Tracking progress towards global biodiversity targets for data-poor longer-lived species: A case study of Northeast Atlantic and Mediterranean sharks and rays

Thesis type
(Thesis) Ph.D.
Date created
Biodiversity loss is increasingly well understood on land, but the extinction risk trajectory remains largely unknown in the comparatively data-poor oceans. The Red List Index is used to track progress towards global biodiversity targets, but it cannot incorporate data-poor species and may not track recovery particularly well. Here, I explore three methods to improve the representation and responsiveness of the Red List Index for data-poor and longer-lived species. I use Class Chondrichthyes in the Northeast Atlantic Ocean and Mediterranean Sea as a case study, representing the most data-poor and long-lived marine species group to be reassessed by the International Union for Conservation of Nature (IUCN), in two regions with widely diverging fisheries management. First, I categorically predict the IUCN status of species assessed as Data Deficient, showing that Data Deficient species are equally or more threatened with an elevated risk of extinction than data-sufficient species. Second, I incorporate these predicted categorisations into a Red List Index with the remaining data-sufficient species. Regional chondrichthyan extinction risk is increasing, with higher overall risk levels and a faster rate of deterioration in the Mediterranean Sea than the Northeast Atlantic. Finally, I incorporate population trend (stable, increasing, or decreasing) into the Red List Index formula to improve index responsiveness to population recovery of species with generation lengths exceeding the ten-year framework of biodiversity targets and Red List reassessments. This 'Red List Population Index' has the potential to forewarn of species' population recovery ten years ahead of the current Red List Index formulation and requires no more data collection or expertise to complete. Together, these studies present novel, cost-effective, and time-sensitive methods for broadening the representativeness of data-poor, longer-lived species in progress tracking towards imminent biodiversity targets and for conservation prioritisation.
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Supervisor or Senior Supervisor
Thesis advisor: Dulvy, Nicholas K
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