The contribution of insects to global forest deadwood decomposition.

The amount of carbon stored in deadwood is equivalent to about 8 per cent of the global forest carbon stocks1. The decomposition of deadwood is largely governed by climate2-5 with decomposer groups-such as microorganisms and insects-contributing to variations in the decomposition rates2,6,7. At the global scale, the contribution of insects to the decomposition of deadwood and carbon release remains poorly understood7. Here we present a field experiment of wood decomposition across 55 forest sites and 6 continents. We find that the deadwood decomposition rates increase with temperature, and the strongest temperature effect is found at high precipitation levels. Precipitation affects the decomposition rates negatively at low temperatures and positively at high temperatures. As a net effect-including the direct consumption by insects and indirect effects through interactions with microorganisms-insects accelerate the decomposition in tropical forests (3.9% median mass loss per year). In temperate and boreal forests, we find weak positive and negative effects with a median mass loss of 0.9 per cent and -0.1 per cent per year, respectively. Furthermore, we apply the experimentally derived decomposition function to a global map of deadwood carbon synthesized from empirical and remote-sensing data, obtaining an estimate of 10.9 ± 3.2 petagram of carbon per year released from deadwood globally, with 93 per cent originating from tropical forests. Globally, the net effect of insects may account for 29 per cent of the carbon flux from deadwood, which suggests a functional importance of insects in the decomposition of deadwood and the carbon cycle.

Ethnomedicinal landscape: distribution of used medicinal plant species in Nepal.

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.

Selection of medicinal plants for traditional medicines in Nepal.

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 (ß1 = 0.0160 ± 0.0009, Z1 = 16.59, p < 0.00001, AIC1 = 1181), and with similar slope and stronger performance for the community dataset (ß2 = 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.