Researchers at the University of California, Berkeley, have developed some of the most detailed maps yet showcasing the distribution of plant species. They used advanced artificial intelligence and citizen science data from the iNaturalist app. iNaturalist is a widely used cellphone app, originally developed by UC Berkeley students, that allows people to upload photos and location data of plants, animals, or any other life they encounter and then crowdsource their identity. The app has more than 8 million users worldwide who have uploaded more than 200 million observations.

The researchers used a type of artificial intelligence called a convolutional neural network, a deep learning model, to correlate the citizen science data for plants in California with high-resolution remote-sensing satellite or aeroplane images of the state. The network discovered correlations that were then used to predict the current range of 2,221 plant species throughout California, down to scales of a few square meters.

Botanists usually build high-quality species maps by painstakingly listing all plant species in an area, but this is not feasible outside of a few small natural areas or national parks. Instead, the AI model, called Deepbiosphere, leverages free data from iNaturalist and remote sensing airplanes or satellites that now cover the entire globe. Given enough observations by citizen scientists, the model could be deployed in countries lacking detailed scientific data on plant distributions and habitats to monitor vegetation change, such as deforestation or regrowth after wildfires.

Protecting plant species

The findings were published on Sept. 5 in the journal Proceedings of the National Academy of Sciences by Moisés "Moi" Expósito-Alonso, a UC Berkeley assistant professor of integrative biology, first author Lauren Gillespie, a doctoral student in computer science at Stanford University, and their colleagues. Gillespie currently has a Fulbright U.S. Student Program grant to use similar techniques to detect patterns of plant biodiversity in Brazil.

"During my year here in Brazil, we've seen the worst drought on record and one of the worst fire seasons on record," Gillespie said. "Remote sensing data so far has been able to tell us where these fires have happened or where the drought is worst, and with the help of deep learning approaches like Deepbiosphere, soon it will tell us what's happening to individual species on the ground."

"That is a goal—to expand it to many places," Expósito-Alonso said. "Almost everybody in the world has smartphones now, so maybe people will start taking pictures of natural habitats, which will be possible globally. At some point, this will allow us to have layers in Google Maps showing where all the species are so we can protect them. That's our dream."

Remote sensing data

Besides being free and covering most of Earth, remote sensing data are also more fine-grained and more frequently updated than other information sources, such as regional climate maps, which often have a few kilometres resolution. Using citizen science data with remote sensing images—just the basic infrared maps that provide only a picture and the temperature—could allow daily monitoring of landscape changes that are hard to track.

Monitoring can help conservationists discover change hotspots or pinpoint species-rich areas needing protection.

In the study, the researchers tested Deepbiosphere by excluding some iNaturalist data from the AI training set and then asking the AI model to predict the plants in the excluded area. The AI model had an accuracy of 89% in identifying the presence of species, compared to 27% for previous methods. They also pitted it against other models developed to predict where plants grow around California and how they will migrate with rising temperatures and changing rainfall. One of these models is Maxent, developed at the American Museum of Natural History, which uses climate grids and geo-referenced plant data.

They also tested Deepbiosphere against detailed plant maps created for some of the state's parks. It predicted with 81.4% accuracy the location of redwoods in Redwood National Park in Northern California and accurately captured (with R2=0.53) the burn severity caused by the 2013 Rim Fire in Yosemite National Park.

Phys Org