Mangrove forests possess the capacity to respond to sea level rise, which can be decomposed into adaptability and resilience. Adaptability is primarily measured by the suitable habitat area of mangroves following landward migration, while resilience refers to the ability of mature mangrove forests to maintain their original distribution and functions under sea level rise. However, existing research rarely distinguishes between adaptability and resilience, nor explicitly differentiates the impacts of anthropogenic pressures on these two aspects. This study developed an integrated framework using Sea Level Affecting Marshes Model (SLAMM) to predict mangrove adaptability and resilience under sea level rise scenarios and Geographical Detectors for Assessing Spatial Factors (GeoDetector) to assess their exposure to anthropogenic disturbances. The research focused on Guangdong Province, the largest mangrove area in China, and provided projections for 2070 under the RCP4.5 and RCP8.5 scenarios. The results suggest that under the combined effects of sea level rise and coastal land use, mangrove resilience would decline more markedly than adaptability. By 2070, suitable mangrove habitat is projected to decline to 49.73 %-72.01 % of the current extent, with only 31.26 %-68.67 % of the present mangrove area persisting as resilient mangroves. A significant portion of the lost mangroves would consist of highly diverse, mature mangrove communities. Furthermore, the spatial distribution of terrestrial anthropogenic pressures, primarily from aquaculture ponds and industrial centers, would exert differential impacts on mangrove responses. Aquaculture would mainly affect mangrove adaptability, while industrial development would primarily influence mangrove resilience. By 2070, 28.89 %-40.23 % of the suitable mangrove habitats would be subjected to high levels of anthropogenic pressure, compared to only 0.92 %-2.08 % of the resilient mangroves. The study's findings suggest that enhancing mangrove adaptability and resilience in response to sea level rise will require differentiated approaches and measures. The proposed framework, which can be adapted to mangrove habitat studies in other regions with appropriate local datasets, provides practical tools for the adaptive management of mangrove ecosystems under global change.

Climate change and anthropogenic activities are driving substantial shifts in mangrove community structure and accelerating species turnover. However, persistent cloud cover, frequent rainfall, and strong tidal dynamics in coastal regions hinder access to high-quality satellite imagery, thereby limiting species-level identification in mangrove ecosystems. To overcome these constraints, this study integrates niche theory with remote sensing by incorporating environmental variables that constrain species growth into species-level spatial distribution analysis. In addition, interpretable machine learning is employed to quantify the relative contributions of environmental and remote sensing data. Specifically, the Jeffries-Matusita distance was employed to quantify species separability across monthly composite datasets, and the month exhibiting optimal separability was selected as the remote sensing baseline for subsequent classification. Compared with the remote sensing baseline, the proposed framework improved overall accuracy by 16.7

The Southern Ocean plays a critical role in global climate regulation and harbors unique biodiversity increasingly threatened by climate change and expanding fisheries. This study establishes a systematic framework to identify Areas of Ecological Significance (AESs) through integrated spatial evaluation of marine environmental, biodiversity, and ecological functional indicators. Combining bibliometric analysis, expert consultation (Delphi method), and geospatial modeling, we identified 10 key evaluation criteria, highlighting benthic organism distribution, chlorophyll-a concentration, seabird presence, and krill abundance as dominant factors. Single- and multi-factor analyses revealed concentrated AES hotspots along coastal regions of the Ross Sea, Weddell Sea, Amundsen Sea, and Cosmonaut Sea. These areas exhibit exceptional productivity but face significant conservation gaps. Although aligned with existing Marine Protected Areas (MPAs) like the Ross Sea region, our results underscore urgent protection needs for climate-vulnerable sectors currently lacking formal protection. The three-tiered evaluation framework (environment-biodiversity-function) advances large-scale marine spatial planning but requires improved data integration, particularly for cetaceans, zooplankton, and carbonate chemistry, to address existing biases. This study provides a science-based foundation for international MPA designation while identifying critical knowledge gaps that must be addressed to safeguard the Southern Ocean's ecological integrity amid rapid environmental change.

