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Record W2557175066 · doi:10.1071/mf16120

Measuring niche overlap between co-occurring Plectropomus spp. using acoustic telemetry and stable isotopes

2016· article· en· W2557175066 on OpenAlexaff
Jordan K. Matley, Michelle R. Heupel, Aaron T. Fisk, Colin A. Simpfendorfer, Andrew J. Tobin

Bibliographic record

VenueMarine and Freshwater Research · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsUniversity of Windsor
FundersJames Cook UniversityAustralian GovernmentGreat Barrier Reef Marine Park Authority
KeywordsLeopardusBiologyReefCoral reef fishNicheEcological nicheFisheryEcologyHabitat

Abstract

fetched live from OpenAlex

Investigating niche overlap in exploited fish species can reveal behavioural information necessary to improve conservation and fisheries management at a species level. The present study examined spatial and dietary overlap between two co-occurring reef fish, namely Plectropomus leopardus and P. maculatus, at an inshore reef in the Great Barrier Reef Marine Park using acoustic telemetry and stable isotopes. Movements of tagged fish within an acoustic array of 19 receivers deployed along a narrow reef portion of Orpheus Island were monitored for up to 3 years. Although space use was similar between species, spatial overlap was rare and P. maculatus (n = 30) was consistently deeper than P. leopardus (n = 32). Dietary overlap between species was high based on overlapping d15N and d13C isotopic niches in muscle tissue (n = 20). The complementary stable isotope and acoustic telemetry data revealed these species had similar isotopic niches but distinct space use patterns, which may be a product of competition for resources. These findings show species-specific behaviours within a genus commonly managed or reported as a single entity, and provide new information on partitioning of resources by Plectropomus spp. in inshore reef environments.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.185
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.076
GPT teacher head0.320
Teacher spread0.244 · 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 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

Citations31
Published2016
Admission routes1
Has abstractyes

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