Genetic diversity and distribution of Senegalia senegal (L.) Britton under climate change scenarios in West Africa

Paul Terwase Lyam; Joaquín Duque-Lazo; Walter Durka; Frank Hauenschild; Jan Schnitzler; Ingo Michalak; Oluwatoyin Temitayo Ogundipe; Alexandra Nora Muellner-Riehl

doi:10.1371/journal.pone.0194726

Abstract

Climate change is predicted to impact species’ genetic diversity and distribution. We used Senegalia senegal (L.) Britton, an economically important species distributed in the Sudano-Sahelian savannah belt of West Africa, to investigate the impact of climate change on intraspecific genetic diversity and distribution. We used ten nuclear and two plastid microsatellite markers to assess genetic variation, population structure and differentiation across thirteen sites in West Africa. We projected suitable range, and potential impact of climate change on genetic diversity using a maximum entropy approach, under four different climate change scenarios. We found higher genetic and haplotype diversity at both nuclear and plastid markers than previously reported. Genetic differentiation was strong for chloroplast and moderate for the nuclear genome. Both genomes indicated three spatially structured genetic groups. The distribution of Senegalia senegal is strongly correlated with extractable nitrogen, coarse fragments, soil organic carbon stock, precipitation of warmest and coldest quarter and mean temperature of driest quarter. We predicted 40.96 to 6.34 per cent of the current distribution to favourably support the species’ ecological requirements under future climate scenarios. Our results suggest that climate change is going to affect the population genetic structure of Senegalia senegal, and that patterns of genetic diversity are going to influence the species’ adaptive response to climate change. Our study contributes to the growing evidence predicting the loss of economically relevant plants in West Africa in the next decades due to climate change.

Citation: Lyam PT, Duque-Lazo J, Durka W, Hauenschild F, Schnitzler J, Michalak I, et al. (2018) Genetic diversity and distribution of Senegalia senegal (L.) Britton under climate change scenarios in West Africa. PLoS ONE 13(4): e0194726. https://doi.org/10.1371/journal.pone.0194726

Editor: Tzen-Yuh Chiang, National Cheng Kung University, TAIWAN

Received: April 20, 2017; Accepted: March 8, 2018; Published: April 16, 2018

Copyright: © 2018 Lyam et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: This work was funded by the Alexander von Humboldt Foundation (AVH), Germany under the auspices of an International Climate Protection Fellowship (ICPC), and partly by the German Academic Exchange Service (DAAD), both granted to Paul Terwase Lyam and hosted by Alexandra Muellner-Riehl. Field work and data collection was additionally supported by the National Centre for Genetic resources and Biotechnology (NACGRAB) Ibadan, Nigeria. Apart from this, the funders have no role in the study design, subsequent data collection and analysis, decision to publish or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Biodiversity is crucial to human well-being as it provides fundamental ecosystem services. At the same time, biodiversity is under threat by human population growth, increased land use, and CO₂ emissions, which are all direct or indirect drivers of climate change [1–3]. One of the regions most threatened by climate change is sub-Saharan Africa [4, 5]. This part of Africa is a developing region where deforestation and desertification are of major concern. Within sub-Saharan Africa lies the so-called arid and semi-arid land traversing the savannah belts of the region and making up the Sudano-Sahelian zone (SSZ) [6]. This region is socio-economically and ecologically important, but at the same time threatened by climate change, land degradation due to unsustainable agriculture, deforestation, and overgrazing [7].

The effect of environmental and climate change has been studied for a few African plant species [3, 8, 9]. Mostly, such studies focussed on predicting the impact on ecosystems and species [10]. These studies are highly valuable, but do not account for intra-specific genetic diversity [11]. Global studies indicate that large proportions of suitable habitat and the respective species will be lost during this century due to climate change [3, 11, 12]. However, climate change will also affect intraspecific genetic diversity [13]. Studying the effects of climatic alterations on genetic diversity is necessary if we are to understand the evolutionary consequences of climate change and its long-term effects on species distribution [14]. To assess future impacts on biodiversity many studies have used the Intergovernmental Panel on Climate Change’s (IPCC) scenarios from the Special Report on Emission Scenarios, which project emissions and socio-economic changes [10, 15]. The latest set of climate model simulations assumes four different sets of possible futures. They are known as the Representation Concentration Pathways (RCPs) representing low (RCP 2.6), medium (RCPs 4.5 and 6.0) or high (RCP 8.5) emissions [16, 17].

In the sub-Saharan zone, one important species is Senegalia senegal (L.) Britton. (Fabaceae), syn. Acacia senegal (L.) Willd. [18]. The species, which is known for its exudate named ‘gum arabic’, is a small deciduous tree, native to arid and semi-desert regions of sub-Saharan Africa, but has also been introduced in other regions (e.g. the Indian sub-continent) [19, 20]. In western Africa, S. senegal naturally occurs either as a dominant extensive pure stand or in co-habitation with other species in a variety of vegetation types, including semi-desert grassland, Anogeissus woodland and rocky hill slopes [21]. The species can grow on sandy, skeletal and slightly loamy soils. However, it shows preference for coarse-texture soils such as fossil dunes, with a soil pH of 5 to 8. Although S. senegal has been observed to grow in areas that receive 100 to 950 mm annual rainfall, it thrives best in areas with 300 to 400 mm annual rainfall. The species can tolerate five to 11 months of drought and can survive temperatures high as 43°C, dry wind and sandstorms, but is highly sensitive to frost. The altitude ranges from 100 to 1700 metres above sea level within the African ASAL. The species is insect-pollinated, and predominantly out-crossing [7, 22]. Seeds are dispersed at least partly by animals, especially ungulates [23]. The astringent, emulsifying, film-forming and encapsulating properties of the gum arabic represents an important economic resource, which is often used in the food-processing, pharmaceutical, cosmetic, and lithographic ink industries [24–27]. In addition to playing a preferential role in the context of land degradation and desertification control, S. senegal has been reported to be resilient to drought and grazing making it an important species in agroforestry systems [28]. The current conservation status of the species is “least concern” (IUCN). However, field observations show that the species is locally under intense pressure from animal grazing, pest and human activities (personal observation) thus corroborating the earlier report of Eisa et al. [29]. In addition, climate change will likely further increase the vulnerability of the species through disturbances of natural habitat. As a consequence, in situ conservation and future utility of S. senegal could be jeopardized [30, 31], with obvious repercussions for human livelihoods. Various aspects including soil physicochemical properties [30], pollination and breeding systems [32], ecology [27, 33], phylogeography [23], and population genetics [34, 35] in S. senegal have been previously investigated. In S. senegal, intraspecific genetic variation has been studied both on regional levels [34, 35] and for range-wide phylogeography [23]. The latter shows a recent range expansion to west Africa with genetic diversity reported to be low in the region [23]. However, no detailed analyses are available for the SSZ of West Africa where the available phylogeographic analysis is characterized by sampling gaps.

This study aims to fill existing sampling gaps and identify genetic diversity and population structure of Senegalia senegal (L.) Britton. in the SSZ of West Africa. We used both nuclear and maternally inherited plastid microsatellite markers. Furthermore, we used species distribution modelling to forecast the potential range loss and loss of intra-specific genetic diversity under climate change and promote strategies towards effective management and conservation of S. senegal in the West African SSZ. Specifically, we address the following questions: 1) Is the genetic diversity in the West African SSZ as low as previously stated for the region? 2) How is genetic diversity structured within the West African SSZ? 3) How would future climate impact the distribution of S. senegal in the West African SSZ? We discuss the potential impact of climate driven range changes of S. senegal in the SSZ of West Africa on extant genetic variation.

Materials and methods

Ethic statement

No specific permissions were required for sampling of leaf material. There is no previous report stating that Senegalia senegal is threatened or under protection in any of the sampled locations. According to IUCN, the species has “least concern” conservation status.

Study area and sampling

Fresh leaf samples of Senegalia senegal were collected from thirteen localities along the fringes of the gum arabic belt, situated between latitude 11°N and 13°N and longitude -0.1°W and 13.2°E between 2012 and 2016. The study area is a mixture of tropical woodland, grassland savannah and semi-desert steppe that transverses the SSZ and extends eastwards from north-eastern Ghana to north-eastern Nigeria (Fig 1). The spatial distances between neighbouring collection sites ranged between 35 km (BIR–GUR) and 1,500 km (BKG–MDG). The sample size per site was between 13 and 32 individuals, with a total of 316 samples. Within sites distance was between 10 m and 10 km, depending on the size of the population. Details of parameters sampled from the sites are shown in Table 1. Field-collected material was dried in silica gel prior to DNA extraction [36]. At least one individual per population was deposited as a voucher specimen at both, the herbaria of the National Centre for Genetic Resources and Biotechnology (NACGRAB), Ibadan, Nigeria, and Leipzig University (LZ), Germany.

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Fig 1. Location of thirteen Senegalia senegal populations sampled from West Africa.

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Table 1. Sampling data for Senegalia senegal plant material analysed in this study.

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Microsatellite genotyping

Total genomic DNA was extracted from 30 mg of silica-dried leaves using a Nucleo-spin plant II kit (Macherey-Nagel, Düren, Germany). We used ten nuclear microsatellites (hereafter nSSR) specifically designed for Senegalia senegal by Assoumane et al. [37], and two universal plastid microsatellites (hereafter cpSSR) designed by Weising et al. [38]: ccmp5 and ccmp10. DNA amplification was performed using a multiplex PCR kit (Qiagen, Venlo, The Netherlands). The PCR mix contained a final volume of 10 μl with 5 μl of 2X Qiagen Multiplex PCR Master Mix, 1 μl of Primer mix (0.2 μM of each forward and reverse primers), 1 μl of Q-Solution, 1 μl of RNase free water and 2 μl of template DNA (10 ng/μl). The PCR was initially heated to 95°C for 15 min, followed by a 3-step touchdown cycling programme consisting of denaturation at 95 ^oC for 15 mins, annealing at 67 ^oC for 1.5 mins, extension at 72 ^oC for 1 min, followed by eight cycles of 94 ^oC for 30 s, 65 ^oC for 1.5 min with 2 ^oC decrease at each cycle, 72 ^oC for 1 min; 24 cycles at 94 ^oC for 30 s, 51 ^oC for 1.5 min, 72 ^oC for 1 min, and a final extension at 60 ^oC for 30 min. Amplification was carried out separately for each primer pair in a singleplex-PCR and combined for fragment analysis. PCR products were amplified using 6-FAM-, VIC-, NED-, and PET-labelled primers (Applied Biosystems, Foster City, California, USA). The touchdown cycling program was used on a Mastercycler 2050 model thermal cycler (Thermo Fisher Scientific, Waltham, Massachusetts, USA). PCR products were sequenced on an ABI 3730 automated sequencer (Applied Biosystems) using LIZ 500 or LIZ 600. Allelic binning and scoring of genotypes were performed using GeneMapper v.5.0 (Applied Biosystems). Raw data matrices containing allelic information were double-checked for scoring errors. Samples with more than two missing loci were excluded from further analysis. The final data set consisted of 287 samples for nSSR and 303 samples for cpSSR analyses.

