eDNA metabarcoding for diet analyses of green sea turtles (Chelonia mydas)

Understanding sea turtle diets can help conservation planning, but their trophic ecology is complex due to life history characteristics such as ontogenetic shifts and large foraging ranges. Studying sea turtle diet is challenging, particularly where ecological foraging observations are not possible. Here, we test a new minimally invasive method for the identification of diet items in sea turtles. We fingerprinted diet content using DNA from esophageal and cloacal swab samples by metabarcoding the 18S rRNA gene. This approach was tested on samples collected from green turtles (Chelonia mydas) from a juvenile foraging aggregation in the Bijagós archipelago in Guinea-Bissau. Esophagus samples (n = 6) exhibited a higher dietary richness (11 ± 5 amplicon sequence variants (ASVs) per sample; average ± SD) than cloacal ones (n = 5; 8 ± 2 ASVs). Overall, the diet was dominated by red macroalgae (Rhodophyta; 48.2 ± 16.3% of all ASVs), with the main food item in the esophagus and cloaca being a red alga belonging to the Rhodymeniophycidae subclass (35.1 ± 27.2%), followed by diatoms (Bacillariophyceae; 7.5 ± 7.3%), which were presumably consumed incidentally. Seagrass and some invertebrates were also present. Feeding on red algae was corroborated by field observations and barcoding of food items available in the benthic habitat, validating the approach for identifying diet content. We conclude that identification of food items using metabarcoding of esophageal swabs is useful for a better understanding of the relationships between the feeding behavior of sea turtles and their environment.

Unique Habitat for Benthic Foraminifera in Subtidal Blue Holes on Carbonate Platforms

Dissolution of carbonate platforms, like The Bahamas, throughout Quaternary sea-level oscillations have created mature karst landscapes that can include sinkholes and off-shore blue holes. These karst features are flooded by saline oceanic waters and meteoric-influenced groundwaters, which creates unique groundwater environments and ecosystems. Little is known about the modern benthic meiofauna, like foraminifera, in these environments or how internal hydrographic characteristics of salinity, dissolved oxygen, or pH may influence benthic habitat viability. Here we compare the total benthic foraminiferal distributions in sediment-water interface samples collected from <2 m water depth on the carbonate tidal flats, and the two subtidal blue holes Freshwater River Blue Hole and Meredith’s Blue Hole, on the leeward margin of Great Abaco Island, The Bahamas. All samples are dominated by miliolid foraminifera (i.e., Quinqueloculina and Triloculina), yet notable differences emerge in the secondary taxa between these two environments that allows identification of two assemblages: a Carbonate Tidal Flats Assemblage (CTFA) vs. a Blue Hole Assemblage (BHA). The CTFA includes abundant common shallow-water lagoon foraminifera (e.g., Peneroplis, Rosalina, Rotorbis), while the BHA has higher proportions of foraminifera that are known to tolerate stressful environmental conditions of brackish and dysoxic waters elsewhere (e.g., Pseudoeponides, Cribroelphidium, Ammonia). We also observe how the hydrographic differences between subtidal blue holes can promote different benthic habitats for foraminifera, and this is observed through differences in both agglutinated and hyaline fauna. The unique hydrographic conditions in subtidal blue holes make them great laboratories for assessing the response of benthic foraminiferal communities to extreme environmental conditions (e.g., low pH, dysoxia).

Benthic habitat mapping using Object-Based Image Analysis (OBIA) on Tidung Island, Kepulauan Seribu, DKI Jakarta

Tidung Island is one of the islands in Kepulauan Seribu, DKI Jakarta, Indonesia. This island has various benthic that live on the coastal areas, and benthic habitat has various functions both ecologically and economically. Nowadays, remote sensing technology is one way to detect benthic habitats in coastal areas. Mapping benthic habitat is essential for sustainable coastal resource management and to predict the distribution of benthic organisms. This study aims to map the benthic habitats using the object-based image analysis (OBIA) and calculate the accuracy of benthic habitat classification results in Tidung Island, Kepulauan Seribu, DKI Jakarta. The field data were collected on June 2021, and the image data used is satellite Sentinel-2 imagery acquired in June 2021. The result shows that the benthic habitat classification was produced in 4 classes: seagrass, rubble, sand, and live coral. The accuracy test result obtained an overall accuracy (OA) of 74.29% at the optimum value of the MRS segmentation scale 15;0,1;0.7 with the SVM algorithm. The results of benthic habitat classification show that the Seagrass class dominates the shallow water area at the research site with an area of 118.77 ha followed by Life Coral 104.809 ha, Sand 43.352 ha, and the smallest area is the Rubble class of 42.28 Ha.

Mapping of shallow water bathymetry and reef geomorphology using Sentinel-2 satellite imagery in Genteng Besar and Genteng Kecil Island, Kepulauan Seribu

Information on seafloor characteristics is one of the essential variables in coastal management and marine ecosystems. Application methods in remote sensing technology to study about characteristics of shallow waters have continuously been done. This research consists of two parts: an estimation of depth using Sentinel 2B satellite imagery with the Lyzenga algorithm and geomorphological classification of the benthic area using the Benthic Terrain Modeler (BTM) approach. BTM is a method to analyze benthic habitat and shallow water geomorphology. Integrated Depth data were analyzed using BTM to obtain bathymetric position index (BPI), slope, and classification of reef geomorphological structures. The resulting BPI value range is directly proportional to the given spatial area (scale factor). The slope is ranged between 0.01° – 19.24°, while optimum depth estimation is applicable until 10-meter. The values of BPI and slope were used as variables to classify the geomorphology of shallow water benthic areas based on the previous classification dictionary. Six geomorphological classes resulting from this study are Back Reef, Deep Depression, Depression, Lower Bank Shelf, Mid-Slope Ridges, and Reef Crest.

