests** The authors declare no competing interests.

**Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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## Authors and Affiliations

Carlos Guadaño-Peyrot<fnref n="1" />,<fnref n="3" /> ID • Natalia Vizcaíno-Palomar<fnref n="1" /> ID • Sven Mutke<fnref n="1" /> ID •

Ricardo Alía<fnref n="1" /> ID • Delphine Grivet<fnref n="1" /> ID • Sondes Fkiri<fnref n="4" />,<fnref n="5" /> ID • Marta Benito Garzón<fnref n="2" /> ID

<figure><img src="image_9.png" /></figure>

Carlos Guadaño-Peyrot
carlos.guadano@inia.csic.es

<figure><img src="image_10.png" /></figure>

Marta Benito Garzón
marta.benito-garzon@inrae.fr

<footnote n="1">¹ Instituto de Ciencias Forestales (ICIFOR-INIA), Consejo Superior de Investigaciones Científicas (CSIC), Madrid 28040, Spain</footnote>

<footnote n="2">² UMR BIOGECO – INRAE, University of Bordeaux, Cestas 33610, France</footnote>

<footnote n="3">³ University Institute of Research on Sustainable Forest Management (iuFOR), ETS Ingenierías Agrarias, University of Valladolid, Palencia 34004, Spain</footnote>

<footnote n="4">⁴ National Research Institute of Rural Engineering, Water and Forests (INRGREF), LR11INRGREF0 Laboratory of Management and Valorization of Forest Resources, Carthage University, Ariana 2080, Tunisia</footnote>

<footnote n="5">⁵ Higher Institute of Social Sciences and Education of Gafsa (ISSSEG), University of Gafsa, Route de Tozeur, Gafsa 2112, Tunisia</footnote>