Restoration success is often measured by comparing target species abundance between restored and reference populations. Abundance may poorly predict long-term success, however, because seed addition may initially inflate restored population abundances, and reference population abundances may fluctuate with environmental variation. A demographic approach, informed by modern coexistence theory, may allow for more accurate diagnosis of restoration trajectories. We modeled population dynamics of an endangered plant (Lasthenia conjugens) in restored vernal pools and compared them to reference populations over 18 years (2000-2017). Model estimates of L. conjugens growth rates were better predictors of long-term trends than observed abundances. Although populations fluctuated in reference pools, annual rainfall variability acted as a stabilizing factor for L. conjugens. In restored pools however, invasive grasses and associated litter accumulation overrode the benefits of environmental variability. Our approach improves assessment of restoration outcomes and indicates when management actions, such as grass removal, will improve future trajectories.
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.
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.