Hindcast and Near Real-Time Monitoring of Green Macroalgae Blooms in Shallow Coral Reef Lagoons Using Sentinel-2: A New-Caledonia Case Study

A new set of N isotopic reference values for monitoring Ulva green tides in coral reef ecosystems

Green tides occurrence has increased in coral reefs, yet few reference values have been documented to support bloom management in these ecosystems. Here, we took advantage of recent Ulva green tides that occurred in New Caledonia to (i) identify the elements limiting the growth of Ulva spp. during these blooms; and (ii) validate the use of isotopic markers for identifying sources of nutrients that generated blooms. N/P ratios highlighted a stronger limitation of algae by phosphorus than by nitrogen on sites under oceanic influence, while the proportions of N and P were optimal for algal growth at sites where green tides occurred. Macroalgae highly exposed to sewage water was characterized by higher δ15N than macroalgae collected in areas exposed to synthetic inorganic fertilizers. From these results, we established a new set of threshold values for using δ15N in Ulva species as an indicator of nitrogen source type in coral reefs.

Seasonal Single-Site Sampling Reveals Large Diversity of Marine Algal Toxins in Coastal Waters and Shellfish of New Caledonia (Southwestern Pacific)

Algal toxins pose a serious threat to human and coastal ecosystem health, even if their potential impacts are poorly documented in New Caledonia (NC). In this survey, bivalves and seawater (concentrated through passive samplers) from bays surrounding Noumea, NC, collected during the warm and cold seasons were analyzed for algal toxins using a multi-toxin screening approach. Several groups of marine microalgal toxins were detected for the first time in NC. Okadaic acid (OA), azaspiracid-2 (AZA2), pectenotoxin-2 (PTX2), pinnatoxin-G (PnTX-G), and homo-yessotoxin (homo-YTX) were detected in seawater at higher levels during the summer. A more diversified toxin profile was found in shellfish with brevetoxin-3 (BTX3), gymnodimine-A (GYM-A), and 13-desmethyl spirolide-C (SPX1), being confirmed in addition to the five toxin groups also found in seawater. Diarrhetic and neurotoxic toxins did not exceed regulatory limits, but PnTX-G was present at up to the limit of the threshold recommended by the French Food Safety Authority (ANSES, 23 μg kg-1). In the present study, internationally regulated toxins of the AZA-, BTX-, and OA-groups by the Codex Alimentarius were detected in addition to five emerging toxin groups, indicating that algal toxins pose a potential risk for the consumers in NC or shellfish export.

Nation-wide hierarchical and spatially-explicit framework to characterize seagrass meadows in New-Caledonia, and its potential application to the Indo-Pacific

Despite their ecological role and multiple contributions to human societies, the distribution of Indo-Pacific seagrasses remains poorly known in many places. Herein, we outline a hierarchical spatially-explicit assessment framework to derive nation-wide synoptic knowledge of the distribution of seagrass species and communities. We applied the framework to New Caledonia (southwest Pacific Ocean) and its 36,200 km² of reefs and lagoons. The framework is primarily field-based but can leverage various habitat maps derived from remote sensing. Field data collection can be stratified by map products and retrospectively contribute to developing new seagrass distribution maps. Airborne and satellite remote sensing alone do not allow for the spatial generalisation of the finest attributes (species distribution and types of seagrass beds), but staged stratified field sampling provides synoptic views of these attributes. Using three examples, we discuss how the hierarchical and spatial information generated from this framework's application can inform conservation and management objectives.

A Machine Learning Approach for Estimating the Trophic State of Urban Waters Based on Remote Sensing and Environmental Factors

To improve the accuracy of remotely sensed estimates of the trophic state index (TSI) of inland urban water bodies, key environmental factors (water temperature and wind field) were considered during the modelling process. Such environmental factors can be easily measured and display a strong correlation with TSI. Then, a backpropagation neural network (BP-NN) was applied to develop the TSI estimation model using remote sensing and environmental factors. The model was trained and validated using the TSI quantified by five water trophic indicators obtained for the period between 2018 and 2019, and then we selected the most appropriate combination of input variables according to the performance of the BP-NN. Our results demonstrate that the optimal performance can be obtained by combining the water temperature and single-band reflection values of Sentinel-2 satellite imagery as input variables (R2 = 0.922, RMSE = 3.256, MAPE = 2.494%, and classification accuracy rate = 86.364%). Finally, the spatial and temporal distribution of the aquatic trophic state over four months with different trophic levels was mapped in Gongqingcheng City using the TSI estimation model. In general, the predictive maps based on our proposed model show significant seasonal changes and spatial characteristics in the water trophic state, indicating the possibility of performing cost-effective, RS-based TSI estimation studies on complex urban water bodies elsewhere.