Evidence map›Paper›PMID 34214237›Full record

ArticleGlobal change biology2021

Mammal assemblage composition predicts global patterns in emerging infectious disease risk.

Yingying X G Wang, Kevin D Matson, Luca Santini, Piero Visconti, Jelle P Hilbers, Mark A J Huijbregts, Yanjie Xu, Herbert H T Prins, Toph Allen, Zheng Y X Huang and 1 more

Abstract read
In one paragraph

Article in Global change biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
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  5. 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

11 authors.

Yingying X G WangWildlife Ecology and Conservation Group, Wageningen University and Research, Wageningen, The Netherlands.ORCID 0000-0003-3066-197X
Kevin D MatsonWildlife Ecology and Conservation Group, Wageningen University and Research, Wageningen, The Netherlands.ORCID 0000-0002-4373-5926
Luca SantiniDepartment of Biology and Biotechnologies "Charles Darwin", Sapienza University of Rome, Rome, Italy.ORCID 0000-0002-5418-3688
Piero ViscontiInternational Institute for Applied System Analysis, Laxenburg, Austria.ORCID 0000-0001-6823-2826
Jelle P HilbersDepartment of Environmental Science, Radboud University, Nijmegen, The Netherlands.ORCID 0000-0002-9401-589X
Mark A J HuijbregtsDepartment of Environmental Science, Radboud University, Nijmegen, The Netherlands.ORCID 0000-0002-7037-680X
Yanjie XuWildlife Ecology and Conservation Group, Wageningen University and Research, Wageningen, The Netherlands.ORCID 0000-0003-4420-6353
Herbert H T PrinsWildlife Ecology and Conservation Group, Wageningen University and Research, Wageningen, The Netherlands.ORCID 0000-0003-1131-5107
Toph AllenEcoHealth Alliance, New York, NY, USA.ORCID 0000-0003-4580-091X
Zheng Y X HuangWildlife Ecology and Conservation Group, Wageningen University and Research, Wageningen, The Netherlands.ORCID 0000-0002-8761-3787
Willem F de BoerWildlife Ecology and Conservation Group, Wageningen University and Research, Wageningen, The Netherlands.ORCID 0000-0003-3208-8521

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As a source of emerging infectious diseases, wildlife assemblages (and related spatial patterns) must be quantitatively assessed to help identify high-risk locations. Previous assessments have largely focussed on the distributions of individual species; however, transmission dynamics are expected to depend on assemblage composition. Moreover, disease-diversity relationships have mainly been studied in the context of species loss, but assemblage composition and disease risk (e.g. infection prevalence in wildlife assemblages) can change without extinction. Based on the predicted distributions and abundances of 4466 mammal species, we estimated global patterns of disease risk through the calculation of the community-level basic reproductive ratio R0, an index of invasion potential, persistence, and maximum prevalence of a pathogen in a wildlife assemblage. For density-dependent diseases, we found that, in addition to tropical areas which are commonly viewed as infectious disease hotspots, northern temperate latitudes included high-risk areas. We also forecasted the effects of climate change and habitat loss from 2015 to 2035. Over this period, many local assemblages showed no net loss of species richness, but the assemblage composition (i.e. the mix of species and their abundances) changed considerably. Simultaneously, most areas experienced a decreased risk of density-dependent diseases but an increased risk of frequency-dependent diseases. We further explored the factors driving these changes in disease risk. Our results suggest that biodiversity and changes therein jointly influence disease risk. Understanding these changes and their drivers and ultimately identifying emerging infectious disease hotspots can help health officials prioritize resource distribution.

Indexed as

Communicable Diseases, EmergingAnimalsBiodiversityClimate ChangeEcosystemMammalsassemblage compositionclimate changeemerging infectious diseaseshabitat lossinfectious disease hotspotsspecies distributions

Identifiers

PMID34214237
PMCPMC8518613

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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.