Enhancing in Situ Conservation of Crop Wild Relatives for Food and Agriculture in Lithuania

Enhancing in Situ Conservation of Crop Wild Relatives for Food and Agriculture in Lithuania | 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 Enhancing in Situ Conservation of Crop Wild Relatives for Food and Agriculture in Lithuania Juozas Labokas, Mantas Lisajevičius, Domas Uogintas, Birutė Karpavičienė This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4412054/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The crop and CWR checklist of Lithuania was created containing 2,630 taxa. The checklist comprises 1,384 native taxa including archaeophytes and 1,246 neophytes. In total, 699 taxa (26.6%) could be quite strictly defined as of food or forage use. A list of 144 CWR priority species with 135 native species and archaeophytes and 9 naturalized species was generated. In total, 53 genera of food and forage species belonging to 15 families are represented by the priority CWR. Two approaches for CWR genetic reserve selection have been employed in this study: (1) CWR-targeted evaluation of preselected sites, including Natura 2000 sites, national protected areas, and other effective area-based conservation measures (OECMs), such as ancient hillfort sites and ecological protection zones of water bodies; and (2) analysis of large georeferenced plant databases. Forty-five potential genetic reserve sites have been selected by the first approach covering 83 species or 57.6% of the national CWR priority list. With the second approach, the in situ CWR National Inventory database has been created by combining data from the Database of EU habitat mapping in Lithuania (BIGIS), Herbarium Database of the Nature Research Centre (BILAS), Lithuanian Vegetation Database (EU-LT-001), and Global Biodiversity Information Facility (GBIF). Hotspot analysis of CWR species richness and number of observations suggested that higher CWR diversity is more likely to be found in protected areas. However, Shannon diversity and Shannon equitability indices showed that the areas outside of the protected areas are also suitable for CWR genetic reserve establishment. Conservation planning Crop wild relative CWR hotspot Food and agriculture Genetic reserve Protected area Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction As stated in the Plant Genetic Resources Strategy for Europe (ECPGR, 2021 ), crop wild relative (CWR) genetic resources native to Europe are related to the many socio-economically important crops cultivated in the region and in other parts of the world (food, fodder, forage, beverage, food-additive, oil, biofuel, biomass, medicinal, ornamental) and contain a wide pool of evolving genetic diversity, not duplicated in the crop itself, and that is of potential value for crop improvement. Some wild species are harvested from the wild for direct use as food or feed and constitute a potential source for further domestication and creation of new crops. This statement is in congruence with the International Treaty on Plant Genetic Resources for Food and Agriculture (FAO, 2001 ), stating that contracting parties shall “promote in situ conservation of wild crop relatives and wild plants for food production, including in protected areas”. Although Lithuania is not among the hotspots of floristic richness, it has quite a few CWRs as well as wild harvested plants with some of which not only being harvested from the wild but also used for domestication and development of the new cultivars. The most recent examples of the latter are bog cranberry ( Vaccinium oxycoccos L.) and guelder rose ( Viburnum opulus L.) with five and three new cultivars, respectively, released at the Botanical Garden of Vytautas Magnus University, Kaunas, Lithuania, and distinguished by enriched contents of fruit bioactive substances and the amount of yield (Česonienė, 2022 ; Bilotaitė-Jokubauskienė, 2022 ). Wild garlic, or ramson ( Allium ursinum L.), is another example of the wild harvested plants being increasingly collected from the wild for food as well as cultivated in amateur gardens. These and other native food-plant species have been contributing to the healthy diets of local people through generations and thus have been naturally adapted by their organisms. It is particularly important to consider this nowadays when globalisation is conditioning a wide-spread consumption of imported food products and fast foods. Then, many wild forage species, predominantly from Poaceae and Fabaceae, are also used as CWRs and thus present valuable genetic resources to be conserved to ensure their sustainable use. It is generally admitted that in situ conservation of crop wild relatives is most feasible within the existing network of protected areas. However, it depends on the development of the protected area network including total area coverage and categories of protected areas established. In Lithuania, the protected areas of conservation protection priority (strict reserves, reserves, and heritage objects) and complex protected areas (public parks – national and regional parks, as well as biosphere monitoring territories – biosphere reserves and biosphere polygons) are the most important ones, so sometimes they are called specially protected areas. They cover 1,152,793 hectares or 17.65% of the country’s territory (VSTT, 2024). The areas of the European ecological network Natura 2000 (European Environment Agency 2023 ), including those established under the Habitats Directive, are largely integrated into the system of the nationally protected areas. The major questions to be answered are (1) whether the existing network of protected areas adequately covers the distribution of the priority CWRs in the country and (2) whether there is a need to establish any additional protected areas for CWR conservation. Thus, in the context of food safety concern combined with climate change, the aim of this paper is to enhance in situ conservation of priority CWR species for food and agriculture through the analysis of their distribution across the country and selection of the most appropriate wild populations (MAWPs) as potential sites for the establishment of genetic reserves with the focus on the existing protected areas. It is expected that this effort will facilitate systematic conservation planning of the target species in the country and contribute to the development of the European CWR genetic reserves network. Materials and methods National CWR checklist A floristic approach was used to prepare the national CWR checklist as described by Maxted et al. ( 2013 ). The checklist of the flora of Lithuania was compiled by using the following sources: Dictionary of Plant Names (Jankevičienė 1998 ), Vascular Plants of Lithuania (Gudžinskas 1999 ), Flora of the Baltic Countries (Laasimer et al. 1993 ; Kuusk et al. 1996 ; Kuusk et al. 2003 ), Orchids (Orchidaceae) of Lithuania (Gudžinskas, Ryla 2006 ), Red Data Book of Lithuania (Rašomavičius 2021 ), and the Euro + Med PlantBase (Euro + Med 2006+). The taxonomic harmonization was carried out by referring to the International Plant Name Index (IPNI 2023 ) and Plants of the World online (POWO 2024 ). Prioritization of CWR species Prioritization of CWR species was carried out with the focus on plant genetic resources for food and agriculture (in a wide sense) with the native species and archaeophytes given top priority. Some close relatives of food crops, such as Lactuca serriola L., Malus toringo (Siebold) de Vriese, Prunus cerasifera Ehrh., Armoracia rusticana G.Gaertn., B.Mey. & Scherb., Avena fatua L., and Raphanus raphanistrum L., were excluded from the priority species list due to their invasive or weedy behavior. Prickly lettuce ( L. serriola ) is a particularly rapidly spreading species and invading different natural plant communities, while cherry plum ( P. cerasifera ) poses threat by hybridization with the native species P. spinosa (Rašomavičius, pers. comm.; Pagad 2022 ; Gudžinskas et al. 2023 ). Wild oat ( A. fatua ) and wild radish ( R. raphanistrum ) are listed among the worst agricultural weeds. The other criteria used are as follows: Annex I. List of crops covered under the multilateral system of the International Treaty on Plant Genetic Resources for Food and Agriculture (ITPGRFA); List of global priority crop wild relative genera; Lithuanian national plant variety lists; Wild species relationships to crops; Socio-economic and cultural importance including use traditions of the species; Threat status of species. All CWR taxa falling within the genera listed in the Annex 1 of the International Treaty on Plant Genetic Resources for Food and Agriculture (FAO, 2001 ) as well as in the list of global priority crop wild relative genera (Vincent et al. 2013 ) were selected as priority ones. Lithuanian national plant variety lists (e. g., VATZUM 2024) were used as proxy indicators of crop importance for the respective consideration of their wild relatives. Similarly, the determination of socio-economic and cultural importance was based on well-established plant cultivation and wild harvesting traditions. CWR relatedness to crops according to the Gene Pool (Harlan and de Wet 1971 ) and Taxon Group concepts (Maxted et al. 2006 ) was determined by using the data available at the Germplasm Resources Information Network (USDA 2024), Nordic Priority CWR Dataset (Fitzgerald et al. 2018 ), Flora of the Baltic Countries (Laasimer et al. 1993 ; Kuusk et al. 1996 ; Kuusk et al. 2003 ), Wikispecies ( 2024 ), World Plants (Hassler 1994–2024), and scientific articles on separate genera or species. Threat status of the target species was cited from the Red Data Book of Lithuania (Rašomavičius 2021 ), which provided assessment results based on IUCN criteria (IUCN Species Survival Commission 2012 ). CWR checklist and inventory data template (Thormann et al. 2017 ) was used to facilitate structural data arrangement and management. Several data sources from other European countries, i.e., Nordic countries (Fitzgerald et al. 2018 ; 2019 ; Weibull & Phillips 2020 ), Czech Republic (Taylor et al. 2017 ), and the Netherlands (van Treuren et al. 2017 ) were used, as well as some crop breeders were consulted to find the best solutions for CWR prioritization. Selection of sites for genetic reserves and hotspot analysis Two approaches of site selection for CWR genetic reserves have been employed: (1) evaluation of preselected sites on state-owned land like ancient hillforts (which are state protected archaeological objects), sites of community importance (SCI, Natura 2000 network) and national protected areas including ecological protection zones of water bodies, and (2) application of several large georeferenced plant databases, both national (BIGIS and EU-LT-001) and international (the Global Biodiversity Information Facility – GBIF), for distribution and hotspot analysis of priority CWR species in 4×4 km grid cells. Shannon diversity index (Shannon 1948 ) was employed for CWR diversity evaluation in grid cells by using the following formula: H = −Σ [(n/N)×ln(n/N)], (1) where: Σ – a Greek symbol for sum, H – Shannon diversity index, n – number of observations of a given CWR species within a grid cell, N – number of observations of all CWR species within a grid cell, ln – natural logarithm. The same formula in a simplified version (Bobbitt 2021 ) could be presented as: H = –Σp i × ln(p i ), (2) where p i is the proportion of the entire community made up of species i . A derivative of the Shannon diversity index, the Shannon equitability index, is a way to measure the evenness of species proportions in a community. It is calculated as follows: E H = H / ln(S), (3) where: E H – Shannon equitability index, H – Shannon diversity index, S – total number of unique species. The cover-abundance of each CWR species was estimated by using Braun-Blanquet scale (Braun-Blanquet 1964 ). However, the presence/absence data were used only to avoid complication of the data analysis. QGIS software (QGIS.org 2023 ) was used for mapping of CWR distribution, hotspot analysis and location of potential genetic reserve sites. Data sources Several georeferenced plant databases were used. BIGIS (Institute of Botany GIS database) contains the countrywide species distribution data related to the inventory of the EU importance natural habitats. BILAS is the major Lithuanian herbarium database, maintained by the Institute of Botany of the Nature Research Centre. EU-LT-001, the vegetation database of Lithuania, is a part of European Vegetation Archive (Chytrý et al. 2016 ). The Global Biodiversity Information Facility (GBIF), which contains citizen science data, was also used. Results and discussion Checklist description The crop and CWR checklist of Lithuania contains a total of 2,630 taxa including species (and microspecies), subspecies, varieties, and hybrids (see Supplementary data, Table S1 ). The checklist is comprised of 1,384 native taxa (incl. archaeophytes) and of 1,246 neophytes, of which 905 are used in cultivation. Among them, 471taxa (17.9% of the checklist) are food crops or their wild relatives and 399 taxa (15.2%) are forage/fodder crops or their wild relatives. A total of 699 taxa (26.6%) could be quite strictly defined as of food or forage use. Additionally, 841 taxa (32%) are of medicinal use, as well as 1,396 taxa (53.1%) are of other use. Furthermore, 178 taxa are related to those included in the Annex I of the ITPGRFA (FAO, 2001 ). The checklist also includes 169 taxa that are under the legal national protection (Minister of the Environment of the Republic of Lithuania 2024 ). In addition, the checklist covers 16 taxa (incl. 13 native ones) from Annex II, one taxon from Annex IV, and 10 taxa (incl. eight natives) from Annex V of the Council Directive 92/43/EEC (1992) on the conservation of natural habitats and of wild fauna and flora. Moreover, the checklist contains 22 species that have been declared invasive in Lithuania and 34 species that are considered potentially invasive (Gudžinskas et al. 2023 ). In general, the above-mentioned numbers are consistent with those of the other European countries, particularly in Scandinavia. For example, of the 2,276 crop and CWR species recorded in Norway, 2,084 species (92%) are included in the agricultural and horticultural crop group (Kell et al. 2008 ). Prioritization of the checklist In total, 53 genera of food (including culinary herbs, aromatic plants, and berries) and forage/fodder species were selected belonging to 15 families. A list of 144 CWR priority species with 135 native species (including archaeophytes) and 9 naturalized ones (mostly those, escaped from cultivation), was generated which still should be considered as a working version. Seventy-four species (51.4%) are representatives of the genera listed in Annex 1 of the ITPGRFA and those of the 92 global