Farmer-led agroecology for biodiversity with climate change.

The increasing pace of climate change is an existential threat to farming continuity and biodiversity. Agricultural innovation is running too slowly but could be accelerated by a change in the agroecological narrative. A farmer-led agroecology prioritising farming continuity for biodiversity would speed up innovation and better serve science and society.

Effects of model choice, network structure, and interaction strengths on knockout extinction models of ecological robustness.

Analysis of ecological networks is a valuable approach to understanding the vulnerability of systems to disturbance. The tolerance of ecological networks to coextinctions, resulting from sequences of primary extinctions (here termed "knockout extinction models", in contrast with other dynamic approaches), is a widely used tool for modeling network "robustness". Currently, there is an emphasis to increase biological realism in these models, but less attention has been given to the effect of model choices and network structure on robustness measures. Here, we present a suite of knockout extinction models for bipartite ecological networks (specifically plant-pollinator networks) that can all be analyzed on the same terms, enabling us to test the effects of extinction rules, interaction weights, and network structure on robustness. We include two simple ecologically plausible models of propagating extinctions, one new and one adapted from existing models. All models can be used with weighted or binary interaction data. We found that the choice of extinction rules impacts robustness; our two propagating models produce opposing effects in all tests on observed plant-pollinator networks. Adding weights to the interactions tends to amplify the opposing effects and increase the variation in robustness. Variation in robustness is a key feature of these extinction models and is driven by the structural heterogeneity of nodes (specifically, the skewness of the plant degree distribution) in the network. Our analysis therefore reveals the mechanisms and fundamental network properties that drive observed trends in robustness.