UW Aquatic & Fishery Sciences Quantitative Seminar

Paul Conn

Alaska Fisheries Science Center, NMML/NOAA

Estimating animal abundance using an automated detection system: Ice-associated seals in the Bering Sea

Abstract

Automated detection systems employing advanced technology (e.g. infrared imagery, auditory recording systems, pattern recognition software) are compelling tools for gathering animal abundance and distribution data since investigators can often collect data more efficiently and reduce animal disturbance relative to surveys using human observers. Even with these improvements, analyzing animal abundance with advanced technology can be challenging because of potential for incomplete detection, false positives, and species misidentification. We argue that double sampling with an independent sampling method can provide the critical information needed to account for such errors. We present a hierarchical modeling framework for jointly analyzing automated detection and double sampling data obtained during animal population surveys. Under our framework, observed counts in different sampling units are conceptualized as having arisen from a thinned log-Gaussian Cox process subject to spatial autocorrelation (where thinning accounts for incomplete detection). For multi-species surveys, our approach handles incomplete species observations owing to (a) structural uncertainties (e.g. in cases where the automatic detection data do not provide species observations), and (b) species misclassification; the latter requires auxiliary information on the misclassification process. As an example of combining an automated detection system and a double sampling procedure, we consider the problem of estimating animal abundance from aerial surveys that use infrared imagery to detect animals, and independent, high-resolution digital photography to provide information on species composition and thermal detection accuracy. We illustrate our approach by analyzing simulated data and data from a survey of four ice-associated seal species in the eastern Bering Sea. Our analysis indicated reasonable performance of our hierarchical modeling approach, but suggests a need to balance model complexity with the richness of the dataset. For example, highly parameterized models can lead to spuriously high predictions of abundance in areas that are not sampled, especially when there are large gaps in spatial coverage. We recommend that ecologists employ double sampling when enumerating animal populations with automated detection systems to estimate and correct for detection errors. Combining multiple datasets within a hierarchical modeling framework provides a powerful approach for analyzing animal abundance over large spatial domains.