Search Results (255)

Marine litter accumulation in aquatic ecosystems presents a dire problem for marine life and the human ecosystem. The presence of algae on the ocean surface can alter its response to wind-driven roughening. This alteration is the basis for a new method to detect and image algae and other roughness suppression inducing pollutants from space. The NASA Surface Water and Ocean Topography (SWOT) radar can measure coastal ocean roughness. An empirical model is developed which relates the Mean Square Slope (MSS), a measure of ocean surface roughness, to wind speed given clear water conditions. Deviations of the roughness observed by SWOT from the clear water model results in roughness anomalies that are found to be empirically associated with the concentration of chlorophyll-a observed by the NASA VIIRS instrument. The relationship between roughness anomaly and chlorophyll concentration is incorporated into a new retrieval algorithm for algae by SWOT. This retrieval algorithm has a valid range of 0–6 mg/m³. Full article

Marine chlorophyll-a (Chl-a) concentration is a key proxy for phytoplankton biomass and an important indicator for eutrophication assessment, ecological monitoring, and harmful algal bloom early warning. Accurate forecasting of Chl-a concentration therefore has substantial scientific and practical value for marine environmental management. Nevertheless, existing deep models still have difficulty coupling relatively stable large-scale spatial structure with locally time-varying interactions, and many of them do not provide reliable uncertainty estimates, which limits their use in harmful algal bloom early warning and high-chlorophyll event assessment. To address these issues, this study proposes an uncertainty-quantified dynamic graph ordinary differential equation network, termed UQDGODE, for marine Chl-a concentration forecasting. The model combines a multivariate diffusion graph convolutional branch for stable spatial diffusion modeling with a dynamic graph ordinary differential equation branch for continuously evolving local interactions. A gating mechanism fuses the two branches, and a multivariate probabilistic prediction module outputs predictive means and covariance information for uncertainty quantification. Experiments on the Bohai Sea and South China Sea datasets show that the UQDGODE performs better in both point forecasting accuracy and probabilistic forecasting quality and produces more informative predictive distributions and more adaptive prediction intervals for marine ecological alerting. Full article

Reef-building corals and coralline algae form the calcium carbonate frameworks that underpin tropical coral reefs, yet in some locations, coral cover has declined by ~50% in recent decades due to marine heatwaves and other stressors. Identifying refugia environments, such as upwelling systems, that may buffer stress, promote recovery, and could enhance resilience by promoting physiological plasticity that supports thermotolerance is therefore critical. Here, we compared benthic community composition, coral percent cover, and photophysiology between an upwelling location in the Gulf of Papagayo and a non-upwelling location in Sámara on the Pacific coast of Costa Rica. Waters in Papagayo were cooler, more acidic, and had higher chlorophyll-a concentrations. Reefs at this location exhibited higher crustose coralline algae, higher sea urchin abundance, and lower macroalgae cover, compared to Sámara. Papagayo also showed higher stony coral cover, driven by Pocillopora spp., while Sámara was dominated by massive, heat-tolerant Porites spp. Photophysiological parameters were significantly different between locations. Specifically, photosynthetic efficiency (F_v′/F_m′) was 10–45% higher, and maximum photosynthetic rate (P_max) was 20–40% lower in corals from Papagayo than in those from Sámara. These results reveal that two locations differing in environmental regime within a relatively small geographic area also differ in coral community composition and photophysiological features. Although further research is needed to resolve whether these environmental contrasts shape the observed biological differences, the observed patterns are consistent with the hypothesis that such regimes may support reef persistence or refugia, providing a basis for future work to test this hypothesis directly. Full article

