Nitrogen-enriched discharges from a highly managed watershed intensify red tide (Karenia brevis) blooms in southwest Florida

Enhancing short-term algal bloom forecasting through an anti-mimicking hybrid deep learning method

Accurately predicting algal blooms remains a critical challenge due to their dynamic and non-stationary nature, compounded by high-frequency fluctuations and noise in monitoring data. Additionally, a common issue in time-series forecasting is data replication, where models tend to replicate historical patterns rather than capturing true future variations, leading to inaccurate forecasts during abrupt changes. To address these challenges, we developed a hybrid deep learning model (TAB) that integrates a Temporal Convolutional Network (TCN), an attention mechanism, and Bidirectional Long Short-Term Memory (BiLSTM) network. Furthermore, we employed a novel distortion loss function-DIstortion Loss including shApe and TimE (DILATE)-which incorporates both shape and temporal losses to enhance the model's predictive robustness. Using in situ algal bloom data from Jiangdong Reservoir, Jiulong River, China, the TAB model accurately forecasted hourly chlorophyll-a dynamics for the subsequent 24 h, achieving an R2 of 0.74, a mean absolute percentage error of 21.22%, a root mean square error of 4.12 μg L-1, and a Kling-Gupta efficiency of 0.78. The DILATE loss function effectively mitigated data replication and improved low-peak chlorophyll-a prediction accuracy by 10.57%, compared to the traditional mean squared error (MSE) loss, particularly in the concentration range of 0-20 μg L-1, which represents the most frequently observed levels in the dataset. SHapley Additive exPlanations (SHAP) analysis identified key environmental drivers, improving model interpretability and computational efficiency. This study demonstrates the potential of advanced deep learning models in algal bloom prediction and provides insights for further optimization in forecasting performance and broader applications.

Physiological and transcriptomic analyses to determine the responses of the harmful algae Akashiwo sanguinea to phosphorus utilization

Phosphorus (P) is an essential nutrient driving algal growth in aquatic ecosystems. Dissolved inorganic and organic P (DIP and DOP) are the main components in the marine P pools and are closely related to harmful algal blooms. The dinoflagellate Akashiwo sanguinea is a cosmopolitan species which frequently causes dense blooms in estuaries and coasts worldwide, while the availability of P to A. sanguinea still remain unclear. Herein, the physiological and transcriptomic responses of A. sanguinea grown under P-deficient, DIP-replete and DOP-replete conditions were compared. P-deficient adversely suppressed the growth and photosynthesis of A. sanguinea, while genes associated with P transport, DOP utilization, sulfolipid synthesis, and energy production, were markedly elevated. Three forms of DOP, namely, glucose-6-phosphate (G-6-P), adenosine 5-triphosphate (ATP), and β-Glycerol phosphate (SG-P), supported A. sanguinea growth as efficiently as DIP (NaH2PO4), and no significant difference was observed in biochemical compositions and photosynthesis of A. sanguinea between the DIP and DOP treatments. While the genes related to P transporter were markedly suppressed in DOP groups compared with the DIP group. Our results indicated that A. sanguinea is a good growth strategist under P-deficient/replete conditions, and this species had evolved a comprehensive strategy to cope with P deficiency, which might be a crucial factor driving bloom formation in a low inorganic P environment.

Short-term exposure to okadaic acid induces behavioral and physiological responses in sea urchin (Strongylocentrotus intermedius)

Massive harmful algal blooms (HABs) have increased the risk of marine organisms encountering the dinoflagellate toxin, okadaic acid (OA). Strongylocentrotus intermedius, a globally significant benthic aquaculture species, has a large appetite for benthic algae. During red tide events, there is a high risk of red tide toxin accumulation. This study systematically evaluated the potential impact of short-term OA exposure on the behavior and physiological functions of juvenile S. intermedius. From typical (5 μg/L) to extreme OA concentrations (20 μg/L) during HAB outbreaks, OA exposure gradually inhibited a series of tube foot-related behaviors (sheltering, foraging, righting, and tube-foot tenacity). At OA concentrations during HAB outbreaks (5 μg/L), the tube foot function of S. intermedius was progressively inhibited. Further physiological indicator analyses revealed that the activity of antioxidants increased over a short period to prevent damage from reactive oxygen species induced by OA. However, OA ultimately suppressed the immune response of S. intermedius, leading to apoptosis. Although HAB-associated concentrations of OA (5 μg/L) did not induce a continuous increase in the integrated biological response index of S. intermedius, this study speculated that HABs pose a future risk to echinoderm species. Notably, principal component analysis results showed that OA exposure eventually induced significant changes in the production of O2-, malondialdehyde, and total glutathione, as well as in glutathione S-transferase activity and caspase-7, -8, and -9 levels. This study provides preliminary evidence of OA's toxic effects on sea urchins and essential data for urgent risk assessments of algal toxin pollution in aquaculture during HABs.

