Identifying conservation priorities of a pantropical plant lineage: a case study in Scleria (Cyperaceae)

© 2025 The authors. [ECOSISTEMAS is not responsible for the misuse of copyrighted material] © 2025 Los autores. [ECOSISTEMAS no se hace responsable del uso indebido de material sujeto a derecho de autor] 1 Ecosistemas 34(1): 2847 [January-April / enero-abril, 2025] https://doi.org/10.7818/ECOS.2847 Associate editor / Editora asociada: Yosune Miquelajauregui RESEARCH ARTICLE / ARTÍCULO DE INVESTIGACIÓN ecosistemas Identifying conservation priorities of a pantropical plant lineage: a case study in Scleria (Cyperaceae) Javier Galán Díaz1,2,* , Steven P. Bachman3 , Félix Forest3 , Marcial Escudero1 , Hannah Rotton3, Isabel Larridon3,4 (1) Department of Plant Biology and Ecology, Faculty of Biology, University of Sevilla, 41012 Sevilla, Spain. (2) Department of Pharmacology, Pharmacognosy and Botany, Complutense University of Madrid, 28040 Madrid, Spain. (3) Royal Botanic Gardens, Kew, Richmond TW9 3AE, United Kingdom. (4) Department of Biology, Systematic and Evolutionary Botany, Ghent University, 9000 Gent, Belgium. * Correspondig author / Autor de correspondencia: Javier Galán Díaz [[email protected]] > Received / Recibido: 06/09/2024 – Accepted / Aceptado: 05/02/2025 How to cite / Cómo citar: Galán Díaz, J., Bachman, S.P., Forest, F., Escudero, M., Rotton, H., Larridon, I. 2025. Identifying conservation priorities of a pantropical plant lineage: a case study in Scleria (Cyperaceae). Ecosistemas 34(1): 2847. https://doi.org/10.7818/ECOS.2847 Identifying conservation priorities of a pantropical plant lineage: a case study in Scleria (Cyperaceae) Abstract: Scleria is a pantropical genus of annual and perennial herbs and the sixth largest genus in the Cyperaceae family with around 261 species. In this study, we produced preliminary extinction risk assessments for the ~30% of Scleria species that do not yet have a global Red List assessment and followed the Evolutionarily Distinct and Globally Endangered (EDGE2) and Ecologically Distinct and Globally Endangered (EoDGE) protocols to identify evolutionary and ecologically unique Scleria species at greatest risk of extinction and hotspots of rare and endangered species. Our results indicate that 38 of the 78 Scleria species not yet included in the Red List, and 26% of species in the genus, are potentially threatened with extinction. The risk of extinction is not equally distributed across the phylogeny, and the Afrotropics and the Neotropics accumulate most threatened species. Eleven ecoregions mostly from four African (Madagascar, D.R. Congo, Zambia and Tanzania) and two South American (Brazil, Venezuela) countries accumulate almost half of Scleria species and stand out in terms of their sum of EDGE2 scores. Phylogenetic and functional distinctiveness metrics were largely uncorrelated, and the EcoDGE metric mostly points towards South American countries as reservoirs of ecologically distinctive and endangered species: Brazil, Venezuela, Bolivia, Peru, Colombia, Guyana and Dominican Republic. Recent methodological advances in the identification of species at-risk of extinction and the novel EDGE2 framework emerge as powerful tools to identify conservation priorities. Keywords: conservation prioritization; EDGE; extinction risk; pantropical; sedges Identificando prioridades en la conservación de taxones vegetales pantropicales: un estudio de caso en Scleria (Cyperaceae) Resumen: Scleria es un género pantropical de plantas anuales y perennes. Es el sexto género más grande de la familia Cyperaceae con 261 especies. En este estudio hemos estimado el riesgo de extinción para aquellas especies de Scleria no incluidas en la Lista Roja de la Unión Internacional para la Conservación de la Naturaleza (~30% del género) y utilizado los protocolos EDGE2 (Evolutionarily Distinct and Globally Endangered) y EoDGE (Ecologically Distinct and Globally Endangered) para identificar especies únicas desde un punto de vista evolutivo y ecológico que se enfrentan a un mayor riesgo de extinción así como ecoregiones del mundo ricas en estas especies. Nuestros resultados indican que 38 de las 78 especies de Scleria aún no incluidas en la Lista Roja, y el 26% de las especies del género, están potencialmente en riesgo de extinción. Además, el riesgo de extinción no se distribuye por igual a lo largo de la filogenia. El Afrotrópico y el Neotrópico acumulan la mayoría de las especies amenazadas. Once ecorregiones, principalmente de cuatro países africanos (Madagascar, R.D. Congo, Zambia y Tanzania) y dos sudamericanos (Brasil, Venezuela), acumulan casi la mitad de las especies de Scleria y son reservorios de especies evolutivamente únicas y en peligro de extinción. Las métricas de distintividad filogenética y funcional no se encuentran correlacionadas en Scleria, y EcoDGE apunta sobre todo a países sudamericanos como reservorios de especies ecológicamente únicas y en peligro de extinción. Palabras clave: ciperáceas; EDGE; priorización de la conservación, riesgo de extinción Introduction Scleria P.J.Bergius (1765: 142), commonly known as nut rushes or razor grasses, is the sixth largest genus in the Cyperaceae family with around 260 species (Bauters et al. 2016; Larridon et al. 2021; 2024), and the only genus of tribe Sclerieae Wight & Arn. (subfamily Cyperoideae) (Larridon 2022). Scleria has four subgenera and 17 sections (Bauters et al. 2016, 2018a, 2019). Most species occur in tropical areas but some occupy more temperate climatic regions (Larridon et al. 2021). Scleria is a functionally diverse genus that includes annual and perennial herbs, the latter often using stoloniferous rhizomes or tubers (Bauters et al. 2016; Galán Díaz et al. 2019). Nut rushes are ecologically important in wetlands and areas undergoing secondary aeet ASOCIACIÓN ESPAÑOLA DE ECOLOGÍA TERRESTRE ISSN 1697-2473 Open access / CC BY-NC 4.0 www.revistaecosistemas.net iD iD iD iD iD 2 Galán Díaz et al. 2025 Ecosistemas 34(1): 2847 succession (Bauters et al. 2016), and some species are reported to have local economic significance (Simpson y Inglis 2001; Galán Díaz et al. 2024).Recent studies have disentangled the evolutionary relationships among Scleria species (Bauters et al. 2016, 2018) and identified major dispersal and niche shift events (Larridon et al. 2021). Still, we lack a global assessment of species and world regions of remarkable interest for Scleria conservation. In the current context of rapid global change, there is a need to adopt new tools to assess biodiversity and ecosystems to set conservation priorities (Cowell et al. 2022). Over the last two decades, the use of phylogenetic and functional indices has emerged as a powerful approach to support conservation assessments by identifying at-risk species and regions of great evolutionary and ecological uniqueness (Kondratyeva et al. 2019). The Evolutionarily Distinct and Globally Endangered (EDGE) metric can be used to rank species conservation priorities where species’ evolutionary distinctiveness (ED) is used as a surrogate of their irreplaceability, and species’ probability of extinction (GE) is used as a proxy of their vulnerability (Isaac et al. 2007). It has been successfully implemented in different groups such as corals (Huang 2012), mammals and amphibians (Safi et al. 2013), gymnosperms (Forest et al. 2018) and tetrapods (Gumbs et al. 2018). The EDGE metric has been recently revised to include new advances (EDGE2; Gumbs et al. 2023): (i) species’ evolutionary distinctiveness (ED2) now considers the extinction risk of their close relatives; (ii) it allows the incorporation of uncertainty in the phylogeny and extinction risk. The latter allows the use of phylogenies which have been expanded using non-molecular information (e.g., see Ramos-Gutiérrez et al. 2023), and provides a flexible framework to assess Not Evaluated and preliminary assessed species rather than only depending on species published in the Red List of Threatened Species of the International Union for Conservation of Nature (IUCN) (e.g., Walker et al. 2023). Machine learning approaches constitute efficient tools to produce extinction risk assessments and their implementation in different plant groups has provided promising results (Zizka et al. 2022; Bachman et al. 2023; Walker et al. 2023). The latest version of the Red List includes ~18% (62 666 species) of all known vascular plant species (IUCN 2023). In the case of Scleria, there are extinction risk assessments available for 183 species (26 of which are not yet publicly available in the latest version of the Red List), which accounts for 70% of the genus. Recent taxonomic revisions of the genus (Bauters et al. 2016, 2018a, 2019; Galán Díaz et al. 2019) and the availability of occurrence datasets curated using expert knowledge (Larridon et al. 2021) can help overcome some of the limitations and challenges that arise from using herbarium collections to support preliminary extinction risk assessments (Nic Lughadha et al. 2019). The genus Scleria is therefore a good study system to train machine learning algorithms and produce preliminary extinction risk assessments. Finally, the phylogenetic and functional components of biodiversity are not necessarily correlated, especially when considering functional attributes which are highly dependent on the environment (Losos 2008). Scleria is an ecologically diverse clade that includes species with variable habits, from tiny annuals with small nutlets and fibrous roots to climbers or stout perennials with big buoyant propagules (Galán Díaz et al. 2019). The variation in functional traits across the genus Scleria therefore warrants independent evaluation. In this regard, the EDGE2 framework can incorporate species functional distinctiveness (FUD) as a measure of ecological irreplaceability. This metric is termed Ecologically Distinct and Globally Endangered (EcoDGE) (HidasiNeto et al. 2015). Here, (i) we first produced preliminary extinction risk assessments for the ~30% of Scleria species that do not yet have a global Red List assessment, and then (ii) followed the EDGE2 protocol to identify evolutionary and ecologically distinct Scleria species at greatest risk of extinction and conservation priority areas. For this, we used extinction risk assessments from the global Red List and the most comprehensive phylogeny of Scleria to date (Larridon et al. 2021), as well as newly compiled occurrence and traits datasets. Material and Methods Taxonomy and species occurrences We used the World Checklist of Vascular Plants (WCVP; Govaerts et al. 2021) and the latest taxonomic revisions of the genus Scleria P.J.Bergius (Bauters et al. 2016, 2019) to compile a dataset of 261 accepted species. Data relating to infraspecific taxa were incorporated at the species level. Species names and authorities are available in in Table A1 of Appendix. Occurrence data was compiled using observations from the Global Biodiversity Information Facility (GBIF 2023), Red List (IUCN 2023), research-grade identifications from iNaturalist and records for collections from BR, K, GENT, L, MO, NY, P, US and WAG which were georeferenced using Google Earth. For each species, we filtered observations following several steps: (1) we removed duplicates and retained one observation per 1km2 raster cell, (2) filtered observations based on the native ranges at the ‘botanical country’ scale, according to the WCVP, and (3) excluded observations 1.5 times outside the interquartile climatic range using the mean annual temperature and annual precipitation BIOCLIM variables (Booth et al. 2014). The final occurrence database included 22 759 observations from 248 species. We projected locality data using the Equal Earth map projection (Šavrič et al. 2019). Traits measurements and calculation We measured maximum height, maximum blade length, maximum blade width, nutlet length and nutlet width from 1254 specimens housed at Royal Botanic Gardens, Kew (K) and the Muséum National d'Histoire Naturelle in Paris (P). For species missing in these herbaria, we completed the dataset using information from protologues and descriptions from regional floras (e.g., Davidse et al. 1994; Simpson and Koyama 1998; Le Roux 2015). This information was used to estimate three traits informative of plant ecological strategies (Westoby 1998): maximum height (i.e., distance from the top inflorescence to the ground), 3 Galán Díaz et al. 2025 Ecosistemas 34(1): 2847 leaf area and size of the propagule (i.e., nutlet volume). We estimated leaf area and nutlet volume using the formula of an ellipse and an ellipsoid, respectively. We also considered plant longevity (annual/perennial). Extinction risk data and preliminary assessments Previous studies have shown that the Red List is biased toward certain plant groups and species, for instance, woody perennials and useful plants (Nic Lughadha et al. 2020). Still, we used the Red List categories as indicators of species’ extinction risk because (i) the Red List constitutes a consensual framework worldwide, (ii) the authors have been continuously involved in the production of several Red List assessments of Scleria over the last decade which facilitates the interpretation of the results, (ii) Scleria is nearly comprehensively assessed according to the Red List criterium (i.e., taxa with over 149 species with at least 80% of the species assessed) and (iv) recent advances in the EDGE protocol used Red List categories as proxies of species’ probability of extinction (Gumbs et al. 2023) We downloaded 157 extinction risk assessments from the Red List (version 2023-1) and considered the extinction risk category of other 26 species that have assessments in preparation. To supplement the 183 species with global (published and in preparation) Red List assessments we carried out preliminary assessments and predictions for the 67 Data Deficient (DD) and Not Evaluated (NE) species for which occurrence data was available so that we had an extinction risk assessment for all species. The extinct species S. chevalieri J.Raynal was excluded from the analyses. We followed two approaches: • We estimated species extinction risk categories under IUCN Red List criterion B using the function ‘ConBatch’ from the R package ‘rCAT’ (Moat & Bachman 2020). Given a dataset of occurrences, this function provides preliminary extinction risk categories based on species’ extent of occurrence (EOO). We did not consider the species' area of occupancy (AOO) because it can lead to an overestimation of extinction risk when using occurrence data derived from herbarium records (Nic Lughadha et al. 2019). To assess the accuracy of this