Intraspecific differences in short- and long-term foraging strategies of reef manta ray (Mobula alfredi) in the Chagos Archipelago
Bibliographic record
Abstract
Assessing the foraging ecology of a threatened species is necessary to understand their movement behaviour and habitat use patterns, which are essential for developing effective protection strategies. Here, the foraging ecology of reef manta rays (Mobula alfredi) in the Chagos Archipelago, a region encompassed by a vast no-take marine protected area (MPA), was investigated using stable isotope analysis of skin and muscle tissue. Enriched δ13C values suggest the population predominantly forages in nearshore environments. Skin δ13C values increased with increased rainfall, likely associated with the boosts in primary production and zooplankton biomass due to the coastal advection of seabird guano. Annual variations in δ13C values of skin and muscle were observed and are consistent with reduced nutrient transport associated with the effects of Indian Ocean Dipole oscillations, including a deepening of the thermocline, a suppression of cold-water upwelling, and reduced rainfall. Short- and long-term foraging strategies and locations were identified by applying hierarchical clustering, isotopic niche analysis, and Bayesian stable isotope mixing models to δ13C and δ15N of paired skin and muscle tissue samples. Two isotopically distinct groups of M. alfredi were identified, employing either local foraging strategies restricted to specific locations or wide-ranging strategies that likely mean they engage in regular migrations throughout the archipelago. Ninety-eight percent of M. alfredi were estimated to switch between strategies utilising and connecting multiple discrete nearshore habitats, emphasising their role in ecosystem functioning by facilitating the transport of nutrients across ecosystem boundaries. However, illegal, unreported, and unregulated fishing and lost or abandoned fishing gear commonly occur within the MPA. Locations of particular concern are Egmont Atoll as it is a highly active aggregation location and Peros Banhos Atoll where IUU frequently occurs and M. alfredi are estimated to be heavily reliant upon for foraging. Frequent migrations between atolls by M. alfredi also raises concern over their vulnerability to these activities along migration corridors. This research bridges current knowledge gaps in this population's foraging ecology and concomitant movement patterns, which should inform conservation strategies in the region.
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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) | 0.000 | 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".