UToledo Scientists Develop New Method to Estimate Hidden Species

UToledo Scientists Develop New Method to Estimate Hidden Species

When researchers survey wildlife in a lake, forest or other habitat, they rarely capture every species present - some are simply too rare or elusive to detect.

A new study published in the May issue of Ecological Informatics addresses this longstanding challenge by offering a more powerful statistical method to estimate not just the species scientists can see, but also the ones they're likely missing.

The study proposing the new data model, titled "A Bayesian hierarchical modeling approach for species diversity in ecology," noted that "estimating the 'true' species richness, which includes identifying the number of missing species, has intrigued ecologists for decades."

The research team from The University of Toledo, the U.S. Geological Survey Great Lakes Science Center and Wittenberg University built on a mathematical framework originally developed in the 1940s to model how species and their populations are distributed in nature.

By combining this framework with modern computational techniques, the researchers created a tool that can estimate the "true" number of species in an ecosystem - including those that went uncounted - while also revealing how evenly or unevenly individuals are spread across different species.

The method was tested against simulated data and three real-world historical datasets and proved both accurate and computationally efficient. Researchers also demonstrated how the approach can pool data from multiple related studies - such as the annual Great Lakes fishery survey - to reveal broader trends across time and geography.

"Beyond counting species, this tool gives scientists a richer picture of ecosystem health by measuring two key qualities at once: how abundant species are overall, and how evenly they share that abundance," said the study's lead author, Dr. Song S. Qian, from UToledo's Department of Environmental Sciences.

The tool will be useful for tracking how ecosystems respond to environmental pressures such as pollution, climate change or habitat loss.

The research team was comprised of Song, Dr. Mark R. DuFour, a UToledo graduate who now works at the U.S. Geological Survey's Great Lakes Science Center-Lake Erie Biological Station in Huron, Dr. Sabrina Jaffe of Wittenberg University, Dr. Corbin Hilling, another UToledo graduate who works at the U.S. Geological Survey's Great Lakes Science Center-Lake Erie Biological Station in Huron and Dr. William D. Hintz from The University of Toledo Department of Environmental Sciences & Lake Erie Center.