method, we ran ‘ConBatch’ on the species included in the Red List and compared the actual assessments with the predicted categories. • We implemented a machine learning algorithm (random forest, henceforth RF) using as predictors: EOO, AOO, mean of latitudinal range, elevation, minimum human population density in 2020 (HPD; CIESIN 2016), human footprint index in 2013 (HFI; Mu et a. 2022), proportion of observations located in protected areas (UNEP-WCMC y IUCN 2023), mean annual temperature, minimum temperature of the coldest month, temperature annual range, annual precipitation, precipitation of the driest month and precipitation seasonality. All predictors were resampled at 30 seconds. For HPD and HFI, we calculated the average index value within a 5 km circular buffer of each unique occurrence point. We used 10 repeats of 5-fold crossvalidation to train and evaluate the model and retained 20% of data for external validation. EDGE2 and EcoDGE calculation and species lists We computed two metrics that combine species extinction risk with their evolutionary or functional distinctiveness to prioritize at-risk Scleria species and world regions of special interest for conservation: the Evolutionarily Distinct and Globally Endangered metric (EDGE2) (Gumbs et al. 2023) and the Ecologically Distinct and Globally Endangered metric (EcoDGE) (Hidasi-Neto et al. 2015). To compute ED2, we used the latest phylogenetic inference of the genus from Larridon et al. (2021b), which included 136 species. We used the R package ‘randtip’ (Ramos-Gutiérrez et al. 2023) to impute 111 species missing in the phylogeny but for which infrageneric information (i.e., section) was available. We excluded 14 species for which infrageneric information was not available. Trees were expanded following several criteria: species were imputed in their sections at random, the probability of branch selection was set as equiprobable, and the stem branch was considered as a candidate for binding. This step was repeated to generate a distribution of 500 randomly imputed phylogenetic trees. To calculate FUD, we adapted the EcoDGE framework proposed by Hidasi-Neto et al. (2015) to the new EDGE2 protocol following Griffith et al. (2022). We generated a dendrogram by calculating pairwise species dissimilarities with a generalized Gower's distance matrix (Gower 1971) using the four traits (i.e., longevity, maximum height, leaf area and size of the propagule) and an unweighted pair group method with arithmetic mean (UPGMA). We repeated the dendrogram construction 11 times (all possible combinations of two, three and four traits) to reduce the impact of specific traits on the results. Finally, we multiplied FUD scores by 100 to balance the weighting of the two component values of the EcoDGE metric. The EDGE2 and EcoDGE protocols allow the incorporation of uncertainty in the quantification of extinction probabilities by associating each Red List category to a distribution of extinction probabilities based on a 50-year time horizon (Mooers et al. 2008). For species not included in the Red List and classified as threatened by the preliminary assessments, the extinction risk component (GE) was randomly drawn from CR, VU and EN categories. For species classified as non-threatened, GE values were randomly withdrawn from those of NT and LC categories. Finally, for each species, we calculated a distribution of EDGE2 and EcoDGE scores (N=500) which were used to sort Scleria species in three lists as proposed by Gumbs et al. (2023): a ‘priority or main EDGE2 species list’ that includes threatened species whose distribution of EDGE2 scores rank above the median of the entire genus at least 95% of the times; a ‘borderline EDGE2 species list’ that includes threatened species whose distribution of EDGE2 scores rank above the median of the entire genus at least 80% of the times; and a ‘watch EDGE2 species list’ that includes nonthreatened species whose distribution of EDGE2 scores rank above the median of the entire genus at least 95% of the times. We further discuss the results in the context of the countries and world terrestrial ecoregions (Olson et al. 2001), which are good descriptors of plant distribution (Smith et al. 2018) and can be used as informative units for conservation planning (Dinerstein et al. 2017). All analyses were performed in R (version 4.3.2). 4 Galán Díaz et al. 2025 Ecosistemas 34(1): 2847 Results Preliminary assessments rCAT and RF yielded different results (Table A2 of Appendix). Under criterion B, rCAT classified 53 out of the 67 non-assessed species as threatened according to their EOO (44 CR, 5 EN, 4 VU, 4 NT, 10 LC). Scleria rCAT assessments correctly classified 79.31% of threatened species (high sensitivity) and 91.39% of non-threatened species (high specificity). RF classified 39 species as threatened and 28 as non-threatened. It achieved an accuracy of 97.22%, correctly classifying 96.55% of non-threatened species and all threatened species (higher sensitivity and specificity than rCAT). The single most important predictor for RF was EOO, followed by AOO. Because of its greater performance, we retained the results of RF for the EDGE2 and EcoDGE calculations. Combined Red List results (published, unpublished and preliminary) are available in Table A1 of Appendix. Considering the published (Red List version 2023-1; IUCN 2023) and preliminary (RF) results, we found that the Afrotropics and the Neotropics had the highest number and proportion of threatened species (Fig. 1a). The sections with the greatest proportion of threatened species were Hypoporum (41.54%), followed by Abortivae, Browniae, Schizolepsis and Foveolidia (28.57%) (Fig. 1b). Figure 1. Number of threatened and non-threatened Scleria species grouped by (a) Realm (Olson et al. 2001) and (b) Section. Threatened refers to species classified as Vulnerable (VU), Endangered (EN) and Critically Endangered (CR) in the Red List, as well as Data Deficient (DD) and Not Evaluated (NE) species classified as threatened by the random forest approach. Nonthreatened refers to species classified as Least Concern (LC) and Near Threatened (NT) in the Red List, as well as Data Deficient (DD) and Not Evaluated (NE) species classified as non-threatened by the random forest approach. Figura 1. Número de especies de Scleria amenazadas y no amenazadas agrupadas por (a) Reino biogeográfico (Olson et al. 2001) y (b) Sección. Amenazadas agrupa especies clasificadas como Vulnerable (VU), En Peligro (EN) y En Peligro Crítico (CR) en la Lista Roja, así como especies en las categorías No Evaluado (NE) y Datos Insuficientes (DD) clasificadas como amenazadas por el random forest. No amenazadas se refiere a las especies clasificadas como Preocupación Menor (LC) y Casi Amenazado (NT) en la Lista Roja, así como a las especies en las categorías No Evaluado (NE) y Datos Insuficientes (DD) clasificadas como no amenazadas por el random forest. 