Ecological-niche modeling of genetically distinct populations revealed Kersting’s groundnut [Macrotyloma geocarpum (Harms) Maréchal et Baudet] as a resilient orphan crop to present and future climates

doi:10.21203/rs.3.rs-763341/v1

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Ecological-niche modeling of genetically distinct populations revealed Kersting’s groundnut [Macrotyloma geocarpum (Harms) Maréchal et Baudet] as a resilient orphan crop to present and future climates

https://doi.org/10.21203/rs.3.rs-763341/v1

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Orphan legume crops play an important role in smallholder farmers’ food systems. Though less documented, they have the potential to contribute to adequate nutrition in vulnerable communities. Unfortunately, data are scarce about the potential of those crops to withstand current and future climate variations. Using Macrotyloma geocarpum as an example, we used genetically informed ecological niche models to explore the role of ecology on the current and future distributions of genetic populations of Kersting’s groundnut. Three main conclusions emerged: i) the models had good predictive power, indicating that M. geocarpum’s distribution was correlated with both climatic and soil layers; ii) identity and similarity tests revealed that the two genetic groups while overlapping, are each, locally adapted and display differences in climate suitability; iii) by integrating the genetic information in niche modeling, niches projections show divergence in the response of the species and genetic populations to ongoing climate change. This study highlights the importance of incorporating genetic data into ENM approaches to obtain a finer information of species’ future distribution, and explores the implications for agricultural adaptation, with a particular focus on identifying priority actions in orphan crops conservation and breeding.

Behavioral Ecology

Evolutionary Biology

Molecular Biology

Plant Molecular Biology and Genetics

Macrotyloma geocarpum

orphan crop

genetic groups

species distribution modeling

climate suitability

niche differentiation

conservation

plant breeding

Defining how and where plant varieties will adequately respond to environmental variations is a central topic in plant science research. This is more preoccupying for orphan crops that are largely grown in marginal areas and neglected in the mainstream research agenda ¹. Availability of the genetic resources of those crops is still a challenge for many genebanks at national, regional, and international levels. This situation jeopardizes the sustainable utilization of the plant genetic diversity that can be useful for current and future food systems and secured nutrition ^2–4. Such genetic resources are important for successful cultivars development and selection of economic and agronomic traits and could confer resilience to evolving climate.

In Kersting’s groundnut [Macrotyloma geocarpum (Harms) Maréchal and Baudet], a multipurpose staple orphan crop with high nutritional and economic values for smallholder farmers in West Africa ^5–9, the need to solve the ecological suitability of the extant genetic resources arose despite the significant achievements made recently on the germplasm collection, conservation, and crop selection. Kersting’s groundnut plays an important role in farming sustainability through its ability to fix atmospheric nitrogen in the soil and enhance soil fertility ¹⁰. Furthermore, It serves in traditional medicine for local populations ^{6, 7, 11}. However, the production of Kersting’s groundnut is declining rapidly and the genetic resources were rarely collected and safeguarded for the future generation. In addition, environmental stresses are among the main causes for declining Kersting’s groundnut production from its cultivated areas ^{5, 12}. Though Kersting’s groundnut has relatively good adaptation to low-input conditions ^{13, 14}, increased frequency of drought, intense precipitations, elevated temperatures, and increased salt and heavy metals in soils will often be accompanied by increased infestation by pests, and pathogens, are expected to limit the plant growth and productivity, and consequently the crop’s yield and production ¹⁵.

Recent studies revealed a low variation within the species ^{10, 16} that limits the extent of its genetic diversity and cultivated zones. Kersting’s groundnut counts six landraces set mostly within three agroclimatic zones; Northern-Guinean (NG), Northern-Sudanian (NS), and Southern-Sudanian (SS) of Benin, Burkina Faso, Ghana, and Togo with the predominance of genetic resources and diversity in the Southern-Sudanian zone ^{5, 12}. Overall, the area of cultivation and adaptation of landraces differ among agroclimatic regions. The Black landrace was largely collected in the Northern-Sudanian environmental conditions and was widely preferred, cultivated, and maintained by farmers ¹². The White landrace was widely grown in the Northern-Guinean transition zone of Benin ^{5, 6} while less cultivated in Burkina Faso ¹² and absent in other countries of West-Africa. The production of the Brown landrace was specifically limited to the Ghana farming system ¹².

Kersting’s groundnut landraces are the direct results of farmer selection, cultivation, and maintenance over the centuries. This continual adaptation of the crop to smallholders farming conditions could continue to play a role in adapting production to climate change. Also, local adaptation of landraces could vary in their climatic response and requirement and therefore, may spread differentially under evolving environmental conditions ¹⁷. To find an adequate referendum where the species can thrive, it has become crucial to approximate the potential distribution of the crop and its genetic resources.

Unfortunately, with the rapid evolution in climate conditions and the further introduction and adoption of new cash crops with high economic importance, local seed systems alone will likely be insufficient to ensure the endurance of the crop genetic resources and diversity. In these conditions, applying ecological research is required to inform conservation and management decisions to mitigate a species genetic erosion ¹⁸, as Kersting’s groundnut at National and Regional levels. Ecological niche modeling (ENM) can identify the environmental parameters that can impact a species’ distribution and project its potential distribution area onto new environmental surfaces to examine the effect of present or future environmental change ^{19, 20}.

Several statistical and mechanistic techniques proved effective in quantifying niches and spatial distribution of natural and cultivated species ^21–25. The basic modeling framework of species distribution models (SDMs) in general has been criticized on a number of gaps, such as ignoring heterogeneity in population and genetic structure in different parts of a species geographical range ²⁶. However, many species are organized into differentiated genetic lineages across their geographical ranges ^{27, 28} and populations differ in their adaptive potential to respond to environmental change ²⁹. Studies proved that incorporating molecular data into SDMs represents an important step forward for modeling the effects of climate change on species geographical ranges ^30–32.

In the case of Kersting’s groundnut ³³, much uncertainty remains concerning the ability of the crop to withstand the changing climate, suggesting that there is a clear need to comprehensively analyze the response of the crop diversity under new environmental conditions of the coming decades.

The present study was undertaken to predict the potential distribution of KG under present and future climate change scenarios. Recent molecular studies involving 281 individuals from Benin and Togo identified two major genetic clusters of KG and these two groups were distributed across Southern-Sudanian and Northern-Guinean agroclimatic zones ¹⁶. Kafoutchoni, et al. ³⁴ also assessed the genetic structure of the species through the GBS approach and Discriminant analysis of principal components (DAPC) and found eight genetically distinct groups from five origins. In this context, the following questions are of high interest: do the agroclimatic niches of KG vary with climate changes? Would KG genetic groups differ in their ability to respond to present and future climatic scenarios? This study examines the response of orphan crops to future climates by using genetic information and ecological niche modeling approach (gENMs) using KG as an example. Therefore, we combined KG population genomics data with ecological niche modeling: 1) to analyze the relationship between climate factors and species populations distribution in agroclimatic zones of Burkina Faso, Benin, Ghana, and Togo, and 2) to predict and examine areas that would be suitable for the species and genetic populations under the future scenarios. We hypothesized that: i) the distribution of KG remains stable under future climates, and ii) genetically distinct populations of orphan crops would respond differently to climate change.

Population structure and admixture

Despite low levels of diversity (He = 0.28 and Ho = 0.023, p.value = < 0.001), Kersting’s groundnut populations remained genetically well differentiated. Admixture models with a putative number of tested genetic clusters (K) from one to five, showed that the most likely number of inferred members was 2 with ΔK = 368.369 (Fig. 1a). The classification of the 361 accessions into populations based on the model-based structure (Fig. 1b) showed that 231 accessions belonged to the Pop1 and 130 accessions were included in the Pop2.

