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Record W4402309777 · doi:10.1111/ddi.13911

Why so many Hemiptera invasions?

2024· article· en· W4402309777 on OpenAlexaffabout
Andrew M. Liebhold, Rebecca M. Turner, Charles R. Bartlett, Cléo Bertelsmeier, Rachael E. Blake, Eckehard G. Brockerhoff, Charlotte E. Causton, Janis N. Matsunaga, Stuart H. McKamey, Helen F. Nahrung, Christopher L. Owen, Deepa S. Pureswaran, Alain Roques, Scott A. Schneider, Allen Sanborn, Takehiko Yamanaka

Bibliographic record

VenueDiversity and Distributions · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsCanadian Forest Service
FundersU.S. Forest ServiceU.S. Department of AgricultureCOmON StichtingNational Socio-Environmental Synthesis CenterSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsHemipteraEcologyGeographyBiology

Abstract

fetched live from OpenAlex

Abstract Aim The Hemiptera is the fifth‐largest insect order but among non‐native insect species is approximately tied with the Coleoptera as the most species‐rich insect order (Hemiptera comprise 20% more species than in world fauna). This over‐representation may result from high propagule pressure or from high species invasiveness. Here, we assess the reasons for over‐representation in this group by analysing geographical, temporal and taxonomic variation in numbers of historical invasions. Location Global. Method We assembled lists of historical Hemiptera invasions in 12 world regions, countries or islands (Australia, Chile, Europe, New Zealand, North America, South Africa, South Korea, Japan and the Galapagos, Hawaiian, Okinawa and Ogasawara Islands) and border interception data from nine countries (Australia, Canada, European Union, United Kingdom, Hawaii, Japan, New Zealand, South Korea, USA mainland and South Africa). Using these data, we identified hemipteran superfamilies that are historically over‐represented among established non‐native species, and superfamilies that are over‐represented among arrivals (proxied by interceptions). We also compared temporal patterns of establishments among hemipteran suborders and among regions. Results Across all regions, patterns of over‐ and under‐representation were similar. The Aphidoidea, Coccoidea, Aleyrodoidea, Cimicoidea and Phylloxeroida were over‐represented among non‐native species. These same superfamilies were not consistently over‐represented among intercepted species indicating that propagule pressure does not completely explain the tendency of some Hemiptera to be over‐represented among invasions. Asexual reproduction is common in most over‐represented superfamilies and this trait may be key to explaining high invasion success in these superfamilies. Conclusions We conclude that both propagule pressure and species invasiveness are drivers of high invasion success in the Sternorrhyncha suborder (aphids, scales, whiteflies) and this group plays a major role in the exceptional invasion success of Hemiptera in general. The high historical rates of invasion by Sternorrhyncha species provide justification for biosecurity measure focusing on exclusion of this group.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.218
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.206
Teacher spread0.189 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations16
Published2024
Admission routes2
Has abstractyes

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