scholarly journals Predicting Harmful Algal Blooms and Impacts on Shellfish Mariculture using Novel Data-Driven Approaches

2021 ◽  
Author(s):  
Oliver Stoner ◽  
Theo Economou ◽  
Ricardo Torres ◽  
Ian Ashton ◽  
Andrew Brown
Keyword(s):  
Harmful Algal Blooms ◽  
Algal Blooms ◽  
Statistical Modelling ◽  
Data Driven ◽  
Short Term ◽  
Marine Life ◽  
Learning Framework ◽  
Modelling Framework ◽  
Bivalve Shellfish

Abstract Harmful algal blooms (HABs) intoxicate and asphyxiate marine life, causing devastating environmental and socio-economic impacts costing at least $8bn/yr globally. Accumulation of phycotoxins from HAB phytoplankton in filter-feeding shellfish can poison human consumers, prompting site harvesting closures if concentrations in shellfish exceed safe levels. To better quantify both long- and short-term HAB risks, we developed novel data-driven approaches to predict phycotoxin concentrations in bivalve shellfish associated with HAB forming Dinophysis species. Our spatiotemporal statistical modelling framework assesses long-term HAB risks for different shellfish species in both data-rich and data-poor locations. This can revolutionise mariculture management by more confidently informing optimal siting of new shellfish operations and safe harvesting periods for businesses. Meanwhile, our machine learning framework forecasts phycotoxin concentrations further into the future than previously possible. Across 6 coastal, estuarine and loch sites, we achieve 87% overall accuracy in predicting future harvesting shutdowns 0-8 weeks ahead.

National coastal management challenges and needs

Shore & Beach ◽  
2020 ◽  
pp. 34-43
Author(s):  
Nicole Elko ◽  
Tiffany Roberts Briggs
Keyword(s):  
Coastal Management ◽  
Harmful Algal Blooms ◽  
Algal Blooms ◽  
Local Community ◽  
Extreme Event ◽  
Technical Information ◽  
Interdisciplinary Approach ◽  
Beach Erosion ◽  
The U.S

In partnership with the U.S. Geological Survey Coastal and Marine Hazards and Resources Program (USGS CMHRP) and the U.S. Coastal Research Program (USCRP), the American Shore and Beach Preservation Association (ASBPA) has identified coastal stakeholders’ top coastal management challenges. Informed by two annual surveys, a multiple-choice online poll was conducted in 2019 to evaluate stakeholders’ most pressing problems and needs, including those they felt most ill-equipped to deal with in their day-to-day duties and which tools they most need to address these challenges. The survey also explored where users find technical information and what is missing. From these results, USGS CMHRP, USCRP, ASBPA, and other partners aim to identify research needs that will inform appropriate investments in useful science, tools, and resources to address today’s most pressing coastal challenges. The 15-question survey yielded 134 complete responses with an 80% completion rate from coastal stakeholders such as local community representatives and their industry consultants, state and federal agency representatives, and academics. Respondents from the East, Gulf, West, and Great Lakes coasts, as well as Alaska and Hawaii, were represented. Overall, the prioritized coastal management challenges identified by the survey were: Deteriorating ecosystems leading to reduced (environmental, recreational, economic, storm buffer) functionality, Increasing storminess due to climate change (i.e. more frequent and intense impacts), Coastal flooding, both Sea level rise and associated flooding (e.g. nuisance flooding, king tides), and Combined effects of rainfall and surge on urban flooding (i.e. episodic, short-term), Chronic beach erosion (i.e. high/increasing long-term erosion rates), and Coastal water quality, including harmful algal blooms (e.g. red tide, sargassum). A careful, systematic, and interdisciplinary approach should direct efforts to identify specific research needed to tackle these challenges. A notable shift in priorities from erosion to water-related challenges was recorded from respondents with organizations initially formed for beachfront management. In addition, affiliation-specific and regional responses varied, such as Floridians concern more with harmful algal blooms than any other human and ecosystem health related challenge. The most common need for additional coastal management tools and strategies related to adaptive coastal management to maintain community resilience and continuous storm barriers (dunes, structures), as the top long-term and extreme event needs, respectively. In response to questions about missing information that agencies can provide, respondents frequently mentioned up-to-date data on coastal systems and solutions to challenges as more important than additional tools.

scholarly journals Saxitoxin and tetrodotoxin bioavailability increases in future oceans

