Errors in aerial survey count data: Identifying pitfalls and solutions

Abstract

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.

How Many Reindeer? UAV Surveys as an Alternative to Helicopter or Ground Surveys for Estimating Population Abundance in Open Landscapes

“…Observer variability is often due to non-detection of individuals that are present (false negatives) or misidentification of individuals (false positives) [15]. In our study, we minimised these forms of detection error by having multiple observers scanning the same UAV imagery and later manually reviewing the detected reindeer to remove misidentified individuals [56].…”

“…Images with high mean blue values decreased reindeer detection, and blue as a dominant reflection can be due to e.g., gravel, rock, or barren ground, which will make the brownish fur of reindeer blend better in and be more difficult to detect. Observers in aerial surveys are prone to underestimate animal abundances, especially group size [15], and integrating forms of detection probability in future model development of animal density functions for drone imagery will improve accuracy. For instance, the strip transect framework by Buckland et al (2001), modelled with a uniform key detection function (in 'ds' in the Distance package), or both model parts of a hurdle model (i.e., presence/absence and count model in 'hurdle' in the pscl package), currently assumes perfect detection.…”

How Many Reindeer? UAV Surveys as an Alternative to Helicopter or Ground Surveys for Estimating Population Abundance in Open Landscapes

“…Wildlife populations are traditionally counted by foot or using aerial surveys from helicopters or planes depending on the species characteristics and area of interest [e.g. 12, [13][14][15]. Aerial surveys are costly, with a high carbon footprint and are challenging when it comes to detectability and uncertainty estimates [16][17][18][19].…”

“…For an aerial image survey (i.e., from crewed aircraft or UAV flying at constant height), detection is independent from distance from the transect line. Yet, there are other errors that can influence detection such as tree cover [17], landscape heterogeneity and image quality that should be accounted for in abundance estimates [15]. Integrating measures of detection error within a survey method and identifying habitat variables strongly correlated with the population of interest can greatly improve the accuracy of density estimates [3].…”

Linking ecological processes and animal movements to inform timing of long‐term surveys of a migratory game bird

“…Alternatively, the relationship may be known for some or all covariates individually, but combined it may be difficult to see how features of the overall survey design, such as stratification criteria or inclusion probabilities, should be determined. A rich variety of methods are available for estimating avian abundance (Ralph & Scott, 1981), and the most appropriate method in any situation depends on the conspicuousness and behavior of the species, its habitat, availability of personnel and other resources, and other factors (Davis et al, 2022). When surveys are conducted with thoughtful and appropriate sampling designs, survey data can be leveraged to construct a model to predict variability in species abundance across the survey region (Underwood, 2012).…”