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Marine Ecology Progress Series

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MEPS 615:177-188 (2019)  -  DOI:

Estimating the density of resident coastal fish using underwater cameras: accounting for individual detectability

Guillermo Follana-Berná1,2,3,*, Miquel Palmer3, Andrea Campos-Candela3,4, Pablo Arechavala-Lopez3, Carlos Diaz-Gil1,2,3, Josep Alós3, Ignacio A. Catalan3, Salvador Balle3, Josep Coll5, Gabriel Morey5, Francisco Verger5, Amalia Grau1,2

1Laboratori d’Investigacions Marines i Aqüicultura, LIMIA (Balearic Government), 07157 Port d’Andratx, Illes Balears, Spain
2Instituto de Investigaciones Agroambientales y de Economía del Agua, INAGEA (INIA_Govern Balear-UIB), 07122 Palma, Illes Balears, Spain
3Fish Ecology Lab, Instituto Mediterráneo de Estudios Avanzados, IMEDEA (CSIC-UIB), 07190 Esporles, Illes Balears, Spain
4Department of Marine Sciences and Applied Biology, University of Alicante, 03080 Alicante, Spain
5Tragsatec, 07009 Palma, Illes Balears, Spain
*Corresponding author:

ABSTRACT: Technological advances in underwater video recording are providing novel opportunities for monitoring wild fish. Although extracting data from videos is often challenging, accurate and precise estimates of density for animals whose normal activities are restricted to a bounded area or home range can be obtained from counts averaged across a relatively low number of video frames. However, this method requires that individual detectability (PID, the probability of detecting a given animal provided that it is actually within the area surveyed by a camera) be known. Here we propose a Bayesian implementation for estimating PID after combining counts from cameras with counts from any other reference method. The proposed framework was demonstrated with a case study of Serranus scriba, a widely distributed and resident coastal fish. Density and PID were calculated after combining fish counts from unbaited remote underwater video (RUV) and underwater visual censuses (UVC) as reference methods. The relevance of the proposed framework is that after estimating PID, fish density can be estimated accurately and precisely at the UVC scale (or at the scale of the preferred reference method) using RUV only. This method is further validated using computer simulations based on empirical data. We provide a simulation tool kit for comparing the expected precision attainable for different sampling effort and for species with different levels of PID. Overall, the proposed method may contribute to substantially enlarge the spatio-temporal scope of density monitoring programmes for many resident fish.

KEY WORDS: Bayesian approach · Fish density · Home range · Individual detectability · Monitoring · Unbaited underwater cameras · Underwater visual census

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Cite this article as: Follana-Berná G, Palmer M, Campos-Candela A, Arechavala-Lopez P and others (2019) Estimating the density of resident coastal fish using underwater cameras: accounting for individual detectability. Mar Ecol Prog Ser 615:177-188.

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