A methodological framework for characterizing fish swimming and escapement behaviors in trawls

Abstract

Knowledge about fish behavior is crucial to be able to influence the capture process and catch species composition. The rapid expansion of the use of underwater cameras has facilitated unprecedented opportunities for studying the behavior of species interacting with fishing gears in their natural environment. This technological advance would greatly benefit from the parallel development of dedicated methodologies accounting for right-censored observations and variable observation periods between individuals related to instrumental, environmental and behavioral events. In this paper we proposed a methodological framework, based on a parametric Weibull mixture model, to describe the process of escapement attempts through time, test effects of covariates and estimate the probability that a fish will attempt to escape. We additionally proposed to better examine the escapement process at the individual level with regard to the temporal dynamics of escapement over time. Our approach was used to analyze gadoids swimming and escapement behaviors collected using a video set up in front of a selective device known to improve selectivity on gadoids in the extension of a bottom trawl. Comparison of the fit of models indicates that i) the instantaneous rate of escape attempts is constant over time and that the escapement process can be modelled using an exponential law; ii) the mean time before attempting to escape increases with the increasing number of attempts; iii) more than 80% of the gadoids attempted to escape through the selective device; and iv) the estimated probability of success was around 15%. Effects of covariates on the probability of success were investigated using binomial regression but none of them were significant. The data set collected is insufficient to make general statements, and further observations are required to properly investigate the effect of intrinsic and extrinsic factors governing gadoids behavior in trawls. This methodology could be used to better characterize the underlying behavioral process of fish in other parts of a bottom trawl or in relation to other fishing gears.

Using fish behavior to design a fish pot: Black seabream (Spondyliosoma cantharus) case study

“…On-going progress on automatic image analysis algorithms and artificial intelligence would facilitate video data treatment. Some development for trawl gear already contributed to reduce fishing gear impacts and increase their selectivity by providing rapid, accurate and standardized data on fish behavior in relation to gear (Robert et al, 2020). In parallel, the development of acoustic imaging would overcome dark and turbid conditions to describe fish behavior in relation to fishing gear on a longer period of time (Rose et al, 2005;Fujimori et al, 2018).…”

Computer Vision Reveals Fish Behaviour Through Structural Equation Modelling of Movement Patterns

“…Tidal phases (ebb, ow, high and low) often have a strong in uence on the distribution, abundance and movement of sh, and this in uence changes with the time of day, especially as several sh species are more active at dawn or dusk [64-66]. Any effect of these environmental or sampling covariates on sh behaviour might be modi ed by sh responses to the presence or absence of predators or even shing pressures [44,90]. We expect that adding more environmental covariates into the SEMs and increasing the sampling period (e.g., more sampling days) would, in the future, increase the chance that any relationships between covariates and behaviours were revealed.…”

Artificial intelligence for fish behavior recognition may unlock fishing gear selectivity

“…Classes can also be separated into action, and non-action classes (see Table 3), where a defined behavior present in a video clip is labeled as the action class, and another clip presenting unchanged or normal fish movement is labeled with the non-action class. McIntosh et al (2020) defined four features that translate the startling behavior of sablefish from their trajectories into measurable metrics: direction of travel, speed, aspect ratio, and Local Momentary Change metric. They combined the four features into a form suited to train an AI-based classifier with an LSTM architecture (i.e., tensor data).…”