SEARAM – Seagrass Bed Rapid Assessment Method: A Spatially Explicit Approach to Assessing Seagrass Ecological Condition with Multiple Applications
Abstract
Assessments of seagrass ecological condition traditionally rely on station-based monitoring networks, whose limited spatial representativeness may fail to capture the heterogeneity of these ecosystems. To address this limitation, the SEARAM (Seagrass Bed Rapid Assessment Method) provides a standardized, spatially explicit approach for rapidly assessing seagrass ecological condition. Developed since 2020 within the framework of IFRECOR and MERCI-Cor initiatives, the method relies on in situ measurements of ecological metrics sensitive to environmental pressures, including seagrass cover, fragmentation, epiphytic colonization, and leaf necrosis. Following standardization and normalization against regional reference conditions, these metrics are combined into a multimetric ecological condition index ranging from 0 to 10. Between 2020 and 2024, SEARAM was implemented at 1,249 sampling stations covering 756.5 hectares of seagrass habitats across Réunion Island, Mayotte, and Martinique. High-density sampling and spatial interpolation enable the mapping of ecological condition, species distributions, and morphological characteristics, including in areas where seagrass boundaries cannot be reliably detected through remote sensing. Additional applications include identifying environmental drivers of seagrass and associated fauna distributions, assessing conservation priorities, examining interactions with marine megafauna, and informing the design and scaling of ecological compensation measures. SEARAM therefore complements conventional monitoring networks by providing spatially continuous ecological information to support environmental management and conservation planning. However, its application to long-term temporal monitoring and its transferability to additional geographical regions require further investigation and validation.
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BibTeX
@article{pinault2026searam,
title = {{SEARAM – Seagrass Bed Rapid Assessment Method: A Spatially Explicit Approach to Assessing Seagrass Ecological Condition with Multiple Applications}},
author = {Mathieu Pinault and Julien Wickel and Jean Benoit Nicet and Fanny Kerninon},
journal = {La feuille marine},
year = {2026},
month = oct,
doi = {10.5281/zenodo.22669784},
url = {https://hal.science/hal-05799314v1},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - SEARAM – Seagrass Bed Rapid Assessment Method: A Spatially Explicit Approach to Assessing Seagrass Ecological Condition with Multiple Applications
AU - Mathieu Pinault
AU - Julien Wickel
AU - Jean Benoit Nicet
AU - Fanny Kerninon
PY - 2026
DA - 2026/10/14
JO - La feuille marine
DO - 10.5281/zenodo.22669784
UR - https://hal.science/hal-05799314v1
AB - Assessments of seagrass ecological condition traditionally rely on station-based monitoring networks, whose limited spatial representativeness may fail to capture the heterogeneity of these ecosystems. To address this limitation, the SEARAM (Seagrass Bed Rapid Assessment Method) provides a standardized, spatially explicit approach for rapidly assessing seagrass ecological condition. Developed since 2020 within the framework of IFRECOR and MERCI-Cor initiatives, the method relies on in situ measurements of ecological metrics sensitive to environmental pressures, including seagrass cover, fragmentation, epiphytic colonization, and leaf necrosis. Following standardization and normalization against regional reference conditions, these metrics are combined into a multimetric ecological condition index ranging from 0 to 10. Between 2020 and 2024, SEARAM was implemented at 1,249 sampling stations covering 756.5 hectares of seagrass habitats across Réunion Island, Mayotte, and Martinique. High-density sampling and spatial interpolation enable the mapping of ecological condition, species distributions, and morphological characteristics, including in areas where seagrass boundaries cannot be reliably detected through remote sensing. Additional applications include identifying environmental drivers of seagrass and associated fauna distributions, assessing conservation priorities, examining interactions with marine megafauna, and informing the design and scaling of ecological compensation measures. SEARAM therefore complements conventional monitoring networks by providing spatially continuous ecological information to support environmental management and conservation planning. However, its application to long-term temporal monitoring and its transferability to additional geographical regions require further investigation and validation.
ER -