Over het archief
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.
Dit archief wordt uitgebouwd en beheerd volgens de principes van de Open Access Movement, en het daaruit ontstane Open Archives Initiative.
Basisinformatie over ‘Open Access to scholarly information'.
Selecting variables for habitat suitability of Asellus (Crustacea, Isopoda) by applying input variable contribution methods to artificial neural network models
Mouton, A.M.; Dedecker, A.P.; Lek, S.; Goethals, P.L.M. (2010). Selecting variables for habitat suitability of Asellus (Crustacea, Isopoda) by applying input variable contribution methods to artificial neural network models. Environ. Model. Assessm. 15(1): 65-79. dx.doi.org/10.1007/s10666-009-9192-8
In: Environmental Modeling and Assessment. Springer: Bussum; Amsterdam; Dordrecht. ISSN 1420-2026; e-ISSN 1573-2967, meer
|  |
| Trefwoord |
|
| Author keywords |
Biological assessment; Environmental impact; Macroinvertebrates;Predictive modelling; Rivers; Sensitivity analysis; Multiple regression |
| Auteurs | | Top |
- Mouton, A.M., meer
- Dedecker, A.P., meer
- Lek, S.
- Goethals, P.L.M., meer
|
|
|
| Abstract |
This study aimed to compare different methods to analyse the contribution of individual river characteristics to predict the abundance of Asellus (Crustacea, Isopoda). Six methods which provide the relative contribution and/or the contribution profile of the input variables of artificial neural network models were therefore compared: (1) the ‘partial derivatives’ method; (2) the ‘weights’ method; (3) the ‘perturb’ method; (4) the ‘profile’ method; (5) the ‘classical stepwise’ method; (6) the ‘improved stepwise’ method. Consequently, the key variables which affect the habitat preferences of Asellus could be identified. To evaluate the performance of the artificial neural network model, the model predictions were compared with the results of a multiple linear regression analysis. The dataset consisted of 179 samples, collected over a 3-year period in the Zwalm catchment in Flanders, Belgium. Twenty-four environmental variables as well as the log-transformed abundance of Asellus were used in this study. The different contribution methods seemed to give similar results concerning the order of importance of the input variables. Nevertheless, their diverse computation led to differences in sensitivity and stability of the methods and the derived outcomes on the habitat preferences. From an ecological point of view, the environmental variables describing the stream type (width, depth, stream order and distance to mouth) were the most significant variables for Asellus in the Zwalm catchment during the period 2000–2002 for all applied methods. Indirectly, one can conclude that the water quality is not the limiting factor for the survival of Asellus in the Zwalm catchment. |
IMIS is ontwikkeld en wordt gehost door het VLIZ.
