Recruitment of temperate eel species Anguilla anguilla, A. rostrata & A. japonica has declined over the last few decades due to human activities, such as overfishing and construction of migratory barriers (e.g. dams, weirs and sluices) and hazardous energy infrastructure (e.g. turbines, intakes and outfalls). Numerical models, substantiated with data from field and laboratory studies, can potentially predict and quantify the relative impacts of such activities, thereby assisting in the sustainable management of eel populations. Here, we present an agent-based model (ABM) of juvenile eel migration up estuaries. The model includes relevant eel behaviours and environmental conditions that, according to the literature, influence upstream migration. Crucially, by assessing the local salinity gradient and relative flow direction, the modelled eels (agents) self-determine whether the tide is flooding or ebbing and orientate themselves for navigation, with no top-down instructions. This allows the agents to decide which particular behaviour to undertake as part of Selective Tidal Stream Transport (STST). The developed ABM is coupled to a hydrodynamic model of the Thames Estuary and the results substantiated by comparison against eel trap data. Combinations of the various STST behaviours are systematically tested and the influence they have on up-estuary migration is assessed in terms of relative energy expenditure. The parameterised model is then used predictively at Milford Haven Waterway to investigate potential impacts on the juvenile eel population due to entrainment in a power plant cooling water intake and outfall. Results from the Thames model case study indicate that including bed anchoring behaviour is essential for achieving a good comparison with the eel trap data and the choice of salinity detection threshold is also important. If daylight avoidance (diel) behaviour is not included, the most energy efficient migration is achieved using just two STST behaviours (ebb tide bed anchoring and upward migration during flood). With diel behaviour included, energy expenditure is greater, but some efficiency is regained by including all of the STST behaviours. For the Milford Haven case study, the model predicted a juvenile eel intake and outfall entrainment rate of 2.0% and 4.7%, respectively. It is concluded that the ABM is a valuable tool for assessing potential impacts on the recruitment of eels (extendable to other species) and could be used to assist in site-selection and low impact design of energy infrastructure in tidal environments.

招聘温带鳗鳗，鳗鱼和日本粳稻在过去的几十年中，由于人类活动，例如过度捕捞和建造移民屏障（例如水坝，堰和水闸）和危险能源基础设施（例如涡轮机，进水口和排污口），水土保持有所下降。用来自实地和实验室研究的数据证实的数值模型可以潜在地预测和量化此类活动的相对影响，从而有助于鳗鱼种群的可持续管理。在这里，我们提出了一个基于代理的少年鳗migration河口迁移模型（ABM）。根据文献，该模型包括相关的鳗鱼行为和环境条件，它们会影响上游迁移。至关重要的是，通过评估局部盐度梯度和相对流向，建模的鳗鱼（代理人）可以自行确定潮汐是泛滥还是消退，并自行定向航行，而无需自上而下的说明。这使代理可以决定将哪些特定行为作为选择性潮汐流传输（STST）的一部分进行。所开发的ABM与泰晤士河口的水动力模型耦合，并通过与鳗鱼捕集器数据进行比较来证实结果。系统地测试了各种STST行为的组合，并根据相对能量消耗评估了它们对河口迁移的影响。然后，将参数化模型用于米尔福德避风港航道，以研究由于电厂冷却水进水口和排水口的夹带而对鳗鱼种群造成的潜在影响。泰晤士模型案例研究的结果表明，包括床锚定行为对于与鳗鱼陷阱数据进行良好的比较至关重要，盐度检测阈值的选择也很重要。如果不包括避光（diel）行为，则仅使用两种STST行为即可实现最节能的迁移（退潮潮汐锚定和洪水期间向上迁移）。包括diel行为，能量消耗会更大，但是通过包含所有STST行为，可以重新获得一些效率。对于Milford Haven案例研究，该模型预测幼鳗的摄食量和引水率分别为2.0％和4.7％。