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Electrohydrogenesis effluent as a potential biofertilizerTo characterize the electrohydrogenesis left-over residues as potential biofertilizers, the sample from the operating reactors was performed a 16S rRNA sequencing test, and interestingly, the results revealed that the bio-electrohydrogenesis effluent was enriched with various microorganisms including plant growth-promoting microbes that display biofertilizer-like features. Among the well-known plant-promoting bacterial genera observed in DF-MEC residues included Azospirillum, Mycobacterium, Chryseobacterium, Paenibacillus, Rhizobacter, Pseudomonas, Achromobacter, Bradyrhizobium, Actinomyces, Sphingomonas, Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium, Gordonia, Rhodococcus, Bacillus, Methylobacterium-Methylorubrum, Microbacterium, Flavobacterium, Devosia, Acinetobacter, Mesorhizobium, Enterobacter, Aeromonas, Beijerinckia, etc.24,25,26 (Fig. 2). Lots of investigations working on the feasibility of using biofertilizers other than chemical fertilizers have revealed that those aforementioned microbes play a major role in providing the required nutrients for enhanced crop yield.Figure 2The abundance of the Plant growth-promoting bacteria (genus level) detected from the DF-MEC digestate (%).Full size imageNitrogen-fixing microorganismsThe detected nitrogen-fixing microorganisms from the electrohydrogenesis effluent include Azospirillum sp. (0.11 ± 0.02%), rhizobia (Rhizobium (0.058 ± 0.02%), Bradyrhizobium (0.11 ± 0.04%), and Mesorhizobium (0.1 ± 0.03%)), and Beijerinckia (0.08 ± 0.03%) (Fig. 2) and were repeatedly reported for their superior contribution to the plants’ nitrogen requirements through biological nitrogen fixation, which is an important component of sustainable agriculture25. Although the atmosphere counts about 78% N2, it couldn’t be used by plants in its natural state. Prior to getting used by plants, it needs to be converted to ammonia, which is the readily assimilable form of nitrogen by plants/or crops via a biological nitrogen fixation mechanism25. The biological Nitrogen fixation mechanism is summarized in Fig. 3.Figure 3Mechanism of nitrogen fixation bio-catalyzed by nitrogenase enzyme. The plant growth-promoting bacteria produce nitrogenase which is a complex enzyme consisting of dinitrogenase reductase and dinitrogenase. This complex enzyme plays a major role in molecular N2 fixation. Dinitrogenase reductase provides electrons and dinitrogenase uses those electrons to reduce N2 to NH3. However, oxygen is a potential threat to this process since it has the ability to get bound to the enzyme complex and make it inactive and consequently inhibit the process. Interestingly, bacterial leghemoglobin has a strong affinity for O2 and thus gets bound to free oxygen more strongly and effectively to suppress the available oxygen effects on the whole process of nitrogen fixation.Full size imagePhosphate-solubilizing microorganismsFurthermore, various phosphate-solubilizing and mineralizing strains were also found in bioelectrohydrogenesis residues collected from our DF-MEC integrated reactors. Among those microorganisms with the ability to solubilize/metabolize the insoluble inorganic phosphorus, the dominant bacterial genera included Pseudomonas (0.65 ± 0.15%), Bacillus (0.44 ± 0.11%), Rhodococcus (0.04 ± 0.009%), Rhizobium (0.05 ± 0.02%), Microbacterium sp. (0.04 ± 0.01%), Achromobacter (0.16 ± 0.07%), and Flavobacterium (0.058 ± 0.014%) (Fig. 2). Though enormous amounts of phosphorus are available in the soil, its high portion never contributes to plant growth in its primitive state, unless it is bio-transformed into absorbable forms including monobasic and dibasic. Microbial phosphate solubilizing mechanisms are well described in Fig. 4.Figure 4Inorganic phosphorus solubilization by phosphate-solubilizing rhizobacteria. A bacterium solubilizes inorganic phosphorus through the action of low molecular weight organic acids such as gluconic and citric acids. The hydroxyl (OH) and carboxyl (COOH) groups of these acids chelate the cations bound to phosphate and thus convert insoluble phosphorus into a soluble organic form. The mineralization of soluble phosphorus occurs by synthesizing different phosphatases which catalyze the hydrolysis process. When plants incorporate these solubilized and mineralized phosphorus molecules, eventually, overall plant growth and crop yield significantly increase.Full size imagePhytohormone-producing microorganismsIn this current work, the electrohydrogenesis effluent also contained bacterial genera such as Mycobacterium (0.77 ± 0.18%), Allorhizobium (0.05 ± 0.02%), Pararhizobium (0.05 ± 0.02%), Paenibacillus (1.18 ± 0.24%), Bradyrhizobium (0.11 ± 0.04%), Rhizobium (0.05 ± 0.02%), Acinetobacter (0.14 ± 0.02%), and Azospirillum (0.11 ± 0.025%) (Fig. 2) that have the ability to synthesize indole-3-acetic acid/indole acetic acid (IAA) through indole-3-pyruvic acid and indole-3-acetic aldehyde25. IAA is a well-known type of phytohormone that enhances plant/crop growth. Particularly, Azospirillum sp., also produce various phytohormones namely cytokinins, gibberellins, ethylene, abscisic acid and salicylic acid, auxins, vitamins such as niacin, pantothenic acid, and thiamine. The conceptional model delineating the positive effects of inoculation with Azospirillum sp. a phytohormones-producer plant growth-promoting rhizobacteria and its detailed functions on plant growth are summarized and illustrated in Fig. S1. Therefore, the existence of those rhizobacteria in the bioelectrohydrogenesis residues further implies the suitability of considering the DF-MEC left-over residues as potential biofertilizers.Heavy metals-bioremediating microorganismsSome other bacterial genera with the ability to bioremediate the heavy metal toxicity were also found within the bioelectrohydrogenesis left-over residues as well. Among the detected plant growth-promoting bacterial genera; Rhizobium (0.058 ± 0.023%), Mesorhizobium (0.1 ± 0.026%), Bradyrhizobium (0.11 ± 0.04%), Pseudomonas (0.65 ± 0.15%), and Achromobacter (0.16 ± 0.077%) were reported for their key contribution to alleviate the toxicity of the heavy metals via bioremediation process and improve the soil quality for a relief plant development26 (Fig. 2). Other detected heavy metals-bioremediating microorganisms’ species were Chryseobacterium sp. (0.08 ± 0.007%), Azospirillum (0.11 ± 0.02%), Bacillus (0.44 ± 0.11%), Enterobacter (8.57 ± 0.9%), Gordonia (0.06 ± 0.02%), Paenibacillus (1.18 ± 0.24%), Pseudomonas (0.65 ± 0.15%), and Actinomycetes (0.36 ± 0.05%) that either use microbial siderophores or enzymatic biodegradation process.Electrohydrogenesis left-over residues as a potential source of essential elements for plant growthAs aforementioned in “Materials and methods” section, the electrohydrogenesis left-over residues contained diverse microbial communities that degraded the MEC substrate and generate biogas and inorganic compounds. Moreover, it has been reported that those inorganic nutrients are generally available in fermentation effluent in readily plant-utilizable formats owing to substrate mineralization27. Beside detecting