Seagrasses such as Zostera marina L. play a key role in coastal ecosystems because of the ecological goods and services that they provide, enhancing biodiversity, productivity and carbon sequestration. Despite their ecological relevance, their distribution is, to date, insufficiently documented and it is estimated that only one-quarter of their global extent is mapped.
This study aims to develop a new method to accurately detect and map subtidal seagrass meadows, using Irish seagrass populations as a case study. This method consists of four steps: (i) the development of a species distribution model (SDM); (ii) the use of satellite-derived images to visually appraise the potential presence and extent of seagrass beds; (iii) field surveys to validate the presence or absence of the seagrass; and finally (iv) the construction of an up-to-date detailed map of the seagrass distribution for the region under investigation.
Results indicate that along the Irish coast, and in western regions in particular, the actual distribution of seagrass is considerably greater than is currently reported. Using the proposed method, 16 new regions occupied by seagrass in areas of interest in County Galway (Kilkieran Bay, Bertraghboy Bay, and Chasla Bay) were identified, accounting for a total of 267.92 ha, which increased the previously documented distribution in this area by 44.74%.
In this study, we demonstrate the potential of this novel method to efficiently identify and map undocumented subtidal seagrass meadows. As seagrass habitats are under threat globally, the development of new mapping strategies is a critical contribution to current international efforts in seagrass monitoring and management.
Beca‐Carretero P., Varela S. and Stengel D.B. (2020) A novel method combining species distribution models, remote sensing, and field surveys for detecting and mapping subtidal seagrass meadows. Aquatic Conservation Marine and Freshwater Ecosystems 30(6): 1098-1110. 10.1002/aqc.3312
@article{Beca‐Carretero2020,
Title = {A novel method combining species distribution models, remote sensing, and field surveys for detecting and mapping subtidal seagrass meadows},
Author = {Beca‐Carretero, Pedro and Varela, Sara and Stengel, Dagmar B.},
Editor = {},
Journal = {Aquatic Conservation Marine and Freshwater Ecosystems},
Year = {2020},
Pages = {1098-1110},
Volume = {30},
Doi = {10.1002/aqc.3312},
Abstract = {Seagrasses such as Zostera marina L. play a key role in coastal ecosystems because of the ecological goods and services that they provide, enhancing biodiversity, productivity and carbon sequestration. Despite their ecological relevance, their distribution is, to date, insufficiently documented and it is estimated that only one-quarter of their global extent is mapped.
This study aims to develop a new method to accurately detect and map subtidal seagrass meadows, using Irish seagrass populations as a case study. This method consists of four steps: (i) the development of a species distribution model (SDM); (ii) the use of satellite-derived images to visually appraise the potential presence and extent of seagrass beds; (iii) field surveys to validate the presence or absence of the seagrass; and finally (iv) the construction of an up-to-date detailed map of the seagrass distribution for the region under investigation.
Results indicate that along the Irish coast, and in western regions in particular, the actual distribution of seagrass is considerably greater than is currently reported. Using the proposed method, 16 new regions occupied by seagrass in areas of interest in County Galway (Kilkieran Bay, Bertraghboy Bay, and Chasla Bay) were identified, accounting for a total of 267.92 ha, which increased the previously documented distribution in this area by 44.74%.
In this study, we demonstrate the potential of this novel method to efficiently identify and map undocumented subtidal seagrass meadows. As seagrass habitats are under threat globally, the development of new mapping strategies is a critical contribution to current international efforts in seagrass monitoring and management.},
}
TY - JOUR
AU - Beca‐Carretero, Pedro
AU - Varela, Sara
AU - Stengel, Dagmar B.
TI - A novel method combining species distribution models, remote sensing, and field surveys for detecting and mapping subtidal seagrass meadows
T2 - Aquatic Conservation Marine and Freshwater Ecosystems
PY - 2020
SP - 1098-1110
VL - 30
DO - 10.1002/aqc.3312
AB - Seagrasses such as Zostera marina L. play a key role in coastal ecosystems because of the ecological goods and services that they provide, enhancing biodiversity, productivity and carbon sequestration. Despite their ecological relevance, their distribution is, to date, insufficiently documented and it is estimated that only one-quarter of their global extent is mapped.
This study aims to develop a new method to accurately detect and map subtidal seagrass meadows, using Irish seagrass populations as a case study. This method consists of four steps: (i) the development of a species distribution model (SDM); (ii) the use of satellite-derived images to visually appraise the potential presence and extent of seagrass beds; (iii) field surveys to validate the presence or absence of the seagrass; and finally (iv) the construction of an up-to-date detailed map of the seagrass distribution for the region under investigation.
Results indicate that along the Irish coast, and in western regions in particular, the actual distribution of seagrass is considerably greater than is currently reported. Using the proposed method, 16 new regions occupied by seagrass in areas of interest in County Galway (Kilkieran Bay, Bertraghboy Bay, and Chasla Bay) were identified, accounting for a total of 267.92 ha, which increased the previously documented distribution in this area by 44.74%.
In this study, we demonstrate the potential of this novel method to efficiently identify and map undocumented subtidal seagrass meadows. As seagrass habitats are under threat globally, the development of new mapping strategies is a critical contribution to current international efforts in seagrass monitoring and management.
ER -
