Marine Research Centre, Finnish Environment Institute, Helsinki, Finland.
Department of Marine Science-Tjärnö, University of Gothenburg, Gothenburg, Sweden.
Environ Monit Assess. 2020 Nov 27;192(12):795. doi: 10.1007/s10661-020-08764-7.
Benthic habitats and communities are key components of the marine ecosystem. Securing their functioning is a central aim in marine environmental management, where monitoring data provide the base for assessing the state of marine ecosystems. In the Baltic Sea, a > 50-year-long tradition of zoobenthic monitoring exists. However, the monitoring programmes were designed prior to the current policies, primarily to detect long-term trends at basin-scale and are thus not optimal to fulfil recent requirements such as area-based periodic status assessments. Here, we review the current monitoring programmes and assess the precision and representativity of the monitoring data in status assessments to identify routes for improvement. At present, the monitoring is focused on soft-bottoms, not accounting for all habitat types occurring in the Baltic Sea. Evaluating the sources of variance in the assessment data revealed that the component accounting for variability among stations forms the largest proportion of the uncertainty. Furthermore, it is shown that the precision of the status estimates can be improved, with the current number of samples. Reducing sampling effort per station, but sampling more stations, is the best option to improve precision in status assessments. Furthermore, by allocating the sampling stations more evenly in the sub-basins, a better representativity of the area can be achieved. However, emphasis on securing the long-term data series is needed if changes to the monitoring programmes are planned.
底栖生境和生物群落是海洋生态系统的关键组成部分。确保其功能正常是海洋环境管理的核心目标，监测数据为评估海洋生态系统的状况提供了基础。在波罗的海中，已经有超过 50 年的底栖生物监测传统。然而，这些监测计划是在当前政策之前设计的，主要是为了在流域尺度上检测长期趋势，因此，它们并不完全符合最近的要求，如基于区域的定期状况评估。在这里，我们回顾了当前的监测计划，并评估了监测数据在状况评估中的精度和代表性，以确定改进的途径。目前，监测主要集中在软底生境上，没有考虑到波罗的海中所有的生境类型。评估评估数据中的方差来源表明，站间变异的组成部分构成了不确定性的最大比例。此外，研究表明，在当前样本数量的情况下，可以通过改进状态估计的精度来提高状态估计的精度。通过减少每个站位的采样工作量，但增加更多的站位，是提高状态评估精度的最佳选择。此外，通过更均匀地在次流域分配采样站，可以更好地代表该区域。然而，如果计划对监测计划进行更改，则需要强调确保长期数据系列的连续性。
