Predicting coastal algal blooms in southern California

Local-manifold-distance-based regression: an estimation method for quantifying dynamic biological interactions with empirical time series

Quantifying species interactions based on empirical observations is crucial for ecological studies. Advancements in nonlinear time-series analyses, particularly S-maps, are promising for high-dimensional and non-equilibrium ecosystems. S-maps sequentially perform a local linear model fitting to the time evolution of neighbouring points on the reconstructed attractor manifold, and the coefficients can approximate the Jacobian elements corresponding to interaction effects. However, despite that the advantages in nonlinear forecasting with noise-contaminated data, these methodologies have a limitation in the Jacobian estimation accuracy owing to non-equidistantly stretched local manifolds in the state space. Herein, we therefore introduced a local manifold distance (LMD) concept, a non-equidistant measure based on the multi-faceted state dependency. By integrating LMD with advanced computation techniques, we presented a robust and efficient analytical method, LMD-based regression (LMDr). To validate its advantages in prediction and Jacobian estimation, we analysed synthetic time series of model ecosystems with different noise levels and applied it to an experimental protozoan predator-prey system with established biological information. The robustness to noise was the highest for LMDr, which also showed a better correspondence to expected predator-prey interactions in the protozoan system. Thus, LMDr can be applied to study complex ecological networks under dynamic conditions.

A tale of two blooms: do ecological paradigms for algal bloom success and succession require revisiting?

Lake Erie algal bloom discussions have historically focused on cyanobacteria, with foundational "blooms like it hot" and "high nutrient" paradigms considered as primary drivers behind cyanobacterial bloom success. Yet, recent surveys have rediscovered winter-spring diatom blooms, introducing another key player in the Lake Erie eutrophication and algal bloom story which has been historically overlooked. These blooms (summer vs. winter) have been treated as solitary events separated by spatial and temporal gradients. However, new evidence suggests they may not be so isolated, linked in a manner that manifests as an algal bloom cycle. Equally notable are the emerging reports of cyanobacterial blooms in cold and/or oligotrophic freshwaters, which have been interpreted by some as shifts in classical bloom paradigms. These emerging bloom reports have led many to ask "what is a bloom?". Furthermore, questioning classic paradigms has caused others to wonder if we are overlooking additional factors which constrain bloom success. In light of emerging data and ideas, we revisited foundational concepts within the context of Lake Erie algal blooms and derived five key take-aways: 1) Additional bloom-formers (diatoms) need to be included in Lake Erie algal discussions, 2) The term "bloom" must be reinforced with a clear definition and quantitative metrics for each event, 3) Algal blooms should not be studied solitarily, 4) Shifts in physiochemical conditions serve as an alternative interpretation to potential shifts in ecological paradigms, 5) Additional factors which constrain bloom success and succession (i.e., pH and light) require consideration.

Dryland sensitivity to climate change and variability using nonlinear dynamics

Primary productivity response to climatic drivers varies temporally, indicating state-dependent interactions between climate and productivity. Previous studies primarily employed equation-based approaches to clarify this relationship, ignoring the state-dependent nature of ecological dynamics. Here, using 40 y of climate and productivity data from 48 grassland sites across Mongolia, we applied an equation-free, nonlinear time-series analysis to reveal sensitivity patterns of productivity to climate change and variability and clarify underlying mechanisms. We showed that productivity responded positively to annual precipitation in mesic regions but negatively in arid regions, with the opposite pattern observed for annual mean temperature. Furthermore, productivity responded negatively to decreasing annual aridity that integrated precipitation and temperature across Mongolia. Productivity responded negatively to interannual variability in precipitation and aridity in mesic regions but positively in arid regions. Overall, interannual temperature variability enhanced productivity. These response patterns are largely unrecognized; however, two mechanisms are inferable. First, time-delayed climate effects modify annual productivity responses to annual climate conditions. Notably, our results suggest that the sensitivity of annual productivity to increasing annual precipitation and decreasing annual aridity can even be negative when the negative time-delayed effects of annual precipitation and aridity on productivity prevail across time. Second, the proportion of plant species resistant to water and temperature stresses at a site determines the sensitivity of productivity to climate variability. Thus, we highlight the importance of nonlinear, state-dependent sensitivity of productivity to climate change and variability, accurately forecasting potential biosphere feedback to the climate system.

