HOUSTON – (Sept. 17, 2025) – Artificial intelligence (AI) is opening new ground in ecology. At Rice University, César A. Uribe is developing computational tools to help scientists better understand ecosystems with recent studies using AI to glean new insights from different kinds of ecological data — from African mammal food webs to tropical forest soundscapes.
"AI allows us to analyze ecological data in ways that were not possible before," said Uribe, the Louis Owen Assistant Professor of Electrical and Computer Engineering and a member of the Ken Kennedy Institute at Rice. "These recent projects look at two different questions using different types of data from two different continents. We can span a large set of regions and types of data with these tools."
One project introduces a new way to compare biological networks — the webs of interaction among species that underpin every ecosystem. The goal is to identify structural similarities between ecosystems in different regions, even when they are made up of completely different species. Such comparisons can inform large-scale monitoring of ecosystem health and guide conservation priorities. Traditional methods, however, often struggle with data this complex.
Uribe, together with Lydia Beaudrot at Michigan State University and other colleagues, applied a new class of mathematical tools known as optimal transport distances to analyze over a hundred African mammal food webs across six different regions on the continent.
Optimal transport describes the minimum amount of work needed to transform one object into another: If each object is represented by a mound of dirt, then optimal transport, or "earth mover's" distance, represents the most efficient way to move dirt around so that the two mounds become analogous. In ecology, each network of species interactions can be thought of as one of those "mounds." Optimal transport distances let researchers align the overall structure of two networks, showing how their patterns of connection compare even when the networks consist of different species.
Using these tools, the researchers analyzed data from multiple sources and were able to identify functionally equivalent species, i.e. different species that play the same ecological role in their respective ecosystems.
"This allows us to determine, for example, if the lion in this food web plays the same role as the jaguar in this other one or the leopard in this other one," Uribe said.
The effort to quantify the ecological data was led by former Rice undergraduates Kai Hung, now a doctoral student at the Massachusetts Institute of Technology, and Alex Zalles, now pursuing a doctorate at the University of California, Berkeley.
"They did so well here at Rice that they were recruited into the top programs in the nation," Uribe said. "It really speaks to the caliber of training and undergraduate research experience we provide."
An earlier project focused on the tropical forests of Colombia and used sound to map biodiversity. Led by Maria Guerrero, a doctoral student in Colombia who is joining Rice this fall as a visiting scholar on a Fulbright Scholarship, the study placed 17 microphones across a range of habitats within a Colombian oil palm plantation. Over 10 days, the team recorded hundreds of hours of sound, capturing the calls of frogs, birds and insects.
Through AI analysis, the researchers created what Uribe called a "tropical forest connectome," borrowing a term from neuroscience to describe how different areas of the forest are linked through sound.
"Instead of connections inside the brain, we were looking at the connections in the tropical forest ⎯ how information and energy flows," Uribe said. "We were using bioacoustics data as a proxy to understand the health status of an ecosystem. The novelty here is being able to automatically identify and segment the sounds."
The results showed that habitat matters more than distance: Two patches of intact forest can sound alike even when far apart, while a forest and a nearby region planted with oil palms may be completely different. The study confirmed the fact that converting native forests to monoculture plantations drastically reduces biodiversity, highlighting how bioacoustics can serve as a low-cost tool for large-scale monitoring.
For Uribe, who is from Colombia, the project carried special weight.
"It is personally meaningful because I am doing research that has global impact, using techniques that I am developing here in the United States with many local, regional and international collaborators," Uribe said. "In terms of impact, both papers are meaningful because the research entails applying AI for something other than maximizing profit or gaining a competitive edge: This is AI for ecology and conservation."
Both papers are published in Methods in Ecology and Evolution, the leading journal in the field.
For the African mammals' food webs study, the research was supported by the National Science Foundation (2211815, 2213568, 2443064) and Google. For the bioacoustics study, the research was supported by Universidad de Antioquia, the Alexander von Humboldt Institute for Research on Biological Resources, the National Science Foundation (2213568, 2443064) and Rice, with data from Puerto Wilches funded by Universidad de Antioquia, SGI and Ecopetrol under contract FOGR09. The content in this press release is solely the responsibility of the authors and does not necessarily represent the official views of funding organizations and institutions.
