# Understanding how the Argan tree adapts its performance in a heterogeneous environment using wood anatomical and leaf functional traits

Jean-Frédéric Terral, Goeffrey Bédé, Laure Paradis, Sarah Ivorra, Jérôme Ros, Vincent Girard, Mohammed Ater, Bertrand Limier, Jalal Kassout

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## To cite this version:

Jean-Frédéric Terral, Goeffrey Bédé, Laure Paradis, Sarah Ivorra, Jérôme Ros, et al. Understanding how the Argan tree adapts its performance in a heterogeneous environment using wood anatomical and leaf functional traits. Journal of Arid Environments, 2025, 231, pp.105473. 10.1016/j.jaridenv.2025.105473. hal-05297302

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Submitted on 4 Oct 2025

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# Understanding how the Argan tree adapts its performance in a heterogeneous environment using wood anatomical and leaf functional traits

Jean-Frédéric Terral<sup>a,*</sup> <sup>□</sup>, Goeffrey Bédé<sup>a</sup>, Laure Paradis<sup>a</sup>, Sarah Ivorra<sup>a</sup>, Jérôme Ros<sup>a</sup>, Vincent Girard<sup>a</sup>, Mohammed Ater<sup>b</sup>, Bertrand Limier<sup>a,c</sup>, Jalal Kassout<sup>d</sup>

## ARTICLE INFO

**Keywords:**

Sideroxylon spinosum

Functional traits

Biogeography

Climate

Phenotypic plasticity

Resistance

Vulnerability

## ABSTRACT

Plant responses to environmental factors—specifically, in this study, sap conduction potential, resource use, and photosynthetic capacity—generally involve the simultaneous variation of both independent or correlated traits. To better understand these responses, we develop an integrated approach that examines multiple functional dimensions, defined as groups of correlated phenotypic traits that reflect the constraints and trade-offs shaping the Argan tree phenotype. Our aim is to assess the Argan tree's ability to withstand ongoing climate change, particularly the significant increase in temperature and aridity. In this study, we focus on wood anatomical traits related to sap conduction and reserve storage, alongside structural and physiological leaf traits. To date, this combination of traits has not been systematically studied in the Argan tree. By integrating these functional dimensions, our approach allows us to identify key traits and trait combinations that explain the species' distribution and resilience to heterogeneous environmental and climatic stressors. Furthermore, it enables the identification of bioindicator characters that are sensitive to variations in biogeographical, geographical and climatic factors, facilitating the development of trait-based models of plasticity or adaptation of performances along environmental gradients. Ultimately, this study establishes the eco-functional foundations of the Argan tree, providing insights into its tolerance and resilience to drastic environmental changes. Given its ecological, anthropological and socio-economic significance, understanding the adaptive strategies of this emblematic woody species is crucial for predicting its future persistence under increasing environmental pressures.

## 1. Introduction

The Argan tree (Sideroxylon spinosum L., syn. Argania spinosa (L.) Skeels, Sapotaceae) is a fruit tree endemic to southwestern Morocco (Msanda et al., 2005; Fennane et al., 2021) and classified as a threatened species (Fennane et al., 2021; Oldfield, 2021). The species is primarily distributed across southwestern Morocco, covering approximately 830, 000 ha, including the westernmost part of the High Atlas, the Souss-Massa region, and the Anti-Atlas Mountains (Kenny, 2007). Argan groves are found at altitudes ranging from 0 to 1500 m (m. a.s.l.) and are particularly prevalent in coastal plains and mountainous areas (Fig. 1).

The Argan tree is well adapted to arid and semi-arid conditions. It thrives in poor, rocky, and sandy soils, which are often subject to severe erosion. Annual rainfall in these regions ranges between 150 and 350 mm, with prolonged periods of drought. From a bioclimatic and phytogeographic perspective, the Argan grove represents an exceptional ecosystem at the intersection of Mediterranean, Macaronesian, and Sahelo-Saharan influences (Ros et al., 2021).

Several characteristics highlight the Argan tree's adaptation to xeric conditions. It possesses a deep and extensive root system, enabling access to deeper soil layers and the absorption of residual moisture from surface layers (Kenny, 2007). Notably, the roots lack absorbent hairs,

<footnote><sup>a</sup> ISEM, Institut des Sciences de l'Evolution-Montpellier. Université de Montpellier-CNRS-IRD-EPHE. Place Eugène Bataillon, 34090, Montpellier, France</footnote>

<footnote><sup>b</sup> Laboratoire Botanique Appliquée, Equipe Bio-Agrodiversité. Département de biologie de la Faculté des sciences. Université Abdelmalek-Essaádi, BP 2062, 93030, Tétouan, Morocco</footnote>

<footnote><sup>c</sup> INRAE, Centre de recherche Occitanie-Montpellier, 2 place Viala, 34060 Montpellier Cedex 1, France</footnote>

<footnote><sup>d</sup> Centre Régional de la Recherche Agronomique de Marrakech, Institut National de la Recherche Agronomique, Avenue Ennasr, BP 415 Rabat Principale, 10090, Rabat, Morocco</footnote>

<footnote>* Corresponding author.
E-mail address: jean-frederic.terral@umontpellier.fr (J.-F. Terral).</footnote>
but this deficiency is compensated for by the presence of endomycorrhizal fungi, which facilitate nutrient absorption (Ouallal et al., 2018). Additionally, the high density of its wood (Sideroxylon translates to “iron wood”) is a reliable indicator of its resistance to drought-induced cavitation (Greenwood et al., 2017). The leaves are small, tough, and covered with a thick cuticle, which reduces evapotranspiration and water loss. Although the foliage is considered as evergreen, the tree can shed all its leaves during intense and prolonged droughts to minimize water loss and survive stressful periods in a state of reduced metabolic activity.

From a phenological perspective, flowering and fruiting are synchronized with periods of optimal water availability (Bani-Aameur, 2002). Flowering begins in late September for early-flowering trees, while late-flowering trees start in December, peaking in March. On some trees, flowering may persist throughout the “humid” period, with new flowers emerging after each rainfall, resulting in fruits of varying sizes on the same tree (Bani-Aameur, 2002; Kenny, 2007).

