Mabhaudhi, T. et al. Prospects of orphan crops in climate change. Planta 250, 695–708. https://doi.org/10.1007/s00425-019-03129-y (2019).
Google Scholar
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 Plant Sci. 20, 604–613. https://doi.org/10.1016/j.tplants.2015.07.007 (2015).
Google Scholar
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. 431–487, 2015. https://doi.org/10.1155/2015/431487 (2015).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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 Agric. 40, 259–262. https://doi.org/10.5367/oa.2011.0050 (2011).
Google Scholar
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).
Google Scholar
Ajayi, O. B. & Oyetayo, F. L. Potentials of Kerstingiella geocarpa as a health food. J. Med. Food 12, 184–187. https://doi.org/10.1089/jmf.2008.0100 (2009).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
AchiganDako, E. G. & Vodouhe, S. R. Macrotyloma geocarpum (Harms) Marechal & Baudet. In Plant Resources of Tropical Africa 1: Cereals and Pulses (ed. Brink, M. B. G.) 111–114 (Backhuys Publishers CTA, PROTA, 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. Cell 161, 56–66. https://doi.org/10.1016/j.cell.2015.03.019 (2015).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Araújo, M. B., Whittaker, R. J., Ladle, R. J. & Erhard, M. Reducing uncertainty in projections of extinction risk from climate change. Glob. Ecol. Biogeogr. 14, 529–538. https://doi.org/10.1111/j.1466-822X.2005.00182.x (2005).
Google Scholar
Araujo, M. B. & Peterson, A. T. Uses and misuses of bioclimatic envelope modeling. Ecology 93, 1527–1539. https://doi.org/10.1890/11-1930.1 (2012).
Google Scholar
Martínez-Meyer, E. Climate change and biodiversity: Some considerations in forecasting shifts in species’ potential distributions. Biodiv. Inform. 2, 42–55. https://doi.org/10.17161/bi.v2i0.8 (2005).
Google Scholar
Elith, J. et al. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29, 129–151 (2006).
Google Scholar
Pironon, S. et al. Potential adaptive strategies for 29 sub-Saharan crops under future climate change. Nat. Clim. Chang. 9, 758–763. https://doi.org/10.1038/s41558-019-0585-7 (2019).
Google Scholar
Ramirez-Cabral, N. Y. Z., Kumar, L. & Taylor, S. Crop niche modeling projects major shifts in common bean growing areas. Agric. For. Meteor. 218–219, 102–113. https://doi.org/10.1016/j.agrformet.2015.12.002 (2016).
Google Scholar
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. Am. J. Bot. 103, 1–17. https://doi.org/10.3732/ajb.1500539 (2016).
Google Scholar
Blonder, B. Hypervolume concepts in niche- and trait-based ecology. Ecography 41, 1441–1455. https://doi.org/10.1111/ecog.03187 (2018).
Google Scholar
Hampe, A. & Petit, R. J. Conserving biodiversity under climate change: The rear edge matters. Ecol. Lett. 8, 461–467. https://doi.org/10.1111/j.1461-0248.2005.00739.x (2005).
Google Scholar
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).
Google Scholar
Hereford, J. A quantitative survey of local adaptation and fitness trade-offs. Am. Naturalist. 173, 579–588. https://doi.org/10.1086/597611 (2009).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Ikeda, D. H. et al. Genetically informed ecological niche models improve climate change predictions. Glob. Change Biol. 23, 164–176. https://doi.org/10.1111/gcb.13470 (2016).
Google Scholar
Alvarado-Serrano, D. F. & Knowles, L. L. Ecological niche models in phylogeographic studies: Applications, advances and precautions. Mol. Ecol. Resour. 14, 233–248. https://doi.org/10.1111/1755-0998.12184 (2014).
Google Scholar
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).
Google Scholar
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. https://doi.org/10.1007/s10722-021-01187-4 (2021).
Google Scholar
Brown, J. L. & Carnaval, A. C. A tale of two niches: Methods, concepts, and evolution. Front. Biogeogr. https://doi.org/10.21425/f5fbg44158 (2019).
Google Scholar
Marcer, A., Mendez-Vigo, B., Alonso-Blanco, C. & Pico, F. X. Tackling intraspecific genetic structure in distribution models better reflects species geographical range. Ecol. Evol. 6, 2084–2097. https://doi.org/10.1002/ece3.2010 (2016).
Google Scholar
Oney, B., Reineking, B., O’Neill, G. & Kreyling, J. Intraspecific variation buffers projected climate change impacts on Pinus contorta. Ecol. Evol. 3, 437–449. https://doi.org/10.1002/ece3.426 (2013).
Google Scholar
Tamini, Z. Etude ethnobotanique et analyses morphophysiologiques du développement de la lentille de terre [Macrotyloma geocarpum (harms) Maréchal et Baudet] (Université de Ouagadougou, 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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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. Ecology 93, 1353–1366. https://doi.org/10.1890/11-1181.1 (2012).
Google Scholar
Akohoue, F., Achigan-Dako, E. G., Coulibaly, M. & Sibiya, J. Correlations, path coefficient analysis and phenotypic diversity of a West African germplasm of Kersting’s groundnut [Macrotyloma geocarpum (Harms) Maréchal & Baudet]. Genet. Resour. Crop Evol. 66, 1825–1842. https://doi.org/10.1007/s10722-019-00839-w (2019).
Google Scholar
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).
Google Scholar
Adu-Gyamfi, R., Dzomeku, I. K. & Lardi, J. Evaluation of growth and yield potential of genotypes of Kersting’s groundnut (Macrotyloma geocarpum Harms) in Northern Ghana. Int. Res. J. Agric. Sci. Soil Sci. 2, 509–515 (2012).
