Evidence map›Paper›PMID 26553973›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2015

Mapping landscape friction to locate isolated tsetse populations that are candidates for elimination.

Jérémy Bouyer, Ahmadou H Dicko, Giuliano Cecchi, Sophie Ravel, Laure Guerrini, Philippe Solano, Marc J B Vreysen, Thierry De Meeûs, Renaud Lancelot

Open access · bronzeAbstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
35citing papers in PubMed, 1 pooled it
8.3field-weighted citation impact, top 3% 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

35 citing papers in PubMed, 1 synthesis or guideline pooled it, 64 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Modeling framework to demonstrate elimination of a vector population: Tsetse elimination in Chad.Proceedings of the National Academy of Sciences of the United States of America · 2026
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  16. A machine-learning approach to map landscape connectivity inProceedings of the National Academy of Sciences of the United States of America · 2021
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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

9 authors at 6 institutions in 6 countries.

Jérémy BouyerCentre de Coopération Internationale en Recherche Agronomique pour le Développement, Unité Mixte de Recherche Contrôle des Maladies Animales Exotiques et Emergentes, Campus International de Baillarguet, 34398 Montpellier, France; Institut National de la Recherche Agronomique, Unité Mixte de Recherche 1309 Contrôle des Maladies Animales Exotiques et Emergentes, 34398 Montpellier, France; Centre de Coopération Internationale en Recherche Agronomique pour le Développement, Unité Mixte de Recherche Interactions Hôtes-Vecteurs-Parasites-Environnement dans les Maladies Tropicales Négligées Dues aux Trypanosomatides, 34398 Montpellier, France; Institut Sénégalais de Recherches Agricoles, Laboratoire National d'Elevage et de Recherches Vétérinaires, Service de Parasitologie, BP 2057 Dakar, Senegal; bouyer@cirad.fr.ORCID http://orcid.org/0000-0002-1913-416X
Ahmadou H DickoWest African Science Service in Climate Change and Adapted Land Use, Climate Change Economics Research Program, Cheikh Anta Diop University, BP 5683, Dakar, Senegal;
Giuliano CecchiFood and Agriculture Organization of the United Nations, Sub-Regional Office for Eastern Africa, P.O. Box 5536, Addis Ababa, Ethiopia;
Sophie RavelInstitut de Recherche pour le Développement, Unité Mixte de Recherche Interactions Hôtes-Vecteurs-Parasites-Environnement dans les Maladies Tropicales Négligées Dues aux Trypanosomatides, 34398 Montpellier, France;
Laure GuerriniUnité de Recherche Animal et Gestion Intégrée de Risques, Centre de Coopération Internationale en Recherche Agronomique pour le Développement, 34398 Montpellier, France; Department Environment and Societies, University of Zimbabwe, Harare, Zimbabwe;
Philippe SolanoInstitut de Recherche pour le Développement, Unité Mixte de Recherche Interactions Hôtes-Vecteurs-Parasites-Environnement dans les Maladies Tropicales Négligées Dues aux Trypanosomatides, 34398 Montpellier, France;
Marc J B VreysenInsect Pest Control Laboratory, Joint Food and Agriculture Organization of the United Nations/International Atomic Energy Agency Programme of Nuclear Techniques in Food and Agriculture, A-1400 Vienna, Austria;
Thierry De MeeûsInstitut de Recherche pour le Développement, Unité Mixte de Recherche Interactions Hôtes-Vecteurs-Parasites-Environnement dans les Maladies Tropicales Négligées Dues aux Trypanosomatides, 34398 Montpellier, France; Centre International de Recherche-Développement sur l'Elevage en Zone Sub-humide, B.P. 454, Bobo-Dioulasso, Burkina Faso.
Renaud LancelotCentre de Coopération Internationale en Recherche Agronomique pour le Développement, Unité Mixte de Recherche Contrôle des Maladies Animales Exotiques et Emergentes, Campus International de Baillarguet, 34398 Montpellier, France; Institut National de la Recherche Agronomique, Unité Mixte de Recherche 1309 Contrôle des Maladies Animales Exotiques et Emergentes, 34398 Montpellier, France;
Centre de Coopération Internationale en Recherche Agronomique pour le Développement · FRInstitut de Recherche pour le Développement · FRCentre international de recherche-développement sur l'elevage en zone subhumide · BFCheikh Anta Diop University · SNInternational Atomic Energy Agency · ATUnited Nations Economic Commission for Africa · ET

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tsetse flies are the cyclical vectors of deadly human and animal trypanosomes in sub-Saharan Africa. Tsetse control is a key component for the integrated management of both plagues, but local eradication successes have been limited to less than 2% of the infested area. This is attributed to either resurgence of residual populations that were omitted from the eradication campaign or reinvasion from neighboring infested areas. Here we focused on Glossina palpalis gambiensis, a riverine tsetse species representing the main vector of trypanosomoses in West Africa. We mapped landscape resistance to tsetse genetic flow, hereafter referred to as friction, to identify natural barriers that isolate tsetse populations. For this purpose, we fitted a statistical model of the genetic distance between 37 tsetse populations sampled in the region, using a set of remotely sensed environmental data as predictors. The least-cost path between these populations was then estimated using the predicted friction map. The method enabled us to avoid the subjectivity inherent in the expert-based weighting of environmental parameters. Finally, we identified potentially isolated clusters of G. p. gambiensis habitat based on a species distribution model and ranked them according to their predicted genetic distance to the main tsetse population. The methodology presented here will inform the choice on the most appropriate intervention strategies to be implemented against tsetse flies in different parts of Africa. It can also be used to control other pests and to support conservation of endangered species.

Indexed as

Insect ControlTsetse FliesAnimalsCattleDemographyFemaleGenotypeHumansInsect VectorsLinear ModelsLinkage DisequilibriumMaleTrypanosomiasisarea-wide integrated pest managementeradicationremote sensingresistance surfacevector control

Identifiers

PMID26553973
PMCPMC4664298
OpenAlexW2149714999

What OpenQuestion holds

Textmetadata
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.