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Record W4313561981 · doi:10.1038/s41559-022-01931-8

Terrestrial invasive species alter marine vertebrate behaviour

2023· article· en· W4313561981 on OpenAlexaff
Rachel L. Gunn, Cassandra E. Benkwitt, Nicholas A. J. Graham, Ian R. Hartley, Adam C. Algar, Sally A. Keith

Bibliographic record

VenueNature Ecology & Evolution · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsLakehead University
FundersNatural Environment Research CouncilResearch Councils UKSight Research UKFondation Bertarelli
KeywordsEcologyEcosystemForagingBiologySeabirdReefCoral reefTerritorialityHabitatTrophic levelCoral reef fishInvasive speciesVertebrateBiodiversityBenthic zoneFisheryPredation

Abstract

fetched live from OpenAlex

Human-induced environmental changes, such as the introduction of invasive species, are driving declines in the movement of nutrients across ecosystems with negative consequences for ecosystem function. Declines in nutrient inputs could thus have knock-on effects at higher trophic levels and broader ecological scales, yet these interconnections remain relatively unknown. Here we show that a terrestrial invasive species (black rats, Rattus rattus) disrupts a nutrient pathway provided by seabirds, ultimately altering the territorial behaviour of coral reef fish. In a replicated ecosystem-scale natural experiment, we found that reef fish territories were larger and the time invested in aggression lower on reefs adjacent to rat-infested islands compared with rat-free islands. This response reflected changes in the economic defendability of lower-quality resources, with reef fish obtaining less nutritional gain per unit foraging effort adjacent to rat-infested islands with low seabird populations. These results provide a novel insight into how the disruption of nutrient flows by invasive species can affect variation in territorial behaviour. Rat eradication as a conservation strategy therefore has the potential to restore species interactions via territoriality, which can scale up to influence populations and communities at higher ecological levels.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.244
Teacher spread0.234 · 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

Citations18
Published2023
Admission routes1
Has abstractyes

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