Evidence map›Paper›PMID 34994978›Full record

ArticleThe Journal of animal ecology2022

Examination of the interaction between age-specific predation and chronic disease in the Greater Yellowstone Ecosystem.

Ellen E Brandell, Paul C Cross, Douglas W Smith, Will Rogers, Nathan L Galloway, Daniel R MacNulty, Daniel R Stahler, John Treanor, Peter J Hudson

Open access · hybridAbstract read
In one paragraph

Article in The Journal of animal ecology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
4.0field-weighted citation impact, top 7% of its field
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

4 citing papers in PubMed, 18 citations in OpenAlex.

  1. Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors at 6 institutions in 1 country.

Ellen E BrandellCenter for Infectious Disease Dynamics and Department of Biology, Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, PA, USA.ORCID 0000-0002-2698-7013
Paul C CrossU.S. Geological Survey, Northern Rocky Mountain Science Center, Bozeman, MT, USA.ORCID 0000-0001-8045-5213
Douglas W SmithYellowstone Center for Resources, Yellowstone National Park, Wyoming, WY, USA.
Will RogersDepartment of Ecology, Montana State University, Bozeman, MT, USA.ORCID 0000-0001-9899-8064
Nathan L GallowayBiological Resources Division, National Park Service, Fort Collins, CO, USA.ORCID 0000-0002-0614-2988
Daniel R MacNultyDepartment of Wildland Resources, Utah State University, Logan, UT, USA.ORCID 0000-0002-9173-8910
Daniel R StahlerYellowstone Center for Resources, Yellowstone National Park, Wyoming, WY, USA.
John TreanorYellowstone Center for Resources, Yellowstone National Park, Wyoming, WY, USA.
Peter J HudsonCenter for Infectious Disease Dynamics and Department of Biology, Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, PA, USA.
Yellowstone Ecological Research Center · USPennsylvania State University · USMontana State University · USNational Park Service · USUnited States Geological Survey · USUtah State University · US

Funding

National Science Foundation LTREB DEB-1245373NSF LTREB DEB-1245373U.S. Geological Survey 140G0320P0145U.S. Geological Survey G17AC00427Verne Willaman
6 · The paper itself

Abstract

Predators may create healthier prey populations by selectively removing diseased individuals. Predators typically prefer some ages of prey over others, which may, or may not, align with those prey ages that are most likely to be diseased. The interaction of age-specific infection and predation has not been previously explored and likely has sizable effects on disease dynamics. We hypothesize that predator cleansing effects will be greater when the disease and predation occur in the same prey age groups. We examine the predator cleansing effect using a model where both vulnerability to predators and pathogen prevalence vary with age. We tailor this model to chronic wasting disease (CWD) in mule deer and elk populations in the Greater Yellowstone Ecosystem, with empirical data from Yellowstone grey wolves and cougars. Model results suggest that under moderate, yet realistic, predation pressure from cougars and wolves independently, predators may decrease CWD outbreak size substantially and delay the accumulation of symptomatic deer and elk. The magnitude of this effect is driven by the ability of predators to selectively remove late-stage CWD infections that are likely the most responsible for transmission, but this may not be the age class they typically select. Thus, predators that select for infected young adults over uninfected juveniles have a stronger cleansing effect, and these effects are strengthened when transmission rates increase with increasing prey morbidity. There are also trade-offs from a management perspective-that is, increasing predator kill rates can result in opposing forces on prey abundance and CWD prevalence. Our modelling exploration shows that predators have the potential to reduce prevalence in prey populations when prey age and disease severity are considered, yet the strength of this effect is influenced by predators' selection for demography or body condition. Current CWD management focuses on increasing cervid hunting as the primary management tool, and our results suggest predators may also be a useful tool under certain conditions, but not necessarily without additional impacts on host abundance and demography. Protected areas with predator populations will play a large role in informing the debate over predator impacts on disease.

Indexed as

DeerWolvesAge FactorsAnimalsChronic DiseaseEcosystemFood ChainPopulation DynamicsPredatory Behaviorage structuredemographyhealthy herdsinfectious diseasematrix modelpredator-preyprionsimulation

Identifiers

PMID34994978
PMCPMC9912199
OpenAlexW4206336567

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.