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Background: The risk of losing traditional knowledge of medicinal plants and their use and conservation is very high. Documenting knowledge on distribution and use of medicinal plants by different ethnic groups and at spatial scale on a single platform is important from a conservation planning and management perspective. The sustainable use, continuous practice, and safeguarding of traditional knowledge are essential. Communication of such knowledge among scientists and policy makers at local and global level is equally important, as the available information at present is limited and scattered in Nepal. Methods: In this paper, we aimed to address these shortcomings by cataloguing medicinal plants used by indigenous ethnic groups in Nepal through a systematic review of over 275 pertinent publications published between 1975 and July 2021. The review was complemented by field visits made in 21 districts. We determined the ethnomedicinal plants hotspots across the country and depicted them in heatmaps. Results: The heatmaps show spatial hotspots and sites of poor ethnomedicinal plant use documentation, which is useful for evaluating the interaction of geographical and ethnobotanical variables. Mid-hills and mountainous areas of Nepal hold the highest number of medicinal plant species in use, which could be possibly associated with the presence of higher human population and diverse ethnic groups in these areas. Conclusion: Given the increasing concern about losing medicinal plants due to changing ecological, social, and climatic conditions, the results of this paper may be important for better understanding of how medicinal plants in use are distributed across the country and often linked to specific ethnic groups. [ABSTRACT FROM AUTHOR]

Background: There are handful hypothesis-driven ethnobotanical studies in Nepal. In this study, we tested the non-random medicinal plant selection hypothesis using national- and community-level datasets through three different types of regression: linear model with raw data, linear model with log-transformed data and negative binomial model. Methods: For each of these model, we identified over-utilized families as those with highest positive Studentized residuals and underutilized families with highest negative Studentized residuals. The national-level data were collected from online databases and available literature while the community-level data were collected from Baitadi and Darchula districts. Results: Both dataset showed larger variance (national dataset mean 6.51 < variance 156.31, community dataset mean 1.16 < variance 2.38). All three types of regression were important to determine the medicinal plant species selection and use differences among the total plant families, although negative binomial regression was most useful. The negative binomial showed a positive nonlinear relationship between total plant family size and number of medicinal species per family for the national dataset (β1 = 0.0160 ± 0.0009, Z1 = 16.59, p < 0.00001, AIC1 = 1181), and with similar slope and stronger performance for the community dataset (β2 = 0.1747 ± 0.0199, Z2 = 8.76, p < 0.00001, AIC2 = 270.78). Moraceae and Euphorbiaceae were found over-utilized while Rosaceae, Cyperaceae and Caryophyllaceae were recorded as underutilized. Conclusions: As our datasets showed larger variance, negative binomial regression was found the most useful for testing non-random medicinal plant selection hypothesis. The predictions made by non-random selection of medicinal plants hypothesis holds true for community-level studies. The identification of over-utilized families is the first step toward sustainable conservation of plant resources and it provides a baseline for pharmacological research that might be leading to drug discovery. [ABSTRACT FROM AUTHOR]

A correction to the article "Selection of Medicinal Plants for Traditional Medicines in Nepal" in a 2021 issue is presented.

• Total 1041 presence points of seven species were used for ensemble species distribution modeling. • Modeling carried out using Shared Socioeconomic Pathways 2–4.5 and 5–8.5 for years 2050 and 2070. • Elevation, BIO2, BIO7, BIO18 and BIO19 were found as important for determining distribution of medicinal plants. • The range of alpine species was projected to be increased in central mountains. • Future habitats of (sub)tropical and temperate species were found to be constrained. Climate change is causing shifts in the habitat, distribution, ecology, and phenology of Himalayan plants. These changes are predicted to continue, jeopardizing the survival of medicinal plant species and local livelihoods that rely on them. We analyzed the present and future diversity and distribution of medicinal plant species influenced by different climate change scenarios, and calculated the climatic niche of the species using ensemble species distribution modeling (eSDM). We compiled 1041 (N) geospatial data of seven high-value medicinal plant species of Nepal: Aconitum spicatum (n = 100), Allium wallichii (n = 151), Bergenia ciliata (n = 48), Nardostachys jatamansi (n = 121), Neopicrorhiza scrophulariiflora (n = 94), Paris polyphylla (n = 310) and Valeriana jatamansi (n = 217) including over 85 % from field surveys and the rest from literature and online database. We used bioclimatic variables from Models for Interdisciplinary Research on Climate (MIROC) of version MIROC6, and selected Shared Socioeconomic Pathways (SSP)2-4.5 and SSP5-8.5 for the year of 2050 and 2070 for modeling. We found elevation, mean diurnal and annual temperature ranges (BIO2 and BIO7), and precipitation of warmest and coldest quarters (BIO18 and BIO19) to be the most high weight cofactors for projecting the future potential distribution of high-value medicinal plants in Nepal. Results showed that the suitable range of distribution for high-value medicinal plants would increase and concentrate in mountainous areas of central Nepal, but decline in (sub)tropical and temperate areas, suggesting both in-situ and ex-situ conservation practices, respectively. [ABSTRACT FROM AUTHOR]