This blog post is provided by Susanne Kortsch, Romain Frelat, Ivars Putnis, and Marie Nordström and tells the #StoryBehindThePaper for their article ‘Disentangling temporal food web dynamics facilitates understanding of ecosystem functioning’, which was recently published in the Journal of Animal Ecology.
Communities are organized into consumer networks, or food webs, describing who eats whom. Food webs provide the “energetic” backbones of ecosystems and are essential for energy and matter cycling. Moreover, a given food web structure can potentially buffer or accelerate human and environmental impacts such as climate change. Hence, it is important to understand how food web structure changes over time to better comprehend and anticipate ecosystem changes. In this study, we set out to uncover how food web structure changes over a 34-year period and how shifts in food web structure relate to changes in functioning.
One of the biggest challenges in the study of empirical temporal food webs is data availability. To study long-term variability in food web structure, one needs detailed information on species feeding relationship as well as the variation in species biomass through time. Marine biological monitoring programs that estimate species’ biomasses often focus on a single group of species (e.g. trawling for fish), but rarely sample the entire food web– ranging from primary producers (e.g. phytoplankton) to secondary consumers (e.g. zooplankton) to top predators (e.g. cod). The Gulf of Riga, a sub-basin of the Baltic Sea, is one of the few places with long-term data (>30 years of sampling) on multiple trophic groups. The Gulf of Riga presents a nice case study illustrating the importance of collecting long-term data for understanding the variability of natural systems.
At the end of the 1980s, the Gulf of Riga underwent an ecosystem-wide structural and functional reorganization related to the disappearance of cod, a top predator in the food web, and an increasing importance of the pelagic compartment. We were curious to learn how species compositional changes alter food web structure and function. One might expect that a major compositional change will translate into a change at the food web level, but species appearing in or disappearing from a network can play similar structural and functional network roles. This means that overall food web function can be maintained despite compositional changes. We were also curious to find out how variable food web structure is over time. Although this may seem a trivial question, much of our current empirical understanding of how food webs vary over time comes from analyses on unweighted networks, which have suggested that food web macro-descriptors (e.g. connectance) are constant or invariant over time.
To disentangle how food webs vary over time in the Gulf of Riga, we explored multiple approaches to describe food web structure and function. We compared the traditional topological approach based on species presence/absence using unweighted food webs and two weighted approaches – one node-weighted and one link-weighted – which include information on species biomasses and magnitude of feeding interactions. Node-weighted food web metrics weigh nodes by species biomass and describe the dominance of species in the food webs, whereas link-weighted metrics, based on energetic modeling, can capture changes in the magnitude of interactions. The advantage of link-weighted metrics is that they can reveal changes in energy flow and food web functioning.
Our results show that unweighted and weighted food web descriptors vary substantially, and distinctively, over the 34-year time series. We identified five periods with unique food web characteristics that represent distinct ecosystem structures and functions. The full extent of the temporal food web changes reported was only revealed through the complementarity between unweighted and weighted network approaches linking structure and function. Thus, our study demonstrates the benefit of using multiple methods to draw a more complete picture of temporal ecosystem dynamics. We therefore recommend using a range of descriptors from both unweighted topology-based and weighted (e.g. flux-based) food web approaches in order to characterize the dynamic and multifaceted nature of structural and functional changes in ecosystems.
If you are curious about the methods used in our study, have a look at our online tutorial: https://rfrelat.github.io/BalticFoodWeb.html