A Bayesian network approach to trophic metacommunities shows that habitat loss accelerates top species extinctions

Abstract

We develop a novel approach to analyse 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 that 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 parameterisability 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.

Predictors of psychological stress and behavioural diversity among captive red panda in Indian zoos and their implications for global captive management

“…Biodiversity is vulnerable to the current rapidly changing environmental conditions. Landscape change and habitat loss cause range shift, leading to a higher extinction rate [1][2][3] , especially among range-restricted species such as those endemic to high-elevation 4 . Conservation breeding programmes have been criticised 5 , but in some circumstances they offer the only current way of saving rare species, allowing captive stock to be bred for possible reintroduction 4,6 ; for a review of the issues see 7 .…”

Metapopulation persistence can be inferred from incomplete surveys

“…Our study has the opposite focus to many other studies on metapopulation capacity. We focus on inference with unsampled patches, while previous works have mostly focused on habitat destruction or degradation by removing sampled patches [10,11,63,64]. Our analytic arguments apply as well to removing patches (a caveat though is that we would expect a larger prediction error due to a smaller patch number; see electronic supplementary material, appendix E).…”

“…Furthermore, based on the contribution of each patch to metapopulation capacity, we can quantify the conservation importance of each patch [10]. Beyond applicability for metapopulations, metapopulation capacity and patch importance are also useful for the study of metacommunities [11,12] and in conservation planning and management [13][14][15][16].…”

Global relationships between crop diversity and nutritional stability

“…Finally, we normalize this curve so that max(|nbr( C k )|) = 1 and max( k ) = 1 before finding the area under the curve for R N . It is worth noting that a Bayesian network approach could be used to efficiently derive a similar robustness metric via analytical solutions, rather than permutation 48 , 49 . Our unitless measure represents how robust a food system is to the sequential elimination of crops.…”