Skill Assessment for Coupled Biological/Physical Models of Marine Systems

Combining mesocosms with models reveals effects of global warming and ocean acidification on a temperate marine ecosystem

Ocean warming and species exploitation have already caused large-scale reorganization of biological communities across the world. Accurate projections of future biodiversity change require a comprehensive understanding of how entire communities respond to global change. We combined a time-dynamic integrated food web modeling approach (Ecosim) with previous data from community-level mesocosm experiments to determine the independent and combined effects of ocean warming, ocean acidification and fisheries exploitation on a well-managed temperate coastal ecosystem. The mesocosm parameters enabled important physiological and behavioral responses to climate stressors to be projected for trophic levels ranging from primary producers to top predators, including sharks. Through model simulations, we show that under sustainable rates of fisheries exploitation, near-future warming or ocean acidification in isolation could benefit species biomass at higher trophic levels (e.g., mammals, birds, and demersal finfish) in their current climate ranges, with the exception of small pelagic fishes. However, under warming and acidification combined, biomass increases at higher trophic levels will be lower or absent, while in the longer term reduced productivity of prey species is unlikely to support the increased biomass at the top of the food web. We also show that increases in exploitation will suppress any positive effects of human-driven climate change, causing individual species biomass to decrease at higher trophic levels. Nevertheless, total future potential biomass of some fisheries species in temperate areas might remain high, particularly under acidification, because unharvested opportunistic species will likely benefit from decreased competition and show an increase in biomass. Ecological indicators of species composition such as the Shannon diversity index decline under all climate change scenarios, suggesting a trade-off between biomass gain and functional diversity. By coupling parameters from multilevel mesocosm food web experiments with dynamic food web models, we were able to simulate the generative mechanisms that drive complex responses of temperate marine ecosystems to global change. This approach, which blends theory with experimental data, provides new prospects for forecasting climate-driven biodiversity change and its effects on ecosystem processes.

Subsurface temperature estimates from a Regional Ocean Modelling System (ROMS) reanalysis provide accurate coral heat stress indices across the Main Hawaiian Islands

As ocean temperatures continue to rise, coral bleaching events around the globe are becoming stronger and more frequent. High-resolution temperature data is therefore critical for monitoring reef conditions to identify indicators of heat stress. Satellite and in situ measurements have historically been relied upon to study the thermal tolerances of coral reefs, but these data are quite limited in their spatial and temporal coverage. Ocean circulation models could provide an alternative or complement to these limited data, but a thorough evaluation against in situ measurements has yet to be conducted in any Pacific Islands region. Here we compared subsurface temperature measurements around the nearshore Main Hawaiian Islands (MHI) from 2010 to 2017 with temperature predictions from an operational Regional Ocean Modeling System (ROMS) to evaluate the potential utility of this model as a tool for coral reef management. We found that overall, the ROMS reanalysis presents accurate subsurface temperature predictions across the nearshore MHI region and captures a significant amount of observed temperature variability. The model recreates several temperature metrics used to identify coral heat stress, including predicting the 2014 and 2015 bleaching events around Hawai'i during the summer and fall months of those years. The MHI ROMS simulation proves to be a useful tool for coral reef management in the absence of, or to supplement, subsurface and satellite measurements across Hawai'i and likely for other Pacific Island regions.

Novel assessment tools for inland aquaculture in the western Godavari delta region of Andhra Pradesh

The production of fisheries and shrimp has been twice every 10 years for the previous five decades, making it the most rapidly expanding food industry. This growth is due to intensive farming and the conversion of agriculture into aquaculture in many parts of South Asia. Furthermore, intensive aquaculture generates positive economic growth but leads to environmental degradation without proper monitoring. Unfortunately, technical innovation is less in aquaculture than agricultural and manufacturing industries. The advent of remote sensing and soft computing has expanded various opportunities for utilizing and integrating technological advances in civil and environmental disciplines. This paper presents the aquaculture scenario in the western Godavari delta region of Andhra Pradesh and proposes various novel assessment tools to monitor the aquaculture environment. An experimental investigation was carried out on the physicochemical characteristics of the inland aquaculture ponds to evaluate water quality in the aquaculture ponds. Furthermore, to assess the intensity of inland aquaculture, the current work concentrates on the potential application of remote sensing and soft computing approaches. Geospatial models of kriging and inverse distance weighing (IDW) show higher performance in estimating ammonia levels in the intensive aquaculture groundwaters with coefficient of determination (R2) values of 0.947 and 0.901, respectively. Teaching learning-based optimization (TLBO) and adaptive particle swarm optimization (APSO), two of the five soft computing techniques utilized in the study, perform better than the others. Additionally, it was found that remote sensing-based assessment tools and soft computing prediction models were both trustworthy, accurate, and easy to use. Furthermore, these methods could assist in the real-time evaluation of inland aquaculture waters by stakeholders and policymakers.

Development of a web-based modelling framework for harmful algal blooms transport simulation using open-source technologies

Desktop-based modelling packages presented typical limitations in interactive simulation. This study presents a web-based modelling framework that fully consolidated the simulation work-flow into a WebGIS application, providing a one-step solution for HABs transport simulation within an intuitive and interactive modelling environment. An improved Lagrangian particle-tracking scheme was proposed using fractional Brownian motion technique. The presented model was devoted to quickly forecast the transport pathways in both temporal and spatial dimensions, and evaluate the approximate trends and qualitative understanding of HABs development in data-poor situations. The web modelling platform was developed using multiple open-source JavaScript libraries. The developed WebGIS application provides user-friendly interfaces to prepare inputs, configure simulation settings, visualize, analyse, and validate simulation results within the same framework. The feasibility, capacity, and advantage of the proposed framework were tested and evaluated in a real-world application of red tide transport simulation in the Qinhuangdao coastal waters. The model results showed qualitative agreement with the red tide observed from remote sensing. Our experimental results demonstrated that the developed web-based modelling prototype would present a useful performance for study cases related to HABs transport simulation.

The Transformation and Export of Organic Carbon Across an Arctic River-Delta-Ocean Continuum

The Arctic Ocean is surrounded by land that feeds highly seasonal rivers with water enriched in high concentrations of dissolved and particulate organic carbon (DOC and POC). Explicit estimates of the flux of organic carbon across the land-ocean interface are difficult to quantify and many interdependent processes makes source attribution difficult. A high-resolution 3-D biogeochemical model was built for the lower Yukon River and coastal ocean to estimate biogeochemical cycling across the land-ocean continuum. The model solves for complex reactions related to organic carbon transformation, including mechanistic photodegradation and multi-reactivity microbial processing, DOC-POC flocculation, and phytoplankton dynamics. The baseline DOC and POC flux out of the delta from April to September 2019, was 977 and 536 Gg C (∼80% of the annual total), but only 50% of the DOC and 25% of the POC exited the plume across the 10 m isobath. Microbial breakdown of DOC accounted for a net loss of 168 Gg C (17% of delta export) within the plume and photodegradation accounted for a net loss of 46.6 Gg C DOC (5% of delta export) in 2019. Flocculation decreased the total organic carbon flux by only 6.4 Gg C (∼1%), while POC sinking accounted for 63.3 Gg C (10%) settling in the plume. The loss of chromophoric dissolved organic matter due to photodegradation increased the light available for phytoplankton growth throughout the coastal ocean, demonstrating the secondary effects that organic carbon reactions can have on biological processes and the net coastal carbon flux.

