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

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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.

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Acknowledgments

We thank the three anonymous referees for their helpful comments. We are also grateful to Cymon Cox for his guidance to run R on the CCMAR GYRA cluster and to Damien Georges and Wilfried Thuiller for their help with biomod2. RC was supported by the postdoctoral fellowship SFRH/BPD/85040/2012 from the Fundação para a Ciência e a Tecnologia (FCT, Portugal). JA was supported by the postdoctoral fellowship CCMAR/BPD/0045/2013 from FCT. We acknowledge FCT project EXTANT (EXCL/AAG-GLO/0661/2012).

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  1. Centro de Ciências do Mar (CCMAR), Universidade do Algarve, Campus de Gambelas, 8005-139, Faro, Portugal

    Rosa M. Chefaoui, Jorge Assis & Ester A. Serrão

  2. Red Sea Research Center (RSRC) Building 2, Level 3, Room 3219, King Abdullah University of Science and Technology, Thuwal, 23955-6900, Kingdom of Saudi Arabia

    Carlos M. Duarte

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  1. Rosa M. Chefaoui
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Chefaoui, R.M., Assis, J., Duarte, C.M. et al. Large-Scale Prediction of Seagrass Distribution Integrating Landscape Metrics and Environmental Factors: The Case of Cymodocea nodosa (Mediterranean–Atlantic). Estuaries and Coasts 39, 123–137 (2016). https://doi.org/10.1007/s12237-015-9966-y

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