Population decline and the effects of disturbances on the structure and recovery of octocoral communities (Coelenterata: Octocorallia) in Pacific Panama
Bibliographic record
Abstract
Community structure, species composition, and changes over time after disturbances are frequently studied using common descriptors. We used rank abundance distribution plots (RADs), Rényi entropy plots, common theoretical community models, ordination analysis of similarities (ANOSIM and Clusters), and abundance spectra analyses to study the effects of a gradual natural population decline and an anthropogenic punctuated disturbance on the structure of octocoral communities in Panama, considered a hot spot area for octocoral diversity in the Tropical Eastern Pacific. Over a 17-month period, no significant change was found in community structure after a natural yearly population decline of 25.2%. After a disturbance, however, different recovery trajectories were observed in various coral communities. Possible physical and biological explanations for the observed differences include initial local species diversity and abundance, species life history patterns, colony morphology, and the geographical location of the community. Differences in community structure between study sites were best described using a combination of community descriptors, RADs, and abundance spectra. Rényi plots were useful in identifying changes in community structure, whereas the extent of the changes was best evaluated using ANOSIM and cluster analysis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".