A statistical method using microbial and plankton environmental DNA (eDNA) has been developed to predict baleen whale densities along the California coast, offering a more accurate alternative to traditional forecasting.
New statistical model boosts whale prediction accuracy by 53%
A research team from Scripps Institution of Oceanography at UC San Diego and Cal Poly has created a novel statistical method that uses environmental DNA (eDNA) from microbes and plankton to forecast the density of baleen whales along the California coast. The study, published on May 6, 2026, in PLOS One, analyzed data collected by the California Cooperative Oceanic Fisheries Investigations program from 2014 to 2020, spanning the region from San Diego to Morro Bay.
eDNA reveals the habitat of filter-feeding whales
The method relies on eDNA to characterize the community structure of microbes and small plankton, serving as an ecological habitat proxy for filter-feeding whales such as blue, fin, and humpback whales. By analyzing the composition of these microscopic organisms, the model effectively maps the environmental conditions that attract large whales.
On average, predictions based on microbial communities were 53% more accurate than traditional forecasts, marking a significant improvement over existing methods used to estimate whale distributions.
Open-source tools for broader adoption
To ensure the method can be widely used, the researchers have provided a portable software implementation of the statistical models, allowing other scientists and conservation groups to apply the approach in different regions and contexts without needing to develop the complex algorithms from scratch.