Do Two Different Approaches to the Season in Modeling Affect the Predicted Distribution of Fish? A Case Study for Decapterus maruadsi in the Offshore Waters of Southern Zhejiang, China

The relationships between environmental factors and fish density are closely related, and species distribution models (SDMs) have been widely used in exploring these relationships and predicting the spatial distribution of fishery resources. When exploring the prediction of the spatial distribution of species in different seasons, the method of choosing the appropriate approach to the season will help to improve the predictive performance of the model. Based on data collected from 2015 to 2020 during a survey off southern Zhejiang, the Tweedie-GAM was used to establish the relationship between the density of Decapterus maruadsi and environmental factors at different modeling approaches. The results showed that water temperature, salinity and depth were the main factors influencing D. maruadsi, and they operated through different mechanisms and even resulted in opposite trends of density in different seasons. Spatially, the two modeling approaches also differed in predicting the spatial distribution of D. maruadsi, with the seasonal model showing a higher density trend in inshore waters than in offshore waters in spring but showing the opposite trend in summer and autumn, which was more consistent with the actual spatial distribution of the resource. By analyzing the effects of two different approaches on the prediction of fishery resources, this study aims to provide research ideas and references for improving the predictive performance of SDMs.

A validation of satellite derived cyanobacteria detections with state reported events and recreation advisories across U.S. lakes

Cyanobacteria harmful algal blooms (cyanoHABs) negatively affect ecological, human, and animal health. Traditional methods of validating satellite algorithms with data from water samples are often inhibited by the expense of quantifying cyanobacteria indicators in the field and the lack of public data. However, state recreation advisories and other recorded events of cyanoHAB occurrence reported by local authorities can serve as an independent and publicly available dataset for validation. State recreation advisories were defined as a period delimited by a start and end date where a warning was issued due to detections of cyanoHABs over a state's risk threshold. State reported events were defined as any event that was documented with a single date related to cyanoHABs. This study examined the presence-absence agreement between 160 state reported cyanoHAB advisories and 1,343 events and cyanobacteria biomass estimated by a satellite algorithm called the Cyanobacteria Index (CI_cyano). The true positive rate of agreement with state recreation advisories was 69% and 60% with state reported events. CI_cyano detected a reduction or absence in cyanobacteria after 76% of the recreation advisories ended. CI_cyano was used to quantify the magnitude, spatial extent, and temporal frequency of cyanoHABs; each of these three metrics were greater (r > 0.2) during state recreation advisories compared to non-advisory times with effect sizes ranging from small to large. This is the first study to quantitatively evaluate satellite algorithm performance for detecting cyanoHABs with state reported events and advisories and supports informed management decisions with satellite technologies that complement traditional field observations.

Arctic marine forest distribution models showcase potentially severe habitat losses for cryophilic species under climate change

The Arctic is among the fastest-warming areas of the globe. Understanding the impact of climate change on foundational Arctic marine species is needed to provide insight on ecological resilience at high latitudes. Marine forests, the underwater seascapes formed by seaweeds, are predicted to expand their ranges further north in the Arctic in a warmer climate. Here, we investigated whether northern habitat gains will compensate for losses at the southern range edge by modelling marine forest distributions according to three distribution categories: cryophilic (species restricted to the Arctic environment), cryotolerant (species with broad environmental preferences inclusive but not limited to the Arctic environment), and cryophobic (species restricted to temperate conditions) marine forests. Using stacked MaxEnt models, we predicted the current extent of suitable habitat for contemporary and future marine forests under Representative Concentration Pathway Scenarios of increasing emissions (2.6, 4.5, 6.0, and 8.5). Our analyses indicate that cryophilic marine forests are already ubiquitous in the north, and thus cannot expand their range under climate change, resulting in an overall loss of habitat due to severe southern range contractions. The extent of marine forests within the Arctic basin, however, is predicted to remain largely stable under climate change with notable exceptions in some areas, particularly in the Canadian Archipelago. Succession may occur where cryophilic and cryotolerant species are extirpated at their southern range edge, resulting in ecosystem shifts towards temperate regimes at mid to high latitudes, though many aspects of these shifts, such as total biomass and depth range, remain to be field validated. Our results provide the first global synthesis of predicted changes to pan-Arctic coastal marine forest ecosystems under climate change and suggest ecosystem transitions are unavoidable now for some areas.

Relationship between Engraulis japonicus Resources and Environmental Factors Based on Multi-Model Comparison in Offshore Waters of Southern Zhejiang, China

In order to accurately explore the relationship between the density of Engraulis japonicus and environmental factors, five types of models, including Tweedie-Generalized Additive Model (GAM), two-stage GAM, Ad hoc-GAM, and Generalized Additive Mixing Model (GAMM), were compared based on the survey data in offshore waters of southern Zhejiang, China from 2015 to 2021 in this study. The results showed the best goodness of fit for two-stage GAM when processing the data of E. japonicus resource density. The deviance explained of GAM1 and GAM2 were 19.9 and 53.8%, respectively. According to this study, water temperature and salinity are important environmental factors affecting the distribution of E. japonicus, which are also closely related to latitude. In general, the resource density of E. japonicus decreases gradually with the increase in water temperature. When the salinity was between 26 ppt and 34 ppt, the resource density was higher. Also, there were some differences in the spatial distribution of E. japonicus in different seasons. The relationship between the resource density of E. japonicus and environmental factors was analyzed through various models to provide a scientific basis for the conservation management of E. japonicus in offshore waters of southern Zhejiang, China.

Inter-model comparison of simulated Gulf of Mexico hypoxia in response to reduced nutrient loads: effects of phytoplankton and organic matter parameterization

Complex simulation models are a valuable tool to inform nutrient management decisions aimed at reducing hypoxia in the northern Gulf of Mexico, yet simulated hypoxia response to reduced nutrients varies greatly between models. We compared two biogeochemical models driven by the same hydrodynamics, the Coastal Generalized Ecosystem Model (CGEM) and Gulf of Mexico Dissolved Oxygen Model (GoMDOM), to investigate how they differ in simulating hypoxia and their response to reduced nutrients. Different phytoplankton nutrient kinetics produced 2-3 times more hypoxic area and volume on the western shelf in CGEM compared to GoMDOM. Reductions in hypoxic area were greatest in the western shelf, comprising 72% (~4,200 km2) of the total shelfwide hypoxia response. The range of hypoxia responses from multiple models suggests a 60% load reduction may result in a 33% reduction in hypoxic area, leaving an annual hypoxic area of ~9,000 km2 based on the latest 5-yr average (13,928 km2).