various plant growth-promoting microorganisms in the electrohydrogenesis effluent, a larger number of mineral elements essential for promoted growth and development of crop plants were also investigated and analyzed from the residues. The detected primary and secondary macro-elements’ concentrations in the residues were arranged in decreasing order as follows P  > S  > Na  > K  > N  > Ca  > Mg. Interestingly the findings show that the residues abundantly contained Phosphorus (2.766 × 103 mg/L), Nitrogen (274 mg/L), Potassium (282 mg/L), Calcium (17.66 mg/L), Magnesium (16.3 mg/L), Sulfur (1.225 × 103 mg/L), and Sodium (294.3 mg/L) which are well known as macro-nutrients needed in larger amounts for enhanced plant/ crop growth (Fig. 5).Figure 5Macro-, and micronutrients detected from the bio-electrohydrogenesis left-over residues (mg/L).Full size imageMoreover, small amounts of the microelements including Ni, Pb, Zn, Cu, Cr, Hg, Cd were also found in the electrohydrogenesis residues, and consistently these elements are generally required in small quantities for the development of plants (Fig. 5), otherwise, their high concentrations are toxic for the plant cells thus suppress or inhibit plant growth. The detected concentrations for the main microelements in this current research ranged only from 0.36 to 9.6 × 10–5 mg/L and were all reported to play fundamental roles in plant metabolic reactions.Cultivation of the leguminous crops using electrohydrogenesis left-over residues as fertilizerAfter evaluating the plant-growth promoting bacterial communities and the macro- and micronutrients required for plant/crop growth in the electrohydrogenesis left-over residues, the latter was directly used as fertilizer to grow three different plant species including tomato, chili, and brinjal as afore-described in the “Materials and methods” section. To access the potentials of the electrohydrogenesis effluent as fertilizer, the plants grown in the soil amended with the effluent (Soil + Effluent), were directly compared with their corresponding control plants (Soil + water). The results indicated that at the end of 1st month, the plants with effluent grew faster and generated a good amount of branching than the control plants (see Fig. 6), possibly due to the availability of both microbial species with bio-fertilizing aspects and micro-and macronutrients in the effluent.Figure 6Analysis of the plant growth at the end of the 1st month of cultivation. (a) Tomato in soil with effluent, and its control without effluent (b); (c) Chilli grown in soil with effluent, and its control without effluent (d); and (e) brinjal grown in soil with effluent, and its corresponding control grown without effluent (f) (after 2 months).Full size imageFor instance, tomato (Solanum lycopersicum L.) and chilli (Capsicum annuum L.) height in soil + electrohydrogenesis effluent was ~ 36.9 ± 2.1 cm and ~ 32.6 ± 0.8 cm respectively which was ~ 2.03 and ~ 1.2 times the height of their corresponding plant species in the control protocol, respectively (see Fig. 7). However, the brinjal species (Solanum melongena L.) didn’t show any remarkable height differences in both protocols after a month of cultivation (data not shown), probably due to their low adaptative characteristics to the new environment. However, after the 2nd month, the brinjal height in soil + effluent became 2.7 times that of the brinjal control cultivated without effluent (see Fig. 6e,f). Moreover, both the number of the plants’ leaves and their length in plants cultivated in soil + effluent, were remarkably higher than in plants grown without the supply of the effluent.Figure 7Daily plant growth analysis within one month of cultivation. (a) Tomato growth monitoring, (b) Chili growth analysis.Full size imageAt the end of the 3rd month, the plants in soil + electrohydrogenesis effluent generated more fruit with big size than the control plants (see Fig. 8), but the tomato (Solanum lycopersicum L.) didn’t generate fruits in both protocols at that time probably due to the high weather temperature that inhibitory affected its continuous growth, as previously reported that tomato species are generally so sensitive to temperature change28,29. The final yield was evaluated in terms of the size and number of fruits per cultivated plant. Chili cultivated in soil with MEC effluent generated 3 fruits/plant and its corresponding control without effluent produced only 1 fruit/plant. The chili fruit size in soil + effluent was 16 cm, approximately 18.7% higher than its corresponding control. Moreover, at the time of collecting data, the brinjal plant cultivated in soil with MEC effluent generated brinjal fruits whereas its corresponding cultivated without electrohydrogenesis effluent started flowering (see Fig. 8). These further indicate the significant contribution of the electrohydrogenesis effluent in speeding up the plant growth. Herein, the electrohydrogenesis left-over residues have notably improved the soil quality and significantly promoted the plants’ phenology characterized by plant growth, the generation of new leaves, flowering, and the production of fruits.Figure 8Analysis of plant growth characterized by the flowering and fruiting process at the end of the 3 months. (a) Chili grown in soil with effluent, and its control without effluent (b); (c) brinjal grown in soil with effluent and its corresponding control grown without effluent (d).Full size image More

The current study is the first to directly compare the differences in eukaryotic community diversity by metabarcoding eRNA and eDNA from an estuarine benthic ecosystem. This is also the first study to compare the diversity of environmental RNA and DNA using two of the most common loci examined in metabarcoding applications: COI and 18S. Only a handful of studies have used eRNA to assess diversity through metabarcoding and have demonstrated some differences in detected diversity between eRNA and eDNA in metabarcoding19,29,38. Environmental DNA has many useful applications, but one of the downfalls of DNA is that it can be detected long after an organism has inhabited an area, such as from dead organisms and even significant distances away from the source1. While the persistence of eDNA may be useful in detecting the presence of endangered, rare, or invasive species in marine systems, the persistence of eDNA, such as legacy DNA, may skew the accurate representation of community structure immediately after a disturbance event or during the exact moment of sampling21. Environmental RNA proves to be a useful tool because it is far less persistent in marine environments2,27. In the current study, RNA provides a snapshot of living organisms present in the mesocosms at the time of sampling, compared to DNA which detects past and present organisms in the sample.Benthic ecosystems often have high biodiversity because they are dynamic environments with various substrates favorable for hosting communities39. To date, there is no one loci and associated primer pair that will effectively detect all eukaryotic organisms, and in particular metazoans, so it is beneficial to use multiple loci when looking for a variety of taxa6. Previous marine eDNA studies have