5 Galán Díaz et al. 2025 Ecosistemas 34(1): 2847 Evolutionarily and ecologically distinct species at greatest risk of extinction The phylogenetic diversity (PD) across trees was 900.96 ± 24.01 MY (mean ± SD). Species with the highest mean ED2 scores were Scleria corymbosa, Scleria lithosperma and Scleria melanomphala with 22.28, 20.89 and 19.04 MY, respectively. The species with the greatest mean EDGE2 score was Scleria porphyrocarpa with 3.97 MY of avertable expected PD loss, followed by Scleria zambesica (2.53 MY), Scleria pulchella (2.25 MY) and Scleria madagascariensis (2.04 MY). We detected 45 EDGE2 Scleria species (17.24% of species in the genus). Safeguarding these species, we would secure 68.24% (41.49 MY) of avertable expected PD loss in a 50-year time horizon. The main list included 23 species (Table 1), of which 17 belong to section Hypoporum, three to section Acriulus, two to section Abortivae and one to Corymbosae. The EDGE2 borderline and watch lists included 17 and 5 species respectively (Table A2 of Appendix). The watch list included widespread and locally abundant species that belong to evolutionary distinctive sections such as S. lithosperma, S. melanomphala and S. tonkinensis. Table 1. EDGE2 main list, i.e., threatened species that scored above the median EDGE2 score of the entire genus at least 95% of the time. Species are ranked based on their EDGE2 score. IUCN RL: Red List category of published species (RL IUCN: DD Data Deficient, VU Vulnerable, EN Endangered, CR Critically Endangered), and preliminary assessment of Not Evaluated species (i.e., Threatened). EDGE2: Evolutionarily Distinct and Globally Endangered metric, ED2: evolutionary distinctiveness. Tabla 1. Lista principal EDGE2: especies amenazadas que obtuvieron una puntuación superior a la mediana de la puntuación EDGE2 de todo el género al menos el 95% de las veces. Las especies están ordenadas en función de su puntuación EDGE2. IUCN RL: categoría de la Lista Roja de las especies publicadas (RL IUCN: DD Datos Insuficientes, VU Vulnerable, EN En Peligro, CR En Peligro Crítico), y evaluación preliminar de especies No Evaluadas. EDGE2: índice de Especies Evolutivamente Distintas y Globalmente Amenazadas, ED2: distintividad evolutiva. Scientific name Section IUCN RL EDGE2 (mean ± sd) ED2 (mean ± sd) S. porphyrocarpa E.A.Rob. Corymbosae Threatened 3.97 ± 6.26 8.89 ± 8.27 S. zambesica E.A.Rob. Hypoporum Threatened 2.53 ± 1.46 5.1 ± 0.87 S. pulchella Ridl. Hypoporum CR 2.25 ± 0.45 2.44 ± 0.42 S. madagascariensis Boeckeler Abortivae EN 2.04 ± 0.66 4.62 ± 1.01 S. ankaratrensis Bauters Hypoporum DD (Threatened) 1.45 ± 0.82 2.77 ± 0.48 S. maypurensis Bauters Hypoporum CR 1.45 ± 0.35 1.65 ± 0.34 S. pachyrrhyncha Nelmes Acriulus EN 1.37 ± 1.68 2.81 ± 3.32 S. induta Turrill Acriulus Threatened 1.35 ± 2.43 2.73 ± 3.53 S. thwaitesiana Boeckeler Acriulus Threatened 1.2 ± 2.4 2.56 ± 3.33 S. fulvipilosa E.A.Rob. Hypoporum EN 1.07 ± 0.3 2.31 ± 0.38 S. richardsiae E.A.Rob. Hypoporum EN 0.96 ± 0.27 2.09 ± 0.37 S. nusbaumeri Bauters Abortivae Threatened 0.96 ± 1.34 1.92 ± 1.76 S. pantadenia Meganck & Bauters Hypoporum VU 0.94 ± 0.26 4.17 ± 0.75 S. tricristata Meganck & Bauters Hypoporum EN 0.84 ± 0.29 1.85 ± 0.44 S. calcicola E.A.Rob. Hypoporum EN 0.82 ± 0.22 1.74 ± 0.29 S. burchellii C.B.Clarke Hypoporum EN 0.81 ± 0.26 1.83 ± 0.39 S. liberica Bauters Hypoporum VU 0.59 ± 0.16 2.57 ± 0.43 S. polyrrhiza E.A.Rob. Hypoporum EN 0.57 ± 0.16 1.24 ± 0.21 S. mongomoensis Bauters Hypoporum EN 0.41 ± 0.16 0.87 ± 0.27 S. pedicellata Bauters Hypoporum EN 0.4 ± 0.16 0.88 ± 0.27 S. pseudohispidior Bauters Hypoporum VU 0.31 ± 0.1 1.39 ± 0.29 S. tricholepis Nelmes Hypoporum VU 0.28 ± 0.08 1.18 ± 0.24 S. spicata (Spreng.) J.F.Macbr. Hypoporum VU 0.27 ± 0.08 1.14 ± 0.23 6 Galán Díaz et al. 2025 Ecosistemas 34(1): 2847 Scleria species with the highest mean FUD scores were Scleria skutchii, the tallest species in the genus, and Scleria depressa, the species with the largest nutlet. Overall, 6 of 10 species with the highest FUD scores belonged to the sections Ophryoscleria and Schizolepis which include stout perennials with large leaf areas and climbers. The species with the greatest mean EcoDGE score was Scleria porphyrocarpa, followed by Scleria tropicalis, Scleria williamsii and Scleria chlorantha. We found 38 EcoDGE species, 6 in the main list (Table 2) and 32 in the borderline EDGE2 species list (Table A3 of Appendix). These species (14.56% of species of the genus) account for 64.28% of avertable expected functional diversity loss in the genus in a 50-year time horizon. The evolutionary distinctiveness (ED2) metric was a poor explanatory variable of functional distinctiveness (FUD) in Scleria (F1,237=0.03, p=0.86). Table 2. EcoDGE main list, i.e., threatened species that scored above the median EcoDGE score of the entire genus at least 95% of the time. Species are ranked based on their EcoDGE score. IUCN RL: Red List category of published species (RL IUCN: CR Critically Endangered), and preliminary assessment of Not Evaluated species (i.e., Threatened). EcoDGE: Ecologically Distinct and Globally Endangered metric, FUD: functional distinctiveness. Tabla 2. Lista principal de EcoDGE: especies amenazadas que obtuvieron una puntuación superior a la mediana de la puntuación EcoDGE de todo el género al menos el 95% de las veces. Las especies están ordenadas en función de su puntuación EcoDGE. IUCN RL: categoría de la Lista Roja de las especies publicadas (RL IUCN: CR En Peligro Crítico), y evaluación preliminar de especies No Evaluadas. EcoDGE: Especies Ecológicamente Distintas y Globalmente Amenazadas, FUD: distintividad funcional. Scientific name Section IUCN RL EcoDGE (mean ± sd) FUD (mean ± sd) S. tropicalis M.T.Strong Ophryoscleria Threatened 1.05 ± 1.22 2.34 ± 1.46 S. rubrostriata A.C.Araújo & N.A.Brummitt Schizolepis Threatened 0.61 ± 0.98 1.09 ± 1.36 S. orchardii C.D.Adams Schizolepis CR 0.35 ± 0.19 0.38 ± 0.21 S. chasmema Bonet Mayedo & W.W.Thomas Virgatae Threatened 0.35 ± 0.3 0.66 ± 0.34 S. junghuhniana Boeckeler Elatae Threatened 0.21 ± 0.83 0.44 ± 1.17 S. ovinux J.Raynal ex Fosberg Elatae Threatened 0.19 ± 0.26 0.48 ± 0.34 Regions of special interest for conservation The countries with the highest cumulative EcoDGE scores were Brazil, Venezuela, Bolivia, Democratic Republic of the Congo, Peru and Colombia. The countries with the greatest number of listed EcoDGE species were Democratic Republic of the Congo, Brazil and Madagascar (Table 3). The ecoregions with the highest sum of EDGE2 scores and the greatest number of listed EDGE2 species were the Central Zambezian Miombo woodlands, Madagascar subhumid forests, Cerrado, East Sudanian savanna, Llanos, Guinean forest-savanna mosaic and Madagascar lowland forests (Table 4; Fig. 2a). The countries with the highest cumulative EcoDGE scores were Brazil, Venezuela, Bolivia, Democratic Republic of the Congo, Peru and Colombia. The countries with the greatest number of listed EcoDGE species were Democratic Republic of the Congo, Brazil and Madagascar (Table 3). The ecoregions with the highest sum of EcoDGE scores and number of listed EcoDGE species were Central Zambezian Miombo woodlands, Southwest Amazon moist forests, Bahia coastal forests, Cerrado and Western Congolian forest-savanna mosaic (Table 4; Fig. 2b). According to the assessment of the extent of remaining natural habitat and protected land in the world ecoregions by Dinerstein et al. (2017), there are 22 Scleria species which are restricted to ‘Nature Imperiled’ ecoregions (i.e., ecoregions where the percentage of natural habitat remaining and the amount of the total ecoregion that is protected is less than or equal to 20%) and 34 species only occur in ecoregions where ‘Nature Could Recover (i.e., ecoregions where the percentage of natural habitat remaining and area protected is less than 50% but more than 20% and that require restoration programs to reach the 50% of natural habitat). 