The Efficiency of Hybrid Intelligent Models in Predicting Fiber-Reinforced Polymer Concrete Interfacial-Bond Strength

Fiber-reinforced polymer (FRP) has several benefits, in addition to excellent tensile strength and low self-weight, including corrosion resistance, high durability, and easy construction, making it among the most optimum options for concrete structure restoration. The bond behavior of the FRP-concrete (FRPC) interface, on the other hand, is extremely intricate, making the bond strength challenging to estimate. As a result, a robust modeling framework is necessary. In this paper, data-driven hybrid models are developed by combining state-of-the-art population-based algorithms (bald eagle search (BES), dynamic fitness distance balance-manta ray foraging optimization (dFDB-MRFO), RUNge Kutta optimizer (RUN)) and artificial neural networks (ANN) named “BES-ANN”, “dFDB-MRFO -ANN”, and “RUN-ANN” to estimate the FRPC interfacial-bond strength accurately. The efficacy of these models in predicting bond strength is examined using an extensive database of 969 experimental samples. Compared to the BES-ANN and dFDB-MRFO models, the RUN-ANN model better estimates the interfacial-bond strength. In addition, the SHapley Additive Explanations (SHAP) approach is used to help interpret the best model and examine how the features influence the model’s outcome. Among the studied hybrid models, the RUN-ANN algorithm is the most accurate model with the highest coefficient of determination (R² = 92%), least mean absolute error (0.078), and least coefficient of variation (18.6%). The RUN-ANN algorithm also outperformed mechanics-based models. Based on SHAP and sensitivity analysis method, the FRP bond length and width contribute more to the final prediction results.

A modeling study to determine the contribution of interbasin versus intrabasin phosphorus loads on the southwestern nearshore of Lake Ontario

Elevated phosphorus and nuisance algae such as Cladophora have been persistent environmental concerns in the coastal areas of Lake Ontario. Phosphorus is regarded as one of the drivers of nearshore Cladophora and the most likely mitigation that can be used to control levels of this nuisance algae in the lakes. The Niagara River, carrying the Lake Erie interbasin load, is the major contributor of the overall phosphorus load to Lake Ontario. Due to circulation patterns in the lake, this contribution is especially significant in the southwestern nearshore areas. Here we apply a mathematical model to provide insight into the relative contribution of the Niagara River versus loadings from local rivers (intrabasin loads) on the nearshore phosphorus concentrations in this region. We performed numerical experiments to determine to what extent the Niagara, Genesee and smaller local rivers impact the nearshore (< 20 m depth) phosphorus concentrations. Our model results show that the Niagara River dominates the nearshore region between its discharge location and the Genesee River's mouth, but the Genesee River strongly impacts the nearby Ontario Beach region in the very nearshore (< 5 m depth). Smaller rivers have some impact close to their discharge locations. However, uncertainty with the Niagara River phosphorus load is the limiting factor in making any credible nearshore phosphorus predictions. Model accuracy is also impacted by insufficient short time scale phosphorus loads for all of the rivers, the dynamic nature of the lake circulation in shallow nearshore areas, and the simplified assumptions of the model.

A Review of Modeling Approaches for Understanding and Monitoring the Environmental Effects of Marine Renewable Energy

Understanding the environmental effects of marine energy (ME) devices is fundamental for their sustainable development and efficient regulation. However, measuring effects is difficult given the limited number of operational devices currently deployed. Numerical modeling is a powerful tool for estimating environmental effects and quantifying risks. It is most effective when informed by empirical data and coordinated with the development and implementation of monitoring protocols. We reviewed modeling techniques and information needs for six environmental stressor–receptor interactions related to ME: changes in oceanographic systems, underwater noise, electromagnetic fields (EMFs), changes in habitat, collision risk, and displacement of marine animals. This review considers the effects of tidal, wave, and ocean current energy converters. We summarized the availability and maturity of models for each stressor–receptor interaction and provide examples involving ME devices when available and analogous examples otherwise. Models for oceanographic systems and underwater noise were widely available and sometimes applied to ME, but need validation in real-world settings. Many methods are available for modeling habitat change and displacement of marine animals, but few examples related to ME exist. Models of collision risk and species response to EMFs are still in stages of theory development and need more observational data, particularly about species behavior near devices, to be effective. We conclude by synthesizing model status, commonalities between models, and overlapping monitoring needs that can be exploited to develop a coordinated and efficient set of protocols for predicting and monitoring the environmental effects of ME.

Modeling the Morphodynamics of Coastal Responses to Extreme Events: What Shape Are We In?

This review focuses on recent advances in process-based numerical models of the impact of extreme storms on sandy coasts. Driven by larger-scale models of meteorology and hydrodynamics, these models simulate morphodynamics across the Sallenger storm-impact scale, including swash,collision, overwash, and inundation. Models are becoming both wider (as more processes are added) and deeper (as detailed physics replaces earlier parameterizations). Algorithms for wave-induced flows and sediment transport under shoaling waves are among the recent developments. Community and open-source models have become the norm. Observations of initial conditions (topography, land cover, and sediment characteristics) have become more detailed, and improvements in tropical cyclone and wave models provide forcing (winds, waves, surge, and upland flow) that is better resolved and more accurate, yielding commensurate improvements in model skill. We foresee that future storm-impact models will increasingly resolve individual waves, apply data assimilation, and be used in ensemble modeling modes to predict uncertainties.

Making spatial-temporal marine ecosystem modelling better - A perspective

Marine Ecosystem Models (MEMs) provide a deeper understanding of marine ecosystem dynamics. The United Nations Decade of Ocean Science for Sustainable Development has highlighted the need to deploy these complex mechanistic spatial-temporal models to engage policy makers and society into dialogues towards sustainably managed oceans. From our shared perspective, MEMs remain underutilized because they still lack formal validation, calibration, and uncertainty quantifications that undermines their credibility and uptake in policy arenas. We explore why these shortcomings exist and how to enable the global modelling community to increase MEMs' usefulness. We identify a clear gap between proposed solutions to assess model skills, uncertainty, and confidence and their actual systematic deployment. We attribute this gap to an underlying factor that the ecosystem modelling literature largely ignores: technical issues. We conclude by proposing a conceptual solution that is cost-effective, scalable and simple, because complex spatial-temporal marine ecosystem modelling is already complicated enough.