The ancient history of the Argan tree remains somewhat enigmatic, but its status as a relict species is well established. Its closest phylogenetic relative, *Sideroxylon mascatense*, is supposed to have diverged more than 15 million years ago during the Middle Miocene (Neogene period) (Stride et al., 2014). While *S. mascatense* is distributed from East Africa to Central Asia, *S. spinosum* is the only non-tropical species within the Sapotaceae family. Palaeobiological and palaeoecological data on the

Argan tree are sparse but suggest that the aridification phase following the Miocene, along with the cooling periods of the Pliocene and Quaternary, led to the extinction of tropical and subtropical species in the region. It is within this context of palaeogeographic and palaeoclimatic upheaval that the Argan tree is thought to have emerged. Palynological data from the Pliocene-Quaternary transition, derived from marine surveys off the Moroccan coast (Hooghiemstra et al., 1992; Leroy and Dupont, 1994; Marret and Turon, 1994) and continental Holocene records (Ballouche and Damblon, 1988), support the hypothesis of a long-standing and geographically restricted distribution area and an ancient endemism.

However, it is only more recently that the Argan tree has gained recognition as an emblematic species (Ballouche, 2015). A palynological study of marine sediments extracted off the Moroccan coast and spanning the last 2000 years indicates that the Argan tree experienced a decline during the early Middle Ages, as did matorral, alongside other evergreen species such as oaks (Quercus), olive (Olea europaea), and Pistachio (Pistacia lentiscus) (Mc Gregor et al., 2009). Significant environmental changes accelerated around the 10th century, marked by an increase in steppe plants such as Mugwort (Artemisia) and plantain (Plantago) and a corresponding decline in woody species. During this period, the Argan tree emerged as the dominant tree species. With the establishment of agro-pastoral communities, it became a keystone species, playing critical ecological, cultural, and socio-economic roles, as

<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Location of the study area and Argan populations.</figcaption></figure>
evidenced by archaeobotanical studies in the Anti-Atlas region (Ruas et al., 2015; Ros et al., 2021).

The emblematic Argan tree is not only a symbol of ecological resilience in the face of extreme environmental conditions but also a central element in the social, economic, and cultural life of local Amazigh populations (Kenny, 2007). Although not domesticated or actively cultivated, the tree serves a variety of purposes: the seeds extracted from its nuts are crushed to produce oil for cooking, cosmetics, and traditional medicine (Charrouf and Guillaume, 2002). Argan oil is rich in unsaturated fatty acids, vitamin E, and antioxidants, which, combined with its rarity, make it a highly prized product worldwide (Charrouf and Guillaume, 2002). The dense wood of the Argan tree is used as fuel and for crafting furniture and farming tools. Additionally, its foliage and fruit serve as fodder for goats and sheep, particularly when grass is scarce. Traditionally, the Argan tree is neither planted nor propagated directly by farmers. Instead, its nuts are efficiently dispersed by goats (Ruas et al., 2015). While herds play a positive role in the dispersal of the species, they can also have a negative impact when Argan groves are overexploited for fodder (Zunzunegui et al., 2010).

In recognition of its ecological and socio-economic importance, the Moroccan Argan grove was designated a Biosphere Reserve by UNESCO in 1998 (Msanda et al., 2021). This classification aims to promote sustainable management, protect natural resources, and support the economic development of local communities. In Morocco, reforestation and legal protection efforts have been implemented to prevent the degradation of Argan groves. Beyond its traditional uses, the Argan grove provides essential ecosystem services, such as controlling soil erosion, acting as a bulwark against desertification, regulating local climatic conditions by increasing soil humidity and moderating extreme temperatures, and serving as a habitat for biodiversity, as the groves are hotspots of animal and plant endemism.

Today, the primary threats to the Argan tree include dramatically increasing temperatures, aridification, human impacts on landscapes, and overexploitation, which hinder its natural regeneration. Despite concrete measures being developed and implemented to preserve and conserve the species (Msanda et al., 2021), enhance the value of its products and by-products, and support local rural communities, the mechanisms underlying its resistance and resilience remain poorly understood.

To address this knowledge gap, it is essential to explore the species' functional responses by examining variations in traits associated with life-history strategies and their relationships with environmental gradients (Shipley et al., 2016). This trait-based approach relies on the measurement of functional traits, defined as morphological, physiological, or phenological characteristics that influence plant performance and determine how individuals interact with their environment under varying conditions (Violle et al., 2007).

Understanding how plant species respond to climate change requires an ecophysiological perspective that integrates both carbon and water economy (Rosas et al., 2019). Leaf traits such as leaf area (LA) and leaf mass per area (LMA) are closely linked to resource acquisition strategies and drought avoidance (Poorter et al., 2009; Osnas et al., 2013), while wood anatomical traits—including vessel diameter and wood density—govern hydraulic conductivity and resistance to embolism under water stress (Salleo et al., 2004; Terral et al., 2025). For example, increased LMA combined with high vessel density may enhance water-use efficiency by reducing water loss by transpiration while maintaining sufficient water transport capacity, reflecting an integrated strategy of stress tolerance and resource use (Rosas et al., 2019). Moreover, intraspecific variability and phenotypic plasticity (i.e. variation in morphological or physiological characteristics along an environmental gradient) in these traits can buffer plants against environmental stress, and are therefore considered key components of ecological resilience (Nicotra et al., 2010; Kassout et al., 2019, 2021). Investigating how these traits vary within species across environmental gradients helps identify potential adaptive responses and predict

species' vulnerability or tolerance under future climate scenarios (Valladares et al., 2007; Anderegg et al., 2016). Consequently, combining the study of leaf and wood traits offers a more comprehensive and integrative framework to understand plant performance and persistence in a changing climate.

This work therefore aims to understand the Argan tree's capacity to adapt to global change by characterizing its phenotypic plasticity through wood anatomical characters and leaf traits. The variation and covariation of wood anatomical characters involved in sap conduction and reserve storage, along with leaf traits, will help reveal coordination between wood and leaf plasticity, and establish the eco-functional basis for understanding the Argan tree's tolerance and resilience to xeric conditions and ongoing climate change.

