Finding out when rare and common species change their interactions using multi-site interaction turnover

This blog post is provided by Marie V. Henriksen and tells the #StoryBehindThePaper for the paper “A multi-site method to capture turnover in rare to common interactions in bipartite species networks”, which was recently published in the Journal of Animal Ecology.

In ecological networks, species are linked by their interactions to form complex interaction networks. How species interact in these networks reveals what role they play in maintaining the function and stability of the system. Ecological networks are usually made up of many rare species that have few interactions and a few common generalists that interact with many species. Because of their many interactions, generalists are particularly important for maintaining the overall structure of the system. On the other hand, rare species are found in the periphery of the network and can be lost without large impact on other species. Due to their high numbers, rare specialists are, however, important for overall species richness. To conserve both species and their ecological networks, it is therefore necessary to know how both rare and common species respond to changes in their environment.

In the last decade, different measures of interaction turnover have been used to show when, and why, species change their interactions across space and time. However, due to how they are calculated, these methods are dominated by the interactions of species that are rare across networks and largely overlook the response of common species. Interaction turnover is adapted from the species turnover literature and a focus on rare species is recognised as a general challenge for calculating turnover in ecology. In 2014, Melodie McGeoch and Cang Hui therefore proposed zeta diversity as a multi-site measure of species turnover that describes change in both rare and common species. Even though zeta diversity was developed for species communities, it has the potential to be used as a comprehensive measure of change in many types of systems and contexts, including ecological networks.

While zeta diversity was being developed, I was working on my PhD at Monash University in Australia with Melodie McGeoch as my supervisor. For this work, I was studying species interactions in an ecological network of gall wasps and their natural enemies on Acacia trees. As part of my PhD, along with our co-authors, we applied the concept of zeta diversity to ecological networks to develop a measure of interaction turnover that calculates change in the interactions of both rare and common species.

Galls made by gall wasps on Acacia trees (left) attract a range of different natural enemy wasps that use their ovipositors to lay eggs inside the gall chambers (right). Photo credit: Marie V. Henriksen.

To understand the mechanisms driving network change, we also expanded on two components of interactions turnover that have previously been proposed – species turnover and interaction rewiring. Interactions can change due to species turnover when species disappear from the network, for example, because the environment is no longer suitable for them. Alternatively, interactions can change due to rewiring when species switch, or rewire, their interactions to feed on a different resource species. Since rare species are typically less flexible in their food choice than common species, rare and common species are expected to contribute differently to species turnover and interaction rewiring. We therefore developed this multi-site method to reflect these potential differences between rare and common species.

When multiple ecological networks are compared (M1, M2 and M3), interactions between species (black bars) can be divided into interactions that are shared (grey) and interactions that change because species disappear (green), species change their resource (orange) or a mixture of both (purple). When many networks are compared, it is the common interactions that dominate interaction turnover.

With this method we could then calculate several factors that influence species interactions, including environment, commonness, species turnover and interaction rewiring. To illustrate this, we used the gall wasp-natural enemy system which was sampled across locations in Melbourne. This landscape is highly impacted by human activity and there is large variation in the amount and fragmentation of available insect habitat such as host plants and urban green space. Over a few weeks in early summer 2014, with help from two great research assistants, we collected 10,000 galls on Acacia trees along roads and in city parks and stored them in the laboratory at the university.

The gall wasp larvae feed on plant tissue in the gall where they also encounter larvae of natural enemy wasps. The natural enemies eventually eat the gall wasp larvae or compete with them by eating the plant tissue. Both gall wasps and their natural enemies can be identified when they emerge from the galls as adults, weeks to months later. So, for the rest of the summer, we checked the collected galls and identified adult wasps that emerged.

Interactions between wasp larvae in gall chambers are revealed when the surviving adult gall wasps (left) and natural enemies (right) emerge from the galls. Photo credit: Marie V. Henriksen.

From the emerged gall wasps and natural enemies, we constructed 13 interaction networks from sites across the urban landscape and calculated how interactions of rare and common wasps in the system changed with habitat loss and fragmentation. The results showed that different types of habitats were needed to maintain the interactions of rare and common species in the networks. Rare interactions changed in response to urban green space while common interactions were strongly related to host plant availability and habitat fragmentation. Interactions of both rare and common species changed most rapidly when there was little of these habitats left in the surrounding landscape, revealing their different threshold tolerances to habitat loss. These habitat loss thresholds are critical for guiding management actions aimed at conserving ecological networks and their species diversity. Change in interactions of common species is particularly important for the conservation of ecological networks because of their central role in maintaining network structure.

Read the paper

Read the full paper here: Henriksen, M. V., Latombe, G., Chapple, D. G., Chown, S. L., & McGeoch, M. A. (2021). A multi-site method to capture turnover in rare to common interactions in bipartite species networks. Journal of Animal Ecology, 00, 1– 13.

Disentangling temporal food web structure

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.

Figure 1. The Gulf of Riga and our research vessel at sunset (photograph by Ivars Putnis) along with the Gulf of Riga food web metaweb.

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.

Figure 2. The Gulf of Riga with our scientific research vessel to the left (photograph by Janis Gruduls). The upper right photograph shows a fourhorn sculpin – one of the few glacial relict fish species in the Baltic Sea (photograph by Kalvis Grinvalds), and the lower right photograph shows herring (photograph by Kalvis Grinvalds). The herring is the most dominant fish in the Gulf of Riga in terms of numbers (~90% of total fish abundance).

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.

Figure 3. Example showing three measures of connectance: unweighted, node-weighted and link-weighted. This example demonstrates how a metric such as ‘connectance’ can display distinct and complementary temporal dynamics depending on food web approach.

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.

Figure 4. Average link-weighted food webs for the five main periods with unique food web structure and function identified. The arrows and associated text summarize some of the main ecological changes throughout the study period.

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: