期刊论文详细信息
Marine Ecology Progress Series
Occupancy estimation of marine species: dealing with imperfect detectability
Nikos Katsiaras1  Maria Salomidi1  Yiannis Issaris1  Stelios Katsanevakis1  Kostas Tsiamis1  George Verriopoulos1 
关键词: Occupancy;    Underwater visual survey;    Detectability;    Monitoring;   
DOI  :  10.3354/meps09668
学科分类:海洋学与技术
来源: Inter-Research
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【 摘 要 】

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.

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