demonstrated the preferred method of using two loci to achieve comprehensive metabarcoding for taxonomically diverse environments5,30,40. The current study further demonstrates the need to use at least two different loci for targeted PCR and sequencing to truly capture the wide diversity in rich systems, such as the top oxygenated layer of the marine benthos. Our results demonstrate the types of organisms detected using the 18S loci vary widely from the organisms detected by COI. The 18S loci also detected a higher number of organisms than using the COI loci. The 18S allowed greater detection of metazoans whereas COI was useful in the detection of Oomycota (i.e., a eukaryotic microorganism that resembles fungi) protozoa. Protozoa are often food sources for meiofauna41; therefore, using COI for protozoan detection and 18S for metazoan detection showcases multiple trophic levels present in the mesocosms of the current study. Utilizing multiple loci not only broadens taxonomic diversity detection but can also highlight multiple trophic levels for understanding food webs originating in benthic environments15.A study evaluating arctic benthic diversity similarly found that COI detected fewer taxa than 18S using eDNA metabarcoding, and there was only ~ 40% taxa overlap between markers at the class level42. In the current study, the COI marker using both nucleic acid templates yielded a higher percentage of unassigned taxa after filtering for presence in the majority of mesocosms compared to 18S. A possible explanation for the low metazoan detection by COI may be that the unassigned taxa are metazoans rather than more SAR organisms. Additionally, singly detected metazoans were filtered out of the analysis if they were not detected in at least 4 mesocosms. For example, one type of amphipod was detected in only 3 mesocosm by RNA using the COI marker, but was not detected in any mesocosms using DNA. Therefore, the amphipod observation was excluded from the COI results in Fig. 3 and Table S3 because it was not found in at least 4 mesocosms.Overall, RNA provides a broader assessment of benthic community structure than DNA, particularly when using two loci/ markers for sequencing. In the current study, we used nuclear 18S ribosomal RNA and DNA and mitochondrial COI RNA and DNA sequences as markers for metabarcoding. The number of copies of ribosomal RNA per cell is higher than the copies of ribosomal DNA, and the ratio of RNA: DNA is higher in single-cell organisms, such as protists43. The higher number of unique ASVs detected using eRNA is likely attributed to the higher number of RNA copies of each marker in small, single-cell organisms successfully amplified during PCR, therefore making rare organisms easier to detect. The DNA of highly abundant or higher biomass organisms may “drown out” (i.e., mask) the sequences from lower abundance and biomass organisms during PCR amplification, thus resulting in lower detected α-diversity. The increased detection of ciliates and protozoa using eRNA are consistent with other recent eRNA metabarcoding results that found higher ciliate and protozoan diversity compared to eDNA using the same Uni18S primers19. Positive correlations between organism biomass and sequence copy numbers have been demonstrated for DNA metabarcoding conducted on invertebrate species44. In the current study, RNA allowed for the detected of both larger meiofauna and smaller microfauna, which is optimal for assessing true biodiversity with molecular assays. Chaetonotida, a type of gastrotrich, was the only taxa detected in 4 of the mesocosms using DNA that was not found with RNA. It is possible the chaetonotida may have died during the experiment due to sensitivities to new environmental conditions, and therefore were not detected with RNA. The temperature of the flowing seawater in the mesocosms was approximately 18 °C, and many marine chaetonotida prefer 23–28 °C and high organic matter substrate45.Previous studies have compared the accuracy of conventional morphological identification to molecular metabarcoding methods for assessing biodiversity. In estuarine-specific studies, metabarcoding methods are able to detect the majority of taxa identified with traditional methods and often detected higher species richness, or higher numbers of unique organisms, that was not found conventionally10,46,47. The limiting factor for higher resolution of taxonomic identification is the availability of species-specific sequences in barcoding databases48. However, barcoding databases are becoming more robust as an increasing number of researchers contribute high quality sequencing data to databases7. Therefore, eRNA metabarcoding techniques perform similarly to conventional morphological methods, and may even uncover higher biodiversity in systems like estuaries where meiofauna have been historically understudied and identified.Although eRNA α-diversity is higher compared to eDNA, there is some overlap between ASVs detected with eRNA and eDNA. The higher percentage of overlap between eDNA and eRNA ASVs is predominantly seen in the ASVs detected in all 7 of the mesocosms. The increased overlap in ASVs detected in all 7 mesocosms compared to the relatively lower overlap of ASVs found in only 1 of the 7 mesocosms is due to the filtering of random organisms found in only 1 mesocosm. Detection of a unique organism in a single mesocosm is likely not representative of the sample community and filters potential artifacts that may be introduced during cDNA synthesis from eRNA. However, all uniquely identified ASVs are used in the β-diversity analysis, so the higher number of unique ASVs detected from eRNA are likely driving the significant differences observed between eRNA and eDNA β-diversity. It is possible that using eRNA could increase the statistical power of a study design compared to eDNA because eRNA detects a higher number of ASVs. It is unlikely that a higher number of RNA ASVs could be due to splice variants contributing to unique sequences because the amplified regions of both markers do not contain introns. Therefore, no splicing of transcripts would be expected. Thus, the detected DNA ASVs are from living organisms in the mesocosms and the higher diversity of ASVs detected from RNA demonstrate that RNA is a more suitable option for assessing diversity of living organisms.It is evident that the mesocosms in the current study were rich with meio- and microfauna due to the number of unique organisms and broad diversity of different taxa. It is likely that collecting samples for nucleic acid extraction directly from the field site may result in higher diversity because there is no mechanical disturbance during the laboratory acclimation period and the presence of other organisms in the system, such as fish, macroinvertebrates, or birds. eDNA molecular abundance in samples has been shown to correlate to actual organismal abundance in laboratory environments, but the same correlation is not as apparent in field samples, which is likely due to a variety of collection and processing methods49. eRNA may be the better nucleic acid template for field collection once flash frozen, especially for the detection of protozoans. Future studies will