The traditional collection of plankton samples, often using nets followed by taxonomic analysis is a labour intensive and time-consuming process, and ultimately expensive. The increasing demand for pelagic data combined with ever reducing budgets for monitoring has driven the development of new tools and techniques for the sampling and analysis of this key ecosystem component.
Technological advances have allowed monitoring of the pelagic environment to evolve towards a more integrated approach to data collection at a finer scale. There is nowadays a plethora of methods including for example molecular, optical, remote sensing and automated techniques that allow to further our understanding of biodiversity and species interactions within the pelagic ecosystem.
Ongoing and new challenges however need to be addressed, for example, associated with taxonomic resolution from image analysis methods; or the ability to collect huge amounts of fine-resolution data which has led to a situation where the data collection rate is faster than the processing rate. Furthermore, there is a large variety of data formats including, for example, images, acoustics, taxonomy, remote sensing etc. collected at different spatial and temporal scales. There is therefore a need to (1) improve on how these emerging technologies, collecting multi-modal data, work together, and (2) develop new methods embedding technological advances in data collection and data analytics, for a fully integrated understanding of pelagic processes.
We welcome contributions addressing the following topics:
• Latest developments in emerging technologies for measuring planktonic components of the pelagic ecosystem.
• Ongoing and new challenges associated with data collection, analysis, and interpretation from such technological advances.
• Combining technologies to optimize monitoring of the pelagic environment and/or allow a more integrated approach to understanding complex processes.
• Integration and analysis of pelagic data collected under different formats and at different spatial and temporal scales.
• Applying emerging methods of data analytics/modelling with large multi-modal datasets for a fully integrated understanding of pelagic processes.
The traditional collection of plankton samples, often using nets followed by taxonomic analysis is a labour intensive and time-consuming process, and ultimately expensive. The increasing demand for pelagic data combined with ever reducing budgets for monitoring has driven the development of new tools and techniques for the sampling and analysis of this key ecosystem component.
Technological advances have allowed monitoring of the pelagic environment to evolve towards a more integrated approach to data collection at a finer scale. There is nowadays a plethora of methods including for example molecular, optical, remote sensing and automated techniques that allow to further our understanding of biodiversity and species interactions within the pelagic ecosystem.
Ongoing and new challenges however need to be addressed, for example, associated with taxonomic resolution from image analysis methods; or the ability to collect huge amounts of fine-resolution data which has led to a situation where the data collection rate is faster than the processing rate. Furthermore, there is a large variety of data formats including, for example, images, acoustics, taxonomy, remote sensing etc. collected at different spatial and temporal scales. There is therefore a need to (1) improve on how these emerging technologies, collecting multi-modal data, work together, and (2) develop new methods embedding technological advances in data collection and data analytics, for a fully integrated understanding of pelagic processes.
We welcome contributions addressing the following topics:
• Latest developments in emerging technologies for measuring planktonic components of the pelagic ecosystem.
• Ongoing and new challenges associated with data collection, analysis, and interpretation from such technological advances.
• Combining technologies to optimize monitoring of the pelagic environment and/or allow a more integrated approach to understanding complex processes.
• Integration and analysis of pelagic data collected under different formats and at different spatial and temporal scales.
• Applying emerging methods of data analytics/modelling with large multi-modal datasets for a fully integrated understanding of pelagic processes.