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Record W3174782241 · doi:10.3389/fmars.2021.639772

Quantifying the Effects of Diver Interactions on Manta Ray Behavior at Their Aggregation Sites

2021· article· en· W3174782241 on OpenAlexaff
Miguel de Jesús Gómez-García, María del Carmen Blázquez-Moreno, Joshua D. Stewart, Vianey Leos‐Barajas, Iliana A. Fonseca-Ponce, Aldo A. Zavala‐Jiménez, Karen Fuentes, James T. Ketchum

Bibliographic record

VenueFrontiers in Marine Science · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicIchthyology and Marine Biology
Canadian institutionsUniversity of Toronto
FundersCentro de Investigaciones Biológicas del NoroesteComisión Nacional de Áreas Naturales ProtegidasConsejo Nacional de Ciencia y Tecnología
KeywordsBayGeographyArchipelagoFisheryOceanographyBiologyGeologyArchaeology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.489

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.252
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2021
Admission routes1
Has abstractyes

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