Differentiation strategies for planktonic bacteria and eukaryotes in response to aggravated algal blooms in urban lakes

Aggravated algal blooms potentially decreased environmental heterogeneity. Different strategies of planktonic bacteria and eukaryotes in response to aggravated algal blooms. Environmental constraints of plankton showed different patterns over time.

Seasonal nearshore ocean acidification and deoxygenation in the Southern California Bight

The California Current System experiences seasonal ocean acidification and hypoxia (OAH) owing to wind-driven upwelling, but little is known about the intensity, frequency, and depth distribution of OAH in the shallow nearshore environment. Here we present observations of OAH and dissolved inorganic carbon and nutrient parameters based on monthly transects from March 2017 to September 2018 extending from the surf zone to the ~ 40 m depth contour in La Jolla, California. Biologically concerning OAH conditions were observed at depths as shallow as 10 m and as close as 700 m to the shoreline. Below 20 m depth, 8% of observations were undersaturated with respect to aragonite, 28% of observations had a pH_T less than 7.85, and 19% of observations were below the sublethal oxygen threshold of 157 µmol kg^-1. These observations raise important questions about the impacts of OAH on coastal organisms and ecosystems and how future intensified upwelling may exacerbate these conditions.

Decomposing predictability to identify dominant causal drivers in complex ecosystems

Ecosystems are complex systems of various physical, biological, and chemical processes. Since ecosystem dynamics are composed of a mixture of different levels of stochasticity and nonlinearity, handling these data is a challenge for existing methods of time series-based causal inferences. Here, we show that, by harnessing contemporary machine learning approaches, the concept of Granger causality can be effectively extended to the analysis of complex ecosystem time series and bridge the gap between dynamical and statistical approaches. The central idea is to use an ensemble of fast and highly predictive artificial neural networks to select a minimal set of variables that maximizes the prediction of a given variable. It enables decomposition of the relationship among variables through quantifying the contribution of an individual variable to the overall predictive performance. We show how our approach, EcohNet, can improve interaction network inference for a mesocosm experiment and simulated ecosystems. The application of the method to a long-term lake monitoring dataset yielded interpretable results on the drivers causing cyanobacteria blooms, which is a serious threat to ecological integrity and ecosystem services. Since performance of EcohNet is enhanced by its predictive capabilities, it also provides an optimized forecasting of overall components in ecosystems. EcohNet could be used to analyze complex and hybrid multivariate time series in many scientific areas not limited to ecosystems.

Near-term phytoplankton forecasts reveal the effects of model time step and forecast horizon on predictability

As climate and land use increase the variability of many ecosystems, forecasts of ecological variables are needed to inform management and use of ecosystem services. In particular, forecasts of phytoplankton would be especially useful for drinking water management, as phytoplankton populations are exhibiting greater fluctuations due to human activities. While phytoplankton forecasts are increasing in number, many questions remain regarding the optimal model time step (the temporal frequency of the forecast model output), time horizon (the length of time into the future a prediction is made) for maximizing forecast performance, as well as what factors contribute to uncertainty in forecasts and their scalability among sites. To answer these questions, we developed near-term, iterative forecasts of phytoplankton 1-14 days into the future using forecast models with three different time steps (daily, weekly, fortnightly), that included a full uncertainty partitioning analysis at two drinking water reservoirs. We found that forecast accuracy varies with model time step and forecast horizon, and that forecast models can outperform null estimates under most conditions. Weekly and fortnightly forecasts consistently outperformed daily forecasts at 7-day and 14-day horizons, a trend that increased up to the 14-day forecast horizon. Importantly, our work suggests that forecast accuracy can be increased by matching the forecast model time step to the forecast horizon for which predictions are needed. We found that model process uncertainty was the primary source of uncertainty in our phytoplankton forecasts over the forecast period, but parameter uncertainty increased during phytoplankton blooms and when scaling the forecast model to a new site. Overall, our scalability analysis shows promising results that simple models can be transferred to produce forecasts at additional sites. Altogether, our study advances our understanding of how forecast model time step and forecast horizon influence the forecastability of phytoplankton dynamics in aquatic systems and adds to the growing body of work regarding the predictability of ecological systems broadly.