7 Galán Díaz et al. 2025 Ecosistemas 34(1): 2847 Table 3. Top 20 countries in which Scleria is present ranked by their sum of EDGE2 scores. Richness: number of species present, N: number of EDGE2 and EcoDGE species, Main: number of species included in the main EDGE2 and EcoDGE lists, Sum: sum of EDGE2 and EcoDGE scores. Expanded results can be found in Table A4 of Appendix. Tabla 3. Los 20 países con mayor riqueza de Scleria ordenados por la suma de sus puntuaciones EDGE2. Riqueza: número de especies presentes, N: número de especies EDGE2 y EcoDGE, Main: número de especies incluidas en las listas principales EDGE2 y EcoDGE, Sum: suma de las puntuaciones EDGE2 y EcoDGE. Los resultados ampliados se encuentran en la Tabla A4 del Apéndice 4. Country Richness EDGE2 EcoDGE N Main Sum N Main Sum Madagascar 24 7 3 10.11 3 0 1.24 Dem. Rep. Congo 26 5 2 9.85 5 0 1.94 Brazil 63 6 2 9.43 4 2 3.08 Zambia 29 5 4 8.95 1 0 0.79 Tanzania 30 5 3 8.23 2 0 0.63 Venezuela 31 3 1 5.94 1 1 2.00 South Africa 17 2 0 5.23 0 0 0.17 Thailand 20 3 1 5.18 0 0 0.28 China 15 3 0 4.76 0 0 0.24 Bolivia 34 1 0 4.33 1 0 1.98 Guinea 15 4 2 4.29 1 0 0.22 Ghana 20 2 0 4.20 0 0 0.68 Ethiopia 16 4 1 4.20 0 0 0.29 Côte d'Ivoire 20 2 0 4.05 0 0 0.66 Philippines 13 3 0 3.96 1 0 0.32 Gabon 19 3 1 3.95 2 0 0.66 Angola 11 2 1 3.83 1 0 0.32 Nigeria 20 2 0 3.74 0 0 0.63 Guyana 21 1 0 3.58 1 0 1.26 Cameroon 18 3 0 3.56 2 0 0.89 Table 4. Top 20 ecoregions in which Scleria is present ranked by their sum of EDGE2 scores. Richness: number of species present, N: number of EDGE2 and EcoDGE species, Main: number of species included in the main EDGE2 and EcoDGE lists, Sum: sum of EDGE2 and EcoDGE scores. Expanded results can be found in Table A5 of Appendix. Tabla 4. Las 20 principales ecorregiones en las que está presente Scleria clasificadas por su suma de puntuaciones EDGE2. Riqueza: número de especies presentes, N: número de especies EDGE2 y EcoDGE, Main: número de especies incluidas en las listas principales EDGE2 y EcoDGE, Sum: suma de las puntuaciones EDGE2 y EcoDGE. Los resultados ampliados se encuentran en la Tabla A5 del Apéndice 5. Ecoregion Richness EDGE2 EcoDGE N Main Sum N Main Sum Central Zambezian Miombo woodlands 39 8 7 15.32 3 0 2.04 Madagascar subhumid forests 22 7 3 9.50 2 0 1.09 Cerrado 45 3 1 7.09 2 0 1.72 East Sudanian savanna 22 5 1 5.76 1 0 0.85 Llanos 24 3 1 5.68 0 0 0.86 Guinean forest-savanna mosaic 28 4 1 5.61 1 0 0.81 Madagascar lowland forests 16 2 1 5.48 1 0 0.65 Northwestern Congolian lowland forests 20 4 1 5.28 2 0 0.37 Zambezian and Mopane woodlands 15 3 1 5.28 0 0 0.18 Mato Grosso seasonal forests 28 2 1 4.87 1 0 1.05 Western Congolian forest-savanna mosaic 18 3 1 4.87 3 0 0.80 Tenasserim-South Thailand semi-evergreen rain forests 13 3 1 4.82 0 0 0.19 Zambezian flooded grasslands 13 2 0 4.76 0 0 0.24 Eastern Arc forests 14 3 1 4.70 1 0 0.39 South China-Vietnam subtropical evergreen forests 12 3 0 4.70 0 0 0.19 Guianan savanna 21 2 0 4.61 0 0 0.73 Drakensberg montane grasslands, woodlands and forests 13 2 0 4.33 0 0 0.11 Eastern Guinean forests 21 2 0 4.28 0 0 0.70 West Sudanian savanna 21 2 0 4.23 0 0 0.64 Western Guinean lowland forests 20 3 2 4.15 0 0 0.69 8 Galán Díaz et al. 2025 Ecosistemas 34(1): 2847 Figure 2. Ecoregions of notable conservation concern for Scleria according to their sum of EDGE2 scores (a) and sum of EcoDGE scores (b). The definition of ecoregion follows Olson et al. (2001). Figura 2. Ecorregiones de notable interés para la conservación de Scleria según su suma de puntuaciones EDGE2 (a) y suma de puntuaciones EcoDGE (b). La definición de ecorregión se basa en Olson et al. (2001). Discussion In this study, we estimated the extinction risk for all species of the genus Scleria, as well as identified regions of special interest for its conservation. Our results suggest that half of Scleria species (49%, N=38) not yet included in the Red List are potentially threatened with extinction. Evolutionary and ecologically distinct and endangered Scleria mostly occur across African and South American regions. Preliminary assessments Similar to previous studies, RF outperformed the method that explicitly followed Red List criterion B (Nic Lughadha et al. 2019) and distribution range properties were the best predictors for RF (Nic Lughadha et al. 2019; Bachman et al. 2023; Walker et al. 2023). Up to 15 species were classified as threatened by rCAT and non-threatened by RF; thus, RF set lower thresholds of extent of occurrence (EOO) to classify Scleria species as threatened. This seems a sensible approach to assess Scleria because the 9 Galán Díaz et al. 2025 Ecosistemas 34(1): 2847 size of the distribution range as estimated from herbarium collections is potentially underestimated in most cases (see below), and widespread and locally dominant species are common (Holm et al. 1979; Galán Díaz et al. 2019). Considering species included in the Red List and the results of RF, 26% of all Scleria species are potentially threatened with extinction. In a recent study, Bachman et al. (2023) obtained a similar estimate for Cyperaceae. They also found that, of the 21 families with more than 3000 species, Cyperaceae showed the smallest proportion of threatened species. Whereas 18% of Scleria species currently included in the Red List are threatened, we found that 49% of unassessed species are potentially threatened with extinction. This could be because several Madagascan and South American Scleria species discovered over the last four decades were classified as threatened by RF, which might support that newly described species are geographically restricted and therefore less likely to be encountered in the wild (Brown et al. 2023). This is the case of Scleria nusbaumeri, Scleria ankaratrensis, Scleria attenuatifolia, Scleria chasmema, Scleria millespicula, Scleria pernambucana, Scleria rubrostriata and Scleria tropicalis. It is therefore necessary to prioritize the assessment of all unassessed species to get realistic estimates of extinction risk in the genus and to galvanize conservation support for these species. Evolutionarily distinct and globally endangered Scleria Our analysis identified 23 species that met the criteria to be included in the EDGE2 main list. Scleria porphyrocarpa was the species with the highest EDGE2 and ED2 scores due to its high vulnerability (i.e., it was classified as threatened by both rCAT and RF) and high evolutionary distinctiveness. Scleria porphyrocarpa belongs to section Corymbosae, the oldest lineage in subgenus Scleria (crown age 22.28 Ma) which only includes four other species (Bauters et al. 2016, Larridon et al. 2021). Section Hypoporum was represented in the EDGE2 main list by 17 species. This is due to two reasons: (i) molecular analyses indicated that section Hypoporum originated 8.4 Ma and showed a rapid increase in its diversification rate soon after (Larridon et al. 2021) to become the richest section in Scleria (Bauters et al. 2019); (ii) it holds the highest percentage of threatened species among Scleria sections according to the Red List and our preliminary assessments (42%; Fig. 1b). This is important in the context of the