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Het OWA, het open archief van het Waterbouwkundig Laboratorium heeft tot doel alle vrij toegankelijke onderzoeksresultaten van dit instituut in digitale vorm aan te bieden. Op die manier wil het de zichtbaarheid, verspreiding en gebruik van deze onderzoeksresultaten, alsook de wetenschappelijke communicatie maximaal bevorderen.
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| one publication added to basket [239863] |
A Mixed Modeling Approach to Predict the Effect of Environmental Modification on Species Distributions
Cozzoli, F.; Eelkema, M.; Bouma, T.J.; Ysebaert, T.; Escaravage, V.; Herman, P.M.J. (2014). A Mixed Modeling Approach to Predict the Effect of Environmental Modification on Species Distributions. PLoS One 9(2): e89131. http://dx.doi.org/10.1371/journal.pone.0089131
In: PLoS One. Public Library of Science: San Francisco. ISSN 1932-6203; e-ISSN 1932-6203, meer
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| Abstract |
Human infrastructures can modify ecosystems, thereby affecting the occurrence and spatial distribution of organisms, as well as ecosystem functionality. Sustainable development requires the ability to predict responses of species to anthropogenic pressures. We investigated the large scale, long term effect of important human alterations of benthic habitats with an integrated approach combining engineering and ecological modelling. We focused our analysis on the Oosterschelde basin (The Netherlands), which was partially embanked by a storm surge barrier (Oosterscheldekering, 1986). We made use of 1) a prognostic (numerical) environmental (hydrodynamic) model and 2) a novel application of quantile regression to Species Distribution Modeling (SDM) to simulate both the realized and potential (habitat suitability) abundance of four macrozoobenthic species: Scoloplos armiger, Peringia ulvae, Cerastoderma edule and Lanice conchilega. The analysis shows that part of the fluctuations in macrozoobenthic biomass stocks during the last decades is related to the effect of the coastal defense infrastructures on the basin morphology and hydrodynamics. The methodological framework we propose is particularly suitable for the analysis of large abundance datasets combined with high-resolution environmental data. Our analysis provides useful information on future changes in ecosystem functionality induced by human activities. |
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