The ocean contains distinct ecosystems, but they’re much harder to see than terrestrial forests or savannas. A new model helps scientists better understand patterns of undersea life, which is threatened by pollution, invasive species, and warming temperatures.
What’s new: Researchers from MIT and Harvard used neural networks to update existing maps of undersea ecosystems.
How it works: The authors used unsupervised learning to analyze relationships between different species of plankton and the nutrients they consume.
- Drawing on data from simulations of plankton populations built by MIT’s Darwin Project, the model used a clustering algorithm to draw boundaries around areas where plankton and nutrients showed high levels of interdependence.
- The model generated a map of 115 unique ecological areas, each with a distinct balance of plankton species and nutrients.
- The researchers organized these areas into 12 ecoregions based on the life they contain. Nutrient-poor zones form aquatic deserts, while nutrient-rich areas near coastlines support biodiversity comparable to rainforests.
Results: The model’s predictions aligned well with measurements taken by scientific surveys and satellite data.
Behind the news: Deep learning is being used to tackle a variety of environmental problems.
- Researchers at Austria’s University of Natural Resources and Life Sciences devised a neural network to predict harmful outbreaks of bark beetles in Germany.
- Columbia University scientists trained a model to recognize bird songs and used it to evaluate the impact of climate change on avian migration.
Why it matters: Phytoplankton feed aquatic creatures from microorganisms to whales, produce half of the world’s oxygen, and absorb enormous amounts of atmospheric carbon. Models like this could help oceanographers gauge the planet’s capacity to sustain life.
We’re thinking: As educators, we’re all for algorithms that help fish. We don’t want them to drop out of school.