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

TL;DR

This paper develops a data workflow to standardize and analyze heterogeneous community science datasets, enabling modeling of avian influenza impacts on subantarctic bird populations and estimating mortality rates.

Contribution

It introduces a novel data harmonization method and applies it to model HPAI effects in subantarctic birds using multiple community science datasets.

Findings

Predicted population sizes for several bird species.

Estimated potential mortality rates from HPAI in the subantarctic.

Created an aggregated dataset of bird mortality rates.

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.