In Situ and Ex Situ Conservation Gap Analyses of West African Priority Crop Wild Relative

In Situ and Ex Situ Conservation Gap Analyses of West African Priority Crop Wild Relative | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article In Situ and Ex Situ Conservation Gap Analyses of West African Priority Crop Wild Relative Michael Ugochukwu Nduche, Joana Magos Brehm, Nigel Maxted, Mauricio Parra-Quijano This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1953821/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Crop wild relatives are genetically related wild taxa of crops with unique resources for crop improvement through the transfer of novel and profitable genes. The in situ and ex situ conservation gap analyses for priority crop wild relatives from West Africa were evaluated using species distribution modelling, ecogeographic diversity, and complementary analyses. A total of 20, 125 unique occurrence records were used for the conservation gap analysis, however, 26 taxa had no occurrence data. 64 taxa (62.7%) occurred in protected areas, 56 taxa (55%) were conserved ex situ , while 76.7% (43) of the accessions are underrepresented with less than 50 accessions conserved ex situ . Areas of highest potential diversity were found in the Woroba and Montangnes districts in Cote d’Ivoire, Nzerekore, Faranah, Kindia, and Boke regions of Guinea, South-South, and North-East zones of Nigeria, and Kono and Koinadugu districts in Sierra Leone. Hotspots were found in Atlantique, Littoral, Mono, Kouffo, Atakora, Donga, and Colline provinces of Benin, Accra, and Volta regions of Ghana, North – Central Nigeria, and Lacs district of Cote d’Ivoire and Nzerekore region of Guinea. 29 reserve sites for active in situ conservation were identified, 11 occur in protected areas, while 18 are located outside protected areas. The establishment of the reserve sites will complement existing PAs and ensure long-term active in situ and ex situ conservation and sustainable utilization of priority CWR to underpin food security and mitigate climate change in the region. Crop wild relatives in situ ex situ genetic conservation CAPFITOGEN diversity analysis and species distribution modelling. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction The flora of West Africa is diverse, heterogeneous, and abundant with numerous plant species. The region harbours over 9000 vascular plants with an estimated 1,800 species native to West Africa (Carr et al., 2015 ). The endemic crop species of West Africa include but are not limited to Vigna unguiculata (L.) Walp (Cowpea), V. subterranean (L.) Verdc (Bambara groundnut), Dioscorea cayennensis subsp. rotundata (Poir.) J. Miege (Guinea yam), Elaeis guineensis Jacq (Oil Palm), and Diospyros gracilis Fletcher (African ebony). The climate of West Africa is characterized by abundant year-round rainfall in the Gulf of Guinea to a mean annual rainfall of 165 mm in the Agadez of Niger (USGS, 2017 ). Five bioclimate regions have been recognized in West Africa; Saharan, Sahelian, Sudanian, Guinean, and Guinea – Congolian regions (USGS, 2017 ). As such West African plant species are adapted and resilient to the region's erratic, and diverse ecogeographic conditions and may possess useful genes/traits for crop improvement. Global food production in the next few decades will be determined by several factors including climate change. Climate change will negatively impact agricultural productivity in a global yield decline of an estimated 1.5% per decade (David and Sharon, 2012). This trend can at least be partially mitigated by the genetic and agronomic improvement of cultivated crops using trait diversity from crop wild relatives (CWR) (Maxted et al., 2008 ; David and Sharon, 2012). CWR are wild plant species relatively closely related to crops, including crop’s wild ancestors, that retain indirect use value as gene donors for crop improvement and a high level of genetic diversity having not passed through the genetic bottleneck of domestication. Maxted et al. ( 2006 ) defined CWR broadly as all taxa within the same genus as a crop and more precise as wild plant taxon that have indirect use derived from its relatively close genetic relationship to a crop; this relationship is defined in terms of the CWR belonging to gene pools 1 or 2, or taxon groups 1 to 4 of the related crop. CWR contain resilient genes for crop improvement with several domesticated crops in West Africa improved using adaptive genes from CWR (Nduche et al., 2021 ). Such crops include cassava (David and Sharon, 2012; Kawuki et al., 2016 ), maize (David and Sharon, 2012), yam (Lopez- Montes et al., 2012 ; Saini et al., 2016 ), cowpea (Andargie et al., 2014 ; Badiane et al., 2014 ), millet (Sood et al., 2015 ), sorghum (Park et al., 2015a ), rice (Jena, 2010 ; Atwell et al., 2014 ), barley (Wendler et al., 2015 ), and for an overview (Nduche et al., 2021 ). Despite the important role of CWR in food security in the West African region and the world, their conservation has received little attention. The neglect of CWR is because of lack of appreciation of its potential value in breeding and has resulted in underutilization of its profitable genetic diversity in crop improvement. The adaptive diversity of CWR is a safety net for urgent global food security needs. Globally, in situ conservation of CWR in protected area (PAs) is inadequate, with insufficient number of genetic reserves established (Iriondo et al., 2012 ). In West Africa, 1938 nationally protected sites exist covering about 9.6% of the region. Another 53 internationally designated protected areas are also found in the region (Mallon et al., 2015 ). The number of CWR accessions conserved ex situ in genebanks is relatively low compared to accessions of cultivated crops. Globally, there are an estimated 7 million plant accessions conserved in 1750 genebanks (FAO, 2010a ; Fu, 2017 ), however, about 29% of CWR lack genebank accessions, while over 24% have less than ten accessions represented in genebanks (Castañeda-Álvarez et al., 2016 ). Despite this shortfall in ex situ conservation of CWR, a comprehensive collection of CWR is still lacking. The combined use of in situ and ex situ conservation of plant genetic diversity will lessen the erosion of valuable genetic diversity (Maxted et al., 1997a ; Zegeye, 2017 ). Despite the wide agreement that in situ and ex situ techniques should be applied in a complementary manner (CBD, 1992 ), almost 100% of CWR diversity when conserved are conserved using ex situ seed storage alone (Maxted et al., 2016 ). In situ conservation is only recently being implemented and involves the designation of and management of populations to preserve a particular plant species in its natural abode where its intrinsic features are found (Maxted et al., 1997c ). To help ensure more ex situ and in situ conservation coverage more recently, gap analysis has been applied for the planning of CWR conservation (Maxted et al., 2013 ). It involves identifying CWR diversity that is not well represented in conservation action and prioritizing these ‘gaps’ for more active conservation (Maxted et al., 2008 a ; Magos Brehm et al., 2017a ; Ng’uni et al., 2019 ; Mponya et al., 2020 ; Magos Brehm et al., 2022 ) West Africa is recognized as a region that played a significant role in crop domestication and still retains significant crop landraces and CWR diversity (Castañeda-Álvarez et al., 2016 ; Vincent et al., 2019 ; Maxted and Vincent, 2021 ). For instance, fonio [ Digitaria exilis (Kippist) Stapf] was domesticated in Senegal (Harlan, 1992 ) and Pearl millet was domesticated between Mali and Mauritania (Burgarella et al., 2018 ). The zone between Ghana and Nigeria, down to Cameroon have been identified as the source of yam domestication (Scarcelli et al., 2019 ). Recognizing the crop genepool importance of the region, this paper reports the result of in situ and ex situ conservation gap analyses of West African CWR as a major step towards development of a regional CWR conservation and use strategy for the region. Materials And Methods Collation and Verification of Occurrence Data The distributional data for the 102 West African priority CWR defined by Nduche et al. ( 2021 ) was collated using a standard occurrence data template (Magos Brehm et al., 2017b ). The occurrence data of the West African priority CWR were collated from Global Biodiversity Information Facility (GBIF, 2020 ), Genesys Global Portal on Plant Genetic Resources (Genesys, 2020 ), Royal Botanical Gardens, Kew ( https://www.kew.org/kew-gardens ), and RainBio (Dauby et al., 2016 ). A total of 54,924 distributional records were collated for the 102 West African priority CWR. Records that lacked coordinates but with collection sites information were georeferenced, using Google maps ( https://www.maps.google.com ). A quality check was done on the distributional data to ensure all records were expressed in decimal degrees. Locational records without decimal degree coordinates were converted to a decimal degree using Canadensys ( https://www.data.canadensys.net/tools/coordinates ). Duplicate records were removed before the analysis, and records that lied abnormally in neighbouring countries were reviewed. Duplicate records are distributional records that are associated with the same record but from different sources or was documented twice from the same source (Magos Brehm et al., 2017a ). The West African countries included in this study are Benin, Burkina Faso, Cote D’ Ivoire, Gambia, Ghana, Guinea, Guinea- Bissau, Liberia, Mali, Mauritania, Niger, Nigeria, Senegal, Sierra Leone and Togo. The 20,125 records without duplicate records were entered in the occurrence data template required by the CAPFITOGEN tool which makes use of the FAO- Biodiversity’s multi- crop descriptor (FAO-BIOVERSITY, 2015 ). The ‘TesTable tool’ of CAPFITOGEN3 was used to verify the occurrence data table to ensure it meets the requirements for other CAPFITOGEN3 tools analyses. GEOQUAL tool of CAPFITOGEN3 was used to assess the quality of coordinates and collection sites of the records (Parra - Quijano et al., 2021 ) Ecogeographical Land Characterization Map Ecogeographic land characterization (ELC) (Parra - Quijano et al., 2021 ) was used to evaluate the delineation and depiction of ecogeographic variables and determine appropriate sites for in situ and ex situ conservation of priority CWR (Parra-Quijano et al., 2011 ; Magos Brehm et al., 2022 ). Eighteen environmental variables (6 bioclimatic, 6 edaphic, and 6 geophysical) were selected in the selecVar tool of CAPFITOGEN3, to generate the generalist ELC map. To accommodate those taxa with distributional records of < 10, a generalist ELC map was generated using the ELC maps tool of CAPFITOGEN3. This is because these taxa cannot generate species – specific ELC map. Using the kmeanbic method, at a resolution of the ecogeographic layer of 10 x 10 km (approximately 5 arc – minutes), the ELC map was created. The kmeanbic method was used because it identifies an optimal number of groups with discriminant analysis of principal components. Species Distribution Modelling Based on environmental layers of various components of ecogeographic variables, predicted taxa distribution was identified by the distribution models produced by the individual taxa with more than 10 occurrence records in Maximum Enthropy Algorithm (MaxEnt) (Phillips et al., 2006 ) (Table S6 ). and by circular buffer (CA 50 ) for taxa with less than 10 occurrence records used in the species distribution modelling (SDM), MaxEnt is a common SDM algorithm used to predict taxa distribution (Fourcade et al., 2014 ). The species distribution data of the taxa for model calibration was classified into a training set (75% of total occurrence data) and test set (25% of total occurrence records) for design evaluation. Raster files of bioclimatic variables were obtained from WorldClim ( https://www.worldclim.org/bioclim ), edaphic variables, from ISRIC – World Soil Information ( https://files.isric.org/soilgrids/ ), while geophysical data were downloaded as Digital Elevation Map (DEM) files from the National Aeronautics and Space Administration (NASA) ( https://www.nasa.gov .) All ecogeographic raster files were clipped to the same extent, resampled to the same cell size (0.41666666667 m), and reprojected to the same grid (WGS − 84), in ASCII raster grid format, using ArcMap 10.4.1 (ESRI, 2015 ). With Random Forest, integrated in the SelectVar of the CAPFITOGEN tools, variables for each ecogeographic component (bioclimatic, edaphic and geophysical) at resolution of 10 X 10 Km (approximately 5 arc minutes at Equator) were selected for each priority taxon (Parra-Quijano et al., 2016 ). Bivariate correlation analysis was also evaluated in SelecVar, to reduce dimensionality, and only variables with weak correlation (p- value ≤ 0.33) or not correlated (p – value = 0) were used to create the distribution model for each taxon (Tables S7 and S8). Maximum training sensitivity plus specificity threshold was applied, as recommended by Liu et al. ( 2005 ). The robustness of the models were evaluated using three criteria: (a) average area under the test receiver operating characteristics curve [(ATAUC) ˃ 0.7] (b) standard deviation of ATAUC (STAUC) < 0.15 (c) the proportion of potential distribution area with a STAUC ˃ 0.15, being < 10% were stable and used for evaluating taxa predicted distribution (Ramírez-Villegas et al., 2010 ; Mponya et al., 2020 ). All three criteria had to be met for a model to be valid. However, for those taxa that failed the above MaxEnt model validation criteria, and for taxa with occurrence records < 10, predicted distribution were identified by a circular buffer technique, using a radius of 50 km (CA50) around each observational point as recommended by Hijmans and Spooner ( 2001 ). In this case, intersecting sites are not counted more than once. In Situ Conservation Gap Analysis Gap analysis is a method of evaluation of the extent of conservation which helps to hierarchize CWR for preservation by locating gaps in the conservation (Rodrigues et al., 2004 ; Langhammer et al., 2007 ; Magos Brehm et al., 2017a ). In situ conservation gap analysis involves a comparative study of intrinsic diversity and the element of diversity that is under active conservation action (Maxted et al., 2008b ; Magos Brehm et al., 2017a ) The method was described by Maxted et al. ( 2008 ),Scheldeman and van Zonneveld ( 2010 ) and Parra-Quijano et al. ( 2012b ), where in situ and ex situ conservation gap analyses were determined at taxon and ecogeographic levels. At the taxon level, the West African PA map was overlapped with the passport data in QGIS. Subsequently, using ‘the join attribute by location’ in the ‘data management tool’ of QGIS, the West African PA maps was intersected to identify records within and outside PA. The in situ conservation gaps were obtained by comparing the number of populations of taxa present in PAs against those not represented in PAs (Mponya et al., 2020 ). To estimate the extent of representativeness of in situ conservation of priority CWR at the ecogeographic level, the ELC zones from the ELC map tool analysis and the occurrence data were inputted in the ‘Representa tool’ of CAPFITOGEN3 (Parra - Quijano et al., 2021 ). The West African PA maps were overlapped with the ELC maps produced in the ‘Representa tool’ to determine the representativeness of the ELC zones in PAs. Complementarity analysis was done to identify potential sites for in situ conservation of priority CWR. Maxted et al. ( 1997b ) described these sites as genetic reserve for long – term active conservation of plant genetic resources. They are defined designated locations either within PAs or outside PAs as informal sites for CWR conservation (Magos Brehm et al., 2017a ). Such locations are aimed at conserving a large number of CWR taxa in the smallest available area (Kati et al., 2004 ). Using the ‘Reserve selection’ tool in DIVA – GIS 7.5, at resolution of 10 x 10 km (approximately 5 arc minutes), potential genetic reserve sites were identified according to their priority for the conservation of priority CWR. The PA map for West Africa, obtained from UNEP-WCMC ( 2019 ) was overlapped with the complementarity genetic reserve site and taxon richness maps to determine the level of current passive in situ conservation of the priority CWR and identify areas that require further active in situ conservation actions. Passive in situ conservation means that CWR in PAs are not actively monitored and managed to preserve their genetic diversity and protect them from pest, diseases, fragmentation, habitat degradation and natural disaster (Vincent et al., 2019 ). The maps produced were visualized in DIVA-GIS 7.5 (Hijmans et al., 2012 ) and QGIS 3.16.8 (QGIS-Development Team, 2021 ) Ex Situ Conservation Gap Analysis Ex situ conservation gap analyses were determined at taxon and ecogeographic levels. At the taxon level, a map of observed ex situ collection was subtracted from the predicted distribution map to obtain the gap in current ex situ conservation and locate the priority site for further ex situ collection. To determine the current germplasm representativeness of the ecogeographic diversity, the resulting ELC map and passport data were inputted in the ‘Representa’ tool of CAPFITOGEN to assess the degree of representativeness of the ELC categories in the ex situ collection (Parra-Quijano et al., 2016 ). The maps were processed in DIVA-GIS 7.5 (Hijmans et al., 2012 ), ArcMap 10.7 (ESRI, 2011 ) and QGIS 3.16.8 (QGIS-Development Team, 2021 ) at a resolution of 10 x 10 km (approximately 5 arc minutes). At the ecogeographic level, the categories of representativeness of the diversity were analysed using the ‘Representa tool’ of CAPFITOGEN3 (Parra - Quijano et al., 2021 ). Based on the frequencies of the ELC map, the ELC map was categorized into quartiles, using the ELC zones in the ELC map. The four frequency classes were low, mid-low, mid-high, and high. However, zones where occurrence records were not found were categorized as ‘null’. Ex situ conservation gap were determined by estimating the diversity present in ex situ conservation against that conserved in situ (Mponya et al., 2020 ; Parra - Quijano et al., 2021 ). Results In situ gap analysis A total of 20,125 unique occurrence points were used for the in situ conservation gap analysis, however 26 CWR had no occurrence data. The highest occurrence points were recorded in Benin and Nigeria with 31.9% (6428) and 11.7% (2,358) present points, respectively (Fig.s 1 and S1). Hotspots were found in Atlantique, Littoral, Mono, Kouffo, Atakora, Donga and Colline provinces of Benin. These areas correspond to the location of protected areas with the highest number of taxa such as Pendjari (28), Quari Maro (18), La Lama Nord (16), Monts Kouffe and Boucle de la Pendjari (18) (Table S1) There were also hotspots in Accra and Volta regions of Ghana, corresponding to the location of the Volta River reserve site. Location of high diversity were also spotted around Nasarawa, Plateau States of North- Central Nigeria, where Nasarawa Forest Reserve is located and South- Western zone of Nigeria. High species richness is also observed at the Lacs district of Cote d’Ivoire where the Mando forest reserve is situated, Montagnes district of Cote d’ Ivoire where Mont Nimba is located and Nzerekore region of Guinea where Mont Nimba, Pic de Fon and Pic de Tibe Classified Forests are located (Fig. 2 ). Analysis of the occurrence records showed that 18.5% (3,730) of the total unique present points were recorded in PAs. PAs with the highest number of taxa are Pendjari in Benin (28), Comoe National Park in Cote d’ Ivoire (24), Niokolo – Koba National Park in Senegal (21), Quari Maro in Benin (18) and Queme Superieur in Benin (18), while PAs with the highest population of taxa are Sahel (708), Comoe National Park (407), Kouffe (250) and Pemdjari (239) (Table S1). 62.7% (64) of the priority taxa were represented in a PA, 34.3% (35) of the taxa were present in ≥ 5 PA, while the remaining 27.4% (28) had less than five populations in different PA, the minimum recommended by Dulloo et al. ( 2008 ) for the CWR in situ conservation in PA (Table S2). However, 38 taxa (37.3%) did not occur in any PA. Digitaria cilaris (Retz) Koeler, Vigna racemosa (G. Don) Hutch and Eragrostis pilosa (L.) P. Beauv., had the highest number of taxa populations in PA network with 443, 425 and 234 taxa population, respectively. Similarly, Vigna racemosa (G. Don) Hutch, Eleusine indica (L.) Gaertn. and Oryza. glaberrima Steeud. occurred in more PAs, appearing in 40, 38 and 37 PAs, respectively, while all the rice crop genepool occurred in the PA network. Cowpea (17), yam (13), and potato (9) crop genepools were the highest number of prioeity taxa that occurred in PA (Table S2). Nigeria, Benin and Cote d’ Ivoire had the highest number of PAs where taxa are present, with 46, 25 and 18 PAs, respectively. Conversely, no PA with taxa was identified in Mauritania (Fig. S2). Similarly, the highest number of taxa population in PAs were found in Benin, Burkina -Faso and Cote d’Ivoire had, with 1351, 768 and 463 populations, respectively. Also, Benin, Nigeria and Guinea had the highest number of CWR in PAs, numbering 207, 87 and 76 taxa respectively (Fig. S3)). 