Ecology meets high-tech methods

Biological communities are rarely stable. Their composition is constantly changing, depending on the environmental conditions in the respective ecosystems – and sometimes this change is so vast that individual species completely disappear from a community. In order to predict such developments, researchers employ ecological models. The most promising ones are mechanistic models – those based on the foundational biological mechanisms determining the coexistence of different species. Such models have the potential to reliably predict the composition of biological communities in different habitats.

But do the models live up to expectations when put to the empirical test? This question is exactly what Konstanz researchers have now studied in communities of freshwater algae. For their study in Nature Communications, the team expands upon and tests a mechanistic consumer-resource model and confirms its high predictive capacity. Using the model, the researchers refine current rules on the coexistence of species, too. Their findings can be applied to any situation in which communities of organisms compete for resources and where people want to predict or influence the future development of these biological communities. The spectrum ranges from natural biological communities, such as oceanic plankton communities or our intestinal microbiome, to artificially constructed communities used, for example, in biotechnological processes.

Enabled by the latest methods
The theoretical foundation for the recent study was already laid, in part, in the 1960s. So why could the team only test these theories experimentally now? "Some previous attempts did prove successful in certain aspects. A true pioneer in this was, for example, my direct predecessor at the University of Konstanz, Karl-Otto Rothhaupt", say Lutz Becks, a professor of limnology in the Department of Biology at the University of Konstanz who led the recent study. "Yet we needed an extremely large number of experiments in order to complete a comprehensive study of the model and to expand upon it – and this could only be done within a reasonable time frame using modern laboratory equipment."

Even the first step in the study – determining the nutrient requirements and consumption of different species of freshwater algae – meant conducting 864 growth experiments. Thanks to high-tech lab equipment, each individual monoculture was prepared by a lab robot instead of countless students, technicians and scientists. The corresponding algae counts in the samples were also conducted automatically using a modern high-throughput microscope. In further experiments with communities of organisms where it was necessary to count individuals of different species, artificial intelligence helped identify the algae species.

Prediction and reality align
The researchers used the data from their initial experiments to expand upon the existing model. "The conventional model already accounted for factors that limit the growth of species. Our newly collected data enabled us to add the use of resources as an additional parameter to the model", Becks explains. Afterwards, the researchers conducted 960 further experiments in which they brought together the algae previously grown in monoculture to examine different combinations of species under different nutrient conditions. The team then compared the observed development of these communities with the predictions made by the model. The result: The mechanistic model predicted the composition of the different communities with high precision.

The researchers also conducted computer simulations based on their model to test two ecological rules formulated by biologist David Tilman. They explain how two species that compete with each other for limited resources either coexist or displace each other. The rules state that each species must be limited by different resources and that each species consumes more of the resource that limits its growth. The simulations show: Only the first rule is universally valid. The second rule only applies when the species compete for replaceable resources, but not for essential resources. "When applying the rule, we must always distinguish between these two classes of nutrients", explains Zhijie Zhang, first author of the study.

Climate protection application
In a next step, the approach developed in the study will be applied in a project focused on CO₂ sequestration through phytoplankton. The project is supported by the Vector Stiftung's programme to fund research promoting climate protection. "Together with my colleague Daniel Dietrich, we will use our screening to identify phytoplankton communities that are as resilient as possible to environmental influences. Such communities could then be used to reliably metabolize – and thus sequester – CO₂ from the atmosphere, even in cases where environmental factors such as nutrient availability, temperature or solar radiation vary", Becks projects.

Key facts

• Original publication: Zhijie Zhang & Lutz Becks (2025) Mechanistic prediction of community composition across resource conditions and species richness. Nature Communications; doi: 10.1038/s41467-025-64935-5
• Researchers at the University of Konstanz expand upon and test a mechanistic consumer-resource model and confirm its high predictive capacity for the composition of biological communities.
• Professor Lutz Becks is an ecologist and evolutionary biologist in the Department of Biology at the University of Konstanz who is also spokesperson of the DFG Research Unit "Density dependent symbiosis in planktonic systems" ("DynaSym").
• Funding: German Research Foundation (DFG) and Young Scholar Fund of the University of Konstanz

Note to the editors:
A photo can be downloaded here:

Link: https://www.uni-konstanz.de/fileadmin/pi/fileserver/2025_ab_Oktober/oekologie_trifft_hightech.jpg
Caption: Representative image of an algal community
Photo: Pia Mahler, Universität Konstanz