## Abstract

Baited cameras are often used for abundance estimation wherever alternative techniques are precluded, e.g. in abyssal systems and areas such as reefs. This method has thus far used models of the arrival process that are deterministic and, therefore, permit no estimate of precision.

Furthermore, errors due to multiple counting of fish and missing those not seen by the camera have restricted the technique to using only the time of first arrival, leaving a lot of data redundant. Here, we reformulate the arrival process using a stochastic model, which allows the precision of abundance

estimates to be quantified. Assuming a non-gregarious, cross-current-scavenging fish, we show that prediction of abundance from first arrival time is extremely uncertain. Using example data, we show

that simple regression-based prediction from the initial (rising) slope of numbers at the bait gives good precision, accepting certain assumptions. The most precise abundance estimates were obtained

by including the declining phase of the time series, using a simple model of departures, and taking account of scavengers beyond the camera’s view, using a hidden Markov model.

Furthermore, errors due to multiple counting of fish and missing those not seen by the camera have restricted the technique to using only the time of first arrival, leaving a lot of data redundant. Here, we reformulate the arrival process using a stochastic model, which allows the precision of abundance

estimates to be quantified. Assuming a non-gregarious, cross-current-scavenging fish, we show that prediction of abundance from first arrival time is extremely uncertain. Using example data, we show

that simple regression-based prediction from the initial (rising) slope of numbers at the bait gives good precision, accepting certain assumptions. The most precise abundance estimates were obtained

by including the declining phase of the time series, using a simple model of departures, and taking account of scavengers beyond the camera’s view, using a hidden Markov model.

Original language | English |
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Pages (from-to) | 223-234 |

Number of pages | 12 |

Journal | Marine Ecology: Progress Series |

Volume | 350 |

Publication status | Published - 22 Nov 2007 |

## ASJC Scopus subject areas

- Aquatic Science
- Ecology