Drivers and hotspots of extinction risk in marine mammals.

The world's oceans are undergoing profound changes as a result of human activities. However, the consequences of escalating human impacts on marine mammal biodiversity remain poorly understood. The International Union for the Conservation of Nature (IUCN) identifies 25% of marine mammals as at risk of extinction, but the conservation status of nearly 40% of marine mammals remains unknown due to insufficient data. Predictive models of extinction risk are crucial to informing present and future conservation needs, yet such models have not been developed for marine mammals. In this paper, we: (i) used powerful machine-learning and spatial-modeling approaches to understand the intrinsic and extrinsic drivers of marine mammal extinction risk; (ii) used this information to predict risk across all marine mammals, including IUCN "Data Deficient" species; and (iii) conducted a spatially explicit assessment of these results to understand how risk is distributed across the world's oceans. Rate of offspring production was the most important predictor of risk. Additional predictors included taxonomic group, small geographic range area, and small social group size. Although the interaction of both intrinsic and extrinsic variables was important in predicting risk, overall, intrinsic traits were more important than extrinsic variables. In addition to the 32 species already on the IUCN Red List, our model identified 15 more species, suggesting that 37% of all marine mammals are at risk of extinction. Most at-risk species occur in coastal areas and in productive regions of the high seas. We identify 13 global hotspots of risk and show how they overlap with human impacts and Marine Protected Areas.

Assessment of street-level greenness and its association with housing prices in a metropolitan area.

Rapid global urbanization has made environmental amenities scarce despite their considerable advantages, ranging from aesthetics to health benefits. Street greenness is a key urban environmental amenity. This study developed a green index as an objective measure of greenness using street view images and assessed its predictive power along with that of other environmental amenities for metropolitan housing prices. Spatial interpolation was used to transform point data into areal data, enabling effective analysis of a dataset covering an entire metropolis. A series of hedonic models revealed that (1) street greenness is significantly and negatively associated with housing prices, (2) a traditional greenness indicator and the green index provide complementary information, indicating that they could be used for different purposes, and (3) environmental amenities, in general, demonstrated significant relationships with housing prices. Our analysis strategy including spatial interpolation can be widely employed for studies using different types of data. The findings demonstrating a complementary relationship between our two greenness indicators provide valuable insights for policymakers and urban planners to improve street-level greenness and green accessibility. Considering the significance of environmental amenities, this study provides practical approaches for executing sustainable and healthy city development.

Food deserts in Leon County, FL: disparate distribution of Supplemental Nutrition Assistance Program-accepting stores by neighborhood characteristics.

OBJECTIVE: Examine whether neighborhood characteristics of racial composition, income, and rurality were related to distribution of Supplemental Nutrition Assistance Program (SNAP)-accepting stores in Leon County, Florida. DESIGN: Cross-sectional; neighborhood and food store data collected in 2008. SETTING AND PARTICIPANTS: Forty-eight census tracts as proxy of neighborhoods in Leon County, Florida. All stores and SNAP-accepting stores were identified from a commercial business directory and a United States Department of Agriculture SNAP-accepting store list, respectively (n = 288). MAIN OUTCOME MEASURES: Proportion of SNAP-accepting stores across neighborhoods. ANALYSIS: Descriptive statistics to describe distribution of SNAP-accepting stores by neighborhood characteristics. Proportions of SNAP-accepting stores were compared by neighborhood characteristics with Wilcoxon-Mann-Whitney and Kruskal-Wallis tests. RESULTS: Of 288 available stores, 45.1% accepted SNAP benefits. Of the 48 neighborhoods, 16.7% had no SNAP-accepting stores. Proportions of SNAP-accepting grocery stores were significantly different by neighborhood racial composition and income. Primarily black neighborhoods did not have any supermarkets. Results were mixed with regard to distribution of food stores and SNAP-accepting stores by neighborhood racial composition, income, and rurality. CONCLUSIONS AND IMPLICATIONS: This study suggests disparities in distribution of SNAP-accepting stores across neighborhood characteristics of racial composition, income, and rurality.