ARPHA Preprints, doi: 10.3897/arphapreprints.e124640

Prototype Biodiversity Digital Twin: Real-time bird monitoring with citizen science data

Julian Lopez Lopez Gordillo^‡, Patrik Lauha^§, Ari Lehtiö^|, Ossi Nokelainen^|, Anis Rahman^|, Allan Souza^§, Jussi Talaskivi^|, Gleb Tikhonov^§, Aurélie Vancraeyenest^¶, Otso Ovaskainen

‡ Naturalis Biodiversity Center, Leiden, Netherlands§ University of Helsinki, Helsinki, Finland| University of Jyväskylä, Jyväskylä, Finland¶ CSC – IT Center for Science Ltd, Espoo, Finland

Corresponding author: Otso Ovaskainen ( otso.t.ovaskainen@jyu.fi )

This is an open access preprint distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Citation: Lopez Gordillo J, Lauha P, Lehtiö A, Nokelainen O, Rahman A, Souza A, Talaskivi J, Tikhonov G, Vancraeyenest A, Ovaskainen O (2024) Prototype Biodiversity Digital Twin: Real-time bird monitoring with citizen science data. ARPHA Preprints. https://doi.org/10.3897/arphapreprints.e124640

Abstract

Bird populations respond rapidly to environmental change making them excellent ecological indicators. Climate shifts advance migration, causing mismatches in breeding and resources. Understanding these changes is crucial to monitor the state of environment. Citizen science offers vast potential to collect biodiversity data. We outline a project that combines citizen science with AI-based bird sound classification. The mobile app records bird vocalizations that are classified by AI and stored for re-analysis. Also, it shows a shared observation board that visualizes collective classifications. By merging long-term monitoring and modern citizen science, this project harnesses both approaches’ strengths for comprehensive bird population monitoring.

Keywords

Citizen science, bird monitoring, acoustic monitoring, artificial intelligence, species distribution modelling