International Journal of Aquaculture, 2024, Vol.14, No.2, 101-111 http://www.aquapublisher.com/index.php/ija 108 7 Policy Developments and Future Directions 7.1 Recent policy innovations Recent advancements in technology have significantly influenced policy innovations aimed at monitoring and managing aquatic ecosystems. The integration of smart devices and the Internet of Things (IoT) has enabled more precise and extensive monitoring capabilities, which are crucial for responding to environmental threats such as oil spills and other pollutants (Jankowski et al., 2021). The use of environmental DNA (eDNA) for non-invasive sampling has also been recognized for its potential to revolutionize data collection, providing policymakers with more accurate and comprehensive biodiversity assessments. Additionally, the development of real-time biological early warning systems (BEWS) that monitor behavioral and physiological parameters of aquatic species offers a proactive approach to water quality management, allowing for timely interventions (Yang and Zhang, 2019). 7.2 Trends in policy making Current trends in policy making emphasize the importance of integrating technological advancements with traditional monitoring methods to enhance the management of aquatic ecosystems. Policies are increasingly focusing on the use of high-frequency environmental sensing and statistical approaches to measure ecosystem metabolism, which provides valuable insights into the health of aquatic environments4. There is also a growing trend towards the use of multi-community monitoring and assessment methods that support sustainable development goals (SDGs), particularly those related to clean water and sanitation (SDG6) and life below water (SDG14) (Forio and Goethals, 2020). The incorporation of IoT-based environmental assessment tools into policy frameworks is another emerging trend, as these tools offer continuous and accurate monitoring of water quality parameters. 7.3 Future directions in policy development Future policy development should aim to further integrate advanced monitoring technologies with environmental management practices. The use of IoT and smart monitoring systems should be expanded to cover larger areas and more diverse ecosystems, providing real-time data that can inform policy decisions (Glaviano et al., 2022). Policies should also promote the use of eDNA metabarcoding for ecological status assessments, as this method has shown promise in improving the accuracy and efficiency of biodiversity monitoring. Additionally, there is a need for policies that support the development and implementation of automated calibration systems and machine learning algorithms to enhance the reliability and accuracy of monitoring data (Narmadha et al., 2023). Moreover, future policies should encourage the inclusion of microbial community dynamics in routine biomonitoring programs, as these communities play a crucial role in nutrient cycling and ecosystem functioning. Finally, there should be a focus on developing integrated socio-environmental models that link monitoring data to ecosystem interactions and functions, providing a holistic approach to managing aquatic ecosystems in the context of sustainable development. By embracing these technological advancements and integrating them into policy frameworks, we can improve the monitoring and management of aquatic ecosystems, ensuring their health and sustainability for future generations. 8 Challenges and Opportunities 8.1 Technical and methodological challenges The integration of advanced technologies in monitoring aquatic ecosystems presents several technical and methodological challenges. One significant issue is the frequent need for sensor calibration to ensure data accuracy, as highlighted in the study using IoT technology for environmental assessment (Narmadha et al., 2023). Additionally, the risk of system failure due to sensor malfunction or data loss poses a considerable challenge. The complexity of modeling ecosystem metabolism, particularly in rivers and lakes, also remains a hurdle, despite advancements in high-frequency environmental sensing and statistical approaches (Jankowski et al., 2021). Furthermore, the vast amounts of data generated by modern sensor technologies and autonomous platforms can overwhelm current data analysis capacities, creating a bottleneck in effective data utilization (Malde et al., 2020).
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