compare the diversity of eukaryotic communities detected using eDNA and eRNA collected directly from the field rather than from sediment core mesocosms. Repeated sampling from a field site may help reduce transient eRNA detection when establishing accurate baseline community composition in field-based biomonitoring studies.Recently, meiofaunal organisms and communities are being explored as bioindicators, which are organisms whose presence are indicators for environmental stress and pollution17. For example, lower meiofaunal diversity and abundance is associated with higher pollution in harbors, and the presence of some genera of nematodes are correlated with higher concentrations of polycyclic aromatic hydrocarbons because they are more tolerant to pollution50. A previous study that used field-collected mesocosms from the same location as our current study found similar phyla detected with a metabarcoding approach; the majority of the sequences detected in benthic communities were from nematodes, arthropods, and the microfaunal SAR clade10. Similarly, the majority of the ASVs detected from the current study also corresponded with nematodes, arthropods, and the SAR clade, as well as other commonly detected meiobenthic organisms, such as polychaetes and Homalorhagida (i.e., mud dragons). A recent study exposed a benthic foraminiferal community to chromium, and found that eRNA metabarcoding was more robust for detecting changes in diversity at lower chromium concentrations compared to eDNA51. The eRNA metabarcoding method used in the current study detected meiofaunal taxa typical of marine or estuarine environments. Therefore, eRNA metabarcoding may be useful for efficiently identifying bioindicator species or taxa impacted by exposures to different contaminants and environmental stressors to aid with management of aquatic systems. Another advantage of eRNA is that RNA provides functional information about how organisms response to stress through altered transcription of activated pathways. eRNA will likely be a more powerful tool than eDNA because it allows for the detection of both bioindicator species and, in the future with increase development of genetic databases, environmental detection of biomarkers of stress through increased transcription of response genes.Many academic researchers are adopting molecular methods using High Throughput Sequencing as the future of biomonitoring surveys; however, few regulatory and environmental management organizations/ agencies have adopted metabarcoding into their routine biomonitoring practices for regulatory purposes. In marine benthic communities, metabarcoding provides a comprehensive assessment of diversity and is useful for detecting a broader array of organisms in biomonitoring surveys especially when using two markers47. eDNA is also useful for monitoring discrete communities (i.e., benthic versus pelagic)52, so it is possible that using eRNA could provide vital information about living organisms in specific environments compared to eDNA. Metabarcoding sequencing and bioinformatic approaches for benthic environments vary among studies, thus requiring some standardization between methods to further advance the use of metabarcoding in conservation and regulatory applications30,53.Like eDNA, some advancements must be made with eRNA to be used as a quantitative tool in molecular ecology. Validation of eDNA metabarcoding for assessing relative abundance of species is rapidly progressing by correlating laboratory studies of DNA shedding with field experiments1,54. Similar validation techniques can be used to develop eRNA metabarcoding as a quantitative or semi-quantitative method. There is growing interest in optimizing eRNA extraction protocols from different types of environmental media to standardize the use of eRNA in downstream molecular applications55. Thus, standardizing eRNA protocols will help with integration into environmental management toolkits for regulatory purposes.RNA poses unique barcoding challenges compared to DNA because the number of RNA transcripts from a gene are not always present in the same proportion compared to the gene copy number per genome (i.e., one DNA copy per cell), especially for differentially expressed genes. However, one possible way to work around this issue is to utilize constitutively expressed marker loci where transcription is generally stable and unaffected by environmental stressors, such as those used as reference genes for quantitative real-time PCR49. Fortunately, many loci chosen for metabarcoding purposes fit this criterium; the transcription of 18S and COI remain steady within the cell regardless of environmental stress.Collecting sediment cores from the environment and bringing them into controlled laboratory settings for community analysis through eRNA metabarcoding is a powerful tool that opens opportunities for this method to be used in a broad range of fields. For instance, field-collected mesocosms could be used in controlled settings to investigate the effects of individual or mixtures of toxicants on entire community and population-level outcomes. Marine sediments are often the ultimate sink for environmental contaminants, such as organic pollutants , heavy metals56, and plastic particles57, yet few studies investigate actual community-level changes in contaminant exposures. Marine benthic environments have high biodiversity, but the breadth of diversity in micro- and meiofaunal organisms is often understudied because traditional morphological methods are immensely time consuming. Sediment core mesocosms could also be used to understand the effects of global climate change stressors, such as fluctuating temperatures, surface water salinity and pH, on communities as well as conventional and emerging contaminants in combination with climate change stressors. Additionally, this method could also be used to understand how communities respond to a significant disturbance event or smaller series of stressors, which are often difficult to measure in environmental settings32,58. In these applications, eRNA is favorable for constructing community composition at a specific moment of the experiment to better regulate anthropogenic causes of environmental stress. More