priority CWRs (Vincent et al. 2013 ). The current CWR priority list is shorter by 20% if compared to the previous version of 180 species (Labokas et al. 2016 ) which included a portion of non-food CWR species, and is quite consistent with the Swedish priority list, containing 121 CWR taxa (Weibull & Phillips 2020 ). The summary of prioritized CWR inventory by botanical families is presented in Table 1 (see Supplementary data for the full CWR priority list, Table S2). Table 1 Summary of the prioritized Lithuanian CWR inventory by families Family Genera Species Species % Genera with numbers of species Poaceae 19 47 32.6 Agrostis (5), Alopecurus (4), Anthoxanthum (3), Arrhenatherum (1), Avenula (1), Briza (1), Bromus (1), Cynosurus (1), Dactylis (1), Deschampsia (2), Elymus (1), Festuca (8), Glyceria (4), Helictochloa (1), Leymus (1), Lolium (1), Phalaris (1), Phleum (2), Poa (8) Fabaceae 11 46 31.9 Anthyllis (1), Astragalus (3), Lathyrus (7), Lotus (2), Medicago (2), Melilotus (2), Onobrychis (2), Ononis (1), Securigera (1), Trifolium (14), Vicia (11) Rosaceae 5 16 11.1 Fragaria (3), Malus (2), Prunus (3), Pyrus (2), Rubus (6) Lamiaceae 3 6 4.2 Mentha (3), Origanum (1), Thymus (2) Brassicaceae 2 5 3.5 Barbarea (2), Rorippa (3) Amaryllidaceae 1 6 4.2 Allium (6) Ericaceae 1 5 3.5 Vaccinium (5) Apiaceae 4 4 2.8 Angelica (1), Carum (1), Daucus (1), Pastinaca (1) Grossulariaceae 1 3 2.1 Ribes (3) Asparagaceae 1 1 0.7 Asparagus (1) Asteraceae 1 1 0.7 Cichorium (1) Betulaceae 1 1 0.7 Corylus (1) Cannabaceae 1 1 0.7 Humulus (1) Papaveraceae 1 1 0.7 Papaver (1) Elaeagnaceae 1 1 0.7 Hippophae (1) Total: 15 53 144 100 As seen from Table 1 , nearly 2/3 (64.5%) of the prioritized species are members of two families, Poaceae (47 species) and Fabaceae (46 species). The third richest CWR family is Rosaceae with 16 species. These three families make up 75.7% of the total CWR priority list. Analysis of the priority CWRs by their use categories showed that 88 species (61.1%) could be attributed to the CWRs of forage/fodder crops and 58 species (40.3%) to those of the food crops with only two species, Daucus carota and Vicia lathyroides , overlapping both categories (Table 2 ). Table 2 Summary of the prioritized Lithuanian CWR inventory by use categories Use category Families with numbers of genera/species Number of species* Percent of priority CWR list* Forage/fodder Fabaceae 11/46 Poaceae 16/41 Apiaceae (1/1) 88 61.1 Food Amaryllidaceae (1/6) Apiaceae (4/4) Asparagaceae (1/1) Asteraceae (1/1) Betulaceae (1/1) Brassicaceae (2/5) Cannabaceae (1/1) Elaeagnaceae (1/1) Ericaceae (1/5) Fabaceae 1/1 Grossulariaceae (1/3) Lamiaceae (3/6) Papaveraceae (1/1) Poaceae (3/6) Rosaceae (5/16) 58 40.3 * Includes two overlapping species, Daucus carota L. and Vicia lathyroides L. Regarding the legal protection, 17 CWR priority species (11.8%) are protected by the Order of the Minister of Environment (2024). These are evaluated according to the IUCN categories and criteria at the national level (Table 3 ). Table 3 CWR priority species under the legal national protection with the regional IUCN assessments No. Species IUCN category and criteria* 1 Allium angulosum L. EN B1ab(ii,iii) + 2ab(ii,iii) 2 Allium scorodoprasum L. VU A4ac 3 Allium vineale L. EN B2ab(iii,iv,v) 4 Alopecurus arundinaceus Poir. VU D2 5 Astragalus danicus Retz. NT B2b(iii); B1b(iii) 6 Festuca altissima All. DD 7 Festuca psammophila (Čelak.) R. M. Fritsch EN B1ab(ii,iii,v) + 2ab(ii,iii,v) 8 Glyceria lithuanica (Gorski) Gorski VU B1ab(iii) + 2ab(iii) 9 Helictochloa pratensis (L.) Romero Zarco VU D2 10 Lathyrus laevigatus (Waldst. & Kit.) Gren. NT B2 11 Lathyrus pisiformis L. EN B1ab(iv) + 2ab(iv) 12 Poa remota Forselles NT B2 13 Prunus spinosa L. VU B1ab(ii,iii,v) + 2ab(ii,iii,v) 14 Trifolium lupinaster L. EN B2b(iii)c(iv) 15 Trifolium rubens L. EN B2ab(i,ii,iii,iv) 16 Vicia lathyroides L. EN B2b(iii)c(ii) 17 Vicia pisiformis L. NT B1 + 2 * Source: Rašomavičius 2021 . Based on tentative observations, most of the rest of CWR priority species fall into the IUCN category LC (Least Concern). However, a detailed assessment of at least some species, like Lathyrus palustris , Mentha longifolia , Onobrychis arenaria , and Vaccinium microcarpum , is needed to improve their conservation planning. Analysis of the priority CWR relatedness to crops revealed that 75 species (52.1%) represent the closest relationships with their respective crops: 62 species are within primary gene pool (GP1) and 13 species are within taxon group one (TG1) to their respective crops (see Supplementary data, Table S2). Twenty-four CWR species fall into the second closest group to crops (GP2, TG2, and TG3). And the most distant group (GP3 and TG4) comprises 45 CWR species. As lack of data on genetic relatedness is quite evident, the taxon group concept allows to compensate this data gap, at least, partly. However, the CWR of the most distant taxonomic group, TG4, should still be studied by molecular methods to better reveal their use possibilities in breeding. This does not apply when wild species are being introduced into cultivation themselves. Selection of sites for genetic reserves By using the method of preselected site evaluation, 45 potential CWR genetic reserve sites were identified. Out of these, 29 sites were established in 2022 through 2023. The rest of the sites were originally identified in 2011–2021 (see Labokas, Karpavičienė 2018 ) with the oldest ones inventoried repeatedly. These 45 sites contain 83 CWR priority species (57.6%) with the total number of 748 occurrence records. The suggested sizes of the sites vary from 0.22 to 23.40 ha with the total area of 177.61 ha (see Supplementary data, Table S3). A weak correlation has been established between site area and number of CWR species (R = 0.298, P-value = 0.046). Although it has been reported (Kahilainen et al. 2014 ) that locality area and connectivity between similar localities in conservation planning best conserves both species and intrapopulation genetic diversity, some other factors, such as different land use category, different land ownership, limited size of protected areas (e.g., protected hillfort sites usually are small), prevent from following this recommendation more closely. Within the 45 sites, the most frequent species inventoried were Dactylis glomerata (33 sites), Vicia cracca (32 sites), Corylus avellana (28 sites), Phleum pratense (26 sites), Prunus padus (26 sites), Rubus idaeus (24 sites), Thymus pulegioides (23 sites), Fragaria viridis (22 sites), Poa angustifolia (21 sites), Fragaria vesca (20 sites), and Trifolium medium (20 sites). The least represented populations were those of Allium angulosum, A. scorodoprasum, A. vineale, Asparagus officinalis, Hippophae rhamnoides, Mentha aquatica, Onobrychis viciifolia, Poa trivialis, Rubus nessensis, R. plicatus, Trifolium campestre, T. hybridum, Vaccinium myrtillus, V. oxycoccos, V. vitis-idaea, Vicia pisiformis , and V. tetrasperma , each occurring in one single site out of the 45 sites investigated. We have grouped the distribution of the 83 CWR species across the 45 sites into 5 frequency groups (Table 4 ). As seen from Table 4 , the required minimum of 5 populations, as proposed by Dulloo et al. ( 2008 ), has not yet been met for 36 CWR species, while the minimum of 10 populations, as suggested by Whitlock et al. ( 2016 ) for relatively widespread species, has not been met for 20 more species. Table 4 Distribution of 83 CWR priority species across 45 potential CWR genetic reserve sites Frequency group No. of CWR occurrence sites No. of CWR species Percent of CWR priority list 1 1–4 36 25.0 2 5–9 20 13.9 3 10–14 3 2.1 4 15–19 13 9.0 5 ≥ 20 11 7.6 Total species in 45 sites 83 57.6 Full priority list 144 100.0 Total records in 45 sites 748 The selected 45 potential CWR genetic reserve sites were mapped with QGIS showing that they represent all four climate regions and seven of the 10 subregions of the country (Fig. 1) and are in different national protected areas and/or Natura 2000 sites of Community importance (SCIs) (Supplementary data, Table S3). As seen from Fig. 1, only three climatic subregions (A1, A2, and B5) are not represented by the current approach, while three subregions (C7, D8 and D9) are the least represented ones. Advantages of the first approach, the evaluation of the preselected sites, include known concrete locations, usually in a protected area, state-owned land status, and relatively easily distinguishable site boundaries around of the easier manageable relatively small sites. This is in line with the concept of the so-called micro-reserves, initiated in Spain for rare species conservation (Laguna et al. 2004 ), which could also be applied in the selection of the most appropriate wild populations (MAWPs), a key object in in situ conservation, by treating the MAWPs as analogues of rare species. In such small reserves, the target species are relatively highly concentrated per site area, i.e. 16.6 species per 3.9 ha on average (see Supplementary data, Table S3), compared to 17.8 species per grid cell 4×4 km in protected areas (Table 6 below). It has been reported that small reserves allow to take advantage of conservation opportunities provided by cultural sites, sacred natural sites, and other faith-based sites in otherwise transformed landscapes (Dudley et al. 2024 ). And that is the case in the current study as 30 potential CWR reserve sites were identified in the ancient hillfort sites. These, along with the water protection zones may be attributed to the other effective area-based conservation measures (OECM) as defined by the Convention on Biological Diversity in Decision 14/8 (IUCN-WCPA 2019) as they deliver the effective in-situ conservation of biodiversity, including CWRs, regardless of their primary objectives. From the legal point of view, it is easier to establish small reserves outside of protected areas than large ones. However, the coverage of all target species, let alone their distinctive populations, by such preselected small sites may require a high number of small reserves. This leads to a more comprehensive approach, the application of large georeferenced plant databases. By applying the second approach, the in situ CWR National Inventory database has been created by combining target taxa occurrence data from four major datasets: 1) Database of EU habitat mapping in Lithuania (BIGIS); 2) Database of Herbarium of Nature Research Centre (BILAS); 3) Lithuanian Vegetation Database (EU-LT-001); and 4) Global Biodiversity Information Facility (GBIF). Most of the recent data are contained in BIGIS and GBIF (Table 5 ). The compiled database can run in both Microsoft Access and QGIS formats. Table 5 The numbers of occurrences of CWR priority species 1 in four databases by data oldness Data collected BIGIS BILAS EU-LT-001 GBIF 2 Total 3 Before 2010 0 7,673 19 21 7,713 After 2010 284,264 9 2,155 7,187 293,615 Total 284,264 7,682 2,174 7,208 301,328 1 The numbers refer to 140 out of 144 CWR priority species. No data on Barbarea stricta, Lathyrus pisiformis, Onobrychis arenaria, Vaccinium microcarpum is presented. A lack of distribution data of the four CWR priority species has been attributed to the species identification issues ( Barbarea stricta vs B. vulgaris and Vaccinium microcarpum vs V. oxycoccos ) and rarity of species ( Lathyrus pisiformis and Onobrychis arenaria ). 2 GBIF.org (12 July 2023) GBIF Occurrence Download https://doi.org/10.15468/dl.y43bqn 3 The most up-to-date total is 293,615 which includes multiple occurrences of the same species per grid cell 4×4 km. If counting only distinct species records per grid cell, the total number of occurrences is 68,686. Hotspot analysis of priority CWR occurrences performed with QGIS in 4×4 km grid cells showed that the highest numbers of the target species occurrences are in the north-western and south-eastern parts of the country (Fig. 2 ), where most of the potential genetic reserve sites (green dots) are identified as well (see also Fig. 1 for details). Further, to facilitate conservation actions, we have focused on CWR distribution in protected areas (PAs). These incorporate European Union PAs, i.e., Natura 2000 network, dedicated to the protection of habitats, and national PAs, i.e., national parks, regional parks, strict nature reserves and other nature reserves. It was established that 1882 grid cells at least partly overlap with the Natura 2000 sites and 1941 grid cells overlap with the national protected areas. As most of the national PAs overlap with Natura 2000 sites, we analysed CWR distribution in the grid cells inside both the EU and national PAs. The most species-rich grid cells (up to 60 species per cell) were identified in the PAs in Vilnius County, Southeast Lithuania, including Aukštadvaris and Neris Regional Parks (Fig. 3 ). This is, at least partly, due to diverse ecogeographic conditions, and these sites are valuable in situ reserves for CWR use in research and education as they are easily accessible by researchers and those who use iNaturalist or similar applications for sharing data of species observations. If compared with species richness, the mapping of species observations across the country presented a quite different mosaic of grid cells (Fig. 4 ). A similar pattern was observed when the protected areas were analysed separately revealing the observation-richest grid cells in Žemaitija National Park, Salantai Regional Park, Nemuno Delta Regional Park, and Pagramantis Regional Park (all in Western Lithuania) (Fig. 5 ). The mean number of species per grid cell in PAs was 17.8, while outside of PAs it was 13.8 (Table 6 ). The Kruskal-Wallis test for equal medians showed that there was a significant difference between the area category medians (H = 147.5, p < 0.01). Post-hoc pairwise comparisons using the Mann-Whitney method revealed that the medians differed significantly in all categories (Bonferroni corrected p < 0.001) with the highest number of species per cell in PAs. Regarding the number of observations per grid cell, significant differences were established between all three categories (Kruskal-Wallis test H = 202.8, p < 0.001; Post-hoc Mann-Whitney, Bonferroni corrected p < 0.001). These results suggest that higher CWR diversity is more likely to be found in PAs, where in situ conservation of CWR should be focused on. As the numbers of species and their occurrences taken separately provide an incomplete picture of CWR distribution, the Shannon diversity index (SDI), a combined indicator of species richness and abundance, was calculated for each grid cell. The results showed that the mean SDI in PAs was 2.53, while outside of PAs it was 2.41, suggesting statistically significant differences between the inside and outside of PAs (Kruskal-Wallis test for equal medians H = 17.32, p < 0.001; Table 6 ) (see also Supplementary