Anil Madhavapeddy, Professor of Planetary Computing

Achieving the goals of the Kunming-Montreal Global Biodiversity Framework (GBF), requires robust monitoring and reporting to track progress and guide action. However, our ability to understand trends is challenged because biodiversity data are fragmented and biased. This stems from the many different approaches used to record data, aggregate records, and analyze them to detect trends and attribute causes. While this fragmentation reflects a past lack of a unifying mandate and technological capacity, the urgency of the GBF and new capabilities in data science now make a harmonized approach both necessary and feasible.

We propose the Biodiversity Monitoring Standards Framework (BMSF) as the how: a comprehensive, modular, and tiered system designed to guide the standardization of the entire monitoring workflow—from planning and ethical data collection to model-based analysis and reporting. The BMSF integrates Essential Variables, standardized protocols, and the Findable, Accessible, Interoperable, and Reusable (FAIR) and Collective Benefit, Authority to Control, Responsibility, and Ethics (CARE) data-management principles. It promotes accredited analytical workflows, operationalized through open-source tools and platforms that have standardized data protocols.

This framework enables comparison and aggregation of findings across scales by ensuring consistent data capture, quality assurance, and validated analytical pathways with uncertainty reporting. It is designed to support critical decisions, such as prioritizing areas for restoration and conservation, and verifying corporate nature-related disclosures. The implementation of the BMSF is envisioned through a federated model, building on the strengths of existing organizations and observatories. If supported, the BMSF offers a pathway to actionable, globally comparable knowledge.