Large-Scale Prediction of Seagrass Distribution Integrating Landscape Metrics and Environmental Factors: The Case of Cymodocea nodosa (Mediterranean–Atlantic)

Handle URI:
http://hdl.handle.net/10754/566127
Title:
Large-Scale Prediction of Seagrass Distribution Integrating Landscape Metrics and Environmental Factors: The Case of Cymodocea nodosa (Mediterranean–Atlantic)
Authors:
Chefaoui, Rosa M.; Assis, Jorge; Duarte, Carlos M. ( 0000-0002-1213-1361 ) ; Serrão, Ester A.
Abstract:
Understanding the factors that affect seagrass meadows encompassing their entire range of distribution is challenging yet important for their conservation. Here, we predict the realized and potential distribution for the species Cymodocea nodosa modelling its environmental niche in the Mediterranean and adjacent Atlantic coastlines. We use a combination of environmental variables and landscape metrics to perform a suite of predictive algorithms which enables examination of the niche and find suitable habitats for the species. The most relevant environmental variables defining the distribution of C. nodosa were sea surface temperature (SST) and salinity. We found suitable habitats at SST from 5.8 °C to 26.4 °C and salinity ranging from 17.5 to 39.3. Optimal values of mean winter wave height ranged between 1.2 and 1.5 m, while waves higher than 2.5 m seemed to limit the presence of the species. The influence of nutrients and pH, despite having weight on the models, was not so clear in terms of ranges that confine the distribution of the species. Landscape metrics able to capture variation in the coastline enhanced significantly the accuracy of the models, despite the limitations caused by the scale of the study. We found potential suitable areas not occupied by the seagrass mainly in coastal regions of North Africa and the Adriatic coast of Italy. The present study describes the realized and potential distribution of a seagrass species, providing the first global model of the factors that can be shaping the environmental niche of C. nodosa throughout its range. We identified the variables constraining its distribution as well as thresholds delineating its environmental niche. Landscape metrics showed promising prospects for the prediction of coastal species dependent on the shape of the coast. By contrasting predictive approaches, we defined the variables affecting the distributional areas that seem unsuitable for C. nodosa as well as those suitable habitats not occupied by the species. These findings are encouraging for its use in future studies on climate-related marine range shifts and meadow restoration projects of these fragile ecosystems. © 2015 Coastal and Estuarine Research Federation
KAUST Department:
Red Sea Research Center (RSRC)
Publisher:
Springer Science + Business Media
Journal:
Estuaries and Coasts
Issue Date:
5-May-2015
DOI:
10.1007/s12237-015-9966-y
Type:
Article
ISSN:
15592723
Appears in Collections:
Articles; Red Sea Research Center (RSRC)

Full metadata record

DC FieldValue Language
dc.contributor.authorChefaoui, Rosa M.en
dc.contributor.authorAssis, Jorgeen
dc.contributor.authorDuarte, Carlos M.en
dc.contributor.authorSerrão, Ester A.en
dc.date.accessioned2015-08-12T09:29:21Zen
dc.date.available2015-08-12T09:29:21Zen
dc.date.issued2015-05-05en
dc.identifier.issn15592723en
dc.identifier.doi10.1007/s12237-015-9966-yen
dc.identifier.urihttp://hdl.handle.net/10754/566127en
dc.description.abstractUnderstanding the factors that affect seagrass meadows encompassing their entire range of distribution is challenging yet important for their conservation. Here, we predict the realized and potential distribution for the species Cymodocea nodosa modelling its environmental niche in the Mediterranean and adjacent Atlantic coastlines. We use a combination of environmental variables and landscape metrics to perform a suite of predictive algorithms which enables examination of the niche and find suitable habitats for the species. The most relevant environmental variables defining the distribution of C. nodosa were sea surface temperature (SST) and salinity. We found suitable habitats at SST from 5.8 °C to 26.4 °C and salinity ranging from 17.5 to 39.3. Optimal values of mean winter wave height ranged between 1.2 and 1.5 m, while waves higher than 2.5 m seemed to limit the presence of the species. The influence of nutrients and pH, despite having weight on the models, was not so clear in terms of ranges that confine the distribution of the species. Landscape metrics able to capture variation in the coastline enhanced significantly the accuracy of the models, despite the limitations caused by the scale of the study. We found potential suitable areas not occupied by the seagrass mainly in coastal regions of North Africa and the Adriatic coast of Italy. The present study describes the realized and potential distribution of a seagrass species, providing the first global model of the factors that can be shaping the environmental niche of C. nodosa throughout its range. We identified the variables constraining its distribution as well as thresholds delineating its environmental niche. Landscape metrics showed promising prospects for the prediction of coastal species dependent on the shape of the coast. By contrasting predictive approaches, we defined the variables affecting the distributional areas that seem unsuitable for C. nodosa as well as those suitable habitats not occupied by the species. These findings are encouraging for its use in future studies on climate-related marine range shifts and meadow restoration projects of these fragile ecosystems. © 2015 Coastal and Estuarine Research Federationen
dc.publisherSpringer Science + Business Mediaen
dc.subjectCoastal morphologyen
dc.subjectCymodocea nodosaen
dc.subjectEnvironmental nicheen
dc.subjectLandscape metricsen
dc.subjectSeagrassen
dc.subjectSpecies distribution modellingen
dc.titleLarge-Scale Prediction of Seagrass Distribution Integrating Landscape Metrics and Environmental Factors: The Case of Cymodocea nodosa (Mediterranean–Atlantic)en
dc.typeArticleen
dc.contributor.departmentRed Sea Research Center (RSRC)en
dc.identifier.journalEstuaries and Coastsen
dc.contributor.institutionCentro de Ciências do Mar (CCMAR), Universidade do Algarve, Campus de GambelasFaro, Portugalen
kaust.authorDuarte, Carlos M.en
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