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Record W6967790143 · doi:10.5061/dryad.z8w9ghxmg

Habitat quality effects on the abundance of a coral-dwelling fish across spatial scales

2024· dataset· en· W6967790143 on OpenAlexaff

Bibliographic record

VenueDRYAD · 2024
Typedataset
Languageen
Field
Topic
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsGobyAbundance (ecology)Coral reefCoralCoral reef fishHabitatHost (biology)

Abstract

fetched live from OpenAlex

Microhabitat-associated fishes are expected to be negatively affected by coral reef degradation, given that many species are coral dwellers. However, the factors underlying this negative impact and the spatial scale(s) at which it occurs are poorly understood. We explored how habitat quality metrics and host preferences influence fish abundance across multiple spatial scales, using the functionally important cleaner fish Elacatinus evelynae as a study species. We surveyed fish at 10 sites in Curaçao that varied in coral cover and health. At the microhabitat scale, we found that E. evelynae group size increases on large, healthy corals and on some coral host species, namely Montastraea cavernosa. We also found that, although E. evelynae can occupy at least 10 coral host species, it selectively inhabits just three corals: M. cavernosa, Colpophyllia natans, and Diploria labrynthiformis. Scaling up to explore goby abundance along 30-m transects, we did not find a clear relationship between live coral cover and goby abundance. However, goby abundance was substantially higher at one location with elevated coral cover and a high relative abundance of E. evelynae host species. Collectively, these results confirm that E. evelynae abundance is impacted by reef health. They also indicate that the species’ long-term persistence may depend on both the maintenance of healthy coral hosts and the gobies’ plasticity in host preferences on changing reefscapes. Cryptobenthic fishes such as E. evelynae play a vital role in the ecosystem and understanding drivers of their abundance is important as reefs face increased degradation.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.033
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.033

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 head0.030
GPT teacher head0.351
Teacher spread0.321 · 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
Published2024
Admission routes1
Has abstractyes

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