Genetic data analysis

We used the program MicroChecker v.2.2.3 [39] to estimate the frequency of Null alleles across all samples. For the nSSR data set, intrapopulation genetic diversity was estimated as the total number of alleles per locus and per population (NA), the average number of alleles per locus for each population over all loci (A_avr), the unbiased estimate of expected (H_E) and observed (H_O) heterozygosity [40] within populations using Arlequin v.3.5.1.3 [41]. The number of private alleles (A_PRIV) was estimated using GDA v.1.0 [42]. FSTAT v.1.2 [43] was used to calculate allelic richness (A_R), thus correcting for different sample sizes, and inbreeding coefficient (F_IS). The package genepop v.1.2 [44] was used to perform exact tests of Hardy–Weinberg equilibrium (HWE), and F-statistics estimated by Weir and Cockerhan [45]. Population genetic structure was evaluated using a Bayesian clustering approach, implemented in STRUCTURE v.2.3.4 [46]. Individual’s genotype association from 1 to 13 distinct genetic clusters (K), allowing for admixture, was performed 20 times for each K with 500,000 generations as burn-in period, followed by 500,000 iterations of MCMC analysis assuming that the different populations had correlated allele frequencies without using prior information on the sampling locations of each individual [47]. The “Evanno method” [48] was used to estimate the most likely K [48], as implemented in STRUCTURE HARVESTER [49], but we also scrutinized results of other K for biologically meaningful solutions. To estimate population genetic differentiation, an analysis of molecular variance (AMOVA) was performed based on the clusters suggested by the STRUCTURE analysis using GenAlex v.6. [50]. We tested for isolation by distance (IBD) based on the correlation between genetic and geographical distances between populations using a Mantel test as implemented in GenAlex v.6. [50].

For the cpSSR data, HAPLOTYPE v.1.05 [51] was used to estimate the number of alleles at chloroplast SSR loci (N_acpSSR), number of haplotypes detected in each population (N_b), effective number of haplotypes (N_e), genetic diversity (Dv), haplotype richness (H_r) and the number of private haplotypes (Prv). A parsimony network illustrating genetic relationships between haplotypes was inferred using the software PopArt [52], assuming single-step mutations between alleles. Total genetic variation among samples was calculated using the phi-statistics of AMOVA which was performed using GenAlex [50]. This total variation was partitioned at three levels—within populations (Phi-PT), among populations within clusters (Phi-PR), and among clusters (Phi-RT) [53].

Distribution modelling

The sampled point locations (n = 316) were initially increased with 30 records from the GBIF database [54]. The dataset was extensively cleaned with doubtful localities and multiple records within a 30 arc-second gridcell (approximately 1x1 km, see below) being removed. The final set of point locations (n = 251) was then used for estimating the potential distribution of the species. Additionally, pseudo-absence data were created with the same number of points as the presence data (n = 251). Pseudo-absences were randomly generated across the study area with a minimum distance of 1.5 km to each other and the presence localities. Equal numbers of presence and pseudo-absence were used as recommended by Liu et al. [55]. Nineteen bioclimatic variables, together with three topographic (DEM; DEM-derived) variables with a spatial resolution of 30 arc-seconds were downloaded from the public WorldClim database [56]. These variables describe interpolated monthly means of climatic/environmental factors. Additional 66 soil variables with a resolution of 250 meters (S5 Table) were downloaded from the online database Soildgrids250m [57]. The complete set of variables was resampled to 30 arc-seconds spatial resolution in the WGS 84 coordinate system.

The impact of climate change on the current distribution of Senegalia senegal was assessed using WorldClim’s future climate change scenarios. We selected two future climate change projections (2050 and 2070) from two global climate models (GISS-ER2 [58, 59] and CCSM4 [60], hereafter GCM) and four RCP scenarios [2]. Moreover, topographic and soil variables were considered constant in future scenarios. Distribution modeling (current and future) of S. senegal was computed with a maximum entropy approach [61] as implemented in MaxEnt v.3.3.3k [62]. Model performance was evaluated by the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) plot. The final model was selected as the one with highest AUC score [63, 64]. Variable selection was performed in two steps: First, collinear variables were deleted stepwise considering a variance inflation factor (VIF) > 10 as critical threshold [65] using the 'usdm' R package [66], reducing the number of variables to n = 12. Second, the final set of environmental variables was chosen based on the optimization of the AUC value by the Random Forest (RF) model using the 'AUC-RF' R package [67], resulting in seven environmental predictors for the final model. In order to assess area gains and losses, we re-classified current and future habitat suitability probability scores into binary data by a threshold equal to prevalence [68]. We then estimated the degree of total area change as the percentage of remaining area in comparison with the current distribution. Values above 100% represent expansion (gain of area distribution) and values below 100% represent the diminution of the total extend from the current prediction. In addition, we mapped the changes between the current and future habitat suitability scores of S. senegal for each 30 arc-second grid cell. Differences were classified as follows: 1) not present, low probability of occurrence in current and future predictions (<75%); 2) area loss, high probability in the current prediction and low in the future; 3) area gain, low probability of occurrence (<75%) in the current situation and high in future predictions (>75%) and 4) remain, high probability of occurrence (>75%) as previously done by [69].

Results

Within-Population genetic diversity

In total, 110 alleles were scored across ten nSSR loci for the 287 genotyped individuals (S1 Table). None of the loci exhibited null allele presence. At the population level, observed and expected heterozygosity values for nSSR markers ranged from 0.42 to 0.73 (mean 0.61) and 0.43 to 0.63 (mean 0.56), respectively. Allelic richness ranged from 2.86 to 4.55 with a mean of 3.71. Private alleles were identified in eleven populations, with GOU having the highest number of four private alleles. Significant deviations from HWE were detected at seven sites with heterozygote deficiency in one site (HAD) and heterozygote excess in six sites (Table 2) while measures of inbreeding coefficient (Fis) were generally negative or very low ranging from -0.28 to 0.12 (mean = -0.06).

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Table 2. Genetic characteristics of 13 populations of Senegalia senegal revealed by ten nuclear and two chloroplast markers.

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Genetic diversity parameters and locus level estimates for cpSSR loci are summarised in Tables 2 and S4 respectively. Both cpSSR loci were polymorphic, exhibiting three (ccmp5) and five (ccmp10) alleles per locus among the 303 samples. The combination of alleles resulted in 6 unique haplotypes. Haplotypes H4, H5 and H2 were most frequent occuring in 119, 85 and 79 individuals respectively, whereas H6 was found 14 times and haplotypes H1 and H3 only once. Three haplotypes (H1, H3 and H6) were present in only one population each. Populations YUS harboured two private haplotypes while BRN harboured one private haplotype (Tables 2 and S3). Between one and four haplotypes were found per population, resulting in haplotype richness (H_R) between 0 and 3.25 (Table 2).

Population differentiation

Bayesian analysis of population structure using nSSR suggested the presence of two genetically distinct clusters based on the Evanno method [48], with individuals showing only little mixed ancestry (Fig 2A, S1 Fig). However, K = 3 (Fig 2B) cannot be neglected as it had higher mean likelihood L(K) and still a large value of ΔK. Samples from four geographical populations (BKG, ZUR, SOK and MDG) were affiliated to cluster 1 (green, cluster membership coefficient > 96%). This cluster comprised three populations from the western-most and one from the eastern-most part of the study area. Samples from nine geographical populations made up the red cluster at K = 2 (membership coefficient between 65.4 and 98%), which was again separated at K = 3 into two: cluster 2 (MAD, AGU, GOU–brown) and cluster 3 (RUM, HAD, BRN, GUR, JAK–orange). Population YUS was highly mixed and shared ancestry between clusters 2 and 3 (49% vs 47%). This separation reflected a latitudinal pattern with the northern-most populations making up cluster 2 leaving six populations in the central part of the study area as cluster 3. Population YUS showed mixed ancestry with 49% and 47% affiliation to clusters 2 and 3, respectively (S2 Table).

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Fig 2. Bar plots of proportional group membership for the 287 individuals genotyped at 10 nSSR loci.

(A). K = 2 and (B). K = 3. Vertical bars represent samples. Lines separate populations with colours representing the proportion of ancestry derived from each group. Cluster 1 is shown in green, cluster 2 in brown and cluster 3 in orange.

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With respect to cpSSR, 12 out of the 13 populations were either fixed to a single cpDNA haplotype or had a highly dominant haplotype (Fig 3, S3 Table). The population structure at cpSSR loci closely resembled the one at nSSR. While H2 was present in all four populations making up cluster 1, H5 was dominant in populations of cluster 2 and H4 in populations that corresponded to cluster 3. Similar to the nSSR data set, population YUS was mixed of two haplotypes (H4, H6).

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Fig 3. Distribution range, sampling sites and genetic structure for Senegalia senegal populations analysed in the present study.

(A) nuclear (B) study area and (C) chloroplast genomes. Each population is represented by a pie chart showing proportional membership of clusters or share of haplotypes. Haplotype network was generated by TCS in PopArt with circle sizes proportional to the relative frequency of a particular haplotype.

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With the nuclear data set, non-hierarchical AMOVA revealed 13% variation among and 87% of genetic variation residing within populations (F_ST = 0.14; P ≤ 0.001). A hierarchical AMOVA revealed that 18% of the variation resided among clusters at K = 2, while 5% and 77% resided among and within populations (F_ST = 0.23; P ≤ 0.001). Considering three clusters, differences among the clusters accounted for 13.5% while variation among and within populations accounted for 4% and 82.5% (F_ST = 0.18; P ≤ 0.001) respectively. By contrast, cpSSR non-hierarchical data showed 85% variation among and 15% of genetic variation residing within populations (PhiPT = 0.86; P ≤ 0.001). A hierarchical AMOVA (K = 2) found 51% of the total genetic variance between populations from different clusters (PhiPT = 0.86). Among populations within clusters contributed 39% of the total genetic variance (PhiPR), and 10% of the genetic variation was obtained from within individual populations (PhiPT = 0.89). All three levels contributed significantly to the overall genetic variation as determined through the permutation analyses (Table 3).

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Table 3. Analysis of molecular variance (AMOVAs) for nSSR and cpDNA in Senegalia Senegal.

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Pairwise F_ST values between populations from different clusters (as suggested by STRUCTURE) were generally higher than those between populations from the same cluster (S6 Table). The Mantel test with 999 permutations revealed that genetic divergence of populations was correlated with geographic distance (r = 0.53; P = 0.001) indicating that the isolation by distance model cannot be rejected (Fig 4). Despite this significant IBD pattern, the geographically very distant but genetically close population MDG within the green nSSR cluster indicates the presence of a larger-scale phylogeographical structure that is not consistent with IBD.

