Due to Covid-19 travel restrictions, the organisers of this workshop have decided to postpone it until May 2021.
Harmful algal blooms (HABs) are proliferations of certain photosynthetic organisms (including unicellular phytoplankton and phytobenthos, macroalgae, cyanobacteria, and particular ciliates) that can cause massive fish kills, produce toxins that bioaccumulate in seafood, and/or cause ecological damage through the development of hypoxia/anoxia and other habitat alterations. While HABs are natural processes that occur in all aquatic systems, there is a concern that the frequency and severity of HABs may be increasing due to a combination of natural and human-driven forces, including climate change. Over the last 20 years advances in technology, observation and modelling techniques have provided an avenue towards better management of HAB risks.
Modelling is a particularly essential tool for HAB prediction and management, however existing models still require a great deal of improvement in order to more thoroughly protect against HAB threats. Notably, the modelling of HABs requires better parameterization of the biological, physical and chemical processes of interest, as well as model validation. These improvements in turn require high-resolution sampling of the appropriate parameters, resolving small scales (e.g., thin layers in stratified systems, rheological processes at the micrometre-length scale), and sustaining long time series of observing systems that measure environmental forces in relation to HAB and plankton community response. In 2009 a major HAB Modelling Workshop was held in Galway, Ireland under Global HAB’s predecessor programme GEOHAB, which strove to addressing this need for improved modelling.
This GlobalHAB workshop, co-funded by EuroMarine, will evaluate how the field has progressed in the decade since the Galway meeting. Notably the scietists will look at the evolution of several different types of HAB models. The ones that have the most predictive power for short-term HAB forecasts are often site-and organism-specific. These models do not always give general biological insight and are the hardest to scale up to be used for producing scenarios for novel combinations of environmental conditions (e.g., under future climate or other anthropogenic change). However, modelling efforts focused on short-term predictive power can be complemented with analysis of positive ecological feedbacks or trait-based approaches that model phytoplankton community strategies instead of species. These approaches have the potential to generalize across many HAB species or populations. In any effort at generalization and upscaling, international coordination is fundamental for advancement.
One of the key general objectives of the workshop will be to simply bring the leading HAB researchers together for a collaborative knowledge exchange. The HAB science community is particularly diffuse, in the sense that most modellers, oceanographers, and technologists working on HAB problems do not focus on it as their primary speciality; this makes interdisciplinary gatherings especially important to progress in the field. The organisers also anticipate pursuing four key objectives through dedicated sessions:
- Explore the diversity of HAB modelling approaches
- Identify emerging technologies and platforms to support HAB monitoring
- Link models, observations and stakeholder needs
- Scale up in order to address the global impact of environmental change on HABs
There are four main targeted outcomes from the workshop:
- Education of the next generation of modellers in the range of tools in the community toolbox
- Development of multidisciplinary approaches to implement improved future monitoring systems
- Stakeholder knowledge exchange; Summary for Stakeholders document synthesizing the workshop
- Meta-analysis of long-term trends and predictions of future change
The workshop will contribute to the development of safeguards against the future continued proliferation of HABs, helping to protect marine resources upon which a growing human population will increasingly rely.