The consequences of climate change are extremely pronounced in the Arctic, which is experiencing more intense warming than anywhere else on the planet. Ecosystems here are a critical area of study as the region’s rapidly declining sea ice and increasing water temperatures drastically alter habitats.

Awender et al. used the relatively new method of generalized modeling to examine the stability of the food web — the intricate and complex network of predator and prey species — in the Southern Beaufort Sea off the coast of Alaska and northern Canada.

“Within the last 20 years, the rarely utilized but powerful general modeling technique has emerged and may help us better understand the most complex or mysterious dynamical systems,” said author Stefan Awender.

The method represents a promising alternative to mathematical models when handling sparse amounts of data.

“Generalized modeling can help determine food web stability despite uncertain empirical data like population sizes, feeding processes, and birth and death rates,” said author Renate Wackerbauer.

By sequentially refining the model, the researchers showed that improved parameterization reflecting the real-world situation increases stability. They tracked stability changes as they introduced more complicated and realistic features, such as the role of minor species usually ignored by most modeling approaches.

“Typically, ecosystem models focus on a small number of the most abundant and observed species,” said author Greg Breed. “One of the aspects we focused on was how these background species can actually impart stability to trophic networks.”

They also found that the presence of apex predators, such as polar bears and killer whales, can have a destabilizing effect. Their results showed that, in the absence of these dominant species, the predator role is filled by several smaller species, leading to a more stable ecosystem overall.

Source: “How realistic features affect the stability of an Arctic marine food web model,” by Stefan Awender, Renate Wackerbauer, and Greg A. Breed, Chaos (2024). The article can be accessed at http://doi.org/10.1063/5.0176718.