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Fig 4. Relationship between genetic and geographic distances (isolation by distance) of Senegalia senegal in West Africa.

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Distribution modelling

The final response variable data set to model the current distribution of Senegalia senegal included four climatic and three soil variables (Table 4; S5 Table). The MaxEnt model predicting the current distribution (Fig 5A and 5B) was statistically supported with an AUC value of 0.958 (S2 Fig). The distribution of S. senegal in West Africa is most strongly correlated with extractable nitrogen, coarse fragments, soil organic carbon stock, precipitation of warmest and coldest quarter, precipitation of wettest month and mean temperature of driest quarter (Table 4). Trends in future distribution were similarly forecasted by both GCM but only the resulting maps from GISSER2 are displayed. Our dataset shows that 35.34% to 11.39% of the current potential range of the species will favourably support the species’ ecological requirements across all the scenarios by the year 2050 (Fig 5C–5F). Consequently, by the year 2070, projections show that only 40.96% and 6.34% of the species’ current potential distribution might persist favourably under low emission (RCP 2.6) and high emission (RCP 8.5) scenarios, respectively (Fig 5G–5J). In addition, both GCMs predict a stabilization of habitat loss between 2050 and 2070 under RCP 2.6, while all other scenarios predict additional habitat loss from 2050 to 2070 (S3 Fig).

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Fig 5. Potential current and future distribution maps for Senegalia senegal across the study area.

Location of sample points (A, red triangles) and habitat suitability map for Senegalia senegal based on present-day climatic conditions (B, brown shaded areas). Predicted potential distribution maps under future conditions: 2050 (C–F) and 2070 (G–J) is given according to the representative concentration pathway climate scenarios. Yellow areas indicate unsuitable conditions for S. senegal. The numbers identifying each of the RCPs (C–J) refer to the magnitude of the energy imbalance measured in watts per square meters in the scenario in the year under consideration [16].

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Table 4. Contribution of the seven most important variables to the model.

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Discussion

Within-Population genetic diversity

Genetic diversity is a critical measure in population genetics because it provides insights into the current and likely future health of a population [70]. Heterozygosity, described as the average portion of loci with two varying alleles at a single locus within an individual, is a fundamental measure of genetic diversity [71]. Our analyses revealed high levels of genetic diversity within populations of Senegalia senegal. Mean expected heterozygosity (H_E = 0.56; Table 2) found in this study is comparable to values found in other microsatellite studies of Senegalia species (S. senegal: H_E = 0.67, [7]; H_E = 0.53, [72]; S. dudgeoni, H_E = 0.6 [72]; S. tortilis, H_E = 0.7, [73]) and other tropical tree species, including Milicia excelsa (H_E = 0.81, [74]), Koompassia malaccensis (H_E = 0.85, [75]), and Vitellaria paradoxa (H_E = 0.73, [76]). Conversely, low levels of genetic diversity can lead to inbreeding depression in the short-term, and to reduced evolutionary potential in the longer term [70, 77].

Half of the investigated sites significantly deviated from HWE. Moderate F_IS values were found in one population, indicating a heterozygote deficit. Such a reduction can either be explained by the presence of null alleles [31], the Wahlund effect, or a non-panmictic mating system [72]. As null alleles or Wahlund effects are not evident in our findings, we attribute the deviation from HWE at location HAD (F_IS = 0.12) to inbreeding and limited gene-flow consistent with isolation and small population size. On the other hand, heterozygote excess, which was found in six sites, could potentially be due to selection for heterozygosity (i.e. heterozygote advantage), or self-incompatibility and obligate outcrossing [78]. This finding coupled with F_IS estimates (mean = 0.06) obtained from our study is in agreement with earlier reports [7, 32] suggesting that S. senegal is almost exclusively outcrossed and self-incompatible. Studies on morphology and genetic variation of some tropical species showed that outcrossed genotypes also grew faster and had lower mortality than progeny from selfing, resulting in a greater heterozygosity in the populations due to the selective loss of homozygous individuals [78, 79].

Furthermore, Wang et al., [80] reported that a species’ geographic range and its ecological attributes influenced genetic diversity; and that high heterozygosity favoured long-lived plants especially in the arid zones. Our study is congruent with this assumption and is of particular importance when the historical range shifts Senegalia senegal had experienced [23], the complex landscapes and fragile ecosystems it occupies [81], and its ecological characteristics, are taken into consideration [82]. Asynchrony in flowering season has been observed for nearby S. senegal populations (personal observation), which limits inter-population gene flow [83]. Perennial insurgence of wild fires, insect pest invasion, soil erosion, overexploitation and grazing are common ecological challenges throughout these areas [81, 84, 85]. These factors potentially influence or impact gene flow of S. senegal, as shown for several tropical species [83, 86] and was further supported by Robledo-Arnuncio et al. [87]. The impact of these factors on gene flow could have slightly affected the heterozygosity and allele estimates detected in our study, although S. senegal has been shown to be an outcrossing species [32].

Population genetic structure and differentiation

There was a strong difference in the extent of genetic structuring revealed by the two types of markers (F_ST = 0.14 for nSSRs and F_ST = 0.86 for cpSSRs), indicating a much stronger differentiation of the chloroplast genome compared to the nuclear genome. Such differences are commonly observed and are to be expected due to the different types of inheritance and mode of dispersal [88]. Low to moderate values of nuclear population differentiation have previously been documented in Senegalia senegalia and were attributed to likely intense gene flow [7, 31, 72]. However, as F_ST values only mirror the effects of past events, weak genetic differentiation among populations could also be observed if populations were young, shared a common ancestry and were only weakly differentiated despite lack of current gene flow. The high F_ST values in the chloroplast data set suggest that gene flow via seeds is negligible, thus highlighting the importance of pollen vs. seed dispersal for the connectivity of population cohesion in the species [89].

In the analysis of STRUCTURE, two clusters (K = 2), representing a southern and a northern group, may be the most parsimonious explanation for the structure present in the data, yet we consider it not to offer the most biologically meaningful explanation. This is because three clusters (K = 3) better reflect the structure in the data, showing groups of populations that are geographically coherent. Moreover, the three genetic clusters, as revealed by nSSRs were further fully supported by the analysis of cpDNA (Fig 3). Here, the green cluster matched haplotype H2, and the brown and orange clusters matched the closely related haplotypes H5 and H4. Although dispersal via pollen in Senegalia senegal might be effective within its natural landscapes coupled with the fact that open low density forest facilitates longer pollen dispersal distances [7], human-mediated dispersal through decades of economic usage might have also influenced the disjunct pattern of populations of cluster 1 observed in the dataset. Because of its agroforestry importance and high quality gum production, Senegalia seeds have been traded among nomads, pastorialists and traders of various agricultural products across the region for centuries [25]. Various animals such as cattle, sheep, goat, donkey and camels that graze on the fields for pasture across the entire region play a role in the dispersal of seeds among population. These human-mediated seed transfer is usually along specific routes of similar arid environmental condition, such as MDG, ZUR, and BKG. Frequent gene flow is propagated among those populations without severe interference with populations aside those routes, such as BRN, JAK, and HAD. In addition, sharp environmental transition due to climate change and land use has been shown to be responsible for the increasing aridity from north to south, hence the progressive expansion of Senegalia species from the driest to the wettest zones in the SSZ [28]. Consequently, this gradient rather than geographic distance accounts for gene flow among the populations making up cluster 2 and 3 (K = 3), and thus the divergence of populations.

Overall, there is isolation by distance which is in line with gene-flow drift equilibrium and seed dispersal by animals because with geographical clustering, effects of environment cannot be totally excluded. Therefore, isolation-by-environment [90] may have as well contributed to the seemingly disjunct populations. However, because there is congruence in the result obtained from both nSSR and cpSSR, the whole pattern is very likely to be primarily historic (seed dispersal), rather than adaptive, but this study cannot establish if the disjunct pattern is due to isolation by distance or/and isolation by environment.

Odee et al. [23], hypothesized a recent east to west range expansion of African populations of Senegalia senegal using sequences of ITS and psbA. They illustrated that West African S. senegal was represented by a single genetic group (nuclear), or two haplotypes (chloroplast), respectively. However, the data set in the study was characterized by a low resolution for intraspecific differentiation and contained a sampling gap in the SSZ of West Africa. Our study unveiled more diversity and population structure in S. senegal (Table 2, Table 3), thus suggesting a more complex pattern than previously reported for this region.

The detected haplotypes, however, showed relationships amongst them and the populations to which they were affiliated. Of particular importance is H6 which is quite distinct appearing in only one population (YUS), with at least six and nine mutations between the two closest dominant relatives (H2 and H4). Although this study does not account for the origin of haplotypes, a colonization event probably from MDG to YUS, or somewhere farther from the east, cannot be neglected.

Genetic diversity and distribution under climate change

The MaxEnt model confirms the role of soil and climate properties in shaping the distribution of Senegalia senegal in the SSZ, as reported by Traore et al. [28]. Given the future climate scenarios (Fig 5C–5J), there will be likely a reduction of suitable habitats and population sizes, and potentially a loss of private alleles and private haplotypes leading to declining genetic diversity. All four future climate scenarios vary in magnitude of energy imbalance and the impact of the variation is reflected in the model forecast, as the extent of reduction in suitable range is largely dependent on the species’ ecological requirements. The observed predicted pattern shows a progressive reduction (RCP2.6 > RCP4.5 > RCP6.0 > RCP8.5) of the current potential distribution of S. senegal within the study area. This shows that S. senegal may be under threat due to environmental change in the study area and the species might be facing an increased risk of local extinction. However, considering the high allele diversity found in this study, populations may show a predisposition to maintain genetic diversity which may ultimately be a favorable potential for adaptability and persistence should they disperse to track a new suitable range. Consequently, the ability of S. senegal to persist in an increasingly marginal range will depend on factors such as ecological fitness, genetic make-up and dispersal ability. However, there is no certainty that the dispersal rates of plant species could keep pace with fast rates of environmental change [91].

Conclusions

Our study unveiled a high genetic and haplotypic variation in the West African Sudano-Sahelian range of Senegalia senegal in contrast to previous report that found this part of the range genetically homogenous. We suggest that the species’ colonization history might have played an important role in the distribution of genetic diversity and structuring of populations in the study area. Distribution modeling indicates that depending on the climate scenario, only 41% (RCP 2.6), 35% (RCP 4.5) or down to only 6.3 (RCP 8.5) of suitable area will be available. Consequently, a large part of the geographically structured genetic variation is threatened. In particular the genotypes distributed to the West of the study area, microsatellite cluster 1 and chloroplast haplotype 2, may face strong declines, as hardly any surviving S. senegal are predicted in this region. Our findings therefore offer insights into how to manage threats or predict responses to disturbances in relation to environmental changes. Finally, the findings of this study will provide a valuable base to reinforce the information available to conservationists and policy makers.