Influence of Spatial Scale Selection of Environmental Factors on the Prediction of Distribution of Coilia nasus in Changjiang River Estuary

An estuary region is a complex environment with a transition from fresh to brackish to salt water, and in which some environmental factors change dramatically over small ranges. Therefore, it is important to understand the impact of the selection of spatial scale on the prediction of the distribution of estuarine species. As the largest estuary in China, the Changjiang River estuary is the spawning ground, feeding ground, and migration channel for many species. Based on Coilia nasus, an important economic fish species in the Changjiang River estuary, this study uses the two-stage generalized additive model (GAM) to investigate the potential differences in the response of species’ spatial distribution when environmental factors are assessed at different spatial scales (1′ × 1′, 2′ × 2′, 3′ × 3′, 4′ × 4′, 5′ × 5′). The results showed the following: (1) according to the analysis of the variance inflation factor (VIF), the values of all environmental factors were less than three and we found no correlation among the environmental variables selected. (2) The first stage GAM retained six variables, including year, month, latitude (Lat), water depth (Depth, m), bottom salinity (Sal, mg/L), and chemical oxygen demand (COD, mg/L). The second stage GAM retained four variables, including Year, Lat, pH, and chlorophyll a (Chl-a, μg/L). (3) The mean value of the Chla for the 3′ × 3′ spatial scale was significantly lower than that of the other spatial scales, and the mean value of Sal for the 5′ × 5′ spatial scale was higher than that of the other spatial scales. (4) In terms of the spatial distribution of abundance, the distribution patterns of C. nasus predicted by all scales were not very similar, and the distribution patterns predicted by the 5′ × 5′ scale, in the autumn of 2012, were significantly different from those at other scales. Therefore, the selection of spatiotemporal scales may affect predictions of the spatial distributions of species. We suggest that potential spatiotemporal scale effects should be evaluated in future studies.

Modeling and comparison of count data containing zero values: a case study of Setipinna taty in the south inshore of Zhejiang, China

To effectively use the fishery count data containing zero values, Setipinna taty in the coastal waters of south inshore of Zhejiang in China from 2017 to 2019 was used in this study. Environmental factors, such as water temperature, water depth, and salinity, were selected to establish models and compare based on the generalized additive model (GAM) of the Tweedie distribution (Tweedie-GAM) and two-stage GAM, Ad hoc method, and generalized additive mixed model (GAMM). The results showed that each station accounted for a higher proportion of zero values and the two-stage GAM model had a higher deviation interpretation rate, and GAM I and GAM II had 19.6% and 60.4% deviation interpretation rates. The cross-validation results showed that the performance evaluation of the two-stage GAM model was the best and showed the highest R² value, the lowest average absolute error, and the relatively small root mean square error. This study found that the abundance of S. taty in the south inshore of Zhejiang was highest at around 21°C and 18°C in spring and autumn, and the abundance reached the highest at a water depth of about 20 m. In spatial distribution, the high value of the abundance of S. taty was mostly distributed in the coastal waters in the south of 28°N. In future research, models should be fitted and compared for different sampling zero-value ratios, and more environmental factors should be included to accurately find an optimal model and provide references for the conservation of fishery resources.

Responses of marine ecosystems to climate change impacts and their treatment in biogeochemical ecosystem models

To predict the effects of climate change on marine ecosystems and the effectiveness of intervention and mitigation strategies, we need reliable marine ecosystem response models such as biogeochemical models that reproduce climate change effects. We reviewed marine ecosystem parameters and processes that are modified by climate change and examined their representations in biogeochemical ecosystem models. The interactions among important aspects of marine ecosystem modelling are not often considered due to complexity: these include the use of multiple IPCC scenarios, ensemble modelling approach, independent calibration datasets, the consideration of changes in cloud cover, ocean currents, wind speed, sea-level rise, storm frequency, storm intensity, and the incorporation of species adaptation to changing environmental conditions. Including our recommendations in future marine modelling studies could help improve the accuracy and reliability of model predictions of climate change impacts on marine ecosystems.

Modeling Spatiotemporal Patterns of Ecosystem Metabolism and Organic Carbon Dynamics Affecting Hypoxia on the Louisiana Continental Shelf

The hypoxic zone on the Louisiana Continental Shelf (LCS) forms each summer due to nutrient enhanced primary production and seasonal stratification associated with freshwater discharges from the Mississippi/Atchafalaya River Basin (MARB). Recent field studies have identified highly productive shallow nearshore waters as an important component of shelf-wide carbon production contributing to hypoxia formation. In this study we present results from a three-dimensional hydrodynamic-biogeochemical model named CGEM (Coastal Generalized Ecosystem Model) applied to quantify the spatial and temporal patterns of hypoxia, carbon production, respiration, and transport between nearshore and middle shelf regions where hypoxia is most prevalent. We first demonstrate that our simulations successfully reproduced spatial and temporal patterns of carbon production, respiration, and bottom-water oxygen gradients compared to field observations. We then used interannual simulations to identify transport of particulate organic carbon (POC) from nearshore areas where riverine organic matter and phytoplankton carbon production are greatest. The spatial disconnect between carbon production and respiration in our simulations was driven by westward and offshore POC flux, a pattern that supported heterotrophic respiration on the middle shelf where hypoxia is frequently observed. These results validate the importance of offshore carbon flux to hypoxia formation, particularly on the west shelf where hypoxic conditions are more variable.

The impact of alternative nutrient kinetics and computational grid size on model predicted primary production and hypoxic area in the northern Gulf of Mexico

Model structure uncertainty is seldom calculated because of the difficulty and time required to perform such analyses. Here we explore how a coastal model using the Monod versus Droop formulations and a 6 km × 6 km versus 2 km 2 × km computational grid size predict primary production and hypoxic area in the Gulf of Mexico. Results from these models were compared to each other and to observations, and sensitivity analyses were performed. The different models fit the observations almost equally well. The 6k-model calculated higher rates of production and settling, and especially a larger hypoxic area, in comparison to the 2k-model. The Monod-based model calculated higher production, especially close to the river delta regions, but smaller summer hypoxic area, than the model using the Droop formulation. The Monod-based model was almost twice as sensitive to changes in nutrient loads in comparison to the Droop model, which can have management implications.

From data compilation to model validation: a comprehensive analysis of a full deep-sea ecosystem model of the Chatham Rise

The Chatham Rise is a highly productive deep-sea ecosystem that supports numerous substantial commercial fisheries, and is a likely candidate for an ecosystem based approach to fisheries management in New Zealand. We present the first end-to-end ecosystem model of the Chatham Rise, which is also to the best of our knowledge, the first end-to-end ecosystem model of any deep-sea ecosystem. We describe the process of data compilation through to model validation and analyse the importance of knowledge gaps with respect to model dynamics and results. The model produces very similar results to fisheries stock assessment models for key fisheries species, and the population dynamics and system interactions are realistic. Confidence intervals based on bootstrapping oceanographic variables are produced. The model components that have knowledge gaps and are most likely to influence model results were oceanographic variables, and the aggregate species groups ‘seabird’ and ‘cetacean other’. We recommend applications of the model, such as forecasting biomasses under various fishing regimes, include alternatives that vary these components.

