Evidence map›Paper›PMID 32708047›Full record

ReviewInsects2020

Insights from Population Genomics to Enhance and Sustain Biological Control of Insect Pests.

Arun Sethuraman, Fredric J Janzen, David W Weisrock, John J Obrycki

Open access · goldAbstract readReview
In one paragraph

Review in Insects, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
2.8field-weighted citation impact, top 10% 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

19 citing papers in PubMed, 39 citations in OpenAlex.

  1. Article
  2. Landscape Genomics and Evolutionary History ofEvolutionary applications · 2026
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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

4 authors at 3 institutions in 1 country.

Arun SethuramanDepartment of Biological Sciences, California State University San Marcos, San Marcos, CA 92096, USA.
Fredric J JanzenDepartment of Ecology, Evolution, & Organismal Biology, Iowa State University, Ames, IA 50010, USA.
David W WeisrockDepartment of Biology, University of Kentucky, Lexington, KY 40506, USA.
John J ObryckiDepartment of Entomology, University of Kentucky, Lexington, KY 40506, USA.
University of Kentucky · USCalifornia State University, San Marcos · USMichigan State University · US

Funding

National Science Foundation AS was also supported by NSF ABI Development grant #1564659.U.S. Department of Agriculture USDA grant #2017-06423 to Drs. George Vourlitis and Arun Sethuraman.
6 · The paper itself

Abstract

Biological control-the use of organisms (e.g., nematodes, arthropods, bacteria, fungi, viruses) for the suppression of insect pest species-is a well-established, ecologically sound and economically profitable tactic for crop protection. This approach has served as a sustainable solution for many insect pest problems for over a century in North America. However, all pest management tactics have associated risks. Specifically, the ecological non-target effects of biological control have been examined in numerous systems. In contrast, the need to understand the short- and long-term evolutionary consequences of human-mediated manipulation of biological control organisms for importation, augmentation and conservation biological control has only recently been acknowledged. Particularly, population genomics presents exceptional opportunities to study adaptive evolution and invasiveness of pests and biological control organisms. Population genomics also provides insights into (1) long-term biological consequences of releases, (2) the ecological success and sustainability of this pest management tactic and (3) non-target effects on native species, populations and ecosystems. Recent advances in genomic sequencing technology and model-based statistical methods to analyze population-scale genomic data provide a much needed impetus for biological control programs to benefit by incorporating a consideration of evolutionary consequences. Here, we review current technology and methods in population genomics and their applications to biological control and include basic guidelines for biological control researchers for implementing genomic technology and statistical modeling.

Indexed as

biological controldemographic modelspest managementpopulation genomics

Identifiers

PMID32708047
PMCPMC7469154
OpenAlexW3043871564

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.