Supporting information

S1 Table. Genetic characteristics of ten nuclear SSR loci in 13 populations of Senegalia senegal (N = 297).

https://doi.org/10.1371/journal.pone.0194726.s001

(DOCX)

S2 Table. Proportion of membership of each predefined population in each of the inferred clusters at both K = 2 and K = 3.

https://doi.org/10.1371/journal.pone.0194726.s002

(DOCX)

S3 Table. List of haplotypes detected at two cpSSR loci in 13 populations of Senegalia senegal.

Population YUS is characterized by 2 two unique haplotypes that are not present in any other population. Private haplotypes are highlighted in grey/bold.

https://doi.org/10.1371/journal.pone.0194726.s003

(DOCX)

S4 Table. Genetic characteristics of two chloroplast microsatellite markers and result of genotyping in S. senegal (N = 303).

https://doi.org/10.1371/journal.pone.0194726.s004

(DOCX)

S5 Table. List of variables used for modelling the distribution of Senegalia senegal in West Africa.

https://doi.org/10.1371/journal.pone.0194726.s005

(DOCX)

S6 Table. Matrix of pairwise F_ST values based on allele data at ten nuclear (above diagonal) and two chloroplast SSR (above diagonal) loci among 13 populations of Senegalia senegal.

Most of the values estimated for both marker types were significant (P ≤ 0.001). For abbreviations of populations see Table 1.

https://doi.org/10.1371/journal.pone.0194726.s006

(DOCX)

S1 Fig. Plots for detecting the number of K groups that best fit the data according to [48].

(A) Delta K and (B) Plot of mean likelihood L (K) and variance per K value from STRUCTURE on a dataset containing 287 individuals genotyped for ten nSSR.

https://doi.org/10.1371/journal.pone.0194726.s007

(TIF)

S2 Fig. Plot of sensitivity vs. specificity.

Area under the curve (AUC) value of 0.958 indicates the accuracy of the model prediction.

https://doi.org/10.1371/journal.pone.0194726.s008

(TIF)

S3 Fig. Predicted area (%) gain or loss of Senegalia senegal relative to the current distribution under four future climate scenarios (RCP’s) as estimated by two models (CCSM4, GISSER2) for 2050 and 2070.

https://doi.org/10.1371/journal.pone.0194726.s009

(TIF)

Acknowledgments

We are grateful to Stephen Akpalu (CSIR-FORIG, Ghana), Moussa Massaoudou (INRAN, Maradi-Niger), and Kafil Ibrahim (Rumah-Batsari LGA, Kastina), Olabisi Alamu, Olubuiyi Mayowa (NACGRAB), Ali Abba-Gana Benisheikh (UNIMAID) and Bulama Gado for assisting with sample collections in the field. We thank Dr. Peter Otto for his role in facilitating our sampling efforts, and Dr. Adrien Favre for comments on a previous version of this manuscript. We appreciate all members of the working group “Molecular Evolution and Plant Systematics & Herbarium (LZ)” at Leipzig University, especially Jana Ebersbach and Claudia Krüger, for suggestions and support in the molecular laboratory.