Sensitivity of the Norwegian and Barents Sea Atlantis end-to-end ecosystem model to parameter perturbations of key species

Using end-to-end models for ecosystem-based management requires knowledge of the structure, uncertainty and sensitivity of the model. The Norwegian and Barents Seas (NoBa) Atlantis model was implemented for use in ‘what if’ scenarios, combining fisheries management strategies with the influences of climate change and climate variability. Before being used for this purpose, we wanted to evaluate and identify sensitive parameters and whether the species position in the foodweb influenced their sensitivity to parameter perturbation. Perturbing recruitment, mortality, prey consumption and growth by +/- 25% for nine biomass-dominating key species in the Barents Sea, while keeping the physical climate constant, proved the growth rate to be the most sensitive parameter in the model. Their trophic position in the ecosystem (lower trophic level, mid trophic level, top predators) influenced their responses to the perturbations. Top-predators, being generalists, responded mostly to perturbations on their individual life-history parameters. Mid-level species were the most vulnerable to perturbations, not only to their own individual life-history parameters, but also to perturbations on other trophic levels (higher or lower). Perturbations on the lower trophic levels had by far the strongest impact on the system, resulting in biomass changes for nearly all components in the system. Combined perturbations often resulted in non-additive model responses, including both dampened effects and increased impact of combined perturbations. Identifying sensitive parameters and species in end-to-end models will not only provide insights about the structure and functioning of the ecosystem in the model, but also highlight areas where more information and research would be useful—both for model parameterization, but also for constraining or quantifying model uncertainty.

Integration of GIS and a Lagrangian Particle-Tracking Model for Harmful Algal Bloom Trajectories Prediction

Harmful algal bloom (HAB) is a major environmental problem in coastal waters around the world. The technologies and approaches for short-term forecasting of the HABs trajectories have obtained increasing attention from researchers. In this paper, we present a straightforward physical-based model based on a non-Fickian Lagrangian particle-tracking scheme for understanding the movement of detected HABs. The model adopts the fractional Brownian motion (fBm) technology, and is coupled with the Delft3D and WRF models and GIS. The fBm based Lagrangian particle-tracking model can flexibly control the scale of the particle clouds diffusion through Hurst value, which can be used to account for uncertainties and adjust for better representing the trajectories of HABs. Simulation results demonstrate that the presented model can successfully predict the trends and the main features of red tide drifting. The developed simulation tool enables users to create the model configuration, manage data inputs, run the model, and generate model maps and animations within a GIS environment. It is believed that the model and the tool outlined herein can be very useful for rapidly evaluating potential areas at risk from the HABs events.

Dermal mycobacteriosis and warming sea surface temperatures are associated with elevated mortality of striped bass in Chesapeake Bay

Temperature is hypothesized to alter disease dynamics, particularly when species are living at or near their thermal limits. When disease occurs in marine systems, this can go undetected, particularly if the disease is chronic and progresses slowly. As a result, population-level impacts of diseases can be grossly underestimated. Complex migratory patterns, stochasticity in recruitment, and data and knowledge gaps can hinder collection and analysis of data on marine diseases. New tools enabling quantification of disease impacts in marine environments include coupled biogeochemical hydrodynamic models (to hindcast key environmental data), and multievent, multistate mark-recapture (MMSMR) (to quantify the effects of environmental conditions on disease processes and assess population-level impacts). We used MMSMR to quantify disease processes and population impacts in an estuarine population of striped bass (Morone saxatilis) in Chesapeake Bay from 2005 to 2013. Our results supported the hypothesis that mycobacteriosis is chronic, progressive, and, frequently, lethal. Yearly disease incidence in fish age three and above was 89%, suggesting that this disease impacts nearly every adult striped bass. Mortality of diseased fish was high, particularly in severe cases, where it approached 80% in typical years. Severely diseased fish also had a 10-fold higher catchability than healthy fish, which could bias estimates of disease prevalence. For both healthy and diseased fish, mortality increased with the modeled average summer sea surface temperature (SST) at the mouth of the Rappahannock River; in warmer summers (average SST ≥ 29°C), a cohort is predicted to experience >90% mortality in 1 year. Regression of disease signs in mildly and moderately diseased fish was <2%. These results suggest that these fish are living at their maximum thermal tolerance and that this is driving increased disease and mortality. Management of this fishery should account for the effects of temperature and disease on impacted populations.

Performance metrics for the assessment of satellite data products: an ocean color case study

Performance assessment of ocean color satellite data has generally relied on statistical metrics chosen for their common usage and the rationale for selecting certain metrics is infrequently explained. Commonly reported statistics based on mean squared errors, such as the coefficient of determination (r²), root mean square error, and regression slopes, are most appropriate for Gaussian distributions without outliers and, therefore, are often not ideal for ocean color algorithm performance assessment, which is often limited by sample availability. In contrast, metrics based on simple deviations, such as bias and mean absolute error, as well as pair-wise comparisons, often provide more robust and straightforward quantities for evaluating ocean color algorithms with non-Gaussian distributions and outliers. This study uses a SeaWiFS chlorophyll-a validation data set to demonstrate a framework for satellite data product assessment and recommends a multi-metric and user-dependent approach that can be applied within science, modeling, and resource management communities.

Model application niche analysis: Assessing the transferability and generalizability of ecological models

The use of models by ecologists and environmental managers, to inform environmental management and decision-making, has grown exponentially in the past 50 years. Due to logistical, economical, and theoretical benefits, model users frequently transfer preexisting models to new sites where data are scarce. Modelers have made significant progress in understanding how to improve model generalizability during model development. However, models are always imperfect representations of systems and are constrained by the contextual frameworks used during their development. Thus, model users need better ways to evaluate the possibility of unintentional misapplication when transferring models to new sites. We propose a method of describing a model's application niche for use during the model selection process. Using this method, model users synthesize information from databases, past studies, and/or past model transfers to create model performance curves and heat maps. We demonstrated this method using an empirical model developed to predict the ecological condition of plant communities in riverine wetlands of the Appalachian Highland physiographic region, U.S.A. We assessed this model's transferability and generalizability across (1) riverine wetlands in the contiguous U.S.A., (2) wetland types in the Appalachian Highland physiographic region, and (3) wetland types in the contiguous U.S.A. With this methodology and a discussion of its critical steps, we set the stage for further inquiries into the development of consistent and transparent practices for model selection when transferring a model.

Improved method for calibration of exchange flows for a physical transport box model of Tampa Bay, FL USA

We report the results for both sequential and simultaneous calibration of exchange flows between segments of a 10-box, 1-dimensional, well mixed, bifurcated tidal mixing model for Tampa Bay. Calibrations were conducted for three model options having different mathematical expressions for evaporative loss. In approaching this project we asked three questions: does simultaneous calibration or sequential calibration yield better box model performance; which evaporation option best predicts observed salinities; and how well does model performance compare to more complex hydrodynamic models. Sequential calibration followed the classical salt balance and steady state approach. The nonlinear parameter estimator (PEST) was used for simultaneous calibration. The sequential approach proved useful in evaluating the three evaporation options. However, simultaneous calibration proved superior in predicting observed salinities but was ineffective in discerning differences between evaporation options. The simultaneously calibrated model produced residence times that fell within the range of more complex hydrodynamic models of Tampa Bay.

