November 8, 2024
Summary: Ecological forecasts can be used to predict changes in ecosystems and subsequent impacts on communities. In addition to long-term projections, there is an important need for forecasts in shorter-term decision-making time periods of weeks and months. Scientists at the Ecological Forecasting Initiative are working to advance the field through a process that enables them to continually update model predictions with observed data in order to improve our ability to foresee what may happen in the future. These scientists are calling on greater investment in these efforts, asking the scientific community to collectively commit to building the capacity to improve the field while calling on world nations, major corporations, and NGOs to integrate ecological forecasting into their climate adaptation and mitigation strategies.
Climate and biodiversity crises threaten our ability to manage and conserve natural resources, putting many ecosystems at risk of collapse. Ecological forecasts are a tool used to predict changes in ecosystems and how communities may be impacted. These forecasts can then be used to make decisions to mitigate environmental impacts and build a future that is climate resilient. But while many ecological forecasts have focused on predictions for 2100 and beyond, climate change is happening now and there is an urgent need for forecasts in shorter-term decision-making time periods of weeks and months.
This urgency has led scientists organized through the Ecological Forecasting Initiative (EFI) to call for increased infrastructure for predicting nature and environmental events. This call to action was recently published in Nature Climate Change. EFI is also urging world nations and UN bodies, major international corporations, and NGOs to integrate ecological forecasting into their climate adaptation and mitigation strategies while encouraging the scientific community to commit to building the technological and institutional capacity to respond to this urgent need.
Ecological forecasting is at a critical point for future growth according to Michael Dietze, EFI’s chair and lead of the Ecological Forecasting Laboratory at Boston University. “We have a lot of know-how and advances in sensing technologies, AI, and computing have opened new doors, but we now need to scale up the overall forecasting enterprise considerably to help society mitigate and adapt to widespread change in ecosystems in the face of climate change.”
Specifically, EFI scientists are talking about advancing the field of near-term iterative ecological forecasting, through which forecasts are regularly updated given new data. The forecasts can be compared to what actually happened, and the models can then be improved given what is learned to make more accurate predictions.
“Because you are constantly updating the forecasts as new information becomes available, near-term iterative ecological forecasting establishes a learning loop that is a win-win situation for ecology and decision making,” added Dietze. “The same forecasts that improve decision making on actionable timeframes also accelerate scientific understanding and help us probe new frontiers of discovery about how nature works.”
This iterative approach has been used by atmospheric scientists for decades, who, helped by large-scale investments in weather monitoring, modeling, and data assimilation, were able to continuously improve weather forecasts. In the early days of numerical weather prediction, weather forecasters had the choice between stepping away from forecasting until the mechanics of the atmosphere were better understood or stepping forward into an iterative forecast cycle of learning by doing – through choosing the latter, they achieved a critical win-win of relentless improvements.
Ecological forecasting is at a similar crossroads, though the challenge is more complicated because of the complexity of biodiversity and environmental systems. Scientists are seeing dramatic improvements in the field of ecological forecasting that were unimaginable even a decade ago, much of it fueled by advances in sensor technologies, satellites, computation methods, and machine learning. And, due to shifts towards large-scale networked science, data access has become more standardized and equitable. Thus, the time is right to accelerate our investment.
One of the ways that EFI has supported these advancements is through the ongoing National Ecological Observatory Network (NEON) Forecasting Challenge, which has the goal of predicting NEON data before it is collected. This challenge has involved over 200 teams and developed new educational resources and community cyberinfrastructure. “Ecological forecasting needs to build communities of practice to support its growth as a discipline to leverage these technical advances,” Quinn Thomas, who co-directs the Virginia Tech Center for Ecosystem Forecasting and leads the EFI NEON Forecasting Challenge.
One of the areas where there has been traction is exploring opportunities for cross-government agency collaborations on cyberinfrastructure. Cyberinfrastructure – such as models, community standards, consistent server time, and workflows – have community-scale benefits by reducing the costs, time, and learning curve involved in launching and maintaining forecasts. As an international grassroots consortium, EFI has taken a leadership role in piloting and hosting conversations about scaling cyberinfrastructure for the benefit of the larger community of practice.
“The field of ecological forecasting has included partnerships across many sectors – academia, governmental agencies, industry, and NGOs,” stated Melissa Kenney, Director of Research at the University of Minnesota’s Institute on the Environment and a founding leader of EFI. “Such partnerships are critical to support the design and improvement of regularly updated forecasts that both improve the science and support decisions.”
“This is a new field of science that is at a transition point,” says Dietze. “Key investments in investments in education, training, community building, sensing and computational infrastructure, tool and model development, and research to operations is critical to rapidly scale forecast innovations in ways that will help address climate and biodiversity crises.”