Wild waterbirds are reservoir hosts for avian influenza viruses (AIV), which can cause devastating outbreaks in multiple species, making them a focus for surveillance efforts. Traditional AIV surveillance involves direct sampling of live or dead birds, but environmental substrates present an alternative sample for surveillance. Environmental sampling analyzes AIV excreted by waterbirds into the environment and complements direct bird sampling by minimizing financial, logistic, permitting, and spatial-temporal constraints associated with traditional surveillance. Our objectives were to synthesize the literature on environmental AIV surveillance, to compare and contrast the different sample types, and to identify key themes and recommendations to aid in the implementation of AIV surveillance using environmental samples. The four main environmental substrates for AIV surveillance are feces, feathers, water, and sediment or soil. Feces were the most common environmental substrate collected. The laboratory analysis of water and sediment provided challenges, such as low AIV concentration, heterogenous AIV distribution, or presence of PCR inhibitors. There are a number of abiotic and biotic environmental factors, including temperature, pH, salinity, or presence of filter feeders, that can influence the presence and persistence of AIV in environmental substrates; however, the nature of this influence is poorly understood in field settings, and field data from southern, coastal, and tropical ecosystems are underrepresented. Similarly, there are few studies comparing the performance of environmental samples to each other and to samples collected in wild waterbirds, and environmental surveillance workflows have yet to be validated or optimized. Environmental samples, particularly when used in combination with new technology such as eDNA and next generation sequencing, provided information on trends in AIV detection rates and circulating subtypes that complemented traditional, direct waterbird sampling. The use of environmental samples for AIV surveillance also shows significant promise for programs whose goal is early warning of high-risk subtypes.

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