Understanding public perceptions of cultural ecosystem services (CES) in urban coastal wetland ecological restoration areas was essential for coastal resource management and sustainable development. Although social media data has been increasingly utilized to develop CES indicators, significant technical challenges remained in conducting CES assessments and analyzing the primary influencing factors intelligently, accurately, and efficiently from large volumes of textual comments. To address these challenges, this study developed two artificial intelligence (AI)-based methods-using large language models with prompt engineering-to automatically identify CES categories and associated sentiments, and to analyze the key influencing factors. Using the coastal wetland ecological restoration area in Xiamen, China, as a case study, the results indicated that recreation (31.69%) and aesthetics (23.54%) were the two most commonly perceived CES categories. The average sentiment score across the nine CES categories in all restoration areas was positive (4.0-4.6). Differences in CES perceptions among the three distinct types of restoration areas-mangroves, beaches, and bays-were minimal. Public perceptions were primarily influenced by the ecological environment, historical culture, and management practices. These findings provide targeted recommendations for improving restoration planning and sustainable management in urban coastal wetlands. This study demonstrated an innovative interdisciplinary integration of computer science and marine ecology, highlighting the advantages of AI in advancing CES research and offering a new paradigm for understanding public perception.

Modern pollen assemblages from mangrove wetlands provide sensitive ecological indicators for separating climate-driven biogeographic patterns from human disturbance in threatened coastal ecosystems. This study examines pollen and spore assemblages from 32 surface-sediment samples collected at eight mangrove wetlands along a 10° latitudinal transect (18–28°N) on the southeastern coast of China. The assemblages clearly captured temperature-related mangrove zonation: Tropical southern sites, particularly Dongzhaigang and Qingmeigang, contained high proportions of mangrove pollen dominated by Rhizophora and Sonneratia, whereas the northern range-margin site at Yueqingwan was dominated by regional arboreal pollen, especially Pinus and Quercus. Redundancy analysis further identified the latitudinal thermal gradient as the main control on pollen composition. Superimposed on this climatic signal, anthropogenic disturbance was evidenced by reductions in mangrove pollen content alongside significant increases in disturbance indicator pollen taxa, including Poaceae, Chenopodiaceae and fern spores (Dicranopteris). Disturbed mid-latitude sites, including Quanzhouwan, showed high palynological richness but relatively low Shannon diversity, indicating assemblage dominance by opportunistic or disturbance-tolerant taxa. In contrast, relatively intact tropical mangroves showed lower richness but higher evenness, consistent with mature canopy structure and niche differentiation. Furthermore, this research establishes diagnostic pollen thresholds indicative of ecosystem degradation (mangrove pollen <12%; fern spores >64%) and fragmentation (presence of Corylus and Myrica) within mangrove communities. Collectively, these results demonstrate that pollen assemblages constitute highly sensitive proxies for assessing climatic and anthropogenic pressures, thereby providing robust scientific evidence to guide mangrove restoration efforts and paleo-vegetation reconstructions in vulnerable coastal ecosystems

Understanding changes in cultural ecosystem services (CES) perceptions across different COVID-19 pandemic phases is vital for human well-being. While social media big data are increasingly used for CES evaluation, few studies harness AI's intelligent advantages to process massive data and reveal changes across pre-, during, and post-COVID-19 phases. This study integrates prompt engineering and aspect-based sentiment analysis (ABSA) into large language models (LLMs). Four LLMs are compared with three baseline models trained on different datasets using Chinese RoBERTa; the best-performing LLM (DeepSeek-V3.2-Reasoner) is selected, and a topic model is applied to analyze changes in public concern, based on social media comments from Wuyuan Bay, China. Results show that in three ABSA tasks—aspect identification, sentiment score, and sentiment polarity—LLMs outperform baselines with limited training data. When data increase to 30000, three LLMs perform slightly lower than the baseline models on the aspect identification task, but still outperform them on remaining tasks. Nine CES categories were identified, with recreation, aesthetic, social relation, and health/wellness showing higher frequencies and positive sentiment (>3.0). Across phases, perception frequencies and sentiment scores of recreation and social relation continuously increased, while aesthetic and health/wellness first increased then declined. Public concern content included four topics; parent-child leisure proportion increased continuously. Overall, compared to baselines, all prompt engineering-based LLMs demonstrated data efficiency advantages. Meanwhile, they exhibited cross-model generalizability in certain tasks. This study demonstrates the relative advantages of the method in CES evaluation and can offer recommendations for further optimizing urban coastal parks.