An early-warning forecast model for red tide (Karenia brevis) blooms on the southwest coast of Florida

Karenia brevis blooms occur nearly annually along the southwest coast of Florida, and effective mitigation of ecological, public health, and economic impacts requires reliable real-time forecasting. We present two boosted random forest models that predict the weekly maximum K. brevis abundance category across the Greater Charlotte Harbor estuaries over one-week and four-week forecast horizons. The feature set was restricted to data available in near-real time, consistent with adoption of the models as decision-support tools. Features include current and lagged K. brevis abundance statistics, Loop Current position, sea surface temperature, sea level, and riverine discharges and nitrogen concentrations. During cross-validation, the one-week and four-week forecasts exhibited 73 % and 84 % accuracy, respectively, during the 2010-2023 study period. In addition, we assessed the models' reliability in forecasting the onset of 10 bloom events on time or in advance; the one-week and four-week models anticipated the onset eight times and five times, respectively.

Fusing remote sensing data with spatiotemporal in situ samples for red tide (Karenia brevis) detection

We present a novel method for detecting red tide (Karenia brevis) blooms off the west coast of Florida, driven by a neural network classifier that combines remote sensing data with spatiotemporally distributed in situ sample data. The network detects blooms over a 1-km grid, using seven ocean color features from the MODIS-Aqua satellite platform (2002-2021) and in situ sample data collected by the Florida Fish and Wildlife Conservation Commission and its partners. Model performance was demonstrably enhanced by two key innovations: depth normalization of satellite features and encoding of an in situ feature. The satellite features were normalized to adjust for depth-dependent bottom reflection effects in shallow coastal waters. The in situ data were used to engineer a feature that contextualizes recent nearby ground truth of K. brevis concentrations through a K-nearest neighbor spatiotemporal proximity weighting scheme. A rigorous experimental comparison revealed that our model outperforms existing remote detection methods presented in the literature and applied in practice. This classifier has strong potential to be operationalized to support more efficient monitoring and mitigation of future blooms, more accurate communication about their spatial extent and distribution, and a deeper scientific understanding of bloom dynamics, transport, drivers, and impacts in the region. This approach also has the potential to be adapted for the detection of other algal blooms in coastal waters. Integr Environ Assess Manag 2024;20:1432-1446. © 2024 SETAC.

Integrating temporal decomposition and data-driven approaches for predicting coastal harmful algal blooms

Frequent coastal harmful algal blooms (HABs) threaten the ecological environment and human health. Biscayne Bay in southeastern Florida also faces algal bloom issues; however, the mechanisms driving these blooms are not fully understood, emphasizing the importance of HAB prediction for effective environmental management. The overarching goal of this study is to offer a robust HAB predictive framework and try to enhance the understanding of HAB dynamics. This study established three scenarios to predict chlorophyll-a concentrations, a recognized representative of HABs: Scenario 1 (S1) using single nonlinear machine learning (ML) algorithms, hybrid Scenario 2 (S2) combining linear models and nonlinear ML algorithms, and hybrid Scenario 3 (S3) combining temporal decomposition and ML (TD-ML) algorithms. The novel-developed S3 TD-ML hybrid models demonstrated superior predictive capabilities, achieving all R2 values above 0.9 and MAPE under 30% in tests, significantly outperforming the S1 with an average R2 of 0.16 and the S2 with an R2 of -0.06. S3 models effectively captured the algal dynamics, successfully predicting complex time series with extremes and noise. In addition, we unveiled the relationship between environmental variables and chlorophyll-a through correlation analysis and found that climate change might intensify the HABs in Biscayne Bay. This research developed a precise predictive framework for early warning and proactive management of HABs, offering potential global applicability and improved prediction accuracy to address HAB challenges.

