ReviewEnvironmental monitoring and assessment2020
Developing benthic monitoring programmes to support precise and representative status assessments: a case study from the Baltic Sea.
Review in Environmental monitoring and assessment, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Structured machine learning modeling to support conservation of deep-sea benthic biodiversity.Conservation biology : the journal of the Society for Conservation Biology · 2026Article
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Authors and funding
6 authors.
Funding
Abstract
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.
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