Coastal water quality in the Bothnian Sea is shaped by interactions among local nutrient inputs, internal nutrient cycling, and basin-scale phosphorus enrichment, complicating the assessment and management of eutrophication. This study analyses long-term time series of nutrients (total phosphorus (TP), dissolved inorganic phosphorus (DIP), dissolved inorganic nitrogen (DIN), and total nitrogen (TN)) and phytoplankton indicators (chlorophyll a and biomass) from contrasting Finnish coastal systems off Uusikaupunki and Rauma. Despite higher external phosphorus loading in Rauma, nutrient concentrations and phytoplankton biomass remain lower than in the semi-enclosed Uusikaupunki coastal zone. In contrast, Uusikaupunki exhibits higher chlorophyll a concentrations and lower TP:Chl a ratios, suggesting greater phosphorus bioavailability. At the offshore station SR5, TP and DIP increase below the surface layer, while surface concentrations show no significant trends, indicating phosphorus accumulation in deeper waters. Declining DIN:DIP ratios indicate a shift toward nitrogen limitation, under which primary production increasingly depends on phosphorus-supported nitrogen fixation. Chlorophyll a increases across the coastal gradient, including the outer archipelago, indicating a spatial expansion of eutrophication. Together, these findings are consistent with a system-level shift toward phosphorus-driven production. The results demonstrate a dual-control system in which basin-scale phosphorus enrichment determines long-term background conditions, while local nutrient loading and legacy effects regulate spatial variability in ecosystem response. More broadly, the findings highlight the importance of cross-scale interactions between regional nutrient enrichment and local ecosystem processes for understanding and managing eutrophication in inland and semi-enclosed marine systems. Full article

Introduction: Elasmobranchs play a vital role in marine food webs through top-down control and the structuring of ecosystem stability, yet more than one-third of species face extinction. The Maldives, a recognised Indian Ocean hotspot for shark and batoid diversity, designated its EEZ as a shark sanctuary in 2010, but multispecies elasmobranch occurrence patterns and environmental drivers remain poorly characterised in Lhaviyani Atoll in the central Maldives, which hosts two Important Shark and Ray Areas (ISRAs). Recreational SCUBA networks can turn routine dive activity into long-term conservation evidence, already informing nearly 10% of the western Indian Ocean ISRAs. Objective: To characterise spatiotemporal patterns of elasmobranch assemblages in Lhaviyani Atoll (2017–2024), quantify how environmental and geomorphic drivers shape relative abundance, diversity, and hotspots, and provide evidence for targeted elasmobranch conservation. Methodology: A seven-year opportunistic dive-log dataset of 12,732 SCUBA surveys and 142,994 elasmobranch records across 94 dive sites was analysed. Effort-standardised relative abundance and community metrics (Shannon diversity, Pielou’s evenness) were modelled against sea surface temperature (SST), salinity, dissolved oxygen, chlorophyll-a, zonal current velocity, substrate type, and reef geomorphology using generalised additive models (GAMs). Spatial analyses identified persistent northern-rim aggregation areas aligned with ISRAs. Results: Twenty-eight species (14 sharks, 14 batoids) were recorded, including 23 threatened on the IUCN Red List (4 Critically Endangered, 12 Endangered, 7 Vulnerable). Relative abundance and diversity peaked during the late southwest monsoon (August–September) and declined during the northeast monsoon (December–March). After 2021, diversity and evenness increased while overall abundance declined. Relative abundance was primarily driven by SST, salinity, and current velocity; for sharks, dissolved oxygen and chlorophyll-a were additionally significant, whereas batoid abundance was driven mainly by temperature, oxygen, and current velocity. Four persistent hotspots along the northern atoll rim were identified, with sharks concentrated along exposed slopes and channels, and batoids distributed broadly within lagoonal habitats. Conclusions: Long-term citizen science dive-log monitoring is cost-effective for elasmobranch conservation in remote tropical seascapes. These results show how dive-industry partnerships can inform conservation governance over a decade after sanctuary designation, supporting targeted, habitat-focused management as shark and batoid conservation frameworks continue to evolve. Full article

The chlorophyll-a (chl-a) concentration is a major indicator of marine ecosystem status, harmful algal blooms, and marine primary productivity. In coastal waters, however, complex hydrodynamic and ecological conditions lead to highly variable chl-a dynamics, driven by diverse and interacting mechanisms, posing substantial challenges for chl-a forecasts. To assess the applicability of machine learning approaches in predicting chl-a under complex coastal environments, we present a case study in the Taiwan Strait, where harmful algal blooms occur a few times every year. Based on satellite remote sensing data, a spatiotemporal imputation and prediction framework (STIMP), temporal models (Transformer, CrossFormer, Tsmixer), and spatiotemporal models (MTGNN and PredRNN) were applied to simulate chl-a spatiotemporal variability. A hydrodynamic–biogeochemical model was compared with these machine learning approaches to assess the model skills in coastal chl-a simulations. Results indicate that machine learning models trained with satellite data exhibit reasonable predictive skill offshore with pronounced seasonal variability and low data missing ratio, while their performance weakens in regions where seasonal signals are masked by short-term chl-a fluctuations with more missing data. In contrast, the hydrodynamic–biogeochemical model represents short-term variations in chl-a in nearshore regions with higher temporal resolution and accounts for the underlying mechanisms of phytoplankton biomass accumulation and die-off. When trained with model output, the machine learning approach shows improved performance in coastal chl-a forecasts, with much higher computational efficiency compared to the hydrodynamic–biogeochemical model. This study highlights the advantage of mechanistic and machine learning models in deciphering the spatiotemporal scales and governing mechanisms of chl-a variability in coastal regions and extracting spatiotemporal variability with computational efficiency, respectively. With input data of sufficient temporal resolution (e.g., daily to 3 days) and duration (5–10 years), a combination of the machine learning and mechanistic modeling approaches is recommended for operational coastal phytoplankton bloom forecasting. Full article

