Biodiversity Metrics and Indicators
Measuring what we cannot directly see
Biodiversity is extraordinarily difficult to measure. Unlike carbon, which can be expressed in a single unit (tCO₂e), biodiversity encompasses millions of species, their interactions, the health of entire ecosystems, and the genetic diversity within populations. The field has therefore developed a range of metrics, each capturing different facets of the same complex reality. Understanding their strengths and limitations is essential for credible reporting.
What Are We Actually Measuring?
Biodiversity metrics can target three different levels of biological organisation: genetic diversity (the variety of genes within a species), species diversity (the variety of species in a defined area), and ecosystem diversity (the variety of different habitats and ecological communities). Most corporate biodiversity metrics focus on species and ecosystem levels because they are most tractable with available data and most directly linked to the ecosystem services that businesses depend on.
Within these levels, metrics typically measure one or more of three dimensions: composition (what species or habitats are present), structure (how they are arranged and how many of each), and function (what ecological processes they perform). A healthy ecosystem typically scores well across all three dimensions; degraded ecosystems often maintain some species while losing structural complexity or ecological function.
Mean Species Abundance (MSA)
Mean Species Abundance (MSA) is one of the most widely used biodiversity impact metrics in corporate and financial contexts, forming the basis of tools such as GLOBIO and the Netherlands Environmental Assessment Agency (PBL) models. MSA measures the average abundance of original species relative to their abundance in an undisturbed reference ecosystem.
An MSA of 1.0 (or 100%) indicates an ecosystem with fully intact native species communities at undisturbed abundance levels. An MSA of 0 indicates complete loss of all original species. Land-use change scenarios modelled with GLOBIO estimate that intensive agriculture reduces MSA to around 0.3-0.4 compared to natural ecosystems, while plantation forestry yields MSA values of around 0.5-0.7 depending on species composition.
MSA as an Ecosystem Health Score
Think of MSA as an ecosystem's health score on a scale from 0 to 100%. A pristine old-growth forest scores close to 100%: all the species that should be there are present in roughly the numbers they would naturally occur at. A heavily grazed pasture might score 40%: some original grassland species persist, but many have been replaced by a few dominant grasses and the total community is greatly simplified. An urban car park approaches 0%: virtually no original species remain. MSA is useful because it provides a single summary number that captures the net effect of multiple pressures on overall biodiversity.
Biodiversity Intactness Index (BII)
The Biodiversity Intactness Index (BII) is a closely related metric developed by the Natural History Museum London in collaboration with UNEP-WCMC. BII measures the average proportion of natural biodiversity remaining in local ecological assemblages, relative to a baseline of minimal human impact. Like MSA, it generates a value between 0 and 100%.
A key difference from MSA is that BII is calibrated against observed species abundance data from a large global database of over 2.38 million records across 78,000 species. This empirical grounding makes BII particularly useful for tracking global trends. Analyses using BII suggest that approximately 58% of global land area falls below the 90% BII threshold considered safe for supporting essential ecosystem services. BII is increasingly referenced in TNFD guidance as a state-of-nature metric suitable for aggregate reporting.
Species Richness and Abundance-Based Metrics
Species richness, the count of distinct species in a defined area, is the most intuitive biodiversity metric and the most commonly used in ecological field surveys. It is easy to understand and communicate but has well-known limitations: a habitat with 100 species of which 95 are introduced invasives and 5 are native might show high richness while actually representing severe native biodiversity loss.
More sophisticated abundance-based metrics address this limitation by weighting species counts by their population sizes or by their conservation status. The Species Habitat Index (SHI), for example, combines range maps and habitat condition data to estimate the proportion of a species' historical habitat that remains in suitable condition. This makes it a valuable complement to richness counts for assessing cumulative pressure on target species.
The STAR Metric: Species Threat Abatement and Restoration
The STAR (Species Threat Abatement and Restoration) metric, developed by IUCN and partners, takes a fundamentally different approach. Rather than measuring biodiversity state, STAR measures corporate or landscape-level potential contributions to avoiding species extinction. It is structured around the IUCN Red List threat categories and calculates the share of global extinction risk attributable to specific threats in specific locations.