References

1. Sala EO, Chapin FS, Armesto JJ, Berlow E, Bloomfield J, Dirzo R, et al. Global Biodiversity Scenarios for the Year 2100. Science 2000; 287(1770):1770–4.
- View Article
- Google Scholar
2. Leadley P, Pereira H, Alkemade R, Fernandez-Manjarrés J, Proença V, Scharlemann J et al. Biodiversity scenarios: Projections of 21st century change in biodiversity, and associated ecosystem services. Montréal; 2010. CBD technical series.
- View Article
- Google Scholar
3. Heubes J, Schmidt M, Stuch B, Marquez J, Wittig R, Zizka Gea. The projected impact of climate and land use change on plant diversity: An example from West Africa. J Arid Environ 2013; 96:48–54.
- View Article
- Google Scholar
4. Pachauri RK, Meyer LA. Climate change: Synthesis report. Contribution of working groups I, II and III to the fifth Assessment report of the Intergovernmental Panel on Climate Change. Geneva; 2015;
- View Article
- Google Scholar
5. Parry ML, Canziani O, Palutikof J, van der Linden P, Hanson CE. Climate change: Impacts, adaptation and vulnerability. contribution of Working Group II to the fourth assessment report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press; 2007. Available from: URL: http://www.loc.gov/catdir/enhancements/fy0827/2008295434-d.html.
6. Bocksberger G, Schnitzler J, Chatelain C, Daget P, Janssen T, Schmidt M, et al. Climate and the distribution of grasses in West Africa. Journal of Vegetation Science 2016; 27(2):306–17.
- View Article
- Google Scholar
7. Omondi SF, Kireger E, Dangasuk OG, Chikamai B, Odee DW, Cavers S et al. Genetic Diversity and Population Structure of Acacia senegal (L) Willd. in Kenya. Tropical Plant Biol. 2010; 3(1):59–70.
- View Article
- Google Scholar
8. Zerbo I, Bernhardt-Römermann M, Ouédraogo O, Hahn K, Thiombiano A. Effects of Climate and Land Use on Herbaceous Species Richness and Vegetation Composition in West African Savanna Ecosystems. J Bot 2016; 2016(2):1–11.
- View Article
- Google Scholar
9. Serdeczny O, Adams S, Baarsch F, Coumou D, Robinson A, Hare W, et al. Climate change impacts in Sub-Saharan Africa: From physical changes to their social repercussions. Reg Environ Change 2016; 17(6):1585–600.
- View Article
- Google Scholar
10. Pfenninger M, Balint M, Pauls SU. Methodological framework for projecting the potential loss of intraspecific genetic diversity due to global climate change. BMC Evol Biol 2012; 12(1):224.
- View Article
- Google Scholar
11. McClean CJ, Lovett JC, Kuper W, Hannah L, Sommer JH, Barthlott W et al. African plant diversity and climate change. Ann Mo Bot Gard 2005; 92(2):139–52.
- View Article
- Google Scholar
12. Sommer JH, Kreft H, Kier G, Jetz W, Mutke J, Barthlott W. Projected impacts of climate change on regional capacities for global plant species richness. Proc Biol Sci 2010; 277(1692):2271–80. pmid:20335215
- View Article
- PubMed/NCBI
- Google Scholar
13. Heywood HV. Global biodiversity assessment. Cambridge: Cambridge University Press Cambridge; 1995.
14. Pauls SU, Nowak C, Balint M, Pfenninger M. The impact of global climate change on genetic diversity within populations and species. Mol Ecol 2013; 22(4):925–46. pmid:23279006
- View Article
- PubMed/NCBI
- Google Scholar
15. Nakicenovic N, Davidson O, Davis G, Grübler A, Kram T, Lebre La Rovere E. Emissions scenarios: Summary for policymakers; a special report of IPCC Working Group III. Geneva, Switzerland; 2000.
- View Article
- Google Scholar
16. van Vuuren DP, Edmonds J, Kainuma M, Riahi K, Thomson A, Hibbard K et al. The representative concentration pathways: an overview. Climatic Change 2011; 109:5–31.
- View Article
- Google Scholar
17. Arnell NW, Lloyd-Hughes B. The global-scale impacts of climate change on water resources and flooding under new climate and socio-economic scenarios. Climatic Change 2014; 122:127–40.
- View Article
- Google Scholar
18. Kyalangalilwa B, Boatwright JS, Daru BH, Maurin O, van der Bank M. Phylogenetic position and revised classification of Acacia s.l. (Fabaceae: Mimosoideae) in Africa, including new combinations in Vachellia and Senegalia. Bot J Linn Soc 2013; 172(4):500–23.
- View Article
- Google Scholar
19. Bernhard-Reversat F, Poupon H. Nitrogen cycling in a soil-tree system in a sahelian savanna: example of Acacia senegal. Proceedings of a workshop arranged by the SCOPE/UNEP 1980:363–9.
- View Article
- Google Scholar
20. Beshai AA. The economics of a primary commodity: Gum Arabic. Oxf Bull Econ Stat 1984; 46(4):371–81.
- View Article
- Google Scholar
21. Orwa C, Mutua A, Kindt R, Jamnadass R, Simons A. Agroforestree database: a tree reference and selection guide version 4.0. 2009. Available from: URL: http://www.worldagroforestry.org/af/treedb.
22. Fagg C, Allison GE. Acacia senegal and the gum Arabic trade. Monograph and annotated bibliography. Tropical Forestry Papers No. 42, Alden Group, Oxford 2004:261.
- View Article
- Google Scholar
23. Odee DW, Telford A, Wilson J, Gaye A, Cavers S. Plio-Pleistocene history and phylogeography of Acacia senegal in dry woodlands and savannahs of sub-Saharan tropical Africa: evidence of early colonisation and recent range expansion. Heredity 2012; 109(6):372–82. pmid:22929152
- View Article
- PubMed/NCBI
- Google Scholar
24. Brenan JP. Manual on taxonomy of Acacia species: present taxonomy of four species of Acacia (A. albida, A. senegal, A. nilotica, A. tortilis). Rome: FAO; 1983.
25. Jøker D. Acacia senegal (L.) Willd. Seed Leaflet-Danida Forest Seed Centre 2000; (5).
- View Article
- Google Scholar
26. Maundu P, Tengnäs B. Useful trees and shrubs for Kenya. ICRAF Technical handbook series 2005.
- View Article
- Google Scholar
27. Raddad EAY, Salih AA, Fadl MAE, Kaarakka V, Luukkanen O. Symbiotic nitrogen fixation in eight Acacia senegal provenances in dryland clays of the Blue Nile Sudan estimated by the 15 N natural abundance method. Plant and Soil 2005; 275(1):261–9.
- View Article
- Google Scholar
28. Traore S., Zerbo L., Schmidt M., Thiombiano L. Acacia communities and species responses to soil and climate gradients in the Sudano-Sahelian zone of West Africa. J Arid Environ 2012; 87:144–52.
- View Article
- Google Scholar
29. Eisa MA, Roth M, Sama G., editor. Acacia senegal (gum Arabic tree): Present role and need for future conservation/Sudan. Hohenheim: Deutscher Tropentag; 2008.
30. Githae EW, Gachene CKK, Njoka JT. Soil physicochemical properties under Acacia senegal varieties in the dryland areas of Kenya. Afri J Plant Sci 2011; 5(8):475–82.
- View Article
- Google Scholar
31. Mulumba JW, Nyakaana S, Jamnadas R. Genetic Diversity and structure of Acacia senegal (L.) Willd. in Uganda. Int. J. Botany 2012; 3(8):107–16.
- View Article
- Google Scholar
32. Tandon R, Shivanna KR, Ram HM. Pollination biology and breeding system of Acacia senegal. Bot J Linn Soc 2001; 135(3):251–62.
- View Article
- Google Scholar
33. Gray A, Odee D, Cavers S, Wilson J, Telford A, Grant F et al. Does geographic origin dictate ecological strategies in Acacia senegal (L.) Willd.?: Evidence from carbon and nitrogen stable isotopes. Plant Soil 2013; 369(1–2):479–96.
- View Article
- Google Scholar
34. Chiveu C, Dangasuk OG, Omunyin M, Wachira FN. Genetic diversity in Kenyan populations of Acacia senegal (L:) Willd revealed by combined RAPD and ISSR markers. Afr. J. Biotechnol. 2008; 7(14):2333–40.
- View Article
- Google Scholar
35. Habeballa RS, Hamza NB, El Gaali EI. Genetic variability in Sudanese Acacia senegal (L.) assessed by random amplified polymorphic DNA. Afr. J. Biotechnol. 2010; 9(30):4655–60.
- View Article
- Google Scholar
36. Chase MW, Hills HH. Silica gel: an ideal material for field preservation of leaf samples for DNA studies. Taxon 1991:215–20.
- View Article
- Google Scholar
37. Assoumane AA, Vaillant A, Mayaki AZ, Verhaegen D. Isolation and characterization of microsatellite markers for Acacia senegal (L.) Willd., a multipurpose arid and semi-arid tree. Mol Ecol Resour 2009; 9(5):1380–3. pmid:21564912
- View Article
- PubMed/NCBI
- Google Scholar
38. Weising K, Gardner RC. A set of conserved PCR primers for the analysis of simple sequence repeat polymorphisms in chloroplast genomes of dicotyledonous angiosperms. Genome 1999; 42(1):9–19. pmid:10207998
- View Article
- PubMed/NCBI
- Google Scholar
39. van Oosterhout C, Hutchinson WF, Wills DPM, Shipley P. MICRO‐CHECKER: Software for identifying and correcting genotyping errors in microsatellite data. Mol Ecol Resour 2004; 4(3):535–8.
- View Article
- Google Scholar
40. Nei M. Estimation of average heterozygosity and genetic distance from a small number of individuals. Genetics 1978; 89(3):583–90. pmid:17248844
- View Article
- PubMed/NCBI
- Google Scholar
41. Excoffier L, Lischer HEL. Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. Mol Ecol Resour 2010; 10(3):564–7. pmid:21565059
- View Article
- PubMed/NCBI
- Google Scholar
42. Lewis, PO, Zaykin D. Genetic data analysis: computer program for the analysis of allelic data 2001. Available from: URL: http://lewis.eeb.uconn.edu/lewishome/software.html.
43. Goudet J. FSTAT (Version 1.2): AComputer Program to Calculate F-Statistics. J Hered 1995; 6(86):245–6.
- View Article
- Google Scholar
44. Raymond M, Rousset F. GENEPOP (version 1.2): population genetics software for exact tests and ecumenicism. J Hered 1995; 86(3):248–9.
- View Article
- Google Scholar
45. Weir BS, Cockerham CC. Estimating F-Statistics for the Analysis of Population Structure. Evolution 1984; 38(6):1358. pmid:28563791
- View Article
- PubMed/NCBI
- Google Scholar
46. Pritchard J, Stephens M, Donelly P. Inference of Population Structure Using Multilocus Genotype Data. Genetics 2000; (155):945–59.
- View Article
- Google Scholar
47. Falush D, Stephens M, Pritchard JK. Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics 2003; 164(4):1567–87. pmid:12930761
- View Article
- PubMed/NCBI
- Google Scholar
48. Evanno G, Rednaut S, Goudet J. Detecting the number of clusters of individuals using the software structure: A simulation study. Mol Ecol 2005; 14(8):2611–20. pmid:15969739
- View Article
- PubMed/NCBI
- Google Scholar
49. Earl DA, vonHoldt BM. STRUCTURE HARVESTER: A website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv Genet Resour 2012; 4(2):359–61.
- View Article
- Google Scholar
50. Peakall ROD SP. GENALEX 6: genetic analysis in Excel. Population genetic software for teaching and research. Mol Ecol Notes 2006; 6(1):288–95.
- View Article
- Google Scholar
51. Eliades NG, Eliades DG. Haplotype Analysis: Software for analysis of haplotype data. Forest Genetics and Forest Tree Breeding, Georg-August University Goettingen, Germany. 2009.
52. Leigh JW BD. PopArt: Full‐feature software for haplotype network construction. Methods Ecol Evol 2015; 6(9):1110–6.
- View Article
- Google Scholar
53. Xu J, Sha T, Li YC, Zhao ZW, Yang ZL. Recombination and genetic differentiation among natural populations of the ectomycorrhizal mushroom Tricholoma matsutake from southwestern China. Mol Ecol 2008; 17(5):1238–47. pmid:18302686
- View Article
- PubMed/NCBI
- Google Scholar
54. GBIF.org. GBIF Occurrence Download http://doi.org/10.15468/dl.c8atij 9th November 2016.
55. LIU C, Berry PM, Dawson TP, Pearson RG. Selecting thresholds of occurrence in the prediction of species distributions. Ecography 2005; 28(3):385–93.
- View Article
- Google Scholar
56. Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 2005; 25(15):1965–78.
- View Article
- Google Scholar
57. Hengl T, Mendes de Jesus J, Heuvelink GBM, Ruiperez Gonzalez M, Kilibarda M, Blagotic A et al. SoilGrids250m: Global gridded soil information based on machine learning. PLoS ONE 2017; 12(2):1–40.
- View Article
- Google Scholar
58. Miller RL, Schmidt GA, Nazarenko LS, Tausnev N, Bauer SE, DelGenio AD et al. CMIP5 historical simulations (1850–2012) with GISS ModelE2. J Adv Model Earth Syst 2014; 6(2):441–78.
- View Article
- Google Scholar
59. Nazarenko L, Schmidt GA, Miller RL, Tausnev N, Kelley M, Ruedy R et al. Future climate change under RCP emission scenarios with GISS ModelE2. J Adv Model Earth Syst 2015; 7(1):244–67.
- View Article
- Google Scholar
60. Gent PR, Danabasoglu G, Donner LJ, Holland MM, Hunke EC, Jayne SR et al. The community climate system model version 4. J Clim 2011; 24(19):4973–91.
- View Article
- Google Scholar
61. Phillips SJ, Anderson RP, Schapire RE. Maximum entropy modeling of species geographic distributions. Ecol Modell 2006; 190(3–4):231–59.
- View Article
- Google Scholar
62. Phillips SJ, Dudík M. Modeling of species distributions with Maxent: New extensions and a comprehensive evaluation. Ecography 2008; 31(2):161–75.
- View Article
- Google Scholar
63. Fielding AH, Bell JF. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ Conserv 1997; 24(1):38–49.
- View Article
- Google Scholar
64. Duque-Lazo J, van Gils H, Groen TA, Navarro-Cerrillo RM. Transferability of species distribution models: The case of Phytophthora cinnamomi in Southwest Spain and Southwest Australia. Ecol Modell 2016; 320:62–70.
- View Article
- Google Scholar
65. Quinn GP, Keough MJ. Experimental design and data analysis for biologists. Cambridge: Cambridge University Press; 2002.
66. Naimi B. usdm: Uncertainty analysis for species distribution models. R package version 2013; 1:1–12.
- View Article
- Google Scholar
67. Calle ML, Urrea V Boulesteix A-L, Malats N. AUC-RF: a new strategy for genomic profiling with random forest. Hum Hered 2011; 72(2):121–32. pmid:21996641
- View Article
- PubMed/NCBI
- Google Scholar
68. Duque-Lazo J, Navarro-Cerrillo RM. What to save, the host or the pest? The spatial distribution of xylophage insects within the Mediterranean oak woodlands of Southwestern Spain. For Ecol Manage 2017; 392:90–104.
- View Article
- Google Scholar
69. Navarro-Cerrillo RM, Rumbaó IC, Vidaña AL, Pérez J, Duque-Lazo J. Integración de datos de inventario y modelos dehábitat para predecir la regeneración de especies leñosas mediterráneas en repoblaciones forestales. Revista Ecosistemas 2016; 25(3):6–21.
- View Article
- Google Scholar
70. Kirk H, Freeland JR. Applications and implications of neutral versus non-neutral markers in molecular ecology. Int J Mol Sci 2011; 12(6):3966–88. pmid:21747718
- View Article
- PubMed/NCBI
- Google Scholar
71. Nei M. Analysis of gene diversity in subdivided populations. Proc Natl Acad Sci USA 1973; 70(12):3321–3. pmid:4519626
- View Article
- PubMed/NCBI
- Google Scholar
72. Assoumane A, Mayaki AZ, Favreau B, Bezancon G, Saadou M, Verhaegen D. Differentiation between two sub-species of Acacia senegal complex: Acacia senegal (L.) Willd. and Acacia dudgeoni Craib ex Holland using morphological traits and molecular markers. Genet Resour Crop Evol 2012; 59(1):39–48.
- View Article
- Google Scholar
73. Winters G, Shklar G, Korol L. Characterizations of microsatellite DNA markers for Acacia tortilis. Conserv Genet Resour 2013; 5(3):807–9.
- View Article
- Google Scholar
74. Ouinsavi C, Sokpon N, Khasa DP. Genetic Diversity and Population Structure of a Threatened African Tree Species, Milicia excelsa, Using Nuclear Microsatellites DNA Markers. International Journal of Forestry Research 2009; 2009(1345):1–8.
- View Article
- Google Scholar
75. Noreen AME, Webb EL. High Genetic Diversity in a Potentially Vulnerable Tropical Tree Species Despite Extreme Habitat Loss. PLoS ONE 2013; 8(12).
- View Article
- Google Scholar
76. Gwali S, Vaillant A, Nakabonge G, Okullo JL, Eilu G, Muchugi A et al. Genetic diversity in shea tree (Vitellaria paradoxa subspecies nilotica) ethno-varieties in Uganda assessed with microsatellite markers. Forests, Trees and Livelihoods 2014; 24(3):163–75.
- View Article
- Google Scholar
77. Poczai P, Varga I, Bell NE, Hyvonen J. Genomics Meets Biodiversity: Advances in Molecular Marker Development and their Applications in Plant Genetic Diversity Assessment. In: Caliskan M, editor. The Molecular Basis of Plant Genetic Diversity: INTECH Open Access Publisher; 2012. pp 3–31.
78. Stoeckel S, Grange J, Fernandez-Manjarres JF, Bilger I, Frascaria-Lacoste N, Mariette S. Heterozygote excess in a self-incompatible and partially clonal forest tree species—Prunus avium L. Mol Ecol 2006; 15(8):2109–18. pmid:16780428
- View Article
- PubMed/NCBI
- Google Scholar
79. Liu W, Zhao Y, You J, Qi D, Zhou Y, Chen J et al. Morphological and Genetic Variation along a North-to-South Transect in Stipa purpurea, a Dominant Grass on the Qinghai-Tibetan Plateau: Implications for Response to Climate Change. PLoS ONE 2016; 11(8):1–22.
- View Article
- Google Scholar
80. Wang Z, Kang M, Liu H, Gao J, Zhang Z, Li Y et al. High-level genetic diversity and complex population structure of Siberian apricot (Prunus sibirica L.) in China as revealed by nuclear SSR markers. PLoS ONE 2014; 9(2):1–13.
- View Article
- Google Scholar
81. Chidumayo EN, Gumbo DJ. The dry forests and woodlands of Africa: Managing for products and services. London, UK: Earthscan; 2010.
82. Bouchenak-Khelladi Y, Maurin O, Hurter J, van der Bank M. The evolutionary history and biogeography of Mimosoideae (Leguminosae): An emphasis on African acacias. Mol Phylogenet Evol 2010; 57(2):495–508. pmid:20696261
- View Article
- PubMed/NCBI
- Google Scholar
83. Rozzi R, Arroyo MK, Amesto JJ. Ecological factors affecting gene flow between populations of Anarthrophyllum cumingii (Papilionaceae) growing on equatorial- and polar-facing slopes in the Andes of Central Chile. Plant Ecol 1997; 132:171–9.
- View Article
- Google Scholar
84. Mujawamariya G, Madi OP, Zoubeirou AM, Sene A, Maisharou A, D'Haese M. Common challenges in gum arabic production and commercialization in West Africa: a comparative study of Cameroon, Niger and Senegal. International Forestry Review 2013; 15(2):182–99.
- View Article
- Google Scholar
85. Gaisberger H, Kindt R, Loo J, Schmidt M, Bognounou F, Da SS, Diallo OB, et al. Spatially explicit multi-threat assessment of food tree species in Burkina Faso: A fine-scale approach. PLoS ONE 2017; 12(9):1–26.
- View Article
- Google Scholar
86. Hamilton B. Tropical tree gene flow and tree dispersal: deforestation affects the genetic structure of the surviving forest fragments. Nature 1999; 401:129–30.
- View Article
- Google Scholar
87. Robledo-Arnuncio JJ, Klein EK, Muller-Landau HC, Santamaría L. Space, time and complexity in plant dispersal ecology. Mov Ecol 2014; 2(1):1–17.
- View Article
- Google Scholar
88. Petit RJ, Duminil J, Fineschi S, Hampe A, Salvini D, Vendramin GG. Comparative organization of chloroplast, mitochondrial and nuclear diversity in plant populations. Mol Ecol 2005; 14(3):689–701. pmid:15723661
- View Article
- PubMed/NCBI
- Google Scholar
89. Gonçalves-Oliveira RC, Wöhrmann T, Benko-Iseppon AM, Krapp F, Alves M, Wanderley ML et al. Population genetic structure of the rock outcrop species Encholirium spectabile (Bromeliaceae): The role of pollination vs. seed dispersal and evolutionary implications. Am J Bot 2017; 104(6):868–78. pmid:28611073
- View Article
- PubMed/NCBI
- Google Scholar
90. Sexton JP, Hangartner SB, Hoffmann AA. Genetic isolation by environment or distance: Which pattern of gene flow is most common? Evolution 2014; 68(1):1–15. pmid:24111567
- View Article
- PubMed/NCBI
- Google Scholar
91. Malcolm JR, Markham A, Neilson RP, Garaci M. Estimated migration rates under scenarios of global climate change. J Biogeogr 2002; 29(7):835–49.
- View Article
- Google Scholar

