Predator–prey landscapes of large sharks and game fishes in the Florida Keys
Bibliographic record
Abstract
Interspecific interactions can play an essential role in shaping wildlife populations and communities. To date, assessments of interspecific interactions, and more specifically predator-prey dynamics, in aquatic systems over broad spatial and temporal scales (i.e., hundreds of kilometers and multiple years) are rare due to constraints on our abilities to measure effectively at those scales. We applied new methods to identify space-use overlap and potential predation risk to Atlantic tarpon (Megalops atlanticus) and permit (Trachinotus falcatus) from two known predators, great hammerhead (Sphyrna mokarran) and bull (Carcharhinus leucas) sharks, over a 3-year period using acoustic telemetry in the coastal region of the Florida Keys (USA). By examining spatiotemporal overlap, as well as the timing and order of arrival at specific locations compared to random chance, we show that potential predation risk from great hammerhead and bull sharks to Atlantic tarpon and permit are heterogeneous across the Florida Keys. Additionally, we find that predator encounter rates with these game fishes are elevated at specific locations and times, including a prespawning aggregation site in the case of Atlantic tarpon. Further, using machine learning algorithms, we identify environmental variability in overlap between predators and their potential prey, including location, habitat, time of year, lunar cycle, depth, and water temperature. These predator-prey landscapes provide insights into fundamental ecosystem function and biological conservation, especially in the context of emerging fishery-related depredation issues in coastal marine ecosystems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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) | 0.006 | 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 teacher head, 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".