38 taxa (37.3%) did not occur in any PA, simimarly none of the Sorghum, fonio and yam wild relatives occurred in PA. Other taxa not represented in PA are Echinochloa crus- galli (L.) P. Beauv., Gossypium herbaceum var. acerifolium (Guill. & Perr.) A. Chev., Ipomoea ochracea (Lindl.) Sweet, Manihot dichotoma Ule, Triticum turgidum L. and Vigna. unguiculata subsp. stenophylla (Harv.) Marechal & al. (Table S2). Complementarity analysis identified 29 potential genetic reserve sites with grid square size of 0.4 degrees for the conservation of West African priority CWR (Fig. 3 ). Apart from Burkina – Faso, Liberia, Mauritania and Gambia, genetic reserve sites were identified in all the other West African countries. The highest number of reserve sites were found in Nigeria with 9, while Benin and Guinea have 4 each (Fig. 3 ). Eleveen reserve sites are located in PA, wth 9 of the sites conserving 37% (38) of the CWR, however priority CWR were absent in Eleiyele and Volta River (Table S1 and S3). A total of 458 records were present in 9 of the reserve sites with taxa. 36.3% (37 taxa) of the priority CWR were found in the reserve sites (Table S3). Vigna racemosa (G. Don) Hutch. & Dalz, Oryza glabarrima Steud, Vigna gracilis (Guill. & Perr.) Hoof. f. and O. barthi A. Chev. had the highest number of taxa population; 56, 51, 46 and 35 respectively in the genetic reserve sites (Table S2 and Table S5). Cowpea (10), yam (7), sweet potato (7), and rice (4), are the crop genepools with the highest number of CWR present in the reserve sites (Table S). Conversely, cowpea (13), yam (8), sweet potato (6) and cassava (5) are the crop genepools with the highest number of taxa not represented in reserve sites. V. racemosa (G. Don) Hutch & Dalz., O. barthi A. Chev., Ipomoea aquatica Forssk., O. longistiminata A. Chev. & Roehr and Eleusine indica (L.) Gaertn were found in more genetic resesrve sites than other taxa and were found in 4 reserve sites each (Table S3 and S5). In situ conservation gap analysis of the 102 priority CWR showed that the areas of predicted distribution is present in all the West African countries (Fig. 4 ). The areas of highest potential diversity was found at Woroba and Montangnes districts of Cote d’Ivoire where some protected areas such as Mont Tia, Mont Sangbe, Pic de Fon, Pic de Tibe, Mt Yonon and Mont Nimba reserve site are located (Fig. 4 ). Also of high predicted CWR taxon richness are Nzerekore, Faranah, Kindia and Boke regions of Guinea where the Mont Nimba and Diecke reserve sites are situated. Other areas of high predicted taxon richness are in the South – South zone of Nigeria around Cross River National Park, North Eastern Nigeria, Kono and Koinadugu districts in Sierra Leone (Fig. 4 ), where these areas are predicted to harbour 51 to 63 CWR. However, areas from Abidjan in Cote d’Ivoire, Ghana, Togo, Benin to South- West Nigeria had low areas of predicted distribution (Fig. 4 ). Eighteen ecogeographic variables (6 bioclimatic, 6 edaphic and 6 ecogeographic) were used to generate 24 ELC zones (Fig. 5 ), which represents the predicted ecogeographic scenarios of the region (Parra-Quijano et al., 2012a ; Mponya et al., 2020 ). The ecogeographic diversity of 17 ELC zones are present in 152 PAs, while ELC zones 11 and 2 had the higest diversity in PAs (Table S4 and Fig. 6 ) Ex situ gap analysis The SDM of 55 taxa met the validation whereas for the remaining 8 CWR, a CA 50 buffer area created around each occurrence point (Table S6). The number of ecogeographic variables for the SDM varied from 15 in Vigna filicaulis Hepper and V. desmodiodes Wilczek to 43 in Ipomoea aquatica Forssk (Table S7 and Table 8). A total of 5720 (28.4%) accessions from 56 (55%) priority CWR are represented ex situ . 13 taxa had occurrence data but did not pass the validation criteria for predicted distribution map (Table S6). 55% (56) priority CWR had at least one accession represented in genebank, of these, 23% (13) of the taxa had at least 50 accessions conserved ex situ , while 76.7% (43) of the accessions are underrepresented with less than 50 accessions conserved in genebanks. Nigeria had the highest number of accessions in genebanks, with 23.8% (1366) accessions, while Mauritania had the least 0.2% (13) (Fig. S1). Benin had the highest number of occurrence data (6428), while Mauritania had the least (150) (Fig. 1 and Fig. S1). Oryza glabarrima Steud, O. barthi A. Chev. and O. longistaminata A. Chev. & Roehr. had the highest number of accessions conserved in genebanks, with 2670, 610 and 562 accessions respectively (Table S2). All the Hordeum and Phaseolus CWR species had no occurrence data. Of the taxa that have occurrence data, 20 were not represented in genebanks, while Cola nitida (Vent.) Schott. & Endl. (3), D. rotundata Poir (3), I. batatas (L.) Lam. (3), Sorghum bicolor (L.) Moench (3) and Vigna unguiculata (Linn.) Walp. (3) represent the crop genepools with the highest number that were not present in both genebanks and PA (Table S9). Similarly, of the 13 taxa that did not occur in PA, 7 were not also represented in genebanks. However, all the taxa with ≥ 50 accessions in genebanks also occurred in ≥ 5 PAs (Table S2 and Table S3). The areas of further collection are found in all the West African countries (Fig. 7 ), while 87.27% (89) priority CWR needs further collecting (Table S2). Areas of further collection are Assaba and Guidimaka provinces of Mauritania; Saint – Louis and Tambocounda regions in Senegal. Nzerekore region of Guinea; Koinadugu, Bombali and Tonkolili districts of Sierra Leone. Loffa, Bomi, Montserrado and Grand Cape Mount counties of Liberia. Montagnes, Lacs and Lagunes districts of Cote d’ Ivoire; Mopti region of Mali; Upper West, Bono East, Eastern, Volta and Ashanti regions of Ghana. Other areas are Haut – Bassins, Cascades, Est and Centre – Est regions of Burkina Faso; Plateau, Queme, Atlantique and Alibori provinces of Benin; North – East and North – Central zones of Nigeria (Fig. 7 ) Ecogeographic diversity of 16 ELC zones are conserved in genebanks (Table S9), while the CWR diversity of 8 zones are not represented. ELC zones 2,8 and 11 had the highest population which corresponds to the ELC map category. 50% of the ELC zones had ≥ 25% of their accessions represented in genebanks (Table S4), while ELC zones 2 and 11 had the highest collection. Table 1 Reserve sites for in situ conservation of West African CWR and protected areas where they are located Reserve site Protected Area Total occurrence record Number of CWR ELC zones Total area (Km 2 ) Total area (ha) country 1 Niokolo – Koba National Park 252 21 1,7 9,130 913000 Senegal 2 Boucle de la Pendjari 65 16 1 2755 275,500 Benin 3 Dosso 48 13 1,7 5,440.87 5.44,087 Niger 4 Mount Nimba 63 11 10,12 175.40 17, 540 Guinea 5 Yankari 24 9 1,4 2,254 225, 400 Nigeria 6 Diecke 2 2 - 640 64,000 Guinea 7 Nasarawa 1 1 6 15,076,526 150,765.26 Nigeria 8 Eto 2 1 10 116.02 14.763 Togo 9 Goudi 1 1 10 96 9600 Cote d’Ivoire 10 Eleiyele - - - 5.261 526.092 Nigeria 11 Volta River - - - Ghana Discussion West Africa is rich with taxa diversity, endemism and biodiversity heritage, while CWR diversity and flora distribution of the region have been reported in various studies (Huchinson and Dalziel, 1958 ; Oates et al., 2004 ; Bergl et al., 2007 ; Idohou et al., 2013 ; Hounsou-Dindin et al., 2022 ). However, as a purpose of this, further study is needed to determine the gaps in in situ and ex situ conservation action in the region, as this will complement and consolidate the national efforts of the individual countries. According to recent CWR ecogeographic diversity analysis, West Africa has been identified as a region of global importance with high CWR diversity for food security (Castañeda-Álvarez et al., 2016 ; Vincent et al., 2019 ). The highest CWR diversity identified in the provinces of Benin, is because of the recent Flora of Benin (Akoègninou et al., 2006 ), and the high number of occurrence records found in Benin, relative to other countries in the region (Figs. 1 and S1). High CWR diversity was also identified at Accra and Volta regions of Ghana, North – Central and South- Western zone of Nigeria. Other areas include Lacs district of Cote d’ Ivoire and Nzerekore region of Guinea (Fig. 2 ). These areas correspond to some areas of predicted distribution such as Nzerekore region of Guinea where Mont Nimba, Diecke, Pic de Fon, Pic de Tibe Classified Forests are located (Fig. 4 ). Similarly, these areas of species richness are in congruence with the Guinean forest, categorized as one of the 36 biodiversity hotspots in the world (Maxted and Vincent, 2021 ; Vincent et al., 2022 ) and the highest conservation value in Africa (Luiselli et al., 2019 ). The Guinean Forest covers an area of 621,705 km 2 , extending from Guinea, Sierra Leone, Liberia, Cote d’ Ivoire, Ghana, Togo, Benin to Nigeria. However, Guinean Forest is one of the most exploited biodiversity hotspots in the world, though 15% of the original forest is still unexploited (Conservation International, 2007 ). The network of PAs in West Africa conserves a substantial number of the priority CWR with 61% (63) of the taxa found in PAs (Table S2). However, further field survey should be carried out to ascertain the presence of the priority CWR in those PAs where they were identified. For the 28 taxa (27.4%) that were found in less than five PAs, field survey should be done in areas of predicted distribution to determine their locations and to identify more taxa populations in network of PAs, to ensure they meet or surpass the required minimum number for active in situ conservation (Dulloo et al., 2008 ). Also, effective management and monitoring should be put in place to ensure active in situ conservation of the priority CWR in their respective PAs (Maxted et al., 2008b ). Relevant institutions, stakeholders, non – governmental organizations (NGOs), and protected area managers should synergistically, ensure the maintenance of the PAs for optimal and active conservation action. Pendjari National Park in Benin with an area of 2,765 km 2 and Comoe National Park in Cote d’ Ivoire occupying an area of 11,500km 2 are the PAs with the highest number of CWR (Table S1). The presence of more CWR in Pendjari National Park may have resulted from the fact that the site was better surveyed than other PAs, as shown in the number of occurrence data recorded in Benin as compared to other countries (Fig.s 1 and S1). UNESCO ( 2019 ) reported that 620 plant species are found in Comoe National Park, which agrees with the high number of CWR present in Comoe National Park. The site contains great diversity of plants, endemic species and diverse ecological habitats ranging from savannah, forest to grasslands. (UNESCO, 2019 ). Large PAs such as Sahel (30,693 km 2 ), Comoe National Park (11,500 km 2 ), W National Park Benin (10,000 km 2 ), Niokolo – Koba National Park (9,130 km 2 ), were design to conserve diverse ecogeographical populations, which CWR is a subset. However, they contain only small of CWR population per unit area. The designation and development of the 18 reserve site found outside PAs, as other effective based conservation measures (OECM) will augment the preservation of CWR population outside PA network (Iriondo et al., 2021 ). Identifying priority sites for the in situ conservation of CWR, based on species richness may be misleading since the approach relays only on taxa richness sites neglecting those taxa that require urgent protection (Brooks et al., 2006 ). However, to overcome this challenge, complementarity analysis through reserve site selection is used (Fielder et al., 2015 ; Contreras-Toledo et al., 2019 ; Mponya et al., 2020 ). Complementarity analysis have been used to identity priority site in regions such as Southern African Development Commission (SADC) (Magos Brehm et al., 2022 ) and Middle East (Zair et al., 2021 ). Twenty-nine reserve sites were identified in this study, with 11 in PA and 18 spotted outside PAs. The 11 reserve sites located in PAs will require minimal cost to establish and manage, being in existing PAs. It will augment and complement the protective function offered by the existing PAs and provide benefits to the local communities (Maxted et al., 2008b ; Maxted and Kell, 2009 ). The remaining 18 reserve sites not located in PAs also present an opportunity for those countries with low number of PAs where taxa were found such as Guinea – Bissau (3), Mali (4), Niger (5), Sierra Leone (7) and Senegal (9) (Fig. 3 ). The outcome of the complementary analysis showed that the location of some reserve sites corresponds with some CWR hotspots in West Africa. These areas are Atakora, Alibori, Donga and Bongou provinces in Benin; Accra region of Ghana; North – Central and South – West zones of Nigeria and Lacs district of Cote d’ Ivoire (Figs. 2 and 3 ) The areas of predicted distribution were highest in Woroba and Montangnes districts of Cote d’Ivoire where the reserve site; Mont Nimba is located and some protected areas such as Mont Tia, Mont Sangbe, Pic de Fon, Pic de Tibe, Mt Yonon are found. The area of high predicted distribution also extended to the Nzerekore, Faranah, Kindia, and Boke regions of Guinea, where the reserve site; Mont Nimba and Diecke Classified Forest are located (Fig. 4 ). Mont Nimba is strategic because it is located between Guinea and Cote d’ Ivoire. It occupies a total land area of 175.4 km 2 , with 125.4 km 2 in Guinea and 50 km 2 in Cote d’ Ivoire. UNESCO ( 2019 ) reported diverse flora and endemic plant species in the site, including epiphytes and over 2000 vascular plant species. Similarly, Diecke Classified Forest is one of the largest undisturbed areas of the Guinee Forestiere with diverse plant species including several threatened tree species. The presence of Cola attiensis Aubrev. Pellegr. in the site (Table S3) was also reported by (Couch and Haba, 2021 ). The area of predicted distribution appears to be larger than the area of observed distribution, which shows that the region is under surveyed. Efforts should be made for ex situ collection of taxa in predicted areas outside PAs, as they may be under threat by urbanization, change in land use and habitat destruction (Mponya et al., 2020 ). For active in situ CWR conservation, effective ex situ conservation is needed to complement it. Ex situ conservation methods include seed bank, genebank, DNA bank, cryopreservation, botanical garden and in- vitro conservation (Maxted et al., 1997c ; Maxted, 2013 ). 55% (56) of the priority taxa were represented in genebanks, however more accessions need to be collected for ex situ conservation. Further collection actions should be undertaken for the 20 taxa with occurrence records but not present in genebanks, the 26 taxa without occurrence data and the 44 taxa underrepresented in genebanks to reflect the recommendation by (Brown and Marshall, 1995 ) and (Guerrant et al., 2004 ) of 50 taxa population for effective representation in genebank. Additionally, taxa already present in PAs should be conserved ex situ in genebanks as a back – up to the in situ conservation to protect them in the event of natural disaster, war or fire outbreak (Ford-Lloyd and Maxted, 1993 ). Genebank accessions should be duplicated regionally and internationally to ensure effective and long term ex situ conservation (FAO, 2014 ; Magos Brehm et al., 2022 ). The crop genepools with the highest number of taxa not represented in genebanks are yam (3), potato (3), sorghum (3), cowpea (3) and cola (3). Among the taxa that are not present in genebanks are Dioscorea abyssinica Hochst. ex. Kunth, used to improve yam for resistance against yam mosaic virus and anthranose (Lopez- Montes et al., 2012 ), Manihot carthagenesis (Jacq.) Mull. Arg, M. dichotoma Ule, M. esculenta subsp. peruviana Crantz and M. esculenta subsp. flabellifolia Crantz, used to improve for resistance against cassava brown streak disease (Kawuki et al., 2016 ). Echinichloa frumentacea Link and Eleusine Africana Kenn – O’Byne are used to breed Barnyard millet (Sood et al., 2015 ) and finger millet (Dida and Devos, 2006 ), respectively for high yield. Other taxa that are not present in genebanks include Phaseolus vulgaris var. aborigineus (Burkart) Baude, used for the improvement of common bean against bruchid (Osborn et al., 2003 ), white mould (Mkwaila et al., 2011 ), web and bacterial blight (Beaver et al., 2012 ) and for high yield (Wright and Kelly, 2011 ). Sorghum purpureosericeum (Hochst ex. A. Rich) Schweinf & Asch. has confirmed used in the improvement of sorghum for resistance against sorghum shoot fly (Nwanze et al., 1990 ), while Sorghum bicolor subsp. verticiliforum (L.) Moench is used in breeding sorghum for resistance against stem and leaf rust (Fetch Jr et al., 2009 ; Park et al., 2015b ), increase in seed size and weight (Pillen et al., 2004 ). Hordeum bulbosum L. is used in breeding barley for resistance against barley mild mosaic virus (Ruge et al., 2003 ; Wendler et al., 2015 ), barley yellow virus (Wendler et al., 2015 ), powdery mildew (Pickering and Johnston, 2005 ; Johnston et al., 2009 ), stem and leaf rust (Fetch Jr et al., 2009 ; Johnston et al., 2013 ; Park et al., 2015b ) and leaf scald (Pickering et al., 2006 ). Ex situ conservation will be a safety net for some CWR that have their adaptive scenario outside PA. For instance, some herbs and shrubs thrive on lawns, waste lands, swamps and agricultural lands (Maxted and Kell, 2009 ). A major objective of in situ conservation is to confirm and preserve diverse CWR genes in a defined location for optimal used in crop improvement to ensure food and nutrient security. Ecogeographical diversity can work as proxy for genetic diversity (Korona, 1996 ; Parra-Quijano et al., 2012a ). The frequency of ecogeographical diversity outside PAs is higher, compared to that in PA. Therefore, ex situ collection of priority CWR outside PAs will capture taxa in ELC zones not represented or underrepresented in network of PA. The ELC map shows all resilient environmental conditions present within the geographical location of the target taxa population. ELC zone 2 had more accessions in genebanks and the highest frequency of occurrence in PA compared to other ELC categories (Table S4 and Fig. 6 ). However, taxa found in rare ELC zones present unique genes (Contreras-Toledo et al., 2019 ; Parra - Quijano et al., 2021 ) and should be prioritized in ex situ collection and conservation for use in crop improvement of their related crops. Complementarity analysis showed that 11 ELC categories were present in the reserve sites within PA, compared to 15 ELC categories represented in all PAs. This shows a high degree of complementarity in capturing the ecogeographical categories diversity of the priority CWR. On the average, the diversity of ELC categories per taxa was higher (26. 3%) compared to that for all PA network (23.4%) (Table S4). For ELC zones 12,14,16,17,19,22,23,24 where taxa were not represented in genebanks and ELC zones 4,7,20,21 with low genebank representation, based of frequency of occurrence (Table S9), further collection action should be carried out to ensure their representation. Similarly, ex situ collection should be done to represent all the ELC zones and ensure the preservation of novel and vital genes (Rubio-Teso et al., 2013 ; Parra - Quijano et al., 2021 ). The presence of these taxa in different ELC zones helps to identify those that thrives in adverse and marginal environments, as they may possess profitable genes for adapting their related crops to erratic climatic conditions (Garcia et al., 2017 ). Recommendations Based on the outcome of this study, the following recommendations for the in situ and ex situ conservation of West African priority CWR are proposed: Improved the efficacy of reserve sites in PAs for active in situ conservation through effective management and monitoring of the target CWR to ensure long term preservation. Small PAs should be expanded to ensure full and optimal conservation area and to include CWR diversity that occurs next to them. The eleven reserve sites in PAs should be prioritized for the in situ conservation of West African priority CWR. Ascertain the suitability of the location of the 18 reserve sites that are not in PA, including the topography, accessibility and demography of the taxa in the area. Then initiate the establishment of reserve sites for the in situ conservation of priority CWR not conserved in PAs, to augment the functions of existing PAs. New PAs are crucial for countries with limited PAs such as Guinea – Bissau, Mali, Niger, Sierra Leone and Senegal (Fig S2 and Table S1). The identification of the 29 reserve sites is significant and a footprint for the in situ conservation of the priority CWR. Conduct field survey for the 38 taxa that did not occur in PA to ensure they are present in at least five PAs to meet the minimum number of representations in PAs for active in situ conservation (Dulloo et al., 2008 ). Priority PAs for further field survey are those where CWR are predicted to be present such as Mont Tia, Mont Sangbe, Pic de Fon, Pic de Tibe, Mt Yonon, Cross River National Park and reserve sites such as Mont Nimba and Diecke. Attention should be given to CWR diversity and general biodiversity present in reserve sites located in PAs to ensure the conservation of all available plant genetic diversity. Maintain international genebanks in West Africa such as the International Institute of Tropical Agriculture (IITA) (IITA, 2022 ), Nigeria which conserve accessions of African food crops, AfricaRice M’be Cote d’ Ivoire with over 22, 000 accessions (CGIAR, 2022 ) and ICRISAT, Niger (ICRISAT, 2022 ). Establish national genebank in the areas with high ex situ collection and predicted distribution for the ex situ conservation of priority CWR, while national genebanks like National Centre for Genetic Resources and Biotechnology (NACGRAB) Ibadan, Nigeria with 13, 839 accessions (Crop Trust, 2022 ), National Agricultural Research Center, Cote d’ Ivoire holding 8,000 accessions of coffee (World Coffee Research, 2021 ) and Ghana National Genebank should be upgraded to hold more accessions. Also, genebank accessions should be duplicated in different facilities, while accessions present only in genebanks outside West Africa should be retrieved from internationally genebanks (Table S10), and conserved in area where the taxa have their intrinsic features and taxon richness. Search for occurrence data for the 26 taxa without occurrence records and for those with less than 10 records in their countries of endemism. Conduct field survey for countries with inadequate number of occurrence data such as Mauritania, Gambia, Guinea – Bissau, Liberia, Togo and Sierra Leone (Fig. S1), to identify the location of more priority CWR and taxa population both within and outside PAs for ex situ collection and active in situ conservation. SDM and buffer CA 50 can serve as a guide in locating the taxa in the areas of predicted