A video-based movement analysis system to quantify behavioral stress responses of fish

Aquatic Pathobiology Laboratory, Department of Veterinary Medicine, University of Maryland, 8075 Greenmead Drive, College Park, MD 20742, USA.
Water Research (Impact Factor: 5.32). 12/2004; 38(18):3993-4001. DOI: 10.1016/j.watres.2004.06.028
Source: PubMed

ABSTRACT Behavioral alterations can be measured as endpoints for sublethal toxicity, and serve as a tool for environmental risk assessment and analysis of toxicological impact. Numerous technical and biological factors have made sublethal effects on fish behavior difficult to quantify. In order to investigate stress- and contaminant-induced behavioral alterations, a video analysis system was designed by our laboratory. With this system up to 12 fish may be individually housed in 20 L exposure arenas and automatically videotaped at multiple and discrete intervals during an experimental period. Analog video data can then digitized, converted into x,y coordinates, and finally transformed into relevant behavioral endpoints using software designed for tracking fish movement combined with specific algorithms. These endpoints include velocity, total distance traveled, angular change, percent movement, space utilization, and fractal dimension (path complexity). Data from fish exposed to a reference toxicant, MS222, and simulation experiments, are presented to exemplify alterations in fish behavior associated with exposure, and accuracy and precision, respectively. The system provides flexibility to analyze any observed movement behavior, is remotely controlled, and can be transportable. These movement analyses can be used to identify characteristic behavioral responses to a variety of environmentally-relevant stressors, and assist in risk assessment and the development of more sensitive lowest observable effect level and no observable effect level for sentinel species.

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Available from: Andrew S Kane, Jul 11, 2015
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    • "Behaviors may selectively and continuously adapt in response to direct interaction with physical and chemical aspects of the environment. Thus, behavior has been used to discern and evaluate the effects of exposure to environmental stressors (Baldwin et al., 1994; Balk et al., 1996; Gruber et al., 1994; Kane et al., 2004; Kim et al., 2011). Behavioral monitoring in toxicology provides well-defined endpoints that are practical to measure and to understand in relation to environmental factors that cause variation in the response; use of such indirect monitoring is referred to as surrogacy. "
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    ABSTRACT: Fish are rapidly becoming favored as convenient sentinels for behavioral assays of toxic chemical exposure. Tail-beat frequency (TBF) of fish is highly correlated with swimming speed, which has been used to detect toxicants. Here we examined the effect on TBF of exposure to two chemicals, and evaluated the ability of this novel behavioral parameter to accurately monitor water quality. To further refine our approach, the Wall-hitting rate (WHR) was used to characterize behavioral avoidance after exposure. Overall, exposure to test chemicals at different levels induced significant increase in both behavioral parameters of the red crucian carp during 1-h exposure periods. Furthermore, the TBF achieved better performance as an indicator when it was calculated in cases where the fish hit the tank wall. Collectively, this study demonstrates the capacity of the TBF of fish to assess water quality in a reliable manner.
    Ecotoxicology and Environmental Safety 01/2015; 111. DOI:10.1016/j.ecoenv.2014.09.028 · 2.48 Impact Factor
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    • "The use of videography is now a very common technique in animal movement and behavior study (Kato et al., 1996; Kane et al., 2004; Pennekamp and Schtickzelle, 2013). However, there are several issues of measurement errors that need to be addressed when obtaining measurement through the use of video images and the associated uncertainty in interpreting fish movement. "
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    ABSTRACT: Like many migratory species, anadromous juvenile salmon (Oncorhynchus spp.) rely heavily on light perception to orient themselves in space, capture prey, shoal, avoid predators, and migrate along the shoreline to the ocean. However, the continuous demographic expansion along the US West coast has modified many natural coastal environments and has created a new artificial light environment for these species. Among the contributing factors are the construction of large overwater structures such as ferry terminals that have interfered with juvenile salmon migration and behavior by reducing light availability in the salmon migratory pathway. We examined in this study whether the use of an artificial lighting system can mitigate the dock shading impacts on juvenile salmon behavior. A linear mixed effect model was used to analyze changes in individual fish behavior (due to dock shading and artificial lighting) within a shoal. Two different fish movement metrics were examined to characterize the change in behavior: swimming angular variation, and closest distance to the dock. Juvenile salmon avoided penetrating under the dock when strong shadow was present underneath it. Conversely, when artificial light was used to attenuate the dock edge shadow, it was able to mitigate to some extent the effect on juvenile salmon swimming behavior by making them swim closer to the dock with a higher directionality. But when light was used on a non-shaded area, it caused them to stay further away. Light could potentially be used as a method to mitigate dock shading but precautions need to be paid.
    Ecological Engineering 08/2014; 71:180-189. DOI:10.1016/j.ecoleng.2014.07.010 · 3.04 Impact Factor
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    • "During the last decade, important improvements have been made to automate as much as possible measurements on isolated individuals (Huntingford et al., 2011). Video tracking was developed and allowed the measurement of several behaviours such as speed, changing directions, time spent moving or angular velocity on an isolated individual (Kane et al., 2004; Schjolden et al., 2005). Fish group behaviour still remains very challenging to assess since fish move in a three dimensional environment, difficult to access. "
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    ABSTRACT: Monitoring fish shoal behaviour is a growing concern for scientists studying fish stress and welfare. This study presents an algorithm developed to calculate, from videos taken from above aquaria, two indexes characterizing fish shoal behaviour. These two indexes quantify the dispersion and the swimming activity of the fish shoal in the aquaria. The reliability of these indexes was tested on fish shoal simulations following the rules of Reynolds' model on flocks, herds and schools. Since coordinates of each simulated fish in the shoal was known, these simulations provided true values of dispersion and swimming speed of each fish in the shoal, which were compared to values calculated using the presented algorithm. Further, the two indexes were tested on videos of rainbow trout in aquaria. Behavioural variations of the shoal were estimated before and after food distribution in one test, and before and after a four hours confinement stress in a second test. Data resulting from simulations indicate that the two indexes are sensitive to the simulated changes in the cohesion or the swimming speed of the group. Thus, indexes faithfully translated true values in simulations, with a minimum of 94% of the total variation in true values explained by indexes. Furthermore, the two indexes were sensitive to shoal behaviour modifications observed in the two case studies. Indeed, as expected, a strong group dispersion decrease associated with an important swimming activity increase could be detected just after food distribution using our method. Similarly, our indexes were sensitive to a group behaviour change observed after the four hours confinement stress. Finally, our method was compared to Israeli's, and was found to be more sensitive and more accurate in our conditions. This method provides therefore a sensitive, non-invasive, simple and widely applicable tool to quantify behavioural changes associated with various challenges in aquacultural conditions.
    Aquaculture 06/2014; 430:179–187. DOI:10.1016/j.aquaculture.2014.04.008 · 1.83 Impact Factor
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