Zoeken
Zoeken kan via de modus 'eenvoudig zoeken' (één veld) of uitgebreid via 'geavanceerd zoeken' (meerdere velden). Zo kan je bv. zoeken op een combinatie van een auteursnaam (auteur), een jaartal (jaar) en een documenttype.
Boekenmand
Nuttige resultaten kan je aanvinken en toevoegen aan een mandje. De inhoud hiervan kan je exporteren of afdrukken (naar bv. PDF).
RSS
Op de hoogte blijven van nieuw toegevoegde publicaties binnen uw interessegebied? Dit kan door een RSS-feed (?) te maken van jouw zoekopdracht.
nieuwe zoekopdracht
Community ecological modelling as an alternative to physiographic classifications for marine conservation planning
Rubidge, E.M.; Curtis, J.M.R. (2016). Community ecological modelling as an alternative to physiographic classifications for marine conservation planning. Biodivers. Conserv. 25(10): 1899-1920. https://dx.doi.org/10.1007/s10531-016-1167-x
In: Biodiversity and Conservation. Kluwer Academic Publishers/Springer: London. ISSN 0960-3115; e-ISSN 1572-9710
|  |
| Auteurs | | Top |
- Rubidge, E.M.
- Curtis, J.M.R.
|
|
|
| Abstract |
Accurate mapping of marine species and habitats is an important yet challenging component of establishing networks of representative marine protected areas. Due to limited biological data, marine classifications based on abiotic data are often used as surrogates to represent biological patterns. We tested the surrogacy of an existing physiographic marine classification using non-metric multidimensional scaling and permutational analysis of variance to determine whether species composition was significantly different among physiographic units. We also present an alternative ecological classification that incorporates biological and environmental data in a community modeling approach. We use data on 174 species of demersal fish and benthic invertebrates to identify mesoscale biological assemblages in a 100,000 km2 study area in the northeast Pacific Ocean. We identified assemblages using cluster analysis then used a random forest model with 12 environmental variables to delineate mesoscale ecological units. Our community modelling approach resulted in five geographically coherent ecological units that were best explained by changes in depth, temperature and salinity. Our model showed high predictive performance (AUC = 0.93) and the resulting ecological units represent more distinct species assemblages than those delineated by physiographic variables alone. A strength of our analysis is the ability to map model uncertainty to identify transition zones at unit boundaries. The output of this study provides a biotic driven classification that can be used to better achieve representativity in the MPA planning process. |
IMIS is ontwikkeld en wordt gehost door het VLIZ.
