Search Results (30)

Green tide disasters have erupted consecutively in the Southern Yellow Sea, yet their driving mechanisms remain debated. The “raft-origin-hypothesis” identifies the source as epiphytic Ulva macroalgae attached to laver aquaculture rafts in the Subei Shoal, whereas the “micropropagule-hypothesis” speculates that Ulva micropropagules settling into open sea sediments constitute a cross-year seed reserve. The latter has hitherto relied primarily on laboratory culture simulations, with field-scale assessment lacking. This study established 46 surface sediment survey stations from 2020 to 2022, covering periods before, during, and after green tide outbreaks. Surface sediments were obtained using a box corer, and Ulva micropropagules were quantitatively analyzed through laboratory cultivation and morphological identification. Results showed that micropropagule abundance was generally low and exhibited spatial heterogeneity; high-value areas persistently existed in the Subei Shoal aquaculture zone, extended into the open sea during outbreaks, and generally disappeared in the open sea during and after the decline period. Monthly variations indicated that micropropagules released and settling into sediments (e.g., in the open sea) during large-scale outbreaks could not survive until the following year’s floating period; the initial biomass of subsequent eruptions did not originate from the revival of the seafloor “seed source” in the open sea, which remains speculative. Full article

The invasive brown macroalga Rugulopteryx okamurae has rapidly expanded across the Mediterranean–Atlantic region, generating severe ecological impacts. Nevertheless, the considerable amount of biomass available creates opportunities for valorisation within circular bioeconomy frameworks. This study provides an integrated characterization of the chemical profile and bioactivities of freshly collected floating biomass of R. okamurae from southern Portugal. Proximate composition was determined, and lipophilic (hexane) and hydrophilic (water) extracts were analyzed by GC–MS and spectrophotometric methods. Antioxidant activity was assessed using complementary radical-scavenging, reducing power, and metal-chelation assays, and enzyme inhibition was evaluated against targets associated with neurodegenerative, metabolic, and dermatological disorders. The lipophilic fraction was dominated by long-chain alkanes (≈101 mg/g extract) and sterols, particularly fucosterol (≈43 mg/g extract), but exhibited low radical-scavenging capacity (no EC₅₀ reached in DPPH or ABTS assays), and no relevant enzyme inhibition. In contrast, the water extract contained measurable phlorotannins (6.61 mg PGE/g extract) and showed moderate antioxidant (ABTS: EC₅₀ = 5.17 mg/mL; FRAP: EC₅₀ = 0.78 mg/mL) and enzyme inhibition activities (BChE: IC₅₀ = 5.17 mg/mL; tyrosinase: IC₅₀ = 0.78 mg/mL). Compared with previous studies on R. okamurae, this work applies a systematic fractionation of biomass from southern Portugal into polar and non-polar fractions and, for the first time, correlates the resulting detailed chemical profiles with multiple bioactivities. This approach revealed a clear functional differentiation between fractions, with bioactivity being mainly associated with polar metabolites. Overall, these findings highlight the value of structured extraction strategies for biomass valorisation and support the sustainable management of R. okamurae. Full article

Large-scale green tides dominated by Ulva species have recurred annually in the Southern Yellow Sea for nearly two decades, yet early detection remains challenging due to the patchy distribution of incipient floating macroalgae. This study investigated the spatiotemporal dynamics of Ulva micropropagules during the 2020 outbreak using a systematic cultivation assay. Seawater samples were collected from 23 stations across the Subei Shoal and adjacent waters in April, May, and July, and incubated under controlled laboratory conditions to enumerate Ulva germling densities. Results revealed that Ulva micropropagule abundance peaked in April, with high-density foci concentrated in the Subei Shoal region—particularly in aquaculture areas of Neopyropia J. Brodie & L.-E. Yang, 2020—confirming this zone as one of the important sources. Abundance declined progressively through May and July as macroalgae drifted northward under wind and current forcing. This method effectively identified putative source regions and reconstructed initial dispersal patterns prior to satellite-detectable macroalgal aggregation. These findings demonstrate that Ulva micropropagule monitoring provides a cost-effective, sensitive tool for early warning and Ulva source tracking, offering finer-scale propagule distribution data to inform precision management strategies for mitigating green tide impacts on coastal marine ecosystems. Future research should expand investigations into Ulva micropropagule dynamics to elucidate their mechanistic processes and ecological significance in green tide initiation and development. Full article