distribution. Prioritize the ex situ collection of the 43 taxa with less than 50 accessions in genebanks, using the SDM and CA 50 as a guide to ensure their effective representation ex situ . Also, of priority are the 13 taxa with occurrence data but did not pass the validation criteria. Diversity in ex situ conservation should be increased to include seed banks, cryopreservation, in – vitro storage for recalcitrant taxa, and botanical garden. Government agencies, institutions, local communities, national and international genebanks should be involved in the collection mission. Conduct field survey to identify priority CWR in the ELC zones with low frequency to ensure that a full range of ELC zones are captured so as to preserve unique and novel genes for use in crop improvement (Parra - Quijano et al., 2021 ). Make crosses between plants from collected seeds and their related crops, as well as between CWR found in PA and their related crops based on genepool levels (Table S11). Advanced methods such as embryo rescue, in - vitro gene transfer can be used for CWR that shows difficulty with conventional methods. This may help in resolving the challenge of hunger and food insecurity in the densely populated West African region. Periodic revision, review and upgrade of the outcome of this study and the recommendations in the event of change in conservation priorities as a result of availability of more occurrence records and a more precise algorithm for ecogeographic modelling and occurrence data analysis. Conclusion In this study, the in situ and ex situ conservation gaps for the 102 West African CWR were evaluated. The 26 taxa without occurrence data, 20 taxa with occurrence records but not present in genebanks, and the 44 taxa underrepresented in genebanks have been prioritized for further ex situ collection to ensure their effective representation in genebanks. The areas of high predicted distribution within PAs such as Mont Tia, Mont Sangbe, Pic de Fon, Pic de Tibe, Mt Yonon, Cross River National Park and reserve site such as Mont Nimba and Diecke were also prioritized for ex situ collection. The 38 taxa that are not present in PA and the 28 taxa with less than five population in different PAs were also target taxa for identification and in situ conservation action. Establishment of the 29 identified reserve sites will further strengthen the CWR in situ conservation effort at national and regional level. Similarly, filling the identified in situ and ex situ conservation gaps will ensure that the priority CWR and agrobiodiversity are availability for use as food, feed and fibre. Additionally, the implementation of the proposed recommendations will enhance the active conservation and sustainable utilization of the priority CWR for crop improvement to mitigate climate change and underpin food security for the rising population in West Africa Declarations Acknowledgement The authors would like to thank the Tertiary Education Trust Fund (TETFUND), Nigeria for the scholarship awarded to Michael Nduche which ensured the funding of this study. Funding Tertiary Education Trust Fund (TETFUND), Nigeria provided funding for this study as a scholarship awarded to the corresponding author Contributions N. Maxted and J. Mago Brehm designed the study. All the authors contributed in the data analysis and revising of the manuscript, while M.U. Nduche wrote the manuscript Data Availability Data area available from the author upon reasonable request Conflict of Interest The authors declare that they have no conflict of interest References Akoègninou A, van der Burg WJ, van der Maesen LJG (2006) Flore analytique du Bénin. Backhuys Publishers, Leiden, p 1034 Andargie M, Pasquet RS, Gowda BS, Muluvi GM, Timko MP (2014) Molecular mapping of QTLs for domestication-related traits in cowpea ( V. unguiculata (L.) Walp.). Euphytica 200(3):401–412 Atwell BJ, Wang H, Scafaro AP (2014) Could abiotic stress tolerance in wild relatives of rice be used to improve Oryza sativa? Plant Sci 215:248–258 Badiane FA, Diouf M, Diouf D (2014) Cowpea. In Broadening the Genetic Base of Grain Legumes. Springer, India pp 95–114 Beaver JS, Zapata M, Alameda M, Porch TG, Rosas JC (2012) Registration of PR0401-259 and PR0650-31 dry bean germplasm lines. J Plant Registrations 6(1):81–84 Bergl RA, Oates JF, Fotso R (2007) Distribution and protected area coverage of endemic taxa in West Africa’s Biafran forests and highlands. Biol Conserv 134:195–208 Brooks TM, Mittermeier RA, Da-Fonseca GAB, Geriach J, Hoffmann M, Lamoreux JF, Mittermeier CG, Pilgrim JD, Rodrigues ASL (2006) Global biodiversity conservation priorities. Science 313:58–61. https://doi.org/10.1126/science.1127609 Brown AHD, Marshall DR (1995) A basic sampling strategy: theory and practice. In: Guarino L, Ramantha RV, Reid R (eds) Collecting Plant Genetic Diversity: Technical Guidelines. CABI Publishing, Wallingford, pp 75–92 Burgarella C, Cubry P, Kane NA, Varshney RK, Mariac C, Liu X, Shi C, Thudi M, Couderc M, Xu X, Chitikineni A, Scarcelli N, Barnaud A, Rhoné B, Dupuy C, François O, Berthouly-Salazar C, Vigouroux Y (2018) A western Sahara centre of domestication inferred from pearl millet genomes. Nat Ecol Evol 2:1377–1380 Carr J, Adeleke A, Angu K, Belle E, Burgess N, Carrizo S, Choimes A, Coulthard N, Darwall W, Foden W (2015) Ecosystem Profile Guinean Forests of West Africa Biodiversity Hotspot; Critical Ecosystem Partnership Fund: Arlington, VA, USA Castañeda-Álvarez NP, Khoury CK, Achicanoy HA, Bernau V, Dempewolf H, Eastwood RJ (2016) Global conservation priorities for crop wild relatives. Nat Plants 2(4):16022 CBD (1992) Convention on biological diversity. http://www.cbd.int/doc/legal/cbd-en.pdf . Accessed May 2022 CGIAR (2022) Genebank - Africa Rice Center (AfricaRice), Genetic Resources Unit https://www.genebanks.org/genebanks/africarice/ Accessed 28 June 2022 Conservation International (2007) Guinean Forests of West Africa. In Biodiversity Hotspots. Accessed June 21, 2022 Contreras-Toledo AR, Cortés-Cruz M, Costich DE, Rico-Arce ML, Magos Brehm J, Maxted N (2019) Diversity and conservation priorities of crop wild relatives in Mexico. Plant Genetic Resources 1–11. doi: 10.1017/S1479262118000540 Couch C, Haba PM(2021) TIPA Assessment: Diecke Classified Forest, Yomou Prefecture http://www.herbierguinee.org/uploads/2/6/3/0/26303479/2._tipas_report_diecke_en_new_final.pdf Crop Trust (2022) PGRFA Hub - Crops, Countries and Genebanks, Nigeria https://www.croptrust.org/pgrfa-hub/crops-countries-and-genebanks/countries/nigeria/ Accessed 28 June 2022 Dauby G, Zaiss R, Blach OA, Catarino L, Damen T, Deblauwe V, Dessin S, Dransfield J, Droissart V, Duarte MC, Engledow H, Fadeur G, Figueira R, Gereau RE, Hardy OJ, Harris DJ, de Heij J, Janssens SB, Klomberg Y, Ley AC, Mackinder BA, Meerts P, van de Poel JL, Sonké B, Sosef MSM, Stévart T, Stoffelen P, Svenning JC, Sepulchre P, van der Burgt XM, Wieringa JJ, Couvreur TLP (2016) RAINBIO: a mega-database of tropical African vascular plants distributions. PhytoKeys 74:1–18 David BL, Sharon MG The Influence of Climate Change on Global Crop Productivity.American Society of Plant Biologists, Physiology P(2012) https://doi.org/10.1104/pp.112.208298 . Dida MM, Devos KM (2006) Finger millet. Cereals and Millets. Springer, Berlin Heidelberg Dulloo ME, Labokas J, Iriondo JM, Maxted N, Lane A, Laguna E, Jarvis A, Kell S (2008) Chap. 2. Genetic reserve location and design. In: Iriondo JM, Maxted N, Dulloo ME (eds) Conserving Plant Genetic Diversity in Protected Areas. CABI, Wallingford, UK, pp 23–64 ESRI (2011) ArcGIS Desktop release version 10.7. Environmental Systems Research Institute. Redlands. CA ESRI (2015) ArcMap for desktop release version 10.4.1.Environmental Systems Research Institute, Redlands, Califonia FAO-BIOVERSITY (2015) FAO/Bioversity multi-crop Passport descriptors V.2. URL: https://bioversityinternational.org/e-library/publications/detail/faobioversity-multi-crop-passport-descriptors-v21-mcpd-v21/ Accessed 27 July 2022 FAO (2010a) Second report on the State of the World’s Plant Genetic Resources for Food and Agriculture. Food and Agriculture Organization of the United Nations, Rome, Italy. Available online: http://www.fao.org/agriculture/seed/sow2/en/ Accessed 25 July 2021 FAO (2014) Genebank standards for plant genetic resources for food and agriculture. FAO. http://www.fao.org/3/a-i3704e.pdf Fetch T Jr, Johnston PA, Pickering R (2009) Chromosomal location and inheritance of stem rust resistance transferred from Hordeum bulbosum into cultivated barley ( H. vulgare ). Phytopathology 99(4):339–343 Fielder H, Brotherton P, Hosking J, Hopkins JJ, Ford-Lloyd B, Maxted N(2015) Enhancing the conservation of crop wild relatives in England. PLoS ONE, 10 (6) Ford-Lloyd BV, Maxted N (1993) Preserving diversity. Nature 361:579. https://doi.org/10.1038/361579a0 Fourcade Y, Engler JO, Rödder D, Secondi J (2014) Mapping species distributions with MAXENT using a geographically biased sample of presence data: a performance assessment of methods for correcting sampling bias. PLoS ONE 9:e97122 Fu YB (2017) The Vulnerability of Plant Genetic Resources Conserved Ex Situ . Crop Sci. https://doi.org/10.2135/cropsci2017.01.0014 Garcia RM, Parra-Quijano M, Iriondo JM (2017) Identification of ecogeographical gaps in the Spanish Aegilops collections with potential tolerance to drought and salinity. Peer J 5:e3494 GBIF (2020) Global biodiversity information facility, GBIF occurrence data download https://www.gbif.org/occurrence/search Accessed 24 October 2020 Genesys(2020) Global Portal on Plant Genetic Resources, Genesys. https://www.genesys-pgr.org/welcome . Accessed 20 November 2020 Guerrant EO, Fiedler PL, Havens K, Maunder M (2004) Revised genetic sampling guidelines for conservation collections of rare and endangered plants. In: Guerrant EO, Havens K, Maunder M (eds) Ex Situ Plant Conservation: Supporting Species Survival in the Wild. Island Press, Washington, USA, pp 419–441 Harlan JR (1992) Crops and Man, 2 edn. American Society of Agronomy Hijmans R, Guarino L, Mathur P(2012) DIVA-GIS.7.5.0. https://www.diva-gis.org/documentation . Accessed 26 July 2021 Hijmans RJ, Spooner DM (2001) Geographic distribution of wild potato species. Am J Bot 88:2101–2112 Hounsou-Dindin G, Idohou R, Akakpo AD, Adome N, Adomou AC, Assogbadjo AE, Kakaï RG (2022) Assessment of wild oil plants diversity and prioritization for valorization in Benin (West Africa): A multivariate approach. Trees Forests and People 7. https://doi.org/10.1016/j.tfp.2022.100210 Huchinson J, Dalziel JM(1958) Flora of West Tropical Africa, Vol. 1, Part II.. Crown Agents for Oversea Governments and administrations. London. pp 335–587 ICRISAT (2022) International Crops Research Institute for the Semi-Arid Tropics. Tag, Niger https://www.icrisat.org/tag/niger/ Idohou R, Assogbadjo A, Fandohan A, Gouwakinnou G, Glele -Kakaï RL, Sinsin B, Maxted N (2013) National inventory and prioritization of crop wild relatives: Case study for Benin. Genet Resour Crop Evol 60:1337–1352. 10.1007/s10722-012-9923-6 IITA (2022) Our genetic resources. International Institute of Tropical Agriculture https://www.iita.org/research/genetic-resources/ Accessed 28 June 2022 Iriondo JM, Magos Brehm J, Dulloo ME, Maxted N (eds) (2021) Crop Wild Relative Population Management Guidelines. Farmer’s Pride: Networking, partnerships, and tools to enhance in situ conservation of European plant genetic resources. http://www.farmerspride.eu/ . Accessed July 10 2022 Iriondo JM, Maxted N, Kell SP, Ford-Lloyd BV, Lara-Romano C, Labokas J, Magos Brehm J(2012) Quality standards for genetic reserve conservation of crop wild relatives. In: Maxted N, Dulloo ME, Ford-Lloyd BV, Frese L, Iriondo JM and Pinheiro de Carvalho MAA (eds) Agrobiodiversity Conservation: Securing the Diversity of Crop Wild Relatives and Landraces. CABI Publishing, Wallingford, pp 72–77 Jena KK (2010) The species of the genus Oryza and transfer of useful genes from wild species into cultivated rice. O sativa Breeding Science 60(5):518–523 Johnston PA, Niks RE, Meiyalaghan V, Blanchet E, Pickering R (2013) Rph22: mapping of a novel leaf rust resistance gene introgressed from the non-host Hordeum bulbosum L. into cultivated barley ( Hordeum vulgare L.). 126:1613–1625Theoretical and applied genetics6 Johnston PA, Timmerman-Vaughan GM, Farnden KJ, Pickering R (2009) Marker development and characterisation of Hordeum bulbosum introgression lines: a resource for barley improvement. Theor Appl Genet 118(8):1429–1437 Kati V, Devillers P, Dufrêne M, Legakis A, Vokou D, Lebrun P (2004) Hotspots, complementarity or representativeness? Designing optimal small-scale reserves for biodiversity conservation. Biol Conserv 120(4):471–480 Kawuki RS, Kaweesi T, Esuma W, Pariyo A, Kayondo IS, Ozimati A, Kyaligonza V, Abaca A, Orone J, Tumuhimbise R, Nuwamanya E, Abidrabo P, Amuge T, Ogwok E, Okao G, Wagaba H, Adiga G, Alicai T, Omongo C, Bua A, Ferguson M, Kanju E, Banguma Y (2016) Eleven years of breeding efforts to combat cassava brown streak disease. Breed Sci 66(4):560–571 Korona R (1996) Adaptation to structurally different environments. Proc R Soc Lond Ser B Biol Sci 263:1665–1669. https://doi.org/10.1098/rspb.1996.0243 Langhammer PF, Bakarr MI, Bennun LA, Brooks TM, Clay RP, Darwall W, De Silva N, Edgar GJ, Eken G, Fishpool LDC, Fonseca GAB, Foster MN, Knox DH, Matiku P, Radford EA, Rodrigues ASL, Salaman P, Sechrest W, Tordoff AW (2007) Identification and gap analysis of key biodiversity areas: targets for comprehensive protected area systems. Best Practice Protected Area Guidelines Series, vol 15. IUCN, Gland, Switzerland Liu C, Berry PM, Dawson TP, Pearson RG (2005) Selecting thresholds of occurrence in the prediction of species distributions. Ecography 28:385–393 Lopez- Montes A, Bhattacharjee R, Tessema G(2012) Yam breeding at IITA: achievements, challenges and prospects. http://r4dreview.iita.org/index.php/tag/yam-breeding/ Luiselli L, Dendi D, Eniang EA, Fakae BB, Akani GC, Fa JE (2019) State of knowledge of research in the Guinean forests of West Africa region. Acta Oecol 94:3–11. https://doi.org/10.1016/j.actao.2017.08.006 Magos Brehm J, Gaisberger H, Kell S, Parra-Quijano M, Thormann I, Dulloo ME, Maxted N (2022) Planning complementary conservation of crop wild relative diversity in southern Africa. Divers Distrib. https://doi.org/10.1111/ddi.13512 Magos Brehm J, Kell S, Thormann I, Gaisberger H, Dulloo ME, Maxted N(2017a) Interactive Toolkit for Crop Wild Relative Conservation Planning University of Birmingham, Birmingham, UK and Bioversity International, Rome, Italy http://www.cropwildrelatives.org/conservation-toolkit Magos Brehm J, Kell SP, Thormann I, Gaisberger H, Dulloo E, Maxted N(2017b) Occurrence data collation template v.1. https://doi.org/10.7910/DVN/5B9IV5 , Harvard Dataverse, V1. Mallon CA, Poly F, Roux XL, Marring I, van Elsas JD, Salles JF (2015) Resource pulses can alleviate the biodiversity–invasion relationship in soil microbial communities. Ecology. Ecological Society of America. https://doi.org/10.1890/14-1001.1 Maxted N(2013) In Situ, Ex Situ Conservation. Encyclopedia of Biodiversity (Second Edition), Academic Press, pp 313–323 https://doi.org/10.1016/B978-0-12-384719-5.00049-6 Maxted N, Dulloo E, Ford-Lloyd BV, Iriondo JM, Jarvis A (2008a) Gap analysis: a tool for complementary genetic conservation assessment. Divers Distrib 14:1018–1030 Maxted N, Dulloo ME, Ford-Lloyd B (2016) Enhancing crop genepool use: capturing wild relative and landrace diversity for crop improvement. C. A. B. International Maxted N, Ford- Lloyd BV, Jury S, Kell SP, Scholten MA (2006) Towards a definition of crop wild relative. Biodivers Conserv 15:2673–2685 Maxted N, Ford-Lloyd B, Kell SP, Iriondo JM, Dulloo ME, Turok J(2008) Crop wild relative conservation and use, International Conference on Crop Wild Relative, Conservation and Use, C. A. B. International Maxted N, Ford-Lloyd BV, Hawkes JG (1997a) Plant Genetic Conservation: The In Situ Approach. Chapman & Hall, London Maxted N, Ford-Lloyd BV, Hawkes JG (1997c) Complementary conservation strategies. In: Maxted N, Ford-Lloyd BV, Hawkes JG (eds) Plant Genetic Conservation: The In-situ Approach. Chapman & Hall, London, pp 20–55 Maxted N, Hawkes JG, Guarino LMS (1997b) Towards the selection of data for plant genetic conservation. Genet Resour Crop Evol 44:337–348 Maxted N, Iriondo JM, De Hond L, Dulloo ME, Lefèvre F, Asdal A, Kell SP, Guarino L (2008b) Genetic Reserve Management. In: Iriondo JM, Dulloo ME, Maxted N (eds) Conserving Plant Genetic Diversity in Protected Areas. CAB International, Wallingford, UK, pp 65–87 Maxted N, Kell SP (2009) Establishment of a Global Network for the In situ Conservation of Crop Wild Relatives: Status and needs. Commission on Genetic Resources for Food and Agriculture, Food and Agriculture Organization of the United Nations, Rome, Italy Maxted N, Magos BJ, Kell S(2013) Resource book for preparation of national conservation plans for crop wild relatives and landraces.. In: Magos Brehm, J., Kell, S., Thormann, I., Gaisberger, H., Dulloo, M.E. & Maxted, N., (2017). Interactive Toolkit for Crop Wild Relative Conservation Planning version 1.0. University of Birmingham, Birmingham, UK and Bioversity International, Rome, Italy Maxted N, Vincent H (2021) Review of congruence between global crop wild relative hotspots and centres of crop origin/diversity. Genet Resour Crop Evol 68:1283–1297. https://doi.org/10.1007/s10722-021-01114-7 Mkwaila W, Terpstra KA, Ender M, Kelly JD (2011) Identification of QTL for agronomic traits and resistance to white mold in wild and landrace germplasm of common bean. Plant Breeding 130:665–672 Mponya NK, Chanyenga T, Brehm JM, Maxted N (2020) In situ and ex situ conservation gap analyses of crop wild relatives from Malawi. Genet Resour Crop Evol 68:759–771 Nduche M, Brehm JM, Abberton M, Omosun G, Maxted N(2021) West African Crop Wild Relative Checklist, Prioritization, and Inventory.Genetic Resources, 2 (4):55–65 Ng’uni D, Munkombwe G, Mwila G, Gaisberger H(2019) Spatial analyses of occurrence data of crop wild relatives (CWR) taxa as tools for selection of sites for conservation of priority CWR in Zambia Plant Genet Resour Charact Util,1–12 Nwanze KF, Rao KE, Soman P(1990) Understanding and manipulating resistance mechanisms in sorghum for control of the shoot-fly. In: Proceedings of International Symposium on molecular and genetic approaches to plant stress. New Delhi 14:17th Feb 1990 Oates JF, Bergl RA, Linder JM(2004) Africa’s Gulf of Guinea Forests: Biodiversity Patterns and Conservation Priorities.Advances in Applied Biodiversity Science, number 6.Conservation International, Washington D.C Osborn TC, Hartweck LM, Harmsen RH, Vogelzang RD, Kmiecik KA, Bliss FA (2003) Registration of Phaseolus vulgaris genetic stocks with altered seed protein compositions.(Registrations Of Genetic Stocks). Crop Sci 43(4):1570–1572 Park RF, Golegaonkar PG, Derevnina L, Sandhu KS, Karaoglu H, Elmansour HM, Singh D (2015a) Leaf Rust of Cultivated Barley: Pathology and Control. Annu Rev Phytopathol 53:565–589 Park RF, Golegaonkar PG, Derevnina L, Sandhu KS, Karaoglu H, Elmansour HM, Singh D (2015b) Leaf Rust of Cultivated Barley: Pathology and Control. Annu Rev Phytopathol 53:565–589 Parra-Quijano M, Iriondo JM, Torres E (2011) Ecogeographical land characterization maps as a tool for assessing plant adaptation and their implications in agrobiodiversity studies. Genet Resour Crop Evol. https://doi.org/10.1007/s10722-011-9676-7 Parra-Quijano M, Iriondo JM, Torres E (2012a) Ecogeographical land characterization maps as a tool for assessing plant adaptation and their implications in agrobiodiversity studies. Genet Resour Crop Evol 59:205–217 Parra-Quijano M, Iriondo JM, Torres E (2012b) Improving representativeness of genebank collections through species distribution models, gap analysis, and ecogeographical maps. Biodivers Conserv 21:79–96 Parra-Quijano M, Torres E, Iriondo JM, López F, Molina PA(2016) CAPFITOGEN tools. User manual version 2.0. International Treaty on Plant Genetic Resources for Food and Agriculture. FAO. Rome, pp 251 http://www.capfitogen.net/en/ (Accessed 29 July 2022) Parra - Quijano M, Iriondo JM, Torres ME, López F, Maxted N, Kell SP(2021) CAPFITOGEN3: A toolbox for the conservation and promotion of the use of agricultural biodiversity. Bogota, Colombia, pp 45–194 http://www.capfitogen.net/en/ . Accessed 23 December 2021 Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modelling of species geographic distributions. Ecol Model 190(3–4):23 Pickering R, Johnston PA (2005) Recent progress in barley improvement using wild species of Hordeum. Cytogenet Genome Res 109:344–349 Pickering R, Ruge-Wehling B, Johnston PA, Schweizer G, Ackermann P, Wehling P (2006) The transfer of a gene conferring resistance to scald ( Rhynchosporium secalis ) from Hordeum bulbosum into H. vulgare chromosome 4HS. 125:576–579Plant breeding6 Pillen K, Zacharias A, Lon J(2004) Comparative AB-QTL analysis in barley using a single exotic donor of Hordeum vulgare ssp. spontaneum . Theoretical and applied genetics, 108 (8):1591–1601 QGIS-Development Team (2021) QGIS 3.16. 