Table 1 showed the distribution of populations across agroclimatic zones. Most of the accessions were collected in the Southern-Sudanian zone with proportions of 80.519% and 80% comprised in Pop1 and Pop2, respectively. The accessions collected in the Northern-Sudanian zone were all counted in the Pop2. In the Northern-Guinean zone, 19.481% of accessions belonged to Pop1 and 14.615% were included in Pop2.

Table 1

Number (N) and proportions (Freq) of accessions for each genetic Group per Landrace and per agroecological zone
	Populations	Group 1		Group 2
Variance		N	Freq (%)	N	Freq (%)
	Black	4	1.732	57	43.846
	Brown_greyed_orange_eye	0	0	39	30
Landraces seeds coat color	Red	2	0.866	17	13.077
	White	225	97.403	2	1.538
	White_black_eye	0	0	14	10.769
	White_greyed_orange_eye	0	0	1	0.769
	Northern-Guinean	45	19.481	19	14.615
Agroecological Zones	Northern-Sudanian	0	0	7	5.385
	Southern-Sudanian	186	80.519	104	80
Totals		231	100	130	100

The random distribution of landraces (Fig. 2) into the genetic groups indicated that Pop1 was mostly characterized by the White landrace (97.403% of individuals in Pop1). The genetic Pop1 comprised also the Red and the Black landraces. The Pop2 was composed of all the six landraces included in this study with a predominance of Black (43.846%) and Brown ones (30%).

The analysis of genetic distance revealed relatively strong genetic differentiation among the two distinct groups of KG with a pairwise Fst value of 0.704 (Table 2). The estimated values of gene diversity (Hs) showed overall, low genetic divergence between individuals within each Group. Meanwhile, the heterozygosity analysis showed a greater genetic divergence between individuals in Pop2 (Hs = 0.323).

Table 2

Analysis of molecular variance (AMOVA), genetic variation within and among populations
Source of variation	Df	SS	MS	%	Pairwise Fst	He	Ho	P-value
Within Pop	3	157.584	52.528	5.61		0.28	0.023	< 0.001
Between Pop	1	2420.71	2420.710	64.658	0.704			< 0.001
Pop1						0.028
Pop2						0.324

DF: degree of freedom; SS: sum of squares; MS: expected mean squares; %: Percentage of the variance; He: expected heterozygosity; Ho: observed heterozygosity; P-value: Significance tests after 1,000 permutations.

Occurences dataset construction

After data cleaning steps, the final occurrence dataset contained 53 locations points for the species (including 17 from GBIF), 22 for Pop 1 and 25 for Pop 2, distributed across three agroclimatic zones of Benin, Burkina Faso, Ghana, and Togo. Figure 3 shows Pop1 distributed across two agroecologies, the Northern-Guinean and Southern-Sudanian zones whereas the Pop2 and GBIF points were represented in all agroecologies. At the country level, Pop2 was distributed across the four countries while Pop1 was located in Benin and Togo.

Niche overlap and conservatism among M. geocarpum and genetic populations

The results of the niche overlap (D) indicated that the current distribution of Kersting’s groundnut species and its genetic groups was not significantly different. For all pairwise comparisons, between the niches of the species and Pop1, Pop2, and between niches of the two genetic groups, we found Schoener’s D values of 0.935, 0.907, and 0.878, respectively.

In addition, our results suggested that niche overlap tests performed differently based on D Values obtained from identity and similarity tests. We rejected the null hypothesis (P < 0.01, Fig. 4a) that niches between the species and genetic Pop 1 were identical, but they were similar (P > 0.05, Fig. 4d). Predicted niches between M. geocarpum and genetic Pop 2, while identical (P > 0.05, Fig. 4b), were not conserved (P < 0.01, Fig. 4e). We rejected both the null hypothesis that niches were identical (P < 0.01, Fig. 4c) and that niches were conserved (P < 0.05, Fig. 4f) between the two genetic groups.

Ecological niche modeling

Climatic variables analogy

The initial run in Maxent with M. geocarpum without genetic information showed that the most important environmental variables contributing to the predictive model included temperature seasonality, rainfall wettest month, rainfall driest quarter, Bulk density of soil in kg / cubic-meter for 22.5 cm depth, and soil texture fraction of clay at 10 cm. Based on the percent contribution values, the selected climatic variables were more or less important in the three distribution models of Kersting’s groundnut (Fig. 5a), its genetic Pop1 (Fig. 5b), and Pop2 (Fig. 5c). Precipitation during the driest quarter was the most influential predictor for M. geocarpum and Pop2. Soil texture fraction clay at 10 cm was the variable contributing mostly to the Pop1 model whereas, for the model of Pop2, it was the less important (0.3%). In general, soil texture fraction sand at 10 cm presented the least contribution values to the different models.

Models’ outputs and performance evaluation

The environmental predictive models displayed high predictive power according to the AUC values of 0.86, 0.93, and 0.84 for the species, Pop1 and Pop2, respectively (Table 3). These results suggest that the predictions effectively captured relationships between environmental variables and locations points of Pop1 comparing to Pop2 and the species.

Table 3

Maxent results showing the parameters measuring the three models’ performance and projected areas suitability
Species/Population	AUC	MTP	Occ
M. geocarpum	0.86	0.329	53
Mg_Pop1	0.93	0.247	24
Mg_Pop2	0.84	0.468	23
AUC: Area Under the Curve; MTP: Minimum training presence; Occ: Number of occurrences.

Based on the Minimum training presence (MTP), suitable areas for Kersting’s groundnut and population groups were defined. We found that current distributions were significantly different between populations and species. The Maxent model for the species predicted a large area of cultivable conditions across the three agroclimatic zones, Southern Sudanian (SS), Northern Sudanian (NS), and Northern-Guinean (NG) (Fig. 6a1). The SS and NG zones were the areas forecasted to have high suitable climatic conditions for species production. For the Pop1, the areas predicted to have high likely cultivability conditions were concentrated in the NG zone of Benin, but very less and sparsely distributed in the SS zone (Fig. 6b1). The Pop2 was projected across the three studied agroclimatic zones of the four countries with the highest cultivable areas in SS and NS zones (Fig. 6c1).

Furthermore, the potential distribution maps under future (in 2055) climatic conditions revealed varied patterns in KG and genetic populations cultivable areas (Fig. 6 and Fig. 7, see supplemental Figures). Under the two future climatic scenarios RCP4.5 and RCP8.5, an increase in the species cultivable areas for about 2.75%, were observed due to the decrease of the non-suitable areas (Fig. 6a2, a3, Fig. 7). The expansion of This area was observed mainly in the NG zone of Southern Benin, also in SS and NS zones of Burkina Faso, Ghana, and Togo. Similarly, an increase of the cultivability areas of Kersting’s groundnut was observed in the SS zone of Northern Benin. On the other hand, the SS zone of Central Benin became climatically unsuitable to crop production.

The Pop1 showed to be more vulnerable to future scenarios as the suitable areas slightly decreased (0.504 % under RCP4.5 and 0.779 % under RCP8.5), while the unsuitable ranges increased (Fig. 6b2, b3, Fig. 7).

