Decade-long #warming accelerates #antibiotic #resistance in #grassland soils

 

Abstract

Soils are critical reservoirs of antibiotic-resistance genes (ARGs), which are strongly shaped by microbial interactions and environmental conditions and are therefore highly sensitive to disturbance. Although climate warming is recognized as one of the most significant disturbances to microbial communities and their functions, its impacts on soil resistomes remain poorly understood. Here we investigated the effects of decade-long experimental warming on ARGs in grassland soils using integrated experimental and computational approaches. Our results revealed that ARG abundance substantially increased (23.9%) under warming—particularly glycopeptide- and rifamycin-resistance genes. Warming specifically enriched Actinomycetota hosts, including various potential plant pathogens, and enhanced ARG mobility. Large-scale unprecedented isolates-based phenotypic analyses also validated that warming increased bacterial resistance to multiple antibiotics. Further mechanistic analyses revealed that warming increased ARG abundance primarily through co-selection of resistance genes physically linked to adaptive traits (for example, thermal tolerance and nitrogen assimilation) and positive selection for thermal tolerance genes, which could be further amplified via horizontal gene transfer. Together, these findings convincingly demonstrate that climate warming substantially accelerates soil antibiotic resistance at genomic, ecological and evolutionary levels, with broad implications for public health and environmental sustainability in a warming world.

Source: 

Link: https://www.nature.com/articles/s41586-026-10413-x

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Predicting highly pathogenic avian #influenza #H5N1 #outbreak #risk using extreme #weather and bird #migration data in machine learning models

 

Abstract

Background. 

Climate change is intensifying extreme weather events (EWEs) with potentially profound consequences for zoonotic disease dynamics, yet the mechanisms linking EWEs to highly pathogenic avian influenza (HPAI) H5N1 outbreaks remain poorly characterized. The ongoing H5N1 panzootic, responsible for infection in over 500 avian and mammalian species, as well as nearly 1000 human cases and 477 deaths worldwide, provides a critical opportunity to evaluate how climate conditions shape spillover risk at landscape scales. 

Methods. 

We compiled a county-month dataset of confirmed H5N1 detections across the contiguous United States from 2022 to 2024 and integrated it with satellite-derived climate metrics, storm event data, and wild bird activity data. We trained and validated a gradient boosting machine classifier to predict outbreak risk and characterize predictor relationships. 

Results. 

Our model achieved strong discriminative performance (AUC-ROC = 0.856; AUC-PR = 0.237, representing a 7-fold improvement over chance) and high recall (0.726), supporting its utility as an early warning tool. Human population and temperature-related variables were the most influential predictors: cold temperature shocks and prolonged low temperatures were consistently associated with elevated outbreak risk, likely through enhanced environmental viral persistence, wild bird habitat compression, and allostatic stress-driven immunosuppression in reservoir hosts. Among storm variables, high wind coverage elevated risk, potentially via aerosol dispersal of contaminated particulates, while tornado activity showed an inverse relationship, consistent with documented avoidant behavior in migratory birds. Wild bird reservoir density showed a strong positive monotonic relationship with outbreak risk. 

Conclusions. 

Our analyses demonstrate that routinely available environmental and infection data can be used to predict HPAI outbreak risk at fine spatiotemporal scales. These findings demonstrate the divergent roles of short- versus long-term environmental exposures in HPAI spillover dynamics, as well as the potential for machine learning-based surveillance tools to inform targeted biosecurity interventions and early warning systems.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

This research was supported by a subaward agreement between prime award recipient Boston University (PI: Gregory Wellenius) and the subaward recipient Regents of the University of Colorado (PI: Elise Grover) under the National Institute of Environmental Health Sciences of the National Institutes of Health, Award Number U24ES035309 -01. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Source: 

Link: https://www.medrxiv.org/content/10.64898/2026.03.30.26349797v1

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