Abstract The research presented in this paper was conducted as part of a project that aimed at using biosensors on sentinel oysters to provide a biological perspective of environmental changes. The physiological response patterns (e.g. heart rate variability, shell gape) of sentinel oysters can provide valuable insight into their exposure to environmental stressors. Sensors were attached to measure both heart rate and shell gape (physiological behaviour) of oysters placed at different depths in the water column. Sensors were also deployed to measure water quality that represents the environmental condition the oysters were in. The objective of this study is to utilise the data from different sensors to investigate how environmental conditions modulate physiological response of oysters. We have utilised a set of machine learning models to develop data-driven approaches that can predict heart rate and shell gape (opening and closing action) of oysters from water quality variables, and vice-versa. The level of prediction accuracy indicates how well environmental conditions influence the physiological response of oysters. The effectiveness of the developed approaches is evaluated using data collected from two different deployments of sensors in South East Tasmania, Australia. Experimental results demonstrate that the presented data-driven approaches can provide accurate predictions of physiological and water quality variables, for the data set considered in this study. The prediction error (in terms of MAPE) for HR and water quality is in the range of 3.17%-8.21% and 0.72%–2.48%, respectively, and classification accuracy (F-Score) for shell gape varies between 0.96 and 0.99.

中文翻译：

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调查数据驱动的方法以了解水质与自然环境中哨兵牡蛎生理反应之间的相互作用

摘要 本文中介绍的研究是作为一个项目的一部分进行的，该项目旨在在哨兵牡蛎上使用生物传感器以提供环境变化的生物学视角。哨兵牡蛎的生理反应模式（例如心率变异性、贝壳张开）可以为它们暴露于环境压力源提供有价值的见解。连接传感器以测量放置在水体中不同深度的牡蛎的心率和壳张开（生理行为）。还部署了传感器来测量代表牡蛎所处环境条件的水质。本研究的目的是利用来自不同传感器的数据来研究环境条件如何调节牡蛎的生理反应。我们利用一组机器学习模型来开发数据驱动的方法，可以根据水质变量预测心率和牡蛎的壳张开（打开和关闭动作），反之亦然。预测准确度水平表明环境条件对牡蛎生理反应的影响程度。所开发方法的有效性是使用从澳大利亚东南塔斯马尼亚州的两种不同传感器部署收集的数据进行评估的。实验结果表明，对于本研究中考虑的数据集，所提出的数据驱动方法可以提供对生理和水质变量的准确预测。HR和水质的预测误差（就MAPE而言）分别在3.17%-8.21%和0.72%-2.48%的范围内，