Year-Long Microbial Succession on Microplastics in Wastewater: Chaotic Dynamics Outweigh Preferential Growth

Microplastics are a globally-ubiquitous aquatic pollutant and have been heavily studied over the last decade. Of particular interest are the interactions between microplastics and microorganisms, especially the pursuit to discover a plastic-specific biome, the so-called plastisphere. To follow this up, a year-long microcosm experimental setup was deployed to expose five different microplastic types (and silica beads control) to activated aerobic wastewater in controlled conditions, with microbial communities being measured four times over the course of the year using 16S rDNA (bacterial) and ITS (fungal) amplicon sequencing. The biofilm community shows no evidence of a specific plastisphere, even after a year of incubation. Indeed, the microbial communities (particularly bacterial) show a clear trend of increasing dissimilarity between plastic types as time increases. Despite little evidence for a plastic-specific community, there was a slight grouping observed for polyolefins (PE and PP) in 6–12-month biofilms. Additionally, an OTU assigned to the genus Devosia was identified on many plastics, increasing over time while showing no growth on silicate (natural particle) controls, suggesting this could be either a slow-growing plastic-specific taxon or a symbiont to such. Both substrate-associated findings were only possible to observe in samples incubated for 6–12 months, which highlights the importance of studying long-term microbial community dynamics on plastic surfaces.

Using near-term forecasts and uncertainty partitioning to inform prediction of oligotrophic lake cyanobacterial density

Near-term ecological forecasts provide resource managers advance notice of changes in ecosystem services, such as fisheries stocks, timber yields, or water quality. Importantly, ecological forecasts can identify where there is uncertainty in the forecasting system, which is necessary to improve forecast skill and guide interpretation of forecast results. Uncertainty partitioning identifies the relative contributions to total forecast variance introduced by different sources, including specification of the model structure, errors in driver data, and estimation of current states (initial conditions). Uncertainty partitioning could be particularly useful in improving forecasts of highly variable cyanobacterial densities, which are difficult to predict and present a persistent challenge for lake managers. As cyanobacteria can produce toxic and unsightly surface scums, advance warning when cyanobacterial densities are increasing could help managers mitigate water quality issues. Here, we fit 13 Bayesian state-space models to evaluate different hypotheses about cyanobacterial densities in a low nutrient lake that experiences sporadic surface scums of the toxin-producing cyanobacterium, Gloeotrichia echinulata. We used data from several summers of weekly cyanobacteria samples to identify dominant sources of uncertainty for near-term (1- to 4-week) forecasts of G. echinulata densities. Water temperature was an important predictor of cyanobacterial densities during model fitting and at the 4-week forecast horizon. However, no physical covariates improved model performance over a simple model including the previous week's densities in 1-week-ahead forecasts. Even the best fit models exhibited large variance in forecasted cyanobacterial densities and did not capture rare peak occurrences, indicating that significant explanatory variables when fitting models to historical data are not always effective for forecasting. Uncertainty partitioning revealed that model process specification and initial conditions dominated forecast uncertainty. These findings indicate that long-term studies of different cyanobacterial life stages and movement in the water column as well as measurements of drivers relevant to different life stages could improve model process representation of cyanobacteria abundance. In addition, improved observation protocols could better define initial conditions and reduce spatial misalignment of environmental data and cyanobacteria observations. Our results emphasize the importance of ecological forecasting principles and uncertainty partitioning to refine and understand predictive capacity across ecosystems.