Predicting coastal harmful algal blooms using integrated data-driven analysis of environmental factors

Coastal harmful algal blooms (HABs) have become one of the challenging environmental problems in the world's thriving coastal cities due to the interference of multiple stressors from human activities and climate change. Past HAB predictions primarily relied on single-source data, overlooked upstream land use, and typically used a single prediction algorithm. To address these limitations, this study aims to develop predictive models to establish the relationship between the HAB indicator - chlorophyll-a (Chl-a) and various environmental stressors, under appropriate lagging predictive scenarios. To achieve this, we first applied the partial autocorrelation function (PACF) to Chl-a to precisely identify two prediction scenarios. We then combined multi-source data and several machine learning algorithms to predict harmful algae, using SHapley Additive exPlanations (SHAP) to extract key features influencing output from the prediction models. Our findings reveal an apparent 1-month autoregressive characteristic in Chl-a, leading us to create two scenarios: 1-month lead prediction and current-month prediction. The Extra Tree Regressor (ETR), with an R2 of 0.92, excelled in 1-month lead predictions, while the Random Forest Regressor (RFR) was most effective for current-month predictions with an R2 of 0.69. Additionally, we identified current month Chl-a, developed land use, total phosphorus, and nitrogen oxides (NOx) as critical features for accurate predictions. Our predictive framework, which can be applied to coastal regions worldwide, provides decision-makers with crucial tools for effectively predicting and mitigating HAB threats in major coastal cities.

Non-linear impacts of harmful algae blooms on the coastal tourism economy

Harmful algae blooms (HABs) occur in water bodies throughout the globe and can have multi-faceted impacts on tourism. However, little is known of the magnitude of economic losses to the tourism sector as a result of HABs. There is limited understanding of the empirical relationships between HAB intensity and duration, and the effects of this phenomenon on the tourism sector. This study is based in the state of Florida, USA, a notable sun, sand, and sea destination in the western hemisphere, where blooms of a marine harmful algae are a recurrent threat to coastal tourism. The empirical framework is based on a month and county-level panel database that combines sales by tourism-related businesses with observations from the official HAB surveillance system of the state of Florida. We use time and space fixed-effects regressions to estimate the loss in tourism revenue associated with one additional day of red tide. Results indicate that impacts of HABs on tourism do not follow a linear pattern with increasing HAB concentrations, but rather appear to follow an inverted-U pattern. In other words, higher concentrations of the HAB organism do not necessarily imply higher economic losses, suggesting that the impacts of HABs on tourism are not driven solely by the biophysical element of cell density. Rather, these impacts appear to be mediated and amplified by human dimensions. The loss to tourism-related businesses due to the 2018 Florida red tide bloom was estimated to be $2.7 billion USD, which implies that HABs and their impact on tourism can be considered as a potential 'billion-dollar' disaster.

Phytoplankton communities of the west coast of Florida - multiyear and seasonal responses to nutrient enrichment

Blooms of the harmful algae species Karenia brevis are frequent off the southwest coast of Florida despite having relatively slow growth rates. The regional frequency of these harmful algal blooms led to the examination of the dominant estuarine outflows for effects on both K. brevis and the phytoplankton community in general. There is comparatively little information on the growth rates of non-Karenia taxonomic groups other than diatoms. A seasonally based series (Fall, Winter, and Spring) of bioassay experiments were conducted to determine the nutrient response of the coastal phytoplankton community. Treatments included estuarine waters (Tampa Bay, Charlotte Harbor, and the Caloosahatchee River) applied in a 1:25 dilution added to coastal water to mimic the influence of estuarine water in a coastal environment. Other treatments were 5-15 μM additions of nitrogen (N), phosphorus (P), and silica (Si) species, amino acids, and N (urea) + P added to coastal water. Incubations were conducted under ambient conditions with shading for 48 h. Analyses of dissolved and particulate nutrients were coupled with HPLC analysis of characteristic photopigments and taxonomic assignments of biomass via CHEMTAX. The coastal phytoplankton community, dominated by diatoms, cyanophytes and prasinophytes, was significantly different both by bioassay and by season, indicating little seasonal fidelity in composition. Specific growth rates of chlorophyll a indicated no significant difference between any controls, any estuarine treatment, P, or Si treatments. Conditions were uniformly N-limited with the highest growth rates in diatom biomass. Despite differing initial communities, however, there were seasonally reproducible changes in community due to the persistent growth or decline of the various taxa, including haptophytes, cyanophytes, and cryptophytes. For the one bioassay in which K. brevis was present, the slow growth of K. brevis relative to diatoms in a mixed community was evident, indicating that identifying the seasonally based behavior of other taxa in response to nutrients is critical for the simulation of phytoplankton competition and the successful prediction of the region's harmful algal blooms.

