Evidence map›Paper›PMID 33619083›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2021

A machine-learning approach to map landscape connectivity in

Evlyn Pless, Norah P Saarman, Jeffrey R Powell, Adalgisa Caccone, Giuseppe Amatulli

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

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  14. A machine-learning approach to map landscape connectivity inProceedings of the National Academy of Sciences of the United States of America · 2021
    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

5 authors.

Evlyn PlessDepartment of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06511; espless@ucdavis.edu giuseppe.amatulli@yale.edu.ORCID 0000-0002-6402-3397
Norah P SaarmanDepartment of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06511.ORCID 0000-0001-8974-0301
Jeffrey R PowellDepartment of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06511.ORCID 0000-0002-7651-5153
Adalgisa CacconeDepartment of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06511.
Giuseppe AmatulliSchool of the Environment, Yale University, New Haven, CT 06511; espless@ucdavis.edu giuseppe.amatulli@yale.edu.ORCID 0000-0002-8341-2830

Funding

Genetics of Vector Populations: Aedes aegypiR01AI101112 · NIAID · YALE UNIVERSITY · PI POWELL, JEFFREY R · 2012 to 2016
$2.1M
NIAID NIH HHS R01 AI101112
6 · The paper itself

Abstract

Mapping landscape connectivity is important for controlling invasive species and disease vectors. Current landscape genetics methods are often constrained by the subjectivity of creating resistance surfaces and the difficulty of working with interacting and correlated environmental variables. To overcome these constraints, we combine the advantages of a machine-learning framework and an iterative optimization process to develop a method for integrating genetic and environmental (e.g., climate, land cover, human infrastructure) data. We validate and demonstrate this method for the

Indexed as

EnvironmentGene-Environment InteractionMachine LearningAedesAnimalsGenetics, PopulationGenetic VariationHumansModels, BiologicalMosquito VectorsWorkflowgene flowinvasive specieslandscape geneticsrandom forestvector control

Identifiers

PMID33619083
PMCPMC7936321

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.