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. Glob. Environ. Change 19, 317–325. https://doi.org/10.1016/j.gloenvcha.2009.04.003 (2009).
Google Scholar
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).
Google Scholar
Hancock, A. M. et al. Adaptation to climate across the Arabidopsis thaliana genome. Science 334, 83–86. https://doi.org/10.1126/science.1209244 (2011).
Google Scholar
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 Vol. 30 (eds Jackson, M. et al.) (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. C.R. Biol. 334, 450–457. https://doi.org/10.1016/j.crvi.2011.03.003 (2011).
Google Scholar
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, 371. https://doi.org/10.3390/agronomy10030371 (2020).
Google Scholar
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).
Google Scholar
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. Climate envelope modelling reveals intraspecific relationships among flowering phenology, niche breadth and potential range size in Arabidopsis thaliana. Ecol. Lett. 15, 769–777. https://doi.org/10.1111/j.1461-0248.2012.01796.x (2012).
Google Scholar
Kumar, J., Choudhary, A. K., Gupta, D. S. & Kumar, S. Towards exploitation of adaptive traits for climate-resilient smart pulses. Int. J. Mol. Sci. 20, 1–30. https://doi.org/10.3390/ijms20122971 (2019).
Google Scholar
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 (eds Bohra, A. et al.) 129–139 (Elsevier Inc, 2020).
Google Scholar
Gobu, R. et al. Accelerated crop breeding towards development of climate resilient varieties. In Climate Change and Indian Agriculture: Challenges and Adaptation Strategies (eds Srinivasarao, C. et al.) 49–69 (ICAR-National Academy of Agricultural Research Management, 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).
Google Scholar
Al-Khayri, J. M., Jain, S. M., Johnson, D. V. Springer Nature Switzerland AG. Switzerland. 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).
Google Scholar
Illumina, I. HiSeq®2500 Sequencing System: Unsurpassed power and efficiency for production scale sequencing. System Specification Sheet: Sequencing, 1–4. https://www.illumina.com/documents/products/datasheets/datasheet_hiseq2500.pdf (2015).
Buckler, E. et al. User Manual for TASSEL Trait Analysis by association, Evolution and Linkage Version 5.0. The Buckler Lab at Cornell University. 1–70. https://www.maizegenetics.net/tassel (2014).
Pritchard, J. K., Wen, X., Falush, D. Department of Human Genetics, University of Chicago. (2010).
Francis, R. M. pophelper: An R package and web app to analyse and visualize population structure. Mol. Ecol. Resour. 17, 27–32. https://doi.org/10.1111/1755-0998.12509 (2016).
Google Scholar
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).
Google Scholar
Jombart, T. & Ahmed, I. adegenet version 1.3-1: New tools for the analysis of genome-wide SNP data. Bioinformatics https://doi.org/10.1093/bioinformatics/btr521 (2011).
Google Scholar
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).
Google Scholar
Peterson, A. T. et al. NicheBook (Princeton University Press, 2011).
Kass, J. M., Pinilla-Buitrago, G. E., Vilela, B., Aiello-Lammens, M. E., Muscarella, R., Merow, C., Anderson, R. P., 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. Ecography 38, 541–545 (2015).
Google Scholar
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).
Google Scholar
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).
Google Scholar
IPCC, I. P. o. C. C. Cambridge University Press. https://doi.org/10.1017/CBO9781107415324(2013).
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 (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).
Google Scholar
Osorio-Olvera, L. et al. ntbox: An R package with graphical user interface for modeling and evaluating multidimensional ecological niches Methods. Ecol. Evol. 11, 1199–1206. https://doi.org/10.1111/2041-210X.13452 (2020).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Richards, C. L., Carstens, B. C. & Lacey Knowles, L. Distribution modelling and statistical phylogeography: An integrative framework for generating and testing alternative biogeographical hypotheses. J. Biogeogr. 34, 1833–1845. https://doi.org/10.1111/j.1365-2699.2007.01814.x (2007).
Google Scholar
Phillips, S. J. & Dudík, M. Modeling of species distributions with Maxent: New extensions and a comprehensive evaluation. Ecography 31, 161–175. https://doi.org/10.1111/j.2007.0906-7590.05203.x (2008).
Google Scholar
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. Ecography 36, 1058–1069. https://doi.org/10.1111/j.1600-0587.2013.07872.x (2013).
Google Scholar
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).
Google Scholar
Barve, N., Barve, V. ENMGadgets: Pre and post processing in ENM Workflow. R package version 0.1.0.1. (2019).
Kass, J. M. et al. ENMeval 20: Redesigned for customizable and reproducible modeling of species’ niches and distributions. Ecol. Evolut. https://doi.org/10.1111/2041-210X.13628 (2021).
Google Scholar
Warren, D. L. & Seifert, S. N. Ecological niche modeling in Maxent: The importance of model complexity and the performance of model selection criteria. Ecol. Appl. 21, 335–342. https://doi.org/10.1890/10-1171.1 (2011).
Google Scholar
Warren, D. L., Glor, R. E. & Turelli, M. Environmental Niche equivalency versus conservatism: Quantitative approaches to Niche evolution. Evolution 62, 2868–2883. https://doi.org/10.1111/j.1558-5646.2008.00482.x (2008).
Google Scholar
Broennimann, O. et al. Measuring ecological niche overlap from occurrence and spatial environmental data. Glob. Ecol. Biogeogr. 21, 481–497. https://doi.org/10.1111/j.1466-8238.2011.00698.x (2012).
Google Scholar
Benhamou, S. & Cornélis, D. Incorporating movement behavior and barriers to improve kernel home range space use estimates. J. Wildl. Manag. 74, 1353–1360. https://doi.org/10.2193/2009-441 (2010).
Google Scholar
Source: Ecology - nature.com