This study reveals the distribution of floating macroalgae Sargassum in the East China Sea and Yellow Sea using HY-1C/D Coastal Zone Imager (CZI) data. A new inversion model, utilizing green and near-infrared bands, was developed for the 50 m resolution CZI data. This model effectively distinguishes Sargassum from Ulva prolifera and is effective in turbid coastal waters. Sargassum spatiotemporal distribution and drift patterns over five years were analyzed. Key findings demonstrate that (1) floating Sargassum exhibits distinct spatiotemporal distribution patterns. Sargassum initially emerges along Zhejiang’s eastern coast in February. During March and April, it concentrates east of Hangzhou Bay. While in May, Sargassum appears in the Yellow Sea, and is distributed near the Shandong Peninsula by June. Small patches of Sargassum are also found in the Yellow Sea from November to January. (2) Its distribution is influenced by various factors like nutrients, temperature, salinity, currents, and winds. Suitable nutrients, temperature, and salinity promote growth, while currents and winds, particularly in April–May, drive its northward drift from the East China Sea into the Yellow Sea. The Yellow Sea population originates from both drifting populations and local growth. (3) This research highlights the utility of HY-1C/D satellite data in coastal zone research, facilitating ecological monitoring and protection. Full article

Macroalgae are an integral part of estuarine primary production; however, their excessive growth may have severe negative impacts on the ecosystem. Although it is generally believed that algal blooms may be caused by a combination of excessive nutrients and temperature, their occurrences are hard to predict, and quantitative monitoring is a logistical challenge which requires the development of reliable and inexpensive techniques. This can be achieved by implementation of processing algorithms and indices on multi-spectral satellite images. Tuggerah Lakes estuary on the Central Coast of NSW was studied because of the regular occurrences of blooms, primarily of green filamentous algae. The detection of algal blooms based on the red-edge effect of the chlorophyll provided consistent results supported by direct observations. The Floating Algae Index (FAI) was identified as the most accurate index for detecting algal blooms in shallow areas, following a comparative analysis of six commonly used algae detection indices. Logistic regression was implemented where FAI was used as a predictor of two clusters, “bloom” and “non-bloom”. FAI was calculated for multi-spectral satellite images based on pixels of 20 × 20 m, covering the entire area of the Tuggerah Lakes. Seven sample points (pixels) were chosen, and the optimal threshold was found for each pixel to assign it to one of the two clusters. The logistic regression model was trained for each pixel; then the optimal parameters for its coefficients and the optimal classification threshold were obtained by cross-validation based on bootstrapping. Probabilities for classifying clusters as either “bloom” or “non-bloom” were predicted with respect to the optimal threshold. The resulting model can be used to estimate probability of macroalgal blooms in coastal estuaries, allowing quantitative monitoring through time and space. Full article

Green tide area is a crucial indicator for monitoring green tide dynamics. However, scale effects arising from differences in image resolution can lead to estimation errors. Current pixel-level and sub-pixel-level methods often overlook the impact of morphological differences across varying resolutions. To address this, our study examines the influence of morphological diversity on green tide area estimation using GF-1 WFV data and the Virtual-Baseline Floating macroAlgae Height (VB-FAH) index at a 16 m resolution. Green tide patches were categorized into small, medium, and large sizes, and morphological features such as elongation, compactness, convexity, fractal dimension, and morphological complexity were designed and analyzed. Machine learning models, including Extra Trees, LightGBM, and Random Forest, among others, classified medium and large patches into striped and non-striped types, with Extra Trees achieving outstanding performance (accuracy: 0.9844, kappa: 0.9629, F1-score: 0.9844, MIoU: 0.9637). The results highlighted that large patches maintained stable morphological characteristics across resolutions, while small and medium patches were more sensitive to scale, with increased estimation errors at lower resolutions. Striped patches, particularly among medium patches, were more sensitive to scale effects compared to non-striped ones. The study suggests that incorporating morphological features of patches, especially in monitoring striped and small patches, could be a key direction for improving the accuracy of green tide monitoring and dynamic change analysis. Full article

This study focused on evaluating the effect of different concentrations of nutrients and total suspended solids on the removal rate of nutrients and biocompounds from the macroalgae U. lactuca in an integrated system with the shrimp Penaeus vannamei in biofloc. The experiment lasted 45 days and included five treatments with three replicates each, with percentages of 0 (control), 25, 50, 75, and 100% biofloc inoculum (73.3 ± 5.7 and 325.0 ± 21.2 mg L⁻¹ initial nitrate and solids, respectively, in the 100% inoculum), from a shrimp farm, resulting in different concentrations of solids and nutrients. The macroalgae were introduced into 280 L tanks at a density of 0.88 kg m⁻², along with 200 shrimp m⁻³. The algae were separated by a floating structure. Water quality parameters were measured, and the nutrient removal rate was evaluated. The treatment with 75% inoculum showed a removal rate of 55.0 ± 4.0 and 31.0 ± 10.0% of nitrate and phosphate, respectively. There was no difference in macroalgae growth between the treatments; however, macroalgae grown in 75% inoculum had higher protein, chlorophyll-a, and lower ash values compared with the control. The use of macroalgae in integrated production with shrimp under the conditions of the treatment with 75% biofloc inoculum proved to be viable and sustainable. Full article