Phytoplankton primary production (PP) underpins marine ecosystems. In winter marginal seas, the magnitude and size structure of PP not only sustain overwintering zooplankton but also shape larval fish survival and fishery resources in the following year. We conducted two cruises in the fish overwintering grounds of the East China Sea shelf to investigate the spatial distribution, size structure, and environmental controls of net primary production (NPP). Winter NPP was generally low relative to the annual range. Nutrient concentrations at most stations exceeded potential limitation thresholds, whereas the mixed-layer mean light exposure (LE) fell below the light-saturation threshold at most stations, indicating that insufficient light availability was primarily associated with sub-saturating light conditions of low winter productivity. Among size classes, the nano-sized fraction dominated NPP, followed by the pico-sized fraction, while the micro-sized fraction contributed least; however, the relative contribution of the micro-sized fraction increased in February. Measured values of two key parameters widely used in satellite-based NPP models—PBopt (optimal chlorophyll-specific carbon fixation rate) and F (a dimensionless light-related factor for the vertical distribution of primary production)—were both lower than model predictions, and the magnitude of deviation varied with water depth and mixing conditions. These findings refine our understanding of biogeochemical processes in overwintering grounds of winter marginal seas. Full article

Across much of the Arctic, climate warming has reduced the extent of thicker and more persistent sea ice and increased the prevalence of thinner first-year ice. Thin first-year landfast sea ice is ecologically important because reduced ice thickness can increase light transmission to the ice–water interface, while the associated brine conditions, including salinity and permeability, can strongly influence algal biomass accumulation and photophysiology. This thin (0.24–0.55 m), short-lived, seasonal, first-year landfast sea ice already dominates Nuup Kangerlua fjord, southwest Greenland, making it a useful natural example of ice conditions that may become more common in parts of the future Arctic. We focused on late February–early March because this period captures the seasonal transition from very low winter irradiance toward increasing spring light, when sea ice algal communities begin photosynthetic acclimation prior to the main bloom period. Using this site as an example of future Arctic-like conditions, we investigated chlorophyll a (Chl a) concentration and the photobiology of sea ice algal communities during five sampling events between 2017 and 2022. The vertical distribution of Chl a concentration and photobiological parameters measured with variable chlorophyll fluorescence differed between years, as did Chl a concentrations, with integrated biomass ranging from 0.08 to 0.78 mg Chl a m⁻². Direct under-ice PAR measurements showed transmittance values ranging from 0.013 to 0.29. Bottom-ice communities were acclimated to relatively high light intensities, with E_k often exceeding 200 µmol photons m⁻² s⁻¹, and we detected no clear evidence of photoinhibition in the fluorescence data. Boosted regression tree models identified brine salinity as the main predictor of both Chl a concentration, explaining 42.0% of the variation, and, Φ_{PSII_max}, the maximum dark-adapted photosynthetic efficiency, explaining 86.1% of the variation. Both parameters decreased exponentially with increasing sea ice brine salinity (p < 0.0001), indicating that higher brine salinity was associated with reduced algal biomass and lower photosynthetic efficiency. These results show that short-lived first-year landfast sea ice can support physiologically active sea ice algal communities despite relatively low biomass, and suggest that algal performance in this ice type was more strongly associated with brine salinity during the late-winter to early spring sampling period, while light availability also varied substantially among years. As thin and short-lived sea ice conditions become more common in parts of the Arctic, this habitat may represent an increasingly important, though temporally variable, component of Arctic marine primary production. Full article

