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Disentangling temporal food web dynamics facilitates understanding of ecosystem functioning

Citation

Kortsch, Susanne et al. (2021), Disentangling temporal food web dynamics facilitates understanding of ecosystem functioning, Dryad, Dataset, https://doi.org/10.5061/dryad.6t1g1jwwn

Abstract

Studying how food web structure and function varies through time represents an opportunity to better comprehend and anticipate ecosystem changes. Yet, temporal studies of highly resolved food web structure are scarce. With few exceptions, most temporal food web studies are either too simplified, preventing a detailed assessment of structural properties, or binary, missing the temporal dynamics of energy fluxes among species.

Using long-term, multi-trophic biomass data coupled with highly resolved information on species feeding relationships, we analyzed food web dynamics in the Gulf of Riga (Baltic Sea) over more than three decades (1981-2014). We combined unweighted (topology-based) and weighted (biomass- and flux-based) food web approaches, first, to unravel how distinct descriptors can highlight differences (or similarities) in food web dynamics through time, and second, to compare temporal dynamics of food web structure and function.

We find that food web descriptors vary substantially and distinctively through time, likely reflecting different underlying ecosystem processes. While node- and link-weighted metrics reflect changes related to alterations in species dominance and fluxes, unweighted metrics are more sensitive to changes in species and link richness. Comparing unweighted, topology-based metrics and flux-based functions further indicates that temporal changes in functions cannot be predicted using unweighted food web structure. Rather, information on species population dynamics and weighted, flux-based networks should be included to better comprehend temporal food web dynamics.

By integrating unweighted, node- and link-weighted metrics, we here demonstrate how different approaches can be used to compare food web structure and function, and identify complementary patterns of change in temporal food web dynamics, which enables a more complete understanding of the ecological processes at play in ecosystems undergoing change.

Methods

The Excel file contains: 1. species list; 2. food web; 3. food web reference list, 4. fluxweb parameter values, 5. body masses with references

The trophic interactions among the subsampled taxa in the Gulf of Riga food web metaweb were compiled via an extensive literature review on species gut content analyses, primarily from the Baltic Sea.

The fluxweb parameter values were used to compute the energy fluxes among species using the "fluxweb" R package. Body mass estimates for fish, benthos, and phytoplankton are specific for Gulf of Riga communities and derived from local biomonitoring data, whereas zooplankton body mass estimates come from other areas of the Baltic Sea.

An R tutorial to perform the unweighted and weighted food web analyses on the Gulf of Riga metaweb is available through GitHub  https://rfrelat.github.io/BalticFoodWeb.html