RESUMO
Accurate estimates of animal abundance are essential for guiding effective management, and poor survey data can produce misleading inferences. Aerial surveys are an efficient survey platform, capable of collecting wildlife data across large spatial extents in short timeframes. However, these surveys can yield unreliable data if not carefully executed. Despite a long history of aerial survey use in ecological research, problems common to aerial surveys have not yet been adequately resolved. 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. 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. 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. Through our case study, we demonstrated how these challenges can prove problematic by detailing the extent and magnitude of potential errors. Using our online quiz, we showed that aerial observers typically undercount group size and that the magnitude of counting errors increases with group size. 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. 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. 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.
RESUMO
A common challenge for studying wildlife populations occurs when different survey methods provide inconsistent or incomplete inference on the trend, dynamics, or viability of a population. A potential solution to the challenge of conflicting or piecemeal data relies on the integration of multiple data types into a unified modeling framework, such as integrated population models (IPMs). IPMs are a powerful approach for species that inhabit spatially and seasonally complex environments. We provide guidance on exploiting the capabilities of IPMs to address inferential discrepancies that stem from spatiotemporal data mismatches. We illustrate this issue with analysis of a migratory species, the American Woodcock (Scolopax minor), in which individual monitoring programs suggest differing population trends. To address this discrepancy, we synthesized several long-term data sets (1963-2015) within an IPM to estimate continental-scale population trends, and link dynamic drivers across the full annual cycle and complete extent of the woodcock's geographic range in eastern North America. Our analysis reveals the limiting portions of the life cycle by identifying time periods and regions where vital rates are lowest and most variable, as well as which demographic parameters constitute the main drivers of population change. We conclude by providing recommendations for resolving conflicting population estimates within an integrated modeling approach, and discuss how strategies (e.g., data thinning, expert opinion elicitation) from other disciplines could be incorporated into ecological analyses when attempting to combine multiple, incongruent data types.
