News Release

Plant groupings in drylands support ecosystem resilience

Peer-Reviewed Publication

Santa Fe Institute

Many complex systems, from microbial communities to mussel beds to drylands, display striking self-organized clusters. According to theoretical models, these groupings play an important role in how an ecosystem works and its ability to respond to environmental changes. A new paper in PNAS focused on the spatial patterns found in drylands offers important empirical evidence validating the models.

Drylands make up 40 percent of the Earth’s landmass and are places where water is the limiting resource for life. They often display a characteristic clustering of vegetation surrounded by bare soil — patterns that are easy to spot in aerial images. The new study, led by SFI External Professor Sonia Kéfi, who is a researcher at CNRS in France, finds that not only are these spatial patterns caused by the stressful environmental conditions of drylands, but they are also a critical adaptation that allows drylands to function in changing conditions. When a dryland ecosystem tips into a degraded state, the spatial patterns disappear.

“Many people have the idea that ‘interesting’ ecosystems are places like the Amazon, and that drylands are poor in some way,” says SFI External Professor Ricard Solé (Pompeu Fabra University), a co-author on the paper. “But they can be very rich. They are responsible for managing how water is being retained or not in these habitats, and are important for CO2 exchange.” Beyond their ecological importance, drylands are also home to one-third of the world’s human population, making them important economically and culturally.

In healthy dryland ecosystems, islands of vegetation create oases where conditions are a bit better than the rest of the landscape. There’s more water, more nutrients, and more shade. If an ecosystem’s climate becomes drier, those clusters tend to move further apart.

And this, says Kéfi, is a double-edged sword. While improving local conditions, these clusters also create spaces without vegetation — harsh places where a single plant would not survive on its own. If conditions become too harsh, the ecosystem can reach a tipping point into desertification.

Kéfi and her colleagues wondered if aerial images, and their evidence of changes in spatial patterns, could themselves indicate the health or level of degradation in a given plot of land.

“In theory, we could tell something about the ecosystem from the sky — that’s what the models predict, in very broad terms,” says Kéfi. To test this, the team paired aerial images with soil and vegetation data gathered from 115 dryland ecosystems across 13 different countries. “This on-the-ground data shows us where one ecosystem is healthier or functioning better than other ecosystems.” Using the two types of data, the team could test the predictions of the model against real-world observations.

“Our results represent a significant advance in the development of tools for the management and preservation of dryland ecosystems in a warmer, drier world,” says Kéfi. “More specifically, changes in spatial vegetation patterns (or the lack thereof) could be used as indicators of degradation.”

According to Solé, the study offers, for the first time, real validation that the model correctly predicts the nonlinear dynamics of what has been unfolding in dryland ecosystems. “The beauty of this work is that it reveals something that goes beyond the pattern-forming problem. You can talk about ecosystem health in ways that are not metaphoric, and it opens new interesting questions about how to address the future of these ecosystems,” he says.

The authors hope their work will make it easier to spot degrading systems that might be approaching a tipping point. And, because vegetation patterning seems to also be key in other natural systems, such as microbial communities or coastal wetlands, their results could have implications for systems beyond arid zones.

Read the paper "Self-organization as a mechanism of resilience in dryland ecosystems" in PNAS (February 2, 2024) DOI: 10.1073/pnas.2305153121

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