Next Article in Journal
Soil Quality Assessment for Sustainable Management: A Minimum Dataset for Long-Term Fertilization in Subtropical Plantations in South China
Previous Article in Journal
Analysis of Volatile Organic Compounds in Cinnamomum camphora Leaves by Direct Thermal Desorption–Gas Chromatography/Mass Spectrometry (DTD-GC/MS)
Previous Article in Special Issue
Modeling Tree Mortality Induced by Climate Change-Driven Drought: A Case Study of Korean Fir in the Subalpine Forests of Jirisan National Park, South Korea
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Forests
Volume 16
Issue 9
10.3390/f16091434
forests-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Genetic Diversity and Risk of Non-Adaptedness in Natural North Moroccan and Planted South Spanish Atlas Cedar †

by
Belén Méndez-Cea
1,2,*,
Isabel García-García
2,
David Manso-Martínez
2,
Juan Carlos Linares
1,
Francisco Javier Gallego
2 and
Jose Luis Horreo
2,*
1
Departamento de Sistemas Físicos, Químicos y Naturales, Universidad Pablo de Olavide, 41013 Sevilla, Spain
2
Unidad de Genética, Departamento de Genética, Fisiología y Microbiología, Facultad de CC Biológicas, Universidad Complutense de Madrid, 28040 Madrid, Spain
*
Authors to whom correspondence should be addressed.
†
Some sections of this manuscript are part of a Ph.D. Thesis by author Belén Méndez-Cea, available online at https://docta.ucm.es/entities/publication/f5dc82b4-22ce-49a5-99e1-bc799b97df1e, accessed on 1 September 2025.
Forests 2025, 16(9), 1434; https://doi.org/10.3390/f16091434
Submission received: 31 July 2025 / Revised: 29 August 2025 / Accepted: 2 September 2025 / Published: 8 September 2025
(This article belongs to the Special Issue Forest Dieback, Tree Mortality Risks and Post-disturbance Resilience to Environmental Changes)
Browse Figures
Versions Notes

Abstract

The Atlas cedar Cedrus atlantica is a relict and endemic conifer from Morocco and Algeria, although plantations may be found in several locations aside from its natural range. Recurrent droughts have been widely related to Atlas cedar dieback, growth decline, and mortality, but the genetic basis of potential adaptive capacity is unknown. We used the double digest restriction-site associated DNA sequencing technique (ddRAD-seq) to describe the genetic structure and variability of Atlas cedar along an aridity gradient in Morocco. Furthermore, we investigated the potential genetic origin of three Spanish plantations, also along an aridity gradient. The obtained single nucleotide polymorphisms (SNPs) were used to perform genotype–environment associations (GEAs) to define SNPs related to bioclimatic variables of temperature and precipitation. The vulnerability of this species to environmental variations was also estimated by its risk of non-adaptedness (RONA). Population structure showed a divergence between the Moroccan natural stands and some of the Spanish plantations, with each Moroccan nucleus being genetically distinct. The genetic variability was significantly lower in plantations than in natural populations. The drier Spanish plantations (easternmost) were genetically very similar to the driest Moroccan population (southernmost), suggesting that as its origin. A total of 41 loci under selection were obtained with the Moroccan dataset. In relation to temperature and precipitation variables, isothermality showed the highest number of associated loci (10) in GEA studies, and genotype–phenotype associations (GPAs) showed one locus associated with the Specific Leaf Area. RONA value was higher in the southernmost High Atlas population, where rising temperature was the main driver of expected genetic offset by allele frequency changes under the worst emissions scenario. In contrast, Spanish plantations would need smaller genetic changes to cope with the expected climate change. Likely gene flow from southern to northern areas suggests a latitudinal heading, where Spanish plantations might operate as an assisted migration. Moreover, one locus showed a northern/southern pattern in saplings but not in adults, suggesting a potential latitudinal pattern of selection. Our results are discussed on the basis of their management and conservation.

1. Introduction

Conserving forest genetic diversity is required to ensure any potential adaptive capacity to ongoing climate change [1]. The evolutionary potential of trees needs to be realized through conservation efforts, particularly as regards relicts and threatened tree species [2]. Climate change is of particular concern for forest ecosystems restricted to climatic refugia, mostly linked to mountain landscapes with local microclimates, as their drought-sensitive species cannot readily shift their ranges if conditions become warmer and drier [3]. That is the case of several Mediterranean mountain forests [4], such as the Atlas cedar (Cedrus atlantica (Endl.) Manetti ex Carrière), an endangered conifer tree endemic to the mountain ranges of northern Morocco and Algeria [5,6,7,8].
The forests formed by Atlas cedar play a crucial role in regulating regional carbon and water cycles, acting both as carbon sinks through biomass accumulation and as regulators of hydrological processes by enhancing infiltration, moderating runoff, and maintaining watershed stability in semi-arid landscapes [7,8]. Furthermore, these forests provide a habitat for numerous endemic, threatened, and climate-sensitive species, sustaining ecological connectivity across fragmented Mediterranean mountains [8].
Atlas cedar forests have been negatively impacted by recent droughts, showing high mortality rates in some areas [6,9,10]. Under a regional climatic context, drought events are becoming more frequent and severe in the Mediterranean basin, and specifically in North Africa [11,12,13]. Several studies have revealed a link among these drought events, global climate warming, and decreased growth in Atlas cedar [6,9]. One of the most vulnerable areas to climate change and habitat degradation is the southernmost limit of the Atlas cedar distribution range, located in the High Atlas range [6]. Here, microrefugia may be the only alternative to extinction, especially as the pace and likelihood of the Atlas cedar range shift are largely insufficient to track the rate of climate change [14]. In addition to microrefugia conservation, assisted migration allows the movement of species populations, for instance, through large-scale reforestation programs, providing an effective climate change adaptation strategy [15,16,17,18]. Furthermore, the suitability of such assisted migration trials might be partially evaluated by assessing the fitness of former Atlas cedar plantations established outside their natural range [19,20]. Nonetheless, the risks associated with moving tree species outside their current ranges and existing policies hamper the idea of Atlas cedar-assisted migration to forest management and conservation.
Despite the fact that establishing reliable genetic conservation strategies in a spatially explicit context has been previously stated for the Atlas cedar [7,8], our understanding of the genetic mechanisms underlying its response to changing environmental conditions is still limited. Hence, previous genetic studies performed in Atlas cedar used molecular markers such as AFLPs (e.g., refs. [7,21,22]) or SSRs (e.g., refs. [23,24,25,26]), while the application of Next-Generation Sequencing (NGS) techniques is still very scarce. This lack of information is probably due to the characteristics of conifer genomes, with large sizes and abundant repetitive sequences, as well as the absence of a reference genome, which makes NGS studies in tree species such as Atlas cedar challenging [27]. Because of the intrinsic characteristics of conifers, NGS approaches with these species are mainly related to RNA-seq as this technique allows for the sequencing of gene regions and the reads can be assembled de novo. It is remarkable that RNA-seq studies have provided the opportunity to perform genomics with non-model species [27]. However, the large genome size of this species makes it very complex and expensive to work with it, not only due to the limits of the technology but also due to those of bioinformatic tools. Over the years, the improvement of NGS techniques and pipelines has provided the opportunity to sequence complete genomes of conifers (e.g., ref. [28]) and to start population genomics studies [29]. In the case of Atlas cedar, some NGS-based advances have been achieved recently, including experimental transcriptomic studies using RNA-seq, which showed candidate genes mechanistically involved in Atlas cedar’s drought sensitivity, rapid local adaptation [30], and selection patterns [31]. As such, there are currently no genomic studies regarding the population structure and characteristics in natural populations of Atlas cedar.
Reduced representation methods, which are based on NGS, allow us to open new horizons to work with non-model species without the need for having previous genomics knowledge of them [32]. These techniques are used to describe many de novo SNPs, which are very useful for performing studies related to population genetics. It is important to highlight that the reproducibility using restriction enzyme sequencing methods could be complex to obtain due to missingness and genotyping errors [33]. However, this issue can be minimized using the correct bioinformatics pipeline to relieve the potential errors [34].
In addition, Atlas cedar has been used outside its natural range in forest plantations in Europe, including some areas of southern Spain subjected to extensive forest restoration during the last half of the 20th century [20], although the specific origin and genetic characteristics of such plantations are currently unknown. These plantations have been proposed as suitable carbon sinks to mitigate climate change [16,19], while increasing drought may reduce their growth, increasing their vulnerability under a climate change scenario [9,35]. Dendrochronological studies have indicated that the Atlas cedar plantations in southwestern Europe are sensitive to increasing water shortages [20]. Therefore, evaluating the genetic diversity and climate adaptive capacity in Atlas cedar plantations is required to inform assisted migration of this endangered tree species, as climate change is projected to reduce its suitable habitat [36,37].
The aim of this study was to investigate genomic information (more precisely, the double-digest restriction-site associated DNA sequencing technique—ddRAD-seq) to define the genetic structure and characteristics of Moroccan Atlas cedar, as well as several Spanish plantations (including their potential origin), following aridity gradients. Furthermore, genotype–environment associations (GEAs), genotype–phenotype associations (GPAs), and the risk of non-adaptedness (RONA) were also performed to shed light on the genetic mechanisms underlying the vulnerability of this endangered tree. The results will be useful to inform suitable conservation strategies under current and expected climate change scenarios.