Ulva prolifera and Sargassum are two common floating macroalgae in China’s coastal algal bloom events. Ulva prolifera frequently emerges concomitantly with Sargassum outbreaks, thereby presenting challenges to the monitoring of algal blooms, thereby presenting challenges to the monitoring of algae. To tackle the challenge of differentiating between Ulva prolifera and Sargassum, this study employs Sentinel-2 MSI data for spectral analysis. Notably, significant disparities in the Remote Top of Atmosphere Reflectance (Rtoa) between Ulva prolifera and Sargassum are observed. This study proposes a random forest-based algorithm for discriminating between Ulva prolifera and Sargassum in the regions of the Yellow Sea and East China Sea. The algorithm introduced in this study attains remarkable accuracy in distinguishing Ulva prolifera and Sargassum within Sentinel-2 MSI data, achieving identical F1 scores of 99.1% for both. Moreover, when tested with GF-1 WFV data, the algorithm showcases outstanding performance; this demonstrates the algorithm’s robustness and its ability to mitigate the uncertainty linked to threshold selection. Simultaneously, a comparative analysis of algae distribution was conducted for both 2017 and the period from January to May 2023. Experimental results indicate that the algorithm exhibits high accuracy in distinguishing between Ulva prolifera and Sargassum. This capability will significantly enhance the monitoring of large algae in maritime regions; this holds crucial theoretical significance and offers substantial practical value in the realm of marine ecological conservation. Full article

The bloom of pelagic Sargassum in the Atlantic Ocean has become increasingly problematic, especially when the algae have beached. A build-up of decaying beached material has damaging effects on coastal ecosystems and tourism industries. While remote sensing offers an effective tool to assess the spatial and temporal patterns of Sargassum over large spatial extents, its use so far has been limited to a broad discrimination of Sargassum species from other macroalgae and floating vegetation. Knowledge on the spatial distribution of decayed material will help to support management strategies and inform targeted removal. In this study, we aim to characterise the spectral response of fresh and decayed Sargassum and identify regions of the spectra that offer the greatest separability for the detection and classification of decayed material. We assessed the spectral response of fresh and decayed Sargassum (1) in situ on the beach and (2) in mesocosm experiments where Sargassum samples were allowed to decay over time. We found a decrease in the magnitude of reflectance, noticeably in the visible region (400–700 nm), for decayed, in contrast to fresh, Sargassum. Separability analyses also showed that most spectral bands with a wavelength > ~540 nm will be capable of discriminating between fresh and decayed material, although the near-infrared region offers the greatest degree of separability. We demonstrate, for the first time, that there are clear differences in the spectral reflectance of fresh and decayed Sargassum with potential application for remote sensing approaches. Full article

Ulva pertusa (U. pertusa) is a benthic macroalgae in submerged conditions, and it is relatively difficult to monitor with the remote sensing approaches for floating macroalgae. In this work, a novel remote-sensing approach is proposed for monitoring the U. pertusa green tide, which applies a deep learning method to high-resolution RGB images acquired with unmanned aerial vehicle (UAV). The results of U. pertusa extraction from semi-simultaneous UAV, Landsat-8, and Gaofen-1 (GF-1) images demonstrate the superior accuracy of the deep learning method in extracting U. pertusa from UAV images, achieving an accuracy of 96.46%, a precision of 94.84%, a recall of 92.42%, and an F1 score of 0.92, surpassing the algae index-based method. The deep learning method also performs well in extracting U. pertusa from satellite images, achieving an accuracy of 85.11%, a precision of 74.05%, a recall of 96.44%, and an F1 score of 0.83. In the cross-validation between the results of Landsat-8 and UAV, the root mean square error (RMSE) of the portion of macroalgae (POM) model for U. pertusa is 0.15, and the mean relative difference (MRD) is 25.01%. The POM model reduces the MRD in Ulva pertusa area extraction from Landsat-8 imagery from 36.08% to 6%. This approach of combining deep learning and UAV remote sensing tends to enable automated, high-precision extraction of U. pertusa, overcoming the limitations of an algae index-based approach, to calibrate the satellite image-based monitoring results and to improve the monitoring frequency by applying UAV remote sensing when the high-resolution satellite images are not available. Full article

The world’s largest macroalgae bloom (also known as green tide) has been reported since the 29th Olympic Games in 2008, which is verified as the fast reproduction of floating green macroalgae (Ulva prolifera). It is helpful to assess the biomass of macroalgae for the government of marine environment protection. In this study, the synchronization cruise experiment was firstly introduced, which aimed to investigate the biomass evaluation of Ulva prolifera in the Yellow Sea of China. The Floating Algae Index by Polarimetric SAR image (FAIPS) was then proposed. Finally, the floating algae biomass evaluation model was demonstrated and verified, which showed an exponential relationship between FAIPS and wet biomass per area (kg/m²) of macroalgae. The model proposed in this paper can be used in the biomass assessment of floating algae in the presence of polarimetric SAR images, regardless of daylight and cloud coverage over the sea surface. Full article

