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Record W2468006385 · doi:10.1111/gcb.13393

Habitat degradation disrupts neophobia in juvenile coral reef fish

2016· article· en· W2468006385 on OpenAlexaff
Mark I. McCormick, Douglas P. Chivers, Bridie J. M. Allan, Maud C. O. Ferrari

Bibliographic record

VenueGlobal Change Biology · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicCoral and Marine Ecosystems Studies
Canadian institutionsUniversity of Saskatchewan
FundersAustralian Research Council
KeywordsNeophobiaHabitatEcologyPredationALARMCoral reefReefDamselfishBiologyCoral reef fish

Abstract

fetched live from OpenAlex

Habitat degradation not only disrupts habitat-forming species, but alters the sensory landscape within which most species must balance behavioural activities against predation risk. Rapidly developing a cautious behavioural phenotype, a condition known as neophobia, is advantageous when entering a novel risky habitat. Many aquatic organisms rely on damage-released conspecific cues (i.e. alarm cues) as an indicator of impending danger and use them to assess general risk and develop neophobia. This study tested whether settlement-stage damselfish associated with degraded coral reef habitats were able to use alarm cues as an indicator of risk and, in turn, develop a neophobic response at the end of their larval phase. Our results indicate that fish in live coral habitats that were exposed to alarm cues developed neophobia, and, in situ, were found to be more cautious, more closely associated with their coral shelters and survived four-times better than non-neophobic control fish. In contrast, fish that settled onto degraded coral habitats did not exhibit neophobia and consequently suffered much greater mortality on the reef, regardless of their history of exposure to alarm cues. Our results show that habitat degradation alters the efficacy of alarm cues with phenotypic and survival consequences for newly settled recruits.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

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)0.0010.000

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 head0.032
GPT teacher head0.261
Teacher spread0.229 · 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

Citations42
Published2016
Admission routes1
Has abstractyes

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