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We applied a modified version of a planning framework for CWR conservation25,26 which has been used by numerous countries of Europee.g.29,63,64, Americae.g.65, Africa30 and Asia66,67. We addressed the following main steps of the toolkit (see Spanish version49): (i) CWR checklist, i.e., creating a list of CWR taxa distributed in an area (Supplementary Data 1), (ii) CWR inventory, i.e., taxa selection and collation of ancillary data, including taxonomic data (Supplementary Data 2), (iii) taxa extinction risk assessment (Table 1, Supplementary Data 3), and (iv) a systematic conservation planning assessment, i.e., spatial analyses to assess conservation areas (Fig. 1). We only provide a brief description of steps i-iii, as these are thoroughly described in Goettsch et al.2. Here, we focus on the systematic conservation planning assessment, introducing an approach in order to identify conservation areas for CWR that account for genetic differentiation in a spatially explicit way, through the use of proxies of genetic differentiation (Fig. 1).During the process -framed under the project “Safeguarding Mesoamerican crop wild relatives” (https://www.darwininitiative.org.uk/project/23007/)- more than 100 experts from academic, governmental, and non-governmental organizations from El Salvador, Guatemala, Honduras, Mexico, the UK, and IUCN participated in six workshops, shared data, and provided fundamental knowledge and feedback at each project stage to ensure accurate, reliable and robust information for next steps. The checklist, inventory and risk assessment were collaboratively developed between partners of El Salvador, Guatemala, and Mexico (hereafter, Mesoamerica; Goettsch et al.2). The spatial analysis to identify areas for in situ and ex situ conservation of CWR was done independently by each country.To assess conservation areas of CWR in Mexico, we developed proxies of genetic differentiation that account for evolutionary processes by including historical and environmental drivers of genetic diversity (see the Methods section ‘Proxies of genetic differentiation’). In addition, we used criteria such as information on taxon-specific tolerance to human-modified habitats and IUCN extinction risk category. We applied a systematic conservation planning approach and performed spatial analysis using the software Zonation50. We compared different scenarios to represent genetic diversity of CWR based on potential species distribution models (SDM) and proxies of genetic differentiation.Study areaMesoamerica is a cultural region encompassing the territories of Belize, Guatemala, El Salvador, the southern part of Mexico and parts of Honduras, Nicaragua and Costa Ricasee 2. In this study, we also included the dry areas of northern Mexico that are part of Aridamerica68 and the Nearctic biogeographic realm69 to account for the full extent of the geographic range of many taxa included in the extinction risk assessment2.For the assessment of conservation areas, we focused on Mexico, which is one of the most biodiverse countries in the world70. The Mexican territory covers 80% of the landscapes of the region called Mesoamerica. Its high biological diversity is attributed to its geographic, topographic, climatic, geological and cultural characteristics, which, among other factors, shaped the distribution of an extraordinary variety of ecosystems and species with high levels of endemism and species turnover among different regions32,71,72,73. In particular, the high genetic variation within populations of landraces and CWR is the result of past and ongoing sociocultural processes occurring in a wide range of distinct environmental conditions74,75.(i) CWR checklist and (ii) CWR inventoryThe compiled CWR checklist included ~3000 species and subspecies of 92 genera and 45 families of plants that belong to the same genus of a crop cultivated in Mesoamerica, or wild plant collected for food or other uses in the region (Supplementary Data 1).The first set of criteria were established in preparation for the first stakeholder workshop. The following criteria were applied at the genus level to compile the CWR inventory: (1) occurrence of wild relatives of cultivated plants or crops that were domesticated in Mesoamerica; (2) existence of research groups working on taxa that could support the extinction risk assessment; and (3) relation to a crop of economic and nutritional importance at local, national and regional levels, or cultivars known to require genetic improvement.To narrow the list for the inventory and extinction risk assessment, similar criteria were agreed upon in the same workshop and applied at the species level: (1) native distribution in Mesoamerica, incl. Aridamerica; (2) related to a crop of economic or social importance based on production and nutritional value; (3) related to a taxon for which Mesoamerica is the center of origin or domestication; (4) constitutes part of the primary or secondary gene pool, and in some cases the tertiary gene pool76. The primary gene pool consists of wild plants of the same species as the crop and thus their mating produces strong fertile progeny. The secondary gene pool is composed of wild relatives distinct from cultivated species but closely related as to produce some fertile offspring (same taxonomic series or section in the absence of crossing and genetic diversity information, see the ‘taxon group’ concept proposed by Maxted and collaborators77, Supplementary Note 5). The tertiary gene pool (same subgenus in the taxon group concept) corresponds to CWR that are more distant relatives to the taxa of the primary gene pool, but can have important adaptive traits which can be used with specific breeding techniques. This provided a preliminary list of 514 CWR taxa related to avocado, cotton, amaranth, cocoa, squash, sweet potato, chayote, chili pepper, cempasuchil, bean, sunflower, maize, papaya, potato, vanilla, and yuca (Supplementary Data 2).The list had to be further reduced due to time and funding restrictions to include those genera which when added together would include no more than 250 taxa, and that the taxonomic groups could be comprehensively assessed and their taxa evaluated throughout their entire range. Thus, not all species in the group necessarily met the criteria previously mentioned. See the final Mesoamerican CWR inventory in Supplementary Data 3; see summary in Table 1.(iii) Taxa extinction risk assessmentFull methodological details and results of this section are described in Goettsch et al.2. Summarizing, during the process 224 taxa were evaluated according to the International Union for Conservation of Nature, IUCN, Red List Categories and Criteria78. The IUCN Red List is a critical indicator to identify species most vulnerable to extinction considering a set of criteria, i.e., species’ population trends, size, structure, and geographic ranges. A Red List workshop with the participation of 25 experts from different project partner institutions and IUCN specialists was organized to assess the extinction risk of taxa. The threat analysis included not only species, but subspecies and subpopulations (i.e. races) for some groups (Supplementary Data 3, see summary in Table 1).