The distribution of 19 macroinvertebrate taxawas related to 36 environmental variables in 3 Alpine glacial streams. Principal component analysis (PCA) and a self-organising map (SOM) were used to ordinate sample sites according to community composition. Multiple linear regression (MLR) was carried out with the environmental variables as predictors and each macroinvertebrate taxon as criterion variable, a multilayer perceptron (MLP) used the environmental variables as input neurons and each taxon as output neuron. The contribution of each environmental variable to macroinvertebrate response was quantified examining MLR regression coefficients and compared with partial derivative (Pad) and connection weights approach (CW) methods. PCA and SOM emphasized a difference between glacial (kryal) and non-glacial (krenal and rhithral) stations. Canonical correlation analysis (CANCOR) confirmed this separation, outlining the environmental variables (altitude, distance fromsource andwater temperature) which contributed most with macroinvertebrates to site ordination. SOM clustered kryal, rhithral and krenal in three well separated group of sites. MLR and MLP detected the best predictors of macroinvertebrate response. Pad sensitivity analysis and CW method emphasized the importance of water chemistry and substrate in determining the response of taxa, the importance of substratewas overlooked by linear multivariate analysis (MLR).
|Titolo:||Macroinvertebrate assemblages in glacial stream systems: A comparison of linear multi-variate methods with artificial neural networks|
|Autori interni:||ROSSARO, BRUNO (Ultimo)|
|Parole Chiave:||Artificial neural networks; Chironomidae; Glacial streams; Multivariate analysis; Sensitivity analysis|
|Settore Scientifico Disciplinare:||Settore BIO/05 - Zoologia|
|Data di pubblicazione:||24-apr-2007|
|Digital Object Identifier (DOI):||10.1016/j.ecolmodel.2006.04.028|
|Appare nelle tipologie:||01 - Articolo su periodico|