Nitrogen forms and dissolved organic matter optical properties in bulk rainfall, canopy throughfall, and stormwater in a subtropical urban catchment

The study of nitrogen (N) transformation in urban ecosystems is crucial in the protection of coastal water bodies because excess N may fuel harmful algae blooms (HABs). The purpose of this investigation was to study and identify the forms and concentrations of N in rainfall, throughfall, and stormwater runoff for 4 storm events in a subtropical urban ecosystem and to use fluorescence spectroscopy to evaluate the optical properties and expected lability of dissolved organic matter (DOM) in the same samples. The rainfall contained both inorganic and organic N pools, and organic N as nearly 50 % of total dissolved N in the rainfall. As water moved through the urban water cycle, from rainfall to stormwater and from rainfall to throughfall, it was enriched in total dissolved N, with most of the enrichment coming from dissolved organic N. Throughfall fluxes of total dissolved N were as high as 0.67 kg ha-1, compared to 0.44 kg ha-1 from rainfall, suggesting that the urban tree canopy can facilitate anthropogenic subsidies of N to the urban water cycle. Through analysis of sample optical properties, we saw that the throughfall presented the highest humification index and the lowest biological index when compared to rainfall, suggesting throughfall likely consists of higher molecular weight compounds of greater recalcitrance. This study highlights the importance of the dissolved organic N fraction of urban rainfall, stormwater, and throughfall and shows how the chemical composition of dissolved organic nutrients can change as rainfall is transformed into throughfall in the urban tree canopy.

Evaluating red tide effects on the West Florida Shelf using a spatiotemporal ecosystem modeling framework

The West Florida Shelf (WFS), located in the eastern Gulf of Mexico, fosters high species richness and supports highly valuable fisheries. However, red tide events occur regularly that can impact fisheries resources as well as ecosystem state, functioning, and derived services. Therefore, it is important to evaluate and quantify the spatiotemporal impacts of red tides to improve population assessments, mitigate potential negative effects through management, and better understand disturbances to support an ecosystem-based management framework. To model red tide effects on the marine community, we used Ecospace, the spatiotemporal module of the ecosystem modeling framework Ecopath with Ecosim. The inclusion of both lethal and sublethal response functions to red tide and a comprehensive calibration procedure allowed to systematically evaluate red tide effects and increased the robustness of the model and management applicability. Our results suggest severe red tide impacts have occurred on the WFS at the ecosystem, community, and population levels in terms of biomass, catch, and productivity. Sublethal and indirect food-web effects of red tide triggered compensatory responses such as avoidance behavior and release from predation and/or competition.. This study represents a step forward to operationalize spatiotemporal ecosystem models for management purposes that may increase the ability of fisheries managers to respond more effectively and be more proactive to episodic mortality events, such as those caused by red tides.

Conservation connections: incorporating connectivity into management and conservation of flats fishes and their habitats in a multi-stressor world

Coastal marine fisheries and the habitats that support them are under extensive and increasing pressures from numerous anthropogenic stressors that occur at multiple spatial and temporal scales and often intersect in unexpected ways. Frequently, the scales at which these fisheries are managed do not match the scales of the stressors, much less the geographic scale of species biology. In general, fishery management is ill prepared to address these stressors, as underscored by the continuing lack of integration of fisheries and habitat management. However, research of these fisheries is increasingly being conducted at spatial and temporal scales that incorporate biology and ecological connectivity of target species, with growing attention to the foundational role of habitat. These efforts are also increasingly engaging stakeholders and rights holders in research, education, and conservation. This multi-method approach is essential for addressing pressing conservation challenges that are common to flats ecosystems. Flats fisheries occur in the shallow, coastal habitat mosaic that supports fish species that are accessible to and desirable to target by recreational fishers. Because these species rely upon coastal habitats, the anthropogenic stressors can be especially intense-habitat alteration (loss and degradation) and water quality declines are being exacerbated by climate change and increasing direct human impacts (e.g., fishing effort, boat traffic, depredation, pollution). The connections necessary for effective flats conservation are of many modes and include ontogenetic habitat connectivity; connections between stressors and impacts to fishes; connections between research and management, such as research informing spawning area protections; and engagement of stakeholders and rights holders in research, education, and management. The articles included in this Special Issue build upon a growing literature that is filling knowledge gaps for flats fishes and their habitats and increasingly providing the evidence to inform resource management. Indeed, numerous articles in this issue propose or summarize direct application of research findings to management with a focus on current and future conservation challenges. As with many other fisheries, a revised approach to management and conservation is needed in the Anthropocene.

Impacts of the Green Revolution on Rhizosphere Microbiology Related to Nutrient Acquisition

The Green Revolution (GR) involved selective breeding of cereals and the use of high fertilizer inputs with the goal of increasing crop yields to alleviate hunger. As a result of both greater use of inorganic fertilizers and the introduction of semi-dwarf cultivars, grain yield increased globally and hunger was alleviated in certain areas of the world. However, these changes in varietal selection and fertilization regimes have impacted soil fertility and the root-associated microbiome. Higher rates of inorganic fertilizer application resulted in reduced rhizosphere microbial diversity, while semi-dwarf varieties displayed a greater abundance of rhizosphere microbes associated with nitrogen utilization. Ultimately, selection for beneficial aboveground traits during the GR led to healthier belowground traits and nutrient uptake capabilities.

Cascading effects of climate change on recreational marine flats fishes and fisheries

Tropical and subtropical coastal flats are shallow regions of the marine environment at the intersection of land and sea. These regions provide myriad ecological goods and services, including recreational fisheries focused on flats-inhabiting fishes such as bonefish, tarpon, and permit. The cascading effects of climate change have the potential to negatively impact coastal flats around the globe and to reduce their ecological and economic value. In this paper, we consider how the combined effects of climate change, including extremes in temperature and precipitation regimes, sea level rise, and changes in nutrient dynamics, are causing rapid and potentially permanent changes to the structure and function of tropical and subtropical flats ecosystems. We then apply the available science on recreationally targeted fishes to reveal how these changes can cascade through layers of biological organization-from individuals, to populations, to communities-and ultimately impact the coastal systems that depend on them. We identify critical gaps in knowledge related to the extent and severity of these effects, and how such gaps influence the effectiveness of conservation, management, policy, and grassroots stewardship efforts.

Septic system-groundwater-surface water couplings in waterfront communities contribute to harmful algal blooms in Southwest Florida

As human population growth has expanded in Southwest Florida, water quality has become degraded with an increased occurrence of harmful algal blooms (HABs). Red tide (Karenia brevis) originating offshore, intensifies in nearshore waters along Florida's Gulf Coast, and blue-green algae (Microcystis spp.) originating in Lake Okeechobee is discharged into the Caloosahatchee River. These HABs could be enhanced by anthropogenic nitrogen (N) and phosphorus (P) from adjacent watersheds. North Fort Myers is a heavily developed, low-lying city on the Caloosahatchee River Estuary serviced by septic systems with documented nutrient and bacterial pollution. To identify sources of pollution within North Fort Myers and determine connections with downstream HABs, this multiyear (2017-2020) study examined septic system- groundwater- surface water couplings through the analysis of water table depth, nutrients (N, P), fecal indicator bacteria (FIB), molecular markers (HF183, GFD, Gull2), chemical tracers (sucralose, pharmaceuticals, herbicides, pesticides), stable isotopes of groundwater (δ¹⁵N-NH₄, δ¹⁵N-NO₃) and particulate organic matter (POM; δ¹⁵N, δ¹³C), and POM elemental composition (C:N:P). POM samples were also collected during K. brevis and Microcystis spp. HAB events. Most (>80%) water table depth measurements were too shallow to support septic system functioning (<1.07 m). High concentrations of NH₄⁺ and NO_x, up to 1094 μM and 482 μM respectively, were found in groundwater and surface water. δ¹⁵N values of groundwater (+4.7‰) were similar to septic effluent (+4.9‰), POM (+4.7‰), and downstream HABs (+4.8 to 6.9‰), indicating a human waste N source. In surface water, FIB were elevated and HF183 was detected, while in groundwater and surface water sucralose, carbamazepine, primidone, and acetaminophen were detected. These data suggest that groundwater and surface water in North Fort Myers are coupled and contaminated by septic system effluent, which is negatively affecting water quality and contributing to the maintenance and intensification of downstream HABs.

Occurrence of Fibropapillomatosis in Green Turtles (Chelonia mydas) in Relation to Environmental Changes in Coastal Ecosystems in Texas and Florida: A Retrospective Study

Fibropapillomatosis is a neoplastic disease of marine turtles, with green turtles (Chelonia mydas) being the most affected species. Fibropapillomatosis causes debilitating tumor growths on soft tissues and internal organs, often with lethal consequences. Disease incidence has been increasing in the last few decades and the reason is still uncertain. The potential viral infectious agent of Fibropapillomatosis, chelonid herpesvirus 5, has been co-evolving with its sea turtle host for millions of years and no major mutation linked with increased disease occurrence has been detected. Hence, frequent outbreaks in recent decades are likely attributable to external drivers such as large-scale anthropogenic changes in the green turtle coastal marine ecosystem. This study found that variations in sea surface temperature, salinity, and nutrient effluent discharge from nearby rivers were correlated with an increased incidence of the disease, substantiating that these may be among the significant environmental drivers impacting Fibropapillomatosis prevalence. This study offers data and insight on the need to establish a baseline of environmental factors which may drive Fibropapillomatosis and its clinical exacerbation. We highlight the multifactorial nature of this disease and support the inclusion of interdisciplinary work in future Fibropapillomatosis research efforts.