2019 ◽  
Author(s):  
C.C. Roggatz ◽  
N. Fletcher ◽  
D.M. Benoit ◽  
A.C. Algar ◽  
A. Doroff ◽  
...  
Keyword(s):  
Species Interactions ◽  
Harmful Algal Blooms ◽  
Algal Blooms ◽  
Paralytic Shellfish Poisoning ◽  
Ecosystem Stability ◽  
Shellfish Poisoning ◽  
Marine Life ◽  
Paralytic Shellfish ◽  
Water Ph ◽  
Ph Increase

Increasing atmospheric levels of carbon dioxide are largely absorbed by the world’s oceans, decreasing surface water pH1. In combination with increasing ocean temperatures, these changes have been identified as a major sustainability threat to future marine life2. Interactions between marine organisms are known to depend on biomolecules, but the influence of oceanic pH on their bioavailability and functionality remains unexplored. Here we show that global change significantly impacts two ecological keystone molecules3 in the ocean, the paralytic toxins saxitoxin (STX) and tetrodotoxin (TTX). Increasing temperatures and declining pH increase the abundance of the toxic forms of these two neurotoxins in the water. Our geospatial global model highlights where this increased toxicity could intensify the devastating impact of harmful algal blooms on ecosystems in the future, for example through an increased incidence of paralytic shellfish poisoning (PSP). We also use these results to calculate future saxitoxin toxicity levels in Alaskan clams, Saxidomus gigantea, showing critical exceedance of limits save for consumption. Our findings for TTX and STX exemplarily highlight potential consequences of changing pH and temperature on chemicals dissolved in the sea. This reveals major implications not only for ecotoxicology, but also for chemical signals mediating species interactions such as foraging, reproduction, or predation in the ocean with unexplored consequences for ecosystem stability and ecosystem services.

A trajectories' guide to the state space - learning missing terms in bifurcating ecological systems

2021 ◽  
Author(s):  
Rahel Vortmeyer-Kley ◽  
Pascal Nieters ◽  
Gordon Pipa
Keyword(s):  
Machine Learning ◽  
Differential Equations ◽  
Algal Blooms ◽  
Ecological Systems ◽  
Short Term ◽  
Specific System ◽  
Worst Case ◽  
System Components ◽  
Full Knowledge

<p>Ecological systems typically can exhibit various states ranging from extinction to coexistence of different species in oscillatory states. The switch from one state to another is called bifurcation. All these behaviours of a specific system are hidden in a set of describing differential equations (DE) depending on different parametrisations. To model such a system as DE requires full knowledge of all possible interactions of the system components. In practise, modellers can end up with terms in the DE that do not fully describe the interactions or in the worst case with missing terms.</p><p>The framework of universal differential equations (UDE) for scientific machine learning (SciML) [1] allows to reconstruct the incomplete or missing term from an idea of the DE and a short term timeseries of the system and make long term predictions of the system’s behaviour. However, the approach in [1] has difficulties to reconstruct the incomplete or missing term in systems with bifurcations. We developed a trajectory-based loss metric for UDE and SciML to tackle the problem and tested it successfully on a system mimicking algal blooms in the ocean.</p><p>[1] Rackauckas, Christopher, et al. "Universal differential equations for scientific machine learning." arXiv preprint arXiv:2001.04385 (2020).</p>

scholarly journals Comparison of Short-Term Toxicity of 14 Common Phycotoxins (Alone and in Combination) To The Survival of Brine Shrimp Artemia Salina

2021 ◽  
Author(s):  
Yu Ting Zhang ◽  
Shanshan SONG ◽  
Bin ZHANG ◽  
Yang ZHANG ◽  
Miao TIAN ◽  
...  
Keyword(s):  
Ecological Risk ◽  
Harmful Algal Blooms ◽  
Algal Blooms ◽  
Algal Species ◽  
Natural Populations ◽  
Marine Ecosystems ◽  
Artemia Salina ◽  
Marine Organisms ◽  
Short Term ◽  
The Impact