data, Fig. S1 and Fig. S2 for maps of species richness and the number of observations, respectively, outside of PAs, and Fig. S3, Fig. S4, and Fig. S5 for SDI-based species mapping across the country, in its PAs, and outside PAs, respectively). The Shannon equitability index was also calculated to find out how similar the abundances of different CWR species are in a grid cell with the advantage that it is easier interpretable, as its value ranges from 0 to 1 where 1 indicates complete evenness (Bobbitt 2021 ). Differently from the above-mentioned results, this indicates that potential CWR in situ conservation sites can also be established outside of the PA network. Table 6 Total and mean (per grid cell 4×4 km) numbers of CWR species, numbers of CWR species observations, and mean values of Shannon diversity and Shannon equitability indices inside and outside of protected areas (± is followed by standard deviation; different letters indicate significant differences at p = 0.001) Area category No. of unique CWR species No. of CWR species observations Shannon diversity index (H) Shannon equit-ability index E H =H / ln(S) Total (S) Mean per cell Total Mean per cell Mean per cell Mean per cell Inside PAs 138 17.8 ± 10.6 a 190,106 82.6 ± 83.6 a 2.53 ± 0.67 a 0.513 Outside PAs 140 13.8 ± 9.5 c 111,222 50.9 ± 61.3 c 2.41 ± 0.74 b 0.488 Total country 140 16.0 ± 10.3 b 301,328 67.9 ± 75.7 b 2.48 ± 0.71 ab 0.502 The obtained Shannon diversity index values could be interpreted as indicating moderate (2.50–2.99) and low (2.00–2.49) diversity (Fernando, 1998 ). Similalrly, the Shannon equitability index show moderate abundances of CWR species both inside and outside of protected areas. Conclusion A comprehensive and annotated national CWR checklist has been created for Lithuania for the first time. It serves as a baseline information resource for CWR conservation planning and action. Prioritization of CWR taxa could be made based solely on their use in breeding of socio-economically important crops. In this case, even the invasive species can be prioritized (see, e.g., Fitzgerald et al., 2018 ). However, for the in situ conservation, some other criteria, like species nativeness and threat level, are preferred to justify CWR conservation in natural or seminatural habitats and comply with the local regulations. The latter concept complies with the actions to mitigate the impact of climate change on plant communities and whole ecosystems, and thus is highly appreciated. Most of the priority CWR (93.8%) are native species of Lithuanian flora, and 8.3% are threatened (IUCN regional evaluation categories EN and VU). A degree of CWR relatedness to crop is another major criterion for CWR prioritisation as it indicates whether the wild relative could be easily used in breeding or requires more sophisticated techniques. Although the gene pool approach remains the direct indicator of CWR relatedness to crops, the taxonomic group approach plays an important role when the gene pool concept cannot be applied. In the current study, 56.9% of priority CWR species are related to crops based on the gene pool concept and the rest (43.1%) stand for taxonomic group relationships. There are 62 CWR species within GP1 of their respective crops and 22 CWR species within TG1 and TG2 to crops. Two approaches towards CWR genetic reserve selection have been employed in this study which can complement each other: (1) CWR-targeted evaluation of preselected sites, like sites of Community importance (SCI, Natura 2000 network) and national protected areas, as well as other effective area-based conservation measures (OECMs), such as ancient hillfort sites (which are state protected archaeological objects) and ecological protection zones of water bodies; all 45 potential genetic reserve sites have been selected by this way covering 83 species or 57.6% of the national CWR priority list, and (2) analysis of large georeferenced plant databases for CWR species richness and number of observations; multiple hotspots of priority CWR species have been identified by this way covering 140 species, or 97.2%, of the priority list. If the first approach evaluates all conditions at the place, the second approach is based solely on target species distribution and abundance. Hotspot analysis of CWR species richness and number of observations suggested that higher CWR diversity is more likely to be found in protected areas. However, Shannon diversity and Shannon equitability indices showed that the areas outside of the protected areas are also suitable for CWR genetic reserve establishment. It has been reported that area-based conservation efforts, which include both protected areas and other effective area-based conservation measures (OECMs), are likely to extend and diversify (Maxwell et al. 2020 ). Thus, the current study is in congruence with this statement as the use of OECMs (ancient hillfort sites and river protection zones) contributed substantially to selecting the potential CWR genetic reserve sites, developing the national genetic reserve network and in situ CWR dataset inclusion into the European Catalogue EURISCO. Declarations Author Contribution J.L. developed the concept and wrote the main manuscript text, D.U. carried out CWR distribution analyses and created the national CWR database, M.L. compiled the CWR checklist, B.K. and J.L. collected data in the field and carried out primary analysis. All authors reviewed the manuscript. Acknowledgement The research was supported by the German Federal Ministry for Agriculture and Food (BMEL) through the Bioversity International coordinated project CWR in EURISCO, Agreement No: L21ROM196. The bulk part of the species distribution data was provided by the project “Inventory of European Union habitats on the territory of Lithuania”, 2011–2014 (BIGIS database). Potential CWR in situ conservation sites were established with the support of the State Forest Service, Contract No. S-54, 2023.05.03. Conflict of interest The authors declare no conflict of interest. References Bilotaitė-Jokubauskienė Ž (2022) Tarptautiniu mastu patvirtinta nauja lietuviška putinų veislė ‘Dievind’. https://sodospalvos.lt/rubrikos/tarptautiniu-mastu-patvirtinta-nauja-lietuviska-putinu-veisle-dievind/ (in Lithuanian) Accessed Jan 2024 Bobbitt Z (2021) Shannon Diversity Index: Definition & Example. In: Statology: Statistics. Simplified. https://www.statology.org/shannon-diversity-index/ Accessed Apr 2024 Braun-Blanquet J (1964) Pflanzensoziologie. Grundzuge der Vegetationskunde. – Wien–New York. Chytrý M, Hennekens SM, Jiménez-Alfaro B (2016) European Vegetation Archive (EVA): An integrated database of European vegetation plots. Appl Veg Sci, 19:173–180. https://doi.org/10.1111/avsc.12191 Česonienė L (2022) Paprastosios spanguolės, paprastieji putinai ir sodinės šilauogės. In : Ruzgas V, Stanys V (sudarytojai): Augalų Selekcija Lietuvoje Amžių Sandūroje. Lietuvos agrarinių ir miškų mokslų centras. Akademija, Kėdainių r. ISBN 978-609-451-005-2, pp 211–216. https://www.lammc.lt/data/public/uploads/2022/06/augalu-selekcija.pdf (in Lithuanian) Accessed Jan 2024 Council Directive 92/43/EEC of 21 May 1992 on the conservation of natural habitats and of wild fauna and flora. Official Journal L206, 22/07/1992 P. 0007 – 0050. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex%3A31992L0043 Accessed Apr 2024 Dudley N, Timmins HL, Stolton S, Watson JEM (2024) Effectively incorporating small reserves into national systems of protected and conserved areas. Diversity, 16:216. https://doi.org/10.3390/d16040216 Dulloo ME, Labokas J, Iriondo JM, Maxted N, Lane A, Laguna E, Jarvis A, Kell SP (2008) Genetic reserve location and design. – In: Iriondo JM, Maxted N, Dulloo ME (eds), Conserving Plant Genetic Diversity in Protected Areas: Population Management of Crop Wild Relatives: 23–64. – Wallingford, UK ECPGR (2021) Plant Genetic Resources Strategy for Europe. European Cooperative Programme for Plant Genetic Resources, Rome, Italy. https://www.ecpgr.cgiar.org/fileadmin/bioversity/publications/pdfs/PGR_STRATEGY_LP_22_Nov_revised.pdf Accessed Jan 2024 European Environment Agency (2023) The Natura 2000 protected areas network. https://www.eea.europa.eu/themes/biodiversity/natura-2000/the-natura-2000-protected-areas-network Accessed Jan 2024 Euro+Med 2006+ [continuously updated]: Euro+Med PlantBase - the information resource for Euro-Mediterranean plant diversity. – Published at http://www.europlusmed.org Accessed Jan 2024 FAO (2001) The International Treaty on Plant Genetic Resources for Food and Agriculture. FAO, Rome. https://www.fao.org/3/i0510e/i0510e.pdf Accessed Apr 2024 Fernando, E. S. (1998) Forest formations and flora of the Philippines: Handout in FBS 21. College of Forestry and Natural Resources, University of the Philippines at Los Baños (Unpublished). Fitzgerald H, Palmé A, Aronsson M, Asdal Å, Bjureke K, Endresen D et al (2018) The Nordic Priority Crop Wild Relative Dataset. figshare. Dataset. https://doi.org/10.6084/m9.figshare.5688130.v3 Fitzgerald H, Palmé A, Asdal Å, Endresen D, Kiviharju E, Lund B, Rasmussen M, Thorbjörnsson H, Weibull J (2019) A regional approach to Nordic crop wild relative in situ conservation planning. Plant Genetic Resources: Characterization and Utilization 17(2):196–207. https://doi.org/10.1017/S147926211800059X Gudžinskas Z (1999) Vascular Plants of Lithuania. Institute of Botany, Vilnius, 211 pp Gudžinskas Z, Petrulaitis L, Uogintas D et al (2023) Invasive and alien species in Lithuania https://www.researchgate.net/publication/373603395_Invazines_ir_svetimzemes_rusys_Lietuvoje_-_Invasive_and_Alien_Species_in_Lithuania Accessed Nov 2023 Gudžinskas Z, Ryla M (2006) Orchids (Orchidaceae) of Lithuania (in Lithuanian with English Preface and Summary). Institute of Botany, Vilnius, 104 pp Harlan JR, de Wet JMJ (1971) Towards a rational classification of cultivated plants. Taxon 20:509–517. https://doi.org/10.2307/1218252 Hassler M (1994–2024) World Plants. Synonymic Checklist and Distribution of the World Flora. Version 18.4; last update Feb. 11th, 2024. https://www.worldplants.de. Last accessed 13/02/2024. IPNI (2023) International Plant Names Index. Published on the Internet http://www.ipni.org, The Royal Botanic Gardens, Kew, Harvard University Herbaria & Libraries and Australian National Herbarium. Accessed Jan 2024 IUCN Species Survival Commission (2012) IUCN Red List categories and criteria, version 3.1, second edition. Gland, Switzerland, 38 pp IUCN-WCPA Task Force on OECMs (2019) Recognising and reporting other effective area-based conservation measures. Gland, Switzerland: IUCN. https://doi.org/10.2305/IUCN.CH.2019.PATRS.3.en Jankevičienė R (Compiler) (1998) Dictionary of Plant Names. Institute of Botany, Vilnius, 523 pp Kahilainen A, Puurtinen M, Kotiaho JS (2014) Conservation implications of species–genetic diversity correlations. Global Ecol Conserv 2:315–323. https://doi.org/10.1016/j.gecco.2014.10.013 Kell SP, Knüpffer H, Jury SL, Ford-Lloyd BV and Maxted N (2008) Crops and Wild Relatives of the Euro-Mediterranean Region: Making and Using a Conservation Catalogue. In: Maxted N et al (eds). Crop Wild Relative Conservation and Use: 69–109. CAB International, Wallingford, UK. Kuusk V, Tabaka L, Jankevičienė R (eds) (1996) Flora of the Baltic Countries. Compendium of Vascular Plants, Volume 2. Tartu, Estonia, 372 pp Kuusk V, Tabaka L, Jankevičienė R (eds) (2003) Flora of the Baltic Countries. Compendium of Vascular Plants, Volume 3. Tartu, Estonia, 408 pp Laasimer L, Kuusk V, Tabaka L, Lekavičius A (eds) (1993) Flora of the Baltic Countries. Compendium of Vascular Plants, Volume 1. Tartu, Estonia, 378 pp Labokas J, Karpavičienė B (2018) Creation of a network of seed sites for in-situ conservation of medicinal and aromatic plant genetic resources in Lithuania. Botanica 24(1):87–97 Labokas J, Karpavičienė B, Rašomavičius V, Gelvonauskis B (2016) Developing a national crop wild relative in situ conservation strategy for Lithuania: Creation of a national CWR inventory and its prioritization. In: Maxted N, Dulloo ME and Ford-Lloyd BV (eds.) Enhancing Crop Genepool Use: Capturing Wild Relative and Landrace Diversity for Crop Improvement. CAB International. P. 217–230. Laguna E, Deltoro V I, P`erez-Botella J, P`erez-Rovira P, Serra L, Olivares A, et al. (2004) The role of small reserves in plant conservation in a region of high diversity in eastern Spain. Biol Conserv 119:421–426. https://doi.org/10.1016/j.biocon.2004.01.001 Lithuanian Hydrometeorological Service. 2015. Climate regions of Lithuania. http://www.meteo.lt/en/climate-regions-of-lithuania Accessed Jan 2023 Maxted N, Ford-Lloyd BV, Jury S, Kell SP, Scholten M (2006) Towards a definition of a crop wild relative. Biol Conserv 15:2673–2685 Maxted N, Magos Brehm J and Kell S (2013) Resource Book for Preparation of National Conservation Plans for Crop Wild Relatives and Landraces. https://www.fao.org/fileadmin/templates/agphome/documents/PGR/PubPGR/ResourceBook/TEXT_ALL_2511.pdf Maxwell SL, Cazalis V, Dudley N. et al. (2020) Area-based conservation in the twenty-first century. Nature 586:217–227. https://doi.org/10.1038/s41586-020-2773-z Minister of the Environment of the Republic of Lithuania (2024) Order on the approval of the list of species of animals, plants and mushrooms protected by the Republic of Lithuania. https://e-seimas.lrs.lt/portal/legalAct/lt/TAD/TAIS.219902/asr (Consolidated edit from 15 March 2024). Accessed Apr 2024 Pagad S (2022) Global Register of Introduced and Invasive Species - Lithuania. Version 1.2. Invasive Species Specialist Group ISSG. Checklist dataset https://doi.org/10.15468/ewm0v1 accessed via GBIF.org on 2024-01-14 POWO (2024) Plants of the World Online. Facilitated by the Royal Botanic Gardens, Kew. Published on the Internet; http://www.plantsoftheworldonline.org/ Accessed Jan 2024 QGIS.org (2023). QGIS Geographic Information System. Open Source Geospatial Foundation Project. http://qgis.org/en/site Accessed Jan 2024 Rašomavičius V (ed.) (2021) Red Data Book of Lithuania. Animals, plants, fungi. – Vilnius. https://am.lrv.lt/uploads/am/documents/files/Raudonoji%20knyga/Raudonoji_knyga_2021_WEB.pdf Accessed Jan 2024 Shannon CE (1948) A mathematical theory of communication. The Bell System Technical Journal 27(3):379–423. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x Taylor NG, Kell SP, Holubec V, Parra-Quijano M, Chobot K, Maxted N (2017) A systematic conservation strategy for crop wild relatives in the Czech Republic. Diversity Distrib., 23: 448-462. https://doi.org/10.1111/ddi.12539 Thormann I, Kell S, Magos Brehm J, Dulloo ME, Maxted N (2017) CWR checklist and inventory data template v.1, https://doi.org/10.7910/DVN/B8YOQL, Harvard Dataverse, V4. USDA, Agricultural Research Service, National Plant Germplasm System (2024) Germplasm Resources Information Network (GRIN Taxonomy). National Germplasm Resources Laboratory, Beltsville, Maryland. URL: https://npgsweb.ars-grin.gov/gringlobal/taxon/taxonomysearchcwr Accessed Jan 2024 VATZUM, The State Plant Service under the Ministry of Agriculture (2024) Lithuanian National List of Plant Varieties 2024. https://vatzum.lrv.lt/media/viesa/saugykla/2024/2/5ksAXYeAE0s.pdf Accessed Apr 2024 van Treuren R, Hoekstra R, van Hintum TJ (2017) Inventory and prioritization for the conservation of crop wild relatives in The Netherlands under climate change. Biol Conserv 216:123–139. https://doi.org/10.1016/j.biocon.2017.10.003 Vincent H, Wiersema J, Kell SP et al (2013) A prioritized crop wild relative inventory to help underpin global food security. Biol Conserv 167:265–275 https://doi.org/10.1016/j.biocon.2013.08.011 VSTT, State Service for Protected Areas Under the Ministry of Environment (2024) System of protected areas. https://saugoma.lt/en/system-of-protected-areas-ne Accessed Feb 2024 Weibull J, Phillips J (2020) Swedish Crop Wild Relatives: towards a national strategy for in situ conservation of CWR. Genetic Resources 1(1):17–23. https://doi.org/10.46265/genresj.2020.1.17-24 Whitlock W, Hipperson H, Thompson DBA, Butlina RK, Burke T (2016) Consequences of in-situ strategies for the conservation of plant genetic diversity. Biological Conservation, 203: 134–142 Wikispecies: Free species directory (2024) Tracheophyta. https://species.wikimedia.org/wiki/Tracheophyta Accessed Jan 2024 Additional Declarations No competing interests reported. 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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-4412054","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":305180118,"identity":"41df6aea-9fda-45b6-b151-be5bcadc1243","order_by":0,"name":"Juozas Labokas","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIie2PvQrCMBRGrxQ6RVwjaPsKCV1LfZWUC04Ojo6FQl0UNyn4MkrAUVchLi5ODnVzyGD8HYS2joI5EG74yOF+AbBYfpBG8pgdoOCYGT7zYb1CnkrfnNtkrHbbS5H1ipOjLC4aSGuRSnbR22g2364KYLq8WN4XfJoBofs1xtNMYa7QoVVbGvmAiWZiiu0GwbKZKASFbmWxm7LSpphvFHPZoP+FwlPiAmFGQeIuI1arTI7odDNKuPlL0M1QcIUBFSwoVfgY5fmkQ89TqWyfdNTzVHwoipFXriT3Qd9BfA9EqQDgfwa9iscWi8Xyp1wB/H9QPkZDL1UAAAAASUVORK5CYII=","orcid":"","institution":"Nature Research Centre","correspondingAuthor":true,"prefix":"","firstName":"Juozas","middleName":"","lastName":"Labokas","suffix":""},{"id":305180119,"identity":"091aa204-af13-45d9-a098-3500c3133972","order_by":1,"name":"Mantas Lisajevičius","email":"","orcid":"","institution":"Nature Research Centre","correspondingAuthor":false,"prefix":"","firstName":"Mantas","middleName":"","lastName":"Lisajevičius","suffix":""},{"id":305180120,"identity":"5cb03783-7806-4ba5-94ce-51f651d4b284","order_by":2,"name":"Domas Uogintas","email":"","orcid":"","institution":"Nature Research Centre","correspondingAuthor":false,"prefix":"","firstName":"Domas","middleName":"","lastName":"Uogintas","suffix":""},{"id":305180121,"identity":"6d7ce6a9-38b4-41a2-b7dd-6048f20eba5d","order_by":3,"name":"Birutė Karpavičienė","email":"","orcid":"","institution":"Nature Research Centre","correspondingAuthor":false,"prefix":"","firstName":"Birutė","middleName":"","lastName":"Karpavičienė","suffix":""}],"badges":[],"createdAt":"2024-05-13 09:09:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4412054/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4412054/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57011327,"identity":"9fac2e60-8643-453b-a8f2-ea33d90ce5f0","added_by":"auto","created_at":"2024-05-23 11:28:54","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":721563,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of 45 potential CWR genetic reserve sites on climate map of Lithuania (Lithuanian Hydrometeorological Service, 2015) established by evaluation of preselected sites on state-owned land. For the details on CWR genetic reserve sites see Supplementary data (Table S3).\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4412054/v1/5d2e7cc3b0d9ab890e3b3e98.jpeg"},{"id":57011328,"identity":"a05a0bdc-67a0-4dd4-9b57-5c41aabdab8b","added_by":"auto","created_at":"2024-05-23 11:28:54","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1150596,"visible":true,"origin":"","legend":"\u003cp\u003eSpecies richness (numbers of species per grid cell 4×4 km) of priority CWR across Lithuania and locations of the preselected 45 potential CWR reserve sites. Data source: \u003ca href=\"https://doi.org/10.5281/zenodo.11124923\"\u003ehttps://doi.org/10.5281/zenodo.11124923\u003c/a\u003e\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4412054/v1/cdc30412f0a445f44ff76164.jpeg"},{"id":57010983,"identity":"d854fea0-3fbb-4be4-8f84-5db58f5145d7","added_by":"auto","created_at":"2024-05-23 11:20:54","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":945213,"visible":true,"origin":"","legend":"\u003cp\u003eSpecies richness (numbers of species per grid cell 4×4 km) of priority CWR in protected areas of Lithuania including Natura 2000 network.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4412054/v1/fa139fa4fdbe6bcad03c54ca.jpeg"},{"id":57010979,"identity":"1b8e6e3b-1694-4116-a535-cfb000165461","added_by":"auto","created_at":"2024-05-23 11:20:54","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1596588,"visible":true,"origin":"","legend":"\u003cp\u003eNumber of species observations (per grid cell 4×4 km) of priority CWR across Lithuania\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4412054/v1/b2b5f8d0514ef191487aa1d7.png"},{"id":57010984,"identity":"2a8df9e9-963a-48d3-b3a2-ecdf7bd0edf9","added_by":"auto","created_at":"2024-05-23 11:20:54","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2723346,"visible":true,"origin":"","legend":"\u003cp\u003eNumber of species observations (per grid cell 4×4 km) of priority CWR in protected areas of Lithuania including Natura 2000 network.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4412054/v1/ca84c20fdfdb6c7b2754ca15.png"},{"id":61983829,"identity":"40e6216d-b835-46c7-bc73-5b1b16cb53be","added_by":"auto","created_at":"2024-08-07 23:46:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7066271,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4412054/v1/2f36c721-26fc-44cd-8979-fa3a272ca84d.pdf"},{"id":57010981,"identity":"414d0769-bd1e-413d-9bf0-5d9d1bc6d754","added_by":"auto","created_at":"2024-05-23 11:20:54","extension":"zip","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":11312060,"visible":true,"origin":"","legend":"","description":"","filename":"Labokasetal.2024.SUPPLEMENTARYMATERIAL.zip","url":"https://assets-eu.researchsquare.com/files/rs-4412054/v1/ea0bb09c0b323440f3063e3a.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eEnhancing in Situ Conservation of Crop Wild Relatives for Food and Agriculture in Lithuania\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAs stated in the Plant Genetic Resources Strategy for Europe (ECPGR, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), crop wild relative (CWR) genetic resources native to Europe are related to the many socio-economically important crops cultivated in the region and in other parts of the world (food, fodder, forage, beverage, food-additive, oil, biofuel, biomass, medicinal, ornamental) and contain a wide pool of evolving genetic diversity, not duplicated in the crop itself, and that is of potential value for crop improvement. Some wild species are harvested from the wild for direct use as food or feed and constitute a potential source for further domestication and creation of new crops. This statement is in congruence with the International Treaty on Plant Genetic Resources for Food and Agriculture (FAO, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), stating that contracting parties shall \u0026ldquo;promote \u003cem\u003ein situ\u003c/em\u003e conservation of wild crop relatives and wild plants for food production, including in protected areas\u0026rdquo;.\u003c/p\u003e \u003cp\u003eAlthough Lithuania is not among the hotspots of floristic richness, it has quite a few CWRs as well as wild harvested plants with some of which not only being harvested from the wild but also used for domestication and development of the new cultivars. The most recent examples of the latter are bog cranberry (\u003cem\u003eVaccinium oxycoccos\u003c/em\u003e L.) and guelder rose (\u003cem\u003eViburnum opulus\u003c/em\u003e L.) with five and three new cultivars, respectively, released at the Botanical Garden of Vytautas Magnus University, Kaunas, Lithuania, and distinguished by enriched contents of fruit bioactive substances and the amount of yield (Česonienė, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Bilotaitė-Jokubauskienė, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Wild garlic, or ramson (\u003cem\u003eAllium ursinum\u003c/em\u003e L.), is another example of the wild harvested plants being increasingly collected from the wild for food as well as cultivated in amateur gardens. These and other native food-plant species have been contributing to the healthy diets of local people through generations and thus have been naturally adapted by their organisms. It is particularly important to consider this nowadays when globalisation is conditioning a wide-spread consumption of imported food products and fast foods. Then, many wild forage species, predominantly from Poaceae and Fabaceae, are also used as CWRs and thus present valuable genetic resources to be conserved to ensure their sustainable use.\u003c/p\u003e \u003cp\u003eIt is generally admitted that \u003cem\u003ein situ\u003c/em\u003e conservation of crop wild relatives is most feasible within the existing network of protected areas. However, it depends on the development of the protected area network including total area coverage and categories of protected areas established. In Lithuania, the protected areas of conservation protection priority (strict reserves, reserves, and heritage objects) and complex protected areas (public parks \u0026ndash; national and regional parks, as well as biosphere monitoring territories \u0026ndash; biosphere reserves and biosphere polygons) are the most important ones, so sometimes they are called specially protected areas. They cover 1,152,793 hectares or 17.65% of the country\u0026rsquo;s territory (VSTT, 2024). The areas of the European ecological network Natura 2000 (European Environment Agency \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), including those established under the Habitats Directive, are largely integrated into the system of the nationally protected areas. The major questions to be answered are (1) whether the existing network of protected areas adequately covers the distribution of the priority CWRs in the country and (2) whether there is a need to establish any additional protected areas for CWR conservation.\u003c/p\u003e \u003cp\u003eThus, in the context of food safety concern combined with climate change, the aim of this paper is to enhance \u003cem\u003ein situ\u003c/em\u003e conservation of priority CWR species for food and agriculture through the analysis of their distribution across the country and selection of the most appropriate wild populations (MAWPs) as potential sites for the establishment of genetic reserves with the focus on the existing protected areas. It is expected that this effort will facilitate systematic conservation planning of the target species in the country and contribute to the development of the European CWR genetic reserves network.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eNational CWR checklist\u003c/p\u003e\n\u003cp\u003eA floristic approach was used to prepare the national CWR checklist as described by Maxted et al. (\u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e). The checklist of the flora of Lithuania was compiled by using the following sources: Dictionary of Plant Names (Jankevičienė \u003cspan class=\"CitationRef\"\u003e1998\u003c/span\u003e), Vascular Plants of Lithuania (Gudžinskas \u003cspan class=\"CitationRef\"\u003e1999\u003c/span\u003e), Flora of the Baltic Countries (Laasimer et al. \u003cspan class=\"CitationRef\"\u003e1993\u003c/span\u003e; Kuusk et al. \u003cspan class=\"CitationRef\"\u003e1996\u003c/span\u003e; Kuusk et al. \u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003e), Orchids (Orchidaceae) of Lithuania (Gudžinskas, Ryla \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e), Red Data Book of Lithuania (Ra\u0026scaron;omavičius \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), and the Euro\u0026thinsp;+\u0026thinsp;Med PlantBase (Euro\u0026thinsp;+\u0026thinsp;Med 2006+). The taxonomic harmonization was carried out by referring to the International Plant Name Index (IPNI \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e) and Plants of the World online (POWO \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003ePrioritization of CWR species\u003c/p\u003e\n\u003cp\u003ePrioritization of CWR species was carried out with the focus on plant genetic resources for food and agriculture (in a wide sense) with the native species and archaeophytes given top priority. Some close relatives of food crops, such as \u003cem\u003eLactuca serriola\u003c/em\u003e L., \u003cem\u003eMalus toringo\u003c/em\u003e (Siebold) de Vriese, \u003cem\u003ePrunus cerasifera\u003c/em\u003e Ehrh., \u003cem\u003eArmoracia rusticana\u003c/em\u003e G.Gaertn., B.Mey. \u0026amp; Scherb., \u003cem\u003eAvena fatua\u003c/em\u003e L., and \u003cem\u003eRaphanus raphanistrum\u003c/em\u003e L., were excluded from the priority species list due to their invasive or weedy behavior. Prickly lettuce (\u003cem\u003eL. serriola\u003c/em\u003e) is a particularly rapidly spreading species and invading different natural plant communities, while cherry plum (\u003cem\u003eP. cerasifera\u003c/em\u003e) poses threat by hybridization with the native species \u003cem\u003eP. spinosa\u003c/em\u003e (Ra\u0026scaron;omavičius, pers. comm.; Pagad \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Gudžinskas et al. \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). Wild oat (\u003cem\u003eA. fatua\u003c/em\u003e) and wild radish (\u003cem\u003eR. raphanistrum\u003c/em\u003e) are listed among the worst agricultural weeds. The other criteria used are as follows:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eAnnex I. List of crops covered under the multilateral system of the International Treaty on Plant Genetic Resources for Food and Agriculture (ITPGRFA);\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eList of global priority crop wild relative genera;\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eLithuanian national plant variety lists;\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWild species relationships to crops;\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSocio-economic and cultural importance including use traditions of the species;\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThreat status of species.