The model of this genetic group predicted an increase in the suitable areas of the NG zone of Benin, Ghana, and Togo while a decrease was observed in the NS and SS zone of the four countries. The potential cultivable areas of the Pop2 (Fig. 6c2, c3, Fig. 7) slightly decreased by 0.322% under RCP4.5 while increased with extreme conditions of RCP8.5 (0.519%). The future climatic conditions of the SS zone of Central Benin would constitute constraints to this population cultivation. In contrast, the NG zone of Southern Benin and Togo and the SS zone gained in cultivable areas for Pop2.

This study provided evidence of incorporating genetic information in ENM to characterize the adaptation potential of M. geocarpum in four West-African countries (Benin, Burkina Faso, Ghana, and Togo) and suggested implications for sustainable management and breeding perspectives under current and future climate change.

Current potential distribution ranges of Kersting’s groundnut and genetic groups

We used two approaches to model KG distributions across agroclimatic zones of four West African countries: a traditional species SDM using species-wide cultivated areas, and intraspecific models based on genetic information. We found that decomposing a species into intraspecific genetic groups increases our understanding of the potential distribution of KG, which is consistent with results from the previous few studies ³⁰^,³¹^,³⁵^,³⁶. The SDMs for Pop1 performed more accurately in estimating potential cultivation areas of KG rather than the genetic Pop2, which showed lower prediction accuracy. The Pop2 is found in the three agroclimatic zones of all countries; it displays a wide distribution range like the whole species. Indeed, Allouche, et al. ³⁷ reported that the values of predictive accuracy tend to be lower when species are more common within the distribution ranges. Our results reinforce the view that it is necessary for SDMs evaluation, to consider not only the use of genetic data but also the population sizes and the cultivated ranges of each genetic unit ³⁵^,³⁸.

Environmental variables contribution

Our approach allowed to identify six environmental variables correlated with the potential distribution ranges of the species and populations. The rainfall wettest month, rainfall driest quarter, and temperature seasonality were mostly involved in fitting the models. The ecological weight that precipitations may have in KG’s distribution is also supported by a recent ethnobotanical study where farmers identified drought and high moisture during the reproductive stage as the main factors hindering species production ⁵^,¹². Tamini ³⁹ also showed that the different sowing dates and seasons with varied temperatures influenced KG growing cycle by delaying or accelerating the flowering time. To cope with these weather issues, farmers managed their cropping calendar by advancing (in June) or delaying (in August) the date of planting KG. The change in temperature and moisture levels are not only determined by climatic parameters but may also lead to a change in the absorption rate of water, fertilizers, and other minerals in soils, which determine yield output ^40-42. Therefore, the consideration of both climate and soil type was essential to better predict the species and genetic groups’ distribution. The physical composition of soil such as bulk density of soil in kg / cubic-meter for 22.5 cm depth also had a relevant role in KG and populations distribution. Furthermore, based on variables importance shown by Jackknife measures, soil texture fractions clay and sand in percent, at 10 cm depth accounted for the potential distribution ranges of M. geocarpum and the genetic Pop1 while less for Pop2. Kouelo, et al. ⁴³ reported that the soil texture and preparation (type of tillage) influenced the crop productivity in Benin. Although applying SDM to intraspecific genetic groups allowed the detection of environmental variables, a multidisciplinary approach involving agronomists and biologists would allow a clear understanding of the weight that each of these components may have on crop growth and development.

Niche overlapping

We found important geographic overlap among the projected distributions of the species genetic populations. The clusters did not show strict cluster-specific ecological niches. Our results did not support the view that a given species could be considered as an assemblage of genetic units differing in their spatial distribution ³¹^,³⁵. Populations of KG are both cultivated in the Northern-Guinean and the Southern-Sudanian zones of Benin, characterized by bimodal (rainfall ≤ 1500mm) and unimodal (rainfall ≤ 1100mm) growing seasons, respectively. However, Pop 2 showed wide cultivable areas rather than Pop1, as it is also grown in the Northern-Sudanian zone, where rainfalls are relatively low (rainfall ≤ 900mm). The null hypothesis was rejected as climatic niches between the two populations were identical and similar (niche identity test p < 0.01 and niche similarity p < 0.05)), suggesting variation in their environmental niches and adaptation to different climate conditions, as previously reported by Wellenreuther, et al. ⁴⁴ who analyzed the ecological causes of the ranges limits and the coexistence of two congeneric damselflies (Calopteryx splendens and C. virgo). In addition, Maxent’s outputs also revealed that climatic niches of the two genetic populations of M. geocarpum are relatively different in the distribution areas. This finding is in accordance with other studies which reported environmental niche dissimilarity at the intraspecific level ³¹^,³⁸^,⁴⁵^,⁴⁶.

Predicting evolutionary change based on genetic variation

We assumed that KG species will conserve its cultivated areas over space and time. Our results, however, found evidence of variability in the species response to future environmental conditions. This corroborates many other studies which showed the impact of future climatic conditions on crops production, such as cereals, legumes, vegetables ^22-24^,⁴⁷^,⁴⁸.

We also combined genetic information to ENM to test the hypothesis that genetic divergent groups differ in their adaptive potential to respond to environmental change. The projection under future climatic scenarios RCP4.5 and RCP8.5 indicate that the response of Kersting’s groundnut and genetic groups varied significantly across agroclimatic zones. Our results confirmed the idea that different genetic clusters potentially showed adaptive variation to different abiotic conditions within the geographical range of the species ⁴⁹. Globally, although Central Benin is presumed to be a centre of origin for the crop, we observed a loss in suitable areas for the species production in the Southern-Sudanian zone of the country. The genetic Pop 1 is the most concerned by the negative impact of environmental evolution as it was generally found in these regions of the country. Similarly, Ikeda, Max, Allan, Lau, Shuster and Whitham ³¹ tested and demonstrated the hypothesis that species locally adapted to current environments are likely to become maladapted in the future. The same trends were reported on other cereals such as maize, wheat, sorghum, and barley which tend to decrease their area of suitability in their centres of origin ⁵⁰^,⁵¹. In the context of an inevitable agricultural reduction in these agroclimatic zones, the earlier the mitigation actions are taken, the more successful will be the collection and conservation efforts of KG’s resources. On the other hand, severe environmental conditions (RCP8.5) of the Northern-Guinean zone in Southern Benin and Togo remain, even become more favorable to the species cultivation (Pop2). Likewise, the Southern-Sudanian zone of Ghana, Burkina Faso, Togo and Northern Benin, and Northern-Sudanian zone increased in cultivable areas of Pop2. Out of the 361 individuals included in this study, 101 belong to the genetic Pop2, distributed mostly in the Southern-Sudanian zone and counts all the landraces grown - diversity based on seed coat color - ⁵^,¹². Such diversity in this population may explain the adaptive response of the Pop2 to climate variation. This is supported by the conclusion of Vigouroux, et al. ⁵² who described the evolution and adaptation of pearl millet in West Africa. However, the cultivable area of this genetic Pop2 is predicted to decline in the Southern-Sudanian zone of Central Benin. With regards to these results, significant headway can be made by creating more favorable policy environments. Two strategies proposed by Ramirez-Cabral, Kumar and Taylor ²³ can be used to mitigate loss in KG resources and diversity: first, the use of all the resources of Pop2 in regions where the stress of climate conditions become unsuitable for the species production. An example of this is the Southern-Sudanian zone of Central Benin, where the use of KG is more important than other regions. The second option was to address future loss of suitability by shifting Pop1's current cultivation areas to new regions, such as the Northern-Guinean zone of Southern Benin, the southern-Sudanian, and northern-Sudanian zones, where future climatic conditions are projected to become more cultivable for KG.

