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|Comparing the performance of species distribution models of Zostera marina:Implications for conservation|Valle , M.; van Katwijk, M.M.; de Jong, D.J.; Bouma, T.; Schipper, A.M.; Chust, G.; Benito, B.M.; Garmendia, J.M.; Borja, A. (2013). Comparing the performance of species distribution models of Zostera marina:Implications for conservation. J. Sea Res. 83: 56-64. http://dx.doi.org/10.1016/j.seares.2013.03.002
In: Journal of Sea Research. Elsevier/Netherlands Institute for Sea Research: Amsterdam; Den Burg. ISSN 1385-1101; e-ISSN 1873-1414, meer
Intertidal; Dynamics; Conservation; Seagrasses; Ecosystem Management; Wadden Sea
|Auteurs|| || Top |
- Valle, M.
- van Katwijk, M.M.
- de Jong, D.J.
- Bouma, T., meer
- Schipper, A.M.
- Chust, G.
- Benito, B.M.
- Garmendia, J.M.
- Borja, A.
Intertidal seagrasses show high variability in their extent and location, with local extinctions and (re-)colonizations being inherent in their population dynamics. Suitable habitats are identified usually using Species Distribution Models (SDM), based upon the overall distribution of the species; thus, accounting solely for spatial variability. To include temporal effects caused by large interannual variability, we constructed SDMs for different combinations and fusions of yearly distribution data. The main objectives were to: (i) assess the spatio-temporal dynamics of an intertidal seagrass bed of Zostera marina; (ii) select the most accurate SDM techniques to model different temporal distribution data subsets of the species; (iii) assess the relative importance of the environmental variables for each data subset; and (iv) evaluate the accuracy of the models to predict species conservation areas, addressing implications for management. To address these objectives, a time series of 14-year distribution data of Zostera marina in the Ems estuary (The Netherlands) was used to build different data subsets: (1) total presence area; (2) a conservative estimate of the total presence area, defined as the area which had been occupied during at least 4 years; (3) core area, defined as the area which had been occupied during at least 2/3 of the total period; and (4–6) three random selections of monitoring years. On average, colonized and disappeared areas of the species in the Ems estuary showed remarkably similar transition probabilities of 12.7% and 12.9%, respectively. SDMs based upon machine-learning methods (Boosted Regression Trees and Random Forest) outperformed regression-based methods. Current velocity and wave exposure were the most important variables predicting the species presence for widely distributed data. Depth and sea floor slope were relevant to predict conservative presence area and core area. It is concluded that, the fusion of the spatial distribution data from four monitoring years could be enough to establish an accurate habitat suitability model of Zostera marina in the Ems estuary. The methodology presented offers a promising tool for selecting realistic conservation areas for those species that show high population dynamics, such as many estuarine and coastal species.