Methods for Estimating Abundance and Associated Uncertainty from Passive Count Technologies

Abstract

AbstractPassive count technologies (e.g., resistivity counters, infrared cameras, and sonar/hydroacoustic cameras) are increasingly being used to enumerate migratory fish populations, but methodologies for converting counts into abundance estimates with uncertainty are not available. Passive counters are typically paired with a secondary data collection method, such as video, images, or direct observation, to validate or correct the count data for false positives and false negatives. We developed a framework that incorporates measurement error into passive counter estimates based on a statistical comparison with validation data. We demonstrate this framework using resistivity counter and video validation data collected for Gates Creek Sockeye Salmon Oncorhynchus nerka as they migrated through a fish passage facility at the Seton Dam in British Columbia, Canada. We also conducted simulations to evaluate the trade‐offs between validation effort and accuracy and precision of abundance estimates, which can be used to plan passive counter postprocessing and validation. We found our method to be accurate and precise when abundance was high (i.e., >1,000), even when validation effort was low (i.e., 5% validation). There was a positive estimation bias when abundance was low (i.e., 100), and a minimum of 25% validation was required to achieve a CV of 15% and relative error less than 10%. When estimating abundance for small populations, higher validation effort is required to obtain sufficient precision in abundance estimates. Measurement error should not be overlooked in passive fish count technologies, and we provide a robust method for generating uncertainty in abundance estimates that increases their utility for population assessment and conservation.