Seagrass meadows have a high organic carbon (OC) storage capacity, and the long-lived recalcitrant OC (ROC) pool is an important component of the soil OC pool. Halophila beccarii is a widespread species that develops intertidally and is typically associated with mangroves; however, the soil OC stocks and the contribution of mangrove-derived OC to the soil OC pool are still poorly studied. Here we investigated the soil OC and ROC pools, as well as the carbon sources, in an H. beccarii meadow located in Huachang Bay, a tropical lagoon in Hainan, South China. The results showed that the meadow had a low seagrass biomass, and the soil OC contents and stocks down to 90 cm depths were low in the H. beccarii meadow, ranging from 0.51 to 6.27 mg g-1 and from 17.19 to 48.64 t C ha-1, respectively. However, the seagrass soils had high ROC:OC ratios (69.6-100%), and the soil OC content showed a significant linear increase with soil ROC content. Isotopic analyses revealed that the soil delta 13C values were closer to the delta 13C values of mangrove litter than to those of seagrass plant tissues. The results suggest that mangrove-derived OC contributes more to soil OC than seagrass-derived OC in intertidal H. beccarii meadows and that the soil OC contents change as a function of the mangrove ROC accumulation in the soils.

Marine nutrient cycling, particularly of carbon (C), nitrogen (N), phosphorus (P), and silicon (Si), is intricately linked to phytoplankton metabolism, with the Redfield ratio (106:16:1, extended to 15–20 for Si) traditionally serving as a benchmark for nutrient stoichiometry. However, tropical coastal ecosystems experience significant spatial and temporal heterogeneity due to anthropogenic activities, geographic variability, and seasonal shifts, exacerbating imbalances in carbon and nutrient dynamics.Blue-carbon ecosystems, as a "natural solution," offer the potential to mitigate eutrophication and acidification. These highly productive systems can transform CO₂ sources into carbon sinks, contributing to carbon neutrality and improving coastal ecosystem resilience. Xiaohai Lagoon, the largest lagoon in Hainan, China, represents a successful case study of blue-carbon restoration. Over three years of comprehensive restoration measures, including large-scale seagrass and seaweed planting, the lagoon achieved Class I water quality through substantial government investment.Using high-resolution field surveys and real-time water quality monitoring, this study demonstrates how blue-carbon ecosystems dynamically regulate lagoon health through in situ metabolism. During the rainy season (October–December), blue-carbon species rapidly absorbed excess nutrients from land sources, and by November, shifted nutrient dynamics from nitrogen (N) limitation to phosphorus (P) limitation. This transformation converted the lagoon from a CO₂ emission source to a CO₂ sink through photosynthesis. During this process, the combined CO₂ equivalents of three typical greenhouse gases—CO₂, CH₄ (methane), and N₂O (nitrous oxide)—turned negative, −617 g CO₂e m⁻² annually under mean conditions and up to −1,800 g CO₂e m⁻² annually under optimal conditions, underscoring the substantial role of blue-carbon systems in mitigating climate change. In addition, dissolved oxygen (DO) levels increased (107%–136%), and acidification was alleviated (pH 8.41 ± 0.14). However, the decomposition of organic matter from declining blue-carbon species disrupted stoichiometry and caused water quality to deteriorate again, underscoring the critical need for sustained ecological governance.Our findings highlight the pivotal role of blue-carbon restoration in regulating offshore nutrient stoichiometry, mitigating greenhouse gas fluxes, and enhancing coastal ecosystem health. Scaling these results to 10 Hainan lagoons reveals a mitigation potential of ~310,000–500,000 tons CO₂e annually. These insights provide a scientific foundation for advancing Hainan’s ecological civilization pilot zone and offer practical strategies for global coastal management and achieving carbon neutrality.

