Evaluating sampling strategies for the detection of avian influenza viruses in the environment.

Highly pathogenic avian influenza (HPAI) viruses pose an increasing threat to wildlife, livestock and human health, underscoring the need for scalable and early-warning surveillance systems. Environmental RNA (eRNA) monitoring offers a non-invasive, cost-effective alternative to traditional host-based sampling by detecting viral genetic material shed into the environment. Despite its utility, the relative performance of different environmental sampling approaches for avian influenza virus (AIV) detection remains poorly resolved. Here, we conducted a longitudinal study with monthly sampling over approximately one year across two urban waterfowl ponds in Aotearoa New Zealand to evaluate four eRNA sampling strategies - fresh faeces, sediment, active-filtered water and passive-filtered water - for their ability to detect AIV. Using a combination of metagenomic sequencing and RT-qPCR, we show that all sample types can detect AIV, although detections were highly inconsistent across sampling methods, locations and time points. While metagenomic sequencing provided valuable genomic data, including subtype identification and phylogenetic context, RT-qPCR exhibited greater sensitivity, with active-filtered water yielding the highest detection rates, and is currently the more cost-effective approach for large-scale surveillance. Notably, AIV detections were asynchronous among sample types and frequently lacked temporal concordance, suggesting that environmental heterogeneity, RNA persistence, and methodological detection limits strongly influence surveillance outcomes. Despite these inconsistencies, phylogenetic analyses revealed that detected viruses belong to established Australasian lineages, highlighting the ability of environmental surveillance to capture ecologically relevant viral diversity. Our findings demonstrate that while eRNA-based surveillance holds substantial promise as a complementary tool for AIV monitoring, its effectiveness is highly dependent on the environmental sampling strategies and laboratory detection methods used.