Citizen scientists for moveApps : innovations and insights from volunteer coders in wildlife conservation

dc.contributor.authorKolzsch, Andrea
dc.contributor.authorSafi, Kamran
dc.contributor.authorArmfield, Margaux M.
dc.contributor.authorBeaupre, Chloe
dc.contributor.authorBennitt, Emily
dc.contributor.authorBock, Matthias
dc.contributor.authorBrown, William
dc.contributor.authorEichler, Christoph
dc.contributor.authorFielder, Wolfgang
dc.contributor.authorHahn, Clemens
dc.contributor.authorJain, Varalika
dc.contributor.authorKetchin, Matthew W.
dc.contributor.authorLong, Jed A.
dc.contributor.authorMerdian-Tarko, Alexander V.
dc.contributor.authorMinchin, Lauren
dc.contributor.authorNeves de Matos, Tomé
dc.contributor.authorRakesh, Ambati
dc.contributor.authorVan Schalkwyk, Ockert Louis
dc.contributor.authorWikelski, Martin
dc.contributor.authorScharf, Anne K.
dc.date.accessioned2026-02-19T12:36:32Z
dc.date.available2026-02-19T12:36:32Z
dc.date.issued2025-08
dc.descriptionDATA AVAILABILITY STATEMENT : Tracking data for illustrating the functionality of the contributed Apps were collected in previous or parallel efforts and are available on Movebank. The white stork data is stored in the Movebank study ‘LifeTrack White Stork SW Germany’, the African wild dog data in the study ‘African wild dogs—Greater Kruger Monitoring Platform’ and the Cape buffalo data in the study ‘Cape buffalo movements in the Okavango Delta, Botswana’. The white stork data is available to the public and stored in the Movebank Data Repository via https://www.doi.org/10.5441/001/1. ck04mn78 (Fiedler et al., 2019), all others contain sensitive data that cannot be made openly available, but can be provided upon reasonable request. The four MoveApps Workflows with Apps developed in the coding challenge are shared as public Workflows in MoveApps (www. movea pps. org) and published in the Movebank Data Repository. The Stationarity Detection workflow is available via https://doi.org/10.5441/001/1.613, the Interaction of Wildlife and Human Infrastructure workflow via https:// doi.org/10.5441/001/1.635, the Range Estimation workflow via https://doi.org/10.5441/001/1.347 and the Movement Hotspots and Corridors workflow via https://doi.org/10.5441/ 001/1. 639. The code for all Apps is available there and on GitHub, with links provided in the Supporting Information. [Correction added on 26 July 2025 after first online publication: The DOI for the ‘Interaction of Wildlife and Human Infrastructure workflow’ has been corrected here and in the reference list].
dc.description.abstractAmidst numerous global crises, decision-makers have recognized the critical need for fact-based advice, driving unprecedented data collection. However, a significant gap persists between data availability and knowledge generation, primarily due to time and resource constraints. To bridge this gap, we propose involving a novel group of citizen scientists: volunteer code developers. 2. Utilizing the modular, open-source analysis platform MoveApps, we were able to engage 12 volunteer coders in a challenge to create tools for movement ecology, aimed at animal conservation. These volunteers developed functioning applications capable of analysing animal tracking data to identify stationary behaviour, estimate ranges and movement corridors and assess human–wildlife conflicts using data sets from human infrastructure, such as OpenStreetMap. 3. Engaging citizen scientists in developing code has surfaced three primary challenges: (i) Community Building—attracting the right participants; (ii) Community Involvement—maintaining quality standards and directing tasks effectively; and (iii) Community Retention—ensuring long-term engagement. We explore strategies to overcome these challenges and share lessons learnt from our coding challenge experience. Our approaches include engaging the community through their own preferred channels, providing an accessible open-source tool, defining specific use cases in detail, ensuring quality through feedback, fostering self-organized community exchanges and prominently illustrating the impact of contributions. 4. We also advocate for other disciplines to consider leveraging volunteer involvement, alongside artificial intelligence, for data analysis and generating state-of-the-art, fact-based insight to address critical issues such as the global decline in biodiversity.
dc.description.departmentVeterinary Tropical Diseases
dc.description.librarianam2026
dc.description.sdgSDG-09: Industry, innovation and infrastructure
dc.description.sdgSDG-15: Life on land
dc.description.sponsorshipAllen Institute for Artificial Intelligence; Centre for the Advanced Study of Collective Behaviour; Allen Institute for Artificial Intelligence, EarthRanger: Conservation Tech Award 2022.
dc.description.urihttps://besjournals.onlinelibrary.wiley.com/journal/2041210
dc.identifier.citationKölzsch, A., Safi, K., Armfield, M. M., Beaupre, C., Bennitt, E., Böck, M., Brown, W., Eichler, C., Fielder, W., Hahn, C., Jain, V., Ketchin, M. W., Long, J. A., Merdian-Tarko, A. V., Minchin, L., de Matos, T. N., Rakesh, A., van Schalkwyk, O. L., Wikelski, M., & Scharf, A. K. (2025). Citizen scientists for MoveApps: Innovations and insights from volunteer coders in wildlife conservation. Methods in Ecology and Evolution, 16, 1550–1563. https://doi.org/10.1111/2041-210X.70101.
dc.identifier.issn2041-210X (online)
dc.identifier.other10.1111/2041-210X.70101
dc.identifier.urihttp://hdl.handle.net/2263/108463
dc.language.isoen
dc.publisherWiley
dc.rights© 2025 The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND).
dc.subjectCitizen science
dc.subjectConservation technology
dc.subjectHuman–wildlife conflict
dc.subjectKnowledge generation
dc.subjectMovement hotspots
dc.subjectRange estimation
dc.subjectStationarity
dc.subjectVolunteer coding community
dc.titleCitizen scientists for moveApps : innovations and insights from volunteer coders in wildlife conservation
dc.typeArticle

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