We develop a novel approach to trophic metacommunities which allows us to explore how progressive habitat loss affects food webs. Our method combines classic metapopulation models on fragmented landscapes with a Bayesian network representation of trophic interactions for calculating local extinction rates. This means we can repurpose known results from classic metapopulation theory for trophic metacommunities, such as ranking the habitat patches of the landscape with respect to their importance to the persistence of the metacommunity as a whole. We use this to study the effects of habitat loss, both on model communities and the plant-mammal Serengeti food web dataset as a case study. Combining straightforward parameterizability with computational efficiency, our method permits the analysis of species-rich food webs over large landscapes, with hundreds or even thousands of species and habitat patches, while still retaining much of the flexibility of explicit dynamical models.

Ecological processes in food webs depend on species interactions. By identifying broad-scaled interaction patterns, important information on species ecological roles may be revealed. Here, we use the group model to examine how spatial resolution and proximity influence the group structure. We examine a dataset from the Barents Sea, with species occurrences for both the whole region and 25 subregions. Specifically, we test how the group structure in the networks differ comparing i) the regional metaweb to subregions and ii) subregion to subregion. We find that more than half the species in the metaweb change groups when compared to subregions. Between subregions, networks with similar group structure are usually spatially related. Interestingly, although species overlap is important for similarity in group structure, there are notable exceptions. Our results highlight that species ecological roles differ depending on fine-scaled differences in patterns of interactions, and that local network characteristics are important to consider.

We develop a novel approach to trophic metacommunities and use it to study the effect of habitat loss on food webs. Our method assigns a spatially realistic Levins-type metapopulation model to each species, then couples them by making species extinction rates depend on the likelihood of the presence of species’ prey items via a Bayesian network representation of the food web. The method yields general insights into metacommunity ecology, revealing that metacommunity processes alone can restrict the maximum number of trophic levels to a handful at most over fragmented landscapes, independent of energetic or other constraints. It also allows one to repurpose known results of classical metapopulation theory for metacommunities, such as ranking the habitat patches of the landscape with respect to their importance to the persistence of the metacommunity as a whole. Using these tools, we explore how progressive habitat loss affects species extinction rates. The outcome depends on the order of habitat removal: focusing on removing patches which are least crucial to persistence first (best-case scenario) means the metacommunities can often tolerate the removal of more than 90% of their patches. Whereas removing the most crucial patches first (worst-case scenario) leads to the collapse of metacommunities very quickly. Surprisingly, removing patches at random is nearly indistinguishable in its effects from the worst-case scenario. In all cases, species’ vulnerability to habitat loss is greater at higher trophic levels, stressing the risk of network downsizing for food webs under progressive habitat loss.