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAll CWR taxa falling within the genera listed in the Annex 1 of the International Treaty on Plant Genetic Resources for Food and Agriculture (FAO, \u003cspan class=\"CitationRef\"\u003e2001\u003c/span\u003e) as well as in the list of global priority crop wild relative genera (Vincent et al. \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e) were selected as priority ones.\u003c/p\u003e\n\u003cp\u003eLithuanian national plant variety lists (e. g., VATZUM 2024) were used as proxy indicators of crop importance for the respective consideration of their wild relatives. Similarly, the determination of socio-economic and cultural importance was based on well-established plant cultivation and wild harvesting traditions.\u003c/p\u003e\n\u003cp\u003eCWR relatedness to crops according to the Gene Pool (Harlan and de Wet \u003cspan class=\"CitationRef\"\u003e1971\u003c/span\u003e) and Taxon Group concepts (Maxted et al. \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e) was determined by using the data available at the Germplasm Resources Information Network (USDA 2024), Nordic Priority CWR Dataset (Fitzgerald et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e), Flora of the Baltic Countries (Laasimer et al. \u003cspan class=\"CitationRef\"\u003e1993\u003c/span\u003e; Kuusk et al. \u003cspan class=\"CitationRef\"\u003e1996\u003c/span\u003e; Kuusk et al. \u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003e), Wikispecies (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), World Plants (Hassler 1994\u0026ndash;2024), and scientific articles on separate genera or species.\u003c/p\u003e\n\u003cp\u003eThreat status of the target species was cited from the Red Data Book of Lithuania (Ra\u0026scaron;omavičius \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), which provided assessment results based on IUCN criteria (IUCN Species Survival Commission \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eCWR checklist and inventory data template (Thormann et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e) was used to facilitate structural data arrangement and management.\u003c/p\u003e\n\u003cp\u003eSeveral data sources from other European countries, i.e., Nordic countries (Fitzgerald et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Weibull \u0026amp; Phillips \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e), Czech Republic (Taylor et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e), and the Netherlands (van Treuren et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e) were used, as well as some crop breeders were consulted to find the best solutions for CWR prioritization.\u003c/p\u003e\n\u003cp\u003eSelection of sites for genetic reserves and hotspot analysis\u003c/p\u003e\n\u003cp\u003eTwo approaches of site selection for CWR genetic reserves have been employed: (1) evaluation of preselected sites on state-owned land like ancient hillforts (which are state protected archaeological objects), sites of community importance (SCI, Natura 2000 network) and national protected areas including ecological protection zones of water bodies, and (2) application of several large georeferenced plant databases, both national (BIGIS and EU-LT-001) and international (the Global Biodiversity Information Facility \u0026ndash; GBIF), for distribution and hotspot analysis of priority CWR species in 4\u0026times;4 km grid cells. Shannon diversity index (Shannon \u003cspan class=\"CitationRef\"\u003e1948\u003c/span\u003e) was employed for CWR diversity evaluation in grid cells by using the following formula:\u003c/p\u003e\n\u003cp\u003eH = \u0026minus;\u0026Sigma; [(n/N)\u0026times;ln(n/N)], (1)\u003c/p\u003e\n\u003cp\u003ewhere:\u003c/p\u003e\n\u003cp\u003e\u0026Sigma; \u0026ndash; a Greek symbol for sum,\u003c/p\u003e\n\u003cp\u003eH \u0026ndash; Shannon diversity index,\u003c/p\u003e\n\u003cp\u003en \u0026ndash; number of observations of a given CWR species within a grid cell,\u003c/p\u003e\n\u003cp\u003eN \u0026ndash; number of observations of all CWR species within a grid cell,\u003c/p\u003e\n\u003cp\u003eln \u0026ndash; natural logarithm.\u003c/p\u003e\n\u003cp\u003eThe same formula in a simplified version (Bobbitt \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) could be presented as:\u003c/p\u003e\n\u003cp\u003eH = \u0026ndash;\u0026Sigma;p\u003csub\u003ei\u003c/sub\u003e \u0026times; ln(p\u003csub\u003ei\u003c/sub\u003e), (2)\u003c/p\u003e\n\u003cp\u003ewhere p\u003csub\u003ei\u003c/sub\u003e is the proportion of the entire community made up of species \u003cem\u003ei\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eA derivative of the Shannon diversity index, the Shannon equitability index, is a way to measure the evenness of species proportions in a community. It is calculated as follows:\u003c/p\u003e\n\u003cp\u003eE\u003csub\u003eH\u003c/sub\u003e = H / ln(S), (3)\u003c/p\u003e\n\u003cp\u003ewhere:\u003c/p\u003e\n\u003cp\u003eE\u003csub\u003eH\u003c/sub\u003e \u0026ndash; Shannon equitability index,\u003c/p\u003e\n\u003cp\u003eH \u0026ndash; Shannon diversity index,\u003c/p\u003e\n\u003cp\u003eS \u0026ndash; total number of unique species.\u003c/p\u003e\n\u003cp\u003eThe cover-abundance of each CWR species was estimated by using Braun-Blanquet scale (Braun-Blanquet \u003cspan class=\"CitationRef\"\u003e1964\u003c/span\u003e). However, the presence/absence data were used only to avoid complication of the data analysis. QGIS software (QGIS.org \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e) was used for mapping of CWR distribution, hotspot analysis and location of potential genetic reserve sites.\u003c/p\u003e\n\u003cp\u003eData sources\u003c/p\u003e\n\u003cp\u003eSeveral georeferenced plant databases were used. BIGIS (Institute of Botany GIS database) contains the countrywide species distribution data related to the inventory of the EU importance natural habitats. BILAS is the major Lithuanian herbarium database, maintained by the Institute of Botany of the Nature Research Centre. EU-LT-001, the vegetation database of Lithuania, is a part of European Vegetation Archive (Chytr\u0026yacute; et al. \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). The Global Biodiversity Information Facility (GBIF), which contains citizen science data, was also used.\u003c/p\u003e"},{"header":"Results and discussion","content":"\u003cp\u003eChecklist description\u003c/p\u003e\n\u003cp\u003eThe crop and CWR checklist of Lithuania contains a total of 2,630 taxa including species (and microspecies), subspecies, varieties, and hybrids (see Supplementary data, Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). The checklist is comprised of 1,384 native taxa (incl. archaeophytes) and of 1,246 neophytes, of which 905 are used in cultivation. Among them, 471taxa (17.9% of the checklist) are food crops or their wild relatives and 399 taxa (15.2%) are forage/fodder crops or their wild relatives. A total of 699 taxa (26.6%) could be quite strictly defined as of food or forage use. Additionally, 841 taxa (32%) are of medicinal use, as well as 1,396 taxa (53.1%) are of other use. Furthermore, 178 taxa are related to those included in the Annex I of the ITPGRFA (FAO, \u003cspan class=\"CitationRef\"\u003e2001\u003c/span\u003e). The checklist also includes 169 taxa that are under the legal national protection (Minister of the Environment of the Republic of Lithuania \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). In addition, the checklist covers 16 taxa (incl. 13 native ones) from Annex II, one taxon from Annex IV, and 10 taxa (incl. eight natives) from Annex V of the Council Directive 92/43/EEC (1992) on the conservation of natural habitats and of wild fauna and flora. Moreover, the checklist contains 22 species that have been declared invasive in Lithuania and 34 species that are considered potentially invasive (Gudžinskas et al. \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). In general, the above-mentioned numbers are consistent with those of the other European countries, particularly in Scandinavia. For example, of the 2,276 crop and CWR species recorded in Norway, 2,084 species (92%) are included in the agricultural and horticultural crop group (Kell et al. \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003ePrioritization of the checklist\u003c/p\u003e\n\u003cp\u003eIn total, 53 genera of food (including culinary herbs, aromatic plants, and berries) and forage/fodder species were selected belonging to 15 families. A list of 144 CWR priority species with 135 native species (including archaeophytes) and 9 naturalized ones (mostly those, escaped from cultivation), was generated which still should be considered as a working version. Seventy-four species (51.4%) are representatives of the genera listed in Annex 1 of the ITPGRFA and those of the 92 global priority CWRs (Vincent et al. \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e). The current CWR priority list is shorter by 20% if compared to the previous version of 180 species (Labokas et al. \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e) which included a portion of non-food CWR species, and is quite consistent with the Swedish priority list, containing 121 CWR taxa (Weibull \u0026amp; Phillips \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). The summary of prioritized CWR inventory by botanical families is presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e (see Supplementary data for the full CWR priority list, Table S2).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eSummary of the prioritized Lithuanian CWR inventory by families\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFamily\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eGenera\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSpecies\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSpecies %\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eGenera with numbers of species\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePoaceae\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eAgrostis\u003c/em\u003e (5), \u003cem\u003eAlopecurus\u003c/em\u003e (4), \u003cem\u003eAnthoxanthum\u003c/em\u003e (3), \u003cem\u003eArrhenatherum\u003c/em\u003e (1), \u003cem\u003eAvenula\u003c/em\u003e (1), \u003cem\u003eBriza\u003c/em\u003e (1), \u003cem\u003eBromus\u003c/em\u003e (1), \u003cem\u003eCynosurus\u003c/em\u003e (1), \u003cem\u003eDactylis\u003c/em\u003e (1), \u003cem\u003eDeschampsia\u003c/em\u003e (2), \u003cem\u003eElymus\u003c/em\u003e (1), \u003cem\u003eFestuca\u003c/em\u003e (8), \u003cem\u003eGlyceria\u003c/em\u003e (4), \u003cem\u003eHelictochloa\u003c/em\u003e (1), \u003cem\u003eLeymus\u003c/em\u003e (1), \u003cem\u003eLolium\u003c/em\u003e (1), \u003cem\u003ePhalaris\u003c/em\u003e (1), \u003cem\u003ePhleum\u003c/em\u003e (2), \u003cem\u003ePoa\u003c/em\u003e (8)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFabaceae\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eAnthyllis\u003c/em\u003e (1), \u003cem\u003eAstragalus\u003c/em\u003e (3), \u003cem\u003eLathyrus\u003c/em\u003e (7), \u003cem\u003eLotus\u003c/em\u003e (2), \u003cem\u003eMedicago\u003c/em\u003e (2), \u003cem\u003eMelilotus\u003c/em\u003e (2), \u003cem\u003eOnobrychis\u003c/em\u003e (2), \u003cem\u003eOnonis\u003c/em\u003e (1), \u003cem\u003eSecurigera\u003c/em\u003e (1), \u003cem\u003eTrifolium\u003c/em\u003e (14), \u003cem\u003eVicia\u003c/em\u003e (11)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRosaceae\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eFragaria\u003c/em\u003e (3), \u003cem\u003eMalus\u003c/em\u003e (2), \u003cem\u003ePrunus\u003c/em\u003e (3), \u003cem\u003ePyrus\u003c/em\u003e (2), \u003cem\u003eRubus\u003c/em\u003e (6)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLamiaceae\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eMentha\u003c/em\u003e (3), \u003cem\u003eOriganum\u003c/em\u003e (1), \u003cem\u003eThymus\u003c/em\u003e (2)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBrassicaceae\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eBarbarea\u003c/em\u003e (2), \u003cem\u003eRorippa\u003c/em\u003e (3)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAmaryllidaceae\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eAllium\u003c/em\u003e (6)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEricaceae\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eVaccinium\u003c/em\u003e (5)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eApiaceae\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eAngelica\u003c/em\u003e (1), \u003cem\u003eCarum\u003c/em\u003e (1), \u003cem\u003eDaucus\u003c/em\u003e (1), \u003cem\u003ePastinaca\u003c/em\u003e (1)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGrossulariaceae\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eRibes\u003c/em\u003e (3)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAsparagaceae\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eAsparagus\u003c/em\u003e (1)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAsteraceae\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eCichorium\u003c/em\u003e (1)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBetulaceae\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eCorylus\u003c/em\u003e (1)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCannabaceae\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eHumulus\u003c/em\u003e (1)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePapaveraceae\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003ePapaver\u003c/em\u003e (1)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eElaeagnaceae\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eHippophae\u003c/em\u003e (1)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal: 15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e144\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAs seen from Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, nearly 2/3 (64.5%) of the prioritized species are members of two families, Poaceae (47 species) and Fabaceae (46 species). The third richest CWR family is Rosaceae with 16 species. These three families make up 75.7% of the total CWR priority list.