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).

Lencioni, V.; Maiolini, B.; Marziali, L.; Lek, S.; Rossaro, B. (2007). Macroinvertebrate assemblages in glacial stream systems: A comparison of linear multivariate methods with artificial neural networks., 203 (1-2): 119-131. doi: 10.1016/j.ecolmodel.2006.04.028

Macroinvertebrate assemblages in glacial stream systems: A comparison of linear multivariate methods with artificial neural networks

LENCIONI, VALERIA;
2007-01-01

Abstract

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).
Zoologia degli Invertebrati e Idrobiologia
articolo in rivista
2007
pubblicato
203
1-2
119
131
Si
con Impact Factor
si
Lencioni, V.; Maiolini, B.; Marziali, L.; Lek, S.; Rossaro, B.
Lencioni, V.; Maiolini, B.; Marziali, L.; Lek, S.; Rossaro, B. (2007). Macroinvertebrate assemblages in glacial stream systems: A comparison of linear multivariate methods with artificial neural networks., 203 (1-2): 119-131. doi: 10.1016/j.ecolmodel.2006.04.028
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10991/145
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