Abstract Toxic harmful algal blooms (HABs) can cause deleterious effects in marine organisms, threatening the stability of marine ecosystems. It is well known that different strains, natural populations and growth conditions of the same toxic algal species may lead to different amount of phycotoxin production and the ensuing toxicity. To fully assess the ecological risk of toxic HABs, it is of great importance to investigate the toxic effects of phycotoxins in marine organisms. In this study, the short-term toxicity of 14 common phycotoxins (alone and in combination) in the marine zooplankton Artemia salina was investigated. On the basis of 48 h LC50, the order of toxicity in A. salina was AZA3 (with a LC50 of 0.0203 µg/ml)＞AZA2 (0.0273 µg/ml) ＞PTX2 (0.0396 µg/ml)＞DTX1 (0.0819 µg/ml)＞AZA1 (0.106 µg/ml)＞ SPX1 (0.144 µg/ml)＞YTX (0.172 µg/ml)＞dcSTX (0.668 µg/ml)＞OA (0.728 µg/ml)＞STX (1.042 µg/ml)＞GYM (1.069 µg/ml)＞PbTx3 (1.239 µg/ml)＞hYTX (1.799 µg/ml)＞PbTx2 (2.415 µg/ml). For the binary exposure, additive effects of OA and DTX1, DTX1 and hYTX; antagonistic effects of OA and PTX2, OA and STX; and synergetic effects of DTX1 and STX, DTX1 and YTX, DTX1 and PTX2, PTX2 and hYTX on the mortality of A. salina were observed. These results provide valuable toxicological data for assessing the impact of phycotoxins on marine planktonic species and highlight the potential ecological risk of toxic HABs in marine ecosystems.

scholarly journals Solar Irradiance Forecasting Using a Data-Driven Algorithm and Contextual Optimisation

2021 ◽  
Vol 12 (1) ◽  
pp. 134
Author(s):  
Paula Bendiek ◽  
Ahmad Taha ◽  
Qammer H. Abbasi ◽  
Basel Barakat
Keyword(s):  
Solar Irradiance ◽  
Contextual Information ◽  
Management Strategies ◽  
Energy Transition ◽  
Data Driven ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Short Term ◽  
Forecast Horizon

Solar forecasting plays a key part in the renewable energy transition. Major challenges, related to load balancing and grid stability, emerge when a high percentage of energy is provided by renewables. These can be tackled by new energy management strategies guided by power forecasts. This paper presents a data-driven and contextual optimisation forecasting (DCF) algorithm for solar irradiance that was comprehensively validated using short- and long-term predictions, in three US cities: Denver, Boston, and Seattle. Moreover, step-by-step implementation guidelines to follow and reproduce the results were proposed. Initially, a comparative study of two machine learning (ML) algorithms, the support vector machine (SVM) and Facebook Prophet (FBP) for solar prediction was conducted. The short-term SVM outperformed the FBP model for the 1- and 2- hour prediction, achieving a coefficient of determination (R2) of 91.2% in Boston. However, FBP displayed sustained performance for increasing the forecast horizon and yielded better results for 3-hour and long-term forecasts. The algorithms were optimised by further contextual model adjustments which resulted in substantially improved performance. Thus, DCF utilised SVM for short-term and FBP for long-term predictions and optimised their performance using contextual information. DCF achieved consistent performance for the three cities and for long- and short-term predictions, with an average R2 of 85%.

scholarly journals Long-Term Eutrophication and Dynamics of Bloom-Forming Microbial Communities during Summer HAB in Large Arctic Lake

Environments ◽  
2021 ◽  
Vol 8 (8) ◽  
pp. 82
Author(s):  
Nikolay Kashulin ◽  
Tatiana Kashulina ◽  
Alexander Bekkelund
Keyword(s):  
Microbial Communities ◽  
Harmful Algal Blooms ◽  
Algal Blooms ◽  
Temporal Dynamics ◽  
Arctic Lakes ◽  
Genetic Traits ◽  
Genetic Mechanisms ◽  
Redfield Ratios ◽  
Lake Imandra

Harmful algal blooms (HABs) in arctic lakes are recent phenomena. In our study, we performed a long-term analysis (1990–2017) of the eutrophication of Lake Imandra, a large subarctic lake, and explored the biodiversity of bloom-forming microorganisms of a 2017 summer HAB. We performed a 16Sr rRNA metabarcoding study of microbial communities, analysed the associations between N, P, C, and chlorophyll concentrations in the lake water, and developed models for the prediction of HABs based on total P concentration. We have demonstrated that blooms in Lake Imandra occur outside of optimal Redfield ratios and have a nonlinear association with P concentrations. We found that recent summer HABs in a lake occur as simultaneous blooms of a diatom Aulacoseira sp. and cyanobacteria Dolichospermum sp. We have studied the temporal dynamics of microbial communities during the bloom and performed an analysis of the publicly available Dolichospermum genomes to outline potential genetic mechanisms beneath simultaneous blooming. We found genetic traits requisite for diatom-diazotroph associations, which may lay beneath the simultaneous blooming of Aulacoseira sp. and Dolichospermum sp. in Lake Imandra. Both groups of organisms have the ability to store nutrients and form a dormant stage. All of these factors will ensure the further development of the HABs in Lake Imandra and the dispersal of these bloom-forming species to neighboring lakes.

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