Design and assessment of a citizen-science network for avian nest monitoring in forests

Thesis Abstract

The expansion of citizen science networks for ecological monitoring offers scalable avenues to enhance data coverage, particularly for avian nest monitoring in forest ecosystems where professional survey effort is limited and spatial-temporal data gaps persist. This study addresses the problem of data paucity, variable observer reliability, and limited policy translation in forest avifauna monitoring by designing and evaluating a participatory platform that integrates lay observations with expert validation to generate high-quality nest occurrence, success, and phenology datasets. The aim is to develop a replicable framework for citizen-science engagement that yields reliable nest-related indicators, and to assess its ecological and socio-technical performance across diverse forest habitats. Specific objectives include (1) constructing a multi-layered citizen-science network combining smartphone app submissions, structured training modules, and expert verification; (2) evaluating data quality, observer consistency, and spatial-temporal coverage against professional survey benchmarks; (3) examining the network’s impact on forest avifauna monitoring, conservation awareness, and participant retention; (4) identifying socio-technical facilitators and barriers to network adoption; and (5) formulating actionable recommendations for scalable deployment in temperate and boreal forest contexts. The methodology adopts a mixed-methods design anchored in a sequential explanatory approach. The research is conducted in three forested study regions totaling approximately 1,800 square kilometers, incorporating both protected areas and adjacent managed forests. The population comprises registered citizen-science volunteers (n ? 320) and professional ornithologists (n ? 12) involved in nest monitoring. A stratified sampling strategy is applied to recruit volunteers with varying prior experience and to ensure representation across forest types. Data collection instruments include a structured Nest Observation Protocol integrated into a mobile application, educational training curricula, periodic certification tests, and an electronic survey instrument. Nest datasets collected over two breeding seasons (approximately 18 months) include nest location coordinates, nest status, clutch size, hatching success, fledging outcomes, and observational metadata (date, time, observer confidence). Validation data consist of parallel standardized surveys conducted by ornithologists at 15% of nests to assess data accuracy and inter-observer reliability. Instrument validity is established through pilot testing and expert review; reliability is assessed using Cohen’s kappa for categorical nest outcomes and intraclass correlation coefficients (ICC) for repeated measures of observer performance. Quantitative analyses involve data quality assessment, agreement statistics, and spatial-temporal coverage evaluation using generalized linear mixed models (GLMMs) to account for random effects of site and observer, and Bayesian hierarchical models to estimate true nest success probabilities under imperfect detection. In parallel, qualitative data from participant interviews (n ? 40) and focus groups are analyzed thematically to identify motivational drivers, training needs, and perceived barriers, with coding and synthesis performed in line with Braun and Clarke’s thematic analysis approach. The conceptual framework integrates constructs from the Theory of Planned Behavior and the Technology Acceptance Model to explain adoption patterns, complemented by an ecological perspective on nest monitoring uncertainty. A conceptual model is developed that links citizen-science input quality, observer training, and expert validation to ecological indicators and conservation decision-support outputs. Expected findings anticipate high overall data quality with moderate observer variance, improved spatial coverage in under-sampled forest strata, and a positive effect of structured training and certification on data reliability. The study expects to demonstrate that the citizen-science network can reliably estimate nest initiation timing, clutch size, and fledging success when expert validation is employed and sufficient observer replication exists. Findings are anticipated to reveal that social factors, perceived usefulness, and ease of use significantly influence long-term participation, with governance mechanisms such as real-time feedback and transparent data provenance boosting trust and retention. The study contributes to knowledge by presenting a validated design for scalable citizen-science networks in forest avian monitoring, offering a replicable methodological blueprint, data quality benchmarks, and a framework for integrating citizen-generated data into forest management and conservation policy. The main conclusion is that a carefully designed citizen-science network, combining training, validation, and transparent data lineage, can produce robust nest monitoring data that complement professional surveys and support adaptive forest management. Recommendations include adopting modular training curricula, establishing standardized validation protocols, ensuring open data access with clear provenance, and iterating the platform to accommodate region-specific forest types and avifaunal communities.