Detection errors in wildlife abundance estimates from Unmanned Aerial Systems (UAS) surveys: Synthesis, solutions, and challenges

Unmanned aerial systems ( UAS ) are emerging as an accessible and versatile tool for ecologists, promising to revolutionize the way abundance and distribution data are obtained in wildlife studies. Establishment of UAS as an efficient and reliable tool demands understanding how detection errors influence UAS ‐derived counts and possible solutions to address them. We describe two types of false‐negative errors (availability and perception errors) and two types of false‐positive errors (misidentification and double count) that may bias abundance estimates from UAS surveys. Then, we discuss available methods to address detection errors in UAS surveys and point out challenges for future developments. We present hierarchical models as an integrative framework to account for multiple detection errors and datasets in UAS abundance modelling. Methods to address detection errors in UAS surveys depend on how data are collected (flight plan, images processing, and reviewing procedure). Conventional aerial surveys literature offers a set of solutions, especially to deal with false‐negative errors. Available auxiliary information (such as ground counts and telemetry data) facilitates estimating detection errors, although the versatility of UAS permits exploring novel approaches. Solutions involve planning separated strip transects, temporally replicating flights, carrying out counts in orthomosaics, and multiple observer protocol. When automatic image review is used, subsample manual reviewing, trial experiments, and semiautomated procedures might deal with algorithm errors. UAS surveys need to be consciously planned, thinking on what kind of errors can significantly affect counts and the use of raw counts and indices should be avoided. Approaches that formally account for false positives are needed, particularly for double counts. Hierarchical modelling (especially N ‐mixture models) offers a fruitful framework to explore and combine solutions, integrating multiple datasets and accommodating different detection errors.