2. Materials and Methods

2.1. Sample Collection

The Atlas cedar (Cedrus atlantica) is an endangered conifer endemic to northern Morocco and Algeria [5,38]. The Moroccan atlas cedar forests occur in the Rif (RF), Middle Atlas (MA), and High Atlas (HA) between about 1400 and 2300 m asl with an area of 17,200 ha, 86,000 ha, and 26,800 ha, respectively [8], growing under semi-arid-to-humid conditions [5]. In Algeria, Atlas cedar covers about 30,000 ha scattered in the Tell Atlas and Aurès Mountains [10]. We conducted several extensive field works, sampling populations across the three main Atlas cedar locations within their natural distribution range in Morocco [39] and three locations, outside the natural range, in southern Spain (Table 1 and Figure 1; see also refs. [6,20,35,40]). A total of six geographical locations were studied (Figure 1). The natural populations were located in north Morocco, including the Talassemtane National Park (Rif mountains, RF), the Ifrane National Park (Middle Atlas range, MA), and the Eastern High Atlas National Park (High Atlas range, HA). The planted stands located in southern Spain (see also ref. [20]) included the Sierra de las Nieves National Park (Yunquera, YU) and the Sierra Nevada National Park (Dornajo, DO, and Fiñana, FI, respectively; Figure 1 and Supplementary Materials Table S1). The age of the sampled adult trees from natural stands was in all cases older than 100 years, while the trees sampled from plantations were about 60 years old, based on dendrochronological estimations [6,20]. Saplings, which were only collected from Morocco, were about 10–15 years old based on visual counting of the number of scars in the main stem. To our knowledge, there are no reliable records about the origin of the trees used for these plantations. In each selected stand, a 20 m radius circular plot (0.13 ha in size) was established to characterize stand tree size structure and species diversity. All trees with a diameter at 1.3 m (diameter at breast height, DBH) greater than 3 cm were mapped, measured, and classified as living, dead, or stumps.
Due to the lack of long-term, homogeneous local climate data in the study regions, we used the mean local climate for the period 1980–2018 obtained from the CHELSA database (https://chelsa-climate.org/, accessed on 1 September 2025). According to that, the sampling design depicts two climatic gradients (Figure S1). On one hand, we recorded a latitudinal gradient along the natural range of Atlas cedar in Morocco, from the driest sites located in the southernmost High Atlas to milder conditions in the northernmost Rif Mountains (see also ref. [5]). On the other hand, we sampled a longitudinal gradient along southern Spain, from the moister site, located westward under the influence of moist air masses originating from the Atlantic Ocean to the semiarid eastern limit of the Sierra Nevada Range (Figure 1 and Figure S1).

2.2. DNA Extraction and ddRAD-seq

Adult trees and saplings were randomly selected in the area near the circular plots (Table S1). Adult trees were at least 10 m apart, while the saplings were close to sampled adult trees to test relativeness. Fresh leaf samples were collected from 179 individuals of C. atlantica from Moroccan and Spanish populations (Supplementary Materials Table S1). 100 mg of this tissue were lyophilized, and then, the DNeasy Plant Mini Kit (Qiagen®, Berlin, Germany) was used to extract the total DNA following the manufacturer’s instructions with some modifications. To assess the concentration and integrity of the extracted DNA, a spectrophotometer method, NanoDrop OneTM (Thermo Fisher Scientific, Waltham, MA, USA), and electrophoresis in 1% agarose gel were employed, respectively. Afterwards, 179 samples had the necessary quality to proceed with ddRAD-seq [41]. This technique was carried out at LGC Genomics (Berlin, Germany), where the libraries were constructed using two restriction enzymes (RE): PstI (5′CTGCA/G3′), which is a rare cutter enzyme, and ApeKI (5′G/CWGC3′), a common cutter enzyme. After double cutting, a size selection step was performed, minimizing the differences among individuals, making the SNPs described more robust [42], but we have used this threshold to retain a higher number of SNPs due to the large size of the genome and the absence of a reference genome of the species. The size of the fragments selected was 150 bp. Paired-end sequences were obtained by using the Illumina NextSeq500 platform (Illumina, Inc., San Diego, CA, USA) with 1.5 M depth. LGC Genomics (Berlin, Germany) performed QC of the DNA samples sent and of the genetic libraries constructed by them.
The reads were analyzed to determine their quality using FastQC v0.11.9 [43]. Then, fastp v0.12.4 [44] trimmed the adapter sequences and removed the low-quality sequences. A de novo assembly and SNP calling were performed using the resulting paired-end reads. The software ipyrad v0.9.65 [45] was used to carry out both analyses. Subsequently, different filtering steps were performed using VCFtools v0.1.16 program package [46] to create a SNP genetic matrix with the following requirements: minimum allele frequency (MAF) of 5%, maximum missingness of 50%, be biallelic, only 1 SNP per locus was retained to avoid linkage disequilibrium, and finally, individuals with more than 50% of missing data were removed. Several matrices were obtained depending on the original data subset used for the filtration step. It is important to consider that it is difficult to obtain a consensus about the thresholds set for ddRAD-seq data and for the rest of the RAD-seq methods, and they may vary depending on the objective of the study [42], but we have used this threshold in order to retain a higher number of SNPs due to the large size of the genome and the absence of a reference genome of the species.

2.3. Genetic Structure of Populations

Two approaches to describe the genetic structure of the Atlas cedar populations studied herein were carried out. First, a principal component analysis (PCA) was performed using plink2 2.00a2.3 software [47] with the --pca option. Then, a graphic representation was obtained using the R v4.1.2 [48] package ggplot2 v3.3.5 [49]. Then, the fastSTRUCTURE v1.0 software [50] was used to determine the genetic structure of the populations. A total of 10 repetitions with two different K ranges, depending on the dataset studied, were performed. These K ranges were set at 1 to 8 for Moroccan and plantation data. For choosing the K value that best explains the structure of the populations, a tool called chooseK, which is provided by fastSTRUCTURE v1.0, was used. The mean coefficient obtained allowed us to create a graph representation of these results by using pong v1.5 software [51].
Genetic parameters such as fixation indexes (FST) [52] and inbreeding coefficients (GIS) [53], both with significance, were calculated to describe the genetic differences between the populations of study. GenoDive v3.06 software [54] was used to estimate them. Moreover, the number of alleles (Na), the number of effective alleles (Neff), observed and expected heterozygosity (H0 and He), and percentage of polymorphic loci (PPL) were estimated to describe genetic diversity with an R package called adegenet v2.1.10 [55,56]. In addition, fixation indexes were estimated by age cohort in the case of the Moroccan dataset.
Wilcoxon matched-pairs signed-rank tests were performed to describe the presence or absence of significant differences between groups in genetic diversity parameters, specifically H0 and He. These tests use the values calculated for each locus in each group. Prior to running the tests, a Shapiro–Wilk test was carried out to determine the non-parametric nature of our data. All statistical tests were performed in GraphPad Prism 8 for Windows (Boston, MA, USA, www.graphpad.com, accessed on 1 September 2025).

2.4. Selection Signatures

Samples from Moroccan populations were used to perform a selection signature study. Two different approaches were carried out, separated by genetic unit and by age cohort (see Section 2.1.).
BayeScan 2.1 software [57], with default parameters, was utilized to identify selection signatures. This software calculates the FST coefficients for each locus and compares them among and within the populations of study to determine outliers. This version of BayeScan calculates q-values using the FDR correction (false discovery rate). The threshold was set at 5%, so all SNPs with a q-value < 0.05 were considered significant to be under selection.
Those sequences containing significant SNPs were queried against the nucleotide database using BLASTn (NCBI) [58]. When a match was obtained, a BLASTx (NCBI) [59] against the non-redundant protein database was performed to identify protein homology.
The allele frequencies of those significant SNPs were estimated for each population with GenoDive v3.06 software [54].

2.5. Association Studies

Genotype–environment associations (GEA) were identified by using 19 bioclimatic variables obtained from the WorldClim database [60] with a resolution of 30 s and all samples from Morocco. The values of each variable for the localization of our study populations were extracted using the freeware QGIS 3.18 (Quantum Geographic Information System) [61].
An imputation step to fill the missing data of the genetic matrices was conducted using the function “impute” of the R package LEA v3.4.0 [62] with the random method because the function required to perform this study does not allow for the absence of data.
To perform GEA studies, the lfmm (latent factor mixed models) function of the R package LEA v3.4.0 [62] was used. The parameters were 20 repetitions for each run with 100,000 iterations and a burn-in of 50,000. This function requires a K value, which must be the same as that calculated in the fastSTRUCTURE analysis. p-values for each iteration between the SNP and variable were obtained. FDR correction was performed to turn p-values into q-values. The threshold was established at 5%. Finally, in the case of the natural population dataset (Morocco), the sequence containing the significant SNP was used to carry out BLASTn and BLASTx, as described in the Section 2.4.
The rrBLUP v4.6.1 R package [63,64] was used to perform a genotype–phenotype study (GPA). The p-values obtained in the GPA study were corrected by using FDR correction. The significance level was set at 5%, as described for GEA studies. Then, those SNPs significantly associated were analyzed, looking for homologies with the nucleotide and protein NCBI databases, as described previously.
One functional trait, the specific leaf area (SLA), was available for the Moroccan dataset. As described above, the allele frequencies of those significant SNPs were calculated.

2.6. Risk of Non-Adaptedness

To assess the change rate value required for a population to cope with the environmental alterations, a risk of non-adaptedness (RONA) study was carried out [65,66]. The value of RONA shows an absolute average of the changes in allele frequency at loci associated with an environmental variable that is needed for the survival of the population to alterations in that variable. Previous studies have assessed that allele frequencies lower than 0.1 per decade indicate that the species could cope with the environmental perturbations, while when the value is higher than the range from 0.1 to 0.2 per decade, this indicates that this species might not keep pace with climate change [67,68].
pyRona v0.3.6 [66] was used to perform the RONA study. The software uses the predicted bioclimatic data for the future scenario, current bioclimatic data, and the q-values of each bioclimatic variable obtained in GEA. Two RONA studies were performed, one for each of these scenarios. The data information about the predictions was downloaded from the WorldClim database [60], and the points of interest were extracted using QGIS [61], as described above (see Section 2.5). Two emissions scenarios were analyzed: low emissions (RCP 2.6), whose temperature annual range increase is limited to 2 °C, and high emissions (RCP 8.5), limited to a maximum of 4.9 °C. These two scenarios have been previously used to perform RONA studies with populations of Abies pinsapo Boiss. and Abies marocana Trab. [69,70].

