The International Union for Conservation of Nature’s (IUCN) Red List is the global standard for
quantifying extinction risk but assessing population reduction (criterion A) of wide-ranging, long-lived
marine taxa remains difficult and controversial. We show how Bayesian state-space models (BSSM),
coupled with expert knowledge at ...
The International Union for Conservation of Nature’s (IUCN) Red List is the global standard for
quantifying extinction risk but assessing population reduction (criterion A) of wide-ranging, long-lived
marine taxa remains difficult and controversial. We show how Bayesian state-space models (BSSM),
coupled with expert knowledge at IUCN Red List workshops, can combine regional abundance data
into indices of global population change. To illustrate our approach, we provide examples of the
process to assess four circumglobal sharks with differing temporal and spatial data-deficiency: Blue
Shark (Prionace glauca), Shortfin Mako (Isurus oxyrinchus), Dusky Shark (Carcharhinus obscurus),
and Great Hammerhead (Sphyrna mokarran). For each species, the BSSM provided global
population change estimates over three generation lengths bounded by uncertainty levels in intuitive
outputs, enabling informed decisions on the status of each species. Integrating similar analyses into
future workshops would help conservation practitioners ensure robust, consistent and transparent
Red List assessments for other long-lived, wide-ranging species.