Dissolved oxygen (DO) is a critical indicator for assessing marine ecological health and hypoxia risk. Most existing DO prediction studies rely on time-series forecasting models, which require continuous temporal observations and are often unreliable in practical marine monitoring scenarios due to sparse sampling, missing records, and heterogeneous measurement conditions. To address this limitation, this paper investigates the problem of non-temporal DO prediction, aiming to learn a direct nonlinear mapping between environmental drivers and DO concentration. To explicitly model nonlinear pairwise interaction effects between environmental variables, we propose a Factor-Interaction Neural Network (FINN), which decomposes DO estimation into main effects and structured pairwise interaction effects. This interaction-driven design enhances both representation capacity and interpretability compared with conventional multilayer perceptrons. Furthermore, we develop a physics-informed extension, termed PI-FINN, by incorporating oceanographic-consistent regularization priors that reflect key DO formation mechanisms, including temperature-related solubility behavior, depth-wise smoothness associated with stratification, and chlorophyll-driven biological oxygen production tendencies. To evaluate the physical plausibility of model predictions beyond standard accuracy metrics, we introduce a physics-consistency assessment protocol based on Physics Consistency Violation Rate (PCVR) and its robust variant, and further analyze their convergence stability under different driver-weight configurations. Extensive experiments on a real-world marine dataset demonstrate that FINN achieves competitive predictive accuracy compared with strong machine learning baselines (e.g., SVR, Random Forest, and XGBoost), while the proposed physics-informed design mainly improves the physical consistency, robustness, and interpretability of DO estimation under heterogeneous environmental regimes, although it does not necessarily guarantee superior RMSE or MAE performance compared with purely data-driven models. Specifically, FINN achieves an RMSE of 0.3130, an $R^2$ of 0.9831, and a PCVR of 0.4826 on a dataset composed of key environmental variables, including depth, temperature, salinity, and chlorophyll-a, collected under sparse and irregular sampling conditions. Ablation studies confirm the effectiveness of both factor-interaction modeling and physics-guided regularization components. Overall, the proposed framework further provides a reliable tool for sustainable environmental monitoring by enabling physically consistent dissolved oxygen prediction under sparse observational conditions. Such capability is critical for supporting sustainable water resource management, hypoxia risk assessment, and long-term ecological protection. Full article

The Arabian Sea is a key region for global marine biogeochemical research, yet the distribution characteristics and influencing factors of dissolved oxygen and chlorophyll a concentration in its central oxygen minimum zone still require further in-depth investigation. Based on survey data and reanalysis data from 2024, this paper analyzes the distribution characteristics and underlying causes of chlorophyll a concentration and dissolved oxygen using empirical orthogonal function (EOF) decomposition of chlorophyll a concentration and dissolved oxygen saturation along the depth direction, combined with the distribution of the barrier layer, Ekman pumping induced by wind fields, and the diagnostic vertical velocity distribution calculated from ADCP-observed flow velocities. Taking approximately 10° N as the boundary, the chlorophyll a concentration in the layer shallower than 35 m exhibits a distribution pattern of high in the northwest and low in the southeast, while the water layer between 45 m and 95 m shows a pattern of low in the northwest and high in the southeast. A thick barrier layer exists in the southeastern region, whereas the barrier layer in the northwestern region is thinner or absent, resulting in lower surface chlorophyll a concentration in the southeast. ADCP observations indicate that horizontal flow velocities are higher in the south, bringing oxygen-rich water from the south, which leads to higher dissolved oxygen saturation in the southern region compared to the northern region in water shallower than 45 m. At the 65 m water layer, the higher chlorophyll a concentration in the south may result in relatively low dissolved oxygen. The hypoxic zone (dissolved oxygen saturation less than 30%) begins to appear at depths below 105 m, with its southern boundary located between 9° N and 11° N, and this boundary gradually shifts northward as depth increases. The diagnostic vertical velocity between 9° N and 11° N is higher than that in other regions, which may hinder the northward movement of oxygen-rich water from the south. In the southern region, influenced by wind stress, the vertical water movement induced by Ekman pumping is relatively significant, which may lead to a slight increase in dissolved oxygen saturation in water layers with a depth below 125 m. Full article

