‘Designing representative, scalable & policy-useful biodiversity monitoring programmes: Outcomes from the BioMonWeek 2026 workshop “How to design Biodiversity Monitoring Programmes”‘
Published: June 2026
This report summarises the outcomes of the BioMonWeek 2026 workshop “How to design Biodiversity Monitoring Programmes”, held on 7 May 2026 in Montpellier, France. The workshop brought together participants from research, monitoring, policy and data management backgrounds to explore how biodiversity monitoring programmes can be made more representative, scalable and policy-useful across regions and countries.
Context
Many biodiversity monitoring efforts collect valuable data, but not all data can support the same conclusions. What a monitoring programme can say depends on how sites are selected, what population or area the programme is intended to represent, how often observations are repeated, and how uncertainty is handled.
Workshop participants stressed that monitoring design should begin with clear questions: What should the programme represent? What decisions or reporting needs should it support? Which indicators are needed? What sampling design is required to make the results credible?
This is especially important in Europe, where biodiversity monitoring is expected to inform restoration planning, policy evaluation, legal reporting and international commitments. In this context, “design for inference” is not only a scientific concern, as it is becoming a practical requirement for policy-relevant monitoring.
Main takeaways
- Sampling design is central to representativeness. Monitoring programmes need a clear sampling framework. Without this, it may be difficult to know what the data represent or whether results can be generalised beyond sampled locations. The report discusses how probability-based sampling, stratification and spatially balanced designs can help support more robust conclusions.
- Monitoring must balance broad coverage and rare phenomena. A representative monitoring scheme may provide a strong picture of general trends, but still miss rare habitats, species or pressures. Conversely, targeted sampling can capture rare phenomena but may limit broader inference. The report explores how programme design can balance these needs without losing scientific robustness.
- Harmonisation does not mean doing everything the same way. The report supports an approach based on “minimum common requirements” rather than full standardisation. This means agreeing shared principles for sampling design, core variables, metadata, quality control and FAIR data practices, while allowing flexibility for national contexts and existing monitoring schemes.
- Governance and funding are key bottlenecks. Participants identified governance fragmentation, lack of standards and funding continuity as major barriers to coordinated European monitoring data sharing. Technical infrastructure matters, but discussions showed that coordination, institutional responsibilities and long-term support are just as important.
- Indicators need to be designed with policy use in mind: Monitoring data become more useful when there is a clear pathway from observations to indicators. The workshop underlined the need to start from policy questions and decision formats, then work backwards to define indicators, variables, sampling intensity, uncertainty reporting and data workflows.
Future steps
The report identifies practical directions for organisations designing or reforming biodiversity monitoring programmes:
- Strengthen coordination functions for monitoring design and data governance.
- Adopt minimum common requirements to improve transnational comparability.
- Design monitoring around explicit inference statements, including the target population, sampling frame, stratification and valid use of results.
- Build the pathway from monitoring data to policy-relevant indicators early in the process.
- Invest in continuity through stable funding, long-term plot networks, training and sustained data management capacity.