Conservation implications

Successful management of an endangered species often depends on the accurate identification of current and future cultivable areas. Thus, we combined genetic diversity and ecological niche modeling to understand the evolutionary dynamics in Kersting’s groundnut species across its cultivated regions. Although the predictive models showed that future climatic conditions will be more favorable to KG production, areas of the Southern-Sudanian and Northern-Guinean zones of Benin were identified as climatically unsuitable for the species and genetic groups. Notwithstanding the above results, this approach may provide a valuable tool for genetic resources managers for implementing collection and conservation strategies of this orphan legume, as sustained by Sohn, et al. ⁵³ in their study on the endemic bird in South America. In particular, our suggestion is to focus on the Pop1 with particularly high conservation value. This genetic group is composed mainly of the white landrace mostly preferred, grown, and sold by farmers in Benin ⁵^,⁶. However, the local seed system alone will likely be insufficient to adapt the crop production to changing climate. Formal seeds management efforts can improve the decision-making process that would greatly help in the collection and conservation of those populations at higher risk of extinction but also those that may thrive with climate change. In general, African crop genetic resources conservation is unfortunately poorly supported by National Governments, and material from the regions is not fully represented in the important international genebanks which afford the baseline for sustained public breeding efforts ⁴⁷.

Implications of SDM results for selection for adaptation

Undertaking any global initiatives to overcome food insecurity challenges requires an assessment of staple crop productivity, quality, and environmentally suitable areas under climate change ²³. KG is a staple food crop and a source of proteins and nutrients for smallholder farmers in West Africa. In this research, we expected to identify the intraspecific genetic group that would be resilient under future climate scenarios, and which areas are concerned by changes in KG diversity. The model projections indicate that a shift in Kersting’s groundnut productive areas is slightly and likely with a loss of cultivability of the Pop1 cultivation areas and an increase for Pop2 in two agroecologies (Northern-Sudanian and Southern-Sudanian). Although KG is predicted to gain in a suitable area in future environmental changes, its cultivation is, however, decreasing year to year from its cultivated areas ⁵^,⁵⁴. Therefore, it will be crucial to adapt M. geocarpum species to the increasingly challenging environmental conditions through the development of new resilient cultivars that meet farmers’ needs. The Pop2 comprises all landraces (diversity) of the species and can serve as a potential source for diversity on which breeding efforts could be based to confer resilience to changing climates and increase sustainability, growth, and yield of genetic Pop1. Advancements in molecular plant breeding would be an importance of paramount to increase the genetic gains and make more accurate the breeding process. In the particular case of KG, currently, available partial GBS data ¹⁶^,³⁴ would allow the analyses of intraspecific genetic clusters based on gene network variation for various important phenotypic traits (e.g.: grain yield, yield-related traits, flowering time). This novel approach would provide possibilities to assess the extent to which key functional genes and genetic variation may be threatened under future ecological conditions ⁵⁵. Plant breeders have widely and successfully implemented genome-wide breeding approach for the development of climate-resilient varieties ^56-58, through marker-assisted selection and genomic selection. Another approach is to increase variability within the species, particularly in Pop1, through mutation methods (using physical or chemical mutagens) combined with molecular markers (Targeted Induced Local Lesions in Genomes (TILLING)). Such techniques have been successfully used in breeding of many legume crops to enhance diversity and to develop mutant cultivars ²^,⁵⁹^,⁶⁰.

This study assessed the potential impacts of climate variations on environments suitability for Kersting’s groundnut cultivation, and consequently its distribution around four West-African countries. The use of Maxent’ models and genetic information allowed a preliminary understanding of the stress factors influencing the climate suitability of the species and genetic populations under future scenarios (2055, RCP4.5 and RCP8.5). The overall trend shown by our results indicates an increase in climate suitability for the species cultivation in the Northern-Guinean zone of Southern Benin and Togo, the Northern-Sudanian and Southern-Sudanian zones of Burkina Faso, Ghana, Togo, and Northern Benin. A decrease was observed in the Southern-Sudanian zones of Central Benin, which is the major production area. Our findings illustrate also that projected areas of environments cultivability for M. geocarpum and the two populations are on different climate change trajectories. The projected distribution maps presented in this paper have been hence, used to identify strategic measures to manage the impacts of reduced climate cultivability while taking advantage of the opportunities in areas of improved suitability for Kersting’s groundnut cultivation in the future. Our findings could be downscaled to a country level to assist national policymakers in developing strategic control initiatives to prevent the scarcity of this legume.

Although this study represents the first insight into examining the potential of Kersting’s groundnut as a resilient crop under climate change, several limitations are to be noted to develop the right tools to reduce model uncertainty and make better predictions in future research. First, the global distribution of the species and its wild relatives is still incompletely documented online: collecting more and finer occurrence data, especially in regions where its production was previously reported would greatly help in refining or confirming our results. Second, our models identified rainfall, temperature, and soil variables that contributed significantly to their fits. However, socioeconomic factors such as the local market value of the species and cultural preferences must also be considered in the predictions. Combining also measures of key phenotypic traits in the modeling process would contribute to improving predictions of the impact of climate change on this legume crop. Third, the non-availability of whole genome-wide data in the case of Kersting’s groundnut limited access to phylogenetic information and identification of key functional genes for various important phenotypic traits. That may provide means to assess the response of key functional genes under evolutionary climate change.

Nevertheless, using our approach, we identified species, genetic populations, and cultivable areas for further germplasm collecting to enhance available germplasm and better direct Kersting’s groundnut breeding priorities in the future.

Genotyping and genetic clustering

For the present work, a total of 361 accessions of KG collected from Benin, Burkina Faso, Ghana and Togo were used. The DNA of each sample was extracted from young leaves of each accession, using the protocol of the Integrated genotyping service and support (IGSS) at the Biosciences Eastern and Central Africa (BecA: http://hub.africabiosciences.org/activities/services) located in Nairobi, Kenya. The quality of the DNA was confirmed by electrophoresis in 0.8% agarose, and the quantification was carried out using UVP BioDoc-It2 Imaging System. All of the samples were diluted to 50 ng/µl for the DArT genotyping platform. Genotyping was performed using the DArT-Seq™ platform at Diversity Arrays Technology ⁶¹. The quality analysis of the genotypic data was performed using Illumina HiSeq 2500 ⁶². The SilicoDArt calling algorithms (DArTsoft14) was used to score DArTseq markers into a binary format (presence = 1 and absence = 0) for each sample genomic representation. A total of 2844 SNP markers were obtained and processed in TASSEL v5 ⁶³, for quality check. SNPs were filtered with TASSEL v5 for further analysis using the parameters Minor Allele Frequency (MAF) >0.05 and the proportion of missing data < 30%. A total of 142 SNPs were retained and the Nipals model in kdcompute (https://kdcompute.igss-africa.org) was used for data imputation.

The program Structure 2.3.4 ⁶⁴ was used to assign individuals to different genetic clusters based on the admixture model. The population structure was evaluated based on the Bayesian clustering approach using the following settings: correlated allelic frequencies, burn-in period of 10,000 and 10,000 Markov Chain Monte Carlo (MCMC) interactions; and grouping (K) ranging from 1 to 5 in 10 independent runs. The results generated were used as input to the POPHELPER, an R package ⁶⁵, to predict the best K value based on the Evanno method ⁶⁶ and to generate the bar graph and delta K graph.

The pairwise Fixation index (Fst) and molecular variation (AMOVA) were performed using the R package adegenet ⁶⁷ to reveal genetic differentiation among the two distinct groups occupying the three agroclimatic zones of Burkina Faso, Benin, Ghana, and Togo. General patterns of genetic diversity were also evaluated by calculating observed (Ho) versus expected (He) heterozygosity and gene diversity (Hs) within each population.