Introduction Understanding ecosystem degradation and estimating its extent are essential to enact decision-making policies in biodiversity conservation, ecosystem restoration, and management. Although many approaches have been proposed to identify the degradation status of an ecosystem, the heavy data burden required lacks targeted degradation information to inform such decision-making restoration policies. Methods This study proposes a multi-tiered decision-making framework to diagnose ecosystem degradation based on the most common restoration models that link degradation to restoration. The degradation diagnosis process can be executed step-by-step (i.e., in a physical to chemical to biological order). This study selected a limited number of coastal bay indicators from each layer, and a conceptual ecosystem response profile was applied to guide their degradation criteria settings: stepped for physical indicators, hump-shaped for chemical indicators, and smooth for biological indicators. Daya Bay in China was selected for this case study. Results In total, 62% of the bay has degraded by varying degrees, including completely degraded, heavily degraded, moderately degraded, and slightly degraded percentages were 4.64%, 3.78%, 15.16%, and 38.43%, respectively. Moreover, there has been a gradual but continuous spatial change in its degradation degree from north to south and from west to east. Discussion Results from this study can be used to identify and prioritize restoration processes. Human-assisted restoration efforts should prioritize its moderately degraded western section and its heavily degraded coastal areas along Yaling Bay and Fanhe Harbour. Our framework provides an efficient, science-based, and novel approach to diagnose the degradation status of the coast, particularly in the absence of long-term observational data, which can be effectively applied to any region or ecosystem.

Pacific crown-of-thorns starfish (Acanthaster solaris) outbreaks pose a significant threat to coral reef ecosystems, with climate change potentially exacerbating their distribution and impact. However, there remains only a small number of predictive studies on how climate change drives changes in the distribution patterns of A. solaris, and relevant assessments of the impact of these changes on coral reef areas are lacking. To address this issue, this study investigated potential changes in the distribution of A. solaris under climate change and its impact on Acropora coral habitats. Using a novel two-step framework, we integrated both abiotic and biological (Acropora distribution) predictors into species distribution modeling to project future shifts in A. solaris habitats. We created the first reliable set of current and future global distribution maps for A. solaris using a comprehensive dataset and machine learning approach. The results showed significant distribution shifts under three climate change scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5), with expanded ranges under all scenarios, and the greatest expansion occurring near 10° S. Asymmetry in the latitudinal shifts in habitat boundaries suggests that the Southern Hemisphere may face a more severe expansion of A. solaris. Regions previously unsuitable for A. solaris, such as parts of New Zealand, might experience new invasions. Additionally, our findings highlight the potential increase in predatory pressure on coral reefs under SSP2-4.5 and SSP5-8.5 scenarios, particularly in the Western Coral Triangle and Northeast Australian Shelf, where an overlap between A. solaris and Acropora habitats is significant. This study provides critical insights into the ecological dynamics of A. solaris in the context of climate change, and the results have important implications for coral reef management. These findings highlight the need for targeted conservation efforts and the development of mitigation strategies to protect coral reefs from the growing threat posed by A. solaris.

This study investigates the impact of environmental changes induced by systematic manipulation of flooding depth and breeding density on greenhouse gas emissions in the field-based giant rice–fish hybrid farming model. Compared with traditional agricultural practices, increasing cultured density in giant rice–fish co-cultivation significantly alleviated the adverse consequences of flooding on soil nutrient dynamics, microbial activity community structure, and greenhouse gas emissions. Relative to the traditional alternating wet and dry irrigation, the soil concentrations of ammonium, total nitrogen, and phosphate significantly increased. Cultured fish had significantly increased soil microbial biomass carbon, nitrogen, and phosphorus contents and improved soil β-glucosidase and aryl-sulfatase activates relative to flooding alone. Cultured fish increased the relative abundances of Actinobacteria, Nitrospirae, Planctomycetes, Verrucomicrobia, and Aminicenantes. An increasing cultured fish density reduced cumulative methane and nitrous oxide emissions and GWP (global warming potential). Relative to the continuous flooding throughout the growing period, cumulative methane emissions and GWP in the flooding with high-density cultured fish were reduced by 5.32% and 1.48%, respectively. Notably, this co-cultivation strategy has the potential to transform traditional practices for sustainable agriculture. Nevertheless, it is imperative to remain vigilant about the potential consequences of greenhouse gas emissions associated with these innovative practices. Continuous monitoring and refinement are essential to ensure the long-term sustainability and viability of this agricultural approach.

