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Background: Invasive alien insects threaten agriculture, biodiversity, and human livelihoods globally. Unfortunately, insect invasiveness still cannot be reliably predicted. Empirical policies of insect pest quarantine and inspection are mainly designed against species that are already problematic.
Results: We conducted a comparative genomic analysis of 37 invasive insect species and six non-invasive insect species, showing that the gene families associated with defense, protein and nucleic acid metabolism, chemosensory function, and transcriptional regulation were significantly expanded in invasive insects, suggesting that enhanced abilities in self-protection, nutrition exploitation, and locating food or mates are intrinsic features conferring invasiveness in insects. By using these intrinsic genome features, we proposed an invasiveness index and estimated the invasiveness of 99 other insect species with genome data, classifying them as highly, moderately, or minimally invasive. Insects possessing all these aforementioned enhanced abilities are predicted to be highly invasive, and vice versa. Next, a logistic-regression classifier was trained to predict insect invasiveness, achieving 93.2% accuracy.
Conclusions: We present evidence that several traits may confer invasiveness in insects and these features can be used to predict insect invasiveness accurately, and we quantify insect invasiveness with an invasiveness index.
Keywords
Insect pest, Invasiveness, Genome features, Comparative genomics, Invasiveness index
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Peer Review Timeline
CURRENT STATUS: Under Revision
Version 2
Posted 10 Dec, 2020
No community comments so far
Editorial decision: Major revision
On 24 Dec, 2020
Review #3 received
Received 23 Dec, 2020
Reviewer #3 agreed
On 02 Dec, 2020
Reviewer #2 agreed
On 29 Nov, 2020
Review #2 received
Received 29 Nov, 2020
Reviewers invited
Invitations sent on 29 Nov, 2020
Reviewer #1 agreed
On 29 Nov, 2020
Review #1 received
Received 29 Nov, 2020
Editor assigned
On 28 Nov, 2020
Submission checks complete
On 28 Nov, 2020
Editor invited
On 28 Nov, 2020
No comments provided
Review #1 received
Received 20 Sep, 2020
Editorial decision: Major revision
On 20 Sep, 2020
Review #2 received
Received 14 Sep, 2020
Review #3 received
Received 03 Sep, 2020
Reviewer #3 agreed
On 02 Sep, 2020
Reviewer #2 agreed
On 25 Aug, 2020
Reviewer #1 agreed
On 24 Aug, 2020
Reviewers invited
Invitations sent on 21 Aug, 2020
Editor assigned
On 17 Jul, 2020
Submission checks complete
On 15 Jul, 2020
Editor invited
On 15 Jul, 2020
First submitted
On 08 Jul, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Epigenetics & Genomics
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A new preprint design is coming!
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This preprint is under consideration at BMC Genomics. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research article
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Revision
Version 2
Posted 10 Dec, 2020
No community comments so far
Editorial decision: Major revision
On 24 Dec, 2020
Review #3 received
Received 23 Dec, 2020
Reviewer #3 agreed
On 02 Dec, 2020
Reviewer #2 agreed
On 29 Nov, 2020
Review #2 received
Received 29 Nov, 2020
Reviewers invited
Invitations sent on 29 Nov, 2020
Reviewer #1 agreed
On 29 Nov, 2020
Review #1 received
Received 29 Nov, 2020
Editor assigned
On 28 Nov, 2020
Submission checks complete
On 28 Nov, 2020
Editor invited
On 28 Nov, 2020
No comments provided
Review #1 received
Received 20 Sep, 2020
Editorial decision: Major revision
On 20 Sep, 2020
Review #2 received
Received 14 Sep, 2020
Review #3 received
Received 03 Sep, 2020
Reviewer #3 agreed
On 02 Sep, 2020
Reviewer #2 agreed
On 25 Aug, 2020
Reviewer #1 agreed
On 24 Aug, 2020
Reviewers invited
Invitations sent on 21 Aug, 2020
Editor assigned
On 17 Jul, 2020
Submission checks complete
On 15 Jul, 2020
Editor invited
On 15 Jul, 2020
First submitted
On 08 Jul, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Epigenetics & Genomics
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: Invasive alien insects threaten agriculture, biodiversity, and human livelihoods globally. Unfortunately, insect invasiveness still cannot be reliably predicted. Empirical policies of insect pest quarantine and inspection are mainly designed against species that are already problematic.
Results: We conducted a comparative genomic analysis of 37 invasive insect species and six non-invasive insect species, showing that the gene families associated with defense, protein and nucleic acid metabolism, chemosensory function, and transcriptional regulation were significantly expanded in invasive insects, suggesting that enhanced abilities in self-protection, nutrition exploitation, and locating food or mates are intrinsic features conferring invasiveness in insects. By using these intrinsic genome features, we proposed an invasiveness index and estimated the invasiveness of 99 other insect species with genome data, classifying them as highly, moderately, or minimally invasive. Insects possessing all these aforementioned enhanced abilities are predicted to be highly invasive, and vice versa. Next, a logistic-regression classifier was trained to predict insect invasiveness, achieving 93.2% accuracy.
Conclusions: We present evidence that several traits may confer invasiveness in insects and these features can be used to predict insect invasiveness accurately, and we quantify insect invasiveness with an invasiveness index.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
The full text of this article is available to read as a PDF.
This is a list of supplementary files associated with this preprint. Click to download.
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