Methodological Bias in Quadrat-Based Monitoring of Posidonia oceanica: A Structured Narrative Review and Framework for Monitoring Standardization
Mediterranean Marine Science, cilt.27, sa.2, ss.442-458, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Derleme
- Cilt numarası: 27 Sayı: 2
- Basım Tarihi: 2026
- Doi Numarası: 10.12681/mms.44887
- Dergi Adı: Mediterranean Marine Science
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, BIOSIS, Zoological Record
- Sayfa Sayıları: ss.442-458
- Anahtar Kelimeler: Mediterranean Sea, methodological bias, Posidonia oceanica, quadrat-based sampling, sampling design, seagrass monitoring, standardization
- Akdeniz Üniversitesi Adresli: Evet
Özet
Posidonia oceanica meadows form one of the most important coastal habitats in the Mediterranean Sea, providing key ecosystem services including carbon sequestration, sediment stabilization, biodiversity support, and coastal protection. Despite their ecological importance, these meadows have declined in many parts of the Mediterranean over recent decades due to coastal development, pollution, anchoring activities, and climate-related pressures. Detecting such changes requires reliable and comparable monitoring data. Among the available approaches, quadrat-based field surveys remain one of the most widely used methods for describing meadow structure through indicators such as shoot density, percent cover, and leaf biometry. In practice, however, these methods are applied in different ways across monitoring programs. Variations in quadrat size, sampling design, replication strategies, and measurement protocols often make it difficult to compare results among studies or regions. This review examines the methodological foundations of quadrat-based monitoring of P. oceanica and discusses the main sources of bias that may influence monitoring outcomes. A structured literature search identified five recurrent sources of methodological bias across the studies reviewed: sampling design, quadrat size effects, observer variability, depth gradients, and seasonal variability. These factors can affect both the precision of measurements and the interpretation of ecological trends. The review also evaluates commonly used monitoring designs and ecological indices and considers recent technological developments such as photogrammetry, remote sensing, and machine-learning-based image analysis that may help reduce some methodological limitations. Drawing on this synthesis, a conceptual framework is proposed linking sources of methodological bias with their potential consequences for monitoring outcomes, and practical recommendations are outlined to improve methodological consistency and enhance the comparability of P. oceanica monitoring across the Mediterranean basin.