Harmful dinoflagellates are widely distributed in coastal waters worldwide, posing multiple ecological and socioeconomic threats. Climate change may alter the biogeography of these species; however, few studies have linked shifts in harmful dinoflagellates' ecological distribution to their socioeconomic impacts. This study developed a framework to assess the spatiotemporal ecological-social risks posed by harmful dinoflagellates, identifying these algae as risk sources and considering mariculture and coastal populations as the primary risk receptors. China is the world's largest mariculture producer, with approximately 600 million residents living in coastal areas. Focusing on 14 key harmful dinoflagellate species in Chinese coastal waters, we evaluated ecological-social risks under present conditions and two projected climate scenarios for 2100. Our findings indicate that climate change may lead to reductions in suitable habitats for harmful dinoflagellates in tropical and subtropical regions, while habitats in higher-latitude areas are likely to remain stable or expand. Risk area expansion is projected for four species and increased average risk intensity for three, with two species experiencing both. Nationally, total risk area is projected to remain stable, while cumulative risk intensity may decline by 16.64%. Regionally, risk intensity is expected to rise in northern provinces (up to 30.46%) and decline across most southern provinces. Importantly, we reveal a potential spatial "decoupling" of risk sources and receptors along the coast of China in the future. This decoupling demonstrates a reduced overlap between harmful dinoflagellate distributions and areas with dense mariculture or populations. Our findings suggest that, contrary to the common assumption that climate change universally exacerbates harmful algal impacts, these effects may vary across regions and species, highlighting the importance of localized adaptation strategies in risk assessment. This study provides a robust tool for understanding harmful dinoflagellate risks under climate change, thereby supporting the sustainable management of coastal ecosystems.

The present study δ13C, δ15N and fatty acid compositions of two dominant mangrove crabs, Tubuca arcuata and Parasesarma plicatum were compared between a mangrove site frequently receiving dredged wastewater from mariculture ponds and an adjacent reference site, to investigate the impact of wastewater discharge on their diets. A laboratory experiment was also conducted to further test how their diets changed with the wastewater input. The result showed no significant change in the δ13C while clear 15N enrichment of crabs in association with the wastewater discharge. Changes in 15N signature and fatty acid composition of the crabs due to the wastewater discharge indicated that the impact of wastewater discharge was related to crab species, being more apparent on the deposit feeder (T. arcuata) than the herbivorous P. plicatum. The results suggested that the discharge of dredged wastewater into mangroves resulted in the uptake of wastewater-derived materials and nutrients by mangrove crabs.

Twelve commercial species exploited in the eastern Guangdong and southern Fujian waters were assessed using the Catch-Maximum Sustainable Yield (CMSY) and Bayesian Schaefer Model (BSM) methods. The carrying capacity (k), intrinsic rate of population growth (r), maximum sustainable yield (MSY), and relative biomass (Bend/k and B/BMSY) were estimated. The current stock status was defined by B/BMSY and fishing mortality (F/FMSY). The results indicate that seven stocks were overfished or below safe biological limits (B/BMSY < 0.5 or F/FMSY > 1), two stocks were in a recovery phase (0.5 < B/BMSY < 1, F/FMSY < 1), and three stocks were under sustainable fishing pressure with healthy biomass, capable of producing yields close to the MSY (B/BMSY > 1, F/FMSY < 1). The stock statuses are consistent with previous studies on the utilization of pelagic fisheries in the eastern Guangdong and southern Fujian waters and with those assessments in other waters. The results of the assessments suggest that these stocks could be expected to produce higher sustainable catches if permitted to rebuild; thus, more effective and proactive management is needed in this upwelling fishing ground.

