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Record W4402665908 · doi:10.1071/mf24080

No effects of abiotic and anthropogenic factors on reef-associated neonate shark abundance within a shark nursery-area system

2024· article· en· W4402665908 on OpenAlexaff
Ian A. Bouyoucos, Colin A. Simpfendorfer, Gail D. Schwieterman, Kim Eustache, Lauric Thiault, Serge Planes, Jodie L. Rummer

Bibliographic record

VenueMarine and Freshwater Research · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicIchthyology and Marine Biology
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersNational Science Foundation Graduate Research Fellowship ProgramAustralian Research Council
KeywordsAbiotic componentFisheryCarcharhinusAbundance (ecology)ReefEcologyHabitatBiologyEstuaryCatch per unit effortSalinityContext (archaeology)Coral reef fish

Abstract

fetched live from OpenAlex

Context Coastal habitats function as shark nursery areas; however, coastal habitats can experience extreme variation in abiotic conditions and are susceptible to human disturbances. Aims Drivers of abundance were tested within a shark nursery-area system in two populations of reef-associated neonate sharks, namely, blacktip reef sharks (Carcharhinus melanopterus) and sicklefin lemon sharks (Negaprion acutidens). Methods Catch data from a fisheries-independent gill-net survey (n = 90 sets from October 2018 to March 2019) at 10 sites around Moorea, French Polynesia, were used to test for associations between shark abundance and abiotic conditions (temperature, oxygen, pH, salinity, lunar phase and depth). Historical levels of fin-fish fishing effort, trampling (i.e. human movement through habitat), and coastal artificialisation (i.e. walls and embankments) estimated for each site were used to test for anthropogenic effects on shark abundance. Key results There were no effects of any abiotic or anthropogenic factor on abundance of either species. Conclusions Previous work corroborates our findings by demonstrating neonate sharks’ physiological tolerance to extreme abiotic conditions and high survival in response to anthropogenic stressors. Alternatively, populations are already degraded from decades of coastal development. Implications These data can aid in predicting the use of coastal habitats as shark nursery areas.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.077
Threshold uncertainty score0.799

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.018
GPT teacher head0.277
Teacher spread0.259 · 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.

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

Citations3
Published2024
Admission routes1
Has abstractyes

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