Seagrass Health Monitoring Methods: Meta- Analysis and Expert Survey

Description

This dataset was developed to examine how seagrass health is currently monitored and to assess whether commonly used monitoring methods align with expert views on what is most important for evaluating seagrass health. The underlying hypothesis was that, despite the wide range of possible indicators available, seagrass monitoring practice is dominated by a small set of core methods, and that these methods broadly reflect expert judgement, while other potentially informative indicators remain underused. To test this, we compiled two complementary datasets. The first is a systematic meta-analysis of 500 independent studies that reported seagrass health or condition. Studies were identified through structured literature searches and screening, and for each study we recorded basic metadata (including region, species, and study duration) and the types of monitoring methods used. The second dataset consists of responses from a survey of 34 seagrass experts, including researchers and practitioners involved in monitoring, conservation, and management. Survey participants were asked to evaluate the importance and applicability of different seagrass health monitoring methods and to identify approaches expected to become more prominent in the future. Survey responses were annonymised and structured to allow direct comparison with the meta-analysis results. The data show that most seagrass studies rely on a small number of widely used, cost-effective monitoring methods, particularly environmental parameters and structural indicators, while physiological-level indicators are used relatively infrequently. Expert responses largely mirror this pattern, with high importance assigned to core structural and environmental metrics, but also highlight opportunities to strengthen monitoring by integrating early-warning indicators and emerging approaches such as remote sensing, environmental DNA (eDNA), and automated image analysis. These datasets can be interpreted and used to explore patterns in seagrass monitoring practice across regions and species, to compare observed monitoring approaches with expert expectations, and to inform the design of more integrated monitoring frameworks. They are suitable for reuse in future meta-analyses, benchmarking exercises, and evidence-based development of seagrass monitoring and management programs.

Institutions

• University of Derby

  Derby, Derby

Categories

Funders

• Centre for Environment, Fisheries and Aquaculture Science

  Department for Environment Food and Rural Affairs

  United Kingdom

Licence