Harmful dinoflagellates and their resulting blooms pose a threat to marine life and human health. However, to date, global maps of marine life often overlook harmful microorganisms. As harmful algal blooms (HABs) increase in frequency, severity, and extent, understanding the distribution of harmful dinoflagellates and their drivers is crucial for their management. We used MaxEnt, random forest, and ensemble models to map the habitats of the representative HABs species in the genus Alexandrium, including A. catenella, A. minutum, and A. pacificum. Since species occurrence records used in previous studies were solely morphology-based, potentially leading to misidentifications, we corrected these species' distribution records using molecular criteria. The results showed that the key environmental drivers included the distance to the coastline, bathymetry, sea surface temperature (SST), and dissolved oxygen. Alexandrium catenella thrives in temperate to cold zones and is driven by low SST and high oxygen levels. Alexandrium pacificum mainly inhabits the Temperate Northern Pacific and prefers warmer SST and lower oxygen levels. Alexandrium minutum thrives universally and adapts widely to SST and oxygen. By analyzing the habitat suitability of locations with recorded HAB occurrences, we found that high habitat suitability could serve as a reference indicator for bloom risk. Therefore, we have proposed a qualitative method to spatially assess the harmful algae risk according to the habitat suitability. On the global risk map, coastal temperate seas, such as the Mediterranean, Northwest Pacific, and Southern Australia, faced higher risks. Although HABs currently have restricted geographic distributions, our study found these harmful algae possess high environmental tolerance and can thrive across diverse habitats. HAB impacts could increase if climate changes or ocean conditions became more favorable. Marine transportation may also spread the harmful algae to new unaffected ecosystems. This study has pioneered the assessment of harmful algal risk based on habitat suitability.

Ecosystem models and stable isotopes are the important methods to analyse marine food webs, which is more convenient and systematic comparing with traditional way. In this chapter, we picked six case studies to show the marine food web researches by ecosystem models and stable isotope analysis. We focus on the coral reef, seagrass, bay, lagoon and aquaculture ecosystem, including tropical and subtropical regions, where represent the higher biological productivity and biodiversity. EwE and stable isotope analysis are used to describe food sources, tropical level, food webs, moreover, ecosystem function and structure, which became most popular combinational method.

Identifying and quantifying water nitrate pollution is crucial for managing aquatic environment of a bay. Dongshan Bay, a significant semi-enclosed bay in the southeastern coastal area of Fujian Province, features mangrove and coral reef ecosystems at its estuary and bay mouth, respectively. Dongshan Bay is impacted by human activities such as mariculture. We quantified and analyzed nitrate pollution status in the surface waters of Dongshan Bay by measuring physicochemical parameters, stable isotopes (δ15N-NO3-, δ18O-NO3- and δ15N-NH4+) of the surface waters, and using statistical methods including the MixSIAR isotope mixing model. The results showed that the concentrations of chlorophyll a and dissolved inorganic nitrogen in the surface waters exhibited a noticeable gradient change, decreasing from the estuary of the Zhangjiang River to the mouth of Dongshan Bay. The maximum concentrations of chlorophyll a, NH4+, NO3- and NO2- were 45.2 μg·L-1, 52.67 μmol·L-1, 379.2 μmol·L-1 and 3.93 μmol·L-1, respectively. The nitrogen and oxygen isotope values of NH4+ and NO3- in the surface waters showed significant spatial variations. According to the MixSIAR model results, nitrogen sources in the surface waters of Dongshan Bay were mainly freshwater inputs of the Zhangjiang River estuary, aquaculture wastewater, and groundwater. The freshwater input from the Zhangjiang River estuary contributed the most (25.2%), while aquaculture wastewater, groundwater and urban sewage accounted for 24.6%, 19.0%, and 15.1%, respectively. It is evident that freshwater input from the Zhangjiang River estuary is the primary source of nitrate in the surface waters of Dongshan Bay.