bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.64898/2026.10.28.762036

Climate change and extreme weather events to intensify West Nile virus activity

Bliss, D. K.·Fischer, G.·Ernst, K.·Schumaker, N.·Riehle, M. A.·Hilborn, E.·DeFlorio-Barker, S.·Combrink, L.
View original source ↗Export metadata ↓

Abstract

Vector-borne zoonotic diseases pose a substantive risk to public health worldwide, yet predicting outbreaks at scales useful for public health response remains a critical challenge. Transmitted by Culex mosquitoes, West Nile virus is the leading cause of arboviral disease in the United States, progressing to neuroinvasive disease in approximately 1% of infections. Arizona's record-breaking 2021 outbreak, with 1,715 reported human cases and 127 deaths exemplifies this growing burden. Using a decade of mosquito surveillance data coupled with time-lagged weather patterns in a random forest framework, we developed spatial models predicting Culex abundance and West Nile virus occurrence across Arizona at weekly temporal and fine spatial resolution. Our weighted models classified viral occurrence with 98.9% accuracy and explained 65.3% of weekly variation in county-level human cases, with human cases lagging modeled activity by one week. Neither mosquito abundance nor viral occurrence declined at the extreme temperatures characteristic of Arizona summers, suggesting that thermal refugia sustain transmission. Drought-monsoon cycling, rather than temperature alone, emerged as the primary driver of West Nile activity, and projections through 2100 indicate intensifying activity under all climate change scenarios.

Explore related subjects

ecologyPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Bliss, D. K., Fischer, G., Ernst, K., Schumaker, N., Riehle, M. A., Hilborn, E., DeFlorio-Barker, S., Combrink, L.. 2026-10-30. Climate change and extreme weather events to intensify West Nile virus activity. https://doi.org/10.64898/2026.10.28.762036

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Differing patterns in community composition and diversity of biofilm-forming bacteria and fungi along a large salinity gradient

Aim: Biofilms are complex ecosystems formed by phototrophic and heterotrophic organisms, such as bacteria and fungi. Biofilms are hotspots for microbial activity and crucial for the functioning of all aquatic ecosystems, but they are overlooked and poorly studied, especially along large environmental gradients. We aimed at resolving the responses of bacterial and fungal communities to environmental variation along a gradient with strong variation in environmental factors, especially salinity. We also investigated whether the diversity of biofilm-forming bacteria and fungi follows the pattern predicted by the Remane curve, which posits that species diversity is lowest at intermediate salinity. Location: 2300-km-long spatial gradient in the Baltic Sea, Northern Europe Time period: August 2018 Major taxa studied: Biofilm-forming bacteria and fungi Methods: We investigated the composition and diversity of bacteria and fungi by collecting biofilm samples from 42 shallow benthic ecosystems. We also characterized key environmental variables, such as salinity, nutrient concentrations, wind exposure, and climatic conditions. Bacterial and fungal communities in biofilm samples were identified using Illumina MiSeq sequencing. Results: We found that bacteria and fungi responded differently to environmental variation. Bacterial communities were mainly controlled by salinity, and their diversity was lowest in the intermediate salinity. Bacterial communities were also dominated by different bacterial orders in high, intermediate, and low salinity. Fungal communities, instead, were jointly controlled by several environmental variables, i.e., salinity, water temperature, wind exposure, and nutrients, and their diversity or composition did not show a clear pattern along the salinity gradient. Main conclusions: To our knowledge, this is one of the first studies to investigate the biofilm-forming communities, especially fungal communities, along large environmental gradients. Our results highlight the role of biofilms as complex ecosystems where organisms ecological requirements are unique and variable.

ecology↗
bioRxiv2026

Reinforcement Learning Reveals Spatial Strategies for Slowing Biological Invasions

Managing biological invasions requires deciding where, when, and how to act while populations continue to grow and spread. Invasive species threaten ecosystems and livelihoods, but limited management resources mean that managers cannot treat every affected area. This study asks how control efforts can be directed across a landscape to slow an invasion while keeping costs low. We develop a reinforcement-learning framework that can use broader spatial context to control biological invasions, using the spongy moth as a case study. The stochastic simulator captures host-dependent growth, short- and long-distance dispersal, density-dependent mating, and the effects of multiple treatments. The reinforcement-learning strategies are compared with benchmarks that are based on local treatment rules. We find that reinforcement learning achieves the lowest mean cost, with its advantage widening as management complexity increases, such as with simultaneous advancing-front control, nascent-colony management, and established-colony containment on heterogeneous-host landscapes. The reinforcement-learning strategies exhibit highly context-dependent treatment behavior, adjusting treatment choices as invasion dynamics evolve and coordinating interventions across long distances, all of which is learned from scratch.

ecology↗
bioRxiv2026

Mapping long-distance jump dispersal risk by leveraging transport infrastructure data and sampling bias correction: a case study with the invasive spotted lanternfly (Lycorma delicatula)

Long-distance jump dispersal accelerates invasive species spread. Analysis of past jumps can identify invasion pathways and point to locations at risk of future colonization, but spatial bias in survey records obscures patterns. To inform surveillance for the spreading invasive grape pest Lycorma delicatula (Hemiptera: Fulgoridae), we gathered 82,890 agency records of established invasive populations from the United States. We used a clustering algorithm to identify 207 jump dispersal sites, and we developed a bias-aware comparison dataset by filtering records to identify proactive search effort at locations not yet known to hold L. delicatula. We measured points' proximity to a co-produced list of 22 property types encompassing categories with high vehicle traffic, railroads, and related contexts that may transport L. delicatula. Bootstrapping analysis showed that proactive surveys were concentrated near hypothesized influential property types, confirming substantial sampling bias. With this bias accounted for, historical jump dispersal sites were closer than expected to 15 property types, particularly distribution centers, truck rentals, flea markets, and importers. We also compared the lengths of road and rail around these points, finding that jump sites were strongly associated with elevated rail density, whereas models showed limited support for roads as jump sites. Finally, we used the high-risk features to project risk of future jumps across the eastern U.S. using ensemble random forests. Predicted high-risk areas were northeast Illinois, southeast Michigan, and central Ohio. This framework is generalizable, allowing various pathways to be assessed and jump dispersal risk to be projected based on spatially biased surveillance data.

ecology↗