IAP-24-074

Improving spatial estimates of species’ extinction risk for conservation planning

Tackling the global biodiversity crisis, and improving the conservation status of species, requires a concerted effort from actors across the whole of society to identify and act upon opportunities to reduce species extinction risk. The Species Threat Abatement and Restoration (STAR) metric has been designed to support the setting and measuring of progress towards science-based conservation targets for species, allowing all actors to quantify their potential contribution to the reduction of global species extinction risk (Mair et al. 2021). STAR quantifies the potential for threat abatement activities in any given place to contribute towards the reduction of species extinction risk. STAR is now available for both the terrestrial and marine environment, the latter covering 1,646 marine species (Turner et al 2024), and has been made available to businesses and policy makers through the Integrated Biodiversity Assessment Tool (IBAT; www.ibat-alliance.org).

STAR is a spatially explicit metric that uses species Area of Habitat (AOH) data in its calculation (Brooks et al 2019). AOH was mapped for marine species using global data on the distribution of ecosystem types (the IUCN ecosystem typology) combined with species range maps (IUCN red list range maps). These data present a pragmatic approach, given they are global in scope, peer reviewed and publicly available, however resulting AOH and hence STAR data have some limitations. Firstly, the AOH are of coarse spatial and ecological resolution, and AOH inherently over-estimates species expected distribution. Secondly, the approach produces a static AOH map, which does not account for the effects of climate change past, present or future. And thirdly, the resulting STAR data are what we call ‘estimated STAR’, meaning that they do not account for spatial variation in threat impacts. All of these pose challenges to the usefulness of STAR in a decision-making context.

There is, however, potential to address these challenges through the application of species distribution models (SDMs). SDMs have not yet been used in conjunction with STAR, but their use could (i) refine our understanding of the distribution of species, (ii) estimate past and predicted future impacts of climate change, and (iii) allow the spatial impacts of non-climate change threats to be modelled. The integration of climate change considerations into decision making while considering other threats is urgently required to inform effective conservation efforts which aim to reduce of species extinction risk.

Aims and objectives:
This studentship aims to drive scientific advances in the calculation and application of the STAR metric by applying SDMs to sharks and rays (Dulvy et al 2021) in the calculation of marine STAR. Sharks and rays are wide-ranging, pelagic and demersal species that are heavily impacted by fishing pressure and climate change with more than one-third of estimated to be threatened with extinction (Dulvy et al 2021).

The project objectives are to
(1) Develop SDMs for the relevant species and use these to calculate a novel marine STAR output,
(2) Use SDM projections under future climate change scenarios to predict how STAR is likely to change into the future, and
(3) Integrate data on the spatial distribution of fishing pressure to explore how the interaction between fishing pressure and climate change affects species extinction risk.

Objective 1: The student will develop SDMs for sharks and rays using machine learning models (e.g. Stephenson et al., 2023). Species distribution data will be obtained from the IUCN SSC Shark Specialist Group and from open-source repositories, e.g., OBIS, GBIF, IUCN. Current-day environmental data will be drawn from open-source databases, e.g., Biomod and Copernicus (e.g., Assis et al., 2018). SDMs produce a map of species probability of occurrence and where data allows, abundance. The student will develop a novel approach to apply SDM outputs in the calculation of STAR for sharks and rays. Having done this, STAR results will be compared between the output based on AOH and the output based on SDMs.

Objective 2: The student will use future climate change projections (i.e., predicted future environmental data derived from Earth System Models for different greenhouse gas emission scenarios), to predict how the distribution of species will change under future climatic conditions, and therefore, how STAR is likely to change into the future under climate change. These ’future STAR’ results will allow the student to explore whether conservation decisions made today will still be effective for the species into the future.

Objective 3: The student will integrate data on the distribution of fishing pressure (e.g., using high resolution information from Global Fishing Watch, www.globalfishingwatch.org) into the STAR results, which will allow a refined understanding of the spatial impacts of fishing on species. This will be done by developing a novel adaption of the STAR framework to allow consideration of the spatial variation in the distribution of threats.

Year 1

Inception meeting between supervisor team, candidate and case partner. Discussion and prioritisation of specific tasks on the project. Development of species distribution models for sharks and rays (chapter 1). Quarterly meetings with case partner for project input and to plan the student placement with case partner.

Year 2

Calculation of STAR using SDM outputs, and comparison with STAR outputs using AOH (chapter 2). Prediction of how STAR is likely to change into the future under climate change (chapter 3).

Year 3

Refinement of STAR through integration of data on spatial distribution of fishing pressure (chapter 4). Attendance of the Society for Conservation’s International Marine Conservation Congress and the British Ecological Society annual meeting.

Year 3.5

Finish writing thesis and possibly attendance of British Ecological Society annual meeting dependant on conference dates and whether this meeting was not attended in year 3.

The successful candidate will receive training in spatial data analysis, species distribution modelling and predictive mapping, data visualisation, threats and environmental risks. Alongside developing these technical skills, they will also develop skills in research communication, including scientific writing and presentation to stakeholder audiences.

The candidate will be placed in the Modelling, Evidence, and Policy research group at Newcastle University, and will benefit from interaction with a dynamic group of early career researchers working on ecological challenges, supported by experienced academics with diverse analytical and modelling skills. The research group and the school more widely have a strong marine research focus, and the student will have access to diverse marine expertise. In addition, regular interaction with the Ecology & Environmental Change research group at the University of Glasgow, and the Ocean Team at the IUCN will provide additional support and opportunities to develop modelling skills, a detailed understanding of shark ecology and distribution and exposure to approaches for using science to inform international policy.