wrote the paper.The authors declare no conflict of interest. This article is a PNAS Direct Submission.Published under the PNAS license.Data deposition: The datasets generated and analyzed during the current study are available in the University of Exeter ORE repository, https://ore.exeter.ac.uk/repository/ (

Chronic bee paralysis is a well-defined viral disease of honey bees with a global distribution that until recently caused rare but severe symptomatology including colony loss. Anecdotal evidence indicates a recent increase in virus incidence in several countries, but no mention of concomitant disease. We use government honey bee health inspection records from England and Wales to test whether chronic bee paralysis is an emerging infectious disease and investigate the spatiotemporal patterns of disease. The number of chronic bee paralysis cases increased exponentially between 2007 and 2017, demonstrating chronic bee paralysis as an emergent disease. Disease is highly clustered spatially within most years, suggesting local spread, but not between years, suggesting disease burnt out with periodic reintroduction. Apiary and county level risk factors are confirmed to include scale of beekeeping operation and the history of honey bee imports. Our findings offer epidemiological insight into this damaging emerging disease.

Understanding how microbial traits affect the evolution and functioning of microbial communities is fundamental for improving the management of harmful microorganisms, while promoting those that are beneficial. Decades of evolutionary ecology research has focused on examining microbial cooperation, diversity, productivity and virulence but with one crucial limitation. The traits under consideration, such as public good production and resistance to antibiotics or predation, are often assumed to act in isolation. Yet, in reality, multiple traits frequently interact, which can lead to unexpected and undesired outcomes for the health of macroorganisms and ecosystem functioning. This is because many predictions generated in a single‐trait context aimed at promoting diversity, reducing virulence or controlling antibiotic resistance can fail for systems where multiple traits interact. Here, we provide a much needed discussion and synthesis of the most recent research to reveal the widespread and diverse nature of multi‐trait interactions and their consequences for predicting and controlling microbial community dynamics. Importantly, we argue that synthetic microbial communities and multi‐trait mathematical models are powerful tools for managing the beneficial and detrimental impacts of microbial communities, such that past mistakes, like those made regarding the stewardship of antimicrobials, are not repeated.

A cornerstone of classical virulence evolution theories is the assumption that pathogen growth rate is positively correlated with virulence, the amount of damage pathogens inflict on their hosts. Such theories are key for incorporating evolutionary principles into sustainable disease management strategies. Yet, empirical evidence raises doubts over this central assumption underpinning classical theories, thus undermining their generality and predictive power. In this paper, we identify a key component missing from current theories which redefines the growth–virulence relationship in a way that is consistent with data. By modifying the activity of a single metabolic gene, we engineered strains of Magnaporthe oryzae with different nutrient acquisition and growth rates. We conducted in planta infection studies and uncovered an unexpected non‐monotonic relationship between growth rate and virulence that is jointly shaped by how growth rate and metabolic efficiency interact. This novel mechanistic framework paves the way for a much‐needed new suite of virulence evolution theories.

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