整合众包数据建模南极鸟类高致病性禽流感影响
Harmonizing Community Science Datasets to Model Highly Pathogenic Avian Influenza (HPAI) in Birds in the Subantarctic
- 构建多源众包数据清洗与统一流程
- 首次估算南极地区鸟类高致病性禽流感死亡率
- 为种群未知的物种提供数量预测与风险评估
众包观测数据在流行病学与生态学中可用于物种分布建模，但其异构性给标准化、数据质量控制与工作流管理带来挑战。本文提出一种数据处理流程，对eBird、iNaturalist、GBIF等多源众包数据进行清洗与标准化，并应用于南极地区鸟类高致病性禽流感（HPAI）影响的案例研究。基于新构建的南极地区死亡率聚合数据集，我们预测了若干种群结构未知物种的种群规模，并提出了这些物种可能的HPAI致死率新估计。
原文摘要 · Abstract (English)
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.
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