Generalized modeling elucidates complex ecosystem networks

Ecosystems are comprised of plant life along with predator and prey animals. These intricate food webs can be complex and unpredictable, with a small change in one species reverberating through the entire ecosystem. These effects and relationships can be studied by defining each species with a differential equation and observing the behavior of the system. However, this process is time-consuming and suffers from incomplete observational data.

Awender et al. explored an alternative approach for studying the stability of an ecosystem without requiring quantitative information about the species and their interactions. This generalized modeling approach can be a valuable tool for analyzing large and uncertain networks.

“Generalized modeling groups processes into unspecified gain and loss terms that represent biomass flows into and within the network,” said author Stefan Awender. “Through a rescaling formalism, the local stability of a steady state can be extracted without knowing the steady state per se.”

They demonstrated that general and specific approaches can yield useful information and provide best results when employed in tandem. Generalized modeling can identify the conditions required for a stable system, and specific modeling can describe the evolution of one such system over time. The authors believe this method can be used to study real-world ecosystems and similar types of networks in other scientific fields.

“The combination of methods demonstrates the usefulness and power of generalized modeling and validates the technique for the analysis of network stability,” said author Renate Wackerbauer. “This opens up applications across scientific disciplines when processes within the network can be formulated as growth and loss terms with meaningful interpretations of the corresponding generalized parameters.”

Source: “Combining generalized modeling and specific modeling in the analysis of ecological networks,” by Stefan Awender, Renate Wackerbauer, and Greg Breed, Chaos (2023). The article can be accessed at https://doi.org/10.1063/5.0131352 .