Project period: 8 1 24-7 31 25PROJECT title: advancing bayesian statistical methods for BAT acoustic datathe north american BAT monitoring program collects acoustic recordings to monitorbat…

Project period: 8 1 24-7 31 25PROJECT title: advancing bayesian statistical methods for BAT acoustic datathe north american BAT monitoring program collects acoustic recordings to monitorbat populations using echolocation calls. Prior to analysis, recordings are typically processedwith automated species identification software (auto-classifier) that assigns a species labelto a recording deemed to contain a BAT. Regardless of survey effort, these data are subjectto imperfect detection as a species may go undetected and a recording may be misclassifiedas the wrong species by the auto-classifier. One approach for handling potential speciesmisclassification is to collect a calibration dataset that relies on a single human observer toview, or VET, a subset of the recordings. When performing validation, the expert may agreewith the assigned label, assign a different species label, downgrade to a species couplet, or downgrade to a frequency group. Recordings may also be assigned a species coupletby the auto-classifier if the file contains information that two species have in common ortwo distinct species were recorded simultaneously. Due to limitations of existing statisticalmodels designed to accommodate imperfect detection, only the recordings that have onespecies assigned by the auto-classifier and the human expert are included in statisticalapplications. The goal of this project is to develop a multispecies count detection model that 1)accommodates auxiliary information regarding the reliability of the auto-classifier usingcalibration data 2) informs occupancy and misclassification probabilities using knowledgeof species characteristics and behaviors (e.g., species ranges) and other available covariate information (e.g., time of day weather patterns during collection) and 3) incorporatesmultiple observer assignments for the species label of a given recording. These developments will allow researchers to fully leverage acoustic BAT recording data to provide moreaccurate estimates of occupancy, misclassification probabilities, and encounter rates. ASA result, ecologists will have a better understanding of BAT species characteristics, abundances, distributions, and interactions, which can be used to guide future conservationefforts and policy. Further, the proposed methodology has the potential to transform howecologists perform multispecies occupancy modeling in the presence of imperfect detection. The computational scalability of the proposed approach will enable IT to be embedded intointegrated population models and will allow for range-wide surveillance efforts.