Design, implementation and evaluation of a citizen-science monitoring app for urban bat populations

Thesis Abstract

Urban bat populations in densely populated areas face increasing threats from habitat loss, light and noise pollution, and limited public engagement in ecological monitoring. This study addresses the need for scalable, community-driven surveillance that enhances data coverage, quality, and public awareness to inform urban biodiversity management. The aim is to design, implement, and evaluate a citizen-science monitoring application that enables residents to record bat activity, identify species through acoustic cues and social-ecological validation, and contribute standardized data to an open-access urban bat database. Specific objectives are (1) to develop a mobile and web-based app integrating acoustic identification, geolocation, environmental context, and validation workflows; (2) to recruit and train a diverse participant cohort of at least 300 volunteers across three metropolitan districts; (3) to evaluate data quality and reliability through cross-validation with expert-collected acoustic recordings (n = 1,200) and field surveys; (4) to assess user engagement, retention, and behavioral change using a mixed-methods framework; and (5) to model spatial-temporal bat activity patterns and their associations with urban features using regression analyses and spatial statistics. The study adopts a design-research methodology combining software development, field deployment, and evaluative research. A purposive sample of urban households, schools, and community groups (n ? 300 participants) will be recruited through partnerships with municipal parks services, conservation NGOs, and citizen-science networks. Data collection instruments include the designed monitoring app, standardized acoustic analysis protocols (e.g., bat-pass and call-feature extraction using Kaleidoscope Pro), structured survey instruments to measure user experience and motivation, and semi-structured interviews with a subsample of participants (n ? 40). Validity and reliability will be ensured via triangulation of app-derived observations with independently collected audio recordings and expert validation, as well as pilot testing (three iterative cycles) to refine the user interface and data schemas. Data analysis will employ a mixed-methods approach quantitative data will be analyzed using generalized linear models to relate bat activity measures to environmental covariates (street lighting density, green space proportion, building height), and spatial autocorrelation will be assessed with Moran’s I and geographically weighted regression (GWR). Data quality will be appraised through inter-observer agreement metrics (Cohen’s kappa) on a subsample of identifications, and cross-validation with expert hand-verified identifications. Qualitative data from interviews will be subjected to thematic analysis to extract perceptions of usability, motivation, and barriers to participation. Key expected findings include (i) acceptable data quality from citizen contributions with measured reliability approaching expert benchmarks after validation workflows; (ii) meaningful patterns of urban bat activity aligned with roosting resources, green infrastructure, and nocturnal noise/light regimes; (iii) identification of user engagement factors that predict sustained participation and accurate reporting; and (iv) a scalable, open-access data pipeline enabling integration with existing biodiversity portals and city planning tools. The study is anchored in relevant theoretical frameworks, including the Technology Acceptance Model to interpret user uptake, the Social Cognitive Theory to explain citizen motivation and self-efficacy, and a ecological niche theory perspective to interpret bat activity in urban habitats. The contribution to knowledge lies in empirically validating a citizen-science platform for urban chiroptera monitoring, establishing methodological benchmarks for data quality and engagement, and providing a replicable model for urban biodiversity surveillance that can inform municipal decision-making, conservation prioritization, and public education. The main conclusion is that a well-designed citizen-science app can generate reliable, spatially explicit data on urban bat populations while simultaneously fostering community stewardship; recommendations include implementing standardized training modules, continuous quality-control protocols, integration with municipal planning datasets, and scaling the platform to other urban taxa and cities with similar ecological contexts.