[ref1] 1. Sala EO, Chapin FS, Armesto JJ, Berlow E, Bloomfield J, Dirzo R, et al. Global Biodiversity Scenarios for the Year 2100. Science 2000; 287(1770):1770–4.
View Article
Google Scholar

[2] View Article

[3] Google Scholar

[ref2] 2. Leadley P, Pereira H, Alkemade R, Fernandez-Manjarrés J, Proença V, Scharlemann J et al. Biodiversity scenarios: Projections of 21st century change in biodiversity, and associated ecosystem services. Montréal; 2010. CBD technical series.
View Article
Google Scholar

[5] View Article

[6] Google Scholar

[ref3] 3. Heubes J, Schmidt M, Stuch B, Marquez J, Wittig R, Zizka Gea. The projected impact of climate and land use change on plant diversity: An example from West Africa. J Arid Environ 2013; 96:48–54.
View Article
Google Scholar

[8] View Article

[9] Google Scholar

[ref4] 4. Pachauri RK, Meyer LA. Climate change: Synthesis report. Contribution of working groups I, II and III to the fifth Assessment report of the Intergovernmental Panel on Climate Change. Geneva; 2015;
View Article
Google Scholar

[11] View Article

[12] Google Scholar

[ref5] 5. Parry ML, Canziani O, Palutikof J, van der Linden P, Hanson CE. Climate change: Impacts, adaptation and vulnerability. contribution of Working Group II to the fourth assessment report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press; 2007. Available from: URL: http://www.loc.gov/catdir/enhancements/fy0827/2008295434-d.html.

[ref6] 6. Bocksberger G, Schnitzler J, Chatelain C, Daget P, Janssen T, Schmidt M, et al. Climate and the distribution of grasses in West Africa. Journal of Vegetation Science 2016; 27(2):306–17.
View Article
Google Scholar

[15] View Article

[16] Google Scholar

[ref7] 7. Omondi SF, Kireger E, Dangasuk OG, Chikamai B, Odee DW, Cavers S et al. Genetic Diversity and Population Structure of Acacia senegal (L) Willd. in Kenya. Tropical Plant Biol. 2010; 3(1):59–70.
View Article
Google Scholar

[18] View Article

[19] Google Scholar

[ref8] 8. Zerbo I, Bernhardt-Römermann M, Ouédraogo O, Hahn K, Thiombiano A. Effects of Climate and Land Use on Herbaceous Species Richness and Vegetation Composition in West African Savanna Ecosystems. J Bot 2016; 2016(2):1–11.
View Article
Google Scholar

[21] View Article

[22] Google Scholar

[ref9] 9. Serdeczny O, Adams S, Baarsch F, Coumou D, Robinson A, Hare W, et al. Climate change impacts in Sub-Saharan Africa: From physical changes to their social repercussions. Reg Environ Change 2016; 17(6):1585–600.
View Article
Google Scholar

[24] View Article

[25] Google Scholar

[ref10] 10. Pfenninger M, Balint M, Pauls SU. Methodological framework for projecting the potential loss of intraspecific genetic diversity due to global climate change. BMC Evol Biol 2012; 12(1):224.
View Article
Google Scholar

[27] View Article

[28] Google Scholar

[ref11] 11. McClean CJ, Lovett JC, Kuper W, Hannah L, Sommer JH, Barthlott W et al. African plant diversity and climate change. Ann Mo Bot Gard 2005; 92(2):139–52.
View Article
Google Scholar

[30] View Article

[31] Google Scholar

[ref12] 12. Sommer JH, Kreft H, Kier G, Jetz W, Mutke J, Barthlott W. Projected impacts of climate change on regional capacities for global plant species richness. Proc Biol Sci 2010; 277(1692):2271–80. pmid:20335215
View Article
PubMed/NCBI
Google Scholar

[33] View Article

[34] PubMed/NCBI

[35] Google Scholar

[ref13] 13. Heywood HV. Global biodiversity assessment. Cambridge: Cambridge University Press Cambridge; 1995.

[ref14] 14. Pauls SU, Nowak C, Balint M, Pfenninger M. The impact of global climate change on genetic diversity within populations and species. Mol Ecol 2013; 22(4):925–46. pmid:23279006
View Article
PubMed/NCBI
Google Scholar

[38] View Article

[39] PubMed/NCBI

[40] Google Scholar

[ref15] 15. Nakicenovic N, Davidson O, Davis G, Grübler A, Kram T, Lebre La Rovere E. Emissions scenarios: Summary for policymakers; a special report of IPCC Working Group III. Geneva, Switzerland; 2000.
View Article
Google Scholar

[42] View Article

[43] Google Scholar

[ref16] 16. van Vuuren DP, Edmonds J, Kainuma M, Riahi K, Thomson A, Hibbard K et al. The representative concentration pathways: an overview. Climatic Change 2011; 109:5–31.
View Article
Google Scholar

[45] View Article

[46] Google Scholar

[ref17] 17. Arnell NW, Lloyd-Hughes B. The global-scale impacts of climate change on water resources and flooding under new climate and socio-economic scenarios. Climatic Change 2014; 122:127–40.
View Article
Google Scholar

[48] View Article

[49] Google Scholar

[ref18] 18. Kyalangalilwa B, Boatwright JS, Daru BH, Maurin O, van der Bank M. Phylogenetic position and revised classification of Acacia s.l. (Fabaceae: Mimosoideae) in Africa, including new combinations in Vachellia and Senegalia. Bot J Linn Soc 2013; 172(4):500–23.
View Article
Google Scholar

[51] View Article

[52] Google Scholar

[ref19] 19. Bernhard-Reversat F, Poupon H. Nitrogen cycling in a soil-tree system in a sahelian savanna: example of Acacia senegal. Proceedings of a workshop arranged by the SCOPE/UNEP 1980:363–9.
View Article
Google Scholar

[54] View Article

[55] Google Scholar

[ref20] 20. Beshai AA. The economics of a primary commodity: Gum Arabic. Oxf Bull Econ Stat 1984; 46(4):371–81.
View Article
Google Scholar

[57] View Article

[58] Google Scholar

[ref21] 21. Orwa C, Mutua A, Kindt R, Jamnadass R, Simons A. Agroforestree database: a tree reference and selection guide version 4.0. 2009. Available from: URL: http://www.worldagroforestry.org/af/treedb.

[ref22] 22. Fagg C, Allison GE. Acacia senegal and the gum Arabic trade. Monograph and annotated bibliography. Tropical Forestry Papers No. 42, Alden Group, Oxford 2004:261.
View Article
Google Scholar

[61] View Article

[62] Google Scholar

[ref23] 23. Odee DW, Telford A, Wilson J, Gaye A, Cavers S. Plio-Pleistocene history and phylogeography of Acacia senegal in dry woodlands and savannahs of sub-Saharan tropical Africa: evidence of early colonisation and recent range expansion. Heredity 2012; 109(6):372–82. pmid:22929152
View Article
PubMed/NCBI
Google Scholar

[64] View Article

[65] PubMed/NCBI

[66] Google Scholar

[ref24] 24. Brenan JP. Manual on taxonomy of Acacia species: present taxonomy of four species of Acacia (A. albida, A. senegal, A. nilotica, A. tortilis). Rome: FAO; 1983.

