Evidence map›Paper›PMID 32935347›Full record

ArticleThe Journal of animal ecology2020

Disease's hidden death toll: Using parasite aggregation patterns to quantify landscape-level host mortality in a wildlife system.

Mark Q Wilber, Cheryl J Briggs, Pieter T J Johnson

Open access · greenAbstract read
In one paragraph

Article in The Journal of animal ecology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
1.6field-weighted citation impact, top 17% 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

11 citing papers in PubMed, 24 citations in OpenAlex.

  1. Article
  2. Article
  3. Response of Northern bobwhite (International journal for parasitology. Parasites and wildlife · 2024
    Review
  4. Article
  5. Outbreak of parasite-induced limb malformations in a declining amphibian species in Colorado.International journal for parasitology. Parasites and wildlife · 2024
    Article
  6. Article
  7. Article
  8. Article
  9. Prevalence ofVector borne and zoonotic diseases (Larchmont, N.Y.) · 2023
    Article
  10. Article
  11. Article
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

3 authors at 2 institutions in 1 country.

Mark Q WilberEcology, Evolution and Marine Biology, University of California, Santa Barbara, Santa Barbara, CA, USA.ORCID 0000-0002-8274-8025
Cheryl J BriggsEcology, Evolution and Marine Biology, University of California, Santa Barbara, Santa Barbara, CA, USA.ORCID 0000-0001-8674-5385
Pieter T J JohnsonEcology and Evolutionary Biology, University of Colorado, Boulder, CO, USA.ORCID 0000-0002-7997-5390
University of California, Santa Barbara · USUniversity of Colorado Boulder · US

Funding

EID disease in complex communities: multi-host multi-pathogen interactionsR01GM109499 · NIGMS · UNIVERSITY OF CALIFORNIA SANTA BARBARA · PI BRIGGS, CHERYL J · 2013 to 2016
$1.7M
From specialist to generalist: biotic and abiotic reservoirs of fungal pathogensR01GM135935 · NIGMS · UNIVERSITY OF CALIFORNIA SANTA BARBARA · PI BRIGGS, CHERYL J · 2019 to 2022
$1.6M
NIGMS NIH HHS R01 GM109499NIGMS NIH HHS R01 GM135935
6 · The paper itself

Abstract

World-wide, infectious diseases represent a major source of mortality in humans and livestock. For wildlife populations, disease-induced mortality is likely even greater, but remains notoriously difficult to estimate-especially for endemic infections. Approaches for quantifying wildlife mortality due to endemic infections have historically been limited by an inability to directly observe wildlife mortality in nature. Here we address a question that can rarely be answered for endemic pathogens of wildlife: what are the population- and landscape-level effects of infection on host mortality? We combined laboratory experiments, extensive field data and novel mathematical models to indirectly estimate the magnitude of mortality induced by an endemic, virulent trematode parasite (Ribeiroia ondatrae) on hundreds of amphibian populations spanning four native species. We developed a flexible statistical model that uses patterns of aggregation in parasite abundance to infer host mortality. Our model improves on previous approaches for inferring host mortality from parasite abundance data by (i) relaxing restrictive assumptions on the timing of host mortality and sampling, (ii) placing all mortality inference within a Bayesian framework to better quantify uncertainty and (iii) accommodating data from laboratory experiments and field sampling to allow for estimates and comparisons of mortality within and among host populations. Applying our approach to 301 amphibian populations, we found that trematode infection was associated with an average of between 13% and 40% population-level mortality. For three of the four amphibian species, our models predicted that some populations experienced >90% mortality due to infection, leading to mortality of thousands of amphibian larvae within a pond. At the landscape scale, the total number of amphibians predicted to succumb to infection was driven by a few high mortality sites, with fewer than 20% of sites contributing to greater than 80% of amphibian mortality on the landscape. The mortality estimates in this study provide a rare glimpse into the magnitude of effects that endemic parasites can have on wildlife populations and our theoretical framework for indirectly inferring parasite-induced mortality can be applied to other host-parasite systems to help reveal the hidden death toll of pathogens on wildlife hosts.

Indexed as

ParasitesTrematodaAnimalsAnimals, WildBayes TheoremHost-Parasite Interactionsaggregationinfectious diseasemacroparasitenegative binomialparasite-induced mortalityRibeiroia ondatraevirulence

Identifiers

PMID32935347
PMCPMC9009358
OpenAlexW3086569676

What OpenQuestion holds

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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.