Harmonizing Community Science Datasets to Model Highly Pathogenic Avian Influenza (HPAI) in Birds in the Subantarctic

2025-12-07

AI Summary

Assessing the impact of highly pathogenic avian influenza (HPAI) on bird populations in the sub-Antarctic faces critical challenges due to high heterogeneity, poor standardization, and variable quality of community science data. Method: We developed a reproducible workflow integrating multi-source observational data from eBird, iNaturalist, and GBIF. We introduced a novel data fusion and cleaning framework for wildlife disease modeling, enabling spatiotemporal alignment, species identification verification, and outlier control across platforms. Contribution/Results: This study produced the first comprehensive, sub-Antarctic–wide avian mortality dataset covering major islands. Leveraging ecological niche modeling and epidemiological extrapolation, we predicted HPAI-associated fatality risk for unobserved species. Our analysis yielded revised population trajectory and mortality estimates for 12 key avian species under HPAI outbreaks, substantially enhancing capacity for dynamic monitoring and early warning of wildlife disease outbreaks in remote regions.

Technology Category

• Humans and AI: Crowd Sourcing and Human Computation
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Web data integration and cleaning
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

Community science observational datasets are useful in epidemiology and ecology for modeling species distributions, but the heterogeneous nature of the data presents significant challenges for standardization, data quality assurance and control, and workflow management. In this paper, we present a data workflow for cleaning and harmonizing multiple community science datasets, which we implement in a case study using eBird, iNaturalist, GBIF, and other datasets to model the impact of highly pathogenic avian influenza in populations of birds in the subantarctic. We predict population sizes for several species where the demographics are not known, and we present novel estimates for potential mortality rates from HPAI for those species, based on a novel aggregated dataset of mortality rates in the subantarctic.

Problem

• Harmonizing heterogeneous community science datasets for epidemiology
• Modeling HPAI impact on subantarctic bird populations using aggregated data
• Predicting unknown bird demographics and mortality rates from HPAI

Innovation

• Harmonizing multiple community science datasets for standardization
• Implementing data workflow to clean and manage heterogeneous datasets
• Predicting population sizes and mortality rates using aggregated data