Evidence map›Paper›PMID 23977300›Full record

ArticlePloS one2013

Ecological niche modeling of Bacillus anthracis on three continents: evidence for genetic-ecological divergence?

Jocelyn C Mullins, Giuliano Garofolo, Matthew Van Ert, Antonio Fasanella, Larisa Lukhnova, Martin E Hugh-Jones, Jason K Blackburn

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In one paragraph

Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.

0numbers the graph read from it
0cells of the map it votes in
29citing 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

29 citing papers in PubMed.

  1. Spatiotemporal dynamics ofFrontiers in microbiology · 2025
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  13. Predicting the Geographic Distribution of theThe American journal of tropical medicine and hygiene · 2020
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  17. ModelingInternational journal of environmental research and public health · 2019
    Review
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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

7 authors.

Jocelyn C MullinsSpatial Epidemiology & Ecology Research Laboratory, Department of Geography, University of Florida, Gainesville, Florida, United States of America.
Giuliano Garofolo
Matthew Van Ert
Antonio Fasanella
Larisa Lukhnova
Martin E Hugh-Jones
Jason K Blackburn

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We modeled the ecological niche of a globally successful Bacillus anthracis sublineage in the United States, Italy and Kazakhstan to better understand the geographic distribution of anthrax and potential associations between regional populations and ecology. Country-specific ecological-niche models were developed and reciprocally transferred to the other countries to determine if pathogen presence could be accurately predicted on novel landscapes. Native models accurately predicted endemic areas within each country, but transferred models failed to predict known occurrences in the outside countries. While the effects of variable selection and limitations of the genetic data should be considered, results suggest differing ecological associations for the B. anthracis populations within each country and may reflect niche specialization within the sublineage. Our findings provide guidance for developing accurate ecological niche models for this pathogen; models should be developed regionally, on the native landscape, and with consideration to population genetics. Further genomic analysis will improve our understanding of the genetic-ecological dynamics of B. anthracis across these countries and may lead to more refined predictive models for surveillance and proactive vaccination programs. Further studies should evaluate the impact of variable selection of native and transferred models.

Indexed as

EcosystemGeographyModels, BiologicalBacillus anthracisHumansItalyKazakhstanSample SizeUnited States

Identifiers

PMID23977300
PMCPMC3747089

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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.