A GeoHealth Response to a Geoscience Community Climate Change Position Statement.

The American Geophysical Union (AGU) issues position statements reflecting the state of the science and scientific consensus. AGU position statements can be used to support public and institutional policy development, conversations with peers and policymakers, and formal and informal education. The recent climate change position statement, "Society Must Address the Growing Climate Crisis now," provides important baseline information but lacks detail on critical climate and health impacts and actions for AGU and members. This commentary shares feedback from the AGU's GeoHealth Section and encourages members to use the AGU statement and engage in the comment process for other statements.

Identifying Environmental Risk Factors and Mapping the Distribution of West Nile Virus in an Endemic Region of North America.

Understanding the geographic distribution of mosquito-borne disease and mapping disease risk are important for prevention and control efforts. Mosquito-borne viruses (arboviruses), such as West Nile virus (WNV), are highly dependent on environmental conditions. Therefore, the use of environmental data can help in making spatial predictions of disease distribution. We used geocoded human case data for 2004-2017 and population-weighted control points in combination with multiple geospatial environmental data sets to assess the environmental drivers of WNV cases and to map relative infection risk in South Dakota, USA. We compared the effectiveness of (1) land cover and physiography data, (2) climate data, and (3) spectral data for mapping the risk of WNV in South Dakota. A final model combining all data sets was used to predict spatial patterns of disease transmission and characterize the associations between environmental factors and WNV risk. We used a boosted regression tree model to identify the most important variables driving WNV risk and generated risk maps by applying this model across the entire state. We found that combining multiple sources of environmental data resulted in the most accurate predictions. Elevation, late-season humidity, and early-season surface moisture were the most important predictors of disease distribution. Indices that quantified interannual variability of climatic conditions and land surface moisture were better predictors than interannual means. We suggest that combining measures of interannual environmental variability with static land cover and physiography variables can help to improve spatial predictions of arbovirus transmission risk.

Habitat factors influencing distributions of Anaplasma phagocytophilum and Ehrlichia chaffeensis in the Mississippi Alluvial Valley.

Human monocytotropic ehrlichiosis (HME), caused by the bacterium Ehrlichia chaffeensis, and human granulocytic anaplasmosis (HGA), caused by the bacterium Anaplasma phagocytophilum, are two emerging tick-borne zoonoses of concern. Factors influencing geographic distributions of these pathogens are not fully understood, especially at varying spatial extents (regional versus landscape) and resolutions (counties versus smaller land units). We used logistic regression to compare influences of physical environment, land cover composition, and landscape heterogeneity on distributions of A. phagocytophilum and E. chaffeensis at multiple spatial extents. Pathogen presence or absence was determined from white-tailed deer (Odocoileus virginianus) serum samples collected from 1981 to 2005. Ecological predictor variables were derived from spatial datasets that represented deer density, elevation, land cover, normalized difference vegetation index (NDVI), hydrology, and soil moisture. We used three strategies (a priori, exploratory, and spatial extent) to develop models. Best fitting models were applied within a geographic information system to create predictive probability surfaces for each bacterium. Ecological predictor variables generally resulted in better fitting models for E. chaffeensis than A. phagocytophilum (90.5% and 68% sensitivity, respectively), possibly as a result of differences in the natural histories of tick vectors. Although alternative model development strategies produced different models, in all cases bacteria presence or absence was affected by a combination of soil moisture or flooding variables (thought to affect primarily tick vectors) and forest cover or NDVI variables (thought to affect primarily mammalian hosts). This research demonstrates the potential for modeling the distributions of microscopic tick-borne pathogens using coarse regional datasets and emphasizes the importance of forest cover and flooding as environmental constraints, as well as the importance of considering ecological variables at multiple spatial extents.