Improving Demographics and Conservation of Fishes Through Multi‐Frame Individual Distinctions by Size ( MIDS )

Résumé fourni par la source

ABSTRACT Reliable estimation of fish abundance and demographic structure is critical for assessing ecosystem condition and informing conservation management. Non‐invasive surveys, particularly remote video, are increasingly used for ecological monitoring; however, existing analytical approaches cannot distinguish individuals across video sequences unless they are observed simultaneously within the same video frame. Consequently, the number of individuals and their life‐stage compositions may be underestimated relative to the footage captured. Here, we present MIDS (Multi‐frame Individual Distinctions by Size), a novel analytical software that uses length measurements, precision error and statistical inference to distinguish individuals across multiple frames within a video sequence. We applied MIDS to 48 stereo‐video deployments across two estuaries (Wallis Lake and Lake Macquarie, New South Wales, Australia) and two habitat types (rocky reefs and sandy beaches), and compared abundance and life‐stage estimates to the single‐frame MaxN metric. MIDS significantly increased abundance estimates across most stereo‐camera deployments despite differences among habitats and estuaries. The largest increases occurred within juvenile cohorts of Yellowfin Bream ( Acanthopagrus australis ), Sand Whiting ( Sillago ciliata ) and Luderick ( Girella tricuspidata ), where the median difference between MIDS and MaxN ranged from one to six additional juveniles per camera deployment across most estuaries and habitats (all effect sizes r > 0.70). Increases among adult and harvestable life stages were generally smaller and based on fewer observations, reducing statistical power to detect consistent differences between methods. While MIDS provides an improved analytical framework for extracting additional abundance and demographic information from stereo‐video surveys, further validation is required before its broader applications to conservation and management can be fully realised. Nevertheless, MIDS opens opportunities for improved data extraction from spatially separated camera arrays, longer sampling durations and emerging AI‐driven length and identification workflows, by enabling the removal of repeat observations across time. Collectively, these advances position MIDS as a scalable tool for strengthening ecological inference in aquatic systems.

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