Poorly conducted surveys can produce data that obscure animal–environment relationships or introduce biases into inferences (Conroy et al., 2008).įor species that occur at low densities or across large spatial areas, aerial surveys are often the most efficient platform to collect observational count data (Caughley, 1977 Parker et al., 2010). Differing sampling designs, methods, and analysis techniques for count‐based surveys can vary in their ability to yield accurate and precise estimates of abundance. These surveys are typically designed to yield counts of the species within predefined sampling units for a fixed amount of sampling effort (e.g., observation time, travel speed) to make inferences on abundance across a study region. Most estimates of wildlife abundance are derived from surveys that collect count data on target species (Elphick, 2008). Reliable estimates of wildlife abundance are imperative for understanding how environmental variables influence population and community dynamics, assessing trends across time and space, and guiding conservation and management decisions (Williams et al., 2002). Recognizing conditions that can lead to data collection errors and having reasonable solutions for addressing errors can allow researchers to allocate resources effectively to mitigate the most significant challenges for obtaining reliable aerial survey data. We synthesized the information gained from our analyses to evaluate strategies for overcoming the challenges of using aerial survey data to estimate wildlife abundance, such as digital data collection methods, pooling species records by family, and ordinal modeling using binned data. Our results illustrate how each issue can act to bias inferences, highlighting the importance of considering individual methods for mitigating potential problems separately during survey design and analysis. Using our online quiz, we showed that aerial observers typically undercount group size and that the magnitude of counting errors increases with group size. Through our case study, we demonstrated how these challenges can prove problematic by detailing the extent and magnitude of potential errors. We found that nearly three quarters of the aerial survey methodology literature focused on accounting for nondetection errors, while issues of counting error and misidentification were less commonly addressed. Additionally, we used a double‐observer case study focused on waterbird data collected via aerial surveys and an online group (flock) counting quiz to explore the potential extent of each challenge and possible resolutions. Through an extensive review of the aerial survey literature over the last 50 years, we evaluated how common problems encountered in the data (including nondetection, counting error, and species misidentification) can manifest, the potential difficulties conferred, and the history of how these challenges have been addressed. Despite a long history of aerial survey use in ecological research, problems common to aerial surveys have not yet been adequately resolved. However, these surveys can yield unreliable data if not carefully executed. Aerial surveys are an efficient survey platform, capable of collecting wildlife data across large spatial extents in short timeframes. Accurate estimates of animal abundance are essential for guiding effective management, and poor survey data can produce misleading inferences.
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