Eguiraun Harkaitz, Martinez Iciar
Department of Graphic Design & Engineering Projects, Faculty of Engineering in Bilbao, University of the Basque Country UPV/EHU, 48013 Bilbao, Bizkaia, Spain.
Research Center for Experimental Marine Biology and Biotechnology-Plentziako Itsas Estazioa (PiE-UPV/EHU), University of the Basque Country (UPV/EHU), 48620 Plentzia, Bizkaia, Spain.
Entropy (Basel). 2023 Mar 24;25(4):559. doi: 10.3390/e25040559.
In a non-linear system, such as a biological system, the change of the output (e.g., behaviour) is not proportional to the change of the input (e.g., exposure to stressors). In addition, biological systems also change over time, i.e., they are dynamic. Non-linear dynamical analyses of biological systems have revealed hidden structures and patterns of behaviour that are not discernible by classical methods. Entropy analyses can quantify their degree of predictability and the directionality of individual interactions, while fractal dimension (FD) analyses can expose patterns of behaviour within apparently random ones. The incorporation of these techniques into the architecture of precision fish farming (PFF) and intelligent aquaculture (IA) is becoming increasingly necessary to understand and predict the evolution of the status of farmed fish. This review summarizes recent works on the application of entropy and FD techniques to selected individual and collective fish behaviours influenced by the number of fish, tagging, pain, preying/feed search, fear/anxiety (and its modulation) and positive emotional contagion (the social contagion of positive emotions). Furthermore, it presents an investigation of collective and individual interactions in shoals, an exposure of the dynamics of inter-individual relationships and hierarchies, and the identification of individuals in groups. While most of the works have been carried out using model species, we believe that they have clear applications in PFF. The review ends by describing some of the major challenges in the field, two of which are, unsurprisingly, the acquisition of high-quality, reliable raw data and the construction of large, reliable databases of non-linear behavioural data for different species and farming conditions.
在非线性系统中，如生物系统，输出（如行为）的变化与输入（如暴露于应激源）的变化不成比例。此外，生物系统也会随时间变化，即它们是动态的。对生物系统的非线性动力学分析揭示了经典方法无法识别的隐藏结构和行为模式。熵分析可以量化其可预测程度和个体相互作用的方向性，而分形维数（FD）分析可以揭示看似随机的行为模式。将这些技术纳入精准养鱼（PFF）和智能水产养殖（IA）的架构中，对于理解和预测养殖鱼类状态的演变变得越来越必要。本综述总结了近期关于熵和FD技术在受鱼数量、标记、疼痛、捕食/觅食、恐惧/焦虑（及其调节）和积极情绪传染（积极情绪的社会传染）影响的选定个体和群体鱼类行为中的应用研究。此外，还对鱼群中的集体和个体相互作用进行了调查，揭示了个体间关系和等级制度的动态，并对群体中的个体进行了识别。虽然大多数研究是使用模式物种进行的，但我们认为它们在PFF中有明确的应用。综述最后描述了该领域的一些主要挑战，其中两个挑战不出所料，即获取高质量、可靠的原始数据以及构建针对不同物种和养殖条件的大型、可靠的非线性行为数据库。