Stock assessment and end-to-end ecosystem models alter dynamics of fisheries data

Although all models are simplified approximations of reality, they remain useful tools for understanding, predicting, and managing populations and ecosystems. However, a model’s utility is contingent on its suitability for a given task. Here, we examine two model types: single-species fishery stock assessment and multispecies marine ecosystem models. Both are efforts to predict trajectories of populations and ecosystems to inform fisheries management and conceptual understanding. However, many of these ecosystems exhibit nonlinear dynamics, which may not be represented in the models. As a result, model outputs may underestimate variability and overestimate stability. Using nonlinear forecasting methods, we compare predictability and nonlinearity of model outputs against model inputs using data and models for the California Current System. Compared with model inputs, time series of model-processed outputs show more predictability but a higher prevalence of linearity, suggesting that the models misrepresent the actual predictability of the modeled systems. Thus, caution is warranted: using such models for management or scenario exploration may produce unforeseen consequences, especially in the context of unknown future impacts.

Net primary productivity estimates and environmental variables in the Arctic Ocean: An assessment of coupled physical-biogeochemical models

The relative skill of 21 regional and global biogeochemical models was assessed in terms of how well the models reproduced observed net primary productivity (NPP) and environmental variables such as nitrate concentration (NO₃), mixed layer depth (MLD), euphotic layer depth (Z_eu), and sea ice concentration, by comparing results against a newly updated, quality-controlled in situ NPP database for the Arctic Ocean (1959-2011). The models broadly captured the spatial features of integrated NPP (iNPP) on a pan-Arctic scale. Most models underestimated iNPP by varying degrees in spite of overestimating surface NO₃, MLD, and Z_eu throughout the regions. Among the models, iNPP exhibited little difference over sea ice condition (ice-free versus ice-influenced) and bottom depth (shelf versus deep ocean). The models performed relatively well for the most recent decade and toward the end of Arctic summer. In the Barents and Greenland Seas, regional model skill of surface NO₃ was best associated with how well MLD was reproduced. Regionally, iNPP was relatively well simulated in the Beaufort Sea and the central Arctic Basin, where in situ NPP is low and nutrients are mostly depleted. Models performed less well at simulating iNPP in the Greenland and Chukchi Seas, despite the higher model skill in MLD and sea ice concentration, respectively. iNPP model skill was constrained by different factors in different Arctic Ocean regions. Our study suggests that better parameterization of biological and ecological microbial rates (phytoplankton growth and zooplankton grazing) are needed for improved Arctic Ocean biogeochemical modeling.

Evaluation of the complexity and performance of marine planktonic trophic models

Planktonic models represent a powerful tool for creating hypotheses and making predictions about the functioning of marine ecosystems. Their complexity varies according to the number of state variables and the choice of functional forms. We evaluated plankton models during the last 15 years (n =145) with the aims of understanding why they differ in complexity, evaluating model robustness, and describing studies of plankton modelling around the globe. We classified models into four groups: Nutrient-Phytoplankton-Zooplankton (NPZ), Nutrient-Phytoplankton-Zooplankton-Detritus (NPZD), Size-Structured (SS) and Plankton-Functional-Type (PFT). Our results revealed that the number of state variables varied according to the question being addressed: NPZ models were more frequently applied in physical-biological studies, while PFT models were more applied for investigating biogeochemical cycles. Most models were based on simple functional forms which neglect important feedback related to control of plankton dynamics. Modelling studies sometimes failed to describe sensitivity analysis, calibration and validation. The importance of testing different functional forms was commonly overlooked, and the lack of empirical data affected the verification of model robustness. Lastly, we highlight the need to develop modelling studies in the Southern Hemisphere, including Brazil, in order to provide predictions that assist the management of marine ecosystems.

Increasing the Depth of Current Understanding: Sensitivity Testing of Deep-Sea Larval Dispersal Models for Ecologists

Larval dispersal is an important ecological process of great interest to conservation and the establishment of marine protected areas. Increasing numbers of studies are turning to biophysical models to simulate dispersal patterns, including in the deep-sea, but for many ecologists unassisted by a physical oceanographer, a model can present as a black box. Sensitivity testing offers a means to test the models’ abilities and limitations and is a starting point for all modelling efforts. The aim of this study is to illustrate a sensitivity testing process for the unassisted ecologist, through a deep-sea case study example, and demonstrate how sensitivity testing can be used to determine optimal model settings, assess model adequacy, and inform ecological interpretation of model outputs. Five input parameters are tested (timestep of particle simulator (TS), horizontal (HS) and vertical separation (VS) of release points, release frequency (RF), and temporal range (TR) of simulations) using a commonly employed pairing of models. The procedures used are relevant to all marine larval dispersal models. It is shown how the results of these tests can inform the future set up and interpretation of ecological studies in this area. For example, an optimal arrangement of release locations spanning a release area could be deduced; the increased depth range spanned in deep-sea studies may necessitate the stratification of dispersal simulations with different numbers of release locations at different depths; no fewer than 52 releases per year should be used unless biologically informed; three years of simulations chosen based on climatic extremes may provide results with 90% similarity to five years of simulation; and this model setup is not appropriate for simulating rare dispersal events. A step-by-step process, summarising advice on the sensitivity testing procedure, is provided to inform all future unassisted ecologists looking to run a larval dispersal simulation.

Why marine phytoplankton calcify

Calcifying marine phytoplankton-coccolithophores- are some of the most successful yet enigmatic organisms in the ocean and are at risk from global change. To better understand how they will be affected, we need to know "why" coccolithophores calcify. We review coccolithophorid evolutionary history and cell biology as well as insights from recent experiments to provide a critical assessment of the costs and benefits of calcification. We conclude that calcification has high energy demands and that coccolithophores might have calcified initially to reduce grazing pressure but that additional benefits such as protection from photodamage and viral/bacterial attack further explain their high diversity and broad spectrum ecology. The cost-benefit aspect of these traits is illustrated by novel ecosystem modeling, although conclusive observations remain limited. In the future ocean, the trade-off between changing ecological and physiological costs of calcification and their benefits will ultimately decide how this important group is affected by ocean acidification and global warming.

A proposed methodology for the assessment of arsenic, nickel, cadmium and lead levels in ambient air

Air quality assessment, required by the European Union (EU) Air Quality Directive, Directive 2008/50/EC, is part of the functions attributed to Environmental Management authorities. Based on the cost and time consumption associated with the experimental works required for the air quality assessment in relation to the EU-regulated metal and metalloids, other methods such as modelling or objective estimation arise as competitive alternatives when, in accordance with the Air Quality Directive, the levels of pollutants permit their use at a specific location. This work investigates the possibility of using statistical models based on Partial Least Squares Regression (PLSR) and Artificial Neural Networks (ANNs) to estimate the levels of arsenic (As), cadmium (Cd), nickel (Ni) and lead (Pb) in ambient air and their application for policy purposes. A methodology comprising the main steps that should be taken into consideration to prepare the input database, develop the model and evaluate their performance is proposed and applied to a case of study in Santander (Spain). It was observed that even though these approaches present some difficulties in estimating the individual sample concentrations, having an equivalent performance they can be considered valid for the estimation of the mean values - those to be compared with the limit/target values - fulfilling the uncertainty requirements in the context of the Air Quality Directive. Additionally, the influence of the consideration of input variables related to atmospheric stability on the performance of the studied statistical models has been determined. Although the consideration of these variables as additional inputs had no effect on As and Cd models, they did yield an improvement for Pb and Ni, especially with regard to ANN models.

