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.