8 Geographic Information System Open Source Geospatial Foundation Project. http://qgis.osgeo.org Ramírez-Villegas J, Khoury K, Jarvis A, Debouck DG, Guarino L (2010) A gap analysis methodology for collecting crop genepools: a case study with Phaseolus beans. PLoS ONE 5:e13497 Rodrigues ASL, Andelman SJ, Bakarr MI, Boitani L, Brooks TM, Cowling RM, Fishpool LDC, Fonseca GAB, Gaston KJ, Hoffmann M, Long JS, Marquet PA, Pilgrim JD, Pressey RL, Schipper J, Sechrest W, Stuart SN, Underhill LG, Waller RW, Watts MEJ, Yan X (2004) Effectiveness of the global protected area network in representing species diversity. Nature 428:640–643 Rubio-Teso ML, Iriondo JM, Parra-Quijano M, Torres E(2013) National strategy for the conservation of crop wild relatives of Spain. PGR Secure. http://www.pgrsecure.bham.ac.uk/sites/default/files/documents/public/National_CWR_Conservation_Strategy_Spain.pdf Accessed 18 Jun 2022 Ruge B, Linz A, Pickering R, Proeseler G, Greif P, Wehling P (2003) Mapping of Rym14 Hb, a gene introgressed from Hordeum bulbosum and conferring resistance to BaMMV and BaYMV in barley. Theor Appl Genet 107(6):965–971 Saini H, Kashihara Y, Lopez- Montes AJ, Robert A (2016) Interspecific crossing between yam species ( Dioscorea rotundata and Dioscorea bulbifera ) through in vitro ovule culture. Am J Plant Sci 7(8):1268–1274 Scarcelli N, Cubry P, Akakpo R, Thuillet A, Obidiegwu J, Baco MN, Otoo E, Sonké B, Dans A, Djedatin G, Mariac C, Couderc M, Causse S, Alix K, Chaïr H, François O, Vigouroux Y(2019) Yam genomics supports West Africa as a major cradle of crop domestication.Science Advances, 5 (5) Scheldeman X, van Zonneveld M(2010) Training Manual on Spatial Analysis of Plant Diversity and Distribution. Rome, Italy: Bioversity International. https://www.bioversityinternational.org/fileadmin/user_upload/online_library/publications/pdfs/1431.pdf Accessed 18 June 2022 Sood S, Khulbe RK, Gupta AK, Grawal PA, Upadhyaya HD, Bhatt JC (2015) Barnyard millet – a potential food and feed crop of future. Plant Breeding 134:135–147 UNEP-WCMC (2019) The World Database on Protected Areas (WDPA), Cambridge, UK. https://www.protectedplanet.net/ . Accessed 9 Jan 2022 UNESCO (2019) Mount Nimba Strict Nature Reserve. World Heritage Convention https:// whc.unesco.org/en/list/155/ Accessed 24 June 2022 USGS (2017) West Africa: Land Use and Land Cover Dynamics, Agricultural Expansion Across West Africa. US Geographical Survey https://eros.usgs.gov/westafrica/agriculture-expansion Vincent H, Amri A, Castaneda-Alvarez NP, Dempewolf H, Dulloo E, Guarino L, Hole D, Mba C, Toledo A, Maxted N (2019) Modeling of crop wild relative species identifies areas globally for in situ conservation. Commun Biol 2:136 Vincent H, Hole D, Maxted N (2022) Congruence between global crop wild relative hotspots and biodiversity hotspots. Biol Conserv 265. https://doi.org/10.1016/j.biocon.2021.109432 Wendler N, Mascher M, Himmelbach A, Johnston P, Pickering R, Stein N(2015) Bulbosum to Go: A Toolbox to Utilize Hordeum vulgare/bulbosum Introgressions for Breeding and Beyond. Molecular plant, 8 (10):1507–1519 World Coffee Research (2021) Cote d’Ivoire genebank digitizes coffee collection https://worldcoffeeresearch.org/news/2021/ Accessed 28 June 2022 Wright EM, Kelly JD (2011) Mapping QTL for seed yield and canning quality following processing of black bean ( Phaseolus vulgaris L.). Euphytica 179(3):471–484 Zair W, Maxted N, Magos Brehm J, Amri A (2021) Ex-situ and in situ conservation gap analysis of crop wild relative diversity in the Fertile Crescent of the Middle East Genetic Resources and Crop Evolution. 68:693–709 Zegeye H (2017) In situ and ex situ conservation: complementaryapproaches for maintaining biodiversity. Int J Res Environ Stud 4:1–12 Supplementary Files SupplemetaryfileforWAPriorityCWR.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Minor revisions 09 Oct, 2022 Reviewers agreed at journal 07 Sep, 2022 Reviewers invited by journal 27 Aug, 2022 Editor assigned by journal 16 Aug, 2022 First submitted to journal 11 Aug, 2022 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1953821","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":132144207,"identity":"27feffe7-72e2-4bf0-b81f-274e222217e9","order_by":0,"name":"Michael Ugochukwu Nduche","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEklEQVRIiWNgGAWjYFCCBAZmEMXGwAMkC2xgwszEajFII0ELA0TLYcJazNlzDD8XVDBE87GfPfjhg8H5aH6J5GcSDBXWiQ3S7RewabHseWMsPeMMQ24bT16y5AyD27kzZ6SZSTCcSU9skDlTgE2LwY0cM2beNqAWCR4DaR6glg03EswkGNsOJzZI5CTg1vIPrMX49x+Dc0At6d8kGP8R0tIA1mImzWBwAKglB2hLA0hL+gGsWs48K5bmOSYB9EuOmWWPQXLuzJ43xRYJx9KN2yRysIaYwfHkjZ95amxy57efMb7xo8Iut589feONDzXWsv0S6Q+w6oEACTQ+yBPAyDXAowU7YMdnyygYBaNgFIwcAABBd10fxhJTVgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-8880-9052","institution":"University of Birmingham","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Michael","middleName":"Ugochukwu","lastName":"Nduche","suffix":""},{"id":132144208,"identity":"09b65f12-518a-4e36-865c-89e84689d64d","order_by":1,"name":"Joana Magos Brehm","email":"","orcid":"","institution":"University of Birmingham","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Joana","middleName":"Magos","lastName":"Brehm","suffix":""},{"id":132144209,"identity":"6fae1a55-58e8-483b-b122-41d2cdebb9f4","order_by":2,"name":"Nigel Maxted","email":"","orcid":"","institution":"University of Birmingham","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Nigel","middleName":"","lastName":"Maxted","suffix":""},{"id":132144210,"identity":"b808c2a1-a0e0-482c-9f6e-640a932f41c3","order_by":3,"name":"Mauricio Parra-Quijano","email":"","orcid":"","institution":"Universidad Nacional de Colombia Facultad de Ciencias: Universidad Nacional de Colombia Facultad de Ciencias","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mauricio","middleName":"","lastName":"Parra-Quijano","suffix":""}],"badges":[],"createdAt":"2022-08-11 18:24:58","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1953821/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1953821/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":25809350,"identity":"aebbe2d9-8f54-4b5c-94a8-08e562d2c20a","added_by":"auto","created_at":"2022-08-29 19:07:39","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":133722,"visible":true,"origin":"","legend":"\u003cp\u003eObserved records of 102 priority CWR in West Africa\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1953821/v1/3775ea90915a7e6d860329c8.jpg"},{"id":25809238,"identity":"cc73421b-4c24-48aa-8c38-9164046ba915","added_by":"auto","created_at":"2022-08-29 19:02:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":47496,"visible":true,"origin":"","legend":"\u003cp\u003eObserved taxa richness map of 102 priority West African CWR\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-1953821/v1/7ab2a5383f04fce1e96c685d.png"},{"id":25809231,"identity":"10de6dd8-af82-4a7c-83a0-86249ed498c1","added_by":"auto","created_at":"2022-08-29 19:02:39","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":131103,"visible":true,"origin":"","legend":"\u003cp\u003eComplementary analysis showing areas of proposed genetic reserve sites of West African priority CWR. Numbers are in order of conservation priority for the reserve sites. Grid cell size is 0.4 degrees, Geographic coordinate system is WCS 1984\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1953821/v1/d945d0c76e67795c6561ac44.jpg"},{"id":25809522,"identity":"8b100079-9032-40cf-80b8-765b7f372f48","added_by":"auto","created_at":"2022-08-29 19:12:39","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":45997,"visible":true,"origin":"","legend":"\u003cp\u003eTaxa richness based on predicted distribution of 102 priority CWR in West Africa\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-1953821/v1/04510b2f724f50795a829c1f.png"},{"id":25809237,"identity":"29ed32b1-9008-4428-986b-754a49693adf","added_by":"auto","created_at":"2022-08-29 19:02:39","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":73219,"visible":true,"origin":"","legend":"\u003cp\u003eEcogeographic Land Characterization (ELC) generalist map of West Africa based on ecogeographic variables using the method described by Parra - Quijano et al. (2021)\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-1953821/v1/361468614ce448f2e44cc139.png"},{"id":25809352,"identity":"38c82856-804f-443e-8b5e-ae12e8bbaf93","added_by":"auto","created_at":"2022-08-29 19:07:39","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":14809,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eIn situ\u003c/em\u003e conservation gap of priority CWR based on taxa passively conserved in, outside PA and reserve sites across the 24 ELC categories\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-1953821/v1/f23b10067e7a5f5fd213bfb2.png"},{"id":25809235,"identity":"63195a42-c800-4903-b975-a1c4f8f18d76","added_by":"auto","created_at":"2022-08-29 19:02:39","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":33432,"visible":true,"origin":"","legend":"\u003cp\u003ePriority area for further \u003cem\u003eex situ\u003c/em\u003e collection of 102 West African priority CWR based on species distribution models\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-1953821/v1/0aee943f0d717c65d3a74166.png"},{"id":25809523,"identity":"9a687fb5-8df1-42fa-926f-0ed4a4e2ee50","added_by":"auto","created_at":"2022-08-29 19:12:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":616525,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1953821/v1/c8887103-d7bf-4992-a92d-04efe9c7da07.pdf"},{"id":25809234,"identity":"a3219eb2-bfe8-44db-a90b-fcafa1956328","added_by":"auto","created_at":"2022-08-29 19:02:39","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":119914,"visible":true,"origin":"","legend":"","description":"","filename":"SupplemetaryfileforWAPriorityCWR.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-1953821/v1/bd498806f1e6033c21242c44.xlsx"}],"financialInterests":"","formattedTitle":"\u003cp\u003eIn Situ and Ex Situ Conservation Gap Analyses of West African Priority Crop Wild Relative\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe flora of West Africa is diverse, heterogeneous, and abundant with numerous plant species. The region harbours over 9000 vascular plants with an estimated 1,800 species native to West Africa (Carr et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The endemic crop species of West Africa include but are not limited to \u003cem\u003eVigna unguiculata\u003c/em\u003e (L.) Walp (Cowpea), \u003cem\u003eV. subterranean\u003c/em\u003e (L.) Verdc (Bambara groundnut), \u003cem\u003eDioscorea cayennensis\u003c/em\u003e subsp. \u003cem\u003erotundata\u003c/em\u003e (Poir.) J. Miege (Guinea yam), \u003cem\u003eElaeis guineensis\u003c/em\u003e Jacq (Oil Palm), and \u003cem\u003eDiospyros gracilis\u003c/em\u003e Fletcher (African ebony). The climate of West Africa is characterized by abundant year-round rainfall in the Gulf of Guinea to a mean annual rainfall of 165 mm in the Agadez of Niger (USGS, \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Five bioclimate regions have been recognized in West Africa; Saharan, Sahelian, Sudanian, Guinean, and Guinea \u0026ndash; Congolian regions (USGS, \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). As such West African plant species are adapted and resilient to the region's erratic, and diverse ecogeographic conditions and may possess useful genes/traits for crop improvement.\u003c/p\u003e \u003cp\u003eGlobal food production in the next few decades will be determined by several factors including climate change. Climate change will negatively impact agricultural productivity in a global yield decline of an estimated 1.5% per decade (David and Sharon, 2012). This trend can at least be partially mitigated by the genetic and agronomic improvement of cultivated crops using trait diversity from crop wild relatives (CWR) (Maxted et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; David and Sharon, 2012). CWR are wild plant species relatively closely related to crops, including crop\u0026rsquo;s wild ancestors, that retain indirect use value as gene donors for crop improvement and a high level of genetic diversity having not passed through the genetic bottleneck of domestication. Maxted et al. (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) defined CWR broadly as all taxa within the same genus as a crop and more precise as wild plant taxon that have indirect use derived from its relatively close genetic relationship to a crop; this relationship is defined in terms of the CWR belonging to gene pools 1 or 2, or taxon groups 1 to 4 of the related crop. CWR contain resilient genes for crop improvement with several domesticated crops in West Africa improved using adaptive genes from CWR (Nduche et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Such crops include cassava (David and Sharon, 2012; Kawuki et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), maize (David and Sharon, 2012), yam (Lopez- Montes et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Saini et al., \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), cowpea (Andargie et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Badiane et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), millet (Sood et al., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), sorghum (Park et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2015a\u003c/span\u003e), rice (Jena, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Atwell et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), barley (Wendler et al., \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and for an overview (Nduche et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite the important role of CWR in food security in the West African region and the world, their conservation has received little attention. The neglect of CWR is because of lack of appreciation of its potential value in breeding and has resulted in underutilization of its profitable genetic diversity in crop improvement. The adaptive diversity of CWR is a safety net for urgent global food security needs. Globally, \u003cem\u003ein situ\u003c/em\u003e conservation of CWR in protected area (PAs) is inadequate, with insufficient number of genetic reserves established (Iriondo et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In West Africa, 1938 nationally protected sites exist covering about 9.6% of the region. Another 53 internationally designated protected areas are also found in the region (Mallon et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The number of CWR accessions conserved \u003cem\u003eex situ\u003c/em\u003e in genebanks is relatively low compared to accessions of cultivated crops. Globally, there are an estimated 7\u0026nbsp;million plant accessions conserved in 1750 genebanks (FAO, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2010a\u003c/span\u003e; Fu, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), however, about 29% of CWR lack genebank accessions, while over 24% have less than ten accessions represented in genebanks (Casta\u0026ntilde;eda-\u0026Aacute;lvarez et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Despite this shortfall in \u003cem\u003eex situ\u003c/em\u003e conservation of CWR, a comprehensive collection of CWR is still lacking. The combined use of \u003cem\u003ein situ\u003c/em\u003e and \u003cem\u003eex situ\u003c/em\u003e conservation of plant genetic diversity will lessen the erosion of valuable genetic diversity (Maxted et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e1997a\u003c/span\u003e; Zegeye, \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite the wide agreement that \u003cem\u003ein situ\u003c/em\u003e and \u003cem\u003eex situ\u003c/em\u003e techniques should be applied in a complementary manner (CBD, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1992\u003c/span\u003e), almost 100% of CWR diversity when conserved are conserved using \u003cem\u003eex situ\u003c/em\u003e seed storage alone (Maxted et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). \u003cem\u003eIn situ\u003c/em\u003e conservation is only recently being implemented and involves the designation of and management of populations to preserve a particular plant species in its natural abode where its intrinsic features are found (Maxted et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e1997c\u003c/span\u003e). To help ensure more \u003cem\u003eex situ\u003c/em\u003e and \u003cem\u003ein situ\u003c/em\u003e conservation coverage more recently, gap analysis has been applied for the planning of CWR conservation (Maxted et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). It involves identifying CWR diversity that is not well represented in conservation action and prioritizing these \u0026lsquo;gaps\u0026rsquo; for more active conservation (Maxted et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2008\u003c/span\u003ea ; Magos Brehm et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2017a\u003c/span\u003e; Ng\u0026rsquo;uni et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Mponya et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Magos Brehm et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eWest Africa is recognized as a region that played a significant role in crop domestication and still retains significant crop landraces and CWR diversity (Casta\u0026ntilde;eda-\u0026Aacute;lvarez et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Vincent et al., \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Maxted and Vincent, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). For instance, fonio [\u003cem\u003eDigitaria exilis\u003c/em\u003e (Kippist) Stapf] was domesticated in Senegal (Harlan, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1992\u003c/span\u003e) and Pearl millet was domesticated between Mali and Mauritania (Burgarella et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The zone between Ghana and Nigeria, down to Cameroon have been identified as the source of yam domestication (Scarcelli et al., \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Recognizing the crop genepool importance of the region, this paper reports the result of \u003cem\u003ein situ\u003c/em\u003e and \u003cem\u003eex situ\u003c/em\u003e conservation gap analyses of West African CWR as a major step towards development of a regional CWR conservation and use strategy for the region.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCollation and Verification of Occurrence Data\u003c/h2\u003e \u003cp\u003eThe distributional data for the 102 West African priority CWR defined by Nduche et al. (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) was collated using a standard occurrence data template (Magos Brehm et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2017b\u003c/span\u003e ). The occurrence data of the West African priority CWR were collated from Global Biodiversity Information Facility (GBIF, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), Genesys Global Portal on Plant Genetic Resources (Genesys, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), Royal Botanical Gardens, Kew (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.kew.org/kew-gardens\u003c/span\u003e\u003cspan address=\"https://www.kew.org/kew-gardens\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and RainBio (Dauby et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). A total of 54,924 distributional records were collated for the 102 West African priority CWR. Records that lacked coordinates but with collection sites information were georeferenced, using Google maps (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.maps.google.com\u003c/span\u003e\u003cspan address=\"https://www.maps.google.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). A quality check was done on the distributional data to ensure all records were expressed in decimal degrees. Locational records without decimal degree coordinates were converted to a decimal degree using Canadensys (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.data.canadensys.net/tools/coordinates\u003c/span\u003e\u003cspan address=\"https://www.data.canadensys.net/tools/coordinates\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Duplicate records were removed before the analysis, and records that lied abnormally in neighbouring countries were reviewed. Duplicate records are distributional records that are associated with the same record but from different sources or was documented twice from the same source (Magos Brehm et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2017a\u003c/span\u003e). The West African countries included in this study are Benin, Burkina Faso, Cote D\u0026rsquo; Ivoire, Gambia, Ghana, Guinea, Guinea- Bissau, Liberia, Mali, Mauritania, Niger, Nigeria, Senegal, Sierra Leone and Togo. The 20,125 records without duplicate records were entered in the occurrence data template required by the CAPFITOGEN tool which makes use of the FAO- Biodiversity\u0026rsquo;s multi- crop descriptor (FAO-BIOVERSITY, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e ). The \u0026lsquo;TesTable tool\u0026rsquo; of CAPFITOGEN3 was used to verify the occurrence data table to ensure it meets the requirements for other CAPFITOGEN3 tools analyses. GEOQUAL tool of CAPFITOGEN3 was used to assess the quality of coordinates and collection sites of the records (Parra - Quijano et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eEcogeographical Land Characterization Map\u003c/h2\u003e \u003cp\u003eEcogeographic land characterization (ELC) (Parra - Quijano et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) was used to evaluate the delineation and depiction of ecogeographic variables and determine appropriate sites for \u003cem\u003ein situ\u003c/em\u003e and \u003cem\u003eex situ\u003c/em\u003e conservation of priority CWR (Parra-Quijano et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Magos Brehm et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Eighteen environmental variables (6 bioclimatic, 6 edaphic, and 6 geophysical) were selected in the selecVar tool of CAPFITOGEN3, to generate the generalist ELC map. To accommodate those taxa with distributional records of \u0026lt;\u0026thinsp;10, a generalist ELC map was generated using the ELC maps tool of CAPFITOGEN3. This is because these taxa cannot generate species \u0026ndash; specific ELC map. Using the kmeanbic method, at a resolution of the ecogeographic layer of 10 x 10 km (approximately 5 arc \u0026ndash; minutes), the ELC map was created. The kmeanbic method was used because it identifies an optimal number of groups with discriminant analysis of principal components.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSpecies Distribution Modelling\u003c/h2\u003e \u003cp\u003eBased on environmental layers of various components of ecogeographic variables, predicted taxa distribution was identified by the distribution models produced by the individual taxa with more than 10 occurrence records in Maximum Enthropy Algorithm (MaxEnt) (Phillips et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) (Table S6 ). and by circular buffer (CA\u003csub\u003e50\u003c/sub\u003e) for taxa with less than 10 occurrence records used in the species distribution modelling (SDM), MaxEnt is a common SDM algorithm used to predict taxa distribution (Fourcade et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The species distribution data of the taxa for model calibration was classified into a training set (75% of total occurrence data) and test set (25% of total occurrence records) for design evaluation. Raster files of bioclimatic variables were obtained from WorldClim (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.worldclim.org/bioclim\u003c/span\u003e\u003cspan address=\"https://www.worldclim.org/bioclim\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), edaphic variables, from ISRIC \u0026ndash; World Soil Information (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://files.isric.org/soilgrids/\u003c/span\u003e\u003cspan address=\"https://files.isric.org/soilgrids/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), while geophysical data were downloaded as Digital Elevation Map (DEM) files from the National Aeronautics and Space Administration (NASA) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nasa.gov\u003c/span\u003e\u003cspan address=\"https://www.nasa.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.) All ecogeographic raster files were clipped to the same extent, resampled to the same cell size (0.41666666667 m), and reprojected to the same grid (WGS \u0026minus;\u0026thinsp;84), in ASCII raster grid format, using ArcMap 10.4.1 (ESRI, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). With Random Forest, integrated in the SelectVar of the CAPFITOGEN tools, variables for each ecogeographic component (bioclimatic, edaphic and geophysical) at resolution of 10 X 10 Km (approximately 5 arc minutes at Equator) were selected for each priority taxon (Parra-Quijano et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2016\u003c/span\u003e ). Bivariate correlation analysis was also evaluated in SelecVar, to reduce dimensionality, and only variables with weak correlation (p- value\u0026thinsp;\u0026le;\u0026thinsp;0.33) or not correlated (p \u0026ndash; value\u0026thinsp;=\u0026thinsp;0) were used to create the distribution model for each taxon (Tables S7 and S8). Maximum training sensitivity plus specificity threshold was applied, as recommended by Liu et al. (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). The robustness of the models were evaluated using three criteria: (a) average area under the test receiver operating characteristics curve [(ATAUC) ˃ 0.7] (b) standard deviation of ATAUC (STAUC)\u0026thinsp;\u0026lt;\u0026thinsp;0.15 (c) the proportion of potential distribution area with a STAUC ˃ 0.15, being \u0026lt;\u0026thinsp;10% were stable and used for evaluating taxa predicted distribution (Ram\u0026iacute;rez-Villegas et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Mponya et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). All three criteria had to be met for a model to be valid. However, for those taxa that failed the above MaxEnt model validation criteria, and for taxa with occurrence records\u0026thinsp;\u0026lt;\u0026thinsp;10, predicted distribution were identified by a circular buffer technique, using a radius of 50 km (CA50) around each observational point as recommended by Hijmans and Spooner (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). In this case, intersecting sites are not counted more than once.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eIn Situ\u003c/span\u003e \u003cb\u003eConservation Gap Analysis\u003c/b\u003e\u003c/p\u003e \u003cp\u003eGap analysis is a method of evaluation of the extent of conservation which helps to hierarchize CWR for preservation by locating gaps in the conservation (Rodrigues et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Langhammer et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Magos Brehm et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2017a\u003c/span\u003e). \u003cem\u003eIn situ\u003c/em\u003e conservation gap analysis involves a comparative study of intrinsic diversity and the element of diversity that is under active conservation action (Maxted et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2008b\u003c/span\u003e; Magos Brehm et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2017a\u003c/span\u003e) The method was described by Maxted et al. (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2008\u003c/span\u003e),Scheldeman and van Zonneveld (\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and Parra-Quijano et al. (\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2012b\u003c/span\u003e), where \u003cem\u003ein situ\u003c/em\u003e and \u003cem\u003eex situ\u003c/em\u003e conservation gap analyses were determined at taxon and ecogeographic levels. At the taxon level, the West African PA map was overlapped with the passport data in QGIS. Subsequently, using \u0026lsquo;the join attribute by location\u0026rsquo; in the \u0026lsquo;data management tool\u0026rsquo; of QGIS, the West African PA maps was intersected to identify records within and outside PA. The \u003cem\u003ein situ\u003c/em\u003e conservation gaps were obtained by comparing the number of populations of taxa present in PAs against those not represented in PAs (Mponya et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). To estimate the extent of representativeness of \u003cem\u003ein situ\u003c/em\u003e conservation of priority CWR at the ecogeographic level, the ELC zones from the ELC map tool analysis and the occurrence data were inputted in the \u0026lsquo;Representa tool\u0026rsquo; of CAPFITOGEN3 (Parra - Quijano et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The West African PA maps were overlapped with the ELC maps produced in the \u0026lsquo;Representa tool\u0026rsquo; to determine the representativeness of the ELC zones in PAs.\u003c/p\u003e \u003cp\u003eComplementarity analysis was done to identify potential sites for \u003cem\u003ein situ\u003c/em\u003e conservation of priority CWR. Maxted et al. (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e1997b\u003c/span\u003e) described these sites as genetic reserve for long \u0026ndash; term active conservation of plant genetic resources. They are defined designated locations either within PAs or outside PAs as informal sites for CWR conservation (Magos Brehm et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2017a\u003c/span\u003e). Such locations are aimed at conserving a large number of CWR taxa in the smallest available area (Kati et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Using the \u0026lsquo;Reserve selection\u0026rsquo; tool in DIVA \u0026ndash; GIS 7.5, at resolution of 10 x 10 km (approximately 5 arc minutes), potential genetic reserve sites were identified according to their priority for the conservation of priority CWR. The PA map for West Africa, obtained from UNEP-WCMC (\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) was overlapped with the complementarity genetic reserve site and taxon richness maps to determine the level of current passive \u003cem\u003ein situ\u003c/em\u003e conservation of the priority CWR and identify areas that require further active \u003cem\u003ein situ\u003c/em\u003e conservation actions. Passive \u003cem\u003ein situ\u003c/em\u003e conservation means that CWR in PAs are not actively monitored and managed to preserve their genetic diversity and protect them from pest, diseases, fragmentation, habitat degradation and natural disaster (Vincent et al., \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The maps produced were visualized in DIVA-GIS 7.5 (Hijmans et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and QGIS 3.16.8 (QGIS-Development Team, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eEx Situ\u003c/span\u003e \u003cb\u003eConservation Gap Analysis\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eEx situ\u003c/em\u003e conservation gap analyses were determined at taxon and ecogeographic levels. At the taxon level, a map of observed \u003cem\u003eex situ\u003c/em\u003e collection was subtracted from the predicted distribution map to obtain the gap in current \u003cem\u003eex situ\u003c/em\u003e conservation and locate the priority site for further \u003cem\u003eex situ\u003c/em\u003e collection. To determine the current germplasm representativeness of the ecogeographic diversity, the resulting ELC map and passport data were inputted in the \u0026lsquo;Representa\u0026rsquo; tool of CAPFITOGEN to assess the degree of representativeness of the ELC categories in the \u003cem\u003eex situ\u003c/em\u003e collection (Parra-Quijano et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The maps were processed in DIVA-GIS 7.5 (Hijmans et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), ArcMap 10.7 (ESRI, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and QGIS 3.16.8 (QGIS-Development Team, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) at a resolution of 10 x 10 km (approximately 5 arc minutes). At the ecogeographic level, the categories of representativeness of the diversity were analysed using the \u0026lsquo;Representa tool\u0026rsquo; of CAPFITOGEN3 (Parra - Quijano et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Based on the frequencies of the ELC map, the ELC map was categorized into quartiles, using the ELC zones in the ELC map. The four frequency classes were low, mid-low, mid-high, and high. However, zones where occurrence records were not found were categorized as \u0026lsquo;null\u0026rsquo;. \u003cem\u003eEx situ\u003c/em\u003e conservation gap were determined by estimating the diversity present in \u003cem\u003eex situ\u003c/em\u003e conservation against that conserved \u003cem\u003ein situ\u003c/em\u003e (Mponya et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Parra - Quijano et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eIn situ\u003c/span\u003e \u003cb\u003egap analysis\u003c/b\u003e\u003c/p\u003e \u003cp\u003eA total of 20,125 unique occurrence points were used for the \u003cem\u003ein situ\u003c/em\u003e conservation gap analysis, however 26 CWR had no occurrence data. The highest occurrence points were recorded in Benin and Nigeria with 31.9% (6428) and 11.7% (2,358) present points, respectively (Fig.s 1 and S1). Hotspots were found in Atlantique, Littoral, Mono, Kouffo, Atakora, Donga and Colline provinces of Benin. These areas correspond to the location of protected areas with the highest number of taxa such as Pendjari (28), Quari Maro (18), La Lama Nord (16), Monts Kouffe and Boucle de la Pendjari (18) (Table S1) There were also hotspots in Accra and Volta regions of Ghana, corresponding to the location of the Volta River reserve site. Location of high diversity were also spotted around Nasarawa, Plateau States of North- Central Nigeria, where Nasarawa Forest Reserve is located and South- Western zone of Nigeria. High species richness is also observed at the Lacs district of Cote d\u0026rsquo;Ivoire where the Mando forest reserve is situated, Montagnes district of Cote d\u0026rsquo; Ivoire where Mont Nimba is located and Nzerekore region of Guinea where Mont Nimba, Pic de Fon and Pic de Tibe Classified Forests are located (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAnalysis of the occurrence records showed that 18.5% (3,730) of the total unique present points were recorded in PAs. PAs with the highest number of taxa are Pendjari in Benin (28), Comoe National Park in Cote d\u0026rsquo; Ivoire (24), Niokolo \u0026ndash; Koba National Park in Senegal (21), Quari Maro in Benin (18) and Queme Superieur in Benin (18), while PAs with the highest population of taxa are Sahel (708), Comoe National Park (407), Kouffe (250) and Pemdjari (239) (Table S1). 62.7% (64) of the priority taxa were represented in a PA, 34.3% (35) of the taxa were present in \u0026ge;\u0026thinsp;5 PA, while the remaining 27.4% (28) had less than five populations in different PA, the minimum recommended by Dulloo et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) for the CWR \u003cem\u003ein situ\u003c/em\u003e conservation in PA (Table S2). However, 38 taxa (37.3%) did not occur in any PA. \u003cem\u003eDigitaria cilaris\u003c/em\u003e (Retz) Koeler, \u003cem\u003eVigna racemosa\u003c/em\u003e (G. Don) Hutch and \u003cem\u003eEragrostis pilosa\u003c/em\u003e (L.) P. Beauv., had the highest number of taxa populations in PA network with 443, 425 and 234 taxa population, respectively. Similarly, \u003cem\u003eVigna racemosa\u003c/em\u003e (G. Don) Hutch, \u003cem\u003eEleusine indica\u003c/em\u003e (L.) Gaertn. and \u003cem\u003eOryza. glaberrima\u003c/em\u003e Steeud. occurred in more PAs, appearing in 40, 38 and 37 PAs, respectively, while all the rice crop genepool occurred in the PA network. Cowpea (17), yam (13), and potato (9) crop genepools were the highest number of prioeity taxa that occurred in PA (Table S2). Nigeria, Benin and Cote d\u0026rsquo; Ivoire had the highest number of PAs where taxa are present, with 46, 25 and 18 PAs, respectively. Conversely, no PA with taxa was identified in Mauritania (Fig. S2). Similarly, the highest number of taxa population in PAs were found in Benin, Burkina -Faso and Cote d\u0026rsquo;Ivoire had, with 1351, 768 and 463 populations, respectively. Also, Benin, Nigeria and Guinea had the highest number of CWR in PAs, numbering 207, 87 and 76 taxa respectively (Fig. S3)). 38 taxa (37.3%) did not occur in any PA, simimarly none of the Sorghum, fonio and yam wild relatives occurred in PA. Other taxa not represented in PA are \u003cem\u003eEchinochloa crus- galli\u003c/em\u003e (L.) P. Beauv., \u003cem\u003eGossypium herbaceum\u003c/em\u003e var. \u003cem\u003eacerifolium\u003c/em\u003e (Guill. \u0026amp; Perr.) A. Chev., \u003cem\u003eIpomoea ochracea\u003c/em\u003e (Lindl.) Sweet, \u003cem\u003eManihot dichotoma\u003c/em\u003e Ule, \u003cem\u003eTriticum turgidum\u003c/em\u003e L. and \u003cem\u003eVigna. unguiculata\u003c/em\u003e subsp. \u003cem\u003estenophylla\u003c/em\u003e (Harv.) Marechal \u0026amp; al. (Table S2).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eComplementarity analysis identified 29 potential genetic reserve sites with grid square size of 0.4 degrees for the conservation of West African priority CWR (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Apart from Burkina \u0026ndash; Faso, Liberia, Mauritania and Gambia, genetic reserve sites were identified in all the other West African countries. The highest number of reserve sites were found in Nigeria with 9, while Benin and Guinea have 4 each (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Eleveen reserve sites are located in PA, wth 9 of the sites conserving 37% (38) of the CWR, however priority CWR were absent in Eleiyele and Volta River (Table S1 and S3). A total of 458 records were present in 9 of the reserve sites with taxa. 36.3% (37 taxa) of the priority CWR were found in the reserve sites (Table S3). \u003cem\u003eVigna racemosa\u003c/em\u003e (G. Don) Hutch. \u0026amp; Dalz, \u003cem\u003eOryza glabarrima\u003c/em\u003e Steud, \u003cem\u003eVigna gracilis\u003c/em\u003e (Guill. \u0026amp; Perr.) Hoof. f. and \u003cem\u003eO. barthi\u003c/em\u003e A. Chev. had the highest number of taxa population; 56, 51, 46 and 35 respectively in the genetic reserve sites (Table S2 and Table S5). Cowpea (10), yam (7), sweet potato (7), and rice (4), are the crop genepools with the highest number of CWR present in the reserve sites (Table S). Conversely, cowpea (13), yam (8), sweet potato (6) and cassava (5) are the crop genepools with the highest number of taxa not represented in reserve sites. \u003cem\u003eV. racemosa\u003c/em\u003e (G. Don) Hutch \u0026amp; Dalz., \u003cem\u003eO. barthi\u003c/em\u003e A. Chev., \u003cem\u003eIpomoea aquatica\u003c/em\u003e Forssk., \u003cem\u003eO. longistiminata\u003c/em\u003e A. Chev. \u0026amp; Roehr and \u003cem\u003eEleusine indica\u003c/em\u003e (L.) Gaertn were found in more genetic resesrve sites than other taxa and were found in 4 reserve sites each (Table S3 and S5).\u003c/p\u003e \u003cp\u003e \u003cem\u003eIn situ\u003c/em\u003e conservation gap analysis of the 102 priority CWR showed that the areas of predicted distribution is present in all the West African countries (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The areas of highest potential diversity was found at Woroba and Montangnes districts of Cote d\u0026rsquo;Ivoire where some protected areas such as Mont Tia, Mont Sangbe, Pic de Fon, Pic de Tibe, Mt Yonon and Mont Nimba reserve site are located (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Also of high predicted CWR taxon richness are Nzerekore, Faranah, Kindia and Boke regions of Guinea where the Mont Nimba and Diecke reserve sites are situated. Other areas of high predicted taxon richness are in the South \u0026ndash; South zone of Nigeria around Cross River National Park, North Eastern Nigeria, Kono and Koinadugu districts in Sierra Leone (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), where these areas are predicted to harbour 51 to 63 CWR. However, areas from Abidjan in Cote d\u0026rsquo;Ivoire, Ghana, Togo, Benin to South- West Nigeria had low areas of predicted distribution (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Eighteen ecogeographic variables (6 bioclimatic, 6 edaphic and 6 ecogeographic) were used to generate 24 ELC zones (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), which represents the predicted ecogeographic scenarios of the region (Parra-Quijano et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2012a\u003c/span\u003e ; Mponya et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The ecogeographic diversity of 17 ELC zones are present in 152 PAs, while ELC zones 11 and 2 had the higest diversity in PAs (Table S4 and Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eEx situ\u003c/span\u003e \u003cb\u003egap analysis\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe SDM of 55 taxa met the validation whereas for the remaining 8 CWR, a CA\u003csub\u003e50\u003c/sub\u003e buffer area created around each occurrence point (Table S6). The number of ecogeographic variables for the SDM varied from 15 in \u003cem\u003eVigna filicaulis\u003c/em\u003e Hepper and \u003cem\u003eV. desmodiodes\u003c/em\u003e Wilczek to 43 in \u003cem\u003eIpomoea aquatica\u003c/em\u003e Forssk (Table S7 and Table\u0026nbsp;8). A total of 5720 (28.4%) accessions from 56 (55%) priority CWR are represented \u003cem\u003eex situ\u003c/em\u003e. 13 taxa had occurrence data but did not pass the validation criteria for predicted distribution map (Table S6). 55% (56) priority CWR had at least one accession represented in genebank, of these, 23% (13) of the taxa had at least 50 accessions conserved \u003cem\u003eex situ\u003c/em\u003e, while 76.7% (43) of the accessions are underrepresented with less than 50 accessions conserved in genebanks. Nigeria had the highest number of accessions in genebanks, with 23.8% (1366) accessions, while Mauritania had the least 0.2% (13) (Fig. S1). Benin had the highest number of occurrence data (6428), while Mauritania had the least (150) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig. S1). \u003cem\u003eOryza glabarrima\u003c/em\u003e Steud, \u003cem\u003eO. barthi\u003c/em\u003e A. Chev. and \u003cem\u003eO. longistaminata\u003c/em\u003e A. Chev. \u0026amp; Roehr. had the highest number of accessions conserved in genebanks, with 2670, 610 and 562 accessions respectively (Table S2). All the \u003cem\u003eHordeum\u003c/em\u003e and \u003cem\u003ePhaseolus\u003c/em\u003e CWR species had no occurrence data. Of the taxa that have occurrence data, 20 were not represented in genebanks, while \u003cem\u003eCola nitida\u003c/em\u003e (Vent.) Schott. \u0026amp; Endl. (3), \u003cem\u003eD. rotundata\u003c/em\u003e Poir (3), \u003cem\u003eI. batatas\u003c/em\u003e (L.) Lam. (3), \u003cem\u003eSorghum bicolor\u003c/em\u003e (L.) Moench (3) and \u003cem\u003eVigna unguiculata\u003c/em\u003e (Linn.) Walp. (3) represent the crop genepools with the highest number that were not present in both genebanks and PA (Table S9). Similarly, of the 13 taxa that did not occur in PA, 7 were not also represented in genebanks. However, all the taxa with \u0026ge;\u0026thinsp;50 accessions in genebanks also occurred in \u0026ge;\u0026thinsp;5 PAs (Table S2 and Table S3).\u003c/p\u003e \u003cp\u003eThe areas of further collection are found in all the West African countries (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e), while 87.27% (89) priority CWR needs further collecting (Table S2). Areas of further collection are Assaba and Guidimaka provinces of Mauritania; Saint \u0026ndash; Louis and Tambocounda regions in Senegal. Nzerekore region of Guinea; Koinadugu, Bombali and Tonkolili districts of Sierra Leone. Loffa, Bomi, Montserrado and Grand Cape Mount counties of Liberia. Montagnes, Lacs and Lagunes districts of Cote d\u0026rsquo; Ivoire; Mopti region of Mali; Upper West, Bono East, Eastern, Volta and Ashanti regions of Ghana. Other areas are Haut \u0026ndash; Bassins, Cascades, Est and Centre \u0026ndash; Est regions of Burkina Faso; Plateau, Queme, Atlantique and Alibori provinces of Benin; North \u0026ndash; East and North \u0026ndash; Central zones of Nigeria (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) Ecogeographic diversity of 16 ELC zones are conserved in genebanks (Table S9), while the CWR diversity of 8 zones are not represented. ELC zones 2,8 and 11 had the highest population which corresponds to the ELC map category. 50% of the ELC zones had\u0026thinsp;\u0026ge;\u0026thinsp;25% of their accessions represented in genebanks (Table S4), while ELC zones 2 and 11 had the highest collection.