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Record W4388001707 · doi:10.1111/geb.13781

Does non‐native diversity mirror Earth's biodiversity?

2023· article· en· W4388001707 on OpenAlexafffund
Elizabeta Briski, Syrmalenia G. Kotronaki, Ross N. Cuthbert, Alejandro Bortolus, Marnie L. Campbell, Jaimie T. A. Dick, Paul W. Fofonoff, Chad L. Hewitt, Julie L. Lockwood, Hugh J. MacIsaac, Anthony Ricciardi, Gregory M. Ruiz, Evangelina Schwindt, Ulrich Sommer, Aibin Zhan, James T. Carlton

Bibliographic record

VenueGlobal Ecology and Biogeography · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInsect and Arachnid Ecology and Behavior
Canadian institutionsMcGill UniversityUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaLeverhulme TrustAlexander von Humboldt-Stiftung
KeywordsBiodiversityEcologyHabitatGlobal biodiversityTaxonBiologyBiodiversity hotspotAquatic biodiversity researchSpecies richnessGeographySpecies diversityGlobal changeTaxonomic rankClimate change

Abstract

fetched live from OpenAlex

Abstract Aim Human activities have introduced numerous non‐native species (NNS) worldwide. Understanding and predicting large‐scale NNS establishment patterns remain fundamental scientific challenges. Here, we evaluate if NNS composition represents a proportional subset of the total species pool available to invade (i.e. total global biodiversity), or, conversely, certain taxa are disproportionately pre‐disposed to establish in non‐native areas. Location Global. Time period Present day. Major taxa studied Global diversity. Methods We compiled one of the most comprehensive global databases of NNS (36,822 established species) to determine if NNS diversity is a representative proportional subset of global biodiversity. Results Our study revealed that, while NNS diversity mirrors global biodiversity to a certain extent, due to significant deviance from the null model it is not always a representative proportional subset of global biodiversity. The strength of global biodiversity as a predictor depended on the taxonomic scale, with successive lower taxonomic levels less predictive than the one above it. Consequently, on average, 58%, 42% and 28% of variability in NNS numbers were explained by global biodiversity for phylum, class and family respectively. Moreover, global biodiversity was a similarly strong explanatory variable for NNS diversity among regions, but not habitats (i.e. terrestrial, freshwater and marine), where it better predicted NNS diversity for terrestrial than for freshwater and marine habitats. Freshwater and marine habitats were also greatly understudied relative to invasions in the terrestrial habitats. Over‐represented NNS relative to global biodiversity tended to be those intentionally introduced and/or ‘hitchhikers’ associated with deliberate introductions. Finally, randomness is likely an important factor in the establishment success of NNS. Main conclusions Besides global biodiversity, other important explanatory variables for large‐scale patterns of NNS diversity likely include propagule and colonization pressures, environmental similarity between native and non‐native regions, biased selection of intentionally introduced species and disparate research efforts of habitats and taxa.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.554

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)0.0000.000

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 head0.007
GPT teacher head0.224
Teacher spread0.216 · 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.

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

Citations36
Published2023
Admission routes2
Has abstractyes

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