Occurrence data

We used two sets of occurrence data, at the species and genetic analysis levels. M. geocarpum records were collected from its location points using data of self-collected material and the Global Biodiversity and Informatics Facility (GBIF, www.gbif.org), an online available database. We used both the original population location points and those from GBIF in all subsequent analyses. As the different data sources and a large dataset (>500 occurrence records) would likely carry elevated geographical or environmental space biases ⁶⁸^,⁶⁹, the number of records were decreased in Wallace package, an online workspace based on R interface ⁷⁰ using four complementary approaches: 1) we first removed occurrences collected before 1986 to match with environmental layers and soil properties; 2) considerable ambiguity may exist in GBIF data over the identity of the species due to synonymous names (M. geocarpum var. geocarpum, M. geocarpum var. Tisserantii; Kerstingiella geocarpa, Kerstingiella tisserantii). To avoid any confusion arising from this taxonomic ambiguity, we searched through the online databases using the following keywords: Macrotyloma geocarpum, Kerstingiella geocarpa, var. geocarpa or var. geocarpum; orphan legumes. We then harmonized the GBIF database and discarded the reports on var. tisserantii and Kerstingiella tisserantii; 3) we used spatially filtering occurrences located ≤ 10 km from other occurrences using the spThin, an R package ⁷¹; finally, 4) we manually checked isolated locations points in Africa (in ArcGis ver. 10.7.1) and removed occurrences in areas where M. geocarpum is not generally grown.

The defined genetic clusters data with their geographic coordinates were also filtered separately to ensure the real distribution of each population within agroclimatic zones. The filtered dataset comprised in total 64 occurrences (Pop1 = 22; Pop2 = 25 and GBIF = 17) that was used in subsequent analyses.

Environmental variables

We used bioclimate layers combined with soil properties to project current and future niches for the species and each genetic group. A total of 14 bioclimate variables were downloaded from Africlim online regional climate models (RCMs) data portal (https://webfiles.york.ac.uk/KITE/AfriClim/) ⁷². Current and future variables averaged between the periods 1986-2015 (2000) and 2041-2060 (2055) were downloaded at a 30 arc-second (~1 km) spatial resolution. For future climatic conditions, predictions from the Ensemble model ⁷² were used. This model simulates changes based on a set of scenarios. The projections were run under Representative Concentration Pathway (RCP), RCP 4.5, and RCP 8.5 for the 2055 time horizon ⁷³. In all RCPs, the climatic conditions are extreme in RCP 8.5 scenarios compared to RCP 4.5. RCP 4.5 projects temperatures to rise above industrial levels by at least 1.5°C in West Africa, with atmospheric CO₂ reaching 500 ppm while in RCP 8.5 projections, temperatures are predicted to rise by 2.8°C and atmospheric CO₂ to be over 550 ppm ⁷⁴. These climate projections were statistically downscaled to match the bioclimatic variables using the delta method, ⁷⁵.

Data related to soil characteristics were available in the World Soil Information (ISRIC) databases (Soil-property-maps-of-Africa-at-250-m-resolution) at 250 m resolution ⁷⁶. These spatial predictions of soil properties were generated based on two predictive approaches such as random forests and linear regression ⁷⁶. Soil characteristics identified as relevant to M. geocarpum agricultural management included 11 variables related to the soil's physical, chemical, and nutritional properties. Soil data were then converted to 30 arcseconds using ArcGis software v 10.7.1 to match with bioclimate layers. Finally, using shapefile boundaries of four West African countries (Benin, Burkina Faso, Ghana, and Togo) we cropped all variables to encompass the broad geographic regions that define Kersting’s groundnut global distribution.

Jackknife Procedure in Maxent 3.4.4 was used to reduce the number of variables to be included in the prediction models ⁷⁷. The six variables with the highest contribution proportions were selected and were used in the final models of the species, and with genetic information.

Ecological predictive models’ development and evaluation

To calibrate our models, we employed the maximum entropy method ⁷⁷^,⁷⁸ implemented in Maxent ver. 3.4.4. The algorithm has been extensively tested and benchmarked ⁷⁹^,⁸⁰. Many studies have reported Maxent as one of the highest performing presence-background algorithms ²¹^,⁸¹. As the selection of sample points can influence model performance in Maxent ⁸², we restricted the selection of background points using the regularization of 10,000 background points ²¹. For simplification of the modeling algorithms, we used the default settings (feature class and regularization) in Maxent for each of the three models. Models were trained with data from the present and projected in the future. Three Maxent models were generated with different occurrence datasets: first, all accessions location points together without genetic information were used in projecting the entire distribution of KG (model 1); model 2 and model 3 were developed using separately occurrence data of Pop 1 and Pop 2, the genetically defined populations (see Supplemental Table S1).

We used a tenfold cross-validation method, which uses 90% of the data for model training and 10% for model testing for 10 iterations ⁷⁸.

Each model performance was evaluated using traditional Receiver Operator Characteristics (ROC) - area under the curve (AUC) scores ⁸¹ by specifying 500 iterations with the omission threshold set at ten percent ⁸³. A model is considered as having a good fit when its AUC is close to one (AUC≥0.75) ²¹. Minimum training presence (MTP) values were also used as thresholds for testing the performance of each model ⁷⁷.

The outputs from Maxent were processed in ArcGIS ver. 10.7.1 to construct maps of the distribution of Kersting’s groundnut areas cultivability. The continuous probabilities generated by Maxent (Ten Percentile Training Presence) were converted into binary presence-absence maps to identify the levels of areas suitability. Two different levels were, therefore, defined: unsuitable and suitable. Finally, we quantified the dynamic of the cultivated zones of the crop in the scenarios RCP 4.5 and RCP 8.5 of the horizon 2055 using the following equation:

Where, FA_ij corresponds to the extent value (in number of pixels) under the scenario i of the future horizon in the environment j (area suitability); CA is the extent value of current condition; TA corresponds to the total extent of all cultivated zones of the present day. Negative, null, and positive values represent range lost, stable, and gained, respectively.

Furthermore, to visualize the potential changes of suitable areas for Kersting’s groundnut production, we compared current and future distribution ranges of the crop and genetic populations using package “tmap” version 3.3-1 ⁸⁴ in R.

Niche differentiation

To assess the degree of climate niche overlap among species and genetic groups, two randomization tests were executed in ENMTools ver. 1.0.4 ⁸⁵: the niche identity test and the niche similarity test or background test ⁸⁶. The identity test was performed to examine whether the habitat suitability scores generated from the ecological niche models of species and genetic populations are significantly different than expected if they were generated from the same distribution. The similarity test was also performed to examine the degree to which environmental niches are conserved over time, between M. geocarpum and genetic groups.

To quantify niche overlap for pair-wise comparisons, we used Schoener’s D ^86-88 metric, calculated using a generalized linear model (GLM) with 100 randomized pseudoreplicates following the model below:

Where p_x,i, and p_y,i denote the probability assigned in a species distribution model computed for species X and Y to grid cell i. D value ranged from 0 to 1.

The observed values of D in both the niche identity test and the background similarity test were compared to the mean values of five randomized runs using t-test ⁸⁶. The environmental niches were considered significantly different if the observed values of niche overlap were less than 95% or 99% (alpha = 0.05 and 0.01, respectively) of the overlap values derived from the pseudoreplicates.

