Dr Rhosanna Jenkins
At Natcap, we pride ourselves on our academic heritage and ongoing commitment to science.
But why does a science-based approach matter?
In this blog post, we explain why scientific rigour matters when measuring nature and biodiversity, particularly for corporates looking to understand and mitigate their exposure to nature-related risk.
Biodiversity loss and ecosystem degradation are widely recognised as systemic risks. Corporate disclosure frameworks are encouraging companies to explore their nature risks and the industry leaders are already moving towards acting on these risks. There is increasing investment in actions that support nature, such as sustainable agriculture, habitat conservation and nature based solutions. The question is not whether organisations will address nature risk, but how credibly they do so. This is why the quality of the data underpinning the assessment is a critical factor in the success of efforts to mitigate risks and reduce impacts on nature.
Monitoring changes, quantifying potential opportunities or meeting reporting requirements all rely on metrics and data. There’s no shortage of metrics professing to allow companies to quantify their impacts and dependencies on nature, and the corresponding risks to business operations. However, the scientific foundations of these nature metrics are uneven. Some are scientifically robust and thoughtfully constructed, with clear guidance and documentation provided to support their use. Others rely on weak proxies, ambiguous methods and/or untested assumptions.
What do we mean by scientific rigour?
There are several principles of scientific rigour that should be considered when investigating data and metric quality.
- Transparent methodology
- Replicability and reproducibility
- Peer review or independent validation, e.g. ground truthing
- Appropriate uncertainty treatment
- Alignment with ecological theory
Importantly, rigour is different from precision. More precise data points can be less rigorous. For example, a score reported to two or more decimal places may look sophisticated, but if the underlying method or model is not well validated, the extra digits are meaningless.
Why nature is especially vulnerable to weak metrics
Nature is extremely complex; full of non-linear responses and spatially-explicit interactions. Small changes can trigger tipping points and species interactions create cascading effects. For example, reductions in insect numbers can cascade through the entire food chain, as those that rely on them as food sources are put under pressure. In some cases, there will be significant time lags associated with the impact. Ecological quality depends on context and time. Reducing or aggregating this ecological complexity to a single, static number is inherently difficult and risky, but there is significant pressure to translate it into simple, comparable scores for reporting.
Another challenge is data limitations arising from limited consistent biodiversity monitoring efforts. Many species are poorly monitored. Field surveys are expensive. As a result, there is a heavy reliance on proxies and models, such as landcover or habitat based assessments. These metrics can be useful if validated and their limitations are understood.
Action to reverse nature decline is needed urgently, so decisions need to be made with this imperfect information. Even with this imperfect information, we can still be scientifically rigorous. We must make use of the best available data and openly discuss its limitations while committing to continuous improvement.
What happens when we get it wrong
In emerging biodiversity credit markets, for example, the credibility of the entire system depends on whether credits represent genuine ecological gains. If baselines are inflated or condition improvements overestimated, trust erodes quickly.
Consider a sustainability leader at a supermarket who is building the business case for nature in their organisation. Building the business case for nature-positive action requires buy-in from multiple stakeholders across the company, including procurement, operations, finance and leadership teams. Each of these groups relies on data to justify changes to strategy, suppliers, processes, or investments. Presenting a poorly designed biodiversity metric may misrepresent the environmental impact, and ultimately, lose the trust of critical stakeholders.
The Procurement team, acting on the biodiversity analysis, might shift spend toward these seemingly better suppliers in areas of lower environmental impact. Leadership teams may communicate these changes externally as evidence of sustainability progress.
Problems could arise later on, when more robust data later becomes available, or when external stakeholders such as customers, regulators or even NGOs, scrutinise the results. The supermarket may discover it has not reduced its impact, or worse, has shifted to an area of higher risk.
In this way, weak scientific rigour doesn’t just create minor inaccuracies. It can lead to misallocated resources, flawed strategic decisions, and erosion of trust. Conversely, robust, transparent, and scientifically grounded metrics enable confident decision-making, alignment across functions, and credible communication with stakeholders.
How we build scientifically rigorous data products at Natcap
Scientific rigour is not a barrier to scaling nature reporting. It is the foundation that makes scaling possible. Existing metrics can be powerful tools but only if their assumptions, uncertainties and ecological meaning are clearly understood.
At Natcap, we decide which metrics to incorporate and build into our products based on seven qualities of effective biodiversity metrics.
- We use data and methods from peer-reviewed or validated sources
- We are transparent about our methods. Our methodologies are documented clearly, including assumptions and limitations.
- Aggregation beyond the individual metric and location level is designed to preserve meaning
- Our team of scientists regularly reviews the latest developments to ensure we see continuous improvement in our methods
Questions to Ask Your Data Provider
- Have the metrics been independently validated or peer-reviewed?
- How often are the metrics updated?
- What is the reference state or baseline, and why was it chosen?
- How are uncertainties, assumptions, and methodological choices documented?
- Metric purpose matters - in what contexts should the metric not be used?
Ready to move your nature strategy from reporting to real action? Talk to our team about quantifying your risk and securing long-term resilience.