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Record W2384639437 · doi:10.1002/ecy.1461

Fertilization limitation of <i>Diadema antillarum</i> on coral reefs in the Florida Keys

2016· article· en· W2384639437 on OpenAlexaff
Colette J. Feehan, Michael S. Brown, William C. Sharp, Jean‐Sébastien Lauzon‐Guay, Diane K. Adams

Bibliographic record

VenueEcology · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicCoral and Marine Ecosystems Studies
Canadian institutionsAcadian Seaplants (Canada)
FundersRutgers, The State University of New Jersey
KeywordsAllee effectEcologyReefBiologyPopulationHuman fertilizationCoral reefSea urchinFisheryAgronomyDemography

Abstract

fetched live from OpenAlex

Mass mortality of the sea urchin Diadema antillarum due to disease outbreaks in 1983 and 1991 decimated populations in the Florida Keys, and they have yet to recover. Here, we use a coupled advection-diffusion and fertilization-kinetics model to test the hypothesis that these populations are fertilization limited. We found that fertilization success was ≥ 96% prior to the first disease outbreak, decreased substantially following recurrent disease to 3%, and has since remained low. By investigating the combined effects of physical factors (population spatial extent and current velocity) and sea urchin behavior (aggregation) on density-dependent fertilization success, we show that fertilization success at a given density increases with increasing population spatial extent and decreasing current velocity, and is greater under simulated aggregation behavior of D. antillarum. However, at present population densities, the increase in fertilization success due to aggregation is < 1%, even under the most favorable physical conditions. These results indicate that populations are severely fertilization limited, and that Allee effects at low population density will continue to limit recovery. Our results can serve as a practical guide to managers in the development of coral reef restoration strategies, including the design of a D. antillarum restocking program to obtain reproductively viable populations.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.410

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.014
GPT teacher head0.217
Teacher spread0.203 · 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

Citations19
Published2016
Admission routes1
Has abstractyes

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