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

Karenia brevis blooms on Florida's Gulf Coast severely affect regional ecosystems, coastal economies, and public health, and formulating effective management and policy strategies to address these blooms requires an advanced understanding of the processes driving them. Recent research suggests that natural processes explain offshore bloom initiation and shoreward transport, while anthropogenic nutrient inputs may intensify blooms upon arrival along the coast. However, past correlation studies have failed to detect compelling evidence linking coastal blooms to watershed covariates indicative of anthropogenic inputs. We explain why correlation is neither necessary nor sufficient to demonstrate a causal relationship-i.e., a persistent pattern of interaction governed by deterministic rules-and pursue an empirical investigation leveraging the fact that systematic temporal patterns may reveal systematic cause-and-effect relationships. Using time series derived from in-situ sample data, we applied singular spectrum analysis-a non-parametric spectral decomposition method-to recover deterministic signals in the dynamics of K. brevis blooms and upstream water quality and discharge covariates in the Charlotte Harbor region between 2012 and 2021. Next, we applied causal analysis methods based on chaos theory-i.e., convergent cross-mapping and S-mapping-to detect and quantify persistent, state-dependent interaction regimes between coastal blooms and watershed covariates. We discovered that nitrogen-enriched Caloosahatchee River discharges have consistently intensified K. brevis blooms to varying degrees over time. River discharge was typically most influential at the earliest stages of blooms, while total nitrogen concentrations exerted the strongest influence during blooms' growth/maintenance stages. These results indicate that discharges and nitrogen inputs influence blooms through distinct yet synergistic causal mechanisms. Additionally, we traced this anthropogenic influence upstream to Lake Okeechobee (which discharges to the Caloosahatchee River) and the Kissimmee River basin (which drains into Lake Okeechobee), suggesting that watershed-scale nutrient management and modifications to Lake Okeechobee discharge protocols will likely be necessary to mitigate coastal blooms.

Nonlinear causal analysis reveals an effective water level regulation approach for phytoplankton blooms controlling in reservoirs

Reservoirs are a rapidly increasing water body providing water supply, irrigation, and many other benefits for human societies globally. However, due to changes in hydrological conditions, building reservoirs tends to bring adverse effects such as eutrophication and phytoplankton blooms, reducing the ecosystem service values. This study focuses on using the empirical dynamic modeling (EDM), an emerging approach for nonlinear analysis, to investigate the nonlinear causal relationship of water level fluctuation (WLF) on phytoplankton biomass and then develop a quantitative model guiding effective phytoplankton blooms controlling based on water level regulations in reservoirs. Specifically, with 9-year continued daily observed data in the Three Gorges Reservoir, we examined the causal effects of different WLF parameters on the dynamics of phytoplankton blooms for the first time. We found that the water level change in the past 24 h (ΔWL) has the strongest causal effect on the daily dynamics of phytoplankton biomass among all WLF parameters (ΔWL, |ΔWL|, and the water level), with a time lag of 2 days. Moreover, EDM revealed a nonlinear relationship between ΔWL and daily dynamics of phytoplankton biomass and achieved a successful prediction for the chlorophyll a concentration 2-day ahead. Further scenario analyses found that both the rise and fall of water level will significantly reduce the chlorophyll a concentration when phytoplankton blooms occur. Nevertheless, on the whole, the rising water level has a more substantial effect on phytoplankton blooms than falling the water level. This result reveals that regulating ΔWL is a simple and effective approach in controlling phytoplankton blooms in reservoirs. Our study reported the nonlinear causal effect of ΔWL on the dynamics of chlorophyll a and provided a quantitative approach guiding effective phytoplankton blooms controlling based on the water level regulation, which might have a broad application in algal blooms controlling in reservoirs and similar waterbodies.

The Relationship between Environmental Factors and Catch Abundance of Hairtail in the East China Sea Using Empirical Dynamic Modeling

The East China Sea population of hairtail (Trichiurus lepturus, also known as T. japonicus) is a commercially important element of Chinese fisheries. Hairtail has long been widely exploited. Due to overfishing, however, its production declined over the years. One of solutions to this dilemma is to institute reasonable fishery policies. Generally, skillful short-term and long-term prediction of fish catch is a central tool for guiding the development of fishery policy. Accurate predictions require a comprehensive understanding of the relationship between fluctuations in fish catch and variability in both fishing effort and marine environmental conditions. To investigate the combined impact of fishing effort and marine environments on hairtail catch and to develop models to predict hairtail catch, we applied empirical dynamic modeling (EDM) to data on East China Sea fisheries, including hairtail catch, fishing effort, and marine environmental factors. EDM is an equation-free approach that enables the investigation of various complex systems. We constructed all possible multivariate EDM models to investigate the potential mechanisms affecting hairtail catch. Our analysis demonstrates that all key environmental factors (salinity, summer monsoon, sea surface temperature, precipitation, and power dissipation index of tropical cyclones) have an impact on nutrient supply, which we suggest is the central factor influencing hairtail catch. Finally, our comparison of EDM models with parametric models demonstrates that EDM models overwhelmingly outperform parametric models in analysis of these complex interactions.

Time series relationships between chlorophyll-a, physicochemical parameters, and nutrients in the Eastern Harbour of Alexandria, Egypt

The Eastern Harbour of Alexandria, on the Egyptian Mediterranean coast, is characterized by environmental complications due to different types of anthropogenic stressors associated with water dynamics inside the harbor as well as the rapid water exchange with the open sea. These conditions caused chronic eutrophication conditions, with variable levels in the long term. The present study followed daily some physicochemical parameters, nutrients, and phytoplankton biomass, for a complete year. The results indicate coincidence on the short-time scale between the nutrients, phytoplankton biomass, pH, and dissolved oxygen. Spearman's correlation illustrated strong positive correlations between algal blooms and both pH and dissolved oxygen. The present study recorded twelve separate algal blooms, with an average of chlorophyll-a > 16.7 μg/L, confirming the continuity of high eutrophication in the Eastern Harbour. The seasonal Mann-Kendall tests showed that summer attained significant increasing trends for chlorophyll-a, silicate, nitrite, and nitrate, while winter has a significant decreasing trend for chlorophyll-a and pH.

Intraseasonal predictability of natural phytoplankton population dynamics

It is difficult to make skillful predictions about the future dynamics of marine phytoplankton populations. Here, we use a 22-year time series of monthly average abundances for 198 phytoplankton taxa from Station L4 in the Western English Channel (1992-2014) to test whether and how aggregating phytoplankton into multi-species assemblages can improve predictability of their temporal dynamics. Using a non-parametric framework to assess predictability, we demonstrate that the prediction skill is significantly affected by how species data are grouped into assemblages, the presence of noise, and stochastic behavior within species. Overall, we find that predictability one month into the future increases when species are aggregated together into assemblages with more species, compared with the predictability of individual taxa. However, predictability within dinoflagellates and larger phytoplankton (>12 μm cell radius) is low overall and does not increase by aggregating similar species together. High variability in the data, due to observational error (noise) or stochasticity in population growth rates, reduces the predictability of individual species more than the predictability of assemblages. These findings show that there is greater potential for univariate prediction of species assemblages or whole-community metrics, such as total chlorophyll or biomass, than for the individual dynamics of phytoplankton species.

A Remote Sensing and Machine Learning-Based Approach to Forecast the Onset of Harmful Algal Bloom

In the last few decades, harmful algal blooms (HABs, also known as “red tides”) have become one of the most detrimental natural phenomena in Florida’s coastal areas. Karenia brevis produces toxins that have harmful effects on humans, fisheries, and ecosystems. In this study, we developed and compared the efficiency of state-of-the-art machine learning models (e.g., XGBoost, Random Forest, and Support Vector Machine) in predicting the occurrence of HABs. In the proposed models the K. brevis abundance is used as the target, and 10 level-02 ocean color products extracted from daily archival MODIS satellite data are used as controlling factors. The adopted approach addresses two main shortcomings of earlier models: (1) the paucity of satellite data due to cloudy scenes and (2) the lag time between the period at which a variable reaches its highest correlation with the target and the time the bloom occurs. Eleven spatio-temporal models were generated, each from 3 consecutive day satellite datasets, with a forecasting span from 1 to 11 days. The 3-day models addressed the potential variations in lag time for some of the temporal variables. One or more of the generated 11 models could be used to predict HAB occurrences depending on availability of the cloud-free consecutive days. Findings indicate that XGBoost outperformed the other methods, and the forecasting models of 5–9 days achieved the best results. The most reliable model can forecast eight days ahead of time with balanced overall accuracy, Kappa coefficient, F-Score, and AUC of 96%, 0.93, 0.97, and 0.98 respectively. The euphotic depth, sea surface temperature, and chlorophyll-a are always among the most significant controlling factors. The proposed models could potentially be used to develop an “early warning system” for HABs in southwest Florida.

