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. 2021 Jun 23;16(6):e0252470.
doi: 10.1371/journal.pone.0252470. eCollection 2021.

Environmental drivers of reef manta ray (Mobula alfredi) visitation patterns to key aggregation habitats in the Maldives

Joanna L Harris  1   2 , Guy M W Stevens  1
Affiliations

Environmental drivers of reef manta ray (Mobula alfredi) visitation patterns to key aggregation habitats in the Maldives

Joanna L Harris et al. PLoS One. .

Abstract

A detailed understanding of the dynamics of small-scale (10s km) habitat use by the reef manta ray (Mobula alfredi) in the Maldives Archipelago is required to develop an effective national conservation management plan for this wide-ranging species. Here, a combination of photo-ID sightings data and acoustic telemetry were used to investigate both long-term M. alfredi visitation trends and small-scale movement patterns to key habitats on the eastern side of Baa Atoll (Hanifaru Bay feeding area, Dhigu Thila multifunctional site, and Nelivaru Thila cleaning station). All tagged and most of the sighted M. alfredi exhibited high affinity to the eastern side of Baa Atoll, where 99% of detections occurred, and 69% of individuals were re-sighted in multiple years. Sightings data suggests that visitation patterns may be associated with differences in habitat use by sex and maturity status. Boosted regression trees indicated that tag detection probability at Hanifaru Bay increased with increased westerly wind speed (>5ms-1) during the day, close to a new and full moon just after high tide, and when the tidal range was low. Interaction effects between predictors suggest that wind-driven oceanographic processes, such as Langmuir Circulation, maybe working to increase zooplankton concentration at this location. Tag detection probability increased at Dhigu Thila under similar conditions. At Nelivaru Thila, it increased at lower wind speeds (<5ms-1), close to a full moon, three hours after high tide. These results suggest that M. alfredi may utilise cleaning stations during the day when environmental conditions are not suitable for feeding. There was a high level of connectivity between these three locations, which suggests they form part of a network of key habitats that provide essential services to M. alfredi locally. Future conservation efforts should focus on identifying all areas of key habitat use for this species within the Maldives; applying strict protective measures to these sites and any connecting migration corridors which link them.

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Conflict of interest statement

The Four Seasons Resort Maldives at Landaa Giraavaru had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript, but provided logistical support Manta Trust researchers working in the Maldives. This does not alter our adherence to PLOS ONE policies on sharing data and materials. The authors have declared that no other competing interests exist.

Figures

Fig 1
Fig 1. Mobula alfredi tag detections and acoustic receiver network map.
Percentage of detections at each site corresponds to the colour and size of the node at each location. The network map showing how many times individuals were subsequently detected by each pair of acoustic receivers which corresponds to the colour and thickness of the line connecting the nodes. Map created in ArcGIS 10.7 (www.esri.com/) using bathymetry from GEBCO Compilation Group (2020) GEBCO 2020 Grid (doi:10.5285/a29c5465-b138-234d-e053-6c86abc040b9) and reef features from Millennium Coral Reef Mapping Project (MCRMP) (https://data.unep-wcmc.org/datasets/1); MCRMP validated maps provided by the Institute for Marine Remote Sensing, University of South Florida (IMaRS/USF) and Institut de Recherche pour le Développement (IRD, Centre de Nouméa), with support from NASA. MCRMP unvalidated maps provided by IMaRS, USF with support from NASA and further interpreted by UNEP World Conservation Monitoring Centre (www.unep-wcmc.org). IRD, do not endorse these products. Inset top left: the Maldives Archipelago with the study area highlighted in the red box. Inset bottom right: close-up of the key habitats (Hanifaru Bay, Nelivaru Thila and Dhigu Thila) and the Hanifaru and Angafaru marine protected areas (MPAs).
Fig 2
Fig 2. Resident events recorded at each location.
Resident events at each site showing location (by colour) and time at location (by size). Grey shading highlights the SW Monsoon. Note the gap in the timeline between 29/11/2008 and 08/09/2009 when the acoustic receivers were not recording.
Fig 3
Fig 3. Distribution of resident events by hour of the day.
Percentage distribution of resident events (start time) by hour of the day for Hanifaru Bay, Nelivaru Thila, and Dhigu Thila. Data for Fushifaru Kandu and Fares Kandu not shown as only nine resident events occurred between these locations.
Fig 4
Fig 4. Partial dependency plots for Hanifaru Bay.
The effect of each predictor variable on the occurrence of tagged M. alfredi at Hanifaru Bay, while keeping all other variables at their mean. The green dashed line shows locally weighted smoothing (LOESS). Rugs display the distribution of the data for presence (top, blue), and absence (red, bottom).
Fig 5
Fig 5. Interactions between predictor variables for all three locations.
Pairwise interactions between predictor variables while keeping all other variables at their respective mean showing the probability of M. alfredi tag detections at Hanifaru Bay (top row), Dhigu Thila (middle row), and Nelivaru Thila (bottom row). All interactions were significant (p<0.01). Note z-axis scale varies depending on the influence of the interaction.
Fig 6
Fig 6. Partial dependency plots for Dhigu Thila.
The effect of each predictor variable on the occurrence of tagged M. alfredi at Dhigu Thila while keeping all other variables at their mean. The green line shows locally weighted smoothing (LOESS). Rugs display the distribution of the data for presence (top, blue), and absence (red, bottom).
Fig 7
Fig 7. Partial dependency plots for Nelivaru Thila.
The effect of each predictor variable on the occurrence of tagged M. alfredi at Nelivaru Thila, while keeping all other variables at their mean. The green line shows locally weighted smoothing (LOESS). Rugs display the distribution of the data for presence (top, blue), and absence (red, bottom).
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