In 2015, the communities of reef-associated motile macrocrustaceans (decapods and stomatopods) were compared between two coral reefs with contrasting levels of degradation in Puerto Morelos (Mexican Caribbean), “Limones”, less degraded, with a healthy live coral cover, and “Bonanza”, more degraded, with less live coral and more macroalgae. Since then, several stressors have impacted Puerto Morelos. Massive influxes of floating Sargassum, which reached record levels in 2018, 2021, and 2022, have exacerbated the already high eutrophication of the reef lagoon. An outbreak and rapid propagation of the Stony Coral Tissue Loss Disease in 2018 changed the functionality of reefs. Three back-to-back hurricanes struck the coast close to Puerto Morelos in October 2020 and another one in August 2021. We repeated the study in 2022 to examine the potential changes in the habitat and communities of reef-associated crustaceans since 2015. Reef degradation did not increase significantly between 2015 and 2022, but crustacean species richness, diversity, evenness, and dominance, which differed between reefs in 2015, became similar between reefs in 2022, as did the crustacean community composition. The abundance of herbivore crabs increased in Limones, displacing the coral- and hydrocoral-mutualistic crabs and the abundance of detritivore hermit crabs increased in Bonanza. These results suggest a taxonomic homogenization between reefs, apparently related to subtle ecological changes not necessarily captured by standard metrics of reef condition. Full article

The constant production of solids in intensive shrimp and tilapia culture can affect the performance of macroalgae when cultivated in an integrated system, and little is known about culture structures that enhance the performance of macroalgae in biofloc systems. The objective of this work was to evaluate different depths of culture structure for the macroalgae Ulva lactuca in an integrated system with Litopenaeus vannamei and Oreochromis niloticus in a biofloc system. The experiment lasted 70 days, with six systems composed of: a 16 m³ shrimp tank, a 3 m³ tilapia tank, and a 3 m³ macroalgae tank, with water recirculation between tanks. Two treatments were carried out, shallow float, with a structural depth of 10 cm, and bottom float, where the depth was kept at 30 cm from the surface. The shallow float resulted in a growth rate of up to 0.95 ± 0.54% day⁻¹, with biomass loss only at the end of the culture due to the high density of macroalgae, decreasing temperature, and increasing solids concentration. The bottom float had biomass loss throughout the culture cycle. The integrated culture of shrimp, fish, and macroalgae is feasible with the use of shallow floats within 10 cm from the surface. Full article

Extensive floating macroalgae have drifted from the East China Sea to Japan’s offshore area, and field observation cannot sufficiently grasp their extensive spatial and temporal changes. High-spatial-resolution satellite data, which contain multiple spectral bands, have advanced remote sensing analysis. Several indexes for recognizing vegetation in satellite images, namely, the normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and floating algae index (FAI), are useful for detecting floating macroalgae. Thresholds are defined to separate macroalgae-containing image pixels from other pixels, and adaptive thresholding increases the reliability of image segmentation. This study proposes adaptive thresholding using Sentinel-2 satellite data with a 10 m spatial resolution. We compare the abilities of Otsu’s, exclusion, and standard deviation methods to define the floating macroalgae detection thresholds of NDVI, NDWI, and FAI images. This comparison determines the most advantageous method for the automatic detection of floating macroalgae. Finally, the spatial coverage of floating macroalgae and the reproducible combination needed for the automatic detection of floating macroalgae in Kagoshima, Japan, are examined. Full article

Golden tide macroalgae have been accumulating in the Southern Yellow Sea (SYS) for several years, causing serious damage to local coastal economy and ecosystems. However, little is known about the environmental and ecological significance of the epizoans drifting on the floating macroalgae. In this study, floating macroalgae collected from four stations were identified as Sargassum horneri. Furthermore, morphological and molecular (based on the Cytochrome C Oxidase Subunit I, COI) identification revealed that the 28 epizoans on the macroalgae consisted of four crustacean (Ampithoe lacertosa, Idotea metallica, Apohyale sp., and Peramphithoe tea). Apohyale sp. and P. tea were found at all stations, while A. lacertosa is found at only one station. The weight range of Apohyale sp., P. tea, I. metallica, and A. lacertosa is 0.0037~0.0420 g, 0.0057~0.0304 g, 0.0222~0.6592 g, and 0.0047 g, respectively. The specific roles of these epizoans in the golden tide in SYS deserve further study. Our results provide a reference for future studies of invasive macroalgae and epizoans. Full article