Three New Heptelidic Acid Derivatives from the Culture of Mushroom Lentinellus ursinus

Three new heptelidic acid derivatives(1-3)including two new dimeric esters and two known heptelidic acid analogues(4 and 5)were isolated from the solid culture of mushroom Lentinellus ursinus.The structures of new compounds were confirmed by the analysis of NMR and HRESIMS spectroscopic data.The biosynthetic origin of compounds 1-5 was postulated.Compounds 1-5 exhibited no antibacterial activity against Staphylococcus aureus and Escherichia coli at the dose of 100 μM.

Use of the Max Ent model to predict changes in sloth bear(Melursus ursinus)habitats in the Gandaki River Basin,Nepal

The Gandaki River Basin(GRB),located in the central Himalaya,provides habitats for a large number of important flora and fauna species.The habitats of key protected species have changed over time as a result of climate changes and human activities related to land use change,including the development of roads.This study assessed the present and future distributions of sloth bear(Melursus ursinus)habitats using the maximum entropy(Max Ent)model.Bioclimatic data,a digital elevation model and roads were used as environmental layers to assess current and future distributions of habitat.Results show that the overall habitat within the study area is likely to undergo important changes in the future.Bio 8(the mean temperature of the wettest quarter)and slope were found to be important variables affecting the distribution of the species.The habitats very highly suitable for sloth bears currently cover an area of 148 km^(2) within the GRB.These habitats are predicted to increase by 122 km^(2) more by 2050.Overall,a net change by 680.38 km^(2) was found in the GRB.Conservation measures are necessary for this key protected species and its habitat within the GRB and elsewhere in Nepal.

Using machine learning to predict habitat suitability of sloth bears at multiple spatial scales

Background:Habitat resources occur across the range of spatial scales in the environment.The environmental resources are characterized by upper and lower limits,which define organisms’distribution in their communities.Animals respond to these resources at the optimal spatial scale.Therefore,multi-scale assessments are critical to identifying the correct spatial scale at which habitat resources are most influential in determining the specieshabitat relationships.This study used a machine learning algorithm random forest(RF),to evaluate the scaledependent habitat selection of sloth bears(Melursus ursinus)in and around Bandhavgarh Tiger Reserve,Madhya Pradesh,India.Results:We used 155 spatially rarified occurrences out of 248 occurrence records of sloth bears obtained from camera trap captures(n=36)and scats located(n=212)in the field.We calculated focal statistics for 13 habitat variables across ten spatial scales surrounding each presence-absence record of sloth bears.Large(>5000 m)and small(1000–2000 m)spatial scales were the most dominant scales at which sloth bears perceived the habitat features.Among the habitat covariates,farmlands and degraded forests were the essential patches associated with sloth bear occurrences,followed by sal and dry deciduous forests.The final habitat suitability model was highly accurate and had a very low out-of-bag(OOB)error rate.The high accuracy rate was also obtained using alternate validation matrices.Conclusions:Human-dominated landscapes are characterized by expanding human populations,changing landuse patterns,and increasing habitat fragmentation.Farmland and degraded habitats constitute~40%of the landform in the buffer zone of the reserve.One of the management implications may be identifying the highly suitable bear habitats in human-modified landscapes and integrating them with the existing conservation landscapes.