Management Strategy Evaluation Applied to Coral Reef Ecosystems in Support of Ecosystem-Based Management

Ecosystem modelling is increasingly used to explore ecosystem-level effects of changing environmental conditions and management actions. For coral reefs there has been increasing interest in recent decades in the use of ecosystem models for evaluating the effects of fishing and the efficacy of marine protected areas. However, ecosystem models that integrate physical forcings, biogeochemical and ecological dynamics, and human induced perturbations are still underdeveloped. We applied an ecosystem model (Atlantis) to the coral reef ecosystem of Guam using a suite of management scenarios prioritized in consultation with local resource managers to review the effects of each scenario on performance measures related to the ecosystem, the reef-fish fishery (e.g., fish landings) and coral habitat. Comparing tradeoffs across the selected scenarios showed that each scenario performed best for at least one of the selected performance indicators. The integrated ‘full regulation’ scenario outperformed other scenarios with four out of the six performance metrics at the cost of reef-fish landings. This model application quantifies the socio-ecological costs and benefits of alternative management scenarios. When the effects of climate change were taken into account, several scenarios performed equally well, but none prevented a collapse in coral biomass over the next few decades assuming a business-as-usual greenhouse gas emissions scenario.

Progress and challenges in coupled hydrodynamic-ecological estuarine modeling

Numerical modeling has emerged over the last several decades as a widely accepted tool for investigations in environmental sciences. In estuarine research, hydrodynamic and ecological models have moved along parallel tracks with regard to complexity, refinement, computational power, and incorporation of uncertainty. Coupled hydrodynamic-ecological models have been used to assess ecosystem processes and interactions, simulate future scenarios, and evaluate remedial actions in response to eutrophication, habitat loss, and freshwater diversion. The need to couple hydrodynamic and ecological models to address research and management questions is clear, because dynamic feedbacks between biotic and physical processes are critical interactions within ecosystems. In this review we present historical and modern perspectives on estuarine hydrodynamic and ecological modeling, consider model limitations, and address aspects of model linkage, skill assessment, and complexity. We discuss the balance between spatial and temporal resolution and present examples using different spatiotemporal scales. Finally, we recommend future lines of inquiry, approaches to balance complexity and uncertainty, and model transparency and utility. It is idealistic to think we can pursue a "theory of everything" for estuarine models, but recent advances suggest that models for both scientific investigations and management applications will continue to improve in terms of realism, precision, and accuracy.

Rapid evolution of metabolic traits explains thermal adaptation in phytoplankton

Understanding the mechanisms that determine how phytoplankton adapt to warming will substantially improve the realism of models describing ecological and biogeochemical effects of climate change. Here, we quantify the evolution of elevated thermal tolerance in the phytoplankton, Chlorella vulgaris. Initially, population growth was limited at higher temperatures because respiration was more sensitive to temperature than photosynthesis meaning less carbon was available for growth. Tolerance to high temperature evolved after ≈ 100 generations via greater down-regulation of respiration relative to photosynthesis. By down-regulating respiration, phytoplankton overcame the metabolic constraint imposed by the greater temperature sensitivity of respiration and more efficiently allocated fixed carbon to growth. Rapid evolution of carbon-use efficiency provides a potentially general mechanism for thermal adaptation in phytoplankton and implies that evolutionary responses in phytoplankton will modify biogeochemical cycles and hence food web structure and function under warming. Models of climate futures that ignore adaptation would usefully be revisited.

Ecosystem Model Skill Assessment. Yes We Can!

Need to Assess the Skill of Ecosystem Models

Accelerated changes to global ecosystems call for holistic and integrated analyses of past, present and future states under various pressures to adequately understand current and projected future system states. Ecosystem models can inform management of human activities in a complex and changing environment, but are these models reliable? Ensuring that models are reliable for addressing management questions requires evaluating their skill in representing real-world processes and dynamics. Skill has been evaluated for just a limited set of some biophysical models. A range of skill assessment methods have been reviewed but skill assessment of full marine ecosystem models has not yet been attempted.

Northeast US Atlantis Marine Ecosystem Model

We assessed the skill of the Northeast U.S. (NEUS) Atlantis marine ecosystem model by comparing 10-year model forecasts with observed data. Model forecast performance was compared to that obtained from a 40-year hindcast. Multiple metrics (average absolute error, root mean squared error, modeling efficiency, and Spearman rank correlation), and a suite of time-series (species biomass, fisheries landings, and ecosystem indicators) were used to adequately measure model skill. Overall, the NEUS model performed above average and thus better than expected for the key species that had been the focus of the model tuning. Model forecast skill was comparable to the hindcast skill, showing that model performance does not degenerate in a 10-year forecast mode, an important characteristic for an end-to-end ecosystem model to be useful for strategic management purposes.

Skill Assessment Is Both Possible and Advisable

We identify best-practice approaches for end-to-end ecosystem model skill assessment that would improve both operational use of other ecosystem models and future model development. We show that it is possible to not only assess the skill of a complicated marine ecosystem model, but that it is necessary do so to instill confidence in model results and encourage their use for strategic management. Our methods are applicable to any type of predictive model, and should be considered for use in fields outside ecology (e.g. economics, climate change, and risk assessment).

An Integrated Coral Reef Ecosystem Model to Support Resource Management under a Changing Climate

Millions of people rely on the ecosystem services provided by coral reefs, but sustaining these benefits requires an understanding of how reefs and their biotic communities are affected by local human-induced disturbances and global climate change. Ecosystem-based management that explicitly considers the indirect and cumulative effects of multiple disturbances has been recommended and adopted in policies in many places around the globe. Ecosystem models give insight into complex reef dynamics and their responses to multiple disturbances and are useful tools to support planning and implementation of ecosystem-based management. We adapted the Atlantis Ecosystem Model to incorporate key dynamics for a coral reef ecosystem around Guam in the tropical western Pacific. We used this model to quantify the effects of predicted climate and ocean changes and current levels of current land-based sources of pollution (LBSP) and fishing. We used the following six ecosystem metrics as indicators of ecosystem state, resilience and harvest potential: 1) ratio of calcifying to non-calcifying benthic groups, 2) trophic level of the community, 3) biomass of apex predators, 4) biomass of herbivorous fishes, 5) total biomass of living groups and 6) the end-to-start ratio of exploited fish groups. Simulation tests of the effects of each of the three drivers separately suggest that by mid-century climate change will have the largest overall effect on this suite of ecosystem metrics due to substantial negative effects on coral cover. The effects of fishing were also important, negatively influencing five out of the six metrics. Moreover, LBSP exacerbates this effect for all metrics but not quite as badly as would be expected under additive assumptions, although the magnitude of the effects of LBSP are sensitive to uncertainty associated with primary productivity. Over longer time spans (i.e., 65 year simulations), climate change impacts have a slight positive interaction with other drivers, generally meaning that declines in ecosystem metrics are not as steep as the sum of individual effects of the drivers. These analyses offer one way to quantify impacts and interactions of particular stressors in an ecosystem context and so provide guidance to managers. For example, the model showed that improving water quality, rather than prohibiting fishing, extended the timescales over which corals can maintain high abundance by at least 5–8 years. This result, in turn, provides more scope for corals to adapt or for resilient species to become established and for local and global management efforts to reduce or reverse stressors.

