A spatial analysis of seagrass habitat and community diversity in the Great Barrier Reef World Heritage Area

The Great Barrier Reef World Heritage Area (GBRWHA) in north eastern Australia spans 2500 km of coastline and covers an area of ~ 350,000 km2. It includes one of the world’s largest seagrass resources. To provide a foundation to monitor, establish trends and manage the protection of seagrass meadows in the GBRWHA we quantified potential seagrass community extent using six random forest models that include environmental data and seagrass sampling history. We identified 88,331 km2 of potential seagrass habitat in intertidal and subtidal areas: 1111 km2 in estuaries, 16,276 km2 in coastal areas, and 70,934 km2 in reef areas. Thirty-six seagrass community types were defined by species assemblages within these habitat types using multivariate regression tree models. We show that the structure, location and distribution of the seagrass communities is the result of complex environmental interactions. These environmental conditions include depth, tidal exposure, latitude, current speed, benthic light, proportion of mud in the sediment, water type, water temperature, salinity, and wind speed. Our analysis will underpin spatial planning, can be used in the design of monitoring programs to represent the diversity of seagrass communities and will facilitate our understanding of environmental risk to these habitats.

Seagrass habitat suitability model for Redang Marine Park using multibeam echosounder data: Testing different spatial resolutions and analysis window sizes

Integrating Multibeam Echosounder (MBES) data (bathymetry and backscatter) and underwater video technology allows scientists to study marine habitats. However, use of such data in modeling suitable seagrass habitats in Malaysian coastal waters is still limited. This study tested multiple spatial resolutions (1 and 50 m) and analysis window sizes (3 × 3, 9 × 9, and 21 × 21 cells) probably suitable for seagrass-habitat relationships in Redang Marine Park, Terengganu, Malaysia. A maximum entropy algorithm was applied, using 12 bathymetric and backscatter predictors to develop a total of 6 seagrass habitat suitability models. The results indicated that both fine and coarse spatial resolution datasets could produce models with high accuracy (>90%). However, the models derived from the coarser resolution dataset displayed inconsistent habitat suitability maps for different analysis window sizes. In contrast, habitat models derived from the fine resolution dataset exhibited similar habitat distribution patterns for three different analysis window sizes. Bathymetry was found to be the most influential predictor in all the models. The backscatter predictors, such as angular range analysis inversion parameters (characterization and grain size), gray-level co-occurrence texture predictors, and backscatter intensity levels, were more important for coarse resolution models. Areas of highest habitat suitability for seagrass were predicted to be in shallower (<20 m) waters and scattered between fringing reefs (east to south). Some fragmented, highly suitable habitats were also identified in the shallower (<20 m) areas in the northwest of the prediction models and scattered between fringing reefs. This study highlighted the importance of investigating the suitable spatial resolution and analysis window size of predictors from MBES for modeling suitable seagrass habitats. The findings provide important insight on the use of remote acoustic sonar data to study and map seagrass distribution in Malaysia coastal water.

A Biophysical Model and Network Analysis of Invertebrate Community Dispersal Reveals Regional Patterns of Seagrass Habitat Connectivity

The dispersal of marine organisms is a critical process for the maintenance of biodiversity and ecosystem functioning across a seascape. Understanding the patterns of habitat connectivity that arise from the movement of multiple species can highlight the role of regional processes in maintaining local community structure. However, quantifying the probability and scale of dispersal for marine organisms remains a challenge. Here, we use a biophysical model to simulate dispersal, and we conduct a network analysis to predict connectivity patterns across scales for the community of invertebrates associated with seagrass habitat in British Columbia, Canada. We found many possible connections and few isolated habitat meadows, but the probability of most connections was low. Most habitat connections occurred within 3 days of dispersal time over short distances, indicating potential limits to long distance dispersal and little effect of species-specific dispersal abilities on the potential spatial extent of habitat connectivity. We then highlight the different roles that individual seagrass meadows can play in maintaining network connectivity. We also identify clusters of connected meadows and use these clusters to estimate the spatial scale of community dynamics. The connectivity patterns generated by our dispersal simulations highlight the importance of considering marine communities in their broad seascape context, with applications for the prioritization and conservation of habitat that maintains connectivity.

Deep neural networks based automated extraction of dugong feeding trails from UAV images in the intertidal seagrass beds

Dugongs (Dugong dugon) are seagrass specialists distributed in shallow coastal waters in tropical and subtropical seas. The area and distribution of the dugongs’ feeding trails, which are unvegetated winding tracks left after feeding, have been used as an indicator of their feeding ground utilization. However, current ground-based measurements of these trails require a large amount of time and effort. Here, we developed effective methods to observe the dugongs’ feeding trails using unmanned aerial vehicle (UAV) images (1) by extracting the dugong feeding trails using deep neural networks. Furthermore, we demonstrated two applications as follows; (2) extraction of the daily new feeding trails with deep neural networks and (3) estimation the direction of the feeding trails. We obtained aerial photographs from the intertidal seagrass bed at Talibong Island, Trang Province, Thailand. The F1 scores, which are a measure of binary classification model’s accuracy taking false positives and false negatives into account, for the method (1) were 89.5% and 87.7% for the images with ground sampling resolutions of 1 cm/pixel and 0.5 cm/pixel, respectively, while the F1 score for the method (2) was 61.9%. The F1 score for the method (1) was high enough to perform scientific studies on the dugong. However, the method (2) should be improved, and there remains a need for manual correction. The mean area of the extracted daily new feeding trails from September 12–27, 2019, was 187.8 m2 per day (n = 9). Total 63.9% of the feeding trails was estimated to have direction within a range of 112.5° and 157.5°. These proposed new methods will reduce the time and efforts required for future feeding trail observations and contribute to future assessments of the dugongs’ seagrass habitat use.

