Error, Power, and Blind Sentinels: The Statistics of Seagrass Monitoring

Novel approach to large-scale monitoring of submerged aquatic vegetation: A nationwide example from Sweden

According to the EU Habitats directive, the Water Framework Directive, and the Marine Strategy Framework Directive, member states are required to map, monitor, and evaluate changes in quality and areal distribution of different marine habitats and biotopes to protect the marine environment more effectively. Submerged aquatic vegetation (SAV) is a key indicator of the ecological status of coastal ecosystems and is therefore widely used in reporting related to these directives. Environmental monitoring of the areal distribution of SAV is lacking in Sweden due to the challenges of large-scale monitoring using traditional small-scale methods. To address this gap, in 2020, we embarked on a project to combine Copernicus Sentinel-2 satellite imagery, novel machine learning (ML) techniques, and advanced data processing in a cloud-based web application that enables users to create up-to-date SAV classifications. At the same time, the approach was used to derive the first high-resolution SAV map for the entire coastline of Sweden, where an area of 1550 km² was mapped as SAV. Quantitative evaluation of the accuracy of the classification using independent field data from three different regions along the Swedish coast demonstrated relative high accuracy within shallower areas, particularly where water transparency was high (average total accuracy per region 0.60-0.77). However, the classification missed large proportions of vegetation growing in deeper water (on average 31%-50%) and performed poorly in areas with fragmented or mixed vegetation and poor water quality, challenges that should be addressed in the development of the mapping methods towards integration into monitoring frameworks such as the EU directives. In this article, we present the results of the first satellite-derived SAV classification for the entire Swedish coast and show the implementation of a cloud-based SAV mapping application (prototype) developed within the frame of the project. Integr Environ Assess Manag 2022;18:909-920. © 2021 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Submerged aquatic vegetation: Overview of monitoring techniques used for the identification and determination of spatial distribution in European coastal waters

Coastal waters are highly productive and diverse ecosystems, often dominated by marine submerged aquatic vegetation (SAV) and strongly affected by a range of human pressures. Due to their important ecosystem functions, for decades, both researchers and managers have investigated changes in SAV abundance and growth dynamics to understand linkages to human perturbations. In European coastal waters, monitoring of marine SAV communities traditionally combines diver observations and/or video recordings to determine, for example, spatial coverage and species composition. While these techniques provide very useful data, they are rather time consuming, labor-intensive, and limited in their spatial coverage. In this study, we compare traditional and emerging remote sensing technologies used to monitor marine SAV, which include satellite and occupied aircraft operations, aerial drones, and acoustics. We introduce these techniques and identify their main strengths and limitations. Finally, we provide recommendations for researchers and managers to choose the appropriate techniques for future surveys and monitoring programs. Integr Environ Assess Manag 2022;18:892-908. © 2021 SETAC.

Seagrass recovery after fish farm relocation in the eastern Mediterranean

Finfish aquaculture has damaged seagrass meadows worldwide as wastes from the farms can kill these habitat-forming plants. In Cyprus, the Mediterranean endemic Posidonia oceanica is at its upper thermal limits yet forms extensive meadows all around the island. Understanding this under-studied isolated population may be important for the long-term survival of the species given that the region is warming rapidly. When fish farming began around Cyprus in the mid-nineties, cages were moored above seagrass beds, but as production expanded they were moved into deeper water further away from the meadows. Here, we monitored the deepest edge of meadows near fish farms that had been moved into deeper waters as well as at a decommissioned farm site. Four P. oceanica monitoring systems were set up using methods developed by the Posidonia Monitoring Network. Seagrass % coverage, shoot density, % of plagiotropic rhizomes, shoot exposure, leaf morphometry, and sediment organic matter content and grain size were monitored at 11 fixed plots within each system, in 2012-2014 and in 2017. Expansion at the lower depth limit of seagrass meadows was recorded at all monitoring sites. Most other P. oceanica descriptors either did not change significantly or declined. Declines were most pronounced at a site that was far from mariculture activities but close to other anthropogenic pressures. The most important predictor affecting P. oceanica was depth. Monitoring using fixed plots allowed direct comparisons of descriptors over time, removes patchiness and intra-meadow variability increasing our understanding of seagrass dynamics and ecosystem integrity. It seems that moving fish farms away from P. oceanica has helped ensure meadow recovery at the deepest margins of their distribution, an important success story given that these meadows are at the upper thermal limits of the species.