Analysis Of Evolution Features And Development Of Predictive Models On Harmful Algal Blooms In The China Seas Based On Multi-source Data

Harmful algal blooms（HABs）have severe impacts on public health,tourism,mariculture industry and ecosystems,and therefore raise great public attention.The HABs vary at regional and local scale under the joint impacts of multiple environmental stresses,such as climate change,eutrophication,mariculture development and so on.In China,as a type of severe marine ecological disaster,HABs have caused huge economic losses to mariculture industry,and even impose a threat to human health and marine ecosystems.In recent years,HABs show the trend toward more diversification,noxiousness and miniaturization in the coastal waters of China.This leads to different hazardous effects,and results in the difficulty in monitoring of HABs.Therefore,it’s necessary to explore the evolution features of HABs and the major environmental drivers,and to develop predictive methods.With the development of monitoring and research on HABs,data available for HAB analysis are increasing,along with the increasing coverage on spatial and temporal scale.It is possible to explore the evolution features of HABs and develop predictive models through methods like mathematical statistics,geographical information system（GIS）tools,machine learning and so on.So far,the evolution features of HABs,as well as the relationship between HABs and environmental factors in the China Seas,are still not well understood.Meanwhile,there are lack of methods on monitoring and prediction for some special types of HABs.In this study,statistical and GIS tools were used to analyze the evolution features of HABs in the typical sea areas based on the multi-source data related to HABs,and the machine learning methods were used to build predictive models for HABs.Moreover,a platform was established for management,analysis and visualization of data related to HABs.The HABs formed by microalgae,mainly red tides and brown tides,in the Bohai Sea and the Yellow Sea were studied to reveal the evolution features in the two regions based on the historical records of HABs over the last several decades.In the Bohai Sea,datasets of HAB events（1952-2017）,environmental variables（1956-2017）and social and economic data（2017）were compiled.There are 230 HAB events with 64 identified species affecting nearly 70,000 km²area in the Bohai Sea.The history of HABs in the Bohai Sea can be characterized into three periods based on the characteristics of frequency,scale and species composition,and assisted by the analysis of change point detection.Our analysis shows that HABs in the Bohai Sea increase their frequency over the period studied,although the increase has plateaued in the last decade.The seasonal distribution of HAB events has clearly expanded,and the main hotspot moved from Bohai Bay to coastal waters of Qinhuangdao over the three periods.A unique feature of HAB evolution is the rapid shift of typical HAB-forming microalgae from dinoflagellates to haptophytes and pelagophytes over the three periods,with a trend toward diversification,noxiousness and miniaturization.We consider that the rapid shift of HABs in the semi-enclosed Bohai Sea are related to the combined effects of climate change,mariculture development,and eutrophication.The frequency of HABs can be predicted by time series models.Considering the hazard of HABs and the vulnerability of potential targets affected by HABs,the risk of HABs in the Bohai Sea was assessed.The high-risk areas of HABs are mainly located in the Bohai Bay and the coastal waters of Qinhuangdao.In the Yellow Sea,datasets of HAB events（1972-2017）,green tides（2008-2017）and environmental variables（1970-2017）were compiled.There is an increasing dominance of dinoflagellate red tides in terms of frequency,scale,seasonality,spatial distribution and species.The red tides in the northern Yellow Sea and southern Yellow Sea,however,have different evolution features.The recurrent large-scale green tides in the southern Yellow Sea from 2007may lead to the decrease of interannual and seasonal frequency of red tides.Focusing on paralytic shellfish toxins mainly produced by Alexandrium spp.in the East China Sea,and the“giant colony”produced by Phaeocystis globosa in the Beibu Gulf,the South China Sea,prediction methods have been developed through the approach of machine-leaning based on multi-source data.Datasets related to paralytic shellfish toxins from 2 cruises conducted in the Yellow Sea and the East China Sea,and related to Phaeocystis colony from 8 cruises conducted in the Beibu Gulf,the South China Sea,were collected and compiled.Based on these datasets,we compared 7 types of machine learning algorithms to predict the existence of paralytic shellfish toxins and Phaeocystis colony.In the case study of paralytic shellfish toxins,the optimal model is Support Vector Machine（SVM）.The accuracy of the model is 94%after optimization.The predictive indicators for paralytic shellfish toxins are temperature,salinity and phosphate.In the case study of Phaeocystis colony,the optimal model is Light Gradient Boosting Machine（Light GBM）.The accuracy is 84%after optimization,and the predictive indicators include biomass of Synechococcus,prasinoxanthin,diadinoxanthin,temperature and salinity,and chlorophyll c₃,phosphate and nanoeukaryote abundance could serve as optional predictive indicators.Based on analysis of HAB datasets,an integrated platform was designed to manage,analyze and visualize different types of data related to HABs.The platform integrated the database platform,GIS analysis platform,and the visualization platform.The platform realized the dynamic join and storage scalability for HAB data,and integrated different tools ranged from temporal and spatial analysis to preliminary data visualization.A case study on the long-term temporal and spatial evolution of HABs in the Bohai Sea has been applied in the study.In summary,we used the statistical methods and GIS tools to study the evolution features of HABs in the Bohai Sea and the Yellow Sea.The results revealed dramatic changes of HAB causative species in the Bohai Sea,and the combined impacts of multiple drivers like climate change,development of mariculture and eutrophication on the evolution of HABs.In the Yellow Sea,the increasing dominance of dinoflagellate red tides,and the potential effects of large-scale green tide on the evolution red tides,were discovered.Moreover,the machine learning approach was successfully used to predict the distribution of paralytic shellfish toxins and Phaeocystis colony.A platform was established for data management,GIS analysis and data visualization.The results showcase the potential of HAB studies through the approach of data analysis,which could support the research,monitoring and management of HABs.