Navigating nature data can be tough when it’s loaded with heavy jargon. This pocket guide is meant as a reference companion with easy to digest breakdowns of the most common nature data metrics that financial institutions (FIs) use.
We unpack what each metric measures alongside practical examples of usage showing how to evaluate nature dependencies and impacts, as well as risks and opportunities, with confidence.
A note on simplification: The explanations below are intentionally simplified to make these metrics easier to understand. Each one sits on top of detailed, technical methodologies, with important nuances around data sources, modelling assumptions, and scope of application that this guide doesn’t cover.
Biodiversity Intactness Index (BII)
BII measures how intact an ecosystem’s species composition is relative to an undisturbed baseline. But it uses a different modelling approach and dataset (drawing on the PREDICTS database of over 50,000 sites worldwide) than MSA. We provide BII metrics in collaboration with London’s Natural History Museum.
A score of 100% indicates that biodiversity is fully intact, while lower values indicate a greater biodiversity loss. A BII greater than 90% means that the area is approaching the planetary boundary; it has enough biodiversity to be a resilient and functioning ecosystem. Less than 30% is considered ecologically degraded.
How FIs use it:
One example is using BII to refine an MSA-based screening (MSA is defined below) with a second, distinctly modelled read before deciding where to focus attention. Beyond screening, BII can ground engagement conversations, and feed into risk management models tracking ecosystem trends over time.
Why it matters:
BII represents another step in ecosystem-intactness measurement. It doesn’t invalidate MSA-based work already done, it refines the same underlying signal.
Commodity-linked Deforestation Data
In partnership with Global Canopy, we provide in-depth deforestation data using the Forest IQ dataset. Drawing on data from CDP, Trase, ZSL SPOTT, RSPO, and other key initiatives, it evaluates over 2,400 major companies on their commodity supply chain deforestation risks. It scores each company across three dimensions:
- Exposure – how closely the company is tied to forest-risk commodities and vulnerable ecosystems.
- Materiality – how much of the company’s revenue or operations depend on deforestation-linked commodities and land.
- Reported performance – how well the company commits to, discloses, and acts on deforestation and related human rights risks.
How FIs use it:
One example is using BII to refine an MSA-based screening (see the definition of MSA below) with a second, distinctly modelled read before deciding where to focus attention. Beyond screening, BII can ground engagement conversations, and feed into risk management models tracking ecosystem trends over time.
Why it matters:
BII represents another step in ecosystem-intactness measurement. It doesn’t invalidate MSA-based work already done, it refines the same underlying signal.
Land Conversion Equivalence (LCE)
LCE takes an entity’s overall biodiversity footprint (its PDF value – defined below, covering all eight drivers, not land use alone) and expresses it in more intuitive terms.
It’s the equivalent area of natural land that would need to be transformed for urban use to pose the same level of risk to global species. It’s calculated in km².
How FIs use it:
LCE can be used to translate a portfolio company’s biodiversity footprint into a line such as “equivalent to converting 40 km² of natural land to urban use.” This feels easier to communicate to a non-technical audience than a fraction between 0 and 1 (like PDF).
It’s worth noting that LCE simplifies the underlying picture into a single realm and land-use type by design, so it’s read as an easy to grasp headline number.
Why it matters:
It’s a very useful metric when communicating biodiversity footprints to committees, boards, the general public, or clients who find “X square kilometres” more intuitive than a decimal value of PDF.
Mean Species Abundance (MSA)
MSA estimates how much of an area’s original, undisturbed species abundance still remains. It’s expressed as a percentage. A score of 100% means the ecosystem is close to pristine. A lower score means more biodiversity has been lost. MSA only covers the terrestrial realm, using the GLOBIO 4 model.
How FIs use it:
A key use-case is portfolio screening. FIs can flag holdings that sit in ecosystems where a large share of original species abundance is already gone. This helps decide where deeper due diligence and action is worth the effort or investment.
The same score can support stewardship conversations about degradation trends over time. It can also feed into risk management frameworks, or provide evidence for TNFD-aligned disclosure.
Why it matters:
MSA has been a widely used starting point for biodiversity screening for years because it condenses a complex ecological picture into a useful first-pass view across a portfolio.
Nature Value at Risk (NVaR)
NVaR estimates how much of a company’s or portfolio’s economic output could be impaired under severe-but-plausible nature degradation scenarios. This is applied across direct operations and supply chains and is expressed in monetary terms/”dollar value” at risk.
How FIs use it:
In a nutshell, financial materiality. It gives financial decision-makers a single number summarising how much economic output could be impaired under a severe-but-plausible nature degradation scenario. This allows nature risk to be weighed alongside familiar risk metrics.
NVaR figures can also anchor engagement conversations by putting a concrete number behind stewardship asks. NVaR figures can support broader risk management and stress-testing exercises. It strengthens disclosures under frameworks that expect quantified financial materiality assessments.
Why it matters:
NVaR serves as the final link in the chain from location, to ecosystem condition, to financial exposure, translating everything above into the language financial institutions are used to acting on.
Watch our webinar on NVaR to learn more about how we calculate it.
Potentially Disappeared Fraction of Species (PDF)
PDF quantifies an entity’s biodiversity footprint- the risk a company or portfolio poses to global species stocks in a given year as a result of its activities. It’s built from eight drivers, including GHG emissions, air pollution, water consumption, water and land pollution, waste generation, and land use. It covers the terrestrial, freshwater, and marine realms and is calculated as a fraction between 0 – 1.
How FIs use it:
A typical example is using PDF to compare the overall biodiversity footprint of two portfolio companies in different sectors, one whose impact comes mostly from emissions, another mostly from land use, on the same standardised scale. This is something the raw underlying data alone wouldn’t allow.
PDF can also support stewardship reporting on portfolio-wide biodiversity pressure, feed into aggregated risk management strategies, or underpin disclosure metrics under frameworks like TNFD and PBAF, which accept PDF as a measurement approach.
Why it matters:
PDF puts every driver on the same scale, so FIs are able to actually rank and aggregate exposure.
Species Threat Abatement and Restoration (STAR)
STAR quantifies how much a specific conservation or restoration action, at a specific site, could reduce global species extinction risk; it’s a metric built from IUCN Red List data.
There are two STAR scores:
- STAR_T (Threat Abatement) pinpoints locations where mitigating a current threat, such as a development that is putting pressure on a species, could meaningfully reduce extinction risk. It’s based on how much of a species’ current range (its Area of Habitat) falls within a given asset’s assumed area of influence.
- STAR_R (Restoration) looks backward rather than at current conditions. It pinpoints locations where rehabilitating habitat a species used to occupy could meaningfully reduce that species’ extinction risk. It’s based on how much of a species’ formerly occupied range (its Restorable Habitat) falls within a given asset’s assumed area of influence.
How FIs use it:
The clearest advantage is prioritisation. STAR scores can help identify which sites out of hundreds would benefit most from restoration investment or engagement. This ensures a limited stewardship budget goes where it has the most impact.
The same scores can support risk management by highlighting where extinction-risk exposure is highest. Furthermore, they feed into disclosure narratives that need to demonstrate quantified, evidence-based action.
Why it matters:
Not every hectare of degraded habitat carries equal weight for global biodiversity. STAR helps direct effort toward the locations where nature positive action has the greatest measurable impact.
If there’s a nature metric you can’t quite grasp but you don’t see it listed above, fear not. We’ll be periodically updating this pocket guide.
—
Ready to put some of these nature metrics to work? Get in touch with our team.
