The Species Threat Abatement and Restoration (STAR) Metric

The Species Threat Abatement and Restoration (STAR) metric is a science-based tool designed to measure, in a spatially explicit way, the potential reduction in species global extinction risk that could be achieved through conservation action and restoration.

Built on data from the IUCN Red List of Threatened Species™, STAR quantifies how much global species extinction risk could be lowered through actions taken at a particular site or collection of sites, such as a company’s footprint, or even an entire country.

STAR provides a consistent approach to set extinction risk reduction targets both locally and globally, transparently making progress measurable and comparable.

 

Why STAR Matters

Conservation impact measurement has long been fragmented. Different actors, such as governments, NGOs, and businesses, work across diverse geographies and with varied approaches, making joint monitoring and comparison difficult. STAR addresses this challenge by offering a standardised, scalable, and additive framework. Its scores can be summed across sites and actors, which means that contributions to global biodiversity goals can be compared directly and reported with clarity.

 

Methodology

The STAR approach was first introduced by Mair et al. (2021). It combines Red List extinction risk categories, documented threats, and Area of Habitat (AOH) data.

The STAR metric has two components:

• Threat Abatement STAR (STAR_T): which quantifies how much extinction risk could be reduced if current threats were removed. This information is now available at a fine global resolution of 1 km. 
• Restoration STAR (STAR_R): which estimates the potential benefit of restoring habitats where species used to occur. STAR_R is currently mapped at 5 km resolution.

 

The calculation of the STAR score is based on the integration of three main elements. First, it considers how many threatened species are present within the area of study. Second, it assigns a weight to each species according to its category on the IUCN Red List (ranging from 100 for Near Threatened species to 400 for Critically Endangered species). Third, it takes into account the proportion of each species’ global Area of Habitat (AOH) that lies within the pixel or polygon under analysis.

Figure 1. Outline of the steps in calculating STAR_T scores for a defined Area of Interest.

The outcome of this calculation shows, for each point on the map, the potential contribution of that area to reducing the global extinction risk of the species it contains. These values can then be summed and aggregated at larger scales—for example, across a company’s footprint, a protected area, a landscape, or even an entire country—thereby allowing comparisons and enabling clear communication of how different actors can contribute to global conservation targets.

Figure 2. The STAR global layers (version July 2025) for threat abatement. Percentile categories show the STAR score for each grid cell in relation to the global distribution of scores across all grid cells.

One of STAR’s most distinctive features is its ability to break down scores by type of threat. The threats are categorised following the IUCN Threats Classification Scheme and scored for severity and scope to show their impact on a species. The STAR_T score incorporates this information and can be broken down to show the relative contributions of different threats. This means that at any given location STAR_T can show not just the total potential for extinction risk reduction, but also which threats are contributing most—whether agriculture, invasive species, hunting, fishing, or climate change. This disaggregation is powerful because it enables the design of targeted interventions, makes it possible to compare geographies in terms of dominant threats, and allows for transparent reporting aligned with global frameworks such as the Kunming-Montreal Global Biodiversity Framework (KMGBF).

Figure 3. STAR scores for individual species in an area and a break down of the scores by type of threat.

 

From Estimation to Local Implementation

STAR is structured to evolve from broad global estimates to locally grounded, evidence-based measurements.

The first level, Estimated STAR, relies on global Red List and AOH data and assumes that species occupy their entire AOH and that threats act uniformly across it. These assumptions introduce uncertainty. 

The second level, Calibrated STAR, ground-truth’s this estimate by incorporating local data to verify species’ presence and measure the actual intensity of threats within an Area of Interest (AOI). This produces more realistic baselines and actionable targets. 

The third stage, Realised STAR, measures actual progress achieved through conservation actions, such as the reduction of threats or habitat restoration, in relation to the calibrated baseline.

Figure 4. Elements of the global STAR metric and how Estimated STAR relates to Calibrated, Target, and Realised STAR.

 

Recent Developments

In recent years, STAR has expanded into new domains and applications.

• National Red Lists (Mair et al. 2022): By applying STAR with national Red List data in Brazil, Norway, and South Africa, researchers demonstrated that country-specific information can sharpen extinction risk reduction estimates. 
• Marine STAR (Turner et al. 2024): First extension into the ocean realm. Demonstrated that unsustainable fishing accounts for 43% of marine extinction risk, with 75% of potential opportunities lying outside protected areas.
• Regional Applications (Jiménez et al. 2025): STAR was applied to the EU to target invasive alien species, showing that islands like the Canaries and Madeira hold the largest potential opportunities for threat reduction.
• Integration into IUCN RHINO (2025): STAR now underpins the IUCN Rapid High-Integrity Nature-positive Outcomes (RHINO) approach, where it serves as the species-level component linking extinction risk reduction directly to the KMGBF Goal A.
• Calibration Case Studies: In a landscape in Costa Rica, calibration revealed that livestock farming and ranching represented the greatest local opportunity to reduce extinction risk, reshaping priorities compared to global estimates.

 

 

Use of STAR at the Project Level

 

STAR can help to orient management action as it can show where management of particular threats or restoration would bring the greatest extinction risk reduction. Figure 5 is a case study from the Bukit Tigapuluh landscape in Sumatra:

Figure 5. STAR results for the Bukit Tigapuluh Sustainable Landscape and Livelihoods Project. The Bukit Tigapuluh Sustainable Landscape and Livelihoods Project is a sustainable commercial rubber initiative. The study area includes a 5 km buffer (dark grey line), which is set aside to support local livelihoods, wildlife conservation areas and forest protection and restoration, and two ecosystem restoration areas (yellow lines), which form a conservation management zone that protects the Bukit Tigapuluh National Park from encroachment. STAR threat-abatement scores in mapped areas with remaining forest (green) and STAR restoration scores in mapped areas where forest has been lost (purple). Figure taken from Mair et al. (2021) Nature Ecology & Evolution.

 

Applications Across Scales

The flexibility of STAR makes it applicable at multiple levels. 

• At the site scale, it helps land managers and project developers identify which threats to address or where restoration could deliver the greatest benefit. 
• For companies, STAR provides the scientific foundation for setting science-based targets for nature, aligned with disclosure and accountability frameworks such as TNFD and SBTN. 
• At the national scale, governments can use STAR to quantify and report their contributions to the KMGBF Goal A. 

 

 

Limitations and Considerations

• Taxonomic scope: Currently covers comprehensively assessed amphibians, birds, mammals, reptiles, and selected marine taxa. Expansion to other groups is in progress.
• Assumptions in estimated STAR: Species are treated as if they occupy their entire AOH, and threats are assumed to act uniformly across it, which can misrepresent local realities. Calibration is the mechanism to correct for this. 
• Downlisting not automatic: Even complete threat abatement does not always lead to immediate Red List downlisting (e.g., for species with very small populations).
• Emerging threats: STAR cannot anticipate new or emerging threats, which are only incorporated once they are documented in Red List assessments.

 

More Information on STAR:

For general or technical inquiries about STAR, please contact [email protected].

For interest in applying STAR for commercial use, please contact [email protected].

See the IUCN STAR website and an IUCN Issues brief for further information.

See IUCN STAR Guidance for governments and civil society for further information and explanation of the STAR metric.

We acknowledge funding from Newcastle University, the Luc Hoffmann Institute, Vulcan, Synchronicity Earth and the Global Environment Facility, as well as support from the Conservation International GEF Project Agency. The Biodiversity Consultancy pioneered the tool’s private sector applications.