Integrating deep-time palaeontology in conservation prioritisation

Halting biodiversity loss under growing anthropogenic pressure is arguably the greatest environmental challenge we face. Given that not all species are equally threatened and that resources are always limited, establishing robust prioritisation schemes is critical for implementing effective conservation actions. To this end, the International Union for Conservation of Nature (IUCN) Red List of Threatened Species has become a widely used source of information on species' extinction risk. Various metrics have been proposed that combine IUCN status with different aspects of biodiversity to identify conservation priorities. However, current strategies do not take full advantage of palaeontological data, with conservation palaeobiology often focussing on the near-time fossil record (the last 2 million years). Here, we make a case for the value of the deep-time (over 2 million years ago), as it can offer tangible parallels with today's biodiversity crisis and inform on the intrinsic traits that make species prone to extinction. As such, palaeontological data holds great predictive power, which could be harnessed to flag species likely to be threatened but that are currently too poorly known to be identified as such. Finally, we identify key IUCN-based prioritisation metrics and outline opportunities for integrating palaeontological data to validate their implementation. Although the human signal of the current extinction crisis makes direct comparisons with the geological past challenging, the deep-time fossil record has more to offer to conservation than is currently recognised.

Automated summary

Halting biodiversity loss is a major challenge, and prioritization is crucial for effective conservation actions. The IUCN Red List is widely used, but current strategies do not fully utilize paleontological data. Deep-time fossil records can offer insights into today's biodiversity crisis and inform on species prone to extinction. Integrating paleontological data can validate prioritization metrics and contribute to conservation efforts.