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Record W4316368909 · doi:10.1101/2023.01.13.523858

Drivers of asymmetrical insect invasions between three world regions

2023· preprint· en· W4316368909 on OpenAlexafffund
Rylee Isitt, Andrew M. Liebhold, Rebecca M. Turner, Andrea Battisti, Cléo Bertelsmeier, Rachael E. Blake, Eckehard G. Brockerhoff, Stephen B. Heard, Paal Krokene, Bjørn Økland, Helen F. Nahrung, Davide Rassati, Alain Roques, Takehiko Yamanaka, Deepa S. Pureswaran

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2023
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsNatural Resources CanadaCanadian Forest ServiceUniversity of New Brunswick
FundersUniversità degli Studi di PadovaNatural Sciences and Engineering Research Council of CanadaU.S. Forest ServiceEuropean CommissionNational Socio-Environmental Synthesis CenterU.S. Department of AgricultureSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsPropagule pressureDominance (genetics)PropaguleColonizationIntroduced speciesSpecies richnessBiologyContext (archaeology)EcologyInvasive speciesInsectBiological dispersalPopulation

Abstract

fetched live from OpenAlex

Abstract The geographical exchange of non-native insects can be highly asymmetrical, with some world regions ‘exporting’ or ‘importing’ more species than others. Several hypotheses have been proposed to explain such asymmetries, including differences in propagule pressure, environmental features in recipient regions, or biological traits of invaders. We tested aspects of these hypotheses in the context of the exchange of non-native insects between North America, Europe, and Australasia. Europe was the dominant exporter of non-native insect species between the three regions, with most of this asymmetry arising prior to 1950. The European dominance could not be explained by differences in import value, source species pool sizes, or native plant richness in the recipient regions. We identified that the introduction of non-native plants, driven in part by European colonization, best explains the asymmetrical exchange of non-native insects between our focal regions.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.132
GPT teacher head0.225
Teacher spread0.094 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations3
Published2023
Admission routes2
Has abstractyes

Explore more

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