High-resolution aerial imagery reveals that the distribution and arrangement of Acropora palmata patches determine their resistance to hurricane impacts
Bibliographic record
Abstract
Abstract Acropora palmata is the species that contributes the most to the structural complexity of Caribbean reefs. Information concerning the complexity of its populations at the landscape level is relevant to determine how the reef system responds to disturbances, such as cyclonic events. This study examines the repercussions of hurricanes Gamma and Delta (2020) on the patches of A. palmata in Limones reef, one of the best-preserved reefs in the Caribbean. Two orthomosaics were generated using programmed drone flights, one before and one after the passage of both hurricanes. Visually identified polygon files representing A. palmata patches were delineated in both orthomosaics. Regression models were used to analyze the influence of spatial characteristics of those patches, measured through landscape ecology indices, on the probability of patch permanence and, for those patches that remained, the remaining area. Our results show that the A. palmata population suffered a total loss of 25% due to hurricanes. More compact and complex patches at shallower depths exhibited a higher persistence probability. Furthermore, the spatial location of the patches in relation to each other (proximity and size of their neighbors) did not significantly affect the permanence probability. The metrics used were not a good indicator of the area loss of the patches that remained. Here, the damages suffered could mainly be explained by the reef zone, which we attribute to the phenotypic plasticity of A. palmata colonies in high-energy zones, affecting growth characteristics that allow them to better withstand the impact of hurricanes. Overall, we show that using landscape indices to understand the drivers of change in the spatial structure of reefs is an effective method to evaluate and even predict the modifications suffered after disturbance events, information that could be readily available for management and conservation strategies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".