Harmful algal blooms and their eco-environmental indication

Harmful algal blooms (HABs) in freshwater lakes and oceans date back to as early as the 19th century, which can cause the death of aquatic and terrestrial organisms. However, it was not until the end of the 20th century that researchers had started to pay attention to the hazards and causes of HABs. In this study, we analyzed 5720 published literatures on HABs studies in the past 30 years. Our review presents the emerging trends in the past 30 years on HABs studies, the environmental and human health risks, prevention and control strategies and future developments. Therefore, this review provides a global perspective of HABs and calls for immediate responses.

Recurrent microbial community types driven by nearshore and seasonal processes in coastal Southern California

A multitude of concurrent biological and physical processes contribute to microbial community turnover, especially in highly dynamic coastal environments. Characterizing what factors contribute most to shifts in microbial community structure and the specific organisms that correlate with changes in the products of photosynthesis improves our understanding of nearshore microbial ecosystem functions. We conducted high frequency sampling in nearshore Southern California in order to capture sub-weekly microbial community dynamics. Microbial communities were characterized by flow cytometry and 16S rRNA gene sequencing, and placed in the context of physicochemical parameters. Within our time-series, season and nutrient availability corresponded to changes in dominant microbial community members. Concurrent aseasonal drivers with overlapping scales of variability were also apparent when we used network analysis to assess the microbial community as subsets of the whole. Our analyses revealed the microbial community as a mosaic, with overlapping groups of taxa that varied on different timescales and correlated with unique abiotic and biotic factors. Specifically, a subnetwork associated with chlorophyll a exhibited rapid turnover, indicating that ecologically important subsets of the microbial community can change on timescales different than and in response to factors other than those that govern turnover of most members of the assemblage.

Seasonal dynamics of terrestrially sourced nitrogen influenced Karenia brevis blooms off Florida's southern Gulf Coast

Harmful algal blooms (HABs) threaten coastal ecological systems, public health, and local economies, but the complex physical, chemical, and biological processes that culminate in HABs vary by locale and are often poorly understood. Despite broad recognition that cultural eutrophication may exacerbate nearshore bloom events, the association is typically not linear and is often difficult to quantify. Off the Gulf Coast of Florida, Karenia brevis blooms initiate in the open waters of the Gulf of Mexico, and advection of cells supplies nearshore blooms. However, past work has struggled to describe the relationship between terrestrial nutrient runoff and bloom maintenance near the Gulf Coast. This study applied a novel nonlinear time series (NLTS) analytical framework to investigate whether nearshore bloom dynamics observed near Charlotte Harbor, FL were causally and systematically driven by terrestrially sourced inputs of nitrogen, phosphorus, and freshwater between 2012 and 2018. Singular spectrum analysis (SSA) isolated low-dimensional, deterministic signals in K. brevis log₁₀-density dynamics and in the dynamics of nine of 10 candidate drivers. The predominantly seasonal K. brevis signal was strong, explaining 77.6% of the total variance in the observed time series. Causal tests with convergent cross-mapping provided evidence that nitrogen concentrations measured at the discharge point of the Caloosahatchee River systematically influenced K. brevis bloom dynamics. However, further causal testing failed to link these nitrogen dynamics to an upstream basin, possibly due to data limitations. The results support the hypothesis that anthropogenic nitrogen runoff facilitated the growth of K. brevis blooms near Charlotte Harbor and suggest that bloom events would be mitigated by nitrogen source and transport controls within the Caloosahatchee and/or Kissimmee River basins. More broadly, this work demonstrates that management-relevant causal inferences into the drivers of HABs may be drawn from available monitoring records.

