Vulnerability of sea turtles and fishes in response to two catastrophic Caribbean hurricanes, Irma and Maria
Bibliographic record
Abstract
Extreme weather events (e.g., cyclones, floods, droughts) are capable of changing ecosystems and altering how animals obtain resources. Understanding the behavioural responses of animals being impacted by these natural events can help initiate and ameliorate conservation or management programs. This study investigated short- and long-term space-use of the critically endangered hawksbill sea turtle (Eretmochelys imbricata), as well as five species of fishes and stingrays, in response to two of the most destructive Caribbean hurricanes in known history - Irma and Maria, which were at their peak intensity when they passed the US Virgin Islands in September of 2017. Using passive acoustic telemetry in St. Thomas, US Virgin Islands, we show a variety of short-term behavioural patterns initiated across species to reduce exposure to the strong environmental conditions, such as moving to deeper habitats within the study area. Although short-term expansion of activity space was evident for several sea turtles, long-term impacts on space-use and body condition were limited. In contrast, southern stingrays (Hypanus americanus) left the study area shortly after the hurricanes, suggesting vulnerability stemming from altered habitat, prey availability, or temperature/oxygen profiles. This study shows the strong spatial resilience of several nearshore species despite exposure to two consecutive category 5 hurricanes.
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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 | 0.000 | 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".