3. Results

3.1. Stand Structure

The diameter distribution of the plantations (YU, DO, and FI) was relatively homogeneous, with very scarce juvenile trees or samplings and no old trees. Despite adult trees being of a similar age among the south Spain plantations (about 60 years old see ref. [20]), the mean diameter was almost half in the dry zone of Fiñana (Figure 2 and Supplementary Materials Table S2). Natural stands from north Morocco were characterized by abundant regeneration and the presence of very old trees (some of them above seven centuries old and nearly 2 m in diameter), scattered among dense mid-sized trees. HA showed an unbalanced size structure, compared to RF and MA, as small-sized abundant regeneration predominates under the old trees, with almost no intermediate sizes (Figure 3). Stand basal area of alive cedars was higher in the natural stand, with about 26 m2 ha−1, while the basal area of alive cedars was between 15 and 18 m2 ha−1 in YU and DO, respectively, and below 6 m2 ha−1 in FI. The basal area of dead cedars was almost negligible in the planted stands and relatively low in RF and MA (0.2–0.3 m2 ha−1), while HA showed about 10 m2 ha−1 of dead cedar trees. Stump basal area was higher in MA and HA (13.2 and 10.2 m2 ha−1, respectively), compared to RF and the cedar plantations, evidencing a significant logging activity in these sites (Figure 2 and Supplementary Materials Table S2).

3.2. Genetic Structure of Populations

After filtering the data obtained in the de novo assembly with the Moroccan dataset (natural populations), a total of 12,773 SNPs and 124 individuals were maintained. The filtering of the dataset that contained only plantation samples retained 14,085 SNPs and 49 individuals.
FST values were estimated among age groups (adults and saplings) in the Moroccan populations, obtaining the following values: Rif, −0.003; Middle Atlas, 0.002; High Atlas, 0.000. None of these values showed significant difference among the age groups within the same population.
In the Moroccan and Spanish populations matrix, 13,415 SNPs and 173 individuals were retained. PCA results showed a separation between populations with a 49.4% explanation in the PC1 axis (Figure 3). The High and the Middle Atlas are closer to each other than the Rif. Hence, the second axis separated the Western Rif from the other populations (11% PC2) (Figure 4). Moreover, one of the Spanish populations, Fiñana (Almería), formed a group with the High Atlas population (Morocco).
STRUCTURE results (Figure 4) showed that K = 4 is the best value to explain the genetic structure of these six geographical populations of study. It corresponds to the nuclei observed in the PCA studies. These groups were formed by (i) Rif, (ii) Middle Atlas, (iii) High Atlas and Fiñana, and (iv) Dornajo and Yunquera. In addition, all FST values were significant except for the Yunquera and Dornajo pair (Table 1).
Mean number of alleles (Na) ranged between 1.987 and 1.911, mean number of effective alleles (Neff) ranged between 1.779 and 1.843, observed and expected heterozygosity are between 0.246 and 0.268 and 0.376 and 0.411, respectively. Inbreeding coefficient (GIS) showed significant values in the three cases (p-value < 0.001), ranging from 0.069 to 0.091. Finally, the percentage of polymorphic loci ranged between 91.051 and 98.685% (Table 2). In the case of the plantations dataset, mean Na was between 1.720 and 1.851, mean Neff ranged from 1.398 to 1.713, Ho was between 0.198 and 0.237, and He was between 0.316 and 0.352. GIS ranged from 0.086 to 0.143 with significant values (p-value < 0.001), too. The percentage of polymorphic loci was between 72.041 and 85.062.
Wilcoxon matched-pairs signed-rank tests performed with observed and expected heterozygosity showed significant differences for all pairs except for the observed heterozygosity in the Yunquera–Dornajo pair (p-value = 0.0961; see Supplementary Materials Tables S4 and S5).

3.3. Selection Signatures

As Spanish samples come from artificial plantations, this study was carried out using the genetic matrix which contains only the Moroccan populations dataset from the de novo assembly. The results showed a total of 41 loci under selection with a q-value significance of 5%. A total of 13 matches were obtained against the nucleotide database, and 10 of them showed homology with the protein database. All nucleotide matches obtained were with conifer species. Some of the proteins are transcription factors, transferases, or abscisic acid receptors and could be involved in stress abiotic response (Table S3).
To describe differences between adults (over 100 years old) and saplings (10–15 years old) of these populations, another study in which the populations were separated by age was performed (see Table S1). Only 1 locus was significantly under selection, and some trends can be observed when allele frequencies were calculated (Figure 5). In saplings, the frequency of this SNP seems to grow with latitude, but not in adults. No homologous sequences were detected in the nucleotide database of the NCBI. It is remarkable that this locus was not found in the case of Moroccan populations without age separation.

3.4. Association Studies (GEA and GPA)

The 19 bioclimatic variables related to temperature and precipitation were used to perform the GEA studies, both with the Moroccan and Spanish plantations datasets.
A total of 83 associations with 21 different loci were identified using the genetic matrix obtained with the Moroccan dataset (Table 3). In this case, mean temperature of the driest quarter (BIO9) and mean temperature of the warmest quarter (BIO10) could not be used because the variations among the three populations were very low. For this reason, the function was not able to perform the analysis using them. The results obtained with the rest of the variables were as follows: annual mean temperature (BIO1), mean diurnal range (BIO2), seasonality temperature (BIO4), and minimum temperature of the coldest month (BIO6) showed the same three associated loci. Maximum temperature of the warmest month (BIO5), mean temperature of the wettest quarter (BIO8), precipitation of the driest quarter (BIO17), and precipitation of the warmest quarter (BIO18) shared eight loci. Annual precipitation (BIO12), precipitation of the wettest month (BIO13), and precipitation of the wettest quarter (BIO16) shared four associated loci, but BIO12 presented one associated locus more. Precipitation of the driest month (BIO14) and precipitation seasonality (BIO15) had the same three associated loci. Isothermality (BIO3) showed the highest number of associations with 10 loci, and none of them are shared with the rest of the variables. Minimum temperature of the coldest month (BIO6) and temperature annual range (BIO7) showed three and two associated loci, respectively. Some of these loci, specifically 11 of them, were significant in the selection signature analysis too.
In terms of the homologies found with the genome regions, a total of six matches were obtained against nucleotide and protein databases. Some of the functions of the homologous proteins are related to photosynthesis, mRNA modification, or transcription regulation (Supplementary Materials Table S6).
In the case of Spanish plantations, three bioclimatic variables, two of them related to temperature (BIO3 and BIO5) and one related to precipitation (BIO12), did not show any significant association. A total of 1689 associations with 290 different SNPs were described (Table 4). It is remarkable that the highest number of associations was obtained with precipitation of the warmest quarter (BIO18). Mean diurnal range (BIO2) and temperature seasonality (BIO4) shared 56 SNPs. Mean temperature of driest quarter (BIO9) and mean temperature of warmest quarter (BIO10) shared the three loci. Precipitation of the wettest quarter (BIO16) and precipitation of the coldest quarter (BIO19) showed associations with the same 12 loci.
The GPA study performed with the Morocco dataset showed only one locus (SNP 4842) associated with the functional trait SLA (Specific Leaf Area). This region showed a hit with a fragment of the mitochondrial genome of Pinus taeda L. The allele frequencies calculated for each population indicated that the alternative allele was in a higher proportion in the High Atlas population than in the other nuclei (Figure 6).

3.5. Risk of Non-Adaptedness

The first study was performed using the three populations from Morocco and 17 bioclimatic variables (all except for BIO9 and BIO10, which could not be used in the GEAs, see Section 3.4) (Figure 7A,B). The lowest values of the change rate were obtained in the low-emissions scenarios, as expected. The maximum change rate value for each scenario ranges between 2.8 and 6.55, with it being higher in the high-emissions scenario. These values are reached by the High Atlas population in both scenarios and for the maximum temperature of the warmest month (BIO5) variable. The second variable with high RONA values was the annual mean temperature (BIO1). The Middle Atlas showed values slightly lower than the High Atlas, and the Western Rif was the population with the lowest values for both scenarios. The bioclimatic variables related to precipitation showed lower values than those related to temperature. It is important to highlight that the RONA values obtained for precipitation variables reached higher values for the High Atlas and Western Rif than for the Middle Atlas.
The second study was carried out with the Spanish plantations dataset and 16 bioclimatic variables (all except for BIO3, BIO5, and BIO12, which did not show any associations in the GEAs, see Section 3.2) (Figure 7C,D). The maximum values ranged from 1.98 in the case of the low-emissions scenario to 3.72 in the high-emissions scenario. Mean temperature of the warmest quarter (BIO10) showed the highest RONA value, reaching its maximum in Dornajo in both scenarios, closely followed by the value for mean temperature of the driest quarter (BIO9). Yunquera showed 0.0 as the RONA value for the precipitation of the driest month (BIO14) in both scenarios. Fiñana and Yunquera showed lower values than Dornajo.
The RONA values obtained indicate a delay between changes in environmental conditions and allele frequency change rates, which could be important for understanding this species’ response to climate change.