Forecasting unprecedented ecological fluctuations

Forecasting 'Black Swan' events in ecosystems is an important but challenging task. Many ecosystems display aperiodic fluctuations in species abundance spanning orders of magnitude in scale, which have vast environmental and economic impact. Empirical evidence and theoretical analyses suggest that these dynamics are in a regime where system nonlinearities limit accurate forecasting of unprecedented events due to poor extrapolation of historical data to unsampled states. Leveraging increasingly available long-term high-frequency ecological tracking data, we analyze multiple natural and experimental ecosystems (marine plankton, intertidal mollusks, and deciduous forest), and recover hidden linearity embedded in universal 'scaling laws' of species dynamics. We then develop a method using these scaling laws to reduce data dependence in ecological forecasting and accurately predict extreme events beyond the span of historical observations in diverse ecosystems.

Harmful algal blooms under changing climate and constantly increasing anthropogenic actions: the review of management implications

The present review reports all management approaches (physical, chemical, and biological) traditionally adopted in mitigating the global impact of harmful cyanobacterial blooms (cyanoHABs). It recognizes that each mitigation strategy shows characteristic associated limitations and notes that no remedial step has provided a sustainable solution to HABs on a global scale. It emphasizes that the putative anthropogenic N&P inputs reduction through improved wastewater treatment and regulation of point and non-point sources-agricultural fertilizers only offer a short term solution. These approaches are rather preventive than curative hence, do not address concerns relating to the recovery of already-eutrophic and hypereutrophic systems. It raises new concerns on the implications of non-agricultural pollutants such as hydrocarbon fractions in bloom accretions often neglected while addressing HAB triggers. It also accesses the global impacts of HABs as it pertains to socio-economic implications in the geographically diverse world. It, therefore, proposes that Integrated Management Intervention involving the merging of two or more mitigation steps be administered across the aquatic continua as a prudent management solution to complement the current N&P dual management paradigm. It stresses that the contemporaneous adoption of management options with both preventive and curative measures is a key to sustainable HAB management. This review provides sufficient advances and current scenarios for approaching cyanoHABs. Further, it advocates that future research perspectives tackle the mitigation design beyond the short-term nutrient regulations and the parochial attention to the point and non-point N&P input sources.

Causality analysis and prediction of 2-methylisoborneol production in a reservoir using empirical dynamic modeling

2-Methylisobornel (MIB) is one of the most widespread and problematic biogenic compounds causing taste-and-odor problems in freshwater. To investigate the causes of MIB production and develop models to predict the MIB concentration, we have applied empirical dynamic modeling (EDM), a nonlinear approach based on Chaos theory, to the long-term water quality dataset of Kamafusa Reservoir in Japan. The study revealed the dynamic nature of MIB production in the reservoir, and determined causal variables for MIB production, including water temperature, pH, transparency, light intensity, and Green Phormidium. Moreover, EDM established that the system is three-dimensional, and the approach found elevated nonlinearity (from 1.5 to 3) across the whole study period (1996-2015). By taking only one or two candidate predictors with varying time lags, multivariate models for predicting MIB production (best model: r = 0.83, p < 0.001, root mean squared error = 3.1 ng/L) were successfully established. The modeling approach used in this study is a powerful tool for causality identification and odor prediction, thus making important contributions to reservoir management.

Can Community Structure Causally Determine Dynamics of Constituent Species? A Test Using a Host-Parasite Community

Structures of communities have been widely studied with the assumption that they not only are a useful bookkeeping tool but also can causally influence dynamics of the populations from which they emerge. However, convincing tests of this assumption have remained elusive because generally the only way to alter a community property is by manipulating its constituent populations, thereby preventing independent measurements of effects on those populations. There is a growing body of evidence that methods like convergent cross-mapping (CCM) can be used to make inferences about causal interactions using state space reconstructions of coupled time series, a method that relies on only observational data. Here we show that CCM can be used to test the causal effects of community properties using a well-studied Slovakian rodent-ectoparasite community. CCM identified causal drivers across the organizational scales of this community, including evidence that host dynamics were influenced by the degree to which the community at large was connected and clustered. Our findings add to the growing literature on the importance of community structures in disease dynamics and argue for a broader use of causal inference in the analysis of community dynamics.