\u003c/p\u003e\n\u003cp\u003eAnalysis of the priority CWRs by their use categories showed that 88 species (61.1%) could be attributed to the CWRs of forage/fodder crops and 58 species (40.3%) to those of the food crops with only two species, \u003cem\u003eDaucus carota\u003c/em\u003e and \u003cem\u003eVicia lathyroides\u003c/em\u003e, overlapping both categories (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eSummary of the prioritized Lithuanian CWR inventory by use categories\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eUse category\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFamilies with numbers of genera/species\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNumber of species*\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePercent of priority CWR list*\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eForage/fodder\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFabaceae 11/46\u003c/p\u003e\n\u003cp\u003ePoaceae 16/41\u003c/p\u003e\n\u003cp\u003eApiaceae (1/1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e61.1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFood\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAmaryllidaceae (1/6)\u003c/p\u003e\n\u003cp\u003eApiaceae (4/4)\u003c/p\u003e\n\u003cp\u003eAsparagaceae (1/1)\u003c/p\u003e\n\u003cp\u003eAsteraceae (1/1)\u003c/p\u003e\n\u003cp\u003eBetulaceae (1/1)\u003c/p\u003e\n\u003cp\u003eBrassicaceae (2/5)\u003c/p\u003e\n\u003cp\u003eCannabaceae (1/1)\u003c/p\u003e\n\u003cp\u003eElaeagnaceae (1/1)\u003c/p\u003e\n\u003cp\u003eEricaceae (1/5)\u003c/p\u003e\n\u003cp\u003eFabaceae 1/1\u003c/p\u003e\n\u003cp\u003eGrossulariaceae (1/3)\u003c/p\u003e\n\u003cp\u003eLamiaceae (3/6)\u003c/p\u003e\n\u003cp\u003ePapaveraceae (1/1)\u003c/p\u003e\n\u003cp\u003ePoaceae (3/6)\u003c/p\u003e\n\u003cp\u003eRosaceae (5/16)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e40.3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e* Includes two overlapping species, \u003cem\u003eDaucus carota\u003c/em\u003e L. and \u003cem\u003eVicia lathyroides\u003c/em\u003e L.\u003c/p\u003e\n\u003cp\u003eRegarding the legal protection, 17 CWR priority species (11.8%) are protected by the Order of the Minister of Environment (2024). These are evaluated according to the IUCN categories and criteria at the national level (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eCWR priority species under the legal national protection with the regional IUCN assessments\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNo.\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSpecies\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eIUCN category and criteria*\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eAllium angulosum\u003c/em\u003e\u0026nbsp;L.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEN B1ab(ii,iii)\u0026thinsp;+\u0026thinsp;2ab(ii,iii)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eAllium scorodoprasum\u003c/em\u003e\u0026nbsp;L.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVU A4ac\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eAllium vineale\u003c/em\u003e\u0026nbsp;L.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEN B2ab(iii,iv,v)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eAlopecurus arundinaceus\u003c/em\u003e\u0026nbsp;Poir.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVU D2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eAstragalus danicus\u003c/em\u003e\u0026nbsp;Retz.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNT B2b(iii); B1b(iii)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eFestuca altissima\u003c/em\u003e\u0026nbsp;All.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDD\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eFestuca psammophila\u003c/em\u003e\u0026nbsp;(Čelak.) R. M. Fritsch\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEN B1ab(ii,iii,v)\u0026thinsp;+\u0026thinsp;2ab(ii,iii,v)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eGlyceria lithuanica\u003c/em\u003e\u0026nbsp;(Gorski) Gorski\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVU B1ab(iii)\u0026thinsp;+\u0026thinsp;2ab(iii)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eHelictochloa pratensis\u003c/em\u003e\u0026nbsp;(L.) Romero Zarco\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVU D2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eLathyrus laevigatus\u003c/em\u003e\u0026nbsp;(Waldst. \u0026amp; Kit.) Gren.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNT B2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eLathyrus pisiformis\u003c/em\u003e\u0026nbsp;L.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEN B1ab(iv)\u0026thinsp;+\u0026thinsp;2ab(iv)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003ePoa remota\u003c/em\u003e Forselles\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNT B2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003ePrunus spinosa\u003c/em\u003e\u0026nbsp;L.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVU B1ab(ii,iii,v)\u0026thinsp;+\u0026thinsp;2ab(ii,iii,v)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eTrifolium lupinaster\u003c/em\u003e\u0026nbsp;L.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEN B2b(iii)c(iv)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eTrifolium rubens\u003c/em\u003e\u0026nbsp;L.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEN B2ab(i,ii,iii,iv)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eVicia lathyroides\u003c/em\u003e\u0026nbsp;L.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEN B2b(iii)c(ii)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eVicia pisiformis\u003c/em\u003e\u0026nbsp;L.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNT B1\u0026thinsp;+\u0026thinsp;2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e* Source: Ra\u0026scaron;omavičius \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eBased on tentative observations, most of the rest of CWR priority species fall into the IUCN category LC (Least Concern). However, a detailed assessment of at least some species, like \u003cem\u003eLathyrus palustris\u003c/em\u003e, \u003cem\u003eMentha longifolia\u003c/em\u003e, \u003cem\u003eOnobrychis arenaria\u003c/em\u003e, and \u003cem\u003eVaccinium microcarpum\u003c/em\u003e, is needed to improve their conservation planning.\u003c/p\u003e\n\u003cp\u003eAnalysis of the priority CWR relatedness to crops revealed that 75 species (52.1%) represent the closest relationships with their respective crops: 62 species are within primary gene pool (GP1) and 13 species are within taxon group one (TG1) to their respective crops (see Supplementary data, Table S2). Twenty-four CWR species fall into the second closest group to crops (GP2, TG2, and TG3). And the most distant group (GP3 and TG4) comprises 45 CWR species. As lack of data on genetic relatedness is quite evident, the taxon group concept allows to compensate this data gap, at least, partly. However, the CWR of the most distant taxonomic group, TG4, should still be studied by molecular methods to better reveal their use possibilities in breeding. This does not apply when wild species are being introduced into cultivation themselves.\u003c/p\u003e\n\u003cp\u003eSelection of sites for genetic reserves\u003c/p\u003e\n\u003cp\u003eBy using the method of preselected site evaluation, 45 potential CWR genetic reserve sites were identified. Out of these, 29 sites were established in 2022 through 2023. The rest of the sites were originally identified in 2011\u0026ndash;2021 (see Labokas, Karpavičienė \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e) with the oldest ones inventoried repeatedly. These 45 sites contain 83 CWR priority species (57.6%) with the total number of 748 occurrence records. The suggested sizes of the sites vary from 0.22 to 23.40 ha with the total area of 177.61 ha (see Supplementary data, Table S3). A weak correlation has been established between site area and number of CWR species (R\u0026thinsp;=\u0026thinsp;0.298, P-value\u0026thinsp;=\u0026thinsp;0.046). Although it has been reported (Kahilainen et al. \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e) that locality area and connectivity between similar localities in conservation planning best conserves both species and intrapopulation genetic diversity, some other factors, such as different land use category, different land ownership, limited size of protected areas (e.g., protected hillfort sites usually are small), prevent from following this recommendation more closely.\u003c/p\u003e\n\u003cp\u003eWithin the 45 sites, the most frequent species inventoried were \u003cem\u003eDactylis glomerata\u003c/em\u003e (33 sites), \u003cem\u003eVicia cracca\u003c/em\u003e (32 sites), \u003cem\u003eCorylus avellana\u003c/em\u003e (28 sites), \u003cem\u003ePhleum pratense\u003c/em\u003e (26 sites), \u003cem\u003ePrunus padus\u003c/em\u003e (26 sites), \u003cem\u003eRubus idaeus\u003c/em\u003e (24 sites), \u003cem\u003eThymus pulegioides\u003c/em\u003e (23 sites), \u003cem\u003eFragaria viridis\u003c/em\u003e (22 sites), \u003cem\u003ePoa angustifolia\u003c/em\u003e (21 sites), \u003cem\u003eFragaria vesca\u003c/em\u003e (20 sites), and \u003cem\u003eTrifolium medium\u003c/em\u003e (20 sites). The least represented populations were those of \u003cem\u003eAllium angulosum, A. scorodoprasum, A. vineale, Asparagus officinalis, Hippophae rhamnoides, Mentha aquatica, Onobrychis viciifolia, Poa trivialis, Rubus nessensis, R. plicatus, Trifolium campestre, T. hybridum, Vaccinium myrtillus, V. oxycoccos, V. vitis-idaea, Vicia pisiformis\u003c/em\u003e, and \u003cem\u003eV. tetrasperma\u003c/em\u003e, each occurring in one single site out of the 45 sites investigated. We have grouped the distribution of the 83 CWR species across the 45 sites into 5 frequency groups (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). As seen from Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, the required minimum of 5 populations, as proposed by Dulloo et al. (\u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e), has not yet been met for 36 CWR species, while the minimum of 10 populations, as suggested by Whitlock et al. (\u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e) for relatively widespread species, has not been met for 20 more species.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eDistribution of 83 CWR priority species across 45 potential CWR genetic reserve sites\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFrequency group\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNo. of CWR occurrence sites\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNo. of CWR species\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePercent of CWR priority list\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u0026ndash;4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e25.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u0026ndash;9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e13.9\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u0026ndash;14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15\u0026ndash;19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026ge;\u0026thinsp;20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7.6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal species in 45 sites\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e57.6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFull priority list\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e144\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e100.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal records in 45 sites\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e748\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe selected 45 potential CWR genetic reserve sites were mapped with QGIS showing that they represent all four climate regions and seven of the 10 subregions of the country (Fig.\u0026nbsp;1) and are in different national protected areas and/or Natura 2000 sites of Community importance (SCIs) (Supplementary data, Table S3). As seen from Fig.\u0026nbsp;1, only three climatic subregions (A1, A2, and B5) are not represented by the current approach, while three subregions (C7, D8 and D9) are the least represented ones.\u003c/p\u003e\n\u003cp\u003eAdvantages of the first approach, the evaluation of the preselected sites, include known concrete locations, usually in a protected area, state-owned land status, and relatively easily distinguishable site boundaries around of the easier manageable relatively small sites. This is in line with the concept of the so-called micro-reserves, initiated in Spain for rare species conservation (Laguna et al. \u003cspan class=\"CitationRef\"\u003e2004\u003c/span\u003e), which could also be applied in the selection of the most appropriate wild populations (MAWPs), a key object in \u003cem\u003ein situ\u003c/em\u003e conservation, by treating the MAWPs as analogues of rare species. In such small reserves, the target species are relatively highly concentrated per site area, i.e. 16.6 species per 3.9 ha on average (see Supplementary data, Table S3), compared to 17.8 species per grid cell 4\u0026times;4 km in protected areas (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e below). It has been reported that small reserves allow to take advantage of conservation opportunities provided by cultural sites, sacred natural sites, and other faith-based sites in otherwise transformed landscapes (Dudley et al. \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). And that is the case in the current study as 30 potential CWR reserve sites were identified in the ancient hillfort sites. These, along with the water protection zones may be attributed to the other effective area-based conservation measures (OECM) as defined by the Convention on Biological Diversity in Decision 14/8 (IUCN-WCPA 2019) as they deliver the effective \u003cem\u003ein-situ\u003c/em\u003e conservation of biodiversity, including CWRs, regardless of their primary objectives. From the legal point of view, it is easier to establish small reserves outside of protected areas than large ones. However, the coverage of all target species, let alone their distinctive populations, by such preselected small sites may require a high number of small reserves. This leads to a more comprehensive approach, the application of large georeferenced plant databases.\u003c/p\u003e\n\u003cp\u003eBy applying the second approach, the \u003cem\u003ein situ\u003c/em\u003e CWR National Inventory database has been created by combining target taxa occurrence data from four major datasets: 1) Database of EU habitat mapping in Lithuania (BIGIS); 2) Database of Herbarium of Nature Research Centre (BILAS); 3) Lithuanian Vegetation Database (EU-LT-001); and 4) Global Biodiversity Information Facility (GBIF). Most of the recent data are contained in BIGIS and GBIF (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). The compiled database can run in both Microsoft Access and QGIS formats.