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Biodiversity targets are a key tool, used at a global and national policy level, to align biodiversity goals, promote conservation action, and recover nature. Yet most biodiversity targets are not met. In England, the government has committed to legally-binding targets to halt and recover the decline in species abundance by 2030 and 2042. We present evidence from recent population trends of 670 terrestrial animal species (for which abundance time series are available) as a species abundance indicator, together with a synthesis of case studies on species recovery, to assess the degree to which these targets are achievable. The case studies demonstrate that recovery is possible through a range of approaches. The indicator demonstrates that theoretically targets can be achieved by addressing severe declines in a relatively small number of species, as well as creating smaller benefits for many species through landscape-scale interventions. The fact that multiple pathways exist to achieve the species abundance targets in England presents choices but also raises the possibility that targets might be reached with perverse consequences. We demonstrate that evidence on achievability is a necessary but not sufficient condition for determining what is required to deliver conservation outcomes and restore biodiversity.

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.