Comparison of Machine Learning Algorithms for Retrieval of Water Quality Indicators in Case-II Waters: A Case Study of Hong Kong

Anthropogenic activities in coastal regions are endangering marine ecosystems. Coastal waters classified as case-II waters are especially complex due to the presence of different constituents. Recent advances in remote sensing technology have enabled to capture the spatiotemporal variability of the constituents in coastal waters. The present study evaluates the potential of remote sensing using machine learning techniques, for improving water quality estimation over the coastal waters of Hong Kong. Concentrations of suspended solids (SS), chlorophyll-a (Chl-a), and turbidity were estimated with several machine learning techniques including Artificial Neural Network (ANN), Random Forest (RF), Cubist regression (CB), and Support Vector Regression (SVR). Landsat (5,7,8) reflectance data were compared with in situ reflectance data to evaluate the performance of machine learning models. The highest accuracies of the water quality indicators were achieved by ANN for both, in situ reflectance data (89%-Chl-a, 93%-SS, and 82%-turbidity) and satellite data (91%-Chl-a, 92%-SS, and 85%-turbidity. The water quality parameters retrieved by the ANN model was further compared to those retrieved by “standard Case-2 Regional/Coast Colour” (C2RCC) processing chain model C2RCC-Nets. The root mean square errors (RMSEs) for estimating SS and Chl-a were 3.3 mg/L and 2.7 µg/L, respectively, using ANN, whereas RMSEs were 12.7 mg/L and 12.9 µg/L for suspended particulate matter (SPM) and Chl-a concentrations, respectively, when C2RCC was applied on Landsat-8 data. Relative variable importance was also conducted to investigate the consistency between in situ reflectance data and satellite data, and results show that both datasets are similar. The red band (wavelength ≈ 0.665 µm) and the product of red and green band (wavelength ≈ 0.560 µm) were influential inputs in both reflectance data sets for estimating SS and turbidity, and the ratio between red and blue band (wavelength ≈ 0.490 µm) as well as the ratio between infrared (wavelength ≈ 0.865 µm) and blue band and green band proved to be more useful for the estimation of Chl-a concentration, due to their sensitivity to high turbidity in the coastal waters. The results indicate that the NN based machine learning approaches perform better and, thus, can be used for improved water quality monitoring with satellite data in optically complex coastal waters.

Pervasive iron limitation at subsurface chlorophyll maxima of the California Current

The vertical distribution of phytoplankton cells and chlorophyll concentrations throughout the sunlit water column is rarely uniform. In many ocean regions, chlorophyll concentrations peak in distinct and persistent layers deep below the surface called subsurface chlorophyll maximum layers (SCMLs). SCML formation is hypothesized to reflect the consequences of phytoplankton light/macronutrient colimitation, behavior, and/or photoacclimation. We discovered unexpectedly persistent and widespread phytoplankton iron limitation and iron/light colimitation in SCMLs of the California Current and at the edge of the North Pacific Subtropical Gyre using shipboard incubations, metatranscriptomics, and biogeochemical proxies. These results suggest that interactions and feedbacks between iron and light availability play an important and previously unrecognized role in controlling the productivity and biogeochemical dynamics of SCMLs.

Subsurface chlorophyll maximum layers (SCMLs) are nearly ubiquitous in stratified water columns and exist at horizontal scales ranging from the submesoscale to the extent of oligotrophic gyres. These layers of heightened chlorophyll and/or phytoplankton concentrations are generally thought to be a consequence of a balance between light energy from above and a limiting nutrient flux from below, typically nitrate (NO₃). Here we present multiple lines of evidence demonstrating that iron (Fe) limits or with light colimits phytoplankton communities in SCMLs along a primary productivity gradient from coastal to oligotrophic offshore waters in the southern California Current ecosystem. SCML phytoplankton responded markedly to added Fe or Fe/light in experimental incubations and transcripts of diatom and picoeukaryote Fe stress genes were strikingly abundant in SCML metatranscriptomes. Using a biogeochemical proxy with data from a 40-y time series, we find that diatoms growing in California Current SCMLs are persistently Fe deficient during the spring and summer growing season. We also find that the spatial extent of Fe deficiency within California Current SCMLs has significantly increased over the last 25 y in line with a regional climate index. Finally, we show that diatom Fe deficiency may be common in the subsurface of major upwelling zones worldwide. Our results have important implications for our understanding of the biogeochemical consequences of marine SCML formation and maintenance.