[ref25] 25. Jøker D. Acacia senegal (L.) Willd. Seed Leaflet-Danida Forest Seed Centre 2000; (5).
View Article
Google Scholar

[69] View Article

[70] Google Scholar

[ref26] 26. Maundu P, Tengnäs B. Useful trees and shrubs for Kenya. ICRAF Technical handbook series 2005.
View Article
Google Scholar

[72] View Article

[73] Google Scholar

[ref27] 27. Raddad EAY, Salih AA, Fadl MAE, Kaarakka V, Luukkanen O. Symbiotic nitrogen fixation in eight Acacia senegal provenances in dryland clays of the Blue Nile Sudan estimated by the 15 N natural abundance method. Plant and Soil 2005; 275(1):261–9.
View Article
Google Scholar

[75] View Article

[76] Google Scholar

[ref28] 28. Traore S., Zerbo L., Schmidt M., Thiombiano L. Acacia communities and species responses to soil and climate gradients in the Sudano-Sahelian zone of West Africa. J Arid Environ 2012; 87:144–52.
View Article
Google Scholar

[78] View Article

[79] Google Scholar

[ref29] 29. Eisa MA, Roth M, Sama G., editor. Acacia senegal (gum Arabic tree): Present role and need for future conservation/Sudan. Hohenheim: Deutscher Tropentag; 2008.

[ref30] 30. Githae EW, Gachene CKK, Njoka JT. Soil physicochemical properties under Acacia senegal varieties in the dryland areas of Kenya. Afri J Plant Sci 2011; 5(8):475–82.
View Article
Google Scholar

[82] View Article

[83] Google Scholar

[ref31] 31. Mulumba JW, Nyakaana S, Jamnadas R. Genetic Diversity and structure of Acacia senegal (L.) Willd. in Uganda. Int. J. Botany 2012; 3(8):107–16.
View Article
Google Scholar

[85] View Article

[86] Google Scholar

[ref32] 32. Tandon R, Shivanna KR, Ram HM. Pollination biology and breeding system of Acacia senegal. Bot J Linn Soc 2001; 135(3):251–62.
View Article
Google Scholar

[88] View Article

[89] Google Scholar

[ref33] 33. Gray A, Odee D, Cavers S, Wilson J, Telford A, Grant F et al. Does geographic origin dictate ecological strategies in Acacia senegal (L.) Willd.?: Evidence from carbon and nitrogen stable isotopes. Plant Soil 2013; 369(1–2):479–96.
View Article
Google Scholar

[91] View Article

[92] Google Scholar

[ref34] 34. Chiveu C, Dangasuk OG, Omunyin M, Wachira FN. Genetic diversity in Kenyan populations of Acacia senegal (L:) Willd revealed by combined RAPD and ISSR markers. Afr. J. Biotechnol. 2008; 7(14):2333–40.
View Article
Google Scholar

[94] View Article

[95] Google Scholar

[ref35] 35. Habeballa RS, Hamza NB, El Gaali EI. Genetic variability in Sudanese Acacia senegal (L.) assessed by random amplified polymorphic DNA. Afr. J. Biotechnol. 2010; 9(30):4655–60.
View Article
Google Scholar

[97] View Article

[98] Google Scholar

[ref36] 36. Chase MW, Hills HH. Silica gel: an ideal material for field preservation of leaf samples for DNA studies. Taxon 1991:215–20.
View Article
Google Scholar

[100] View Article

[101] Google Scholar

[ref37] 37. Assoumane AA, Vaillant A, Mayaki AZ, Verhaegen D. Isolation and characterization of microsatellite markers for Acacia senegal (L.) Willd., a multipurpose arid and semi-arid tree. Mol Ecol Resour 2009; 9(5):1380–3. pmid:21564912
View Article
PubMed/NCBI
Google Scholar

[103] View Article

[104] PubMed/NCBI

[105] Google Scholar

[ref38] 38. Weising K, Gardner RC. A set of conserved PCR primers for the analysis of simple sequence repeat polymorphisms in chloroplast genomes of dicotyledonous angiosperms. Genome 1999; 42(1):9–19. pmid:10207998
View Article
PubMed/NCBI
Google Scholar

[107] View Article

[108] PubMed/NCBI

[109] Google Scholar

[ref39] 39. van Oosterhout C, Hutchinson WF, Wills DPM, Shipley P. MICRO‐CHECKER: Software for identifying and correcting genotyping errors in microsatellite data. Mol Ecol Resour 2004; 4(3):535–8.
View Article
Google Scholar

[111] View Article

[112] Google Scholar

[ref40] 40. Nei M. Estimation of average heterozygosity and genetic distance from a small number of individuals. Genetics 1978; 89(3):583–90. pmid:17248844
View Article
PubMed/NCBI
Google Scholar

[114] View Article

[115] PubMed/NCBI

[116] Google Scholar

[ref41] 41. Excoffier L, Lischer HEL. Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. Mol Ecol Resour 2010; 10(3):564–7. pmid:21565059
View Article
PubMed/NCBI
Google Scholar

[118] View Article

[119] PubMed/NCBI

[120] Google Scholar

[ref42] 42. Lewis, PO, Zaykin D. Genetic data analysis: computer program for the analysis of allelic data 2001. Available from: URL: http://lewis.eeb.uconn.edu/lewishome/software.html.

[ref43] 43. Goudet J. FSTAT (Version 1.2): AComputer Program to Calculate F-Statistics. J Hered 1995; 6(86):245–6.
View Article
Google Scholar

[123] View Article

[124] Google Scholar

[ref44] 44. Raymond M, Rousset F. GENEPOP (version 1.2): population genetics software for exact tests and ecumenicism. J Hered 1995; 86(3):248–9.
View Article
Google Scholar

[126] View Article

[127] Google Scholar

[ref45] 45. Weir BS, Cockerham CC. Estimating F-Statistics for the Analysis of Population Structure. Evolution 1984; 38(6):1358. pmid:28563791
View Article
PubMed/NCBI
Google Scholar

[129] View Article

[130] PubMed/NCBI

[131] Google Scholar

[ref46] 46. Pritchard J, Stephens M, Donelly P. Inference of Population Structure Using Multilocus Genotype Data. Genetics 2000; (155):945–59.
View Article
Google Scholar

[133] View Article

[134] Google Scholar

[ref47] 47. Falush D, Stephens M, Pritchard JK. Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics 2003; 164(4):1567–87. pmid:12930761
View Article
PubMed/NCBI
Google Scholar

[136] View Article

[137] PubMed/NCBI

[138] Google Scholar

[ref48] 48. Evanno G, Rednaut S, Goudet J. Detecting the number of clusters of individuals using the software structure: A simulation study. Mol Ecol 2005; 14(8):2611–20. pmid:15969739
View Article
PubMed/NCBI
Google Scholar

[140] View Article

[141] PubMed/NCBI

[142] Google Scholar

[ref49] 49. Earl DA, vonHoldt BM. STRUCTURE HARVESTER: A website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv Genet Resour 2012; 4(2):359–61.
View Article
Google Scholar

[144] View Article

[145] Google Scholar

[ref50] 50. Peakall ROD SP. GENALEX 6: genetic analysis in Excel. Population genetic software for teaching and research. Mol Ecol Notes 2006; 6(1):288–95.
View Article
Google Scholar

[147] View Article

[148] Google Scholar

[ref51] 51. Eliades NG, Eliades DG. Haplotype Analysis: Software for analysis of haplotype data. Forest Genetics and Forest Tree Breeding, Georg-August University Goettingen, Germany. 2009.

[ref52] 52. Leigh JW BD. PopArt: Full‐feature software for haplotype network construction. Methods Ecol Evol 2015; 6(9):1110–6.
View Article
Google Scholar

[151] View Article

[152] Google Scholar

[ref53] 53. Xu J, Sha T, Li YC, Zhao ZW, Yang ZL. Recombination and genetic differentiation among natural populations of the ectomycorrhizal mushroom Tricholoma matsutake from southwestern China. Mol Ecol 2008; 17(5):1238–47. pmid:18302686
View Article
PubMed/NCBI
Google Scholar

[154] View Article

[155] PubMed/NCBI

[156] Google Scholar

[ref54] 54. GBIF.org. GBIF Occurrence Download http://doi.org/10.15468/dl.c8atij 9th November 2016.

[ref55] 55. LIU C, Berry PM, Dawson TP, Pearson RG. Selecting thresholds of occurrence in the prediction of species distributions. Ecography 2005; 28(3):385–93.
View Article
Google Scholar

[159] View Article

[160] Google Scholar

[ref56] 56. Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 2005; 25(15):1965–78.
View Article
Google Scholar

[162] View Article

[163] Google Scholar

[ref57] 57. Hengl T, Mendes de Jesus J, Heuvelink GBM, Ruiperez Gonzalez M, Kilibarda M, Blagotic A et al. SoilGrids250m: Global gridded soil information based on machine learning. PLoS ONE 2017; 12(2):1–40.
View Article
Google Scholar

[165] View Article

[166] Google Scholar

[ref58] 58. Miller RL, Schmidt GA, Nazarenko LS, Tausnev N, Bauer SE, DelGenio AD et al. CMIP5 historical simulations (1850–2012) with GISS ModelE2. J Adv Model Earth Syst 2014; 6(2):441–78.
View Article
Google Scholar

[168] View Article

[169] Google Scholar

[ref59] 59. Nazarenko L, Schmidt GA, Miller RL, Tausnev N, Kelley M, Ruedy R et al. Future climate change under RCP emission scenarios with GISS ModelE2. J Adv Model Earth Syst 2015; 7(1):244–67.
View Article
Google Scholar

[171] View Article

[172] Google Scholar

[ref60] 60. Gent PR, Danabasoglu G, Donner LJ, Holland MM, Hunke EC, Jayne SR et al. The community climate system model version 4. J Clim 2011; 24(19):4973–91.
View Article
Google Scholar

[174] View Article

[175] Google Scholar

[ref61] 61. Phillips SJ, Anderson RP, Schapire RE. Maximum entropy modeling of species geographic distributions. Ecol Modell 2006; 190(3–4):231–59.
View Article
Google Scholar

[177] View Article

[178] Google Scholar

[ref62] 62. Phillips SJ, Dudík M. Modeling of species distributions with Maxent: New extensions and a comprehensive evaluation. Ecography 2008; 31(2):161–75.
View Article
Google Scholar

[180] View Article

[181] Google Scholar

[ref63] 63. Fielding AH, Bell JF. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ Conserv 1997; 24(1):38–49.
View Article
Google Scholar

[183] View Article

[184] Google Scholar

[ref64] 64. Duque-Lazo J, van Gils H, Groen TA, Navarro-Cerrillo RM. Transferability of species distribution models: The case of Phytophthora cinnamomi in Southwest Spain and Southwest Australia. Ecol Modell 2016; 320:62–70.
View Article
Google Scholar

[186] View Article

[187] Google Scholar

[ref65] 65. Quinn GP, Keough MJ. Experimental design and data analysis for biologists. Cambridge: Cambridge University Press; 2002.