(iv) Systematic conservation planning assessmentTo undertake the following spatial analyses we focused on the dataset of 224 CWR described above, which is representative of the CWR of the main crops of Mesoamerica (10 genera, Table 1).Species distribution modelingTo compile occurrence records, hundreds of data sources were consulted, including published and personal databases of the project participantse.g.79,80,81,82, the Agrobiodiversity Atlas of Guatemala (https://www.ars.usda.gov/northeast-area/beltsville-md-barc/beltsville-agricultural-research-center/national-germplasm-resources-laboratory/docs/atlas-of-guatemalan-crop-wild-relatives), the Global Biodiversity Information Facility (GBIF, https://www.gbif.org/), and Mexico’s Biodiversity Information System (SNIB, http://snib.mx/).To generate potential species distribution models (SDM), we used more than 13,000 occurrence records (Supplementary Data 4), that were standardized and curated by experts to generate the range maps of taxa as part of the extinction risk assessment, which were published in IUCN Red List (https://www.iucn.org/news/species/202109/threats-crop-wild-relatives-compromising-food-security-and-livelihoods). Spatial resolution of the SDM was 1 km2. SDM were obtained for taxa with more than 20 unique occurrence data in a 1 km2 grid covering the study extent to reduce uncertainty when using smaller sample sizes83. We used 19 bioclimatic variables and other climatic variables, such as annual potential evapotranspiration, aridity index, annual radiation, slope, and altitude84,85,86. Climate data represents annual and seasonal patterns of climate between 1950 and 2000. Also, we used a variable that described the percentage of bare soil and cultivated areas87. Collinearity between variables was assessed with the ‘corselect’ function of the package fuzzySim version 1.088, using a value of 0.8 and the variance inflation factors as criteria to exclude highly correlated variables.We used MaxEnt version 3.3.1, a machine-learning algorithm that uses the maximum entropy principle to identify a target probability distribution, subject to a set of constraints related to the occurrence records and environmental data89,90. Model calibration area for each taxon included those ecoregions where the taxon has been recorded; we used the terrestrial ecoregions dataset69. We did this based on the calibration area or ‘M element’ of the BAM diagram that refers to areas that have been accessible to the taxon via dispersal over relevant periods of time91,92. We randomly sampled 10,000 background localities from the selected areas.To reduce model complexity without compromising model performance, we built several models by varying the feature classes (FC) and regularization multipliers (RM) (see refs. 93,94,95) using R 3.6.096 and ‘ENMeval’ version 0.3.0 package97. FC determines the flexibility of the modeled response to the predictor variables, while the RM penalizes model complexity93. Occurrence records were randomly divided into 70% for model selection, and 30% of data was withheld for model validation. ENMeval carries out an internal partition of localities to test each combination of settings. Therefore, we selected the random k-fold method to divide localities into four bins. We build models with six FC combinations and varied RM values ranging from 0.5 to 4.0 in 0.5 increments. Optimal models were selected using Akaike’s Information Criterion corrected for small sample sizes (⍙AICc = 0). This method penalizes overly complex models and helps to choose those with an optimal number of parameters. However, it has been shown that the number of model parameters may not correctly estimate degrees of freedom98, and that model selection should not be selected solely with one measure99. Thus, we used 30% of the withheld data to test the area under the curve (AUC) of the receiver operating characteristic, and the omission error under a 10 percentile training threshold.We used the ten percentile or minimum training presence threshold to obtain binary maps of the presence and absence of suitable areas for species distribution. We asked experts of each taxonomic group who were also involved in the extinction risk assessment to select one of these two options and to indicate possible overestimated areas, which were then eliminated case by case using the information of Mexican ecoregions100 and watersheds101. Eight models were binarized with the minimum training presence threshold; for the other models we used the 10 percentile threshold. See MaxEnt performance and significance of SDM at Supplementary Data 5. AUC values ranged from 0 to 1; 0.5 indicated a model performance not better than random, while values closer to 1 indicated a better model performance; here we used SDM showing AUC values higher than 0.7. For Phaseolus and Zea, we used SDM that were previously generated by Delgado-Salinas et al.102, and Sánchez González et al.103, respectively. SDM for 116 taxa were validated by experts of each taxonomic group. See references and download links at Supplementary Data 6.For the conservation planning analysis of Mexico, we clipped the models to the Mexican territory, and trimmed the continuous SDM using the binary SDM to keep pixel values of areas with elevated probability of taxa presence. For taxa without SDM, we included the occurrence records of these taxa in the spatial analysis by using the information on observation location, i.e., coordinates (see Supplementary Data 3). This is done by enabling the function ‘species of special interest’ (SSI). See further details in the method section ‘Final conservation analysis’.Proxies of genetic differentiationTo identify proxies of genetic differentiation in an explicit, efficient, and repeatable way, we included environmental and historical drivers of genetic diversity. For this, we first divided Mexico into 27 Holdridge life zones (Supplementary Fig. 2, Supplementary Data 8), which we then subdivided according to phylogeographic studies that have found genetic differentiation among populations of several taxa (see division of each life zone into proxies in Supplementary Fig. 4; Supplementary Fig. 3 provides a general geographical overview of Mexico and main geographic references mentioned in Supplementary Fig. 4). The literature review was done searching for the words “phylogeography” and one of the following: (i) name of the Mexican biogeographic zones, (ii) “Mexico” + an ecosystem name (e.g. “Mexico” “rainforest”) or (iii) “Mexico” + lowlands/highlands. See list of references used in this study in Supplementary Data 9.In addition, we manually reviewed the citations to the most cited papers of the previous search. Reviews and meta-analyses were also included, although we excluded studies performed in CWR to show that our approach can be used without prior information on this group. As more studies on such taxa become available, they can be used to fine-tune the proxies of genetic differentiation. We focused on terrestrial species including plants, animals, and fungi (Supplementary Data 10) except to subdivide a life zone covering the coasts of the California Peninsula, where we could not find studies on terrestrial taxa so we included studies on fish species (see Supplementary Fig. 4).Since most of the life zones cover large territories, and complete phylogeographic congruence among different taxa is uncommon, we targeted to represent general trends that would likely occur across diverse species, instead of trying to represent fine idiosyncratic patterns of genetic differentiation. For instance, although distribution ranges of highland taxa shifted during the Pleistocene climate fluctuations, in general populations persisted (glacial-interglacial periods) within the main mountain ranges, while lowland populations were ephemeral (only glacial periods). So, gene flow among mountain ranges was more limited than within them. As a result, genetic differentiation among mountain ranges of different biogeographic provinces has been widely documented32, so we used this general pattern to subdivide the life zones that occur in highlands. These types of patterns are