## 2. Material and methods

### 2.1. Study locations, sampling and preparation of samples

Samples were collected in May 2022 across the distribution area of the Argan tree, from 14 stations and 140 trees (10 trees per population, except for wood eco-anatomical analysis, for which 2 samples from 1 tree in the Tiout population were lost) (Fig. 1; Table S1; Fig. S1).

Wood sampling was conducted at the branch level rather than from trunks to avoid damaging trees of significant ecological, socio-economic, and cultural importance. Only branch sections with perfect axial symmetry were retained to minimize the influence of biomechanical constraints, such as the formation of reaction wood, on wood anatomical traits. A total of 278 wood samples were analyzed.

After collection, the wood samples were air-dried in the laboratory for one month. They were individually wrapped in tin foil, buried in sand, and charred under oxygen-free conditions at 450 °C during 25 min. This method of wood sample treatment was selected as it will serve as a reference approach for future comparative analyses of archaeological charcoals uncovered from archaeological excavations. The anatomical structure of the wood, preserved during carbonization, can be analyzed with nearly the same precision as thin transverse wood sections obtained using a microtome (Beall et al., 1974; Ros et al., 2021). However, this method has two main drawbacks: the potential formation of radial shrinkage cracks, typically occurring at the level of woody rays, and the uneven surfaces of hand-cut cross-sections, which require extensive fine adjustments under a reflection microscope (Fig. 2A). The minimum and maximum diameters of the wood samples were measured (in mm) using a digital caliper before carbonization. The branch diameter (BD) used in subsequent analyses is the mean of these two measurements.

A total of 4200 fully expanded, mature, and healthy leaves were measured from the 14 populations (30 leaves per tree; 10 trees per population). Leaves were selected from the sun-exposed side (south-southwest) of the upper canopy of each tree (Pérez-Harguindeguy et al., 2013; Kassout et al., 2024). The sampled leaves were transported to the laboratory (INRA Marrakech) for trait measurements (Fig. 2B).

### 2.2. Quantitative eco-anatomical analysis of argan wood

An eco-anatomical approach was employed to measure selected wood traits using a reflected-light microscope connected to an image analysis system (DFC300 FX Leica camera and LAS Leica software) (Kassout et al., 2021; Ros et al., 2021; Terral et al., 2025). This methodology involves measuring wood anatomical characteristics with validated reproducibility, focusing on the peripheral part of the wood to correlate anatomical data with branch diameter (Ros et al., 2021). Data acquisition was followed by statistical analyses. This high-resolution analysis of wood structure, particularly of sap-conducting elements, enables the exploration of ecological, climatic, phenological, agricultural, and anthropological questions, which have been successfully addressed at a local scale for the Argan tree (Ros et al., 2021).
<figure><img src="image_5.png" /><figcaption>**Fig. 2.** A. Transverse cross-section of a wood sample of Argan tree: in blue, anatomical characters; in red, characters involving surface measurements and green, characters involving length measurements. For RD, only the first 4 rays are counted, and for DAP, 3 bands are counted along the defined segment. B. Leaf traits measured using ImageJ (Schneider et al., 2012): leaf length (LL, cm), leaf width (LW, cm), leaf area (LA, $cm^2$). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>
The wood of the Argan tree is diffuse-porous, with a dendritic and diagonal arrangement of vessel elements in transversal section (Neumann et al., 2000) (Fig. 2A). Axial parenchyma bands are tangentially aligned and composed of multicellular strands. In radial alignment, woody rays are predominantly uniseriate but may occasionally be bi- or triseriate, with heterocellular composition. Fenestrated zones are defined by the intersection of parenchyma bands and wood rays (Fig. 2A). Five wood anatomical traits related to sap conduction and reserve storage were observed and measured in the peripheral zone (Fig. 2A): (1)

As demonstrated by Ros et al. (2021), the optimal number of measurements required for accurate assessment of anatomical features is 50 for lumen vessel area (LVA), 10 for vessel density (DVS), 15 for ray density (RD), 15 for density of axial parenchyma (DAP), and 10 for density of fenestrated zones (DFZ).

## 2.3. Measured and calculated leaf traits

Following the methodology outlined by Kassout et al. (2019, 2024), five leaf functional traits were measured (Fig. 2B): leaf length (LL, cm), leaf width (LW, cm), leaf area (LA, cm²), leaf circularity (CIR = 4π*(LA/LP²), unitless – where LP (cm) is the leaf perimeter), and leaf mass per unit area (LMA, g m⁻²). LA, LL, LW, and CIR were measured by scanning the leaves and analyzing the scans using ImageJ software (Schneider et al., 2012) (Fig. 2B). Leaf dry mass was measured after oven-drying the leaves at 70 °C for 72 h, and LMA was calculated as the ratio of leaf dry mass to leaf area (Poorter et al., 2009).

## 2.4. Data analysis

To assess the variability of wood anatomical traits in relation to branch diameter (BD), which is a major parameter of anatomical variation (Kassout et al., 2021; Terral et al., 2025), as well as environmental parameters (biogeographical and climatic data), a Redundancy Analysis (RDA) (Van den Wollenberg, 1977) was conducted using XLStats (Addinsoft, 2024). This multivariate statistical method models the effect of an explanatory matrix, which includes the following parameters: Branch diameter (BD, cm), Mean Annual Temperature (MAT, °C), Mean Annual Precipitation (MAP, mm), Potential Evapotranspiration (PET, mm/month), Aridity Index (AI = P/PET), Amann Continentality Index or Oceanity (ACI, cm), Altitude (m), Latitude (°), and Longitude (°). The response matrix consists of the dataset obtained from the measurement of anatomical traits. Similarly, to analyze the relationships between leaf traits and environmental variables, an RDA was performed. This analysis summarizes the variation in functional traits explained by the environmental variables of the studied Argan populations. Climate parameters were extracted from the Worldclim database, version 2.1 (Fick and Hijmans, 2017). AI was calculated using the CGIAR Global aridity and PET database, version 3 (Zomer et al., 2022). Amann Continentality Index was calculated as: $ACI = MAP.MAT/Tmax.Tmin$, where MAP: Mean annual Precipitation (cm), MAT: Mean Annual Temperature (°C), Tmax: average temperature of the hottest month (July) (°C); Tmin: average temperature of the coldest month (January) (°C) (Amann, 1929). The RDA also constructs a Principal Component Analysis (PCA) on the response variables, with the canonical axes constrained to be linear combinations of the explanatory variables.