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab5\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eThe numbers of occurrences of CWR priority species\u003csup\u003e1\u003c/sup\u003e in four databases by data oldness\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eData collected\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eBIGIS\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eBILAS\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eEU-LT-001\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eGBIF\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTotal\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBefore 2010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7,673\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7,713\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAfter 2010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e284,264\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2,155\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7,187\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e293,615\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e284,264\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7,682\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2,174\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7,208\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e301,328\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003e The numbers refer to 140 out of 144 CWR priority species. No data on \u003cem\u003eBarbarea stricta, Lathyrus pisiformis, Onobrychis arenaria, Vaccinium microcarpum\u003c/em\u003e is presented. A lack of distribution data of the four CWR priority species has been attributed to the species identification issues (\u003cem\u003eBarbarea stricta\u003c/em\u003e vs \u003cem\u003eB. vulgaris\u003c/em\u003e and \u003cem\u003eVaccinium microcarpum\u003c/em\u003e vs \u003cem\u003eV. oxycoccos\u003c/em\u003e) and rarity of species (\u003cem\u003eLathyrus pisiformis\u003c/em\u003e and \u003cem\u003eOnobrychis arenaria\u003c/em\u003e).\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003e GBIF.org (12 July 2023) GBIF Occurrence Download \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.15468/dl.y43bqn\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e3\u003c/sup\u003e The most up-to-date total is 293,615 which includes multiple occurrences of the same species per grid cell 4\u0026times;4 km. If counting only distinct species records per grid cell, the total number of occurrences is 68,686.\u003c/p\u003e\n\u003cp\u003eHotspot analysis of priority CWR occurrences performed with QGIS in 4\u0026times;4 km grid cells showed that the highest numbers of the target species occurrences are in the north-western and south-eastern parts of the country (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), where most of the potential genetic reserve sites (green dots) are identified as well (see also Fig.\u0026nbsp;1 for details).\u003c/p\u003e\n\u003cp\u003eFurther, to facilitate conservation actions, we have focused on CWR distribution in protected areas (PAs). These incorporate European Union PAs, i.e., Natura 2000 network, dedicated to the protection of habitats, and national PAs, i.e., national parks, regional parks, strict nature reserves and other nature reserves. It was established that 1882 grid cells at least partly overlap with the Natura 2000 sites and 1941 grid cells overlap with the national protected areas. As most of the national PAs overlap with Natura 2000 sites, we analysed CWR distribution in the grid cells inside both the EU and national PAs. The most species-rich grid cells (up to 60 species per cell) were identified in the PAs in Vilnius County, Southeast Lithuania, including Auk\u0026scaron;tadvaris and Neris Regional Parks (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). This is, at least partly, due to diverse ecogeographic conditions, and these sites are valuable \u003cem\u003ein situ\u003c/em\u003e reserves for CWR use in research and education as they are easily accessible by researchers and those who use iNaturalist or similar applications for sharing data of species observations.\u003c/p\u003e\n\u003cp\u003eIf compared with species richness, the mapping of species observations across the country presented a quite different mosaic of grid cells (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). A similar pattern was observed when the protected areas were analysed separately revealing the observation-richest grid cells in Žemaitija National Park, Salantai Regional Park, Nemuno Delta Regional Park, and Pagramantis Regional Park (all in Western Lithuania) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe mean number of species per grid cell in PAs was 17.8, while outside of PAs it was 13.8 (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). The Kruskal-Wallis test for equal medians showed that there was a significant difference between the area category medians (H\u0026thinsp;=\u0026thinsp;147.5, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Post-hoc pairwise comparisons using the Mann-Whitney method revealed that the medians differed significantly in all categories (Bonferroni corrected p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) with the highest number of species per cell in PAs. Regarding the number of observations per grid cell, significant differences were established between all three categories (Kruskal-Wallis test H\u0026thinsp;=\u0026thinsp;202.8, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Post-hoc Mann-Whitney, Bonferroni corrected p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These results suggest that higher CWR diversity is more likely to be found in PAs, where \u003cem\u003ein situ\u003c/em\u003e conservation of CWR should be focused on.\u003c/p\u003e\n\u003cp\u003eAs the numbers of species and their occurrences taken separately provide an incomplete picture of CWR distribution, the Shannon diversity index (SDI), a combined indicator of species richness and abundance, was calculated for each grid cell. The results showed that the mean SDI in PAs was 2.53, while outside of PAs it was 2.41, suggesting statistically significant differences between the inside and outside of PAs (Kruskal-Wallis test for equal medians H\u0026thinsp;=\u0026thinsp;17.32, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e) (see also Supplementary data, Fig. \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e and Fig. S2 for maps of species richness and the number of observations, respectively, outside of PAs, and Fig. S3, Fig. S4, and Fig. S5 for SDI-based species mapping across the country, in its PAs, and outside PAs, respectively). The Shannon equitability index was also calculated to find out how similar the abundances of different CWR species are in a grid cell with the advantage that it is easier interpretable, as its value ranges from 0 to 1 where 1 indicates complete evenness (Bobbitt \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Differently from the above-mentioned results, this indicates that potential CWR \u003cem\u003ein situ\u003c/em\u003e conservation sites can also be established outside of the PA network.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab6\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eTotal and mean (per grid cell 4\u0026times;4 km) numbers of CWR species, numbers of CWR species observations, and mean values of Shannon diversity and Shannon equitability indices inside and outside of protected areas (\u0026plusmn;\u0026thinsp;is followed by standard deviation; different letters indicate significant differences at p\u0026thinsp;=\u0026thinsp;0.001)\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eArea category\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eNo. of unique CWR species\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eNo. of CWR species observations\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eShannon diversity index (H)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eShannon equit-ability index\u003c/p\u003e\n\u003cp\u003eE\u003csub\u003eH\u003c/sub\u003e =H / ln(S)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal (S)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMean per cell\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMean per cell\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMean per cell\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMean per cell\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInside PAs\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e138\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17.8\u0026thinsp;\u0026plusmn;\u0026thinsp;10.6 a\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e190,106\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e82.6\u0026thinsp;\u0026plusmn;\u0026thinsp;83.6 a\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.67 a\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.513\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOutside PAs\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e140\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.8\u0026thinsp;\u0026plusmn;\u0026thinsp;9.5 c\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e111,222\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e50.9\u0026thinsp;\u0026plusmn;\u0026thinsp;61.3 c\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74 b\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.488\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal country\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e140\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16.0\u0026thinsp;\u0026plusmn;\u0026thinsp;10.3 b\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e301,328\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e67.9\u0026thinsp;\u0026plusmn;\u0026thinsp;75.7 b\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71 ab\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.502\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe obtained Shannon diversity index values could be interpreted as indicating moderate (2.50\u0026ndash;2.99) and low (2.00\u0026ndash;2.49) diversity (Fernando, \u003cspan class=\"CitationRef\"\u003e1998\u003c/span\u003e). Similalrly, the Shannon equitability index show moderate abundances of CWR species both inside and outside of protected areas.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eA comprehensive and annotated national CWR checklist has been created for Lithuania for the first time. It serves as a baseline information resource for CWR conservation planning and action. Prioritization of CWR taxa could be made based solely on their use in breeding of socio-economically important crops. In this case, even the invasive species can be prioritized (see, e.g., Fitzgerald et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, for the \u003cem\u003ein situ\u003c/em\u003e conservation, some other criteria, like species nativeness and threat level, are preferred to justify CWR conservation in natural or seminatural habitats and comply with the local regulations. The latter concept complies with the actions to mitigate the impact of climate change on plant communities and whole ecosystems, and thus is highly appreciated. Most of the priority CWR (93.8%) are native species of Lithuanian flora, and 8.3% are threatened (IUCN regional evaluation categories EN and VU). A degree of CWR relatedness to crop is another major criterion for CWR prioritisation as it indicates whether the wild relative could be easily used in breeding or requires more sophisticated techniques. Although the gene pool approach remains the direct indicator of CWR relatedness to crops, the taxonomic group approach plays an important role when the gene pool concept cannot be applied. In the current study, 56.9% of priority CWR species are related to crops based on the gene pool concept and the rest (43.1%) stand for taxonomic group relationships. There are 62 CWR species within GP1 of their respective crops and 22 CWR species within TG1 and TG2 to crops.\u003c/p\u003e \u003cp\u003eTwo approaches towards CWR genetic reserve selection have been employed in this study which can complement each other: (1) CWR-targeted evaluation of preselected sites, like sites of Community importance (SCI, Natura 2000 network) and national protected areas, as well as other effective area-based conservation measures (OECMs), such as ancient hillfort sites (which are state protected archaeological objects) and ecological protection zones of water bodies; all 45 potential genetic reserve sites have been selected by this way covering 83 species or 57.6% of the national CWR priority list, and (2) analysis of large georeferenced plant databases for CWR species richness and number of observations; multiple hotspots of priority CWR species have been identified by this way covering 140 species, or 97.2%, of the priority list. If the first approach evaluates all conditions at the place, the second approach is based solely on target species distribution and abundance. Hotspot analysis of CWR species richness and number of observations suggested that higher CWR diversity is more likely to be found in protected areas. However, Shannon diversity and Shannon equitability indices showed that the areas outside of the protected areas are also suitable for CWR genetic reserve establishment.\u003c/p\u003e \u003cp\u003eIt has been reported that area-based conservation efforts, which include both protected areas and other effective area-based conservation measures (OECMs), are likely to extend and diversify (Maxwell et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Thus, the current study is in congruence with this statement as the use of OECMs (ancient hillfort sites and river protection zones) contributed substantially to selecting the potential CWR genetic reserve sites, developing the national genetic reserve network and \u003cem\u003ein situ\u003c/em\u003e CWR dataset inclusion into the European Catalogue EURISCO.\u003c/p\u003e"},{"header":"Declarations","content":" \u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJ.L. developed the concept and wrote the main manuscript text, D.U. carried out CWR distribution analyses and created the national CWR database, M.L. compiled the CWR checklist, B.K. and J.L. collected data in the field and carried out primary analysis. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe research was supported by the German Federal Ministry for Agriculture and Food (BMEL) through the Bioversity International coordinated project CWR in EURISCO, Agreement No: L21ROM196. The bulk part of the species distribution data was provided by the project \u0026ldquo;Inventory of European Union habitats on the territory of Lithuania\u0026rdquo;, 2011\u0026ndash;2014 (BIGIS database). Potential CWR in situ conservation sites were established with the support of the State Forest Service, Contract No. S-54, 2023.05.03.