Leptospirosis is a zoonotic disease of global importance with significant morbidity and mortality. However, the disease is frequently overlooked and underdiagnosed, leading to uncertainty of the true scale and severity of the disease. A neglected tropical disease, leptospirosis disproportionately impacts disadvantaged socioeconomic communities most vulnerable to outbreaks of zoonotic disease, due to contact with infectious animals and contaminated soils and waters. With growing evidence that Leptospira survives, persists, and reproduces in the environment, this paper reviews the current understanding of the pathogen in the environment and highlights the unknowns that are most important for future study. Through a systematic Boolean review of the literature, our study finds that detailed field-based study of Leptospira prevalence, survival, and transmission in natural waters and soils is lacking from the current literature. This review identified a strong need for assessment of physical characteristics and biogeochemical processes that support long-term viability of Leptospira in the environment followed by epidemiological assessment of the transmission and movement of the same strains of Leptospira in the present wildlife and livestock as the first steps in improving our understanding of the environmental stage of the leptospirosis transmission cycle.

Tidal freshwater wetlands are one of the most vulnerable ecosystems to climate change and rising sea levels. However salinification within these systems is poorly understood, therefore, productivity (litterfall, woody biomass, and fine roots) were investigated on three forested tidal wetlands [(1) freshwater, (2) moderately saline, and (3) heavily salt-impacted] and a marsh along the Waccamaw and Turkey Creek in South Carolina. Mean aboveground (litterfall and woody biomass) production on the freshwater, moderately saline, heavily salt-impacted, and marsh, respectively, was 1,061, 492, 79, and $0{\mathrm{g}\mathrm{m}}^{-2}{\mathrm{year}}^{-1}$ versus belowground (fine roots) 860, 490, 620, and $\mathrm{2,128}{\mathrm{g}\mathrm{m}}^{-2}{\mathrm{year}}^{-1}$ . Litterfall and woody biomass displayed an inverse relationship with salinity. Shifts in productivity across saline sites is of concern because sea level is predicted to continue rising. Results from the research reported in this paper provide baseline data upon which coupled hydrologic/wetland models can be created to quantify future changes in tidal forest functions.

The consistent sporadic transmission of West Nile Virus (WNV) in the city of New Orleans justifies the need for distribution risk maps highlighting human risk of mosquito bites. We modeled the influence of biophysical and socioeconomic metrics on the spatio-temporal distributions of presence/vector-host contact (VHC) ratios of WNV vector, Culex quinquefasciatus, within their flight range. Biophysical and socioeconomic data were extracted within 5-km buffer radii around sampling localities of gravid female Culex quinquefasciatus. The spatio-temporal correlations between VHC data and 33 variables, including climate, land use-land cover (LULC), socioeconomic, and land surface terrain were analyzed using stepwise linear regression models (RM). Using MaxEnt, we developed a distribution model using the correlated predicting variables. Only 12 factors showed significant correlations with spatial distribution of VHC ratios (R² = 81.62, p < 0.01). Non-forested wetland (NFWL), tree density (TD) and residential-urban (RU) settings demonstrated the strongest relationship. The VHC ratios showed monthly environmental resilience in terms of number and type of influential factors. The highest prediction power of RU and other urban and built up land (OUBL), was demonstrated during May–August. This association was positively correlated with the onset of the mosquito WNV infection rate during June. These findings were confirmed by the Jackknife analysis in MaxEnt and independently collected field validation points. The spatial and temporal correlations of VHC ratios and their response to the predicting variables are discussed.