Abstract:The increase of tree density in forests of the American Southwest promotes extreme fire events, understory biodiversity losses, and degraded habitat conditions for many wildlife species. To ameliorate these changes, managers and scientists have begun planning treatments aimed at reducing fuels and increasing understory biodiversity. However, spatial variability in tree density across the landscape is not well-characterized, and if better known, could greatly influence planning efforts. We used reflectance values from individual Landsat 8 bands (bands 2, 3, 4, 5, 6, and 7) and calculated vegetation indices (difference vegetation index, simple ratios, and normalized vegetation indices) to estimate tree density in an area planned for treatment in the Jemez Mountains, New Mexico, characterized by multiple vegetation types and a complex topography. Because different vegetation types have different spectral signatures, we derived models with multiple predictor variables for each vegetation type, rather than using a single model for the entire project area, and compared the model-derived values to values collected from on-the-ground transects. Among conifer-dominated areas (73% of the project area), the best models (as determined by corrected Akaike Information Criteria (AICc)) included Landsat bands 2, 3, 4, and 7 along with simple ratios, normalized vegetation indices, and the difference vegetation index (R 2 values for ponderosa: 0.47, piñon-juniper: 0.52, and spruce-fir: 0.66). On the other hand, in aspen-dominated areas (9% of the project area), the best model included individual bands 4 and 2, simple ratio, and normalized vegetation index (R 2 value: 0.97). Most areas dominated by ponderosa, pinyon-juniper, or spruce-fir had more than 100 trees per hectare. About 54% of the study area has medium to high density of trees (100-1000 trees/hectare), and a small fraction (4.5%) of the area has very high density (>1000 trees/hectare). Our results provide a better understanding of tree density for identifying areas in need of treatment and planning for more effective treatment. Our analysis also provides an integrated method of estimating tree density across complex landscapes that could be useful for further restoration planning.

The present paper deals with the study conducted during [2006][2007][2008] to assess trade pattern of important medicinal plants in Chilime Village Development Committee (VDC) of Rasuwa district, Nepal. Chilime VDC is one of the important trade centres of medicinal plants in the district. We documented 60 species of important medicinal plants, including 26 species involved in trade. Among them, 12 most potentially traded species, which have been given high priority by the collectors and traders, were selected for the study of their market potential and their contribution to the local livelihood. About 40% of the households of Chilime VDC were found to be involved in the collection and trade of medicinal plants. Most of the collection (90%) was for trade, which has supported up to 40% of family income contributing average household net profit of NRs 9,000 per year. The local traders were also making a good profit from medicinal plants with annual net contribution of about NRs 0.25 million per trader. But only 50% of actual traded quantity was registered at DFO resulting low revenue collection (only 43% of the expected). This shows that illegal trade is a common practice in the area by which the local traders increase their profit because they do not have to pay revenue. Thus strong mechanisms should be developed to stop illegal trade of medicinal plants and to promote revenue generation.

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Aim: COVID-19 has exerted distress on virtually every aspect of human life with disproportionate mortality burdens on older individuals and those with underlying medical conditions. Variations in COVID-19 incidence and case fatality rates (CFRs) across countries have incited a growing research interest regarding the effect of social factors on COVID-19 case-loads and fatality rates. Our aim in this study was to investigate the effect of population median age, inequalities in human development, healthcare capacity, and pandemic mitigation indicators on country-specific COVID-19 CFRs across countries and regions. Subject and Methods: Using population secondary data from multiple sources, we conducted a cross-sectional study and used geospatial analysis to compare regional differences in COVID-19 CFRs as influenced by the selected indicators. Results: The analysis revealed wide variations in COVID-19 CFRs and the selected indicators across countries and regions. Mean CFR was highest for South America at 1.973% and lowest for Oceania at 0.264%, while the Africa sub-region recorded the lowest scores for pandemic preparedness, vaccination rate, and other indicators. Population Median Age, Vaccination Rate and Inequality-Adjusted Human Development Index (IHDI) emerged as statistically significant predictors of COVID-19 CFR, with directions indicating increasing Population Median Age, higher inequalities in human development and low vaccination rate are predictive of higher fatalities from COVID-19. Conclusion: Regional differences in COVID-19 CFR may be influenced by underlying differences in sociodemographic and pandemic mitigation indicators. Populations with wide social inequalities, increased population Median Age and low vaccination rates are more likely to suffer higher fatalities from COVID-19.

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