4. Discussion

To our knowledge, this study is the first to use the ddRAD-seq technique in Cedrus atlantica. The development of new technologies based on NGS has allowed us to work with non-model organisms and without a reference genome [71]. The reduced representation sequencing minimizes the genome complexity of species with large genomes by using restriction enzymes. The number of molecular markers maintained in this study after filtering (12,773 SNPs for Moroccan populations, 13,415 SNPs for Moroccan and Spanish populations, and 14,085 SNPs for Spanish plantations) is slightly lower than that obtained in other conifer species with the same techniques, such as Pinus contorta Douglas ex Loudon , which showed 17,765 SNPs, and Picea glauca (Moench) Voss., with 17,845 SNPs [72]. On the other hand, the number of SNPs here maintained was higher than the SNPs obtained for other endemic and relict conifers, such as Abies pinsapo, which showed 3982 SNPs [69] and Abies marocana, with 6131 SNPs [70]. This is a huge amount of information for the present genetic study.
All of the Moroccan populations (Figure 1) have their own genetic pool, as shown by the PCA (Figure 3), STRUCTURE (Figure 4), and FST values (Table 1). In addition, STRUCTURE analyses showed genetic introgression from the High Atlas to the Middle Atlas (and, interestingly, not the contrary). Despite the presence of the Rif and the Atlas Mountain Ranges, possibly also acting as an orographic barrier, hindering pollen movement through them, thus hindering gene flow [24], we have also found, but in a smaller proportion, the potential presence of gene flow from the Atlas to the Western Rif; but again, not the contrary. This pattern agrees with gene flow occurring only from southern to northern areas (Figure 1), following an aridity gradient where the northern limit of Atlas cedar distribution has the maximum value of annual precipitation (about 2000 mm). This could reflect not necessarily the unique existence of pollen going from southern to northern areas, but their reproductive success (fitness) when arriving in better conditions, which would be much better in the north (Rif). For this to be confirmed, subsequent analyses should reflect genetic selection in loci related to climatic variables such as temperature and/or precipitation (see below).
From an adaptive conservation perspective, the Atlas cedar range needs to allow in situ selection and capture high levels of the available genetic variation, key for responding to the complex effects of climate change [1]. The natural populations showed low genetic variability with an observed heterozygosity lower than expected, as usual in a relict tree species [3]. However, these results are different from others obtained with relict species such as Abies pinsapo [73] in which this decrease in diversity was not observed. In addition, the inbreeding values (GIS), which showed significant p-values for all nuclei, indicated a high level of inbreeding, again in agreement with the relict condition of Atlas cedar (relict populations are usually small and isolated, with high levels of inbreeding, e.g., ref. [74]). All of this is a risk for the continuity of the species in the area.
A noticeable result obtained in this study is the description of the origin of some Spanish plantations [20]. The individuals present in Fiñana, the easternmost drier site, were very similar to those of the High Atlas, the southernmost drier site (Figure 3), with a small percentage of Middle Atlas introgression (Figure 4), showing their most likely origin. In contrast with this, the other two Spanish plantations (Dornajo and Yunquera) showed a distinct genetic compound from all the other Spanish and Moroccan populations (Figure 3 and Figure 4), with no differences in genetic variability among them (Table 3, Tables S4 and S5). FST values (Table 1) between them were very small (FST = 0.003) and not significant, an order of magnitude smaller than the values among all the other populations (mean FST = 0.038 ± 0.009 se), and very far from the high values (mean FST = 0.123 ± 0.022 se) among these two plantations and all of the other populations. All of this shows Yunquera and Dornajo as coming from a different area than the Moroccan sampled populations (Figure 1). Nonetheless, the results obtained from Dornajo must be treated with caution because of the small sample size. As the Atlas cedar is a relict species from the Atlas and Rif Mountain Ranges of Morocco and Algeria [75], probably the origin of such plantations could be Algeria, but this finding must be confirmed by using Algerian samples in the future. Importantly, all Spanish plantations showed lower diversity (Table 3, Tables S4 and S5) than natural stands, and also presence of inbreeding, which is in line with a founder effect due to their condition (only few specimens were taken from their natural populations to the new plantations); thus, they could be potentially affected by genetic drift in the near future.
The presence of significant loci subjected to evolutionary processes indicated that selective pressure could be acting in this species. Interestingly, some differences between adults and saplings were observed, in a locus that was identified under selection using the dataset separated by age. Moreover, this locus only appears within this dataset. So, it is possible that the new generations of Atlas cedar are experiencing some genetic variations as a response to recent environmental alterations caused by climate change. However, no significant FST values were found between adults and saplings. The selection may be occurring in a few very specific regions of the genome (which was evidenced by only 41 significant SNPs being obtained from the total 12,773 SNPs studied), and therefore, it is not enough to show differences in PCA and FST values, which consider all of the SNPs at the genome level. This also agrees with SNP 212, significantly following selection analyses, and whose frequency in saplings and not in adults grows with latitude (Figure 5). Trees need a long period of time to reproduce, spanning many generations (30 years for the first reproduction in Atlas cedar; ref. [38]), so this process is slow, and it is possible that these changes cannot be described even in the next generation [76]. The absence of a reference genome hindered obtaining most of the gene information from the genome regions containing the loci under selection, but some homologies could be found against the databases (Table S3). This includes genes related, among others, to stress response. However, these kinds of results must be treated with caution due to the proteins found not being able to develop the same function in our species, despite most of the proteins obtained potentially being conserved across species because of the relevant role of their functions. In sum, selection seems to be acting in natural populations of Atlas cedar by changing allele frequencies with generations.
The combined study of environmental variables and genetic data allows us to study the response of tree species to climate perturbations, e.g., refs. [69]. A total of 42 loci identified in GEA analysis with the Moroccan dataset and 997 loci for the Spanish plantations dataset were associated with temperature variables (Table 3 and Table 4), indicating that alterations in temperature potentially limit the survival of Atlas cedar, according to previous ecological studies [31]. In addition, 41 associations for the Moroccan dataset and 692 in the case of Spanish plantations were obtained with variables related to precipitation, evidencing the effect of drought stress observed in several studies [35,40,77]. According to this, studies based on niche models reported bioclimatic variables driving the modern distribution of Cedrus atlantica were related to precipitation: winter precipitation (BIO19), precipitation of the driest month (BIO14), and annual precipitation (BIO12) [36,78]. Both temperature (emphasized by our GEA analyses) and precipitation seem to therefore affect the species dynamics and evolution both in natural populations and plantations and agree with the genetic introgression; thus, the aforementioned gene flow pattern has better fitness individuals, whereby gene flow arrived at better conditions in northern areas (lower temperature and higher precipitation).
Closely related to this, the RONA values estimated for Atlas cedar show a negative lag between the allele frequency change rate and the climate alterations, being more pronounced in the High Atlas population. Variables related to temperature, mainly the annual mean temperature (BIO1), showed the greatest influence on the survival of Atlas cedar in both scenarios. Our results are in line with species distribution models simulating the current distribution of suitable habitat for C. atlantica in Algeria, which showed that the annual mean temperature has the greatest contribution (53%) in determining the Atlas cedar range [37]. Furthermore, correlations with variables related to temperature variability, such as BIO5 (maximum temperature of the warmest month) and BIO6 (minimum temperature of the coldest month), also indicate a significant effect of extreme temperatures [37,78,79].
The highest RONA values obtained in our work for the High Atlas could be related to the intrinsic characteristics of this population, which is the driest and the hottest area where Atlas cedar is placed [6]. For this reason, a reduction in this species could be expected in this southernmost region (High Atlas), and our prediction indicates that it is possible for this to occur because of the highest RONA value obtained for this population in the high-emissions scenario (near to 7). Meanwhile, the values shown by the Western Rif and Middle Atlas for the low-emissions scenario indicated that both areas show lower risk than the High Atlas. These results could be explained by the fact that the Rif and Middle Atlas are the most suitable places for the development of Atlas cedar in Morocco [5]. However, in a low-emissions scenario, the Rif showed a slightly higher value than the Middle Atlas for the annual mean temperature. Moreover, Rif has the highest precipitation and the lowest temperature range of the Moroccan distribution of the Atlas cedar. So, the individuals from this population might be less able to cope with temperature increases [6,80].
The maximum RONA values obtained for Spanish plantations were lower than the estimated values for natural populations. This could be related to the fact that the alterations in the bioclimatic variations are predicted to be less strong in Spain than in Morocco, since some Moroccan nuclei, such as those from the southernmost High Atlas, are already very close to the survival limit for Atlas cedar. Thus, Spanish plantations, with genetics originally coming from southern populations, would need smaller genetic changes in the future to cope with climate change, in spite of the founder effect and consequent reduction in genetic variability coming from their forestry origin.
As it is expected that extreme climate events such as droughts will increase in both severity and intensity as a consequence of forecasted climate change [81,82], it should be expected that there will be a forest range shift upwards in altitude, which has been proposed in this species [14]. Here, we investigate another potential range shift, by northward migration [79]. The fitness of northern Atlas cedar plantations may provide relevant information about species conservation, leading to increase efforts in southern areas and the maintenance of the absence of barriers to gene flow between northern and southern areas, which is especially important regarding conservation and management. Hence, the assisted movement of this endangered tree species outside its historic range may be necessary for conservation purposes [83,84] while it might be, to some extent, already implemented by former plantations [19,20]. Evaluating the genetic diversity and climate adaptive capacity of Atlas cedar plantations from southern Spain supports our understanding of the vulnerability of this relict conifer to ongoing climate change and informs us about the fate of future assisted migration [16]. Nevertheless, our results suggest that these plantations might also be sensitive to impending climate dryness, while climate change is projected to reduce the suitable habitat of cedar [36,37].
In addition, this species has been shown experimentally to be capable of responding to drought events and to produce rapid local adaptation [30]. Our data (RONA analyses) shows that the genetic change rates estimated for Atlas cedar to cope with predicted climatic scenarios range from 0.0 to 6.55, being higher in the High Atlas than in northern populations. These values are higher than those obtained in other relict species located in northern Morocco, which is the case for Abies marocana (which showed a maximum change rate value of 0.9; ref. [70]). It is known that if these change rates are higher than the genetic range from 0.1 to 0.2 per decade, the species shows a lag between allele frequencies and environment perturbations [75]. So, if climate change continues as expected, it will be important to track this pace, limiting the persistence of rear-edge Atlas cedar stands in Morocco and Algeria. In fact, the relationship between extreme drought events in North Africa and an increase in Atlas cedar mortality has already been observed [6,9,10,85], with a 75% decrease in Atlas cedar census in the Rif Mountains [14] between the years 1960 and 2010. All of this shows the species as critically endangered due to current climate change.
Palaeoecological research supports repeated elevation shifts within the Atlas cedar range, at least from the last ice age to the Holocene warming [75,86]. Despite these past elevational migrations and the persistence of isolated populations in scattered refugia, evidence of the ability of Atlas cedar to cope with the last climatic cycles [14], the current rate of climate change, and the land use-related habitat loss likely overcomes their adaptive capacity [8]. Hence, a critical issue is to maintain the most suitable populations locally in microrefugia, while our results also suggest the suitability of some Atlas cedar plantations outside their natural range as a valuable resource for conservation [15,16].
It should be noted that forest plantations constitute a substantial portion of forest ecosystems and contribute significantly to global greening, supporting carbon sinks and mitigating climate change [87]. However, the contrasting drought adaptive capacity of the tree species used for forest management will be determinant for future carbon sinks under a changing climate. Atlas cedar has shown drought adaptive capacity in experimental [30] and forestry studies [19,20]. The genetic potential of Atlas cedar plantations, as a functional element for assisted migration, contrasts with the severe decline expected for the suitable habitat within the Atlas cedar natural range, suggesting that the assisted movement of this endangered tree species within and outside their historic range may be useful for conservation and forestry purposes [17,18].