Acknowledgments

This work was supported by the “Intra-Africa Academic Mobility Scheme” under the project grant number 2016-2988 on “Enhancing training and research mobility for novel crops breeding in Africa (MoBreed)”, funded by the Education, Audiovisual and Culture Executive Agency (EACEA) of the European Commission. The authors gratefully acknowledge Biosciences eastern and central Africa - International Livestock Research Institute (BecA-ILRI Hub), Nairobi, Kenya, for the crop genotyping and assistance for SNPs data treatment. We also highly appreciate the assistance of Mr Gafarou Agounde (from LABEF) and Mr Alban Zossou in the data preparation and data analysis.

Author contributions

M.C.: Conceptualization, Investigation, Data curation, Methodology, molecular data analysis, Writing - Review & Editing; E.G.A.D.: Conceptualization, Writing - Review & Editing, Visualization, Supervision, Funding Management, Project administration; R.I.: Software, data analysis, Review & Editing; F.A.: Additional molecular data resources, Review & Editing; A.T.P.: Review & Editing, Supervision; M.S.: Review & Editing, Supervision.

Supplementary Material

Supplementary materials related to this article are available at Scientific Reports, online.

Data accessibility

The molecular data underlying this article (DarTSeq data) will be submitted to NCBI.

Conflict of Interest

None declared

Mabhaudhi, T. et al. Prospects of orphan crops in climate change., 250, 695–708 https://doi.org/10.1007/s00425-019-03129-y (2019).
Singh, M., Bisht, I. S. & Dutta, M. Springer. India, 221, https://doi.org/10.1007/978-81-322-2023-7 (2014).
Litrico, I. & Violle, C. Diversity in Plant Breeding: A New Conceptual Framework. Trends in plant science, 20, 604–613 https://doi.org/10.1016/j.tplants.2015.07.007 (2015).
Govindaraj, M., Vetriventhan, M. & Srinivasan, M. Importance of genetic diversity assessment in crop plants and its recent advances: an overview of its analytical perspectives. Genet Res Intern, 2015, 431–487 https://doi.org/10.1155/2015/431487 (2015).
Akohoué, F., Sibiya, J. & Achigan-Dako, E. G. On-farm practices, mapping, and uses of genetic resources of Kersting’s groundnut [Macrotyloma geocarpum (Harms) Maréchal et Baudet] across ecological zones in Benin and Togo. Genet Resour Crop Evol, 66, 195–214 https://doi.org/10.1007/s10722-018-0705-7 (2018).
Assogba, P. et al. Indigenous knowledge and agro-morphological evaluation of the minor crop Kersting’s groundnut [Macrotyloma geocarpum (Harms) Maréchal et Baudet] cultivars of Benin. Genet Resour Crop Evol, 63, 513–529 https://doi.org/10.1007/s10722-015-0268-9 (2015).
Adu-Gyamfi, R., Fearon, J., Bayorbor, T. B., Dzomeku, I. K. & Avornyo, V. K. The Status of Kersting's groundnut [Macrotyloma geocarpum (Harms) Marechal and Baudet]. Outlook on Agric, 40, 259–262 https://doi.org/10.5367/oa.2011.0050 (2011).
Obasi, M. O. & Agbatse, A. Evaluation of Nutritive Value and Some Functional Properties of Kersting's groundnut Seeds for Optimum Utilisation as a Food and Feed Resource. E Afr Agric For J, 68, 173–181 https://doi.org/10.4314/eaafj.v68i4.1794 (2003).
Ajayi, O. B. & Oyetayo, F. L. Potentials of Kerstingiella geocarpa as a health food. Journal of medicinal food, 12, 184–187 https://doi.org/10.1089/jmf.2008.0100 (2009).
Mohammed, M., Jaiswal, S. K., Sowley, E. N. K., Ahiabor, B. D. K. & Dakora, F. D. Symbiotic N2 Fixation and Grain Yield of Endangered Kersting's Groundnut Landraces in Response to Soil and Plant Associated Bradyrhizobium Inoculation to Promote Ecological Resource-Use Efficiency. Front Microbiol, 9, 1–14 https://doi.org/10.3389/fmicb.2018.02105 (2018).
Tamini, Z. Étude ethnobotanique de la Lentille de Terre [Macrotyloma geocarpum Maréchal & Baudet] au Burkina Faso. J Agric Trad Bot Appl, 37, 187–199 https://doi.org/10.3406/jatba.1995.3569 (1995).
Coulibaly, M., Agossou, C. O. A., Akohoué, F., Sawadogo, M. & Achigan-Dako, E. G. Farmers’ Preferences for Genetic Resources of Kersting’s groundnut [Macrotyloma geocarpum (Harms) Maréchal and Baudet] in the Production Systems of Burkina Faso and Ghana. Agronomy, 10, 1–20 https://doi.org/10.3390/agronomy10030371 (2020).
Achigan Dako, E. G. & Vodouhe, S. R. Macrotyloma geocarpum (Harms) Marechal & Baudet. In Plant resources of tropical Africa 1: Cereals and pulses Brink M,B. G., (ed. 111–114(Backhuys Publishers CTA, PROTA, Wageningen, 2006).
Mergeai, G. Influence des facteurs sociologiques sur la conservation des ressources phytogenetiques: Le cas de la lentille de terre [Macrotyloma geocarpum (Harms) Marechal et Baudet] au Togo. Bull Rech Agron, 28, 487–500 (1993).
Long, S. P., Marshall-Colon, A. & Zhu, X. G. Meeting the global food demand of the future by engineering crop photosynthesis and yield potential., 161, 56–66 https://doi.org/10.1016/j.cell.2015.03.019 (2015).
Akohoue, F., Achigan-Dako, E. G., Sneller, C., Van Deynze, A. & Sibiya, J. Genetic diversity, SNP-trait associations and genomic selection accuracy in a west African collection of Kersting's groundnut [Macrotyloma geocarpum (Harms) Marechal & Baudet]. PLoS One, 15, 1–24 https://doi.org/10.1371/journal.pone.0234769 (2020).
Schierenbeck, K. A. Population-level genetic variation and climate change in a biodiversity hotspot. Ann Bot, 119, 215–228 https://doi.org/10.1093/aob/mcw214 (2017).
Araújo, M. B., Whittaker, R. J., Ladle, R. J. & Erhard, M. Reducing uncertainty in projections of extinction risk from climate change. Global Ecol Biogeogr, 14, 529–538 https://doi.org/10.1111/j.1466-822X.2005.00182.x (2005).
Araujo, M. B. & Peterson, A. T. Uses and misuses of bioclimatic envelope modeling., 93, 1527–1539 https://doi.org/10.1890/11-1930.1 (2012).
Martínez-Meyer, E. Climate change and biodiversity: some considerations in forecasting shifts in species’ potential distributions. Biodiv Informatics, 2, 42–55 https://doi.org/10.17161/bi.v2i0.8 (2005).
Elith, J. et al. Zimmermann, N. E., Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29, 129–151, doi(2006).
Pironon, S. et al. Potential adaptive strategies for 29 sub-Saharan crops under future climate change. Nature Climate Change, 9, 758–763 https://doi.org/10.1038/s41558-019-0585-7 (2019).
Ramirez-Cabral, N. Y. Z., Kumar, L. & Taylor, S. Crop niche modeling projects major shifts in common bean growing areas. Agricul For Meteor, 218–219, 102–113 (2016).
Syfert, M. M. et al. Crop wild relatives of the brinjal eggplant (Solanum melongena): Poorly represented in genebanks and many species at risk of extinction. American journal of botany, 103, 1–17 https://doi.org/10.3732/ajb.1500539 (2016).
