Anthrax, caused by the bacterium Bacillus anthracis, is a zoonotic disease that persists throughout much of the world in livestock, wildlife, and secondarily infects humans. This is true across much of Central Asia, and particularly the Steppe region, including Kazakhstan. This study employed the Genetic Algorithm for Rule-set Prediction (GARP) to model the current and future geographic distribution of Bacillus anthracis in Kazakhstan based on the A2 and B2 IPCC SRES climate change scenarios using a 5-variable data set at 55 km² and 8 km² and a 6-variable BioClim data set at 8 km². Future models suggest large areas predicted under current conditions may be reduced by 2050 with the A2 model predicting ∼14–16% loss across the three spatial resolutions. There was greater variability in the B2 models across scenarios predicting ∼15% loss at 55 km², ∼34% loss at 8 km², and ∼30% loss with the BioClim variables. Only very small areas of habitat expansion into new areas were predicted by either A2 or B2 in any models. Greater areas of habitat loss are predicted in the southern regions of Kazakhstan by A2 and B2 models, while moderate habitat loss is also predicted in the northern regions by either B2 model at 8 km². Anthrax disease control relies mainly on livestock vaccination and proper carcass disposal, both of which require adequate surveillance. In many situations, including that of Kazakhstan, vaccine resources are limited, and understanding the geographic distribution of the organism, in tandem with current data on livestock population dynamics, can aid in properly allocating doses. While speculative, contemplating future changes in livestock distributions and B. anthracis spore promoting environments can be useful for establishing future surveillance priorities. This study may also have broader applications to global public health surveillance relating to other diseases in addition to B. anthracis.

Oyster reefs provide valuable ecosystem services that contribute to coastal resilience. Unfortunately, many reefs have been degraded or removed completely, and there are increased efforts to restore oysters in many coastal areas. In particular, much attention has recently been given to the restoration of shellfish reefs along eroding shorelines to reduce erosion. Such fringing reef approaches, however, often lack empirical data to identify locations where reefs are most effective in reducing marsh erosion, or fully take into account habitat suitability. Using monitoring data from 5 separate fringing reef projects across coastal Louisiana, we quantify shoreline exposure (fetch + wind direction + wind speed) and reef impacts on shoreline retreat. Our results indicate that fringing oyster reefs have a higher impact on shoreline retreat at higher exposure shorelines. At higher exposures, fringing reefs reduced marsh edge erosion an average of 1.0 m y⁻¹. Using these data, we identify ranges of shoreline exposure values where oyster reefs are most effective at reducing marsh edge erosion and apply this knowledge to a case study within one Louisiana estuary. In Breton Sound estuary, we calculate shoreline exposure at 500 random points and then overlay a habitat suitability index for oysters. This method and the resulting visualization show areas most likely to support sustainable oyster populations as well as significantly reduce shoreline erosion. Our results demonstrate how site selection criteria, which include shoreline exposure and habitat suitability, are critical to ensuring greater positive impacts and longevity of oyster reef restoration projects.

Brucellosis is a common zoonotic disease and remains a major burden in both human and domesticated animal populations worldwide. Few geographic studies of human Brucellosis have been conducted, especially in China. Inner Mongolia of China is considered an appropriate area for the study of human Brucellosis due to its provision of a suitable environment for animals most responsible for human Brucellosis outbreaks. The aggregated numbers of human Brucellosis cases from 1951 to 2005 at the municipality level, and the yearly numbers and incidence rates of human Brucellosis cases from 2006 to 2010 at the county level were collected. Geographic Information Systems (GIS), remote sensing (RS) and ecological niche modeling (ENM) were integrated to study the distribution of human Brucellosis cases over 1951–2010. Results indicate that areas of central and eastern Inner Mongolia provide a long-term suitable environment where human Brucellosis outbreaks have occurred and can be expected to persist. Other areas of northeast China and central Mongolia also contain similar environments. This study is the first to combine advanced spatial statistical analysis with environmental modeling techniques when examining human Brucellosis outbreaks and will help to inform decision-making in the field of public health.