Temperature and residence time controls on an estuarine harmful algal bloom: Modeling hydrodynamics and Alexandrium fundyense in Nauset estuary

A highly resolved, 3-d model of hydrodynamics and Alexandrium fundyense in an estuarine embayment has been developed to investigate the physical and biological controls on a recurrent harmful algal bloom. Nauset estuary on Cape Cod (MA, USA) consists of three salt ponds connected to the ocean through a shallow marsh and network of tidal channels. The model is evaluated using quantitative skill metrics against observations of physical and biological conditions during three spring blooms. The A. fundyense model is based on prior model applications for the nearby Gulf of Maine, but notable modifications were made to be consistent with the Nauset observations. The dominant factors controlling the A. fundyense bloom in Nauset were the water temperature, which regulates organism growth rates, and the efficient retention of cells due to bathymetric constraints, stratification, and cell behavior (diel vertical migration). Spring-neap variability in exchange altered residence times, but for cell retention to be substantially longer than the cell doubling time required both active vertical migration and stratification that inhibits mixing of cells into the surface layer by wind and tidal currents. Unlike in the Gulf of Maine, the model results were relatively insensitive to cyst distributions or germination rates. Instead, in Nauset, high apparent rates of vegetative cell division by retained populations dictated bloom development. Cyst germination occurred earlier in the year than in the Gulf of Maine, suggesting that Nauset cysts have different controls on germination timing. The model results were relatively insensitive to nutrient concentrations, due to eutrophic conditions in the highly impacted estuary or due to limitations in the spatial and temporal resolution of nutrient sampling. Cell loss rates were inferred to be extremely low during the growth phase of the bloom, but increased rapidly during the final phase due to processes that remain uncertain. The validated model allows a quantitative assessment of the factors that contribute to the development of a recurrent harmful algal bloom and provides a framework for assessing similarly impacted coastal systems.

An assessment of phytoplankton primary productivity in the Arctic Ocean from satellite ocean color/in situ chlorophyll-a based models

We investigated 32 net primary productivity (NPP) models by assessing skills to reproduce integrated NPP in the Arctic Ocean. The models were provided with two sources each of surface chlorophyll-a concentration (chlorophyll), photosynthetically available radiation (PAR), sea surface temperature (SST), and mixed-layer depth (MLD). The models were most sensitive to uncertainties in surface chlorophyll, generally performing better with in situ chlorophyll than with satellite-derived values. They were much less sensitive to uncertainties in PAR, SST, and MLD, possibly due to relatively narrow ranges of input data and/or relatively little difference between input data sources. Regardless of type or complexity, most of the models were not able to fully reproduce the variability of in situ NPP, whereas some of them exhibited almost no bias (i.e., reproduced the mean of in situ NPP). The models performed relatively well in low-productivity seasons as well as in sea ice-covered/deep-water regions. Depth-resolved models correlated more with in situ NPP than other model types, but had a greater tendency to overestimate mean NPP whereas absorption-based models exhibited the lowest bias associated with weaker correlation. The models performed better when a subsurface chlorophyll-a maximum (SCM) was absent. As a group, the models overestimated mean NPP, however this was partly offset by some models underestimating NPP when a SCM was present. Our study suggests that NPP models need to be carefully tuned for the Arctic Ocean because most of the models performing relatively well were those that used Arctic-relevant parameters.

Chesapeake Bay nitrogen fluxes derived from a land-estuarine ocean biogeochemical modeling system: Model description, evaluation, and nitrogen budgets

The Chesapeake Bay plays an important role in transforming riverine nutrients before they are exported to the adjacent continental shelf. Although the mean nitrogen budget of the Chesapeake Bay has been previously estimated from observations, uncertainties associated with interannually varying hydrological conditions remain. In this study, a land-estuarine-ocean biogeochemical modeling system is developed to quantify Chesapeake riverine nitrogen inputs, within-estuary nitrogen transformation processes and the ultimate export of nitrogen to the coastal ocean. Model skill was evaluated using extensive in situ and satellite-derived data, and a simulation using environmental conditions for 2001-2005 was conducted to quantify the Chesapeake Bay nitrogen budget. The 5 year simulation was characterized by large riverine inputs of nitrogen (154 × 10⁹ g N yr^-1) split roughly 60:40 between inorganic:organic components. Much of this was denitrified (34 × 10⁹ g N yr^-1) and buried (46 × 10⁹ g N yr^-1) within the estuarine system. A positive net annual ecosystem production for the bay further contributed to a large advective export of organic nitrogen to the shelf (91 × 10⁹ g N yr^-1) and negligible inorganic nitrogen export. Interannual variability was strong, particularly for the riverine nitrogen fluxes. In years with higher than average riverine nitrogen inputs, most of this excess nitrogen (50-60%) was exported from the bay as organic nitrogen, with the remaining split between burial, denitrification, and inorganic export to the coastal ocean. In comparison to previous simulations using generic shelf biogeochemical model formulations inside the estuary, the estuarine biogeochemical model described here produced more realistic and significantly greater exports of organic nitrogen and lower exports of inorganic nitrogen to the shelf.

Assessing the water quality response to an alternative sewage disposal strategy at bathing sites on the east coast of Ireland

A three-dimensional model is used to assess the bathing water quality of Bray and Killiney bathing sites in Ireland following changes to the sewage management system. The model, firstly calibrated to hydrodynamic and water quality data from the period prior to the upgrade of the Wastewater Treatment Works (WwTW), was then used to simulate Escherichia coli (E. coli) distributions for discharge scenarios of the periods prior to and following the upgrade of the WwTW under dry and wet weather conditions. E. coli distributions under dry weather conditions demonstrate that the upgrade in the WwTW has remarkably improved the bathing water quality to a Blue Flag status. The new discharge strategy is expected to drastically reduce the rainfall-related incidents in which environmental limits of the Bathing Water Directive are breached. However, exceedances to these limits may still occur under wet weather conditions at Bray bathing site due to storm overflows that may still be discharged through two sea outfalls offshore of Bray bathing site.