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A new method for measuring group behaviours of fish shoals from recorded videos taken in near aquaculture conditions

机译:从在近水产养殖条件下拍摄的录像中测量鱼群行为的新方法

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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
机译:对于研究鱼类压力和福利的科学家来说,监测鱼类浅滩行为越来越受到关注。这项研究提出了一种算法,该算法可以根据从水族馆上方拍摄的视频来计算表征鱼群行为的两个指标。这两个指标量化了水族馆中鱼群的分散度和游泳活动。根据雷诺兹关于群,牛群和学校的模型的规则,在鱼群模拟中测试了这些指标的可靠性。由于已知浅滩中每条模拟鱼的坐标,因此这些模拟提供了浅滩中每条鱼的真实分散度和游泳速度值,并将其与使用本算法计算的值进行比较。此外,在水族馆的虹鳟鱼视频中测试了这两个指数。在一项测试中估计食物分配前后的浅滩行为变化,在第二项测试中估计四小时的约束压力前后。模拟得出的数据表明,这两个指标对组内聚力或游泳速度的模拟变化敏感。因此,索引在模拟中忠实地转换了真实值,其中至少有94%的真实值变化由索引解释。此外,这两个指标对在两个案例研究中观察到的浅滩行为修改很敏感。确实,正如预期的那样,在使用我们的方法分配食物后,就可以检测到与重要的游泳活动增加相关的强烈的群体分散性下降。同样,我们的指数对四个小时的限制压力后观察到的群体行为变化敏感。最后,将我们的方法与以色列的方法进行了比较,发现在我们的情况下它更敏感,更准确。因此,该方法提供了一种灵敏,无创,简单且可广泛应用的工具,可以量化与水产养殖条件下各种挑战相关的行为变化

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