Abstract

Monitoring programs often collect presence− absence data to understand range ex pansions or contractions, metapopulation dynamics, alien species invasions, or spatial and temporal trends in relative abundance. Using the proportion of sites occupied by a species is misleading, however, if surveys routinely fail to detect species that are present. We used chevron traps paired with underwater videos (N = 1555) in a binomial (presence−absence) generalized additive modeling framework to quantify how environmental conditions, habitat characteristics, and the number of individuals at each site (i.e. site abundance) influenced the detection probabilities of economically important reef fish species in the southeastern USA. After accounting for variable site abundance, trap detection probabilities declined 40% for red porgy Pagrus pagrus, 65% for gray triggerfish Balistes capriscus, and 75% for vermilion snapper Rhomboplites aurorubens as percent hard bottom increased from 0 to 100%. Increasing water temperature caused red porgy trap de tection proba bility to decline modestly, while for gray triggerfish and vermilion snapper it increased substantially. Underwater video was more likely to de tect black sea bass Centropristis striata, red porgy, and gray triggerfish when site abundance and water clarity were high and the video camera was facing downcurrent. Using multiple gears simultaneously, we quantified the ways in which predictor variables influenced the sampling process, which will help in designing surveys that maximize detection probability. Our results also suggest that pairing video cameras to trawls, fisheries acoustics, or nets allows for the estimation of detection probabilities. Adding video cameras to fish traps can enable estimation of reef fish detection probabilities for each gear. Photos: Paul Ritter (left) and SEFSC, NOAA (right) FREE ACCESS

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