[ref66] 66. Naimi B. usdm: Uncertainty analysis for species distribution models. R package version 2013; 1:1–12.
View Article
Google Scholar

[190] View Article

[191] Google Scholar

[ref67] 67. Calle ML, Urrea V Boulesteix A-L, Malats N. AUC-RF: a new strategy for genomic profiling with random forest. Hum Hered 2011; 72(2):121–32. pmid:21996641
View Article
PubMed/NCBI
Google Scholar

[193] View Article

[194] PubMed/NCBI

[195] Google Scholar

[ref68] 68. Duque-Lazo J, Navarro-Cerrillo RM. What to save, the host or the pest? The spatial distribution of xylophage insects within the Mediterranean oak woodlands of Southwestern Spain. For Ecol Manage 2017; 392:90–104.
View Article
Google Scholar

[197] View Article

[198] Google Scholar

[ref69] 69. Navarro-Cerrillo RM, Rumbaó IC, Vidaña AL, Pérez J, Duque-Lazo J. Integración de datos de inventario y modelos dehábitat para predecir la regeneración de especies leñosas mediterráneas en repoblaciones forestales. Revista Ecosistemas 2016; 25(3):6–21.
View Article
Google Scholar

[200] View Article

[201] Google Scholar

[ref70] 70. Kirk H, Freeland JR. Applications and implications of neutral versus non-neutral markers in molecular ecology. Int J Mol Sci 2011; 12(6):3966–88. pmid:21747718
View Article
PubMed/NCBI
Google Scholar

[203] View Article

[204] PubMed/NCBI

[205] Google Scholar

[ref71] 71. Nei M. Analysis of gene diversity in subdivided populations. Proc Natl Acad Sci USA 1973; 70(12):3321–3. pmid:4519626
View Article
PubMed/NCBI
Google Scholar

[207] View Article

[208] PubMed/NCBI

[209] Google Scholar

[ref72] 72. Assoumane A, Mayaki AZ, Favreau B, Bezancon G, Saadou M, Verhaegen D. Differentiation between two sub-species of Acacia senegal complex: Acacia senegal (L.) Willd. and Acacia dudgeoni Craib ex Holland using morphological traits and molecular markers. Genet Resour Crop Evol 2012; 59(1):39–48.
View Article
Google Scholar

[211] View Article

[212] Google Scholar

[ref73] 73. Winters G, Shklar G, Korol L. Characterizations of microsatellite DNA markers for Acacia tortilis. Conserv Genet Resour 2013; 5(3):807–9.
View Article
Google Scholar

[214] View Article

[215] Google Scholar

[ref74] 74. Ouinsavi C, Sokpon N, Khasa DP. Genetic Diversity and Population Structure of a Threatened African Tree Species, Milicia excelsa, Using Nuclear Microsatellites DNA Markers. International Journal of Forestry Research 2009; 2009(1345):1–8.
View Article
Google Scholar

[217] View Article

[218] Google Scholar

[ref75] 75. Noreen AME, Webb EL. High Genetic Diversity in a Potentially Vulnerable Tropical Tree Species Despite Extreme Habitat Loss. PLoS ONE 2013; 8(12).
View Article
Google Scholar

[220] View Article

[221] Google Scholar

[ref76] 76. Gwali S, Vaillant A, Nakabonge G, Okullo JL, Eilu G, Muchugi A et al. Genetic diversity in shea tree (Vitellaria paradoxa subspecies nilotica) ethno-varieties in Uganda assessed with microsatellite markers. Forests, Trees and Livelihoods 2014; 24(3):163–75.
View Article
Google Scholar

[223] View Article

[224] Google Scholar

[ref77] 77. Poczai P, Varga I, Bell NE, Hyvonen J. Genomics Meets Biodiversity: Advances in Molecular Marker Development and their Applications in Plant Genetic Diversity Assessment. In: Caliskan M, editor. The Molecular Basis of Plant Genetic Diversity: INTECH Open Access Publisher; 2012. pp 3–31.

[ref78] 78. Stoeckel S, Grange J, Fernandez-Manjarres JF, Bilger I, Frascaria-Lacoste N, Mariette S. Heterozygote excess in a self-incompatible and partially clonal forest tree species—Prunus avium L. Mol Ecol 2006; 15(8):2109–18. pmid:16780428
View Article
PubMed/NCBI
Google Scholar

[227] View Article

[228] PubMed/NCBI

[229] Google Scholar

[ref79] 79. Liu W, Zhao Y, You J, Qi D, Zhou Y, Chen J et al. Morphological and Genetic Variation along a North-to-South Transect in Stipa purpurea, a Dominant Grass on the Qinghai-Tibetan Plateau: Implications for Response to Climate Change. PLoS ONE 2016; 11(8):1–22.
View Article
Google Scholar

[231] View Article

[232] Google Scholar

[ref80] 80. Wang Z, Kang M, Liu H, Gao J, Zhang Z, Li Y et al. High-level genetic diversity and complex population structure of Siberian apricot (Prunus sibirica L.) in China as revealed by nuclear SSR markers. PLoS ONE 2014; 9(2):1–13.
View Article
Google Scholar

[234] View Article

[235] Google Scholar

[ref81] 81. Chidumayo EN, Gumbo DJ. The dry forests and woodlands of Africa: Managing for products and services. London, UK: Earthscan; 2010.

[ref82] 82. Bouchenak-Khelladi Y, Maurin O, Hurter J, van der Bank M. The evolutionary history and biogeography of Mimosoideae (Leguminosae): An emphasis on African acacias. Mol Phylogenet Evol 2010; 57(2):495–508. pmid:20696261
View Article
PubMed/NCBI
Google Scholar

[238] View Article

[239] PubMed/NCBI

[240] Google Scholar

[ref83] 83. Rozzi R, Arroyo MK, Amesto JJ. Ecological factors affecting gene flow between populations of Anarthrophyllum cumingii (Papilionaceae) growing on equatorial- and polar-facing slopes in the Andes of Central Chile. Plant Ecol 1997; 132:171–9.
View Article
Google Scholar

[242] View Article

[243] Google Scholar

[ref84] 84. Mujawamariya G, Madi OP, Zoubeirou AM, Sene A, Maisharou A, D'Haese M. Common challenges in gum arabic production and commercialization in West Africa: a comparative study of Cameroon, Niger and Senegal. International Forestry Review 2013; 15(2):182–99.
View Article
Google Scholar

[245] View Article

[246] Google Scholar

[ref85] 85. Gaisberger H, Kindt R, Loo J, Schmidt M, Bognounou F, Da SS, Diallo OB, et al. Spatially explicit multi-threat assessment of food tree species in Burkina Faso: A fine-scale approach. PLoS ONE 2017; 12(9):1–26.
View Article
Google Scholar

[248] View Article

[249] Google Scholar

[ref86] 86. Hamilton B. Tropical tree gene flow and tree dispersal: deforestation affects the genetic structure of the surviving forest fragments. Nature 1999; 401:129–30.
View Article
Google Scholar

[251] View Article

[252] Google Scholar

[ref87] 87. Robledo-Arnuncio JJ, Klein EK, Muller-Landau HC, Santamaría L. Space, time and complexity in plant dispersal ecology. Mov Ecol 2014; 2(1):1–17.
View Article
Google Scholar

[254] View Article

[255] Google Scholar

[ref88] 88. Petit RJ, Duminil J, Fineschi S, Hampe A, Salvini D, Vendramin GG. Comparative organization of chloroplast, mitochondrial and nuclear diversity in plant populations. Mol Ecol 2005; 14(3):689–701. pmid:15723661
View Article
PubMed/NCBI
Google Scholar

[257] View Article

[258] PubMed/NCBI

[259] Google Scholar

[ref89] 89. Gonçalves-Oliveira RC, Wöhrmann T, Benko-Iseppon AM, Krapp F, Alves M, Wanderley ML et al. Population genetic structure of the rock outcrop species Encholirium spectabile (Bromeliaceae): The role of pollination vs. seed dispersal and evolutionary implications. Am J Bot 2017; 104(6):868–78. pmid:28611073
View Article
PubMed/NCBI
Google Scholar

[261] View Article

[262] PubMed/NCBI

[263] Google Scholar

[ref90] 90. Sexton JP, Hangartner SB, Hoffmann AA. Genetic isolation by environment or distance: Which pattern of gene flow is most common? Evolution 2014; 68(1):1–15. pmid:24111567
View Article
PubMed/NCBI
Google Scholar

[265] View Article

[266] PubMed/NCBI

[267] Google Scholar

[ref91] 91. Malcolm JR, Markham A, Neilson RP, Garaci M. Estimated migration rates under scenarios of global climate change. J Biogeogr 2002; 29(7):835–49.
View Article
Google Scholar

[269] View Article

[270] Google Scholar

Figures

Abstract

Introduction

Materials and methods

Ethic statement

Study area and sampling

Microsatellite genotyping

Genetic data analysis

Distribution modelling

Results

Within-Population genetic diversity

Population differentiation

Distribution modelling

Discussion

Within-Population genetic diversity

Population genetic structure and differentiation

Genetic diversity and distribution under climate change

Conclusions

Supporting information

S1 Table. Genetic characteristics of ten nuclear SSR loci in 13 populations of Senegalia senegal (N = 297).

S2 Table. Proportion of membership of each predefined population in each of the inferred clusters at both K = 2 and K = 3.

S3 Table. List of haplotypes detected at two cpSSR loci in 13 populations of Senegalia senegal.

S4 Table. Genetic characteristics of two chloroplast microsatellite markers and result of genotyping in S. senegal (N = 303).

S5 Table. List of variables used for modelling the distribution of Senegalia senegal in West Africa.

S6 Table. Matrix of pairwise FST values based on allele data at ten nuclear (above diagonal) and two chloroplast SSR (above diagonal) loci among 13 populations of Senegalia senegal.

S1 Fig. Plots for detecting the number of K groups that best fit the data according to [48].

S2 Fig. Plot of sensitivity vs. specificity.

S3 Fig. Predicted area (%) gain or loss of Senegalia senegal relative to the current distribution under four future climate scenarios (RCP’s) as estimated by two models (CCSM4, GISSER2) for 2050 and 2070.

Acknowledgments

References

S6 Table. Matrix of pairwise F_ST values based on allele data at ten nuclear (above diagonal) and two chloroplast SSR (above diagonal) loci among 13 populations of Senegalia senegal.