A temporal exploration of food webs using biomonitoring data

Since the very first representation of an ecological network (Camerano, 1880), food webs have become an important tool to explore and summarize the trophic interactions between species that coexist in an ecosystem. The architecture of food webs is intimately related to how ecosystems function, and determines the services ecosystems provide.  Changes in the structure of food webs may have drastic consequences for the functioning of ecosystems. Yet, our understanding of how food webs vary over time remains unclear. Accomplishing a complete inventory of species and their interactions requires significant effort, and the diversity and dynamics of nature makes it challenging to follow variability in food webs over time: species come and go, or become more or less abundant.

In this study, we asked the question: How are changes in species composition (presence/absence and abundances) influencing food web structure over time?

Pierre Olivier - Delorean back to the future of food webs - Ocean Fact - Ecography

Fig1. Animation – Back to the past of food webs.

Copyrights © Pierre Olivier

We set out to answer this question using data from a defined area of the North Sea. Knowing what species there are and when, gives insight into how food webs may change over time. In our study, we used the ecosystem survey data–here, the annual monitoring of species composition and abundances of bottom fauna–to identify which species were found in the area over a period of 17 years (1998-2015).

We first built a metaweb: a food web containing the registered species and their possible trophic interactions. Subsampling this metaweb for species occurring at specific years, together with their interactions, provided information on the temporal variability in the architecture of the food webs (Fig. 2). Additionally, we developed node-weighted food web metrics, which integrate the dynamics of populations, by combining traditional food web metrics with species abundances to the node level.

Pierre Olivier - Figure 2. Schematic representation of the objectives of our study. - Ecography

Figure 2. Schematic representation of the objectives of our study. (updated after publication)

(A) Temporal changes in abundance of species results in (B) changes in the topology of food webs that can be investigated with (C) time series of topological indicators. Our case study is (D) located in the German Bight where fish and invertebrates have been sampled intensively.

CC BY 4.0 © Pierre Olivier et al.

The temporal analysis of the 17-year time series revealed that food web structure changed over time. In addition, the node-weighted metrics revealed that two driving forces were at play. In the first half of the time series, the structural changes originated from a change in species composition: flatfish species became more frequently recorded, whereas some species of shrimp became less recorded. In the second half, food web structure followed a shift in dominance of species, going from a benthivore-dominated community to a more planktivorous community. Our results show that traditional metrics alone give an incomplete picture—one where change is only detectable when species e.g. invade or leave the area—and that we can take advantage of additional information, such as species abundances.

With this empirical exercise, we demonstrate how ecosystem surveys can be used to monitor temporal changes in food web structure, which are important ecosystem indicators for building marine management and conservation plans.

Full reference: Olivier, P., Frelat, R., Bonsdorff, E., Kortsch, S., Kröncke, I., Möllmann, C., Neumann, H., Sell, A.F. and Nordström, M.C. Exploring the temporal variability of a food web using long-term biomonitoring data. Ecography. DOI:10.1111/ecog.04461

This work has been licensed under a CC BY 4.0 licensing.

References

  1. Camerano, L. 1880. Dell’equilibrio dei viventi mercé la reciproca distruzione. – Accademia delle Scienze di Torino 15: 393–414.
This work has been published under a CC BY 4.0 licensing.

This article was first published on the MARmaED website.

This research has been funded by the MARmaED PhD training network.
The MARmaED project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 675997.

MARmaED - EU Horizon 2020 Marie Sklodowska-Curie Innovative Training Network
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Pierre
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And co-authors of the publication.