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eReserve sites for \u003cem\u003ein situ\u003c/em\u003e conservation of West African CWR and protected areas where they are located\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReserve site\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProtected Area\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal occurrence record\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNumber of CWR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eELC zones\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTotal area (Km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTotal area (ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ecountry\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNiokolo \u0026ndash; Koba National Park\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9,130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e913000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSenegal\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBoucle de la Pendjari\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e275,500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eBenin\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDosso\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5,440.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.44,087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNiger\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMount Nimba\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10,12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e175.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17, 540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eGuinea\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYankari\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2,254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e225, 400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNigeria\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiecke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e64,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eGuinea\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNasarawa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15,076,526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e150,765.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNigeria\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEto\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e116.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTogo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGoudi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCote d\u0026rsquo;Ivoire\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEleiyele\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e526.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNigeria\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVolta River\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eGhana\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWest Africa is rich with taxa diversity, endemism and biodiversity heritage, while CWR diversity and flora distribution of the region have been reported in various studies (Huchinson and Dalziel, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1958\u003c/span\u003e; Oates et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Bergl et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Idohou et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Hounsou-Dindin et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, as a purpose of this, further study is needed to determine the gaps in \u003cem\u003ein situ\u003c/em\u003e and \u003cem\u003eex situ\u003c/em\u003e conservation action in the region, as this will complement and consolidate the national efforts of the individual countries. According to recent CWR ecogeographic diversity analysis, West Africa has been identified as a region of global importance with high CWR diversity for food security (Casta\u0026ntilde;eda-\u0026Aacute;lvarez et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Vincent et al., \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The highest CWR diversity identified in the provinces of Benin, is because of the recent Flora of Benin (Ako\u0026egrave;gninou et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), and the high number of occurrence records found in Benin, relative to other countries in the region (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and S1). High CWR diversity was also identified at Accra and Volta regions of Ghana, North \u0026ndash; Central and South- Western zone of Nigeria. Other areas include Lacs district of Cote d\u0026rsquo; Ivoire and Nzerekore region of Guinea (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These areas correspond to some areas of predicted distribution such as Nzerekore region of Guinea where Mont Nimba, Diecke, Pic de Fon, Pic de Tibe Classified Forests are located (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Similarly, these areas of species richness are in congruence with the Guinean forest, categorized as one of the 36 biodiversity hotspots in the world (Maxted and Vincent, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Vincent et al., \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and the highest conservation value in Africa (Luiselli et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The Guinean Forest covers an area of 621,705 km\u003csup\u003e2\u003c/sup\u003e\u003csub\u003e,\u003c/sub\u003e extending from Guinea, Sierra Leone, Liberia, Cote d\u0026rsquo; Ivoire, Ghana, Togo, Benin to Nigeria. However, Guinean Forest is one of the most exploited biodiversity hotspots in the world, though 15% of the original forest is still unexploited (Conservation International, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe network of PAs in West Africa conserves a substantial number of the priority CWR with 61% (63) of the taxa found in PAs (Table S2). However, further field survey should be carried out to ascertain the presence of the priority CWR in those PAs where they were identified. For the 28 taxa (27.4%) that were found in less than five PAs, field survey should be done in areas of predicted distribution to determine their locations and to identify more taxa populations in network of PAs, to ensure they meet or surpass the required minimum number for active \u003cem\u003ein situ\u003c/em\u003e conservation (Dulloo et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Also, effective management and monitoring should be put in place to ensure active \u003cem\u003ein situ\u003c/em\u003e conservation of the priority CWR in their respective PAs (Maxted et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2008b\u003c/span\u003e). Relevant institutions, stakeholders, non \u0026ndash; governmental organizations (NGOs), and protected area managers should synergistically, ensure the maintenance of the PAs for optimal and active conservation action. Pendjari National Park in Benin with an area of 2,765 km\u003csup\u003e2\u003c/sup\u003e and Comoe National Park in Cote d\u0026rsquo; Ivoire occupying an area of 11,500km\u003csup\u003e2\u003c/sup\u003e are the PAs with the highest number of CWR (Table S1). The presence of more CWR in Pendjari National Park may have resulted from the fact that the site was better surveyed than other PAs, as shown in the number of occurrence data recorded in Benin as compared to other countries (Fig.s 1 and S1). UNESCO (\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) reported that 620 plant species are found in Comoe National Park, which agrees with the high number of CWR present in Comoe National Park. The site contains great diversity of plants, endemic species and diverse ecological habitats ranging from savannah, forest to grasslands. (UNESCO, \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Large PAs such as Sahel (30,693 km\u003csup\u003e2\u003c/sup\u003e), Comoe National Park (11,500 km\u003csup\u003e2\u003c/sup\u003e), W National Park Benin (10,000 km\u003csup\u003e2\u003c/sup\u003e), Niokolo \u0026ndash; Koba National Park (9,130 km\u003csup\u003e2\u003c/sup\u003e), were design to conserve diverse ecogeographical populations, which CWR is a subset. However, they contain only small of CWR population per unit area. The designation and development of the 18 reserve site found outside PAs, as other effective based conservation measures (OECM) will augment the preservation of CWR population outside PA network (Iriondo et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIdentifying priority sites for the \u003cem\u003ein situ\u003c/em\u003e conservation of CWR, based on species richness may be misleading since the approach relays only on taxa richness sites neglecting those taxa that require urgent protection (Brooks et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). However, to overcome this challenge, complementarity analysis through reserve site selection is used (Fielder et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Contreras-Toledo et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Mponya et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Complementarity analysis have been used to identity priority site in regions such as Southern African Development Commission (SADC) (Magos Brehm et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and Middle East (Zair et al., \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Twenty-nine reserve sites were identified in this study, with 11 in PA and 18 spotted outside PAs. The 11 reserve sites located in PAs will require minimal cost to establish and manage, being in existing PAs. It will augment and complement the protective function offered by the existing PAs and provide benefits to the local communities (Maxted et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2008b\u003c/span\u003e; Maxted and Kell, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The remaining 18 reserve sites not located in PAs also present an opportunity for those countries with low number of PAs where taxa were found such as Guinea \u0026ndash; Bissau (3), Mali (4), Niger (5), Sierra Leone (7) and Senegal (9) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The outcome of the complementary analysis showed that the location of some reserve sites corresponds with some CWR hotspots in West Africa. These areas are Atakora, Alibori, Donga and Bongou provinces in Benin; Accra region of Ghana; North \u0026ndash; Central and South \u0026ndash; West zones of Nigeria and Lacs district of Cote d\u0026rsquo; Ivoire (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThe areas of predicted distribution were highest in Woroba and Montangnes districts of Cote d\u0026rsquo;Ivoire where the reserve site; Mont Nimba is located and some protected areas such as Mont Tia, Mont Sangbe, Pic de Fon, Pic de Tibe, Mt Yonon are found. The area of high predicted distribution also extended to the Nzerekore, Faranah, Kindia, and Boke regions of Guinea, where the reserve site; Mont Nimba and Diecke Classified Forest are located (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Mont Nimba is strategic because it is located between Guinea and Cote d\u0026rsquo; Ivoire. It occupies a total land area of 175.4 km\u003csup\u003e2\u003c/sup\u003e, with 125.4 km\u003csup\u003e2\u003c/sup\u003e in Guinea and 50 km\u003csup\u003e2\u003c/sup\u003e in Cote d\u0026rsquo; Ivoire. UNESCO (\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) reported diverse flora and endemic plant species in the site, including epiphytes and over 2000 vascular plant species. Similarly, Diecke Classified Forest is one of the largest undisturbed areas of the Guinee Forestiere with diverse plant species including several threatened tree species. The presence of \u003cem\u003eCola attiensis\u003c/em\u003e Aubrev. Pellegr. in the site (Table S3) was also reported by (Couch and Haba, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The area of predicted distribution appears to be larger than the area of observed distribution, which shows that the region is under surveyed. Efforts should be made for \u003cem\u003eex situ\u003c/em\u003e collection of taxa in predicted areas outside PAs, as they may be under threat by urbanization, change in land use and habitat destruction (Mponya et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor active \u003cem\u003ein situ\u003c/em\u003e CWR conservation, effective \u003cem\u003eex situ\u003c/em\u003e conservation is needed to complement it. \u003cem\u003eEx situ\u003c/em\u003e conservation methods include seed bank, genebank, DNA bank, cryopreservation, botanical garden and \u003cem\u003ein- vitro\u003c/em\u003e conservation (Maxted et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e1997c\u003c/span\u003e; Maxted, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). 55% (56) of the priority taxa were represented in genebanks, however more accessions need to be collected for \u003cem\u003eex situ\u003c/em\u003e conservation. Further collection actions should be undertaken for the 20 taxa with occurrence records but not present in genebanks, the 26 taxa without occurrence data and the 44 taxa underrepresented in genebanks to reflect the recommendation by (Brown and Marshall, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1995\u003c/span\u003e ) and (Guerrant et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) of 50 taxa population for effective representation in genebank. Additionally, taxa already present in PAs should be conserved \u003cem\u003eex situ\u003c/em\u003e in genebanks as a back \u0026ndash; up to the \u003cem\u003ein situ\u003c/em\u003e conservation to protect them in the event of natural disaster, war or fire outbreak (Ford-Lloyd and Maxted, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). Genebank accessions should be duplicated regionally and internationally to ensure effective and long term \u003cem\u003eex situ\u003c/em\u003e conservation (FAO, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Magos Brehm et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe crop genepools with the highest number of taxa not represented in genebanks are yam (3), potato (3), sorghum (3), cowpea (3) and cola (3). Among the taxa that are not present in genebanks are \u003cem\u003eDioscorea abyssinica\u003c/em\u003e Hochst. ex. Kunth, used to improve yam for resistance against yam mosaic virus and anthranose (Lopez- Montes et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), \u003cem\u003eManihot carthagenesis\u003c/em\u003e (Jacq.) Mull. Arg, \u003cem\u003eM. dichotoma\u003c/em\u003e Ule, \u003cem\u003eM. esculenta\u003c/em\u003e subsp. \u003cem\u003eperuviana\u003c/em\u003e Crantz and \u003cem\u003eM. esculenta\u003c/em\u003e subsp. \u003cem\u003eflabellifolia\u003c/em\u003e Crantz, used to improve for resistance against cassava brown streak disease (Kawuki et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). \u003cem\u003eEchinichloa frumentacea\u003c/em\u003e Link and Eleusine Africana Kenn \u0026ndash; O\u0026rsquo;Byne are used to breed Barnyard millet (Sood et al., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and finger millet (Dida and Devos, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), respectively for high yield. Other taxa that are not present in genebanks include \u003cem\u003ePhaseolus vulgaris\u003c/em\u003e var. \u003cem\u003eaborigineus\u003c/em\u003e (Burkart) Baude, used for the improvement of common bean against bruchid (Osborn et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), white mould (Mkwaila et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), web and bacterial blight (Beaver et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and for high yield (Wright and Kelly, \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). \u003cem\u003eSorghum purpureosericeum\u003c/em\u003e (Hochst ex. A. Rich) Schweinf \u0026amp; Asch. has confirmed used in the improvement of sorghum for resistance against sorghum shoot fly (Nwanze et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e1990\u003c/span\u003e), while \u003cem\u003eSorghum bicolor\u003c/em\u003e subsp. \u003cem\u003everticiliforum\u003c/em\u003e (L.) Moench is used in breeding sorghum for resistance against stem and leaf rust (Fetch Jr et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Park et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2015b\u003c/span\u003e), increase in seed size and weight (Pillen et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). \u003cem\u003eHordeum bulbosum\u003c/em\u003e L. is used in breeding barley for resistance against barley mild mosaic virus (Ruge et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Wendler et al., \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), barley yellow virus (Wendler et al., \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), powdery mildew (Pickering and Johnston, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Johnston et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), stem and leaf rust (Fetch Jr et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Johnston et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Park et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2015b\u003c/span\u003e) and leaf scald (Pickering et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). \u003cem\u003eEx situ\u003c/em\u003e conservation will be a safety net for some CWR that have their adaptive scenario outside PA. For instance, some herbs and shrubs thrive on lawns, waste lands, swamps and agricultural lands (Maxted and Kell, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA major objective of \u003cem\u003ein situ\u003c/em\u003e conservation is to confirm and preserve diverse CWR genes in a defined location for optimal used in crop improvement to ensure food and nutrient security. Ecogeographical diversity can work as proxy for genetic diversity (Korona, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Parra-Quijano et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2012a\u003c/span\u003e ). The frequency of ecogeographical diversity outside PAs is higher, compared to that in PA. Therefore, \u003cem\u003eex situ\u003c/em\u003e collection of priority CWR outside PAs will capture taxa in ELC zones not represented or underrepresented in network of PA. The ELC map shows all resilient environmental conditions present within the geographical location of the target taxa population. ELC zone 2 had more accessions in genebanks and the highest frequency of occurrence in PA compared to other ELC categories (Table S4 and Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). However, taxa found in rare ELC zones present unique genes (Contreras-Toledo et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Parra - Quijano et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and should be prioritized in \u003cem\u003eex situ\u003c/em\u003e collection and conservation for use in crop improvement of their related crops. Complementarity analysis showed that 11 ELC categories were present in the reserve sites within PA, compared to 15 ELC categories represented in all PAs. This shows a high degree of complementarity in capturing the ecogeographical categories diversity of the priority CWR. On the average, the diversity of ELC categories per taxa was higher (26. 3%) compared to that for all PA network (23.4%) (Table S4). For ELC zones 12,14,16,17,19,22,23,24 where taxa were not represented in genebanks and ELC zones 4,7,20,21 with low genebank representation, based of frequency of occurrence (Table S9), further collection action should be carried out to ensure their representation. Similarly, \u003cem\u003eex situ\u003c/em\u003e collection should be done to represent all the ELC zones and ensure the preservation of novel and vital genes (Rubio-Teso et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2013\u003c/span\u003e ; Parra - Quijano et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The presence of these taxa in different ELC zones helps to identify those that thrives in adverse and marginal environments, as they may possess profitable genes for adapting their related crops to erratic climatic conditions (Garcia et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e"},{"header":"Recommendations","content":"\u003cp\u003eBased on the outcome of this study, the following recommendations for the \u003cem\u003ein situ\u003c/em\u003e and \u003cem\u003eex situ\u003c/em\u003e conservation of West African priority CWR are proposed:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eImproved the efficacy of reserve sites in PAs for active \u003cem\u003ein situ\u003c/em\u003e conservation through effective management and monitoring of the target CWR to ensure long term preservation. Small PAs should be expanded to ensure full and optimal conservation area and to include CWR diversity that occurs next to them. The eleven reserve sites in PAs should be prioritized for the \u003cem\u003ein situ\u003c/em\u003e conservation of West African priority CWR. Ascertain the suitability of the location of the 18 reserve sites that are not in PA, including the topography, accessibility and demography of the taxa in the area. Then initiate the establishment of reserve sites for the \u003cem\u003ein situ\u003c/em\u003e conservation of priority CWR not conserved in PAs, to augment the functions of existing PAs. New PAs are crucial for countries with limited PAs such as Guinea \u0026ndash; Bissau, Mali, Niger, Sierra Leone and Senegal (Fig S2 and Table S1). The identification of the 29 reserve sites is significant and a footprint for the \u003cem\u003ein situ\u003c/em\u003e conservation of the priority CWR.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eConduct field survey for the 38 taxa that did not occur in PA to ensure they are present in at least five PAs to meet the minimum number of representations in PAs for active \u003cem\u003ein situ\u003c/em\u003e conservation (Dulloo et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Priority PAs for further field survey are those where CWR are predicted to be present such as Mont Tia, Mont Sangbe, Pic de Fon, Pic de Tibe, Mt Yonon, Cross River National Park and reserve sites such as Mont Nimba and Diecke. Attention should be given to CWR diversity and general biodiversity present in reserve sites located in PAs to ensure the conservation of all available plant genetic diversity.