We are excited to share that our paper “TABMON: design and deployment of a transnational passive acoustic monitoring network for European birds” has been published in Methods in Ecology and Evolution.
What the paper is about
Biodiversity monitoring is urgently needed but ecological surveys remain fragmented, costly, and limited in geographic scope — leaving large knowledge gaps precisely where we need data most. Passive Acoustic Monitoring (PAM) has long promised to change this, but deploying networks at scales that can deliver truly novel insights has remained elusive due to challenges in standardising how data are collected, managed, analysed, and shared.
This paper presents TABMON as a concrete answer to those challenges. We describe the full design and deployment of the network: from site selection criteria and hardware provisioning, to metadata standards, quality assurance, AI-based analysis pipelines, and indicator outputs aligned with Essential Biodiversity Variables (EBVs). The result is a reproducible, interoperable framework that other monitoring programmes can adopt or build upon.
Key contributions
- A standardised, transnational PAM network spanning four biogeographic regions — Norway, the Netherlands, France, and Spain
- A fully documented data pipeline from raw audio to species detections, community metrics, and EBV-aligned indicators
- An explicit treatment of uncertainty at every step, from AI model confidence to aggregated biodiversity indicators
- A discussion of how acoustic data can be integrated with traditional monitoring schemes and reported against EU policy targets
Why it matters
Large-scale, standardised biodiversity data are essential for tracking progress towards the EU Biodiversity Strategy 2030 and the Kunming-Montreal Global Biodiversity Framework. TABMON demonstrates that a paneuropean acoustic monitoring network is not only feasible but operational — and that its outputs can feed directly into the indicators decision-makers rely on.
Read the paper
The published version is available in Methods in Ecology and Evolution: doi.org/10.1111/2041-210x.70308