STAR has two components. The threat abatement component estimates the extinction risk reduction achievable by eliminating a given threat (such as habitat loss, invasive species, or pollution) from a specific location. The restoration component estimates the extinction risk reduction achievable by restoring habitat in a given area. This makes STAR particularly useful for companies seeking to demonstrate their contribution to the global biodiversity agenda in outcome terms, rather than simply reporting activity metrics.
| Metric | What It Measures | Data Required | Best Used For |
|---|---|---|---|
| MSA | Average abundance of original species relative to undisturbed baseline | Land use maps, pressure data | Impact footprinting, land use change modelling |
| BII | Average proportion of natural biodiversity remaining vs. minimal-impact baseline | Species abundance records, land use data | State of nature tracking, aggregate reporting |
| Species Richness | Count of distinct species in a defined area | Field surveys or remote sensing proxies | Site-level assessment, monitoring change over time |
| STAR | Potential contribution to reducing species extinction risk | IUCN Red List data, threat maps | Target setting, outcome-based accountability |
| Species Habitat Index (SHI) | Proportion of historical species habitat remaining in suitable condition | Range maps, habitat condition indices | Portfolio-level screening, species-specific tracking |
Area-Based vs. Abundance-Based Metrics
A practical distinction for corporate users is between area-based and abundance-based metrics. Area-based metrics, such as "hectares of habitat protected or restored," are simple to report and verify but can mask quality differences: protecting low-quality degraded land delivers far less biodiversity value per hectare than protecting intact old-growth forest. Abundance-based metrics such as MSA and BII attempt to capture quality as well as quantity but require more sophisticated data and modelling.
The TNFD recommends reporting both types. Area-based metrics provide transparency and traceability; abundance-based metrics provide ecological relevance. Together they offer a more complete picture than either alone, and help prevent the "junk biodiversity credit" problem where companies claim large conservation impacts from protecting already-degraded land of minimal ecological value.
Example: Interpreting Metrics for a Chocolate Company
A chocolate manufacturer sources cocoa from Ghana and Ivory Coast, two countries that have lost over 80% of their original forest cover. A site-level MSA assessment of its key sourcing landscapes might return values of 0.25-0.35, reflecting the heavily degraded condition of cocoa-growing areas relative to the original tropical forest baseline. BII analysis might indicate that the region is well below the 90% safety threshold across most of its supply shed. Species richness surveys at cocoa farms would reveal low native bird and insect diversity compared to forest remnants nearby. STAR analysis would quantify the extinction risk reduction achievable if deforestation-free cocoa sourcing eliminated the primary driver of ongoing forest loss in the region. Together, these metrics build a coherent picture of biodiversity status and the potential impact of the company's sourcing decisions.
Selecting Metrics for TNFD Reporting
TNFD guidance steers organisations toward metrics appropriate to their sector, their data availability, and the nature of their material risks. No single metric captures everything relevant to biodiversity. The appropriate metric set depends on the ecosystems at risk, the impact drivers involved, and the questions decision-makers need to answer.
For organisations beginning their biodiversity measurement journey, the pragmatic advice from the TNFD is to start with what is available and improve progressively. Reporting area-based metrics with known limitations is more valuable than reporting nothing while waiting for perfect data. Paired with clear explanation of methodology and known gaps, imperfect metrics still provide useful directional information that supports investor and stakeholder decision-making.
Key Takeaways
- 1Biodiversity metrics operate at three levels - genetic, species, and ecosystem - and measure composition, structure, and function; most corporate metrics focus on species and ecosystem levels because these are most tractable and directly linked to ecosystem services
- 2Mean Species Abundance (MSA) measures the average abundance of native species relative to an undisturbed baseline on a 0-100% scale; intensive agriculture typically yields MSA values of 30-40%
- 3The Biodiversity Intactness Index (BII), calibrated against 2.38 million empirical records, indicates that around 58% of global land falls below the 90% threshold considered safe for essential ecosystem services
- 4The STAR metric measures potential corporate contributions to reducing species extinction risk and is structured around IUCN Red List threat categories, making it useful for outcome-based target setting
- 5Area-based and abundance-based metrics are complementary: area metrics provide transparency and traceability while abundance metrics capture ecological quality, and TNFD recommends reporting both