new EDGE formulation, that accounts for the extinction risk of closely related species, because the deeper branches of a clade with a high proportion of threatened species will have a greater probability of being at risk, thus resulting in species belonging to it having higher EDGE scores (Gumbs et al. 2023). Finally, the three threatened species of section Acriulus were also included in the main list, as well as two Madagascan endemics from section Abortivae. Almost half of Scleria species (129 species) are present in the 11 ecoregions with the greatest cumulative EDGE2 scores. Four African (Madagascar, D.R. Congo, Zambia and Tanzania) and two South American (Brazil, Venezuela) countries mainly covered these areas of special interest for conservation. Madagascar comprises 24 species of Scleria in two ecoregions (i.e., subhumid and lowland forests), including five threatened and two recently described species. Madagascan species belong to 11 sections out of the 17 sections recognized in Scleria, which highlights the importance of this country as a reservoir of Scleria diversity and the importance of long-distance dispersal in the genus (Galán Díaz et al. 2019). Four other ecoregions from the Afrotropics (i.e., Central Zambezian Miombo woodlands, East Sudanian Savanna, Guinean forest-savanna mosaic, and Northwestern Congolian lowland forests and Zambezian and Mopane woodlands; Olson et al. 2001) are identified as important conservation areas for Scleria. These ecoregions contain 67 species (26% species of Scleria, from 12 sections) of which 19 are endangered. Brazil has the greatest Scleria richness (63 species) and ranks second in terms of cumulative EDGE2 scores. Two Brazilian ecoregions are shown as areas of special interest for the conservation of the evolutionary history of Scleria: Cerrado and Mato Grosso seasonal forests. These ecoregions include 49 species from 11 sections, of which 4 are endangered. The Venezuelan Llanos is another important ecoregion for evolutionary distinct and endangered Scleria. Unlike the Colombian Llanos, which also includes over 30 species, it includes species from old Scleria lineages (i.e., sections Lithospermae and Foveolidia; Larridon et al. 2021) and two threatened species. According to the revision of the world ecoregions of Dinerstein et al. (2017), which considered the World Database of Protected Areas (UNEP-WCMC y IUCN 2023) along with habitat assessments based on tree cover and human land use, two of the abovementioned ecoregions (i.e., Madagascar subhumid forests and Guinean forest-savanna mosaic) are classified as ‘nature imperiled’ because the percentage of natural habitat remaining and protected area is less than 20%. Other ecoregions of interest for Scleria conservation that need active restoration programs to reach the 50% of protected natural habitat are Madagascar lowland forests, Central Zambezian Miombo woodlands and East Sudanian savanna. Ecologically distinct and globally endangered Scleria We expect uncertainty in the identification of EcoDGE species because the results are largely dependent on the selection of traits, whereas the evolutionary relationship among Scleria species is well resolved at least at the section level (Bauters et al. 2016, 2018a). In this study we used traits which are informative of species ecological strategies (Westoby 1998): plant longevity, leaf area, height and nutlet size. These traits indicate how plants cope with environmental nutrient stress and disturbances, the position of the species in the vertical light gradient of the vegetation, competitive vigor, dispersal distance and seed persistence in the soil bank (Pérez-Harguindeguy et al. 2013). We found ED2 and FUD were largely orthogonal; thus, EcoDGE based prioritizations yielded complementary results to the EDGE2 approach. Our analysis identified six species in the main EcoDGE list. Unlike EDGE2, EcoDGE mostly points towards South American countries as reservoirs of distinctive and endangered species. This is because many EDGE2 species are phylogenetically restricted to section Hypoporum which has its center of diversity in tropical Africa, whereas EcoDGE species (i.e., those with great blade area and nutlet size) belong to different sections mostly restricted to South America. Eight South and Central American 16 Galán Díaz et al. 2025 Ecosistemas 34(1): 2847 Table A1 continued / Continuación Tabla A1 Scientific name RL IUCN RF Rcat Notes S. ovinux J.Raynal ex Fosberg NE T T Preliminary assessment. S. pachyrrhyncha Nelmes EN Included in RL 2023-1. S. panicoides Kunth LC Included in RL 2023-1. S. pantadenia Meganck & Bauters VU Included in RL 2023-1. S. parallella C.B.Clarke NE NT NT Preliminary assessment. S. parvula Steud. LC Included in RL 2023-1. S. patula E.A.Rob. NE T T Preliminary assessment. S. pauciflora Muhl. ex Willd. LC Included in RL 2023-1. S. paupercula E.A.Rob. LC Assessed but not published. S. pedicellata Bauters EN Included in RL 2023-1. S. pergracilis (Nees) Kunth LC Included in RL 2023-1. S. pernambucana Luceño & M.Alves NE T T Preliminary assessment. S. perpusilla Cherm. EN Included in RL 2023-1. S. pilosa Boeckeler NE T T Preliminary assessment. S. pilosissima Britton NE NT T Preliminary assessment. S. plusiophylla Steud. LC Included in RL 2023-1. S. poeppigii (Nees) Steud. NE T T Preliminary assessment. S. poiformis Retz. LC Included in RL 2023-1. S. polycarpa Boeckeler LC Included in RL 2023-1. S. polyrrhiza E.A.Rob. EN Included in RL 2023-1. S. pooides Ridl. LC Included in RL 2023-1. S. porphyrocarpa E.A.Rob. NE T T Preliminary assessment. S. procumbens E.A.Rob. NE NT T Preliminary assessment. S. pseudohispidior Bauters VU Included in RL 2023-1. S. psilorrhiza C.B.Clarke LC Assessed but not published. S. pulchella Ridl. CR Included in RL 2023-1. S. purdiei C.B.Clarke LC Included in RL 2023-1. S. purpurascens Steud. LC Assessed but not published. S. pusilla Pilg. LC Included in RL 2023-1. S. racemosa Poir. LC Included in RL 2023-1. S. radula Hance LC Assessed but not published. S. ramosa C.B.Clarke LC Included in RL 2023-1. S. rehmannii C.B.Clarke LC Included in RL 2023-1. S. remota Ridl. NE T T Preliminary assessment. S. reticularis Michx. LC Included in RL 2023-1. S. richardsiae E.A.Rob. EN Included in RL 2023-1. S. robinsoniana J.Raynal NT Included in RL 2023-1. S. robusta Camelb. & Goetgh. LC Included in RL 2023-1. S. rosea Cherm. LC Included in RL 2023-1. S. rubrostriata A.C.Araújo & N.A.Brummitt NE T T Preliminary assessment. S. rugosa R.Br. LC Included in RL 2023-1. S. rutenbergiana Boeckeler NT Included in RL 2023-1. S. scabra Willd. LC Included in RL 2023-1. S. scabriuscula Schltdl. NE NT NT Preliminary assessment. S. schiedeana Schltdl. LC Included in RL 2023-1. S. schimperiana Boeckeler LC Included in RL 2023-1. S. schulzii Barros NE NT T Preliminary assessment. S. scrobiculata Nees & Meyen LC Assessed but not published. S. secans (L.) Urb. LC Included in RL 2023-1. S. sellowiana Kunth LC Assessed but not published. S. setulosociliata Boeckeler LC Included in RL 2023-1. S. sheilae J.Raynal CR Included in RL 2023-1. S. sieberi Nees ex Kunth NE NT T Preliminary assessment. S. skutchii M.T.Strong & J.R.Grant LC Included in RL 2023-1. S. sobolifera E.F.Franklin LC Included in RL 2023-1. S. sororia Kunth LC Included in RL 2023-1. S. sphacelata F.Muell. LC Included in RL 2023-1. 