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ABSTRACT Mosquito larvae encounter diverse assemblages of bacteria (i.e., “microbiota”) and fungi in the aquatic environments that they develop in. However, while a number of studies have addressed the diversity and function of microbiota in mosquito life history, relatively little is known about mosquito-fungus interactions outside several key fungal entomopathogens. In this study, we used high-throughput sequencing of internal transcribed spacer 2 (ITS2) metabarcode markers to provide the first simultaneous characterization of the fungal communities in field-collected Aedes albopictus larvae and their associated aquatic environments. Our results reveal unprecedented variation in fungal communities among adjacent but discrete larval breeding habitats. Our results also reveal a distinct fungal community assembly in the mosquito gut versus other tissues, with gut-associated fungal communities being most similar to those present in the environment where larvae feed. Altogether, our results identify the environment as the dominant factor shaping the fungal community associated with mosquito larvae, with no evidence of environmental filtering by the gut. These results also identify mosquito feeding behavior and fungal mode of nutrition as potential drivers of tissue-specific fungal community assembly after environmental acquisition. IMPORTANCE The Asian tiger mosquito, Aedes albopictus , is the dominant mosquito species in the United States and an important vector of arboviruses of major public health concern. One aspect of mosquito control to curb mosquito-borne diseases has been the use of biological control agents such as fungal entomopathogens. Recent studies also demonstrate the impact of mosquito-associated microbial communities on various mosquito traits, including vector competence. However, while much research attention has been dedicated to understanding the diversity and function of mosquito-associated bacterial communities, relatively little is known about mosquito-associated fungal communities. A better understanding of the factors that drive fungal community diversity and assembly in mosquitoes will be essential for future efforts to target mosquito-associated bacteria and fungi for mosquito and mosquito-borne disease control. 

Abstract Background West Nile virus (WNV) is the leading cause of mosquito-borne illness in the continental USA. WNV occurrence has high spatiotemporal variation, and current approaches to targeted control of the virus are limited, making forecasting a public health priority. However, little research has been done to compare strengths and weaknesses of WNV disease forecasting approaches on the national scale. We used forecasts submitted to the 2020 WNV Forecasting Challenge, an open challenge organized by the Centers for Disease Control and Prevention, to assess the status of WNV neuroinvasive disease (WNND) prediction and identify avenues for improvement. Methods We performed a multi-model comparative assessment of probabilistic forecasts submitted by 15 teams for annual WNND cases in US counties for 2020 and assessed forecast accuracy, calibration, and discriminatory power. In the evaluation, we included forecasts produced by comparison models of varying complexity as benchmarks of forecast performance. We also used regression analysis to identify modeling approaches and contextual factors that were associated with forecast skill. Results Simple models based on historical WNND cases generally scored better than more complex models and combined higher discriminatory power with better calibration of uncertainty. Forecast skill improved across updated forecast submissions submitted during the 2020 season. Among models using additional data, inclusion of climate or human demographic data was associated with higher skill, while inclusion of mosquito or land use data was associated with lower skill. We also identified population size, extreme minimum winter temperature, and interannual variation in WNND cases as county-level characteristics associated with variation in forecast skill. Conclusions Historical WNND cases were strong predictors of future cases with minimal increase in skill achieved by models that included other factors. Although opportunities might exist to specifically improve predictions for areas with large populations and low or high winter temperatures, areas with high case-count variability are intrinsically more difficult to predict. Also, the prediction of outbreaks, which are outliers relative to typical case numbers, remains difficult. Further improvements to prediction could be obtained with improved calibration of forecast uncertainty and access to real-time data streams (e.g. current weather and preliminary human cases). Graphical Abstract 

Vesicular stomatitis (VS) is the most common vesicular livestock disease in North America. Transmitted by direct contact and by several biting insect species, this disease results in quarantines and animal movement restrictions in horses, cattle and swine. As changes in climate drive shifts in geographic distributions of vectors and the viruses they transmit, there is considerable need to improve understanding of relationships among environmental drivers and patterns of disease occurrence. Multidisciplinary approaches integrating pathology, ecology, climatology, and biogeophysics are increasingly relied upon to disentangle complex relationships governing disease. We used a big data model integration approach combined with machine learning to estimate the potential geographic range of VS across the continental United States (CONUS) under long-term mean climate conditions over the past 30 years. The current extent of VS is confined to the western portion of the US and is related to summer and winter precipitation, winter maximum temperature, elevation, fall vegetation biomass, horse density, and proximity to water. Comparison with a climate-only model illustrates the importance of current processes-based parameters and identifies regions where uncertainty is likely to be greatest if mechanistic processes change. We then forecast shifts in the range of VS using climate change projections selected from CMIP5 climate models that most realistically simulate seasonal temperature and precipitation. Climate change scenarios that altered climatic conditions resulted in greater changes to potential range of VS, generally had non-uniform impacts in core areas of the current potential range of VS and expanded the range north and east. We expect that the heterogeneous impacts of climate change across the CONUS will be exacerbated with additional changes in land use and land cover affecting biodiversity and hydrological cycles that are connected to the ecology of insect vectors involved in VS transmission.