In recent years, climate change has increasingly shaped the potential habitat distribution of marine fishes, rendering this topic a focal area in marine ecology and biogeographical research. Using the MaxEnt modeling approach, this study projects the current and future potential habitats of eggs and larvae of two ecologically and economically important coastal species, the Liza haematocheilus and the Harpadon nehereus, across the nearshore waters of Zhejiang Province. Distribution records of early life stages and key environmental variables were integrated to model suitability under present-day conditions and three Shared Socioeconomic Pathway–Representative Concentration Pathway climate scenarios: SSP2-4.5, SSP3-7.0, and SSP5-8.5. Results identify sea surface salinity, surface current velocity, and sea surface temperature as the primary drivers of habitat suitability for Liza haematocheilus early-life stages; in contrast, chlorophyll-a concentration emerges as an additional significant predictor for Harpadon nehereus, alongside the aforementioned three variables. Under contemporary climatic conditions, high-suitability habitats for Liza haematocheilus eggs and larvae are predominantly concentrated in estuarine and island-adjacent nearshore zones, constituting 40.76% of the total predicted suitable area. For Harpadon nehereus, high-suitability areas are broadly distributed across nearshore shelf waters, representing 65.57% of its total modeled suitable habitat. Projected under future scenarios, the largest absolute increase in high-suitability area for Liza haematocheilus occurs under SSP3-7.0 in the 2050s (+0.38 × 10⁴ km²), whereas Harpadon nehereus exhibits its greatest expansion under SSP5-8.5 in the 2090s (+0.45 × 10⁴ km²). Collectively, the total suitable habitat area for Liza haematocheilus is projected to expand across all three scenarios, while that of Harpadon nehereus remains relatively stable overall—yet its high-suitability fraction increases markedly under high-emission, high-warming conditions. These findings suggest that ongoing climate warming may facilitate range expansion and enhanced nursery habitat availability for both species in Zhejiang’s coastal zone, positioning them as potential ecological beneficiaries of regional climate change. Full article

The sustainable valorization of marine biowaste, particularly shrimp residues, has emerged as a promising strategy to develop eco-friendly agricultural inputs that enhance crop productivity and reduce environmental impacts. This study investigated the effects of a biotechnologically processed fermented shrimp-waste (Parapenaeus longirostris) formulation as a biostimulant on the growth, physiological performance, and development of a local mays variety (Zea mays L., DKC 744) under controlled pot conditions. The experiment evaluated root, foliar, and combined applications of the biostimulant at three concentrations (5%, 10%, and 15%) over a 90-day vegetative cycle. Morphological parameters, including stem height, leaf number, leaf mass, and root biomass, were measured at regular intervals, while chlorophyll a and b contents were assessed to evaluate photosynthetic efficiency. The results indicated that all biostimulant treatments significantly enhanced mays growth. Root-applied biostimulants primarily stimulated root biomass by up to 764.0 ± 66.8 g at the 10% concentration, whereas foliar applications improved above-ground traits, including stem elongation and leaf formation, reaching maximum heights of 200.0 ± 1.9 cm and 17.0 ± 0.4 leaves under intermediate concentrations. Combined root and foliar applications produced the highest stem height (240.0 ± 5.6 cm), leaf number (19.0 ± 0.0), leaf mass (1034.0 ± 11.1 g), and chlorophyll content (2.44 ± 0.9 for chlorophyll a) at 10–15% concentrations. The results also revealed that moderate concentrations generally provided the most balanced stimulation, suggesting the presence of an optimal dose threshold. This study demonstrated the comparative effectiveness of root, foliar, and combined applications of a fermented shrimp-waste biostimulant and identified an optimal concentration. However, its limitations lie in the use of controlled pot conditions and a single crop variety, which restrict the extrapolation of results to field-scale applications and diverse agroecological environments. Therefore, more research is needed to explore the action mechanisms of the studied biostimulant and elicitors, mainly the interaction between biocompounds and the treated plant. Full article

