Search bioRxiv⌕ Search

bioRxiv · 10.1101/2024.07.04.602117

A Systematic Review of the Distribution and Prevalence of Viruses Detected in the Peromyscus maniculatus Species Complex (Rodentia: Cricetidae)

Abstract

The North American Deermouse, Peromyscus maniculatus, is one of the most widespread and abundant mammals on the continent. It is of public health interest as a known host of several viruses that are transmissible to humans and can cause illness, including the acute respiratory disease Hantavirus pulmonary syndrome (HPS). However, recent taxonomic studies indicate that P. maniculatus is a complex of multiple species, raising questions about how to identify and interpret three decades of hantavirus monitoring data. We conducted a systematic review investigating the prevalence and spatial distribution of viral taxa detected in wild populations allocated to P. maniculatus. From the 49 relevant studies published from 2000 to 2022, we extracted and analyzed spatial occurrence data to calculate weighted populational prevalences for hantaviruses. We found that detection efforts have been concentrated in the Western United States and Mexico with a focus on the spread of Sin Nombre virus (Orthohantavirus sinnombreense), the primary causative agent of HPS. There are significant gaps in the existing literature both geographically and in regard to the types of viruses being sampled. These results are significantly impacted by a recent taxonomic split of P. maniculatus into four species, and we were able to update 94% of hantavirus observations to reflect this change. Investigating the uncertain, and likely multiple, phylogenetic histories of these viral hosts should be a key emphasis of future modeling efforts. Author SummaryUnderstanding the interactions of viruses with mammal hosts is critical for monitoring disease spread and identifying species or geographic areas at high risk for future zoonotic disease outbreaks. However, much of the data scientists have collected about viruses in mammals remains fragmented in published literature and is not available in an up-to-date, standardized format online. We conducted a systematic review of virus observations in one of the most abundant, widespread group of rodent species in North America, the Peromyscus maniculatus (North American Deermouse) species complex. We extracted and analyzed the spatial distribution of 62,421 observations reporting the results of tests for viruses in the Hantaviridae, Arenaviridae, and Flaviviridae families. We find that conclusions about reservoir host status may be impacted by recent taxonomic proposals that split P. maniculatus into four distinct species, and we show how the taxonomic identifications of 94% of the reported observations can be updated using expert-generated maps of the ranges for the proposed new species. We also highlight the uneven geographic sampling of viruses in the rodent populations and major gaps that remain in our knowledge.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Finkbeiner, A., Khatib, A., Upham, N., Sterner, B.. 2024-07-08. A Systematic Review of the Distribution and Prevalence of Viruses Detected in the Peromyscus maniculatus Species Complex (Rodentia: Cricetidae). https://doi.org/10.1101/2024.07.04.602117

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

KEEP EXPLORING

Related preprints

Operationalising the context in regenerative agriculture: decision-making and farm variability

Soil degradation is a widespread challenge that requires a broad response at the individual farm level. To ensure effectivity, the practices should be tailored to the farm context: land manager objectives and farm specific challenges. These have however been difficult to quantify. Here we demonstrate that a workable farm context can be created based on a value survey, open satellite and soil data, and published models for vegetation gross primary productivity and soil erosion. Based on the findings, despite individual differences, farmers value profitability and operational efficiency, but also biodiversity and soil health. At least the regenerative farmers surveyed also value working for the greater good more than maintaining tradition or power. In spite of wide differences in farm production orientation, we also found that each farm also had a broad variation in individual fields GPP. Most fields have a stable GPP level, which is either high or low, and that there is a 2-3-fold difference between the weakest and best producing fields indicating the potential for improving GPP by improving the growing conditions on currently weak fields. In addition, soil loss was found to be highly concentrated in critical source areas, where 10% of the field area contributed to 50% of the soil loss. Overall, open data can be linked to modelling workflows to rapidly produce a decision-making context for farmers. This facilitates benchmarking and co-learning as well as enables land managers and advisors to identify the farm context for planning effective responses to soil degradation.

ecology↗

Fly, land, listen: Autonomous intermittent locomotion enables scalable low-noise drone ecoacoustic surveys

Ecoacoustic monitoring is enabling scientists and land managers to monitor and manage biodiversity more effectively and cost-efficiently in the face of human pressures and rapidly changing climates. Currently, most ecoacoustic surveys use manually deployed static sensors to record data, limiting the scale and reach of surveying efforts. Here we present a proof-of-concept autonomous drone platform that can use intermittent locomotion to conduct ecoacoustic surveys using an onboard sensor. Our custom prototype is able to fly, navigate, and avoid obstacles autonomously, land at a pre-determined location, record audio from an onboard microphone whilst static, before taking off and moving to the next sampling site. Autonomous navigation and operation enable greater sampling flexibility, reach, and scalability. Furthermore, by recording audio only whilst landed, noise from the drone's rotors does not mask signals or disturb animals, simplifying signal processing and downstream ecological analyses. We conducted trials in a scrubland habitat at the Knepp Estate in West Sussex, where our prototype demonstrated successful autonomous navigation and obstacle avoidance. Furthermore, we found that avian biodiversity data collected from the drone platform was comparable to that from traditional static acoustic sensor deployments, and that vocalisation patterns were not significantly impacted by the noise of the drone arriving or leaving a site. While scaled deployments of our technology would require further technical and regulatory challenges to be solved, our first demonstration of autonomous intermittent robotics-assisted ecoacoustic surveys lays the foundations for more cost-effective and far-reaching biodiversity surveys, with transformative potential for conservation, agricultural management, biosecurity, and more.

ecology↗

Do higher-order moments improve inference of population dynamics?

Fitting mathematical models of population dynamics to microbial time-series data allows us to estimate the ecological processes and interactions taking place in the microbiome. Repeated experiments of microbial systems yield replicates which slightly differ from each other. Some of this variability arises due to the fact that births and deaths occur at random. Most prior work focuses on fitting a deterministic mathematical model to the average across replicates. We use a stochastic model to fit the variability to the observed variability across replicates. Using a simulation-driven approach, we study the conditions under which our approach allows us to infer a larger fraction of ecological parameters correctly. We observe a substantial improvement in parameter inference. Lastly, our Bayesian approach not only allows us to incorporate prior information about the system, but also provides a distribution of parameters which conveys some idea of the uncertainty of the estimates.

ecology↗