particularly relevant for a country like Mexico, due to its complex topography, tropical latitude, and geographic features of different ages, which promote population differentiation among the Mexican main geographic features. To translate the phylogeographic information into a spatial context, we used biogeographic regions, basins, topographic or edaphic data to split the life zones into different subzones using the best fitting cartography to represent the phylogeographic patterns (Supplementary Fig. 4).We obtained 102 proxies of genetic differentiation for Mexico (Supplementary Fig. 5). We validated our findings by using available genomic data of an empirical study of a wild relative of maize, the teosinte Zea mays subsp. parviglumis, which was not included in the literature review in order to test the usefulness of our approach regarding the lack of genetic data. The dataset includes ca. 1800 occurrence records and ca. 30,000 SNPs48. Sampling localities were not used for distribution modeling. Admixture groups per population were estimated for K1 to 60. According to the population analysis, Z. mays subsp. parviglumis is structured in 13 genetic clusters along a longitudinal gradient (Fig. 3a–c). We used the K = 13 for plotting based on the Cross-Validation error. The proportion of each genetic cluster was estimated by sampling locality and plotted using pie charts over the map (Supplementary Fig. 6). Then, using the data layer of the SDM subdivided by proxies of genetic differentiation, we extracted which was the proxy most frequent in a 5 km buffer for each sampling locality. The Admixture plot was ordered by all genetic clusters and subdivided by the proxy of genetic differentiation most frequent for each locality. In addition, we calculated a principal component analysis (PCA) and projected into a score plot the first three components. Individual samples were colored by the proxies where they fell in the 5 km buffer (Fig. 3c). To compare how genetic variation was represented by the different scenarios we plotted the proportion of the area of each proxy as given by the potential SDM according to two different scenarios (only considering SDM; combining SDM*PGD) considering 20% of Mexico’s terrestrial area (Fig. 3d). Analyses were run in R version 3.5.196 using the R packages pcadapt version 4.3.3104, ggplot2 version 2_3.3.3105, readr version 1.4.0106, gridExtra version 2.3107, ggnewscale version 0.4.5108, scatterpie version 0.1.5109, pophelper version 2.3.1110, raster version 3.4-5111, rgdal version 1.4-8112, rgl version 0.107.10113, and sp version 1.4-4114,115.Habitat preferenceWe considered habitat preference to refine the presence of CWR in the planning process; thus minimizing commission errors and highlighting areas that more probably contain taxa116. For each taxon, experts assessed its habitat preference (1: high preference; 0.5: low preference; 0.1: no preference) according to the following categories: (i) well-conserved vegetation (i.e. primary vegetation), (ii) human-impacted vegetation (i.e. secondary vegetation), (iii) less intensive rainfed and moisture agriculture, (iv) intensive rainfed and moisture agriculture, (v) irrigated agriculture, (vi) induced and cultivated grasslands and forests, and vii) urban areas (Supplementary Data 11). To spatially delimit these classes, we used the land use cover and vegetation map for Mexico117, and assessed seven main categories of land cover by grouping the map legend (Supplementary Fig. 9). To differentiate between less intensive and intensive cultivated areas, we followed Bellon et al.56, who associated the presence of native maize varieties of Mexico to occur in municipalities with average yields of less than or equal to 3 t ha-1 using agricultural production data from 2010 from the Information System of Agrifood and Fisheries (SIAP), and selected the municipalities with the established average maize yield. We combined the municipality layer with the land cover map to differentiate areas of high and low agricultural intensity. To generate taxon-specific habitat layers, we associated the habitat preference classes established by experts to the land cover map aggregated into seven major land cover categories, using R 3.6.096 and the following packages: raster version 3.4-5111 and rgdal version 1.4-8112. We obtained habitat maps for 116 taxa with SDM.Preliminary analysisWe generated five preliminary scenarios to explore different approaches to include conservation features for maximizing the representation of intraspecific diversity as given by taxa and proxies of genetic differentiation, i.e., representation of proxies within a taxa range (Supplementary Fig. 7): (i) “SDM” scenario, included 116 SDM, which we used as base scenario to examine the representation of taxa and proxies of genetic variability (n = 116); (ii) “SDM + LZ” scenario, included 116 SDM and 27 layers representing Holdridge life zones to consider environmental variation (n = 143); (iii) “SDM + PGD” scenario, included 116 SDM and 102 layers representing each proxy of genetic differentiation individually (n = 218); (iv) “SDM*PGD” scenario, included 5004 input layers representing the intersection of SDM and PGD (n = 5004; combining 116 SDM with 102 proxies resulted in 11,832 layers, but as some of the intersections produced empty outputs given the extension of SDM that do not cover all Mexico, for further analysis we used 5004 input layers with value data. To subdivide the layers, we used ArcGIS version 10.2.2118; to filter the layers, we used R 3.5.196.); (v) “SDM and PGD as ADMU” scenario, included 116 SDM as the main conservation features, while integrating one single layer of proxies of genetic differentiation to consider each of them as planning units by using the ‘Administrative units’ function. Analysis was done in Zonation50,119.We compared the results by assessing 20% of Mexico’s terrestrial area (Fig. 5b) to perform statistical analysis in R 3.5.196 using the following packages: purrr version 0.3.4120, ‘dplyr’ version 1.0.2121, ‘ggplot2’ version 2_3.3.3105, ‘raster’ version 3.4-5111, ‘scales’ version 1.2.0122, ‘sp’ version 1.4-4114,115, ‘tidyr’ version 1.0.2123, and ‘vegan’ version 2.6-2124. The area threshold was established based on Aichi target 11 and on comparisons of performance curves to efficiently represent taxa ranges delimited by SDM and proxies of genetic differentiation (Fig. 6). As using SDM combined with proxies of genetic differentiation showed the highest representation of genetic diversity (“SDM*PGD” scenario), we used this approach for the final analyses.Final conservation analysisWe identified areas of high conservation value for CWR in Mexico by using the software Zonation version 4.050,119, a systematic conservation planning tool that allows optimizing representation of species, taxa, or other conservation features, e.g., proxies of genetic differentiation, in a given study area. The program hierarchically ranks areas by removing cells of low conservation value, as given, for example, by a reduced number of taxa or occurrence of low weighted features, while considering multiple criteria such as the weighting of taxa and habitat preference of taxa. We applied the core-area