Blonder, B. Hypervolume concepts in niche- and trait-based ecology., 41, 1441–1455 https://doi.org/10.1111/ecog.03187 (2018).
Hampe, A. & Petit, R. J. Conserving biodiversity under climate change: the rear edge matters. Ecology letters, 8, 461–467 https://doi.org/10.1111/j.1461-0248.2005.00739.x (2005).
Leimu, R. & Fischer, M. A meta-analysis of local adaptation in plants. PLoS One, 3, 1–8 https://doi.org/10.1371/journal.pone.0004010 (2008).
Hereford, J. A quantitative survey of local adaptation and fitness trade-offs. Amer Naturalist, 173, 579–588 https://doi.org/10.1086/597611 (2009).
Shaw, R. G. & Etterson, J. R. Rapid climate change and the rate of adaptation: insight from experimental quantitative genetics. New Phytol, 195, 752–765 https://doi.org/10.1111/j.1469-8137.2012.04230.x (2012).
Gotelli, N. J. & Stanton-Geddes, J. Climate change, genetic markers and species distribution modelling. J Biogeogr, 42, 1577–1585 https://doi.org/10.1111/jbi.12562 (2015).
Ikeda, D. H. et al. Genetically informed ecological niche models improve climate change predictions. Global Change Biol, 23, 164–176 https://doi.org/10.1111/gcb.13470 (2016).
Alvarado-Serrano, D. F. & Knowles, L. L. Ecological niche models in phylogeographic studies: applications, advances and precautions. Molecular ecology resources, 14, 233–248 https://doi.org/10.1111/1755-0998.12184 (2014).
Thoen, M. P. et al. Genetic architecture of plant stress resistance: multi-trait genome-wide association mapping. New Phytol, 213, 1346–1362 https://doi.org/10.1111/nph.14220 (2017).
Kafoutchoni, K. M., Agoyi, E. E., Agbahoungba, S., Assogbadjo, A. E. & Agbangla, C. Genetic diversity and population structure in a regional collection of Kersting’s groundnut [Macrotyloma geocarpum (Harms) Maréchal & Baudet]. Genet Resour Crop Evol, 1–16 https://doi.org/10.1007/s10722-021-01187-4 (2021).
Marcer, A., Mendez-Vigo, B., Alonso-Blanco, C. & Pico, F. X. Tackling intraspecific genetic structure in distribution models better reflects species geographical range. Ecology and evolution, 6, 2084–2097 https://doi.org/10.1002/ece3.2010 (2016).
Oney, B., Reineking, B., O'Neill, G. & Kreyling, J. Intraspecific variation buffers projected climate change impacts on Pinus contorta. Ecology and evolution, 3, 437–449 https://doi.org/10.1002/ece3.426 (2013).
Allouche, O., Tsoar, A. & Kadmon, R. Assessing the accuracy of species distribution models: Prevalence, Kappa and the True Skill Statistic (TSS). J Applied Ecol, 43, 1223–1232 https://doi.org/10.1111/j.1365-2664.2006.01214.x (2006).
Milanesi, P. et al. Combining Bayesian genetic clustering and ecological niche modeling: Insights into wolf intraspecific genetic structure. Ecology and evolution, 8, 11224–11234 https://doi.org/10.1002/ece3.4594 (2018).
Tamini, Z. Etude ethnobotanique et analyses morphophysiologiques du développement de la lentille de terre [Macrotyloma geocarpum (harms) Maréchal et Baudet]. Docteur d'Etat ès Sciences Naturelles Sciences Naturelles, Université de Ouagadougou, Ouagadougou, Burkina Faso, 1997.
Yohannes, H. A Review on Relationship between Climate Change and Agriculture. J Earth Sci Clim Change, 7, 1–8 https://doi.org/10.4172/2157-7617.1000335 (2015).
Sileshi, G. et al. Variation in maize yield gaps with plant nutrient inputs, soil type and climate across sub-Saharan Africa. Field Crops Res, 116, 1–13 https://doi.org/10.1016/j.fcr.2009.11.014 (2010).
Padi, F. K. & Ehlers, J. D. Effectiveness of Early Generation Selection in Cowpea for Grain Yield and Agronomic Characteristics in Semiarid West Africa. Crop Sci, 48, 533–540 https://doi.org/10.2135/cropsci2007.05.0265 (2008).
Kouelo, K. A. F. et al. Impact du travail du sol et de la fertilisation minérale sur la productivité de [Macrotyloma geocarpum (Harms) Maréchal et Baudet] au centre du Bénin. J Appl Biosci, 51, 3625–3632 (2012).
Wellenreuther, M., Larson, K. W. & Svensson, E. I. Climatic niche divergence or conservatism? Environmental niches and range limits in ecologically similar damselflie., 93, 1353–1366 https://doi.org/10.1890/11-1181.1 (2012).
Banerjee, A. K., Mukherjee, A., Guo, W., Ng, W. L. & Huang, Y. Combining ecological niche modeling with genetic lineage information to predict potential distribution of Mikania micrantha Kunth in South and Southeast Asia under predicted climate change. Global Ecol Conserv, 20, 1–16 https://doi.org/10.1016/j.gecco.2019.e00800 (2019).
Gorel, A. P., Duminil, J., Doucet, J. L. & Fayolle, A. Ecological niche divergence associated with species and populations differentiation in Erythrophleum (Fabaceae, Caesalpinioideae). Pl Ecol Evol, 152, 41–52 https://doi.org/10.5091/plecevo.2019.1543 (2019).
Burke, M. B., Lobell, D. B. & Guarino, L. Shifts in African crop climates by 2050, and the implications for crop improvement and genetic resources conservation. Global Environ Change, 19, 317–325 https://doi.org/10.1016/j.gloenvcha.2009.04.003 (2009).
Ramirez-Cabral, N. Y. Z., Kumar, L. & Shabani, F. Global alterations in areas of suitability for maize production from climate change and using a mechanistic species distribution model (CLIMEX). Sci. Rep, 7, 1–13 https://doi.org/10.1038/s41598-017-05804-0 (2017).
Hancock, A. M. et al. Adaptation to climate across the Arabidopsis thaliana genome., 334, 83–86 https://doi.org/10.1126/science.1209244 (2011).
Bellon, M. R. & van Etten, J. Climate Change and On-farm Conservation of Crop Landraces in Centres of Diversity. In Plant Genetic Resources and Climate Change Jackson, M., Ford-Lloyd, B., Parry, M., eds.),30(CAB International, 2014).
Lane, A. & Jarvis, A. Changes in Climate will modify the Geography of Crop Suitability: Agricultural Biodiversity can help with Adaptation. ISI J, 4, 12 (2007).
Vigouroux, Y., Barnaud, A., Scarcelli, N. & Thuillet, A. C. Biodiversity, evolution and adaptation of cultivated crops. Comptes rendus biologies, 334, 450–457 https://doi.org/10.1016/j.crvi.2011.03.003 (2011).
Sohn, N., Fernandez, M. H., Papes, M. & Anciães, M. Ecological Niche Modeling in Practice: Flagship Species and Regional Conservation Planning. Oecol Aust, 17, 429–440 https://doi.org/10.4257/oeco.2013.1703.11 (2013).
Amujoyegbe, B., Obisesan, I., Ajayi, A. & Aderanti, F. Disappearance of Kersting's groundnut [Macrotyloma geocarpum (harms) Maréchal et Baudet] in South-Western Nigeria: an indicator of genetic erosion. Plant Gen Res News, 152, 45–50 (2007).
Banta, J. A. et al. J. H., Climate envelope modelling reveals intraspecific relationships among flowering phenology, niche breadth and potential range size in Arabidopsis thaliana. Ecology letters, 15, 769–777 https://doi.org/10.1111/j.1461-0248.2012.01796.x (2012).
