MétaCan
Menu
Back to cohort
Record W6892161271 · doi:10.5061/dryad.2jm63xsr0

Tropicalization of temperate reef fish communities facilitated by urchin grazing and diversity of thermal affinities

2022· dataset· en· W6892161271 on OpenAlexaff

Bibliographic record

VenueDRYAD · 2022
Typedataset
Languageen
Field
Topic
Canadian institutionsUniversity of VictoriaMemorial University of Newfoundland
Fundersnot available
KeywordsKelpKelp forestTemperate climateHabitatReefSea urchinCoral reef fishBiodiversityEffects of global warming on oceans

Abstract

fetched live from OpenAlex

Aim: Global declines in structurally complex habitats are reshaping both land and seascapes in directions that affect biological communities’ responses to warming. Here, we test whether widespread loss of kelp habitats through sea urchin overgrazing systematically changes sensitivity of fish communities to warming. Location: Global temperate latitudes Time period: Modern Major taxa studied: Fishes Methods: Community thermal affinity shifts related to habitat were assessed by simulating and comparing fish communities from 2,271 surveys across 15 ecoregions. Results: We find that fishes in kelp and urchin barrens differ in realized thermal affinities and range sizes, but only in regions where species pools have high variability in species’ thermal affinities. Barrens on warm-temperate reefs host relatively more warm-affinity fish species than neighbouring kelp beds, highlighting acceleration of tropicalization processes facilitated by urchin herbivory. By contrast, proportionally more cool-affinity fishes colonize barrens at high temperate latitudes, contributing to community lags with ocean warming in these regions. Main conclusions: Our findings implicate urchins as drivers of ecological change, in part by affecting biological resilience to warming.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.266
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.002
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 head0.029
GPT teacher head0.239
Teacher spread0.210 · 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
GenreDataset

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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueDRYADFrench-language works237,207