\u003c/p\u003e\n\u003ch2\u003eConflict of interest\u003c/h2\u003e \u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBilotaitė-Jokubauskienė Ž (2022) Tarptautiniu mastu patvirtinta nauja lietuvi\u0026scaron;ka putinų veislė \u0026lsquo;Dievind\u0026rsquo;. https://sodospalvos.lt/rubrikos/tarptautiniu-mastu-patvirtinta-nauja-lietuviska-putinu-veisle-dievind/ (in Lithuanian) Accessed Jan 2024\u003c/li\u003e\n\u003cli\u003eBobbitt Z (2021) Shannon Diversity Index: Definition \u0026amp; Example. In: Statology: Statistics. Simplified. https://www.statology.org/shannon-diversity-index/ Accessed Apr 2024\u003c/li\u003e\n\u003cli\u003eBraun-Blanquet J (1964) Pflanzensoziologie. Grundzuge der Vegetationskunde. \u0026ndash; Wien\u0026ndash;New York.\u003c/li\u003e\n\u003cli\u003eChytr\u0026yacute; M, Hennekens SM, Jim\u0026eacute;nez-Alfaro B (2016) European Vegetation Archive (EVA): An integrated database of European vegetation plots. Appl Veg Sci, 19:173\u0026ndash;180. https://doi.org/10.1111/avsc.12191 \u003c/li\u003e\n\u003cli\u003eČesonienė L (2022) Paprastosios spanguolės, paprastieji putinai ir sodinės \u0026scaron;ilauogės. \u003cem\u003eIn\u003c/em\u003e: Ruzgas V, Stanys V (sudarytojai): Augalų Selekcija Lietuvoje Amžių Sandūroje. Lietuvos agrarinių ir mi\u0026scaron;kų mokslų centras. Akademija, Kėdainių r. ISBN 978-609-451-005-2, pp 211\u0026ndash;216. https://www.lammc.lt/data/public/uploads/2022/06/augalu-selekcija.pdf (in Lithuanian) Accessed Jan 2024\u003c/li\u003e\n\u003cli\u003eCouncil Directive 92/43/EEC of 21 May 1992 on the conservation of natural habitats and of wild fauna and flora. Official Journal L206, 22/07/1992 P. 0007 \u0026ndash; 0050. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex%3A31992L0043 Accessed Apr 2024\u003c/li\u003e\n\u003cli\u003eDudley N, Timmins HL, Stolton S, Watson JEM (2024) Effectively incorporating small reserves into national systems of protected and conserved areas. Diversity, 16:216. https://doi.org/10.3390/d16040216 \u003c/li\u003e\n\u003cli\u003eDulloo ME, Labokas J, Iriondo JM, Maxted N, Lane A, Laguna E, Jarvis A, Kell SP (2008) Genetic reserve location and design. \u0026ndash; In: Iriondo JM, Maxted N, Dulloo ME (eds), Conserving Plant Genetic Diversity in Protected Areas: Population Management of Crop Wild Relatives: 23\u0026ndash;64. \u0026ndash; Wallingford, UK\u003c/li\u003e\n\u003cli\u003eECPGR (2021) Plant Genetic Resources Strategy for Europe. European Cooperative Programme for Plant Genetic Resources, Rome, Italy. https://www.ecpgr.cgiar.org/fileadmin/bioversity/publications/pdfs/PGR_STRATEGY_LP_22_Nov_revised.pdf Accessed Jan 2024\u003c/li\u003e\n\u003cli\u003eEuropean Environment Agency (2023) The Natura 2000 protected areas network. https://www.eea.europa.eu/themes/biodiversity/natura-2000/the-natura-2000-protected-areas-network Accessed Jan 2024\u003c/li\u003e\n\u003cli\u003eEuro+Med 2006+ [continuously updated]: Euro+Med PlantBase - the information resource for Euro-Mediterranean plant diversity. \u0026ndash; Published at http://www.europlusmed.org Accessed Jan 2024\u003c/li\u003e\n\u003cli\u003eFAO (2001) The International Treaty on Plant Genetic Resources for Food and Agriculture. FAO, Rome. https://www.fao.org/3/i0510e/i0510e.pdf Accessed Apr 2024\u003c/li\u003e\n\u003cli\u003eFernando, E. S. (1998) Forest formations and flora of the Philippines: Handout in FBS 21. College of Forestry and Natural Resources, University of the Philippines at Los Ba\u0026ntilde;os (Unpublished).\u003c/li\u003e\n\u003cli\u003eFitzgerald H, Palm\u0026eacute; A, Aronsson M, Asdal \u0026Aring;, Bjureke K, Endresen D et al (2018) The Nordic Priority Crop Wild Relative Dataset. figshare. Dataset. https://doi.org/10.6084/m9.figshare.5688130.v3 \u003c/li\u003e\n\u003cli\u003eFitzgerald H, Palm\u0026eacute; A, Asdal \u0026Aring;, Endresen D, Kiviharju E, Lund B, Rasmussen M, Thorbj\u0026ouml;rnsson H, Weibull J (2019) A regional approach to Nordic crop wild relative in situ conservation planning. Plant Genetic Resources: Characterization and Utilization 17(2):196\u0026ndash;207. https://doi.org/10.1017/S147926211800059X\u003c/li\u003e\n\u003cli\u003eGudžinskas Z (1999) Vascular Plants of Lithuania. Institute of Botany, Vilnius, 211 pp\u003c/li\u003e\n\u003cli\u003eGudžinskas Z, Petrulaitis L, Uogintas D et al (2023) Invasive and alien species in Lithuania https://www.researchgate.net/publication/373603395_Invazines_ir_svetimzemes_rusys_Lietuvoje_-_Invasive_and_Alien_Species_in_Lithuania Accessed Nov 2023\u003c/li\u003e\n\u003cli\u003eGudžinskas Z, Ryla M (2006) Orchids (Orchidaceae) of Lithuania (in Lithuanian with English Preface and Summary). Institute of Botany, Vilnius, 104 pp\u003c/li\u003e\n\u003cli\u003eHarlan JR, de Wet JMJ (1971) Towards a rational classification of cultivated plants. Taxon 20:509\u0026ndash;517. https://doi.org/10.2307/1218252\u003c/li\u003e\n\u003cli\u003eHassler M (1994\u0026ndash;2024) World Plants. Synonymic Checklist and Distribution of the World Flora. Version 18.4; last update Feb. 11th, 2024. https://www.worldplants.de. Last accessed 13/02/2024.\u003c/li\u003e\n\u003cli\u003eIPNI (2023) International Plant Names Index. Published on the Internet http://www.ipni.org, The Royal Botanic Gardens, Kew, Harvard University Herbaria \u0026amp; Libraries and Australian National Herbarium. Accessed Jan 2024\u003c/li\u003e\n\u003cli\u003eIUCN Species Survival Commission (2012) IUCN Red List categories and criteria, version 3.1, second edition. Gland, Switzerland, 38 pp\u003c/li\u003e\n\u003cli\u003eIUCN-WCPA Task Force on OECMs (2019) Recognising and reporting other effective area-based conservation measures. Gland, Switzerland: IUCN. https://doi.org/10.2305/IUCN.CH.2019.PATRS.3.en \u003c/li\u003e\n\u003cli\u003eJankevičienė R (Compiler) (1998) Dictionary of Plant Names. Institute of Botany, Vilnius, 523 pp\u003c/li\u003e\n\u003cli\u003eKahilainen A, Puurtinen M, Kotiaho JS (2014) Conservation implications of species\u0026ndash;genetic diversity correlations. Global Ecol Conserv 2:315\u0026ndash;323. https://doi.org/10.1016/j.gecco.2014.10.013\u003c/li\u003e\n\u003cli\u003eKell SP, Kn\u0026uuml;pffer H, Jury SL, Ford-Lloyd BV and Maxted N (2008) Crops and Wild Relatives of the Euro-Mediterranean Region: Making and Using a Conservation Catalogue. In: Maxted N et al (eds). Crop Wild Relative Conservation and Use: 69\u0026ndash;109. CAB International, Wallingford, UK.\u003c/li\u003e\n\u003cli\u003eKuusk V, Tabaka L, Jankevičienė R (eds) (1996) Flora of the Baltic Countries. Compendium of Vascular Plants, Volume 2. Tartu, Estonia, 372 pp\u003c/li\u003e\n\u003cli\u003eKuusk V, Tabaka L, Jankevičienė R (eds) (2003) Flora of the Baltic Countries. Compendium of Vascular Plants, Volume 3. Tartu, Estonia, 408 pp\u003c/li\u003e\n\u003cli\u003eLaasimer L, Kuusk V, Tabaka L, Lekavičius A (eds) (1993) Flora of the Baltic Countries. Compendium of Vascular Plants, Volume 1. Tartu, Estonia, 378 pp\u003c/li\u003e\n\u003cli\u003eLabokas J, Karpavičienė B (2018) Creation of a network of seed sites for in-situ conservation of medicinal and aromatic plant genetic resources in Lithuania. Botanica 24(1):87\u0026ndash;97\u003c/li\u003e\n\u003cli\u003eLabokas J, Karpavičienė B, Ra\u0026scaron;omavičius V, Gelvonauskis B (2016) Developing a national crop wild relative in situ conservation strategy for Lithuania: Creation of a national CWR inventory and its prioritization. In: Maxted N, Dulloo ME and Ford-Lloyd BV (eds.) Enhancing Crop Genepool Use: Capturing Wild Relative and Landrace Diversity for Crop Improvement. CAB International. P. 217\u0026ndash;230.\u003c/li\u003e\n\u003cli\u003eLaguna E, Deltoro V I, P`erez-Botella J, P`erez-Rovira P, Serra L, Olivares A, et al. (2004) The role of small reserves in plant conservation in a region of high diversity in eastern Spain. Biol Conserv 119:421\u0026ndash;426. https://doi.org/10.1016/j.biocon.2004.01.001\u003c/li\u003e\n\u003cli\u003eLithuanian Hydrometeorological Service. 2015. Climate regions of Lithuania. http://www.meteo.lt/en/climate-regions-of-lithuania Accessed Jan 2023\u003c/li\u003e\n\u003cli\u003eMaxted N, Ford-Lloyd BV, Jury S, Kell SP, Scholten M (2006) Towards a definition of a crop wild relative. Biol Conserv 15:2673\u0026ndash;2685\u003c/li\u003e\n\u003cli\u003eMaxted N, Magos Brehm J and Kell S (2013) Resource Book for Preparation of National Conservation Plans for Crop Wild Relatives and Landraces. https://www.fao.org/fileadmin/templates/agphome/documents/PGR/PubPGR/ResourceBook/TEXT_ALL_2511.pdf \u003c/li\u003e\n\u003cli\u003eMaxwell SL, Cazalis V, Dudley N. et al. (2020) Area-based conservation in the twenty-first century. Nature 586:217\u0026ndash;227. https://doi.org/10.1038/s41586-020-2773-z\u003c/li\u003e\n\u003cli\u003eMinister of the Environment of the Republic of Lithuania (2024) Order on the approval of the list of species of animals, plants and mushrooms protected by the Republic of Lithuania. https://e-seimas.lrs.lt/portal/legalAct/lt/TAD/TAIS.219902/asr (Consolidated edit from 15 March 2024). Accessed Apr 2024\u003c/li\u003e\n\u003cli\u003ePagad S (2022) Global Register of Introduced and Invasive Species - Lithuania. Version 1.2. Invasive Species Specialist Group ISSG. Checklist dataset https://doi.org/10.15468/ewm0v1 accessed via GBIF.org on 2024-01-14\u003c/li\u003e\n\u003cli\u003ePOWO (2024) Plants of the World Online. Facilitated by the Royal Botanic Gardens, Kew. Published on the Internet; http://www.plantsoftheworldonline.org/ Accessed Jan 2024\u003c/li\u003e\n\u003cli\u003eQGIS.org (2023). QGIS Geographic Information System. Open Source Geospatial Foundation Project. http://qgis.org/en/site Accessed Jan 2024\u003c/li\u003e\n\u003cli\u003eRa\u0026scaron;omavičius V (ed.) (2021) Red Data Book of Lithuania. Animals, plants, fungi. \u0026ndash; Vilnius. https://am.lrv.lt/uploads/am/documents/files/Raudonoji%20knyga/Raudonoji_knyga_2021_WEB.pdf Accessed Jan 2024\u003c/li\u003e\n\u003cli\u003eShannon CE (1948) A mathematical theory of communication. The Bell System Technical Journal 27(3):379\u0026ndash;423. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x\u003c/li\u003e\n\u003cli\u003eTaylor NG, Kell SP, Holubec V, Parra-Quijano M, Chobot K, Maxted N (2017) A systematic conservation strategy for crop wild relatives in the Czech Republic. Diversity Distrib., 23: 448-462. https://doi.org/10.1111/ddi.12539\u003c/li\u003e\n\u003cli\u003eThormann I, Kell S, Magos Brehm J, Dulloo ME, Maxted N (2017) CWR checklist and inventory data template v.1, https://doi.org/10.7910/DVN/B8YOQL, Harvard Dataverse, V4.\u003c/li\u003e\n\u003cli\u003eUSDA, Agricultural Research Service, National Plant Germplasm System (2024) Germplasm Resources Information Network (GRIN Taxonomy). National Germplasm Resources Laboratory, Beltsville, Maryland. URL: https://npgsweb.ars-grin.gov/gringlobal/taxon/taxonomysearchcwr Accessed Jan 2024\u003c/li\u003e\n\u003cli\u003eVATZUM, The State Plant Service under the Ministry of Agriculture (2024) Lithuanian National List of Plant Varieties 2024. https://vatzum.lrv.lt/media/viesa/saugykla/2024/2/5ksAXYeAE0s.pdf Accessed Apr 2024\u003c/li\u003e\n\u003cli\u003evan Treuren R, Hoekstra R, van Hintum TJ (2017) Inventory and prioritization for the conservation of crop wild relatives in The Netherlands under climate change. Biol Conserv 216:123\u0026ndash;139. https://doi.org/10.1016/j.biocon.2017.10.003\u003c/li\u003e\n\u003cli\u003eVincent H, Wiersema J, Kell SP et al (2013) A prioritized crop wild relative inventory to help underpin global food security. Biol Conserv 167:265\u0026ndash;275 https://doi.org/10.1016/j.biocon.2013.08.011 \u003c/li\u003e\n\u003cli\u003eVSTT, State Service for Protected Areas Under the Ministry of Environment (2024) System of protected areas. https://saugoma.lt/en/system-of-protected-areas-ne Accessed Feb 2024\u003c/li\u003e\n\u003cli\u003eWeibull J, Phillips J (2020) Swedish Crop Wild Relatives: towards a national strategy for in situ conservation of CWR. Genetic Resources 1(1):17\u0026ndash;23. https://doi.org/10.46265/genresj.2020.1.17-24 \u003c/li\u003e\n\u003cli\u003eWhitlock W, Hipperson H, Thompson DBA, Butlina RK, Burke T (2016) Consequences of \u003cem\u003ein-situ\u003c/em\u003e strategies for the conservation of plant genetic diversity. Biological Conservation, 203: 134\u0026ndash;142\u003c/li\u003e\n\u003cli\u003eWikispecies: Free species directory (2024) Tracheophyta. https://species.wikimedia.org/wiki/Tracheophyta Accessed Jan 2024\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Conservation planning, Crop wild relative, CWR hotspot, Food and agriculture, Genetic reserve, Protected area","lastPublishedDoi":"10.21203/rs.3.rs-4412054/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4412054/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe crop and CWR checklist of Lithuania was created containing 2,630 taxa. The checklist comprises 1,384 native taxa including archaeophytes and 1,246 neophytes. In total, 699 taxa (26.6%) could be quite strictly defined as of food or forage use. A list of 144 CWR priority species with 135 native species and archaeophytes and 9 naturalized species was generated. In total, 53 genera of food and forage species belonging to 15 families are represented by the priority CWR. Two approaches for CWR genetic reserve selection have been employed in this study: (1) CWR-targeted evaluation of preselected sites, including Natura 2000 sites, national protected areas, and other effective area-based conservation measures (OECMs), such as ancient hillfort sites and ecological protection zones of water bodies; and (2) analysis of large georeferenced plant databases. Forty-five potential genetic reserve sites have been selected by the first approach covering 83 species or 57.6% of the national CWR priority list. With the second approach, the \u003cem\u003ein situ\u003c/em\u003e CWR National Inventory database has been created by combining data from the Database of EU habitat mapping in Lithuania (BIGIS), Herbarium Database of the Nature Research Centre (BILAS), Lithuanian Vegetation Database (EU-LT-001), and Global Biodiversity Information Facility (GBIF). Hotspot analysis of CWR species richness and number of observations suggested that higher CWR diversity is more likely to be found in protected areas. However, Shannon diversity and Shannon equitability indices showed that the areas outside of the protected areas are also suitable for CWR genetic reserve establishment.\u003c/p\u003e","manuscriptTitle":"Enhancing in Situ Conservation of Crop Wild Relatives for Food and Agriculture in Lithuania","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-23 11:20:49","doi":"10.21203/rs.3.rs-4412054/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9487be84-7232-4b32-bb08-b2e61c2e3cd3","owner":[],"postedDate":"May 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-07T23:38:31+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-23 11:20:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4412054","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4412054","identity":"rs-4412054","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}