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eMaintain international genebanks in West Africa such as the International Institute of Tropical Agriculture (IITA) (IITA, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), Nigeria which conserve accessions of African food crops, AfricaRice M\u0026rsquo;be Cote d\u0026rsquo; Ivoire with over 22, 000 accessions (CGIAR, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and ICRISAT, Niger (ICRISAT, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Establish national genebank in the areas with high \u003cem\u003eex situ\u003c/em\u003e collection and predicted distribution for the \u003cem\u003eex situ\u003c/em\u003e conservation of priority CWR, while national genebanks like National Centre for Genetic Resources and Biotechnology (NACGRAB) Ibadan, Nigeria with 13, 839 accessions (Crop Trust, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), National Agricultural Research Center, Cote d\u0026rsquo; Ivoire holding 8,000 accessions of coffee (World Coffee Research, \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Ghana National Genebank should be upgraded to hold more accessions. Also, genebank accessions should be duplicated in different facilities, while accessions present only in genebanks outside West Africa should be retrieved from internationally genebanks (Table S10), and conserved in area where the taxa have their intrinsic features and taxon richness.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eSearch for occurrence data for the 26 taxa without occurrence records and for those with less than 10 records in their countries of endemism. Conduct field survey for countries with inadequate number of occurrence data such as Mauritania, Gambia, Guinea \u0026ndash; Bissau, Liberia, Togo and Sierra Leone (Fig. S1), to identify the location of more priority CWR and taxa population both within and outside PAs for \u003cem\u003eex situ\u003c/em\u003e collection and active \u003cem\u003ein situ\u003c/em\u003e conservation. SDM and buffer CA\u003csub\u003e50\u003c/sub\u003e can serve as a guide in locating the taxa in the areas of predicted distribution.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ePrioritize the \u003cem\u003eex situ\u003c/em\u003e collection of the 43 taxa with less than 50 accessions in genebanks, using the SDM and CA\u003csub\u003e50\u003c/sub\u003e as a guide to ensure their effective representation \u003cem\u003eex situ\u003c/em\u003e. Also, of priority are the 13 taxa with occurrence data but did not pass the validation criteria. Diversity in \u003cem\u003eex situ\u003c/em\u003e conservation should be increased to include seed banks, cryopreservation, \u003cem\u003ein \u0026ndash; vitro\u003c/em\u003e storage for recalcitrant taxa, and botanical garden. Government agencies, institutions, local communities, national and international genebanks should be involved in the collection mission.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eConduct field survey to identify priority CWR in the ELC zones with low frequency to ensure that a full range of ELC zones are captured so as to preserve unique and novel genes for use in crop improvement (Parra - Quijano et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eMake crosses between plants from collected seeds and their related crops, as well as between CWR found in PA and their related crops based on genepool levels (Table S11). Advanced methods such as embryo rescue, \u003cem\u003ein - vitro\u003c/em\u003e gene transfer can be used for CWR that shows difficulty with conventional methods. This may help in resolving the challenge of hunger and food insecurity in the densely populated West African region.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ePeriodic revision, review and upgrade of the outcome of this study and the recommendations in the event of change in conservation priorities as a result of availability of more occurrence records and a more precise algorithm for ecogeographic modelling and occurrence data analysis.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, the \u003cem\u003ein situ\u003c/em\u003e and \u003cem\u003eex situ\u003c/em\u003e conservation gaps for the 102 West African CWR were evaluated. The 26 taxa without occurrence data, 20 taxa with occurrence records but not present in genebanks, and the 44 taxa underrepresented in genebanks have been prioritized for further \u003cem\u003eex situ\u003c/em\u003e collection to ensure their effective representation in genebanks. The areas of high predicted distribution within PAs such as Mont Tia, Mont Sangbe, Pic de Fon, Pic de Tibe, Mt Yonon, Cross River National Park and reserve site such as Mont Nimba and Diecke were also prioritized for \u003cem\u003eex situ\u003c/em\u003e collection. The 38 taxa that are not present in PA and the 28 taxa with less than five population in different PAs were also target taxa for identification and \u003cem\u003ein situ\u003c/em\u003e conservation action. Establishment of the 29 identified reserve sites will further strengthen the CWR \u003cem\u003ein situ\u003c/em\u003e conservation effort at national and regional level. Similarly, filling the identified \u003cem\u003ein situ\u003c/em\u003e and \u003cem\u003eex situ\u003c/em\u003e conservation gaps will ensure that the priority CWR and agrobiodiversity are availability for use as food, feed and fibre. Additionally, the implementation of the proposed recommendations will enhance the active conservation and sustainable utilization of the priority CWR for crop improvement to mitigate climate change and underpin food security for the rising population in West Africa\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the Tertiary Education Trust Fund (TETFUND), Nigeria for the scholarship awarded to Michael Nduche which ensured the funding of this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTertiary Education Trust Fund (TETFUND), Nigeria provided funding for this study as a scholarship awarded to the corresponding author\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eN. Maxted and J. Mago Brehm designed the study. All the authors contributed in the data analysis and revising of the manuscript, while M.U. Nduche wrote the manuscript\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData area available from the author upon reasonable request\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAko\u0026egrave;gninou A, van der Burg WJ, van der Maesen LJG (2006) Flore analytique du B\u0026eacute;nin. Backhuys Publishers, Leiden, p 1034\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndargie M, Pasquet RS, Gowda BS, Muluvi GM, Timko MP (2014) Molecular mapping of QTLs for domestication-related traits in cowpea (\u003cem\u003eV. unguiculata\u003c/em\u003e (L.) Walp.). Euphytica 200(3):401\u0026ndash;412\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAtwell BJ, Wang H, Scafaro AP (2014) Could abiotic stress tolerance in wild relatives of rice be used to improve Oryza sativa? Plant Sci 215:248\u0026ndash;258\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBadiane FA, Diouf M, Diouf D (2014) Cowpea. In Broadening the Genetic Base of Grain Legumes. Springer, India pp\u0026nbsp;95\u0026ndash;114\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeaver JS, Zapata M, Alameda M, Porch TG, Rosas JC (2012) Registration of PR0401-259 and PR0650-31 dry bean germplasm lines. J Plant Registrations 6(1):81\u0026ndash;84\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBergl RA, Oates JF, Fotso R (2007) Distribution and protected area coverage of endemic taxa in West Africa\u0026rsquo;s Biafran forests and highlands. Biol Conserv 134:195\u0026ndash;208\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrooks TM, Mittermeier RA, Da-Fonseca GAB, Geriach J, Hoffmann M, Lamoreux JF, Mittermeier CG, Pilgrim JD, Rodrigues ASL (2006) Global biodiversity conservation priorities. Science 313:58\u0026ndash;61. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1126/science.1127609\u003c/span\u003e\u003cspan address=\"10.1126/science.1127609\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrown AHD, Marshall DR (1995) A basic sampling strategy: theory and practice. In: Guarino L, Ramantha RV, Reid R (eds) Collecting Plant Genetic Diversity: Technical Guidelines. CABI Publishing, Wallingford, pp 75\u0026ndash;92\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurgarella C, Cubry P, Kane NA, Varshney RK, Mariac C, Liu X, Shi C, Thudi M, Couderc M, Xu X, Chitikineni A, Scarcelli N, Barnaud A, Rhon\u0026eacute; B, Dupuy C, Fran\u0026ccedil;ois O, Berthouly-Salazar C, Vigouroux Y (2018) A western Sahara centre of domestication inferred from pearl millet genomes. Nat Ecol Evol 2:1377\u0026ndash;1380\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarr J, Adeleke A, Angu K, Belle E, Burgess N, Carrizo S, Choimes A, Coulthard N, Darwall W, Foden W (2015) Ecosystem Profile Guinean Forests of West Africa Biodiversity Hotspot; Critical Ecosystem Partnership Fund: Arlington, VA, USA\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCasta\u0026ntilde;eda-\u0026Aacute;lvarez NP, Khoury CK, Achicanoy HA, Bernau V, Dempewolf H, Eastwood RJ (2016) Global conservation priorities for crop wild relatives. Nat Plants 2(4):16022\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCBD (1992) Convention on biological diversity. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cbd.int/doc/legal/cbd-en.pdf\u003c/span\u003e\u003cspan address=\"http://www.cbd.int/doc/legal/cbd-en.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed May 2022\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCGIAR (2022) Genebank - Africa Rice Center (AfricaRice), Genetic Resources Unit \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genebanks.org/genebanks/africarice/\u003c/span\u003e\u003cspan address=\"https://www.genebanks.org/genebanks/africarice/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e Accessed 28 June 2022\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eConservation International (2007) Guinean Forests of West Africa. In Biodiversity Hotspots. Accessed June 21, 2022\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eContreras-Toledo AR, Cort\u0026eacute;s-Cruz M, Costich DE, Rico-Arce ML, Magos Brehm J, Maxted N (2019) Diversity and conservation priorities of crop wild relatives in Mexico. Plant Genetic Resources 1\u0026ndash;11. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1017/S1479262118000540\u003c/span\u003e\u003cspan address=\"10.1017/S1479262118000540\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCouch C, Haba PM(2021) TIPA Assessment: Diecke Classified Forest, Yomou Prefecture \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.herbierguinee.org/uploads/2/6/3/0/26303479/2._tipas_report_diecke_en_new_final.pdf\u003c/span\u003e\u003cspan address=\"http://www.herbierguinee.org/uploads/2/6/3/0/26303479/2._tipas_report_diecke_en_new_final.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCrop Trust (2022) PGRFA Hub - Crops, Countries and Genebanks, Nigeria \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.croptrust.org/pgrfa-hub/crops-countries-and-genebanks/countries/nigeria/\u003c/span\u003e\u003cspan address=\"https://www.croptrust.org/pgrfa-hub/crops-countries-and-genebanks/countries/nigeria/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e Accessed 28 June 2022\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDauby G, Zaiss R, Blach OA, Catarino L, Damen T, Deblauwe V, Dessin S, Dransfield J, Droissart V, Duarte MC, Engledow H, Fadeur G, Figueira R, Gereau RE, Hardy OJ, Harris DJ, de Heij J, Janssens SB, Klomberg Y, Ley AC, Mackinder BA, Meerts P, van de Poel JL, Sonk\u0026eacute; B, Sosef MSM, St\u0026eacute;vart T, Stoffelen P, Svenning JC, Sepulchre P, van der Burgt XM, Wieringa JJ, Couvreur TLP (2016) RAINBIO: a mega-database of tropical African vascular plants distributions. PhytoKeys 74:1\u0026ndash;18\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavid BL, Sharon MG The Influence of Climate Change on Global Crop Productivity.American Society of Plant Biologists, Physiology P(2012) \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1104/pp.112.208298\u003c/span\u003e\u003cspan address=\"10.1104/pp.112.208298\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDida MM, Devos KM (2006) Finger millet. Cereals and Millets. Springer, Berlin Heidelberg\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDulloo ME, Labokas J, Iriondo JM, Maxted N, Lane A, Laguna E, Jarvis A, Kell S (2008) Chap. 2. Genetic reserve location and design. In: Iriondo JM, Maxted N, Dulloo ME (eds) Conserving Plant Genetic Diversity in Protected Areas. CABI, Wallingford, UK, pp 23\u0026ndash;64\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eESRI (2011) ArcGIS Desktop release version 10.7. Environmental Systems Research Institute. Redlands. CA\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eESRI (2015) ArcMap for desktop release version 10.4.1.Environmental Systems Research Institute, Redlands, Califonia\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFAO-BIOVERSITY (2015) FAO/Bioversity multi-crop Passport descriptors V.2. URL: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bioversityinternational.org/e-library/publications/detail/faobioversity-multi-crop-passport-descriptors-v21-mcpd-v21/\u003c/span\u003e\u003cspan address=\"https://bioversityinternational.org/e-library/publications/detail/faobioversity-multi-crop-passport-descriptors-v21-mcpd-v21/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e Accessed 27 July 2022\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFAO (2010a) Second report on the State of the World\u0026rsquo;s Plant Genetic Resources for Food and Agriculture. Food and Agriculture Organization of the United Nations, Rome, Italy. Available online: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.fao.org/agriculture/seed/sow2/en/\u003c/span\u003e\u003cspan address=\"http://www.fao.org/agriculture/seed/sow2/en/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e Accessed 25 July 2021\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFAO (2014) Genebank standards for plant genetic resources for food and agriculture. FAO. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.fao.org/3/a-i3704e.pdf\u003c/span\u003e\u003cspan address=\"http://www.fao.org/3/a-i3704e.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFetch T Jr, Johnston PA, Pickering R (2009) Chromosomal location and inheritance of stem rust resistance transferred from Hordeum bulbosum into cultivated barley (\u003cem\u003eH. vulgare\u003c/em\u003e). Phytopathology 99(4):339\u0026ndash;343\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFielder H, Brotherton P, Hosking J, Hopkins JJ, Ford-Lloyd B, Maxted N(2015) Enhancing the conservation of crop wild relatives in England. PLoS ONE, 10 (6)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFord-Lloyd BV, Maxted N (1993) Preserving diversity. Nature 361:579. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/361579a0\u003c/span\u003e\u003cspan address=\"10.1038/361579a0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFourcade Y, Engler JO, R\u0026ouml;dder D, Secondi J (2014) Mapping species distributions with MAXENT using a geographically biased sample of presence data: a performance assessment of methods for correcting sampling bias. PLoS ONE 9:e97122\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFu YB (2017) The Vulnerability of Plant Genetic Resources Conserved \u003cem\u003eEx Situ\u003c/em\u003e. Crop Sci. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2135/cropsci2017.01.0014\u003c/span\u003e\u003cspan address=\"10.2135/cropsci2017.01.0014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarcia RM, Parra-Quijano M, Iriondo JM (2017) Identification of ecogeographical gaps in the Spanish Aegilops collections with potential tolerance to drought and salinity. Peer J 5:e3494\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGBIF (2020) Global biodiversity information facility, GBIF occurrence data download \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gbif.org/occurrence/search\u003c/span\u003e\u003cspan address=\"https://www.gbif.org/occurrence/search\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e Accessed 24 October 2020\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGenesys(2020) Global Portal on Plant Genetic Resources, Genesys. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genesys-pgr.org/welcome\u003c/span\u003e\u003cspan address=\"https://www.genesys-pgr.org/welcome\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 20 November 2020\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuerrant EO, Fiedler PL, Havens K, Maunder M (2004) Revised genetic sampling guidelines for conservation collections of rare and endangered plants. In: Guerrant EO, Havens K, Maunder M (eds) Ex Situ Plant Conservation: Supporting Species Survival in the Wild. Island Press, Washington, USA, pp 419\u0026ndash;441\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHarlan JR (1992) Crops and Man, 2 edn. American Society of Agronomy\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHijmans R, Guarino L, Mathur P(2012) DIVA-GIS.7.5.0. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.diva-gis.org/documentation\u003c/span\u003e\u003cspan address=\"https://www.diva-gis.org/documentation\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 26 July 2021\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHijmans RJ, Spooner DM (2001) Geographic distribution of wild potato species. Am J Bot 88:2101\u0026ndash;2112\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHounsou-Dindin G, Idohou R, Akakpo AD, Adome N, Adomou AC, Assogbadjo AE, Kaka\u0026iuml; RG (2022) Assessment of wild oil plants diversity and prioritization for valorization in Benin (West Africa): A multivariate approach. Trees Forests and People 7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.tfp.2022.100210\u003c/span\u003e\u003cspan address=\"10.1016/j.tfp.2022.100210\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuchinson J, Dalziel JM(1958) Flora of West Tropical Africa, Vol.\u0026nbsp;1, Part II.. Crown Agents for Oversea Governments and administrations. London. pp\u0026nbsp;335\u0026ndash;587\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eICRISAT (2022) International Crops Research Institute for the Semi-Arid Tropics. Tag, Niger \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.icrisat.org/tag/niger/\u003c/span\u003e\u003cspan address=\"https://www.icrisat.org/tag/niger/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIdohou R, Assogbadjo A, Fandohan A, Gouwakinnou G, Glele -Kaka\u0026iuml; RL, Sinsin B, Maxted N (2013) National inventory and prioritization of crop wild relatives: Case study for Benin. Genet Resour Crop Evol 60:1337\u0026ndash;1352. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10722-012-9923-6\u003c/span\u003e\u003cspan address=\"10.1007/s10722-012-9923-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIITA (2022) Our genetic resources. International Institute of Tropical Agriculture \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.iita.org/research/genetic-resources/\u003c/span\u003e\u003cspan address=\"https://www.iita.org/research/genetic-resources/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e Accessed 28 June 2022\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIriondo JM, Magos Brehm J, Dulloo ME, Maxted N (eds) (2021) Crop Wild Relative Population Management Guidelines. Farmer\u0026rsquo;s Pride: Networking, partnerships, and tools to enhance \u003cem\u003ein situ\u003c/em\u003e conservation of European plant genetic resources. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.farmerspride.eu/\u003c/span\u003e\u003cspan address=\"http://www.farmerspride.eu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed July 10 2022\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIriondo JM, Maxted N, Kell SP, Ford-Lloyd BV, Lara-Romano C, Labokas J, Magos Brehm J(2012) Quality standards for genetic reserve conservation of crop wild relatives. In: Maxted N, Dulloo ME, Ford-Lloyd BV, Frese L, Iriondo JM and Pinheiro de Carvalho MAA (eds) Agrobiodiversity Conservation: Securing the Diversity of Crop Wild Relatives and Landraces. CABI Publishing, Wallingford, pp\u0026nbsp;72\u0026ndash;77\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJena KK (2010) The species of the genus Oryza and transfer of useful genes from wild species into cultivated rice. O sativa Breeding Science 60(5):518\u0026ndash;523\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJohnston PA, Niks RE, Meiyalaghan V, Blanchet E, Pickering R (2013) Rph22: mapping of a novel leaf rust resistance gene introgressed from the non-host \u003cem\u003eHordeum bulbosum\u003c/em\u003e L. into cultivated barley (\u003cem\u003eHordeum vulgare\u003c/em\u003e L.). 126:1613\u0026ndash;1625Theoretical and applied genetics6\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJohnston PA, Timmerman-Vaughan GM, Farnden KJ, Pickering R (2009) Marker development and characterisation of \u003cem\u003eHordeum bulbosum\u003c/em\u003e introgression lines: a resource for barley improvement. Theor Appl Genet 118(8):1429\u0026ndash;1437\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKati V, Devillers P, Dufr\u0026ecirc;ne M, Legakis A, Vokou D, Lebrun P (2004) Hotspots, complementarity or representativeness? Designing optimal small-scale reserves for biodiversity conservation. Biol Conserv 120(4):471\u0026ndash;480\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKawuki RS, Kaweesi T, Esuma W, Pariyo A, Kayondo IS, Ozimati A, Kyaligonza V, Abaca A, Orone J, Tumuhimbise R, Nuwamanya E, Abidrabo P, Amuge T, Ogwok E, Okao G, Wagaba H, Adiga G, Alicai T, Omongo C, Bua A, Ferguson M, Kanju E, Banguma Y (2016) Eleven years of breeding efforts to combat cassava brown streak disease. Breed Sci 66(4):560\u0026ndash;571\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKorona R (1996) Adaptation to structurally different environments. Proc R Soc Lond Ser B Biol Sci 263:1665\u0026ndash;1669. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1098/rspb.1996.0243\u003c/span\u003e\u003cspan address=\"10.1098/rspb.1996.0243\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLanghammer PF, Bakarr MI, Bennun LA, Brooks TM, Clay RP, Darwall W, De Silva N, Edgar GJ, Eken G, Fishpool LDC, Fonseca GAB, Foster MN, Knox DH, Matiku P, Radford EA, Rodrigues ASL, Salaman P, Sechrest W, Tordoff AW (2007) Identification and gap analysis of key biodiversity areas: targets for comprehensive protected area systems. Best Practice Protected Area Guidelines Series, vol 15. IUCN, Gland, Switzerland\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu C, Berry PM, Dawson TP, Pearson RG (2005) Selecting thresholds of occurrence in the prediction of species distributions. Ecography 28:385\u0026ndash;393\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLopez- Montes A, Bhattacharjee R, Tessema G(2012) Yam breeding at IITA: achievements, challenges and prospects. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://r4dreview.iita.org/index.php/tag/yam-breeding/\u003c/span\u003e\u003cspan address=\"http://r4dreview.iita.org/index.php/tag/yam-breeding/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuiselli L, Dendi D, Eniang EA, Fakae BB, Akani GC, Fa JE (2019) State of knowledge of research in the Guinean forests of West Africa region. Acta Oecol 94:3\u0026ndash;11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.actao.2017.08.006\u003c/span\u003e\u003cspan address=\"10.1016/j.actao.2017.08.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMagos Brehm J, Gaisberger H, Kell S, Parra-Quijano M, Thormann I, Dulloo ME, Maxted N (2022) Planning complementary conservation of crop wild relative diversity in southern Africa. Divers Distrib. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/ddi.13512\u003c/span\u003e\u003cspan address=\"10.1111/ddi.13512\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMagos Brehm J, Kell S, Thormann I, Gaisberger H, Dulloo ME, Maxted N(2017a) Interactive Toolkit for Crop Wild Relative Conservation Planning University of Birmingham, Birmingham, UK and Bioversity International, Rome, Italy \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cropwildrelatives.org/conservation-toolkit\u003c/span\u003e\u003cspan address=\"http://www.cropwildrelatives.org/conservation-toolkit\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMagos Brehm J, Kell SP, Thormann I, Gaisberger H, Dulloo E, Maxted N(2017b) Occurrence data collation template v.1. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7910/DVN/5B9IV5\u003c/span\u003e\u003cspan address=\"10.7910/DVN/5B9IV5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, Harvard Dataverse, V1.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMallon CA, Poly F, Roux XL, Marring I, van Elsas JD, Salles JF (2015) Resource pulses can alleviate the biodiversity\u0026ndash;invasion relationship in soil microbial communities. Ecology. Ecological Society of America. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1890/14-1001.1\u003c/span\u003e\u003cspan address=\"10.1890/14-1001.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaxted N(2013) In Situ, Ex Situ Conservation. Encyclopedia of Biodiversity (Second Edition), Academic Press, pp\u0026nbsp;313\u0026ndash;323 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/B978-0-12-384719-5.00049-6\u003c/span\u003e\u003cspan address=\"10.1016/B978-0-12-384719-5.00049-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaxted N, Dulloo E, Ford-Lloyd BV, Iriondo JM, Jarvis A (2008a) Gap analysis: a tool for complementary genetic conservation assessment. Divers Distrib 14:1018\u0026ndash;1030\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaxted N, Dulloo ME, Ford-Lloyd B (2016) Enhancing crop genepool use: capturing wild relative and landrace diversity for crop improvement. C. A. B. International\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaxted N, Ford- Lloyd BV, Jury S, Kell SP, Scholten MA (2006) Towards a definition of crop wild relative. Biodivers Conserv 15:2673\u0026ndash;2685\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaxted N, Ford-Lloyd B, Kell SP, Iriondo JM, Dulloo ME, Turok J(2008) Crop wild relative conservation and use, International Conference on Crop Wild Relative, Conservation and Use, C. A. B. International\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaxted N, Ford-Lloyd BV, Hawkes JG (1997a) Plant Genetic Conservation: The In Situ Approach. Chapman \u0026amp; Hall, London\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaxted N, Ford-Lloyd BV, Hawkes JG (1997c) Complementary conservation strategies. In: Maxted N, Ford-Lloyd BV, Hawkes JG (eds) Plant Genetic Conservation: The In-situ Approach. Chapman \u0026amp; Hall, London, pp 20\u0026ndash;55\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaxted N, Hawkes JG, Guarino LMS (1997b) Towards the selection of data for plant genetic conservation. Genet Resour Crop Evol 44:337\u0026ndash;348\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaxted N, Iriondo JM, De Hond L, Dulloo ME, Lef\u0026egrave;vre F, Asdal A, Kell SP, Guarino L (2008b) Genetic Reserve Management. In: Iriondo JM, Dulloo ME, Maxted N (eds) Conserving Plant Genetic Diversity in Protected Areas. CAB International, Wallingford, UK, pp 65\u0026ndash;87\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaxted N, Kell SP (2009) Establishment of a Global Network for the In situ Conservation of Crop Wild Relatives: Status and needs. Commission on Genetic Resources for Food and Agriculture, Food and Agriculture Organization of the United Nations, Rome, Italy\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaxted N, Magos BJ, Kell S(2013) Resource book for preparation of national conservation plans for crop wild relatives and landraces.. In: Magos Brehm, J., Kell, S., Thormann, I., Gaisberger, H., Dulloo, M.E. \u0026amp; Maxted, N., (2017). Interactive Toolkit for Crop Wild Relative Conservation Planning version 1.0. University of Birmingham, Birmingham, UK and Bioversity International, Rome, Italy\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaxted N, Vincent H (2021) Review of congruence between global crop wild relative hotspots and centres of crop origin/diversity. Genet Resour Crop Evol 68:1283\u0026ndash;1297. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10722-021-01114-7\u003c/span\u003e\u003cspan address=\"10.1007/s10722-021-01114-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMkwaila W, Terpstra KA, Ender M, Kelly JD (2011) Identification of QTL for agronomic traits and resistance to white mold in wild and landrace germplasm of common bean. Plant Breeding 130:665\u0026ndash;672\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMponya NK, Chanyenga T, Brehm JM, Maxted N (2020) \u003cem\u003eIn situ\u003c/em\u003e and \u003cem\u003eex situ\u003c/em\u003e conservation gap analyses of crop wild relatives from Malawi. Genet Resour Crop Evol 68:759\u0026ndash;771\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNduche M, Brehm JM, Abberton M, Omosun G, Maxted N(2021) West African Crop Wild Relative Checklist, Prioritization, and Inventory.Genetic Resources, 2 (4):55\u0026ndash;65\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNg\u0026rsquo;uni D, Munkombwe G, Mwila G, Gaisberger H(2019) Spatial analyses of occurrence data of crop wild relatives (CWR) taxa as tools for selection of sites for conservation of priority CWR in Zambia Plant Genet Resour Charact Util,1\u0026ndash;12\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNwanze KF, Rao KE, Soman P(1990) Understanding and manipulating resistance mechanisms in sorghum for control of the shoot-fly. In: Proceedings of International Symposium on molecular and genetic approaches to plant stress. New Delhi 14:17th Feb 1990\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOates JF, Bergl RA, Linder JM(2004) Africa\u0026rsquo;s Gulf of Guinea Forests: Biodiversity Patterns and Conservation Priorities.Advances in Applied Biodiversity Science, number 6.Conservation International, Washington D.C\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOsborn TC, Hartweck LM, Harmsen RH, Vogelzang RD, Kmiecik KA, Bliss FA (2003) Registration of \u003cem\u003ePhaseolus vulgaris\u003c/em\u003e genetic stocks with altered seed protein compositions.(Registrations Of Genetic Stocks). Crop Sci 43(4):1570\u0026ndash;1572\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePark RF, Golegaonkar PG, Derevnina L, Sandhu KS, Karaoglu H, Elmansour HM, Singh D (2015a) Leaf Rust of Cultivated Barley: Pathology and Control. Annu Rev Phytopathol 53:565\u0026ndash;589\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePark RF, Golegaonkar PG, Derevnina L, Sandhu KS, Karaoglu H, Elmansour HM, Singh D (2015b) Leaf Rust of Cultivated Barley: Pathology and Control. Annu Rev Phytopathol 53:565\u0026ndash;589\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParra-Quijano M, Iriondo JM, Torres E (2011) Ecogeographical land characterization maps as a tool for assessing plant adaptation and their implications in agrobiodiversity studies. Genet Resour Crop Evol. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10722-011-9676-7\u003c/span\u003e\u003cspan address=\"10.1007/s10722-011-9676-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParra-Quijano M, Iriondo JM, Torres E (2012a) Ecogeographical land characterization maps as a tool for assessing plant adaptation and their implications in agrobiodiversity studies. Genet Resour Crop Evol 59:205\u0026ndash;217\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParra-Quijano M, Iriondo JM, Torres E (2012b) Improving representativeness of genebank collections through species distribution models, gap analysis, and ecogeographical maps. Biodivers Conserv 21:79\u0026ndash;96\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParra-Quijano M, Torres E, Iriondo JM, L\u0026oacute;pez F, Molina PA(2016) CAPFITOGEN tools. User manual version 2.0. International Treaty on Plant Genetic Resources for Food and Agriculture. FAO. Rome, pp\u0026nbsp;251 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.capfitogen.net/en/\u003c/span\u003e\u003cspan address=\"http://www.capfitogen.net/en/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (Accessed 29 July 2022)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParra - Quijano M, Iriondo JM, Torres ME, L\u0026oacute;pez F, Maxted N, Kell SP(2021) CAPFITOGEN3: A toolbox for the conservation and promotion of the use of agricultural biodiversity. Bogota, Colombia, pp\u0026nbsp;45\u0026ndash;194 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.capfitogen.net/en/\u003c/span\u003e\u003cspan address=\"http://www.capfitogen.net/en/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 23 December 2021\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePhillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modelling of species geographic distributions. Ecol Model 190(3\u0026ndash;4):23\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePickering R, Johnston PA (2005) Recent progress in barley improvement using wild species of Hordeum. Cytogenet Genome Res 109:344\u0026ndash;349\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePickering R, Ruge-Wehling B, Johnston PA, Schweizer G, Ackermann P, Wehling P (2006) The transfer of a gene conferring resistance to scald (\u003cem\u003eRhynchosporium secalis\u003c/em\u003e) from Hordeum bulbosum into \u003cem\u003eH. vulgare\u003c/em\u003e chromosome 4HS. 125:576\u0026ndash;579Plant breeding6\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePillen K, Zacharias A, Lon J(2004) Comparative AB-QTL analysis in barley using a single exotic donor of \u003cem\u003eHordeum vulgare\u003c/em\u003e ssp. \u003cem\u003espontaneum\u003c/em\u003e. Theoretical and applied genetics, 108 (8):1591\u0026ndash;1601\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQGIS-Development Team (2021) QGIS 3.16. 8 Geographic Information System Open Source Geospatial Foundation Project. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://qgis.osgeo.org\u003c/span\u003e\u003cspan address=\"http://qgis.osgeo.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRam\u0026iacute;rez-Villegas J, Khoury K, Jarvis A, Debouck DG, Guarino L (2010) A gap analysis methodology for collecting crop genepools: a case study with Phaseolus beans. PLoS ONE 5:e13497\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRodrigues ASL, Andelman SJ, Bakarr MI, Boitani L, Brooks TM, Cowling RM, Fishpool LDC, Fonseca GAB, Gaston KJ, Hoffmann M, Long JS, Marquet PA, Pilgrim JD, Pressey RL, Schipper J, Sechrest W, Stuart SN, Underhill LG, Waller RW, Watts MEJ, Yan X (2004) Effectiveness of the global protected area network in representing species diversity. Nature 428:640\u0026ndash;643\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRubio-Teso ML, Iriondo JM, Parra-Quijano M, Torres E(2013) National strategy for the conservation of crop wild relatives of Spain. PGR Secure. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.pgrsecure.bham.ac.uk/sites/default/files/documents/public/National_CWR_Conservation_Strategy_Spain.pdf\u003c/span\u003e\u003cspan address=\"http://www.pgrsecure.bham.ac.uk/sites/default/files/documents/public/National_CWR_Conservation_Strategy_Spain.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e Accessed 18 Jun 2022\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRuge B, Linz A, Pickering R, Proeseler G, Greif P, Wehling P (2003) Mapping of Rym14 Hb, a gene introgressed from \u003cem\u003eHordeum bulbosum\u003c/em\u003e and conferring resistance to BaMMV and BaYMV in barley. Theor Appl Genet 107(6):965\u0026ndash;971\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaini H, Kashihara Y, Lopez- Montes AJ, Robert A (2016) Interspecific crossing between yam species (\u003cem\u003eDioscorea rotundata\u003c/em\u003e and \u003cem\u003eDioscorea bulbifera\u003c/em\u003e) through in vitro ovule culture. Am J Plant Sci 7(8):1268\u0026ndash;1274\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eScarcelli N, Cubry P, Akakpo R, Thuillet A, Obidiegwu J, Baco MN, Otoo E, Sonk\u0026eacute; B, Dans A, Djedatin G, Mariac C, Couderc M, Causse S, Alix K, Cha\u0026iuml;r H, Fran\u0026ccedil;ois O, Vigouroux Y(2019) Yam genomics supports West Africa as a major cradle of crop domestication.Science Advances, 5 (5)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eScheldeman X, van Zonneveld M(2010) Training Manual on Spatial Analysis of Plant Diversity and Distribution. Rome, Italy: Bioversity International. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.bioversityinternational.org/fileadmin/user_upload/online_library/publications/pdfs/1431.pdf\u003c/span\u003e\u003cspan address=\"https://www.bioversityinternational.org/fileadmin/user_upload/online_library/publications/pdfs/1431.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e Accessed 18 June 2022\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSood S, Khulbe RK, Gupta AK, Grawal PA, Upadhyaya HD, Bhatt JC (2015) Barnyard millet \u0026ndash; a potential food and feed crop of future. Plant Breeding 134:135\u0026ndash;147\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUNEP-WCMC (2019) The World Database on Protected Areas (WDPA), Cambridge, UK. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.protectedplanet.net/\u003c/span\u003e\u003cspan address=\"https://www.protectedplanet.net/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 9 Jan 2022\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUNESCO (2019) Mount Nimba Strict Nature Reserve. World Heritage Convention https://\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewhc.unesco.org/en/list/155/\u003c/span\u003e\u003cspan address=\"http://whc.unesco.org/en/list/155/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e Accessed 24 June 2022\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUSGS (2017) West Africa: Land Use and Land Cover Dynamics, Agricultural Expansion Across West Africa. US Geographical Survey \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://eros.usgs.gov/westafrica/agriculture-expansion\u003c/span\u003e\u003cspan address=\"https://eros.usgs.gov/westafrica/agriculture-expansion\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVincent H, Amri A, Castaneda-Alvarez NP, Dempewolf H, Dulloo E, Guarino L, Hole D, Mba C, Toledo A, Maxted N (2019) Modeling of crop wild relative species identifies areas globally for in situ conservation. Commun Biol 2:136\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVincent H, Hole D, Maxted N (2022) Congruence between global crop wild relative hotspots and biodiversity hotspots. Biol Conserv 265. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.biocon.2021.109432\u003c/span\u003e\u003cspan address=\"10.1016/j.biocon.2021.109432\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWendler N, Mascher M, Himmelbach A, Johnston P, Pickering R, Stein N(2015) Bulbosum to Go: A Toolbox to Utilize \u003cem\u003eHordeum vulgare/bulbosum\u003c/em\u003e Introgressions for Breeding and Beyond. Molecular plant, 8 (10):1507\u0026ndash;1519\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Coffee Research (2021) Cote d\u0026rsquo;Ivoire genebank digitizes coffee collection \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://worldcoffeeresearch.org/news/2021/\u003c/span\u003e\u003cspan address=\"https://worldcoffeeresearch.org/news/2021/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e Accessed 28 June 2022\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWright EM, Kelly JD (2011) Mapping QTL for seed yield and canning quality following processing of black bean (\u003cem\u003ePhaseolus vulgaris\u003c/em\u003e L.). Euphytica 179(3):471\u0026ndash;484\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZair W, Maxted N, Magos Brehm J, Amri A (2021) Ex-situ and in situ conservation gap analysis of crop wild relative diversity in the Fertile Crescent of the Middle East Genetic Resources and Crop Evolution. 68:693\u0026ndash;709\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZegeye H (2017) In situ and ex situ conservation: complementaryapproaches for maintaining biodiversity. Int J Res Environ Stud 4:1\u0026ndash;12\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"genetic-resources-and-crop-evolution","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gres","sideBox":"Learn more about [Genetic Resources and Crop Evolution](https://www.springer.com/journal/10722)","snPcode":"10722","submissionUrl":"https://submission.nature.com/new-submission/10722/3","title":"Genetic Resources and Crop Evolution","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Crop wild relatives, in situ, ex situ, genetic conservation, CAPFITOGEN, diversity analysis, and species distribution modelling.","lastPublishedDoi":"10.21203/rs.3.rs-1953821/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1953821/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCrop wild relatives are genetically related wild taxa of crops with unique resources for crop improvement through the transfer of novel and profitable genes. The \u003cem\u003ein situ\u003c/em\u003e and \u003cem\u003eex situ\u003c/em\u003e conservation gap analyses for priority crop wild relatives from West Africa were evaluated using species distribution modelling, ecogeographic diversity, and complementary analyses. A total of 20, 125 unique occurrence records were used for the conservation gap analysis, however, 26 taxa had no occurrence data. 64 taxa (62.7%) occurred in protected areas, 56 taxa (55%) were conserved \u003cem\u003eex situ\u003c/em\u003e, while 76.7% (43) of the accessions are underrepresented with less than 50 accessions conserved \u003cem\u003eex situ\u003c/em\u003e. Areas of highest potential diversity were found in the Woroba and Montangnes districts in Cote d\u0026rsquo;Ivoire, Nzerekore, Faranah, Kindia, and Boke regions of Guinea, South-South, and North-East zones of Nigeria, and Kono and Koinadugu districts in Sierra Leone. Hotspots were found in Atlantique, Littoral, Mono, Kouffo, Atakora, Donga, and Colline provinces of Benin, Accra, and Volta regions of Ghana, North \u0026ndash; Central Nigeria, and Lacs district of Cote d\u0026rsquo;Ivoire and Nzerekore region of Guinea. 29 reserve sites for active \u003cem\u003ein situ\u003c/em\u003e conservation were identified, 11 occur in protected areas, while 18 are located outside protected areas. The establishment of the reserve sites will complement existing PAs and ensure long-term active \u003cem\u003ein situ\u003c/em\u003e and \u003cem\u003eex situ\u003c/em\u003e conservation and sustainable utilization of priority CWR to underpin food security and mitigate climate change in the region.\u003c/p\u003e","manuscriptTitle":"In Situ and Ex Situ Conservation Gap Analyses of West African Priority Crop Wild Relative","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-08-29 19:02:37","doi":"10.21203/rs.3.rs-1953821/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Minor revisions","date":"2022-10-09T11:25:06+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2022-09-07T05:08:40+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2022-08-27T13:56:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-08-16T05:14:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"Genetic Resources and Crop Evolution","date":"2022-08-11T14:23:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"genetic-resources-and-crop-evolution","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gres","sideBox":"Learn more about [Genetic Resources and Crop Evolution](https://www.springer.com/journal/10722)","snPcode":"10722","submissionUrl":"https://submission.nature.com/new-submission/10722/3","title":"Genetic Resources and Crop Evolution","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f37a4fa4-f3f5-4046-8bf9-08d31ddbfcdc","owner":[],"postedDate":"August 29th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2022-11-20T21:53:35+00:00","versionOfRecord":[],"versionCreatedAt":"2022-08-29 19:02:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1953821","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1953821","identity":"rs-1953821","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}