Evidence map›Paper›PMID 40510630›Full record

ArticleEcology and evolution2025

Expanding National-Scale Wildlife Disease Surveillance Systems With Research Networks.

Kim M Pepin, Matthew A Combs, Guillaume Bastille-Rousseau, Meggan E Craft, Paul Cross, Maria A Diuk-Wasser, Roderick B Gagne, Travis Gallo, Tyler Garwood, Jonathon D Heale and 12 more

Abstract read
In one paragraph

Article in Ecology and evolution, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

22 authors.

Kim M PepinNational Wildlife Research Center, Wildlife Services, Animal and Plant Health Inspection Service United States Department of Agriculture Fort Collins Colorado USA.ORCID https://orcid.org/0000-0002-9931-8312
Matthew A CombsNational Wildlife Research Center, Wildlife Services, Animal and Plant Health Inspection Service United States Department of Agriculture Fort Collins Colorado USA.
Guillaume Bastille-RousseauCooperative Wildlife Research Laboratory Southern Illinois University Carbondale Illinois USA.ORCID https://orcid.org/0000-0001-6799-639X
Meggan E CraftDepartment of Ecology, Evolution, and Behavior University of Minnesota St. Paul Minnesota USA.
Paul CrossU.S. Geological Survey Northern Rocky Mountain Science Center Bozeman Montana USA.ORCID https://orcid.org/0000-0001-8045-5213
Maria A Diuk-WasserDepartment of Ecology, Evolution, and Environmental Biology Columbia University New York City New York USA.
Roderick B GagneDepartment of Pathobiology, Wildlife Futures Program University of Pennsylvania School of Veterinary Medicine Kennett Pennsylvania USA.
Travis GalloDepartment of Environmental Science and Technology University of Maryland College Park Maryland USA.
Tyler GarwoodDepartment of Ecology, Evolution, and Behavior University of Minnesota St. Paul Minnesota USA.ORCID https://orcid.org/0000-0002-5170-824X
Jonathon D HealeWildlife Services, Animal and Plant Health Inspection Services United States Department of Agriculture Fort Collins Colorado USA.
Joshua HewittNational Wildlife Research Center, Wildlife Services, Animal and Plant Health Inspection Service United States Department of Agriculture Fort Collins Colorado USA.
Jennifer Høy-PetersenDepartment of Pathobiology, Wildlife Futures Program University of Pennsylvania School of Veterinary Medicine Kennett Pennsylvania USA.
Jennifer MalmbergNational Wildlife Research Center, Wildlife Services, Animal and Plant Health Inspection Service United States Department of Agriculture Fort Collins Colorado USA.ORCID https://orcid.org/0000-0001-9066-721X
Jennifer MullinaxDepartment of Environmental Science and Technology University of Maryland College Park Maryland USA.ORCID https://orcid.org/0000-0003-4695-059X
Laura PlimptonDepartment of Ecology, Evolution, and Environmental Biology Columbia University New York City New York USA.ORCID https://orcid.org/0000-0002-2632-9272
Lauren SmithNational Wildlife Research Center, Wildlife Services, Animal and Plant Health Inspection Service United States Department of Agriculture Fort Collins Colorado USA.ORCID https://orcid.org/0009-0003-5652-0865
Meredith C VanAckerDepartment of Ecology, Evolution, and Environmental Biology Columbia University New York City New York USA.
Jeffrey C ChandlerNational Wildlife Research Center, Wildlife Services, Animal and Plant Health Inspection Service United States Department of Agriculture Fort Collins Colorado USA.
W David WalterU.S. Geological Survey, Pennsylvania Cooperative Fish and Wildlife Research Unit The Pennsylvania State University University Park Pennsylvania USA.ORCID https://orcid.org/0000-0003-3068-1073
Grete Wilson-HenjumNational Wildlife Research Center, Wildlife Services, Animal and Plant Health Inspection Service United States Department of Agriculture Fort Collins Colorado USA.
George WittemyerDepartment of Fish, Wildlife and Conservation Biology Colorado State University Fort Collins Colorado USA.ORCID https://orcid.org/0000-0003-1640-5355
Kezia ManloveDepartment of Wildland Resources and Ecology Center Utah State University Logan Utah USA.ORCID https://orcid.org/0000-0002-7200-5236

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Efficient learning about disease dynamics in free-ranging wildlife systems can benefit from active surveillance that is standardized across different ecological contexts. For example, active surveillance that targets specific individuals and populations with standardized sampling across ecological contexts (landscape-scale targeted surveillance) is important for developing a mechanistic understanding of disease emergence, which is the foundation for improving risk assessment of zoonotic or wildlife-livestock disease outbreaks and predicting hotspots of disease emergence. However, landscape-scale targeted surveillance systems are rare and challenging to implement. Increasing experience and infrastructure for landscape-scale targeted surveillance will improve readiness for rapid deployment of this type of surveillance in response to new disease emergence events. Here, we describe our experience developing and rapidly deploying a landscape-scale targeted surveillance system for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in two free-ranging deer species across their ranges in the United States. Our surveillance system was designed to collect data across individual, population, and landscape scales for future analyses aimed at understanding mechanisms and risk factors of SARS-CoV-2 transmission, evolution, and persistence. Our approach leveraged partnerships between state and federal public service sectors and academic researchers in a landscape-scale targeted surveillance research network. Methods describe our approach to developing the surveillance network and sampling design. Results report challenges with implementing our intended sampling design, specifically how the design was adapted as different challenges arose and summarize the sampling design that has been implemented thus far. In the discussion, we describe strategies that were important for the successful deployment of landscape-scale targeted surveillance, development and operation of the research network, construction of similar networks in the future, and analytical approaches for the data based on the sampling design.

Indexed as

chronic wasting diseasedisease emergencemule deeropportunistic surveillanceSARS‐CoV‐2severe acute respiratory syndrome coronavirus 2surveillancetargeted surveillancewhite‐tailed deerwildlife disease

Identifiers

PMID40510630
PMCPMC12158667

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.