Kumar, J., Choudhary, A. K., Gupta, D. S. & Kumar, S. Towards Exploitation of Adaptive Traits for Climate-Resilient Smart Pulses. International journal of molecular sciences, 20, 1–30 https://doi.org/10.3390/ijms20122971 (2019).
Bohra, A., Mir, R. R., Jha, R., Maurya, A. K. & Varshney, R. K. Advances in genomics and molecular breeding for legume improvement. In Advancement in Crop Improvement Techniques Bohra, A., Mir, R. R., Jha, R., Maurya, A. K., Varshney, R. K., eds.), 129–139(Elsevier Inc, 2020).
Gobu, R. et al. Accelerated Crop Breeding Towards Development of Climate Resilient Varieties. In Climate Change and Indian Agriculture: Challenges and Adaptation Strategies Srinivasarao, C., Srinivas, T., Rao, R. V. S., Srinivasarao, N., Vinayagam, S. S., Krishnan, P., eds.),49–69(ICAR-National Academy of Agricultural Research Management, Hyderabad, Telangana, India, 2020).
Aliyu, S., Massawe, F. & Mayes, S. Genetic diversity and population structure of Bambara groundnut [Vigna subterranea (L.) Verdc.]: synopsis of the past two decades of analysis and implications for crop improvement programmes. Genet Resour Crop Evol, 63, 925–943 https://doi.org/10.1007/s10722-016-0406-z (2016).
Al-Khayri, J. M., Jain, S. M., Johnson, D. V. & Switzerland Springer Nature Switzerland AG. doi: https://doi.org/10.1007/978-3-030-23400-3 (2019).
Kilian, A. et al. Diversity arrays technology: a generic genome profiling technology on open platforms. Methods Mol Biol, 888, 67–89 https://doi.org/10.1007/978-1-61779-870-2_5 (2012).
Illumina, I. HiSeq®2500 Sequencing System: Unsurpassed power and efficiency for production scale sequencing. 1–4(2015).
Buckler, E. et al. Zhang, Y. doi: (2014).
Pritchard, J. K., Wen, X. & Falush, D. Department of Human Genetics, University of Chicago. doi(2010).
Francis, R. M. pophelper: an R package and web app to analyse and visualize population structure. Molecular ecology resources, 17, 27–32 https://doi.org/10.1111/1755-0998.12509 (2016).
Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software structure: a simulation study. Mol Ecol, 14, 2611–2620 https://doi.org/10.1111/j.1365-294X.2005.02553.x (2005).
Jombart, T. & Ahmed, I. adegenet version 1.3-1: new tools for the analysis of genome-wide SNP data., 187, https://doi.org/10.1093/bioinformatics/btr521 (2011).
Boria, R. A., Olson, L. E., Goodman, S. M. & Anderson, R. P. Spatial filtering to reduce sampling bias can improve the performance of ecological niche models. Ecol Model, 275, 73–77 https://doi.org/10.1016/j.ecolmodel.2013.12.012 (2014).
Peterson, A. T. et al. NicheBook. Princeton University Press. Princeton and Oxford, doi(2011).
Kass, J. M. et al. Wallace: A Modular Platform for Reproducible Modeling of Species Niches and Distributions. R package version 1.0.6.3(2020).
Aiello-Lammens, M. E., Boria, R. A., Radosavljevic, A., Vilela, B. & Anderson, R. P. spThin: an R package for spatial thinning of species occurrence records for use in ecological niche models., 38, 541–545 (2015).
Platts, P. J., Omeny, P. A. & Marchant, R. AFRICLIM: high-resolution climate projections for ecological applications in Africa. Afr J Ecol, 53, 103–108 (2014).
Meinshausen, M. et al. The RCP greenhouse gas concentrations and their extensions from 1765 to 2300. Clim. Change, 109, 213–241 https://doi.org/10.1007/s10584-011-0156-z (2011).
IPCC, I. P. o. C. C (Cambridge University Press, 2013). doi: 10.1017/CBO9781107415324
Ramirez-Villegas, J. & Jarvis, A. Downscaling global circulation model outputs: the delta method decision and policy analysis Working Paper No.1. Policy Anal Manag 1, 1–18, doi(2010).
Hengl, T. et al. Mapping Soil Properties of Africa at 250 m Resolution: Random Forests Significantly Improve Current Predictions. PLoS One, 10, 1–26 https://doi.org/10.1371/journal.pone.0125814 (2015).
Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum Entropy modeling of species geographic distributions. Ecol Model, 190, 231–259 https://doi.org/10.1016/j.ecolmodel.2005.03.026 (2005).
Elith, J. et al. A statistical explanation of MaxEnt for ecologists. Diversity Distrib, 17, 43–57 https://doi.org/10.1111/j.1472-4642.2010.00725.x (2010).
Richards, C. L., Carstens, B. C. & Knowles, L. L., Distribution modelling and statistical phylogeography: an integrative framework for generating and testing alternative biogeographical hypotheses. J of Biogeography, 34, 1833–1845 https://doi.org/10.1111/j.1365-2699.2007.01814.x (2007).
Phillips, S. J. & Dudík, M. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation., 31, 161–175 https://doi.org/10.1111/j.2007.0906-7590.05203.x (2008).
Merow, C., Smith, M. J. & Silander, J. A. A practical guide to MaxEnt for modeling species’ distributions: what it does, and why inputs and settings matter., 36, 1058–1069 https://doi.org/10.1111/j.1600-0587.2013.07872.x (2013).
Phillips, S. J. et al. Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecol Appl, 19, 181–197 (2009).
Peterson, A. T., Papeş, M. & Soberón, J. Rethinking receiver operating characteristic analysis applications in ecological niche modeling. Ecol Model, 213, 63–72 https://doi.org/10.1016/j.ecolmodel.2007.11.008 (2008).
Tennekes, M. "tmap": Thematic Maps in R. 1-118, doi: 10.18637/jss.v084.i06 (2018).
Warren, D. & Dinnage, R. ENMTools: Analysis of Niche Evolution using Niche and Distribution Models. R package version 1.0.4. 66(2021).
Warren, D. L., Glor, R. E. & Turelli, M. Environmental Niche Equivalency Versus Conservatism: Quantitative Approaches to Niche Evolution., 62, 2868–2883 https://doi.org/10.1111/j.1558-5646.2008.00482.x (2008).
Schoener, T. W. The Anolis lizards of Bimini: Resource Partitioning in a Complex Fauna., 49, 704–726 https://doi.org/10.2307/1935534 (1968).
Rödder, D. & Engler, J. O. Quantitative metrics of overlaps in Grinnellian niches: advances and possible drawbacks. Global Ecol Biogeogr, 20, 915–927 https://doi.org/10.1111/j.1466-8238.2011.00659.x (2011).

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Ecological-niche modeling of genetically distinct populations revealed Kersting’s groundnut [Macrotyloma geocarpum (Harms) Maréchal et Baudet] as a resilient orphan crop to present and future climates

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Abstract

Figures

Introduction

Results

Population structure and admixture

Occurences dataset construction

Ecological niche modeling

Climatic variables analogy

Models’ outputs and performance evaluation

Discussions

Conclusion And Perspectives

Materials And Methods

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1