MEPS 453:95-106 (2012)  -  DOI: https://doi.org/10.3354/meps09668

ABSTRACT: Underwater visual surveys are frequently used in monitoring programmes of marine populations. Species occupancy, defined as the probability of presence in a sampling unit, is a commonly used state variable. Imperfect detectability is a serious issue in such studies and, if ignored, may lead to incorrect inferences and erroneous management decisions. In this paper, we propose a methodology and field protocol for underwater visual surveys implemented by multiple observers. This approach can be applied for an unbiased occupancy estimation of marine species by explicitly incorporating imperfect detection into the modelling process. Based on a case study carried out in a Greek coastal area (Saronikos Gulf), the benefits of the proposed approach were demonstrated. Using a sufficient number of observers, the probability of recording false absences (i.e. the probability that the target species was present in a site but not detected) was minimized and occupancy estimation was greatly improved. For the whelk Stramonita haemastoma in the case study area, single-observer occupancy estimates were negatively biased and varied significantly (between 0.64 and 0.89) depending on the observer, while with the proposed methodology, using 5 observers, the obtained occupancy estimate had the value of 0.93. The probability of false absence was high in the single-observer case (between 0.10 and 0.30), and rather low with any combination of 3 observers (<0.025), while it dropped to practically 0 with 5 observers. As demonstrated in the case of the alien green alga Codium fragile fragile, occupancy models provide a flexible framework for relating occupancy to spatial and environmental covariates, testing ecological hypotheses and producing predictive distributional maps. Overall, the presented methodology and its potential extensions could prove extremely useful in a variety of applications in the marine environment.

KEY WORDS: Occupancy · Underwater visual survey · Detectability · Monitoring