5. Conclusions

While the need to include evolutionary processes that maintain genetic diversity and adaptive potential in Atlas cedar forests has been previously advocated [7], there are still several gaps about how this might occur for effective conservation and management guidance [8]. Here, new emerging genomic tools and modeling improve our understanding of the genetic basis of adaptive responses to environmental change, while they provide the opportunity to consider evolutionary processes in conservation planning. The potential selection fingerprints obtained seem to show that an adaptive response to climate change is taking place in Atlas cedar Moroccan populations. GEA studies indicated that environmental variables related to temperature (but also precipitation) are the most relevant for Atlas cedar populations. Several candidate genes involved in it have been identified, and potential gene flow/introgression patterns (southern to northern areas), as well as RONA analyses, showed the species as critically endangered and were very useful to propose strategies of conservation because they allow us to describe the vulnerability of populations (especially in the south) and their current dynamics. If genetic diversity for adaptive evolution can be conserved, and habitat conservation and restoration are put in place to limit deforestation and soil erosion, this must promote in situ adaptive processes, with potential impacts on biodiversity and the Atlas cedar forest ecosystem function.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/f16091434/s1. Figure S1: Mean local climate for the period 1980–2018 obtained from the CHELSA database (https://chelsa-climate.org/, accessed on 1 September 2025) for natural (north Morocco) Atlas cedar study sites in the Rif (a), RF; Middle Atlas (b), MA; and High Atlas (c), HA; and plantations (south Spain) in Yunquera (d), YU; Dornajo (e), DO; and Fiñana (f) FI. The blue lines indicate the monthly total precipitation, and the red lines indicate the monthly mean temperature; total annual precipitation (p, mm) and mean annual temperature (T, °C) are also noted as insets; Table S1: Sampling location, local climate for the period 1980–2018 obtained from the CHELSA database (https://chelsa-climate.org/, accessed on 1 September 2025) and the number of individuals from each nuclei studied classified as adults and saplings. HA: High Atlas; MA: Middle Atlas; RF: Rif; FI: Fiñana; DO: Dornajo; YU: Yunquera; Table S2: Stand structure characteristic of the Atlas cedar sites studied. Rif (RF), Middle Atlas (MA), High Atlas (HA), Yunquera (YU), Dornajo (DO), and Fiñana (FI); Table S3: Summary of significant loci obtained in the selection signature study when the Moroccan dataset was used. Shown are the SNP ID, the type of sequence that matched against our sequences, and the e-value of the nucleotide matched. Name of the protein that showed homology, its function and e-value of that matched; Table S4: Wilcoxon matched-pairs signed-rank tests with observed heterozygosity values (Ho). The W value obtained for each pair (above the diagonal) and the p-value of each comparison (below the diagonal) are shown. The non-significant p-value appears in bold. HA: High Atlas; MA: Middle Atlas; RF: Rif; YU: Yunquera; FI: Fiñana; DO: Dornajo; Table S5: Wilcoxon matched-pairs signed-rank tests with expected heterozygosity values (He). The W value obtained for each pair (above the diagonal) and the p-value of each comparison (below the diagonal) are shown. HA: High Atlas; MA: Middle Atlas; RF: Rif; YU: Yunquera; FI: Fiñana; DO: Dornajo; Table S6: Summary of significant SNPs obtained in the GEA study with the Moroccan dataset, which showed homologies with nucleotide and protein databases of the NCBI. The nucleotide sequence that matched against our sequence, the protein, and the described function for each of them are indicated. Highlighted in blue are those SNPs that were significant in the selection signature study.

Author Contributions

Conceptualization, J.C.L., F.J.G. and J.L.H.; formal analysis, B.M.-C. and I.G.-G.; funding acquisition, J.C.L. and F.J.G.; investigation, B.M.-C., I.G.-G. and D.M.-M.; resources, J.C.L.; writing—original draft, B.M.-C.; writing—review and editing, B.M.-C., J.L.H., J.C.L. and I.G.-G. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Junta de Andalucía (grant number PAIDI, P18-RT-1170) and the Spanish Ministry of Science and Innovation (grant numbers TED2021-129770B-34C22 and PID2021-123675OB-C44).