The Trophic Basis of Fish Assemblages in Temperate Estuarine and Coastal Ecosystems

More than half of the fish biomass of coastal rocky reefs depends on zooplankton; however, the trophic basis of estuarine fish assemblages remains unknown. We quantified the trophic basis (i.e. basal energy sources) of fish community biomass inhabiting three habitat types (seagrass, natural reef and artificial reef) in two estuaries, and at two coastal rocky reef sites. Estuarine fish assemblages were surveyed with Baited Remote Underwater Video (BRUVs). Species abundance, richness and biomass of fish were classified into 9 functional feeding groups (3 elasmobranch and 6 teleost). Comparable metrics for coastal fish assemblages were obtained from published surveys using BRUV, remote underwater video and visual census survey methods. Using the functional feeding group biomass and the group-specific diet composition, the breakdown of energy sources was calculated using a food web analysis. Estuarine reef habitats had different species and different functional feeding group composition than seagrass habitat. The majority of fish biomass in the seagrass habitat was supported by detritus (51% at one estuary) or macrophytes (58% at the other estuary). In contrast, zooplankton supported most fish biomass (45-59%) at the coastal reef locations, and in reef habitat in one estuary (35-43%), but not the other estuary (33-34%). The trophic basis of estuarine and coastal fish assemblages reveals their potential response to urbanisation including changes to habitat, nutrient supply and current flow.

TIME SERIES ANALYSIS FOR MONITORING SEAGRASS HABITAT AND ENVIRONMENT IN BUSUANGA, PHILIPPINES USING GOOGLE EARTH ENGINE

Seagrasses are marine flowering plants which are part of a highly productive coastal ecosystem and play key roles in the coastal processes. Unfortunately, they are declining in area coverage globally, and seagrass losses can be attributed to climate change such as sea-level rise, increase in sea surface temperature, and decrease in salinity, as well as human-related activities. The objective of this research is to assess the historical changes in the seagrass habitat and environment of Busuanga, Philippines using time series data available in the Google Earth Engine (GEE) platform. These include satellite data such as MODIS, Landsat 5, 7, and 8, and SeaWIFS. Reanalysis data such as HYCOM was also utilized in this research. Results from HYCOM data show that there has been a 0.0098 °C increase in the sea surface temperature per decade in Busuanga while MODIS data indicates an increase of 0.0045 °C per decade. Moreover, HYCOM data also shows an overall average of 0.76 mm in sea surface elevation anomaly and a decreasing trend in salinity values at 0.0026 psu per decade. Chlorophyll-a concentration has a minimal increase based on results from MODIS and SeaWIFS. Aside from changes in water parameters, changes in the land also affect seagrasses. Forest loss may cause increased siltation in the coastal ecosystem which can lead to seagrass loss. Based on the results of Landsat satellite image processing, there has been forest cover loss in Busuanga with the highest loss occurring in 2013 when super typhoon Yolanda ravaged the island. Lastly, results from the linear spectral unmixing of 778 Landsat images from 1987–2000 show that the average percent cover of seagrasses in Busuanga were declining through the years.

Zooplankton species composition and diversity in the seagrass habitat of Lawas, Sarawak, Malaysia

Seagrass habitats are considered to be some of the most biodiverse ecosystems on the planet and safeguard some ecologically and economically important fauna, amongst which are some globally threatened species, including dugong. Malaysian seagrass ecosystems are not widespread, but their existence supports some significant marine fauna. A rigorous zooplankton study was conducted from May 2016 to February 2017, in the seagrass habitat of Lawas, Sarawak, Malaysia, to examine their temporal composition and diversity, together with their ecological influences. A total of 45 zooplankton species from 13 significant groups were recorded in the seagrass habitat. The population density of zooplankton ranged between 2,482 ind/m³ and 22,670 ind/m³ over three different seasons. A single zooplankton copepod was found to be dominant (47.40%), while bivalves were the second largest (31.8%) group in terms of total abundance. It was also noticed that the average relative abundance (0.62) and important species index (62.08) of copepods were higher than for other groups that exist in the seagrass meadow, whereas copepod Parvocalanus crassirostris showed both the highest average relative abundance (0.41) and the highest important species index (41.15). The diversity (H') and richness index of the intermediate season were found to be highest due to favourable physico-chemical conditions. Within the referred seasonal cluster, the wet and dry seasons were almost similar in terms of species abundance, while the intermediate season was distinct, with high species diversity backed by ANOSIM analysis results. Copepod and bivalves formed one group with a common similarity level of 0.80. The CCA (Canonical Correspondence Analysis) model established that abiotic factors, especially turbidity, NO2, rainfall, dissolved oxygen and pH were significantly correlated with abundance of individual groups of zooplankton. Zooplankton assemblage and abundance in Lawas were found to be very rich in multiple seasons, indicating that the productivity of uninterrupted seagrass habitat might be high and the system rich in biodiversity.