Subsurface swimming and stationary diving are metabolically cheap in adult Pacific walruses (Odobenus rosmarus divergens)

Walruses rely on sea-ice to efficiently forage and rest between diving bouts while maintaining proximity to prime foraging habitat. Recent declines in summer sea ice have resulted in walruses hauling out on land where they have to travel farther to access productive benthic habitat while potentially increasing energetic costs. Despite the need to better understand the impact of sea ice loss on energy expenditure, knowledge about metabolic demands of specific behaviours in walruses is scarce. In the present study, 3 adult female Pacific walruses (Odobenus rosmarus divergens) participated in flow-through respirometry trials to measure metabolic rates while floating inactive at the water surface during a minimum of 5 min, during a 180-second stationary dive, and while swimming horizontally underwater for ∼90 m. Metabolic rates during stationary dives (3.82±0.56 l O2 min−1) were lower than those measured at the water surface (4.64±1.04 l O2 min−1), which did not differ from rates measured during subsurface swimming (4.91±0.77 l O2 min−1). Thus, neither stationary diving nor subsurface swimming resulted in metabolic rates above those exhibited by walruses at the water surface. These results suggest that walruses minimize their energetic investment during underwater behaviours as reported for other marine mammals. Although environmental factors experienced by free-ranging walruses (e.g., winds or currents) likely affect metabolic rates, our results provide important information for understanding how behavioural changes affect energetic costs and can be used to improve bioenergetics models aimed at predicting the metabolic consequences of climate change on walruses.

Mapping of Coral Reefs with Multispectral Satellites: A Review of Recent Papers

Coral reefs are an essential source of marine biodiversity, but they are declining at an alarming rate under the combined effects of global change and human pressure. A precise mapping of coral reef habitat with high spatial and time resolutions has become a necessary step for monitoring their health and evolution. This mapping can be achieved remotely thanks to satellite imagery coupled with machine-learning algorithms. In this paper, we review the different satellites used in recent literature, as well as the most common and efficient machine-learning methods. To account for the recent explosion of published research on coral reel mapping, we especially focus on the papers published between 2018 and 2020. Our review study indicates that object-based methods provide more accurate results than pixel-based ones, and that the most accurate methods are Support Vector Machine and Random Forest. We emphasize that the satellites with the highest spatial resolution provide the best images for benthic habitat mapping. We also highlight that preprocessing steps (water column correction, sunglint removal, etc.) and additional inputs (bathymetry data, aerial photographs, etc.) can significantly improve the mapping accuracy.

Shallow-Water Benthic Habitat Mapping Using Drone with Object Based Image Analyses

Spatial information on benthic habitats in Wangiwangi island waters, Wakatobi District, Indonesia was very limited in recent years. However, this area is one of the marine tourism destinations and one of the Indonesia’s triangle coral reef regions with a very complex coral reef ecosystem. The drone technology that has rapidly developed in this decade, can be used to map benthic habitats in this area. This study aimed to map shallow-water benthic habitats using drone technology in the region of Wangiwangi island waters, Wakatobi District, Indonesia. The field data were collected using a 50 × 50 cm squared transect of 434 observation points in March–April 2017. The DJI Phantom 3 Pro drone with a spatial resolution of 5.2 × 5.2 cm was used to acquire aerial photographs. Image classifications were processed using object-based image analysis (OBIA) method with contextual editing classification at level 1 (reef level) with 200 segmentation scale and several segmentation scales at level 2 (benthic habitat). For level 2 classification, we found that the best algorithm to map benthic habitat was the support vector machine (SVM) algorithm with a segmentation scale of 50. Based on field observations, we produced 12 and 9 benthic habitat classes. Using the OBIA method with a segmentation value of 50 and the SVM algorithm, we obtained the overall accuracy of 77.4% and 81.1% for 12 and 9 object classes, respectively. This result improved overall accuracy up to 17% in mapping benthic habitats using Sentinel-2 satellite data within the similar region, similar classes, and similar method of classification analyses.

Regional High-Resolution Benthic Habitat Data from Planet Dove Imagery for Conservation Decision-Making and Marine Planning

High-resolution benthic habitat data fill an important knowledge gap for many areas of the world and are essential for strategic marine conservation planning and implementing effective resource management. Many countries lack the resources and capacity to create these products, which has hindered the development of accurate ecological baselines for assessing protection needs for coastal and marine habitats and monitoring change to guide adaptive management actions. The PlanetScope (PS) Dove Classic SmallSat constellation delivers high-resolution imagery (4 m) and near-daily global coverage that facilitates the compilation of a cloud-free and optimal water column image composite of the Caribbean’s nearshore environment. These data were used to develop a first-of-its-kind regional thirteen-class benthic habitat map to 30 m water depth using an object-based image analysis (OBIA) approach. A total of 203,676 km2 of shallow benthic habitat across the Insular Caribbean was mapped, representing 5% coral reef, 43% seagrass, 15% hardbottom, and 37% other habitats. Results from a combined major class accuracy assessment yielded an overall accuracy of 80% with a standard error of less than 1% yielding a confidence interval of 78%–82%. Of the total area mapped, 15% of these habitats (31,311.7 km2) are within a marine protected or managed area. This information provides a baseline of ecological data for developing and executing more strategic conservation actions, including implementing more effective marine spatial plans, prioritizing and improving marine protected area design, monitoring condition and change for post-storm damage assessments, and providing more accurate habitat data for ecosystem service models.