Data Availability Statement

The original contributions presented in this study are included in the article and Supplementary Materials. Further inquiries can be directed to the corresponding authors.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Sgrò, C.M.; Lowe, A.J.; Hoffmann, A.A. Building evolutionary resilience for conserving biodiversity under climate change. Evol. Appl. 2011, 4, 326–337. [Google Scholar] [CrossRef] [PubMed]
  2. Hampe, A.; Petit, R.J. Conserving biodiversity under climate change: The rear edge matters. Ecol. Lett. 2005, 8, 461–467. [Google Scholar] [CrossRef] [PubMed]
  3. Hampe, A.; Jump, A.S. Climate Relicts: Past, Present, Future. Annu. Rev. Ecol. Evol. Syst. 2011, 42, 313–333. [Google Scholar] [CrossRef]
  4. Médail, F.; Diadema, K. Glacial refugia influence plant diversity patterns in the Mediterranean basin. J. Biogeogr. 2009, 36, 1333–1345. [Google Scholar] [CrossRef]
  5. Benabid, A. Biogéographie phytosociologie et phytodynamique des cédraies de l’Atlas Cedrus atlantica (Manetti). Le cèdre de l’Atlas. Actes du séminaire international sur le cèdre de l’Atlas. Annal. Rech. For. Maroc. 1994, 27, 62–76. [Google Scholar]
  6. Camarero, J.J.; Sánchez-Salguero, R.; Sangüesa-Barreda, G.; Lechuga, V.; Viñegla, B.; Seco, J.I.; Taïqui, L.; Carreira, J.A.; Linares, J.C. Drought, axe and goats. More variable and synchronized growth forecasts worsening dieback in Moroccan Atlas cedar forests. Sci. Total Environ. 2021, 765, 142752. [Google Scholar] [CrossRef]
  7. Bobo-Pinilla, J.; Nieto Lugilde, D.; Terrab, A.; Balao, F.; Peñas, J. Spatially explicit assessment of genetic variation to inform conservation effort for an endangered Mediterranean conifer, Cedrus atlantica. Ecol. Evol. 2022, 12, e9613. [Google Scholar] [CrossRef]
  8. Cheddadi, R.; Taberlet, P.; Boyer, F.; Coissac, E.; Rhoujjati, A.; Urbach, D.; Remy, C.; Khater, C.; Antry, S.; Aoujdad, J.; et al. Priority conservation areas for Cedrus atlantica in the Atlas Mountains, Morocco. Conserv. Sci. Prac. 2022, 4, 12680. [Google Scholar] [CrossRef]
  9. Navarro-Cerrillo, R.M.; Sarmoum, M.; Gazol, A.; Abdoun, F.; Camarero, J.J. The decline of Algerian Cedrus atlantica forests is driven by a climate shift towards drier conditions. Dendrochronologia 2019, 55, 60–70. [Google Scholar] [CrossRef]
  10. Sarmoum, M.; Camarero, J.J.; Abdoun, F. Aridification increases growth resistance of Atlas cedar forests in NW Algeria. For. Ecol. Manag. 2024, 556, 121730. [Google Scholar] [CrossRef]
  11. Esper, J.; Frank, D.; Büntgen, U.; Verstege, A.; Luterbacher, J.; Xoplaki, E. Long-term drought severity variations in Morocco. Geophys. Res. Lett. 2007, 34, L17702. [Google Scholar] [CrossRef]
  12. Touchan, R.; Anchukaitis, K.J.; Meko, D.M.; Sabir, M.; Attalah, S.; Aloui, A. Spatiotemporal drought variability in northwestern Africa over the last nine centuries. Clim. Dyn. 2011, 37, 237–252. [Google Scholar] [CrossRef]
  13. Lelieveld, J.; Proestos, Y.; Hadjinicolaou, P.; Tanarhte, M.; Tyrlis, E.; Zittis, G. Strongly increasing heat extremes in the Middle East and North Africa (MENA) in the 21st century. Clim. Change 2016, 137, 245–260. [Google Scholar] [CrossRef]
  14. Cheddadi, R.; Henrot, A.J.; François, L.; Boyer, F.; Bush, M.; Carré, M.; Coissac, E.; De Oliveira, P.E.; Ficetola, F.; Hambuckers, A.; et al. Microrefugia, climate change, and conservation of Cedrus atlantica in the Rif Mountains, Morocco. Front. Ecol. Evol. 2017, 5, 114. [Google Scholar] [CrossRef]
  15. Twardek, W.M.; Taylor, J.J.; Rytwinski, T.; Aitken, S.N.; MacDonald, A.L.; Van Bogaert, R.; Cooke, S.J. The application of assisted migration as a climate change adaptation tactic: An evidence map and synthesis. Biol. Conserv. 2023, 280, 109932. [Google Scholar] [CrossRef]
  16. McLachlan, J.S.; Hellmann, J.J.; Schwartz, M.W. A framework for debate of assisted migration in an era of climate change. Conserv. Biol. 2007, 21, 297–302. [Google Scholar] [CrossRef]
  17. Chakraborty, D.; Ciceu, A.; Ballian, D.; Garzón, M.B.; Bolte, A.; Bozic, G.; Buchacher, R.; Čepl, J.; Cremer, E.; Ducousso, A.; et al. Assisted tree migration can preserve the European forest carbon sink under climate change. Nat. Clim. Chang. 2024, 14, 845–852. [Google Scholar] [CrossRef]
  18. Szamosvári, E.; Chakraborty, D.; Schüler, S.; van Loo, M. Assisted Migration as a Climate Change Adaptation Strategy. In Ecological Connectivity of Forest Ecosystems; Lapin, K., Oettel, J., Braun, M., Konrad, H., Eds.; Springer: Cham, Switzerland, 2025. [Google Scholar] [CrossRef]
  19. Messinger, J.; Güney, A.; Zimmermann, R.; Ganser, B.; Bachmann, M.; Remmele, S.; Aas, G. Cedrus libani: A promising tree species for Central European forestry facing climate change? Eur. J. For. Res. 2015, 134, 1005–1017. [Google Scholar] [CrossRef]
  20. Camarero, J.J.; Gazol, A.; Colangelo, M.; Linares, J.C.; Navarro-Cerrillo, R.M.; Rubio-Cuadrado, Á.; Silla, F.; Dumas, P.-J.; Courbet, F. Shifting Precipitation Patterns Drive Growth Variability and Drought Resilience of European Atlas Cedar Plantations. Forests 2021, 12, 1751. [Google Scholar] [CrossRef]
  21. Renau-Morata, B.; Nebauer, S.G.; Sales, E.; Allainguillaume, J.; Caligari, P.; Segura, J. Genetic diversity and structure of natural and managed populations of Cedrus atlantica (Pinaceae) assessed using random amplified polymorphic DNA1. Am. J. Bot. 2005, 92, 87–884. [Google Scholar] [CrossRef] [PubMed]
  22. Dagher-Kharrat, M.B.; Mariette, S.; Lefèvre, F.; Fady, B.; March, G.G.-D.; Plomion, C.; Savouré, A. Geographical diversity and genetic relationships among Cedrus species estimated by AFLP. Tree Genet. Genomes 2007, 3, 275–285. [Google Scholar] [CrossRef]
  23. Fady, B.; Lefèvre, F.; Reynaud, M.; Vendramin, G.G.; Dagher-Kharrat, M.B.; Anzidei, M.; Pastorelli, R.; Savouré, A.; Bariteau, M. Gene flow among different taxonomic units: Evidence from nuclear and cytoplasmic markers in Cedrus plantation forests. Theor. Appl. Genet. 2003, 107, 1132–1138. [Google Scholar] [CrossRef]
  24. Terrab, A.; Paun, O.; Talavera, S.; Tremetsberger, K.; Arista, M.; Stuessy, T.F. Genetic diversity and population structure in natural populations of Moroccan Atlas cedar (Cedrus atlantica; Pinaceae) determined with cpSSR markers. Am. J. Bot. 2006, 93, 1274–1280. [Google Scholar] [CrossRef] [PubMed]
  25. Karam, M.-J.; Aouad, M.; Roig, A.; Bile, A.; Dagher-Kharrat, M.B.; Klein, E.K.; Fady, B.; Lefèvre, F. Characterizing the genetic diversity of Atlas cedar and phylogeny of Mediterranean Cedrus species with a new multiplex of 16 SSR markers. Tree Genet. Genomes 2019, 15, 60. [Google Scholar] [CrossRef]
  26. Šeho, M.; Fussi, B.; Kavaliauskas, D. Cedrus atlantica—Possible alternative tree species under changing climate conditions in Central Europe. SilvaWorld 2025, 4, 23–35. [Google Scholar] [CrossRef]
  27. Neale, D.B.; Wheeler, N.C. (Eds.) The Conifers: Genomes, Variation and Evolution; Springer: Cham, Switzerland, 2019; ISBN 978-3-319-46806-8. [Google Scholar] [CrossRef]
  28. Gagalova, K.K.; Warren, R.L.; Coombe, L.; Wong, J.; Ming Nip, K.; Saint Yuen, M.M.; Whitehill, J.G.A.; Celedon, J.M.; Ritland, C.; Taylor, G.A.; et al. Spruce giga-genomes: Structurally similar yet distinctive with differentially expanding gene families and rapidly evolving genes. Plant J. 2022, 111, 1469–1485. [Google Scholar] [CrossRef]
  29. Méndez-Cea, B. Estudio Genómico del Potencial Adaptativo Frente al Cambio Climático en Coníferas Sensibles a la Sequía. Ph.D. Thesis, Facultad de Ciencias Biológicas, Universidad Complutense de Madrid, Madrid, Spain, 2023. Available online: https://docta.ucm.es/entities/publication/f5dc82b4-22ce-49a5-99e1-bc799b97df1e (accessed on 1 September 2025).
  30. Cobo-Simón, I.; Gómez-Garrido, J.; Esteve-Codina, A.; Dabad, M.; Alioto, T.; Maloof, J.N.; Méndez-Cea, B.; Seco, J.I.; Linares, J.C.; Gallego, F.J. De novo transcriptome sequencing and gene co-expression reveal a genomic basis for drought sensitivity and evidence of a rapid local adaptation on Atlas cedar (Cedrus atlantica). Front. Plant Sci. 2023, 14, 1116863. [Google Scholar] [CrossRef]
  31. Scotti, I.; Lalagüe, H.; Oddou-Muratorio, S.; Scotti-Saintagne, C.; Ruiz Daniels, R.; Grivet, D.; Lefevre, F.; Cubry, P.; Fady, B.; González-Martínez, S.C.; et al. Common microgeographical selection patterns revealed in four European conifers. Mol. Ecol. 2023, 32, 393–411. [Google Scholar] [CrossRef]
  32. Andrews, K.R.; Good, J.M.; Miller, M.R.; Luikart, G.; Hohenlohe, P.A. Harnessing the power of RADseq for ecological and evolutionary genomics. Nat. Rev. Genet. 2016, 17, 81–92. [Google Scholar] [CrossRef]
  33. Ulaszewski, B.; Meger, J.; Burczyk, J. Comparative Analysis of SNP Discovery and Genotyping in Fagus sylvatica L. and Quercus robur L. Using RADseq, GBS, and ddRAD Methods. Forests 2021, 12, 222. [Google Scholar] [CrossRef]
  34. O’Leary, S.J.; Puritz, J.B.; Willis, S.C.; Hollenbeck, C.M.; Portnoy, D.S. These aren’t the loci you’re looking for: Principles of effective SNP filtering for molecular ecologists. Mol. Ecol. 2018, 27, 3193–3206. [Google Scholar] [CrossRef] [PubMed]
  35. Linares, J.C.; Taïqui, L.; Camarero, J.J. Increasing Drought Sensitivity and Decline of Atlas Cedar (Cedrus atlantica) in the Moroccan Middle Atlas Forests. Forests 2011, 2, 777–796. [Google Scholar] [CrossRef]
  36. Xiao, S.; Li, S.; Wang, X.; Chen, L.; Su, T. Cedrus distribution change: Past, present, and future. Ecol. Indica. 2022, 142, 109159. [Google Scholar] [CrossRef]
  37. Laala, A.; Adimi, A. Modeling the potential distribution and shift of an Algerian endangered endemic species (Cedrus atlantica) under climate change scenarios: Implications for conservation. J. Nat. Conserv. 2024, 82, 126744. [Google Scholar] [CrossRef]
  38. Thomas, P. Cedrus atlantica. The IUCN Red List of Threatened Species 2013: e.T42303A2970716. 2013. Available online: https://www.iucnredlist.org/species/42303/2970716 (accessed on 29 April 2025).
  39. Caudullo, G. Chorological Data for the Main European Woody Species; Dataset; Mendeley Data: London, UK, 2024. [Google Scholar] [CrossRef]
  40. Linares, J.C.; Taïqui, L.; Sangüesa-Barreda, G.; Seco, J.I.; Camarero, J.J. Age-related drought sensitivity of Atlas cedar (Cedrus atlantica) in the Moroccan Middle Atlas forests. Dendrochronologia 2013, 31, 88–96. [Google Scholar] [CrossRef]
  41. Peterson, B.K.; Weber, J.N.; Kay, E.H.; Fisher, H.S.; Hoekstra, H.E. Double Digest RADseq: An Inexpensive Method for De Novo SNP Discovery and Genotyping in Model and Non-Model Species. PLoS ONE 2012, 7, e37135. [Google Scholar] [CrossRef] [PubMed]