Under the combined impacts of climate change and intensified human activities, harmful algal blooms (HABs) have occurred with increasing frequency in China’s coastal waters, posing growing risks to marine ecosystems and regional sustainability. Chlorophyll-a concentration (Chl-a), a key indicator of phytoplankton biomass, plays a crucial role in HAB monitoring and early warning. This study integrates satellite remote sensing data from 2000 to 2004, 2011 to 2013, and 2023 to 2024 with in situ measurements and environmental variables (e.g., dissolved oxygen) to investigate Chl-a dynamics in the East China Sea. The results indicate pronounced spatiotemporal heterogeneity across the region. Spectral features were represented using band-ratio methods and the BRG model, followed by variable selection based on the Bayesian Information Criterion (BIC) to determine the optimal band combinations for model training. Six mainstream machine learning models were evaluated, and the Backpropagation Neural Network (BP) was selected as the baseline model due to its superior performance. To further improve model robustness and global optimization capability, the Artificial Lemming Algorithm (ALA) was employed to optimize the BP network, resulting in the ALA-BP inversion model. The optimized model achieved correlation coefficients of 0.933 on the test set and 0.940 on the independent validation set, outperforming the other models. The proposed model was further applied to the 2024 algal bloom event in the East China Sea, successfully capturing the spatiotemporal variations of Chl-a. This study provides an effective retrieval framework for Chl-a in optically complex coastal waters and demonstrates its applicability in HAB monitoring. Full article

In the context of global warming, the South China Sea is showing an accelerating warming trend, which highlights the impact of climate change on the marine environment of the South China Sea. The interdecadal variation of chlorophyll a concentration (Chl_a) directly reflects the long-term evolution pattern of the upper marine ecological environment in the South China Sea and has significant indicative significance for marine ecological protection. This study investigated the relationships between Chl_a concentration, sea surface temperature (SST), shortwave radiation (SSRD), mixed layer depth (MLD), wind speed (Wind), and geostrophic current (ugo) in the South China Sea over the past 27 years. Statistical methods were used to analyze the differentiated impacts of marine environmental factors under different climate backgrounds on Chl_a concentration. From 1998 to 2007 (P1), there was a decreasing trend in the early stage, from 2007 to 2015 (P2), there was an upward trend in the middle period, and from 2015 to 2024 (P3), there was an upward trend in the recent period. During these three stages, SST and MLD were the core influencing factors. The threshold of SST gradually increased over time, reaching 27.05 °C, 27.24 °C, and 27.32 °C, respectively. The average normalized information flow (NIF) of Chl_a concentration changed from positive to negative. In the early stage of P1, the SST in most areas of the South China Sea was less than 27.05 °C, and in the P2 and P3 periods, the SST in most areas reached the threshold. The thresholds of MLD were 22.33 m, 21.64 m, and 33.68 m, respectively. The average NIF of Chl_a concentration was positive in all periods. The MLD in most areas of the South China Sea did not reach the threshold in all three periods. These findings emphasize the different roles of marine environmental factors in regulating Chl_a concentration in the South China Sea, providing a scientific basis for the ecological health monitoring of marine fishery areas, disaster warning, and adaptive management in response to climate warming. Full article

Marine organisms that episodically aggregate near coastal nuclear power plant water intakes pose a substantial risk to cooling water security. Predicting the spatial distribution of such high-risk species remains challenging because their occurrence is shaped not only by environmental conditions but also by complex trophic interactions. In this study, we model the habitat distribution of three high-risk nektonic species, Dotted gizzard shad (Konosirus punctatus), Japanese swimming crab (Charybdis japonica) and squid (Loligo sp.), in the cooling water intake area of a coastal nuclear power plant in eastern Liaodong Bay using generalized linear models (GLMs) and joint species distribution models (JSDMs). Based on summer surveys conducted in 2024–2025, we explicitly incorporated trophic linkages among target species, their prey, and predators within JSDMs. Model performance was evaluated using cross-validation based on AUC, RMSE, and coefficient of determination (R²). Our results indicate that water depth was the dominant environmental driver for all three species, while chlorophyll-a concentration and distance to the intake exerted species-specific effects. By incorporating interspecific trophic associations and environmental responses, JSDMs showed consistently improved predictive performance relative to GLMs, with approximately 1.5-fold higher R² values and 10–30% lower RMSE, while offering enhanced ecological interpretability. The models revealed strong positive associations between target species and both lower-trophic prey and higher-trophic predators, suggesting that top–down and bottom–up processes jointly regulate aggregation dynamics. This study demonstrates that integrating trophic interactions into species distribution modeling substantially improves predictions of high-risk marine species near coastal infrastructure and provides an ecological basis for proactive management of cooling water intake systems. Full article