17 Galán Díaz et al. 2025 Ecosistemas 34(1): 2847 Table A1 continued / Continuación Tabla A1 Scientific name RL IUCN RF Rcat Notes S. spicata (Spreng.) J.F.Macbr. VU Included in RL 2023-1. S. spiciformis Benth. LC Included in RL 2023-1. S. splitgerberiana Henrard ex Uittien LC Included in RL 2023-1. S. sprucei C.B.Clarke LC Included in RL 2023-1. S. staheliana Uittien LC Included in RL 2023-1. S. stipitata Uittien NE NT NT Preliminary assessment. S. stipularis Nees LC Included in RL 2023-1. S. stocksiana Boeckeler NE NT T Preliminary assessment. S. suaveolens Nelmes LC Included in RL 2023-1. S. suffulta C.B.Clarke NE T T Preliminary assessment. S. sumatrensis Retz. LC Included in RL 2023-1. S. tenacissima (Nees) Steud. LC Assessed but not published. S. tenella Kunth LC Included in RL 2023-1. S. tepuiensis Core LC Included in RL 2023-1. S. terrestris (L.) Fassett LC Included in RL 2023-1. S. tessellata Willd. LC Included in RL 2023-1. S. testacea Nees ex Kunth LC Included in RL 2023-1. S. thwaitesiana Boeckeler NE T NT Preliminary assessment. S. tonkinensis C.B.Clarke LC Included in RL 2023-1. S. transvaalensis E.F.Franklin NT Included in RL 2023-1. S. trialata Poir. LC Included in RL 2023-1. S. tricholepis Nelmes VU Included in RL 2023-1. S. tricristata Meganck & Bauters EN Assessed but not published. S. tricuspidata S.T.Blake LC Assessed but not published. S. triglomerata Michx. LC Included in RL 2023-1. S. triquetra M.T.Strong LC Assessed but not published. S. tropicalis M.T.Strong NE T T Preliminary assessment. S. tryonii Domin NE T T Preliminary assessment. S. uleana Boeckeler NE NT NT Preliminary assessment. S. unguiculata E.A.Rob. LC Included in RL 2023-1. S. vaginata Steud. LC Assessed but not published. S. variegata (Nees) Steud. LC Assessed but not published. S. venezuelensis Core NE T T Preliminary assessment. S. verrucosa Willd. LC Included in RL 2023-1. S. verticillata Muhl. ex Willd. LC Included in RL 2023-1. S. veseyfitzgeraldii E.A.Rob. LC Assessed but not published. S. violacea Pilg. NE NT NT Preliminary assessment. S. virgata (Nees) Steud. LC Included in RL 2023-1. S. vogelii C.B.Clarke LC Included in RL 2023-1. S. warmingiana Boeckeler NE T T Preliminary assessment. S. welwitschii C.B.Clarke LC Included in RL 2023-1. S. williamsii Gross EN Assessed but not published. S. woodii C.B.Clarke LC Included in RL 2023-1. S. wrightiana Boeckeler NE NT NT Preliminary assessment. S. xerophila E.A.Rob. NE T T Preliminary assessment. S. zambesica E.A.Rob. NE T T Preliminary assessment. Species without occurrence data / Especies sin datos de presencia: S. assamica (C.B.Clarke) D.M.Verma, S. depauperata Boeckeler, S. elongatissima Piérart, S. hirta Boeckeler, S. jiangchengensis Y.Y.Qian, S. lateritica Nelmes, S. macrolomioides H.Pfeiff., S. mutoensis Nakai, S. papuana J.Kern, S. scandens Core, S. schenckiana Boeckeler and S. swamyi Govind. 18 Galán Díaz et al. 2025 Ecosistemas 34(1): 2847 Table A2. Scleria EDGE2 borderline and watch lists (Gumbs et al. 2023). Borderline list: threatened species whose distribution of EDGE2 scores rank above the median of the entire genus at least 80% of the time. Watch list: nonthreatened species whose distribution of EDGE2 scores rank above the median of the entire genus at least 95% of the times. EDGE2: Evolutionarily Distinct and Globally Endangered metric, ED2: evolutionary distinctiveness. Tabla A2. Listas límite y de vigilancia EDGE2 de Scleria (Gumbs et al. 2023). Lista límite: especies amenazadas cuya distribución de las puntuaciones EDGE2 se sitúa por encima de la mediana de todo el género al menos el 80% de las veces. Lista de vigilancia: especies no amenazadas cuya distribución de las puntuaciones EDGE2 se sitúa por encima de la mediana de todo el género al menos el 95% de las veces. EDGE2: Métrica de distintividad evolutiva y en peligro de extinción a escala mundial, ED2: Métrica de distintividad evolutiva. List Scientific name Section EDGE2 (mean ± sd) ED2 (mean ± sd) Borderline S. chasmema Bonet Mayedo & W.W.Thomas Virgatae 0.76 ± 1.85 1.57 ± 2.53 S. guineensis J.Raynal Hypoporum 0.76 ± 0.85 0.85 ± 0.9 S. glomerulata Oliv. Hypoporum 0.74 ± 0.85 0.85 ± 0.9 S. sheilae J.Raynal Hypoporum 0.74 ± 0.81 0.81 ± 0.88 S. baroni-clarkei De Wild. Foveolidia 0.59 ± 0.33 1.34 ± 0.6 S. poeppigii (Nees) Steud. Hymenolytrum 0.58 ± 1.16 1.16 ± 1.58 S. densispicata (C.B.Clarke) J.Kern Browniae 0.45 ± 3.04 0.88 ± 5.1 S. bradei Gross Hypoporum 0.43 ± 0.77 0.83 ± 0.99 S. neocaledonica Rendle Browniae 0.42 ± 3.61 0.9 ± 5.34 S. andringitrensis Cherm. Hypoporum 0.42 ± 0.45 0.86 ± 0.87 S. oligochondra Nelmes Ophryoscleria 0.41 ± 0.94 0.91 ± 1.39 S. perpusilla Cherm. Hypoporum 0.41 ± 0.47 0.87 ± 0.93 S. afroreflexa Lye Hypoporum 0.4 ± 0.54 0.86 ± 1.02 S. chlorantha Boeckeler Naumannianae 0.4 ± 1.68 0.86 ± 2.42 S. remota Ridl. Hypoporum 0.39 ± 0.67 0.77 ± 0.94 S. filiculmis Boeckeler Hypoporum 0.38 ± 0.59 0.89 ± 0.92 S. tropicalis M.T.Strong Ophryoscleria 0.37 ± 0.91 0.72 ± 1.29 Watch S. transvaalensis E.F.Franklin Acriulus 1.52 ± 0.57 13.88 ± 4.08 S. corymbosa Roxb. Corymbosae 1.29 ± 0.59 22.28 ± 2.18 S. lithosperma (L.) Sw. Lithospermae 1.22 ± 0.52 20.89 ± 0 S. melanomphala Kunth Melanomphalae 1.15 ± 0.5 19.04 ± 0 S. tonkinensis C.B.Clarke Corymbosae 0.82 ± 0.36 13.91 ± 1.63 19 Galán Díaz et al. 2025 Ecosistemas 34(1): 2847 Table A3. Scleria EcoDGE borderline list (Griffith et al. 2022; Gumbs et al. 2023): threatened species whose distribution of EcoDGE scores rank above the median of the entire genus at least 80% of the time. EcoDGE: Ecologically Distinct and Globally Endangered metric, FUD: functional distinctiveness. Tabla A3. Lista límite EcoDGE de Scleria (Griffith et al. 2022; Gumbs et al. 2023): especies amenazadas cuya distribución de puntuaciones EcoDGE se sitúa por encima de la mediana de todo el género al menos el 80% de las veces. EcoDGE: Métrica de distintividad ecológica y en peligro de extinción a escala mundial, FUD: distintividad funcional. List Scientific name Section EcoDGE (mean ± sd) FUD (mean ± sd) Borderline S. porphyrocarpa E.A.Rob. Corymbosae 1.14 ± 1.45 2.05 ± 2.1 S. williamsii Gross Scleria 0.86 ± 1.33 1.83 ± 2.56 S. chlorantha Boeckeler Naumannianae 0.84 ± 1.29 1.8 ± 1.78 S. attenuatifolia M.T.Strong Schizolepis 0.54 ± 1.01 1.13 ± 1.36 S. foveolata Cav. Schizolepis 0.46 ± 1.44 0.75 ± 2.07 S. khasiana Boeckeler Elatae 0.31 ± 0.47 0.62 ± 0.63 S. madagascariensis Boeckeler Abortivae 0.24 ± 0.15 0.49 ± 0.28 S. bradei Gross Hypoporum 0.23 ± 0.4 0.67 ± 0.55 S. induta Turrill Acriulus 0.2 ± 0.3 0.63 ± 0.38 S. nusbaumeri Bauters Abortivae 0.2 ± 0.25 0.42 ± 0.35 S. aurantiaca Lye Foveolidia 0.18 ± 0.15 0.2 ± 0.16 S. warmingiana Boeckeler Scleria 0.17 ± 0.26 0.54 ± 0.35 S. burchellii C.B.Clarke Hypoporum 0.15 ± 0.12 0.28 ± 0.24 S. densispicata (C.B.Clarke) J.Kern Browniae 0.13 ± 0.15 0.27 ± 0.2 S. pachyrrhyncha Nelmes Acriulus 0.12 ± 0.07 0.27 ± 0.14 S. hildebrandtii Boeckeler Foveolidia 0.12 ± 0.07 0.21 ± 0.13 S. adpressohirta (Kük.) E.A.Rob. Foveolidia 0.11 ± 0.2 0.18 ± 0.27 S. arcuata E.A.Rob. Foveolidia 0.1 ± 0.3 0.2 ± 0.42 S. suffulta C.B.Clarke Elatae 0.1 ± 0.12 0.21 ± 0.16 S. glomerulata Oliv. Hypoporum 0.1 ± 0.05 0.1 ± 0.05 S. oligochondra Nelmes Ophryoscleria 0.1 ± 0.14 0.18 ± 0.21 S. patula E.A.Rob. Foveolidia 0.09 ± 0.08 0.2 ± 0.09 S. pulchella Ridl. Hypoporum 0.08 ± 0.06 0.09 ± 0.06 S. neocaledonica Rendle Browniae 0.07 ± 0.1 0.13 ± 0.13 S. anomala (Steud.) J.Raynal Elatae 0.06 ± 0.21 0.11 ± 0.29 S. guineensis J.Raynal Hypoporum 0.06 ± 0.04 0.06 ± 0.04 S. cheekii Bauters Hypoporum 0.06 ± 0.04 0.24 ± 0.17 S. aureovillosa Kiaos. & K.Wangwasit Foveolidia 0.05 ± 0.05 0.11 ± 0.06 S. baroni-clarkei De Wild. Foveolidia 0.05 ± 0.12 0.1 ± 0.24 S. multilacunosa T.Koyama Foveolidia 0.04 ± 0.04 0.08 ± 0.05 S. mongomoensis Bauters Hypoporum 0.04 ± 0.07 0.07 ± 0.14 S. sheilae J.Raynal Hypoporum 0.03 ± 0.01 0.03 ± 0.01 20 Galán Díaz et al. 2025 Ecosistemas 34(1): 2847 Table A4. Countries in which Scleria is present ranked by their sum of EDGE2 scores. Richness: number of species present, N: number of listed EDGE2 and EcoDGE species, Main: number of species included in the main EDGE2 and EcoDGE lists, Sum: sum of EDGE2 and EcoDGE scores. Tabla A4. Países en los que Scleria está presente ordenados por su suma de puntuaciones EDGE2. Richness: número de especies presentes, N: número de especies incluidas en las listas EDGE2 y EcoDGE, Main: número de especies incluidas en las listas principales EDGE2 y EcoDGE, Sum: suma de las puntuaciones EDGE2 y EcoDGE. Country Richness EDGE2 EcoDGE N Main Sum N Main Sum Madagascar 24 7 3 10.11 3 0 1.24 Dem. Rep. Congo 26 5 2 9.85 5 0 1.94 Brazil 63 6 2 9.43 4 2 3.08 Zambia 29 5 4 8.95 1 0 0.79 Tanzania 30 5 3 8.23 2 0 0.63 Venezuela 31 3 1 5.94 1 1 2.00 South Africa 17 2 0 5.23 0 0 0.17 Thailand 20 3 1 5.18 0 0 0.28 China 15 3 0 4.76 0 0 0.24 Bolivia 34 1 0 4.33 1 0 1.98 Guinea 15 4 2 4.29 1 0 0.22 Ghana 20 2 0 4.20 0 0 0.68 Ethiopia 16 4 1 4.20 0 0 0.29 Côte d'Ivoire 20 2 0 4.05 0 0 0.66 Philippines 13 3 0 3.96 1 0 0.32 Gabon 19 3 1 3.95 2 0 0.66 Angola 11 2 1 3.83 1 0 0.32 Nigeria 20 2 0 3.74 0 0 0.63 Guyana 21 1 0 3.58 1 0 1.26 Cameroon 18 3 0 3.56 2 0 0.89 Australia 22 1 0 3.51 1 0 0.30 Zimbabwe 8 2 1 3.50 1 0 0.21 Colombia 32 1 0 3.47 0 0 1.41 Mozambique 7 2 0 3.45 0 0 0.23 India 13 2 0 3.45 2 0 0.51 Mexico 23 1 0 3.39 0 0 0.54 Cuba 22 1 0 3.26 0 0 0.36 Indonesia 15 1 0 3.14 1 1 0.40 Belize 16 1 0 3.03 0 0 0.42 eSwatini 6 2 0 2.94 0 0 0.07 Guatemala 13 1 0 2.83 0 0 0.44 Nicaragua 15 1 0 2.82 0 0 0.45 Honduras 16 1 0 2.80 0 0 0.46 Costa Rica 17 1 0 2.70 0 0 0.98 Trinidad and Tobago 13 1 0 2.62 1 1 0.74 Liberia 10 2 1 2.58 0 0 0.18 Burkina Faso 11 1 0 2.51 0 0 0.53 Benin 18 1 0 2.50 0 0 0.61 Peru 24 1 0 2.45 1 0 1.53 Eq. Guinea 9 2 1 2.42 1 0 0.31 Uganda 11 1 0 2.36 0 0 0.27 Botswana 8 1 0 2.27 0 0 0.08 21 Galán Díaz et al. 2025 Ecosistemas 34(1): 2847 Table A4 continued / Continuación Tabla A4 Country Richness EDGE2 EcoDGE N Main Sum N Main Sum Togo 12 1 0 2.16 0 0 0.56 Dominican Rep. 8 2 0 2.08 1 0 1.01 United States of America 14 1 0 2.06 0 0 0.12 Malawi 6 1 1 2.01 0 0 0.21 Taiwan 8 1 0 1.95 0 0 0.09 Sri Lanka 6 1 0 1.84 0 0 0.04 Argentina 12 1 0 1.83 0 0 0.39 Puerto Rico 11 1 0 1.77 0 0 0.25 Sierra Leone 8 1 1 1.68 0 0 0.10 Panama 15 0 0 1.65 1 0 0.96 Congo 11 1 0 1.64 1 0 0.33 Jamaica 6 1 0 1.55 0 0 0.16 Kenya 5 1 0 1.48 0 0 0.17 Paraguay 12 0 0 1.47 0 0 0.40 Central African Rep. 10 0 0 1.31 0 0 0.16 Fiji 2 1 0 1.29 0 0 0.01 Tonga 2 1 0 1.29 0 0 0.01 Suriname 17 0 0 1.25 0 0 0.70 Rwanda 2 1 0 1.24 0 0 0.02 Bahamas 1 1 0 1.22 0 0 0.01 Timor-Leste 1 1 0 1.22 0 0 0.01 Cayman Is. 1 1 0 1.22 0 0 0.01 Ecuador 16 0 0 1.17 0 0 0.47 Guinea-Bissau 5 0 0 1.00 0 0 0.09 Niger 2 0 0 0.76 0 0 0.03 S. Sudan 1 1 0 0.74 1 0 0.10 New Caledonia 4 1 0 0.72 2 1 0.28 Chad 4 0 0 0.50 0 0 0.15 Japan 6 0 0 0.49 0 0 0.07 Mali 7 0 0 0.48 0 0 0.47 Lesotho 3 0 0 0.43 0 0 0.01 Senegal 9 0 0 0.41 0 0 0.50 Nepal 3 0 0 0.41 0 0 0.03 Burundi 5 0 0 0.40 0 0 0.16 Myanmar 2 0 0 0.40 0 0 0.02 El Salvador 5 0 0 0.34 0 0 0.06 Vietnam 5 0 0 0.32 0 0 0.07 Papua New Guinea 3 0 0 0.25 0 0 0.02 Cambodia 3 0 0 0.23 1 0 0.08 Seychelles 2 0 0 0.19 0 0 0.04 Mauritius 1 0 0 0.19 1 0 0.06 Vanuatu 2 0 0 0.15 0 0 0.01 Laos 2 0 0 0.09 0 0 0.02 Uruguay 1 0 0 0.08 0 0 0.00 Malaysia 1 0 0 0.02 0 0 0.01 French Guiana 0 0 0 0.00 0 0 0.00 22 Galán Díaz et al. 2025 Ecosistemas 34(1): 2847 Table A5. Top 50 ecoregions in which Scleria is present ranked by their sum of EDGE2 scores. Richness: number of species present, N: number of EDGE2 and EcoDGE species, Main: number of species included in the main EDGE2 and EcoDGE lists, Sum: sum of EDGE2 and EcoDGE scores. Tabla A5. Las 50 principales ecorregiones en las que Scleria está presente según su suma de puntuaciones EDGE2. Richness: número de especies presentes, N: número de especies EDGE2 y EcoDGE, Main: número de especies incluidas en las listas principales EDGE2 y EcoDGE, Sum: suma de las puntuaciones EDGE2 y EcoDGE. Ecoregion Richness EDGE2 EcoDGE N Main Sum N Main Sum Central Zambezian Miombo woodlands 39 8 7 15.32 3 0 2.04 Madagascar subhumid forests 22 7 3 9.50 2 0 1.09 Cerrado 45 3 1 7.09 2 0 1.72 East Sudanian savanna 22 5 1 5.76 1 0 0.85 Llanos 24 3 1 5.68 0 0 0.86 Guinean forest-savanna mosaic 28 4 1 5.61 1 0 0.81 Madagascar lowland forests 16 2 1 5.48 1 0 0.65 Northwestern Congolian lowland forests 20 4 1 5.28 2 0 0.37 Zambezian and Mopane woodlands 15 3 1 5.28 0 0 0.18 Mato Grosso seasonal forests 28 2 1 4.87 1 0 1.05 Western Congolian forest-savanna mosaic 18 3 1 4.87 3 0 0.80 Tenasserim-South Thailand semi-evergreen rain forests 13 3 1 4.82 0 0 0.19 Zambezian flooded grasslands 13 2 0 4.76 0 0 0.24 Eastern Arc forests 14 3 1 4.70 1 0 0.39 South China-Vietnam subtropical evergreen forests 12 3 0 4.70 0 0 0.19 Guianan savanna 21 2 0 4.61 0 0 0.73 Drakensberg montane grasslands, woodlands and forests 13 2 0 4.33 0 0 0.11 Eastern Guinean forests 21 2 0 4.28 0 0 0.70 West Sudanian savanna 21 2 0 4.23 0 0 0.64 Western Guinean lowland forests 20 3 2 4.15 0 0 0.69 Southern Rift montane forest-grassland mosaic 14 2 1 3.94 0 0 0.29 Angolan Miombo woodlands 11 2 1 3.90 1 0 0.20 Cardamom Mountains rain forests 11 3 1 3.89 1 0 0.18 Southern Congolian forest-savanna mosaic 15 2 1 3.83 1 0 0.52 Guinean montane forests 17 3 2 3.52 0 0 0.22 Petén-Veracruz moist forests 21 1 0 3.52 0 0 0.54 Eastern Miombo woodlands 9 2 0 3.29 0 0 0.22 Alto Paraná Atlantic forests 23 1 0 3.28 0 0 0.77 Central Indochina dry forests 11 2 0 3.22 0 0 0.15 Bahia coastal forests 27 1 0 3.21 2 2 1.82 Atlantic Equatorial coastal forests 16 2 1 3.20 1 0 0.39 Arnhem Land tropical savanna 18 1 0 3.20 0 0 0.18 Southern Miombo woodlands 9 2 2 3.19 1 0 0.35 Southern Africa bushveld 6 2 0 3.05 0 0 0.07 Guianan piedmont and lowland moist forests 22 1 0 3.03 0 0 0.85 Queensland tropical rain forests 15 1 0 2.99 1 0 0.24 Cape York Peninsula tropical savanna 16 1 0 2.96 0 0 0.17 Central American pine-oak forests 18 1 0 2.94 0 0 0.47 Western Java rain forests 12 1 0 2.93 1 1 0.36 KwaZulu-Cape coastal forest mosaic 12 1 0 2.91 0 0 0.10 Caqueta moist forests 21 1 0 2.90 0 0 0.72 23 Galán Díaz et al. 2025 Ecosistemas 34(1): 2847 Table A5 continued / Continuación Tabla A5 Ecoregion Richness EDGE2 EcoDGE N Main Sum N Main Sum Maputaland coastal forest mosaic 10 1 0 2.90 0 0 0.11 Pantanal 14 1 0 2.81 0 0 0.39 Southeastern Indochina dry evergreen forests 7 2 1 2.80 0 0 0.09 Bolivian Yungas 18 1 0 2.69 0 0 0.57 Kimberly tropical savanna 13 1 0 2.68 0 0 0.13 Einasleigh upland savanna 12 1 0 2.65 0 0 0.12 Tocantins/Pindare moist forests 18 1 0 2.59 0 0 0.55 Southern Pacific dry forests 12 1 0 2.58 0 0 0.23 Chiquitano dry forests 26 0 0 2.57 0 0 0.83