  42. Doublet, M.; Degalez, F.; Lagarrigue, S.; Lagoutte, L.; Gueret, E.; Allais, S.; Lecerf, F. Variant calling and genotyping accuracy of ddRAD-seq: Comparison with 20X WGS in layers. PLoS ONE 2024, 19, e0298565. [Google Scholar] [CrossRef]
  43. Andrews, S. FastQC: A Quality Control Tool for High Throughput Sequence Data. 2010. Available online: http://www.bioinformatics.babraham.ac.uk/projects/fastqc (accessed on 11 March 2025).
  44. Chen, S.; Zhou, Y.; Chen, Y.; Gu, J. fastp: An ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 2018, 34, i884–i890. [Google Scholar] [CrossRef]
  45. Eaton, D.A.R.; Overcast, I. Ipyrad: Interactive assembly and analysis of RADseq datasets. Bioinformatics 2020, 36, 2592–2594. [Google Scholar] [CrossRef] [PubMed]
  46. Danecek, P.; Auton, A.; Abecasis, G.; Albers, C.A.; Banks, E.; DePristo, M.A.; Handsaker, R.E.; Lunter, G.; Marth, G.T.; Sherry, S.T.; et al. The variant call format and VCFtools. Bioinformatics 2011, 27, 2156–2158. [Google Scholar] [CrossRef]
  47. Chang, C.C.; Chow, C.C.; Tellier, L.C.A.M.; Vattikuti, S.; Purcell, S.M.; Lee, J.J. Second-generation PLINK: Rising to the challenge of larger and richer datasets. GigaScience 2015, 4, 7. [Google Scholar] [CrossRef]
  48. R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. 2022. Available online: https://www.R-project.org/ (accessed on 1 September 2025).
  49. Wickham, H. ggplot2: Elegant graphics for Data Analysis; Springer: New York, NY, USA, 2016; ISBN 978-3-319-24277-4. Available online: https://ggplot2.tidyverse.org/ (accessed on 1 September 2025).
  50. Raj, A.; Stephens, M.; Pritchard, J.K. fastSTRUCTURE: Variational Inference of Population Structure in Large SNP Data Sets. Genetics 2014, 197, 573–589. [Google Scholar] [CrossRef]
  51. Behr, A.A.; Liu, K.Z.; Liu-Fang, G.; Nakka, P.; Ramachandran, S. Pong: Fast analysis and visualization of latent clusters in population genetic data. Bioinformatics 2016, 32, 2817–2823. [Google Scholar] [CrossRef]
  52. Rousset, F. Genetic differentiation and estimation of gene flow from F-statistics under isolation by distance. Genetics 1997, 145, 1219–1228. [Google Scholar] [CrossRef]
  53. Rousset, F. Inbreeding and relatedness coefficients: What do they measure? Heredity 2002, 88, 371–380. [Google Scholar] [CrossRef]
  54. Meirmans, P.G. genodive version 3.0: Easy-to-use software for the analysis of genetic data of diploids and polyploids. Mol. Ecol. Resour. 2020, 20, 1126–1131. [Google Scholar] [CrossRef] [PubMed]
  55. Jombart, T. adegenet: An R package for the multivariate analysis of genetic markers. Bioinformatics 2004, 24, 1403–1405. [Google Scholar] [CrossRef] [PubMed]
  56. Jombart, T.; Ahmed, I. adegenet 1.3-1: New tools for the analysis of genome-wide SNP data. Bioinformatics 2011, 27, 3070–3071. [Google Scholar] [CrossRef]
  57. Foll, M.; Gaggiotti, O.E. A genome scan method to identify selected loci appropriate for both dominant and codominant markers: A bayesian perspective. Genetics 2008, 180, 977–993. [Google Scholar] [CrossRef]
  58. Altschul, S.F.; Gish, W.; Miller, W.; Myers, E.W.; Lipman, D.J. Basic local alignment search tool. J. Mol. Biol. 1990, 215, 403–410. [Google Scholar] [CrossRef] [PubMed]
  59. Gish, W.; States, D.J. Identification of protein coding regions by database similarity search. Nat. Genet. 1993, 3, 266–272. [Google Scholar] [CrossRef] [PubMed]
  60. Fick, S.E.; Hijmans, R.J. WorldClim 2: New 1km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 2017, 37, 4302–4315. [Google Scholar] [CrossRef]
  61. QGIS Development Team. QGIS Geographic Information System. Open Source Geospatial Foundation Project. 2022. Available online: http://qgis.osgeo.org/ (accessed on 1 September 2025).
  62. Frichot, E.; Francois, O. LEA: An R package for landscape and ecological association studies. Methods Ecol. Evol. 2015, 6, 925–929. [Google Scholar] [CrossRef]
  63. Endelman, J.B. Ridge regression and other kernels for genomic selection with R package rrBLUP. Plant Genome 2011, 4, 250–255. [Google Scholar] [CrossRef]
  64. Endelman, J.B.; Jannink, J.L. Shrinkage estimation of the realized relationship matrix. G3 Genes Genomes Genet. 2012, 2, 1405–1413. [Google Scholar] [CrossRef]
  65. Rellstab, C.; Zoller, S.; Walthert, L.; Lesur, I.; Pluess, A.R.; Graf, R.; Bodénès, C.; Sperisen, C.; Kremer, A.; Gugerli, F. Signatures of local adaptation in candidate genes of oaks (Quercus spp.) with respect to present and future climatic conditions. Mol. Ecol. 2016, 25, 5907–5924. [Google Scholar] [CrossRef]
  66. Pina-Martins, F.; Baptista, J.; Pappas, G., Jr.; Paulo, O.S. New insights into adaptation and population structure of cork oak using genotyping by sequencing. Glob. Change Biol. 2019, 5, 337–350. [Google Scholar] [CrossRef]
  67. Jump, A.S.; Hunt, J.M.; Martinez-Izquierdo, J.A.; Peñuelas, J. Natural selection and climate change: Temperature-linked spatial and temporal trends in gene frequency in Fagus sylvatica. Mol. Ecol. 2006, 15, 3469–3480. [Google Scholar] [CrossRef]
  68. Jump, A.S.; Ruiz-Benito, P.; Greenwood, S.; Allen, C.D.; Kitzberger, T.; Fensham, R.; Martínez-Vilalta, J.; Lloret, F. Structural overshoot of tree growth with climate variability and the global spectrum of drought-induced forest dieback. Glob. Change Biol. 2017, 23, 3742–3757. [Google Scholar] [CrossRef]
  69. Méndez-Cea, B.; García-García, I.; Linares, J.C.; Gallego, F.J. Warming appears as the main risk of non-adaptedness for western Mediterranean relict fir forests under expected climate change scenarios. Front. Plant Sci. 2023, 14, 1155441. [Google Scholar] [CrossRef] [PubMed]
  70. Méndez-Cea, B.; García-García, I.; Sánchez-Salguero, R.; Lechuga, V.; Gallego, F.J.; Linares, J.C. Tree-Level Growth Patterns and Genetic Associations Depict Drought Legacies in the Relict Forests of Abies marocana. Plants 2023, 12, 873. [Google Scholar] [CrossRef] [PubMed]
  71. Elshire, R.J.; Glaubitz, J.C.; Sun, Q.; Poland, J.A.; Kawamoto, K.; Buckler, E.S.; Mitchell, S.E. A robust, simple genotyping-by- sequencing (GBS) approach for high diversity species. PLoS ONE 2011, 6, e19379. [Google Scholar] [CrossRef] [PubMed]
  72. Chen, C.; Mitchell, S.E.; Elshire, R.J.; Buckler, E.S.; El-Kassaby, Y.A. Mining conifers’ mega-genome using rapid and efficient multiplexed high-throughput genotyping-by-sequencing (GBS) SNP discovery platform. Tree Genet. Genomes 2013, 9, 1537–1544. [Google Scholar] [CrossRef]
  73. Cobo-Simón, I.; Méndez-Cea, B.; Jump, A.S.; Seco, J.; Gallego, F.J.; Linares, J.C. Understanding genetic diversity of relict forests. Linking long-term isolation legacies and current habitat fragmentation in Abies pinsapo Boiss. For. Ecol. Manag. 2020, 461, 117947. [Google Scholar] [CrossRef]
  74. Willi, Y.; Van Buskirk, J.; Hoffmann, A.A. Limits to the adaptive potential of small populations. Annu. Rev. Ecol. Evol. Syst. 2006, 37, 433–458. [Google Scholar] [CrossRef]
  75. Cheddadi, R.; Fady, B.; François, L.; Hajar, L.; Suc, J.P.; Huang, K.; Demarteau, M.; Vendramin, G.G.; Ortu, E. Putative glacial refugia of Cedrus atlantica deduced from quaternary pollen records and modern genetic diversity. J. Biogeogr. 2009, 6, 1361–1371. [Google Scholar] [CrossRef]
  76. Dauphin, B.; Rellstab, D.; Schmid, M.; Zoller, S.; Karger, D.N.; Brodbeck, S.; Guillaume, F.; Gugerli, F. Genomic vulnerability to rapid climate warming in a tree species with a long generation time. Glob. Change Biol. 2021, 27, 1181–1195. [Google Scholar] [CrossRef] [PubMed]
  77. Taoufik, A.; Atmane, R.; Abdenbi, Z.E.A. Explaining the Atlas Cedar (Cedrus atlantica M.) dieback by studying Water relations of young plants subject to an edaphic drought. E3S Web Conf. 2021, 234, 00100. [Google Scholar] [CrossRef]
  78. Bouahmed, A.; Vessella, F.; Schirone, B.; Krouchi, F.; Derridj, A. Modeling Cedrus atlantica potential distribution in North Africa across time: New putative glacial refugia and future range shifts under climate change. Reg. Environ. Chang. 2019, 19, 1667–1682. [Google Scholar] [CrossRef]
  79. Arar, A.; Nouidjem, Y.; Bounar, R.; Tabet, S.; Kouba, Y. Modeling of the current and future potential distribution of Atlas cedar (Cedrus atlantica) forests revealed shifts in the latitudinal, longitudinal and altitudinal range towards more humid conditions. Ecol. Quest. 2020, 31, 49–62. [Google Scholar] [CrossRef]
  80. Williams, A.P.; Allen, C.D.; Macalady, A.K.; Griffin, D.; Woodhouse, C.A.; Meko, D.M.; Swetnam, T.W.; Rauscher, S.A.; Seager, R.; Grissino-Mayer, H.D.; et al. Temperature as a potent driver of regional forest drought stress and tree mortality. Nat. Clim. Chang. 2013, 3, 292–297. [Google Scholar] [CrossRef]
  81. Ranasinghe, R.; Ruane, A.C.; Vautard, R.; Arnell, N.; Coppola, E.; Cruz, F.A.; Dessai, S.; Islam, A.S.; Rahimi, M.; Carrascal, D.R.; et al. 2021: Climate Change Information for Regional Impact and for Risk Assessment. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change; Masson-Delmotte, V.P., Zhai, A., Pirani, S.L., Connors, C., Péan, S., Berger, N., Caud, Y., Chen, L., Goldfarb, M.I., Gomis, M., et al., Eds.; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2023; pp. 1767–1926. [Google Scholar] [CrossRef]
  82. Trisos, C.H.; Adelekan, I.O.; Totin, E.; Ayanlade, A.; Efitre, J.; Gemeda, A.; Kalaba, K.; Lennard, C.; Masao, C.; Mgaya, Y.; et al. 2022: Africa. In Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change; Pörtner, H.-O., Roberts, D.C., Tignor, M., Poloczanska, E.S., Mintenbeck, K., Alegría, A., Craig, M., Langsdorf, S., Löschke, S., Möller, V., et al., Eds.; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2023; pp. 1285–1455. [Google Scholar] [CrossRef]
  83. Millar, C.I.; Stephenson, N.L.; Stephens, S.L. Climate change and forests of the future: Managing in the face of uncertainty. Ecol. Appl. 2007, 17, 2145–2151. [Google Scholar] [CrossRef]
  84. Marris, E. Forestry: Planting the forest of the future. Nature 2009, 459, 906–908. [Google Scholar] [CrossRef] [PubMed]
  85. El Abidine, A.Z. Forest decline in Morocco: Causes and control strategy. Sci. Chang. Plan./Sécher. 2003, 14, 209. [Google Scholar]
  86. Terrab, A.; Hampe, A.; Lepais, O.; Talavera, S.; Vela, E.; Stuessy, T.F. Phylogeography of north African Atlas cedar (Cedrus atlantica, Pinaceae): Combined molecular and fossil data reveal a complex quaternary history. Am. J. Bot. 2008, 95, 1262–1269. [Google Scholar] [CrossRef] [PubMed]