zonation removal rule (CAZ) to maximize the representation of all conservation features in a minimal possible area51. Zonation generates two main outputs: (a) a hierarchical landscape priority rank map, that allows decision makers establishing different area thresholds to highlight areas of conservation interest; and (b) a representation curve showing species or conservation features range distribution in a given area. The curve also allows identifying how much area is needed to cover a certain taxon range or the distribution of a feature of conservation interest.For the conservation scenarios, we integrated the following inputs in the Zonation software: (1) 5,004 layers, i.e., SDM intersected with proxies of genetic differentiation (as described by “SDM*PGD” scenario, Fig. 4), (2) occurrence records of 98 taxa; only for those taxa without SDM, see Supplementary Data 3), (3) taxa specific habitat layers (according to Supplementary Data 11 and Supplementary Fig. 9), and (4) IUCN threat category (Supplementary Data 3) as an additional parameter to weight taxa differently to consider their vulnerability to extinction, see details below. See Zonation configuration at Supplementary Note 6.Data from different sources can be mixed in the same analysis, which is useful to not lose or omit information of any taxa of interest in the assessment. Here, we included information of a total of 214 taxa (see Supplementary Data 3). Distribution data of 116 taxa were represented by 5004 layers that resulted from combining 116 SDM and 102 PGD. This approach showed the highest proportion of area of taxa ranges (on average 41%) and highest representation of PGD within the area of each taxon (on average 76%; Fig. 4; see description in the main text). For some taxa, e.g. Cucurbita pepo, Physalis cinerascens, and Zea mays information on its distribution was assessed at subspecies level rather than at species level, explaining the difference in numbers of CWR taxa.In addition, we included occurrence data of 98 taxa without SDM to prevent missing important areas of taxa known distribution that are important to conserve (see Supplementary Data 3). We enabled the function ‘species of special interest’ (SSI) of Zonation, and included a SSI feature list file, listing the taxon names, as well as taxon-specific coordinate file for each of the 98 taxa that have been reviewed by the experts of each group. The spatial reference system was World Mercator projection. Occurrence data and SDM are treated similarly in the Zonation analysis, i.e., cells where taxa occur will be retained in the solution as long as possible to maximize its representation in the solution.We assigned weights to the 116 taxa with SDM by using IUCN threat categories (according to Supplementary Data 3), giving highest values to taxa with highest risk of extinction that urgently need management actions to further avoid genetic erosion. By including conservation feature weights, Zonation estimates the conservation value of a cell not only based on the presences of a taxa and their distribution range, but also on the weight. A high weight indicates a high conservation value of cells where these taxa are distributed. As there is no rule for weight setting, we assigned values between 1 and 0 regardless of taxa distribution ranges, which is automatically considered in the Zonation algorithm to guarantee the representation of locations where limited-range distributed taxa occur within the most valuable conservation area. Thus, weights were assigned as follows: Critically endangered, CR: 1; Endangered, EN: 1; Vulnerable, VU: 0.8; Near threatened, NT: 0.5; Data deficient, DD: 0.3; Least concern, LC: 0.2 Not evaluated, NE: 0.1. SSI taxa were all weighted similarly with 1 in order to represent the 98 SSI taxa and their occurrences in the top fraction of the most valuable conservation area, as these areas could be considered as ‘irreplaceable’ in terms of conservation. The conservation of these taxa that are only known in a few locations is crucial to maintain their populations. Information on weights for taxa with and without SDM is included in the file that lists the 5004 conservation features and the SSI file, respectively.To include the information on habitat, we included 116 habitat maps which guide the selection of cells to areas where its presence is more probable (see the Methods section: “Habitat preference”). This option can only be used for taxa represented by a raster layer, and is not available for SSI taxa included via occurrence records. By enabling the “landscape condition” option of Zonation, each habitat map is linked to a specific conservation feature layer. Areas with unfavorable habitats will quickly be masked out during the selection of cells in order to obtain a solution that favors conservation areas within areas of preferred habitat.We generated three final scenarios to identify conservation areas for (a) all taxa, (b) taxa exclusively distributing in natural vegetation, and (c) taxa associated with a wider range of habitats such as natural vegetation, agricultural and urban areas. The Zonation configuration remained similar among the three scenarios. When taxa were not included in a given scenario, we assigned a value weight of 0. This excluded the feature to be considered for the hierarchical prioritization of the landscape, but still allowed to evaluate the taxa during post-processing.To evaluate the spatial results (Supplementary Fig. 11), we analyzed performance curves to represent proxies of genetic differentiation within each taxon range (Supplementary Fig. 12). Also, we considered the most valuable 20% area of Mexico to calculate the coincidence of the three scenarios (Supplementary Fig. 13), and the overlap with federal protected areas125 and indigenous regions126,127 (Supplementary Fig. 14), and land cover data used in the analyses (Supplementary Figs. 9, 15).We discussed the proposed methodological framework, input layer and criteria during a fourth workshop in Mexico. It is worth mentioning that we ran several analyses including additional layers, such as areas where indigenous communities live that promote the presence of CWR in the landscape6. However, as the output indicated no evident difference by including this information, final analyses did not consider these data. We neither included protected areas nor tried to expand on the current 12% protected area system, because most management plans do not specifically address CWR management (but see the management program of the Protected Area of ‘Sierra de Manantlán’128), and thus generally do not adequately plan for wild and native genetic resources129. We also discussed different approaches to consider connectivity for taxa, habitats and proxies of genetic differentiation in the Zonation processing. Still, we finally decided to run the analysis without particularly accounting for connectivity as we had no taxa-specific information on dispersal abilities or possible effects of fragmentation, and we did not want to lose efficiency of the solution to represent taxa by or include lower-quality habitats by forcing the solution to an aggregation of pixels.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More