After testing the relationship between branch diameter, geographical, and climatic parameters on the anatomy of the Argan tree using RDA, two different approaches were applied where applicable. First, for anatomical traits strongly linked to BD, population-by-population linear regression analyses were performed. Based on the results obtained by Terral et al. (2025), the variation in the parameters of the regression equations was modeled using the explanatory parameter with the

greatest influence. Second, the relationship between the most dependent anatomical trait and biogeographical or climatic factors was modeled using linear regression.

A second RDA was performed to evaluate the variability of measured and calculated leaf traits (mean values per tree) according to the environmental parameters defined above (biogeographical and climatic data).

Finally, to assess correlations among trait pairs, the Pearson correlation coefficient (r) was calculated, integrating wood anatomical traits and leaf traits. To investigate patterns of trait covariation in the Argan tree, robust linear regression was applied using average trait values per tree. This regression method was applied because it is well-suited for large datasets, overcoming limitations of traditional regression methods, such as heteroscedasticity and the influence of outliers. Correlation and regression analyses were conducted using log-transformed data in R software (R Development Core Team, 2017).

# 3. Results

## 3.1. Wood anatomical traits

The first two canonical axes of the Redundancy Analysis (RDA) conducted on data from Argan wood samples i.e. — quantitative anatomical data and potential explanatory environmental parameters, account for 95.06 % of the total variance (Fig. 3A). The distribution of individuals grouped by population along the first canonical axis, which explains 82.63 % of the variance, is significantly driven by differences in Lumen Vessel Area (LVA). This anatomical feature is strongly influenced by branch diameter (BD), with wider branches exhibiting higher LVA values. Ray Density (RD) and, to a lesser extent, Density of Fenestrated Zones (DFZ) and Density of Axial Parenchyma (DAP), which are negatively correlated with LVA, also contribute to the variation in LVA (Fig. 3A). The second canonical axis (12.43 % of the variance explained) is associated with Vessel Density (DVS), whose variation appears to be strongly correlated with Mean Annual Temperature (MAT) and Altitude (ALT), two closely related environmental parameters (Fig. 3A). This trend is particularly evident in the 2–3 canonical plane, where the effect of BD is significantly reduced (Fig. S2). Thus, a high DVS value is indicative of lower temperatures within the thermal range of the Argan tree’s distribution area and, consequently, higher altitudes. The third canonical axis (3.74 % of the variance explained) appears to account for the dispersion of individuals and populations in relation to variations in DFZ and, secondarily, the residual variation in LVA not explained by BD. No environmental variables were found to explain the variations in DFZ. However, based on field observations, distinctions can be made between certain stations. For example, Krima, a grassy agricultural zone (cultivated with crops such as legumes and barley) characterized by deep soil, contrasts with Talat Nifart and part of the Alma station, which are distinguished by skeletal, shallow, and highly stony soils (Fig. S1).

## 3.2. Wood anatomy – environment relationships

As recently demonstrated in olive trees (Kassout et al., 2021 for wild olive and Terral et al., 2025 for cultivated varieties), a log-linear (natural logarithm) relationship was found between branch diameter (BD) and Lumen Vessel Area (LVA) for each Argan population (Table 1; Fig. S3).

The slopes of these linear models, which are strongly correlated with their y-intercepts ($R^2 = 0.95, p < 0.0001$), were compared with the environmental parameters of the sampling stations (see Table S3A for Pearson correlation coefficients and P-values between environmental variables and traits). The results indicate that the overall variation in slope is linearly related to Mean Annual Temperature (MAT) (Fig. 4A).
<figure><img src="image_6.png" /><figcaption>**Fig. 3.** A. Projection of statistical individuals (Argan wood samples) of each population, explanatory variables (BD: Branch Diameter; MAT: Mean Annual Temperature; MAP: Mean Annual Precipitations, PET: Potential Evapotranspiration; AI: Aridity Index; ACI: Amann Continentsality Index; ALT: Altitude; LAT: Latitude; LON: Longitude - Vectors are represented using a multiplication factor of 2 to give a clear idea of the impact of the parameters concerned) and response variables (DVS: Density of Vessels; DAP: Density of Axial Parenchyma; DFZ: Density of Fenestrated Zones; RD: Ray Density; LVA: Lumen Vessel Area) on canonical axes 1 and 2. Confidence ellipses (5%) per population are also shown. B. Projection of statistical individuals (Argan trees) of each population, explanatory variables (vectors are represented using a multiplication factor of 3 to give a clear idea of the impact of the parameters concerned), and response variables (LL: leaf length (cm); LW: leaf width (cm); LA: leaf area (cm²); CIR: Leaf circularity (unitless) and LMA: leaf mass area per unit area (g m⁻²) on canonical axes 1 and 2 of the Redundancy Analysis. Confidence ellipses per population are also shown. Small boxes summarise the main trends, represented by arrows, in terms of trait plasticity and relationships between traits and ecological parameters.</figcaption></figure>

Furthermore, as introduced in Fig. 4, a linear regression fitted between
Density of Vessels (DVS, log-transformed data) and branch diameter
(BD) revealed a significant relationship between the two parameters
(Fig. 4B). This suggests that, across the Argan tree's distribution area, an
increase in MAT leads to a decrease in Density of vessels (DVS), i.e., the

number of vessels per unit area of wood in transverse section (N/mm²).

3.3. *Leaf traits – environment relationships*

The first two canonical axes of the RDA performed on leaf trait data
<caption>**Table 1**</caption>

Summary of data relating to linear regressions modelling for each population the relationship between lumen vessel area (LVA, µm) and branch diameter (BD, mm). Data are Log-transformed. Populations are ordered by increasing Mean Annual Temperature (MAT, °C). Confidence intervals of slopes and intercepts (95 %) are presented in Table S2.

<table><thead><tr><th>Population</th><th>MAT (°C)</th><th>N. samples</th><th>R<sup>2</sup></th><th>P-value</th><th>Slope</th><th>Intercept</th></tr></thead><tbody><tr><td>Tagadirt N Tazrout</td><td>15.44</td><td>20</td><td>0.657</td><td>&lt;0.0001</td><td>0.557</td><td>4.995</td></tr><tr><td>Ait Ouihi</td><td>16.02</td><td>20</td><td>0.341</td><td>0.007</td><td>0.313</td><td>5.578</td></tr><tr><td>Talat Nifart</td><td>16.21</td><td>20</td><td>0.411</td><td>0.002</td><td>0.136</td><td>6.047</td></tr><tr><td>Tafraout</td><td>16.43</td><td>20</td><td>0.562</td><td>&lt;0.001</td><td>0.311</td><td>5.510</td></tr><tr><td>Lakhasas</td><td>16.58</td><td>20</td><td>0.519</td><td>&lt;0.001</td><td>0.382</td><td>5.344</td></tr><tr><td>Ida ou Aazza</td><td>17.43</td><td>20</td><td>0.662</td><td>&lt;0.0001</td><td>0.444</td><td>5.378</td></tr><tr><td>Alma</td><td>17.82</td><td>20</td><td>0.365</td><td>0.005</td><td>0.281</td><td>5.620</td></tr><tr><td>Reggada</td><td>18.13</td><td>20</td><td>0.461</td><td>0.001</td><td>0.537</td><td>5.134</td></tr><tr><td>Tamanar</td><td>18.17</td><td>20</td><td>0.748</td><td>&lt;0.0001</td><td>0.321</td><td>5.498</td></tr><tr><td>Lahsinate</td><td>18.20</td><td>20</td><td>0.612</td><td>&lt;0.0001</td><td>0.513</td><td>5.190</td></tr><tr><td>El Ghazoua</td><td>18.74</td><td>20</td><td>0.732</td><td>&lt;0.0001</td><td>0.651</td><td>4.815</td></tr><tr><td>Ouijane</td><td>19.06</td><td>20</td><td>0.579</td><td>&lt;0.0001</td><td>0.591</td><td>4.958</td></tr><tr><td>Tiout</td><td>19.94</td><td>18</td><td>0.617</td><td>&lt;0.0001</td><td>0.574</td><td>4.834</td></tr></tbody></table>

and explanatory environmental variables explain for 92.72 % of the total variance (Fig. 3B; please refer to Table S3B for Pearson correlation coefficients and P-values between environmental variables and traits). The first axis (82.4 %) distinguishes populations of trees with Large leaves (high values of Leaf Length (LL) and, secondarily, Leaf Area (LA) and Leaf Width (LW)) and thin leaves (low values of Leaf Mass per Area (LMA))—located at low-value coordinates—from populations with small, thick leaves (high-value coordinates) (Fig. 3B). The second canonical axis, which contributes much less to population distinction, is explained by LW and Leaf Circularity (CIR). It separates populations with rounded leaves (high values of LW and CIR, e.g., Lashinate and Krima) from those with narrow leaves, such as El Ghazoua (Fig. 3B).

Along the first axis, LL appears to be positively correlated primarily with the Amann Continentality Index (ACI), Latitude (LAT), Longitude (LON), and Aridity Index (AI), while LMA is positively linked to Altitude (ALT) and Potential Evapotranspiration (PET) (Fig. 3B). Mean Annual Temperature (MAT) seems to have few influences on trait variation, and Mean Annual Precipitation (MAP) is entirely independent. Populations characterized by trees with broad, thin leaves are typically found at low

<figure><img src="image_7.png" /><figcaption>**Fig. 4.** A. Linear regression model (Equation, R² and P-value) explaining the variation of slope relating Log-transformed (natural logarithm) lumen vessel area (LN (LAV)) and branch diameter (LN(BD)), according to MAT (°C). Note that the ‘Tagadirt N Tazrout’ population (grey circle) was removed from the analysis because it was considered as an outlier (Cook distance = 1.1, well above the maximum threshold of 0.33). B. Linear relationship between density of vessels LN(DVS) and MAT (°C).</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Correlation network between leaf and wood traits. Dashed and solid lines refer to negative and positive correlation, respectively. Correlation strength is represented by lines thickness. Only significant correlations (alpha = 0.05) are shown and mean tree values were used. For traits abbreviations see Material and Methods section.</figcaption></figure>

<caption>**Table 2**</caption>

Summary of the robust linear regression analyses between wood anatomical characters and leaf traits ("+": positive correlation among traits; "-": negative correlation; n.s: non-significant).

<table><thead><tr><th>Trait</th><th>Regression indicators</th><th>LA</th><th>LL</th><th>LW</th><th>LMA</th><th>CIR</th></tr></thead><tbody><tr><td rowspan="5">LVA</td><td>R<sup>2</sup></td><td>0.590</td><td>0.810</td><td>0.530</td><td>0.527</td><td>0.589</td></tr><tr><td>P-value</td><td>0.180</td><td><strong>0.004</strong></td><td>0.252</td><td>0.096</td><td>0.075</td></tr><tr><td>Relationship</td><td>n.s</td><td>+</td><td>n.s</td><td>n.s</td><td>n.s</td></tr><tr><td>Estimates</td><td>0.89</td><td>1.11</td><td>1.84</td><td>-0.65</td><td>-3.59</td></tr><tr><td>Standard error</td><td>0.62</td><td>0.31</td><td>1.52</td><td>0.35</td><td>1.84</td></tr><tr><td rowspan="5">DVS</td><td>R<sup>2</sup></td><td>0.756</td><td>0.803</td><td>0.352</td><td>0.895</td><td>0.736</td></tr><tr><td>P-value</td><td><strong>0.015</strong></td><td><strong>0.028</strong></td><td>0.668</td><td><strong>&lt;0.001</strong></td><td><strong>0.018</strong></td></tr><tr><td>Relationship</td><td>-</td><td>-</td><td>n.s</td><td>+</td><td>+</td></tr><tr><td>Estimates</td><td>-1.51</td><td>-1.27</td><td>-2.07</td><td>0.60</td><td>3.83</td></tr><tr><td>Standard error</td><td>0.53</td><td>0.50</td><td>4.71</td><td>0.09</td><td>1.39</td></tr><tr><td rowspan="5">RD</td><td>R<sup>2</sup></td><td>0.255</td><td>0.307</td><td>0.375</td><td>0.0002</td><td>0.295</td></tr><tr><td>P-value</td><td>0.531</td><td>0.496</td><td>0.295</td><td>0.990</td><td>0.492</td></tr><tr><td>Relationship</td><td>n.s</td><td>n.s</td><td>n.s</td><td>n.s</td><td>n.s</td></tr><tr><td>Estimates</td><td>-0.14</td><td>-0.16</td><td>-0.36</td><td>0.001</td><td>0.47</td></tr><tr><td>Standard error</td><td>0.22</td><td>0.22</td><td>0.32</td><td>0.13</td><td>0.66</td></tr><tr><td rowspan="5">DFZ</td><td>R<sup>2</sup></td><td>0.662</td><td>0.322</td><td>0.005</td><td>0.742</td><td>0.641</td></tr><tr><td>P-value</td><td>0.066</td><td>0.549</td><td>0.934</td><td><strong>0.008</strong></td><td><strong>0.049</strong></td></tr><tr><td>Relationship</td><td>n.s</td><td>n.s</td><td>n.s</td><td>+</td><td>+</td></tr><tr><td>Estimates</td><td>-0.81</td><td>-0.38</td><td>0.10</td><td>0.27</td><td>1.85</td></tr><tr><td>Standard error</td><td>0.40</td><td>0.61</td><td>1.14</td><td>0.08</td><td>0.84</td></tr><tr><td rowspan="5">DAP</td><td>R<sup>2</sup></td><td>0.124</td><td>0.283</td><td>0.001</td><td>0.200</td><td>0.454</td></tr><tr><td>P-value</td><td>0.842</td><td>0.716</td><td>0.988</td><td>0.650</td><td>0.219</td></tr><tr><td>Relationship</td><td>n.s</td><td>n.s</td><td>n.s</td><td>n.s</td><td>n.s</td></tr><tr><td>Estimates</td><td>-0.18</td><td>-0.25</td><td>-0.03</td><td>0.10</td><td>0.71</td></tr><tr><td>Standard error</td><td>0.90</td><td>0.68</td><td>2.25</td><td>0.22</td><td>0.54</td></tr><tr><td rowspan="5">Slope<sup>a</sup></td><td>R<sup>2</sup></td><td>0.063</td><td>0.312</td><td>0.087</td><td>0.914</td><td>0.705</td></tr><tr><td>P-value</td><td>0.876</td><td>0.481</td><td>0.855</td><td><strong>&lt;0.001</strong></td><td><strong>0.008</strong></td></tr><tr><td>Relationship</td><td>n.s</td><td>n.s</td><td>n.s</td><td>+</td><td>+</td></tr><tr><td>Estimates</td><td>0.28</td><td>0.64</td><td>0.72</td><td>0.50</td><td>2.97</td></tr><tr><td>Standard error</td><td>1.75</td><td>0.88</td><td>3.85</td><td>0.07</td><td>0.93</td></tr></tbody></table>

altitudes in the northern and western parts of the distribution area (near the ocean), where conditions are less continental (high ACI values), with lower evapotranspiration and aridity (high AI values) (Fig. 3B). Conversely, populations in southern, eastern, and high-altitude regions, which experience higher continentality (lower oceanity),

evapotranspiration, and aridity, are characterized by trees with short, thick leaves (low LL and high LMA). Pearson correlation tests confirm relationships between leaf traits and environmental parameters (Table S3B).

### 3.4. Covariation between wood anatomical and leaf traits

Correlation analysis revealed that Argan tree traits are not independent, with significant correlations observed between wood anatomical and leaf functional traits (Fig. 5). For example, Vessel Density (DVS) is negatively correlated with LL and LA but positively correlated with LMA and CIR. Vessel Lumen Vessel Area (LVA) is positively correlated with leaf size traits (LL, LW, LA). In contrast, Ray Density (RD), Density of Axial Parenchyma (DAP), and Density of Fenestrated Zones (DFZ) show no correlation with any leaf traits (Fig. 5).

Covariation patterns between wood and leaf traits (log-transformed data) reveal significant relationships among the studied functional traits in the Argan tree (Table 2). For instance, DVS exhibits significant relationships with LMA, LL, LA, and CIR. A significant relationship is also observed between LVA and LL. Additionally, DFZ shows a significant relationship with LMA and CIR. Furthermore, when testing the relationship between the slope derived from the scaling relationship of LVA and branch diameter (BD) (Fig. 4A–Table 1) and leaf traits, a significant positive relationship is found with LMA (Table 2).

## 4. Discussion

This study is a comprehensive investigation into variations of wood anatomical and leaf traits of the Argan tree throughout its distribution range. Plasticity is interpreted hereafter in functional and eco-physiological perspectives in order to understand how this species adapts to increasingly arid conditions, with a particular focus on its capacity of resilience and the implications for its conservation in the context of climate change.

### 4.1. Branch size influences sap conduction

We found that the lumen area of vessels (LVA) in the Argan tree depends on branch diameter (BD). LVA varies significantly as a function of the circumference of the vascular cambium, which reflects the age of the branch and, consequently, the volume of the cauline part to be irrigated, located above the studied cross-section. We also observed that three traits anticorrelated with LVA—ray density (RD), density of axial parenchyma (DAP, composed of tangential bands of cells), and density of fenestrated zones (DFZ) — are influenced by branch size. Secondary parenchyma tissues facilitate the horizontal transport of sugars to parenchyma cells located internally within the growth ring (Morris, 2016) and play a critical role in the radial connection between secondary phloem and xylem (Pfautsch et al., 2015). This connection is particularly important for maintaining hydraulic conduction resilience in the event of cavitation (Salleo et al., 2004). As secondary parenchyma cells generally have a longer lifespan than vessel elements, the role of rays and axial parenchyma shifts toward storage of reserves when vessels cease to transport sap (Spicer and Holbrook, 2007; Schweingruber et al., 2013). An increase in the potential for raw sap conduction by vessels would lead to a reduction in the capacity for radial/horizontal conduction and xylem-phloem transfer via secondary parenchyma cells. Rays and axial parenchyma can thus be viewed as sap conduction relays, particularly in cases of conduction failure, which may be linked to cavitation.

### 4.2. Thermal conditions control wood anatomical features

Thermal conditions appear to play a significant role in the plasticity of Argan tree anatomical traits. The temperature–anatomy relationship is expressed in two distinct ways. First, the variation in the slope and,

<footnote><sup>a</sup> slope values were retrieved from the linear relationship between LVA and Branch diameter (BD) (see Table 1). Traits abbreviations are given in the Material and Methods section.</footnote>
correlatively, the y-intercept of the linear relationships between LVA and BD are modulated by thermal conditions. Rising temperatures seem to reduce the initial size of vessels, suggesting a lower conduction potential at the onset of branch development. Higher mean annual temperatures are associated with an increased pattern of vessel enlargement, reflecting a greater sap conduction potential during branch growth and development. This model contrasts with the patterns recently modeled in olive trees (Terral et al., 2025) and their wild ancestor, the oleaster (Kassout et al., 2021). From a functional point of view, olive trees appear to rely on a high acquisition of water resources at the beginning of growth, followed by a strategy of resource conservation. In contrast, the Argan tree exhibits a low sap conduction potential at the start of stem development, transitioning to a strategy of rapid water resource utilization as temperatures rise and heat stress increases. This strong divergence between Olive and Argan may be linked to phenological differences, particularly in the timing and duration of their growing and development periods. The olive tree's growth and development period occurs during the months with the longest photoperiod and highest temperatures (from March/April to

September), with potential pause in July and August. In contrast, the Argan tree's period of active biological activity generally begins in October and ends in June for the latest-flowering individuals, coinciding with the season of highest rainfall probability, shortest photoperiod, and lowest temperatures. In particularly hot years, this period may be further shortened, leading to complete defoliation as early as June. Second, the strong linear relationship between density of vessels (DVS) and Mean Annual Temperatures (MAT) demonstrates that increasing temperatures lead to a decrease in DVS, unlike what it is observed in olive trees (Terral et al., 2025). In the Argan tree, the trade-off between conduction efficiency and safety highlights that its functional strategy cannot be equated to that of a typical Mediterranean species. These findings underscore that the Argan tree's unique ecological and biogeographical position—at the intersection of the Mediterranean, Macaronesian, and Sahelo-Saharan zones—requires it to adopt specific strategies to withstand aridity and high temperatures.

<figure><img src="image_9.png" /><figcaption>**Fig. 6.** Schematic presentation of the plasticity along environmental gradients of the main coordinated wood and leaf traits, interpreted in terms of eco-functional strategies and trade-offs.</figcaption></figure>
### 4.3. Eco-functional significance of leaf trait variation

By analyzing five leaf traits, our results reveal patterns of population variability in response to geographical and environmental conditions. A marked phenotypic diversity in the Argan tree is evidenced along the studied environmental gradients, which likely reflects distinct functional responses to environmental factors within the species' distribution range (Wright et al., 2004; Gratani, 2014). Previous studies on drought tolerance of Argan trees from different provenances have highlighted significant variability in eco-physiological and biochemical traits (e.g., relative water content, leaf dry mass, and stomatal conductance), demonstrating notable plasticity in response to stressful environmental conditions (Chakhchar et al., 2022; Afi et al., 2024). Our data suggest that the Argan tree adjusts its response to environmental stressors, such as continentality (conversely, oceanity) and aridity (correlatively potential evapotranspiration), to maintain essential physiological functions.

Our results further reveal that leaf mass per area (LMA) and leaf size, assessed through leaf length (LL) and leaf area (LA), are negatively correlated along the first RDA axis (Fig. 3B). This relationship represents a fundamental dimension of functional diversity within Argan populations. Along this axis, trees with larger leaves and lower LMA values exhibit traits associated with rapid resource acquisition, whereas trees with smaller leaves and higher LMA values adopt a resource conservation strategy (Poorter et al., 2009). LMA is a key trait within the Leaf Economic Spectrum and is directly linked to leaf-level carbon gain and tissue construction (Wright et al., 2004). Additionally, leaf size-related traits play a crucial role in energy balance, photosynthesis, light interception, water balance, and nutrient use efficiency (Wright et al., 2004; Díaz et al., 2016). Indeed, species with low LMA and larger leaves are typically associated with higher photosynthetic capacity and, consequently, faster growth rates (Poorter et al., 2009; Wright et al., 2004; Osnas et al., 2013). With respect to the control of the studied functional traits, environmental conditions appear to play a key role in shaping the plasticity of Argan tree responses. Our findings indicate that LMA and correlatively, leaf size, are strongly influenced by increasing altitude, potential evapotranspiration, and aridity, which aligns with previous studies (Yang et al., 2022). This relationship, previously documented at both interspecific (Wright et al., 2004; Díaz et al., 2016) and intraspecific levels (Kassout et al., 2019, 2024; Kuppler et al., 2020), may reflect an adaptive response to the contrasting conditions of continental/oceanic climatic influences, as well as variations in altitude within the Argan tree's distribution range. Accordingly, an increase in leaf size and a corresponding decrease in LMA may enhance a plant's ability to optimize energy balance, photosynthesis, water balance, and nutrient use efficiency (Kassout et al., 2024).

### 4.4. Wood anatomical and leaf trait covariation and coordination

Our results provide strong evidence of coordination between wood anatomical and leaf traits, reflecting a whole-plant resource-use strategy that links hydraulic function with the photosynthetic compartment (Rosas et al., 2019) (Fig. 6). We find a significant covariation between LMA and hydraulic conductivity potential, as characterized by the slope of the relationship between LVA and BD. An increase in sap conduction during growth is associated with an acquisitive strategy and enhanced photosynthetic carbon gain (low LMA). This relationship suggests that efficient hydraulic conduction during growth supports key eco-physiological processes by maximizing resource capture at the whole-tree level (Wright et al., 2004; Díaz et al., 2016). Furthermore, given that the Argan tree may follow an avoidance strategy under extreme drought conditions by total defoliation, the relationship between LMA and hydraulic conduction potential may represent a functional mechanism that enables the tree to maintain eco-physiological performance under water stress, while maximizing nutrient acquisition when conditions become favorable. The covariation pattern between

LMA and DVS suggests that individuals with higher LMA—and consequently lower photosynthetic rates—are characterized by a greater degree of hydraulic safety (i.e. ability to resist conduction failure due to gas embolism or cavitation). Although branch diameter plays a role in this relationship, the negative correlation between LVA and DVS supports the existence of a trade-off between hydraulic efficiency and safety, similar to what has been observed in the olive tree (Kassout et al., 2021; Terral et al., 2025) (Fig. 6). This trade-off is further supported by the negative correlation between DVS, LL, and LA, as well as the positive correlation between LVA and LL. An increase in LL and LA has previously been linked to greater resource acquisition capacity (Wright et al., 2004; Kassout et al., 2024), while an increase in DVS enhances hydraulic safety (Kassout et al., 2021; Terral et al., 2025). Additionally, the increase in the density of fenestrated zones (DFZ), positively associated with LMA, suggests that the Argan tree may enhance its capacity for nutrient conservation and storage within secondary parenchyma tissues (Spicer and Holbrook, 2007).

## 5. Conclusion

Despite a relatively restricted distribution area, the Argan tree exhibits high plasticity in both wood anatomical and leaf traits. These findings provide valuable insights into the species' functional responses and its capacity for resilience. Phenotypic plasticity may help predict the potential impacts of climate change on this emblematic tree, which plays a crucial role for local human communities.

Trait variation is not directly linked to rainfall, which is typically considered a major limiting climatic factor. Instead, thermo-climatic parameters emerge as the primary drivers of the studied functional traits. The relationships between sap conduction, reserve storage, and photosynthetic capacity appear to be primarily mediated by the interactions between DVS, pattern of vessel enlargement, and LMA. These relationships reflect shifts in eco-functional strategies along a spectrum from resource conservation to rapid resource use, as illustrated by Terral et al. (2025).

While rising temperatures and unpredictable inter-annual rainfall fluctuations theoretically increase the vulnerability of the Argan tree to climate change, its primary response to intense heat and summer water deficits is an avoidance strategy by shedding leaves. Since the Argan tree's entire growth and development cycle occurs outside the summer period, it appears to exhibit a degree of resilience—provided that adequate water resources are available and that its habitat retains a seasonal climatic regime characterized by a cooler and wetter period (typically from October to April). However, as far as we know, human practices and the over-exploitation of Argan groves represent the most significant threats to the long-term sustainability of Argan populations (Fassih et al., 2025).

Although original and innovative, this eco-functional study does not provide insight into the evolutionary history of the Argan tree. Using the same populations and trees studied, genomic analyses should enable the development of a complementary molecular ecology approach aimed at reconstructing the Argan tree's past history and its adaptation to environmental changes, understanding the biogeographical structure of its genetic diversity, identifying priority conservation areas, and predicting the species' resilience to climate change through links between genotype, phenotype and environment.

Beyond the potential of population genomics, this innovative pilot study enables the examination of archaeological charred wood, particularly from the iconic medieval site of Îgîlîz (Tifigit, Anti-Atlas). This upcoming study will help characterize the ecological conditions, particularly climatic conditions, under which Argan trees grew in the past. It will contribute significantly to our understanding of eco-anatomical variations, both in the territory exploited by humans and over time. Finally, let us hope that we can contribute to the preservation of populations and the conservation of the species.
## CRediT authorship contribution statement

**Jean-Frédéric Terral:** Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. **Goeffrey Bédé:** Writing – review & editing, Methodology, Investigation, Formal analysis. **Laure Paradis:** Writing – review & editing, Visualization, Resources, Investigation, Data curation. **Sarah Ivorra:** Writing – review & editing, Visualization, Resources, Methodology, Data curation. **Jérôme Ros:** Writing – review & editing, Resources, Methodology, Investigation. **Vincent Girard:** Writing – review & editing, Resources, Investigation. **Mohamed Ater:** Writing – review & editing, Validation, Resources, Methodology, Investigation, Conceptualization. **Bertrand Limier:** Writing – review & editing, Visualization, Resources, Methodology, Investigation, Formal analysis. **Jalal Kassout:** Writing – review & editing, Writing – original draft, Visualization, Validation, Resources, Methodology, Investigation, Formal analysis, Data curation, Conceptualization.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgements

We are grateful to the authorities and the people we met during our trip who enabled us to collect samples. Ahmed and Malika Jaafar, and their children (“Oasis de Tiout”), as well as Mohamed Sabir from Tiout village, are warmly acknowledged for their help, logistical support and kindness. We would also like to thank the İgîlîz archaeological mission for its invaluable assistance, as well as Ahmed S. Ettahiri, Abdallah Fili, Jean-Pierre Van Staëvel and Marie-Pierre Ruas. This work was conducted with the support of the ARGAN project (PI: J.-F. Terral) financed by the “Mission pour les Initiatives Transverses et Interdisciplinaires (MITI) – Adaptation du vivant à son environnement” of the CNRS (France).

## Abbreviation list:

### Wood anatomical traits

**DVS** density of vessels (number of vessels/mm²)

**LVA** lumen vessel area (µm²)

**RD** ray density (N/mm), calculated by counting the number of rays intersecting a transverse segment defined by the operator, with the segment's endpoints located on a radius and its length measured

**DAP** density of axial parenchyma (DAP, N/mm²) calculated as the number of tangential parenchyma strips crossing a radial segment defined by the operator, with endpoints positioned at the center of a parenchyma band and its length measured

**DFZ** Density of fenestrated zones (N/mm²)

### Leaf traits

**LL** leaf length (cm)

**LW** leaf width (cm)

**LA** leaf area (cm²)

**CIR** leaf circularity (=4 π*(LA/LP2), unitless – where LP (cm) is the leaf perimeter (cm))

**LMA** leaf mass per unit area (LMA, g m⁻²)

### Explanatory parameters including environmental factors

**BD** Branch diameter (cm)

**MAT** Mean Annual Temperature (°C)

**MAP** Mean Annual Precipitation (mm)

**PET** Potential Evapotranspiration (mm/month)

**AI** Aridity Index (=P/PET)

**ACI** Amann Continentality Index or Oceanity (cm)

**ALT** altitude (m)

**LAT** Latitude (°)

**LON** Longitude (°)

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.jaridenv.2025.105473.

## Data availability

Data will be made available on request.

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