  87. Yang, H.; Ciais, P.; Frappart, F.; Li, X.; Brandt, M.; Fensholt, R.; Fan, L.; Saatchi, S.; Besnard, S.; Deng, Z.; et al. Global increase in biomass carbon stock dominated by growth of northern young forests over past decade. Nat. Geosci. 2023, 16, 886–892. [Google Scholar] [CrossRef]
Forests 16 01434 g001
Figure 1. Location of the Atlas cedar study sites in Morocco and Spain. The dotted polygons show the distribution of Atlas cedar [39]. The color scale indicates a 90 m digital elevation model obtained from copernicus (https://dataspace.copernicus.eu/explore-data/data-collections/copernicus-contributing-missions/collections-description/COP-DEM, accessed on 1 September 2025). Rif (RF), Middle Atlas (MA), and High Atlas (HA) are natural populations, while Yunquera (YU), Dornajo (DO), and Fiñana (FI) are plantations.
Figure 1. Location of the Atlas cedar study sites in Morocco and Spain. The dotted polygons show the distribution of Atlas cedar [39]. The color scale indicates a 90 m digital elevation model obtained from copernicus (https://dataspace.copernicus.eu/explore-data/data-collections/copernicus-contributing-missions/collections-description/COP-DEM, accessed on 1 September 2025). Rif (RF), Middle Atlas (MA), and High Atlas (HA) are natural populations, while Yunquera (YU), Dornajo (DO), and Fiñana (FI) are plantations.
Forests 16 01434 g001
Forests 16 01434 g002
Figure 2. Sampling site images and diameter (dbh, cm) distribution of the studied Atlas cedar stands (photographs taken by J.C. Linares).
Figure 2. Sampling site images and diameter (dbh, cm) distribution of the studied Atlas cedar stands (photographs taken by J.C. Linares).
Forests 16 01434 g002
Forests 16 01434 g003
Figure 3. PCA representation of the Moroccan and Spanish plantations datasets. The first two axes (PC1 and PC2) are shown. The legend indicates the nuclei of study and the color for each of them.
Figure 3. PCA representation of the Moroccan and Spanish plantations datasets. The first two axes (PC1 and PC2) are shown. The legend indicates the nuclei of study and the color for each of them.
Forests 16 01434 g003
Forests 16 01434 g004
Figure 4. Structure results of the Moroccan and Spanish datasets. The K = 4, which was the best value to explain the genetic structure, is shown. The colors indicate the genetic pool of each population. RF: Rif; MA: Middle Atlas; HA: High Atlas; FI: Fiñana; DO: Dornajo; YU: Yunquera.
Figure 4. Structure results of the Moroccan and Spanish datasets. The K = 4, which was the best value to explain the genetic structure, is shown. The colors indicate the genetic pool of each population. RF: Rif; MA: Middle Atlas; HA: High Atlas; FI: Fiñana; DO: Dornajo; YU: Yunquera.
Forests 16 01434 g004
Forests 16 01434 g005
Figure 5. Allele frequencies of SNP 212, which showed significance in the selection signature study by age cohort.
Figure 5. Allele frequencies of SNP 212, which showed significance in the selection signature study by age cohort.
Forests 16 01434 g005
Forests 16 01434 g006
Figure 6. Allele frequency of SNP 4842, which showed a significant association with SLA in the GPA study for each population.
Figure 6. Allele frequency of SNP 4842, which showed a significant association with SLA in the GPA study for each population.
Forests 16 01434 g006
Forests 16 01434 g007
Figure 7. Risk of non-adaptedness (RONA) results obtained for Moroccan stands (A,B) and Spanish plantations (C,D). The two study scenarios are represented: the low-emissions scenario, RCP2.6 (A,C), and the high-emissions scenario, RCP8.5 (B,D). The X axes show the RONA value, which indicates the allele frequency change rate, which is necessary for the species to be able to respond to the alteration in the bioclimatic variable of study. The values of the X axes are different among the figures due to the value ranges obtained differing between scenarios and datasets. The Y axis indicates the population. The color legend is related to the bioclimatic variables represented as bars. They are colored from red to orange for temperature variables and with a blue scale for precipitation variables.
Figure 7. Risk of non-adaptedness (RONA) results obtained for Moroccan stands (A,B) and Spanish plantations (C,D). The two study scenarios are represented: the low-emissions scenario, RCP2.6 (A,C), and the high-emissions scenario, RCP8.5 (B,D). The X axes show the RONA value, which indicates the allele frequency change rate, which is necessary for the species to be able to respond to the alteration in the bioclimatic variable of study. The values of the X axes are different among the figures due to the value ranges obtained differing between scenarios and datasets. The Y axis indicates the population. The color legend is related to the bioclimatic variables represented as bars. They are colored from red to orange for temperature variables and with a blue scale for precipitation variables.
Forests 16 01434 g007
Table 1. Fixation indexes (FST) between nuclei of study using SNPs from the Moroccan and Spanish datasets. Above the diagonal are the FST values, and below, the p-values. HA: High Atlas, MA: Middle Atlas; RF: Western Rif; YU: Yunquera; FI: Fiñana; and DO: Dornajo.
Table 1. Fixation indexes (FST) between nuclei of study using SNPs from the Moroccan and Spanish datasets. Above the diagonal are the FST values, and below, the p-values. HA: High Atlas, MA: Middle Atlas; RF: Western Rif; YU: Yunquera; FI: Fiñana; and DO: Dornajo.
HAMARFYUFIDO
HA—0.0340.0690.2180.0150.207
MA0.001—0.0540.2040.0240.193
RF0.0010.001—0.1730.0640.163
YU0.0010.0010.000—0.2150.003
FI0.0010.0010.0000.001—0.202
DO0.0010.0010.0000.1710.001—
Table 2. Parameters of genetic diversity with standard errors for the Moroccan and Spanish plantation studied sites. The number of alleles (Na), the number of effective alleles (Neff), the observed heterozygosity (Ho), the expected heterozygosity (He), the inbreeding coefficient (GIS), with the level of significance (*** p-value < 0.001), and the percentage of polymorphic loci (PPL) are shown. HA: High Atlas; MA: Middle Atlas; RF: Rif.
Table 2. Parameters of genetic diversity with standard errors for the Moroccan and Spanish plantation studied sites. The number of alleles (Na), the number of effective alleles (Neff), the observed heterozygosity (Ho), the expected heterozygosity (He), the inbreeding coefficient (GIS), with the level of significance (*** p-value < 0.001), and the percentage of polymorphic loci (PPL) are shown. HA: High Atlas; MA: Middle Atlas; RF: Rif.
PopulationNaNeffHoHeGISPPL
HA1.978 ± 0.0011.825 ± 0.0050.264 ± 0.0010.401 ± 0.0020.069 ***97.761%
MA1.987 ± 0.0011.843 ± 0.0050.268 ± 0.0010.411 ± 0.0020.071 ***98.685%
RF1.911 ± 0.0031.779 ± 0.0050.246 ± 0.0010.376 ± 0.0020.091 ***91.051%
YU1.851 ± 0.0031.713 ± 0.0050.234 ± 0.0020.352 ± 0.0020.086 ***85.062%
FI1.720 ± 0.0041.668 ± 0.0050.198 ± 0.0020.316 ± 0.0020.143 ***72.041%
DO1.784 ± 0.0031.398 ± 0.0040.237 ± 0.0020.333 ± 0.0020.099 ***78.417%
Table 3. Bioclimatic variables obtained from WorldClim to perform GEA studies with the Moroccan dataset. The number of significant associations (q-value < 5%) for each of the variables studied is shown.
Table 3. Bioclimatic variables obtained from WorldClim to perform GEA studies with the Moroccan dataset. The number of significant associations (q-value < 5%) for each of the variables studied is shown.
VariableCodeNo. of Associations
BIO1Annual mean temperature3
BIO2Mean diurnal range3
BIO3Isothermality10
BIO4Temperature seasonality3
BIO5Max. temperature of the warmest month8
BIO6Min. temperature of the coldest month3
BIO7Temperature annual range2
BIO8Mean temperature of the wettest quarter8
BIO11Mean temperature of the coldest quarter2
BIO12Annual precipitation5
BIO13Precipitation of the wettest month4
BIO14Precipitation of the driest month4
BIO15Precipitation seasonality4
BIO16Precipitation of the wettest quarter4
BIO17Precipitation of the driest quarter8
BIO18Precipitation of the warmest quarter8
BIO19Precipitation of the coldest quarter4
Table 4. Bioclimatic variables obtained from WorldClim to perform GEA studies with the Spanish plantation dataset. The number of significant associations (q-value < 5%) for each of the variables studied is shown.
Table 4. Bioclimatic variables obtained from WorldClim to perform GEA studies with the Spanish plantation dataset. The number of significant associations (q-value < 5%) for each of the variables studied is shown.
VariableCodeNo. of Associations
BIO1Annual mean temperature119
BIO2Mean diurnal range57
BIO3Isothermality0
BIO4Temperature seasonality56
BIO5Max. temperature of the warmest month0
BIO6Min. temperature of the coldest month236
BIO7Temperature annual range26
BIO8Mean temperature of the wettest quarter248
BIO9Mean temperature of the driest quarter3
BIO10Mean temperature of the warmest quarter3
BIO11Mean temperature of the coldest quarter249
BIO12Annual precipitation0
BIO13Precipitation of the wettest month11
BIO14Precipitation of the driest month129
BIO15Precipitation seasonality34
BIO16Precipitation of the wettest quarter12
BIO17Precipitation of the driest quarter235
BIO18Precipitation of the warmest quarter259
BIO19Precipitation of the coldest quarter12
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Méndez-Cea, B.; García-García, I.; Manso-Martínez, D.; Linares, J.C.; Gallego, F.J.; Horreo, J.L. Genetic Diversity and Risk of Non-Adaptedness in Natural North Moroccan and Planted South Spanish Atlas Cedar. Forests 2025, 16, 1434. https://doi.org/10.3390/f16091434

AMA Style

Méndez-Cea B, García-García I, Manso-Martínez D, Linares JC, Gallego FJ, Horreo JL. Genetic Diversity and Risk of Non-Adaptedness in Natural North Moroccan and Planted South Spanish Atlas Cedar. Forests. 2025; 16(9):1434. https://doi.org/10.3390/f16091434

Chicago/Turabian Style

Méndez-Cea, Belén, Isabel García-García, David Manso-Martínez, Juan Carlos Linares, Francisco Javier Gallego, and Jose Luis Horreo. 2025. "Genetic Diversity and Risk of Non-Adaptedness in Natural North Moroccan and Planted South Spanish Atlas Cedar" Forests 16, no. 9: 1434. https://doi.org/10.3390/f16091434

APA Style

Méndez-Cea, B., García-García, I., Manso-Martínez, D., Linares, J. C., Gallego, F. J., & Horreo, J. L. (2025). Genetic Diversity and Risk of Non-Adaptedness in Natural North Moroccan and Planted South Spanish Atlas Cedar. Forests, 16(9), 1434. https://doi.org/10.3390/f16091434

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop