Ecological Forecasting and Operational Information Systems Support Sustainable Ocean Management
Abstract
:1. Introduction
2. Case Studies
2.1. Ecological Forecasts for Fisheries
2.1.1. Case Study 1—Southern Bluefin Tuna in the Great Australia Bight
2.1.2. Case Study 2—Forecasts in a Multispecies Longline Fishery in Eastern Australia
2.2. Ecological Forecasts in Aquaculture
2.2.1. Case Study 1—Dissolved Oxygen Forecasts in Tasmania
2.2.2. Case Study 2—An Operational Information System for Managing the Chilean Aquaculture Industry
2.3. Ecological Forecasts of Harmful Algal Blooms
2.3.1. Case Study 1—Real-Time Forecasting of Harmful Algal Blooms in the Yellow SEA
2.3.2. Case Study 2—Toxic Algal Blooms in Tasmanian Coastal Waters
2.4. Ecological Forecasting for Risks to Iconic Habitats or Their Users
2.4.1. Case Study 1—Near Real-Time Forecasts of Coral Bleaching on the Great Barrier Reef
2.4.2. Case Study 2—Forecasting to Inform Suppression of Crown-of-Thorns Starfish on the Great Barrier Reef
2.4.3. Case Study 3—Real-Time Forecasting to Manage Risks Posed by Irukandji Jellyfish on the Great Barrier Reef
2.5. Common Features amongst Case Studies
3. Discussion
3.1. Insights from the Case Studies
3.1.1. A Clearly Identified Management Problem Benefits from Environmental Intelligence
3.1.2. Operational Information Systems can Enhance Forecast Value
3.1.3. An Enduring Funding Model Is Needed to Sustain and Develop the Field
3.2. Gaps and Improvements
3.3. Recommendations
- Information systems can help end users. To integrate ecoforecasting into decision making, an operational information system that synthesises observations and modelling to provide easy-access information delivered through cyberinfrastructure can be used as a powerful tool to communicate a complex forecast. Not all forecasts need to be developed by mechanism-based models, which tend to be more expensive to run. Sometimes empirical models relying on correlations of past events (which can be run very quickly) can be effective. The trade-off between these two types of models needs to be considered for optimal outcome. Similarly, not all forecasts need to be delivered by operational information systems, and in some situations, a simple alert or warning might be sufficient. However, in complex situations or when the model results need expert interpretation, an easy-to-use information system will be essential. Significant and ongoing investment in cyberinfrastructure is needed to support operational forecasting systems.
- Active engagement with end users is essential to ensure forecasts are reliable and useful, with their assumptions, uncertainties, and results clearly communicated, and the needs of decision makers addressed [16,23]. Effective partnerships should be formed between scientists and stakeholders. These could aim to develop effective communication tools that recognise stakeholders’ level of forecasting knowledge, priorities, and interests related to the forecast [39]. Ethical issues associated with forecasting should be considered to allow societal, ecological, and economic benefits (e.g., [15]).
- National ecoforecasting agencies are best able to support long-term delivery. The project-based funding model needs to be backed by strategic funding or commercial investment to execute ongoing delivery and operationalisation. Research projects and teams have delivered excellent forecast systems, but dedicated national programs to provide marine ecoforecasts (e.g., [24]) are needed to bring together scientists and resource managers together to solve resource management challenges in a rapidly changing world, and deliver consistent, timely, and reliable forecasts to a wide range of users. The Ecological Forecasting Initiative is a grass roots coordination approach connecting forecast developers in the USA, Canada, and Oceania regions, but is still supported by project-based funding. We proposed that funding agencies consider supporting a national agency responsible for coordinating existing monitoring, modelling, and dissemination capabilities for nationally important priority areas of ecoforecasting.
- Real-time data access will require new technologies. New technologies need to be developed to provide real-time in situ observation data and fit-for-purpose models (e.g., hydrodynamic, ecological, disease). For example, DNA-based techniques and data could inform ecological models, especially when cryptic, sporadic, remote, organisms are involved.
3.4. The Future of Forecasts in Supporting Sustainable Ocean Management
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Case study and Location | Method | Nowcast | Forecast Lead Time | Delivery Mode | Part of an Information System | Uptake |
---|---|---|---|---|---|---|
Fisheries | ||||||
Tuna—eastern Australia | Habitat preferences | Yes | 0–3 months | No | Yes | |
Tuna—southern Australia | Habitat preferences | Yes | 0–3 months | Website | No | Yes |
Aquaculture | ||||||
Hypoxia—Tasmania | Coastal model | Yes | <10 days | Dashboard | Yes, with interactive data explorer | Yes, but not sustained |
SIMA-Austral Chile | Integrated models | Yes | Days to months | Website | Yes | Yes |
Algal blooms | ||||||
Yellow Sea, China | Ocean model | Yes | 6.5 days | Website | No | Yes, but not sustained |
Tasmania | Environmental correlation | Yes | 0–3 months | Not yet | No | No |
Iconic habitats and their users | ||||||
Coral bleaching—GBR | eReefs models (hydrodynamic and biogeochemistry) | Yes | 3 days | Email and reports | Yes (nowcast) | Yes (through reports, papers) |
Crown of Thorns—GBR | Combined hydrodynamic and ecosystem models | No | A few years | Reports and publications | No | No |
Jellyfish—Queensland | GLM | Yes | <3 days | Publications | No | Only as guidelines |
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Sun, C.; Hobday, A.J.; Condie, S.A.; Baird, M.E.; Eveson, J.P.; Hartog, J.R.; Richardson, A.J.; Steven, A.D.L.; Wild-Allen, K.; Babcock, R.C.; et al. Ecological Forecasting and Operational Information Systems Support Sustainable Ocean Management. Forecasting 2022, 4, 1051-1079. https://doi.org/10.3390/forecast4040057
Sun C, Hobday AJ, Condie SA, Baird ME, Eveson JP, Hartog JR, Richardson AJ, Steven ADL, Wild-Allen K, Babcock RC, et al. Ecological Forecasting and Operational Information Systems Support Sustainable Ocean Management. Forecasting. 2022; 4(4):1051-1079. https://doi.org/10.3390/forecast4040057
Chicago/Turabian StyleSun, Chaojiao, Alistair J. Hobday, Scott A. Condie, Mark E. Baird, J. Paige Eveson, Jason R. Hartog, Anthony J. Richardson, Andrew D. L. Steven, Karen Wild-Allen, Russell C. Babcock, and et al. 2022. "Ecological Forecasting and Operational Information Systems Support Sustainable Ocean Management" Forecasting 4, no. 4: 1051-1079. https://doi.org/10.3390/forecast4040057