Evidence map›Paper›PMID 35397182›Full record

ArticleEcological applications : a publication of the Ecological Society of America2022

A machine-learning approach to predict success of a biocontrol for invasive Eurasian watermilfoil reduction.

Diana T White, Thibaud M Antoniou, Jonathan M Martin, William Kmetz, Michael R Twiss

Open access · hybridAbstract read
In one paragraph

Article in Ecological applications : a publication of the Ecological Society of America, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.5field-weighted citation impact, top 33% 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

1 citing paper in PubMed, 4 citations in OpenAlex.

  1. A machine-learning approach to predict success of a biocontrol for invasive Eurasian watermilfoil reduction.Ecological applications : a publication of the Ecological Society of America · 2022
    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 at 1 institution in 1 country.

Diana T WhiteDepartment of Mathematics, Clarkson University, Potsdam, New York, USA.
Thibaud M AntoniouDepartment of Mathematics, Clarkson University, Potsdam, New York, USA.
Jonathan M MartinDepartment of Mathematics, Clarkson University, Potsdam, New York, USA.
William KmetzDepartment of Biology, Clarkson University, Potsdam, New York, USA.
Michael R TwissDepartment of Biology, Clarkson University, Potsdam, New York, USA.
Clarkson University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Myriophyllum spicatum, more commonly known as Eurasian watermilfoil (EWM), is one of the most invasive aquatic plants in North America, causing negative ecological and economic impacts in ecosystems where it proliferates. Many control strategies have been developed and implemented to mitigate EWM growth and spread, although the results are mixed and there is no consensus on lake-specific strategies. Here, we describe the development of a predictive model using a support vector technique, that predicts the success of biological pest control using Euhrychiopsis lecontei (the milfoil weevil), a milfoil specialist, to reduce EWM in lakes. Such a model is informed by lake characteristics (limnological and landscape) and augmentation strategies. To develop our predictive model, we performed a metadata analysis from 133 published peer-reviewed literature and professional reports of milfoil weevil augmentation field experiments that contained information on lake characteristics. The predictive model's algorithm uses a support vector machine (SMV) to learn patterns among lake characteristics, along with the recorded augmentation strategy and the reported success of each study, where success is a measure of EWM change over a season and is recorded in a variety of ways (e.g., EWM biomass change, EWM percent change, EWM visual change, etc.,). Overall, the model results suggests that shallower lakes, more frequent weevil augmentations, and larger weevil overwintering habitat are the most important predictors for EWM reduction success by weevil augmentation. Although watermilfoil weevil augmentation is a promising mitigation strategy, it may not work for all lakes. However, in terms of suggesting weevil augmentation, our model is a valuable tool for lake stakeholders and resource managers, who can use it to determine whether milfoil weevil augmentation, which can be very costly due to the difficulties in finding and raising milfoil weevils, will be a useful and sustainable approach to control EWM in their lake community.

Indexed as

EcosystemWeevilsAnimalsLakesMachine LearningPlantsbiocontrolEurasian watermilfoilmachine learningmilfoil weevilspredictive modelingsupport vector machine

Identifiers

PMID35397182
PMCPMC9539498
OpenAlexW4223581324

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.