Quantifying the Effects of Diver Interactions on Manta Ray Behavior at Their Aggregation Sites
Bibliographic record
Abstract
Manta rays ( Mobula birostris, Mobula. cf. birostris , and Mobula alfredi ), the largest mobulid rays, are subjected to exploitation and overfishing in certain parts of the world. Tourism has been supported as a sustainable alternative for the conservation of the species, and a potential source of economic spillover to local populations. Nevertheless, the effects of tourism over these highly social animals remains unknown. Manta rays aggregate at three sites in Mexico: Oceanic manta rays ( M. birostris) in The Revillagigedo Archipelago and Banderas Bay in the Pacific. Caribbean manta rays ( M. cf. birostris ) around Isla Contoy National Park in the Caribbean. We analyzed the behavior of manta rays using video data collected by local researchers and tourism operators to determine how diver behaviors and techniques (SCUBA and free diving) affect them. Diver activities were grouped into passive and active categories. We described 16 behaviors and grouped them into four behavioral states: Directional, erratic, attraction and evasion to divers. We modeled the sequence of behaviors exhibited by manta rays via first order Markov chains. Our models accounted for passive and active diver behavior when modeling the changes in manta behavior. Manta rays in Banderas Bay and Revillagigedo displayed a higher frequency of erratic behaviors than at Isla Contoy, while Banderas Bay manta rays transitioned to evasion behaviors more often. Manta rays responded similarly in both sites to active divers. At freediving sites, manta rays from Isla Contoy displayed evasion less frequently than at Banderas Bay. Changes in manta ray behavior were similar for both sites, but mantas in Banderas Bay transitioned to evasion more with active divers. The increased food availability for Isla Contoy manta rays could be the reason for the reduced response toward divers in this site. The existence of additional stressors such as both traffic in Banderas Bay could be causing the mantas in this site to respond more frequently to active divers. This study, the first of its kind in oceanic and Caribbean manta rays, highlights that regulations and the use of best practices are vital for achieving longer and less disturbing encounters for both manta rays and divers.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".