Spatial analysis of stream invertebrates distribution in the Adour-Garonne drainage basin (France), using Kohonen self organizing maps

• Published in: Ecological modelling Vol. 146; no. 1; pp. 167 - 180
• Main Authors: Céréghino, R., Giraudel, J.L., Compin, A.
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Amsterdam Elsevier B.V 01.12.2001; Elsevier
• Subjects: Animal, plant and microbial ecology; Aquatic insects; Benthic fauna; Biological and medical sciences; Classification; Coleoptera; Ephemeroptera; Freshwater; Fundamental and applied biological sciences. Psychology; General aspects. Techniques; Methods and techniques (sampling, tagging, trapping, modelling...); Neural networks; Plecoptera; Self-organizing maps; Spatial distribution; Trichoptera; France; Europe; Garonne River; France, Adour R; France, Garonne R; Spatial distribution; Neural networks; Ephemeroptera; Coleoptera; Self-organizing maps; Trichoptera; Classification; Plecoptera; Benthic fauna; Aquatic insects; Insecta; Drainage basins; Spatial analysis; Zoobenthos; Neural network; Lotic environment; Freshwater environment; Self organization; Faunal survey; Animal community; Arthropoda; Stream; Models; Invertebrata
• ISSN: 0304-3800; 1872-7026
• DOI: 10.1016/S0304-3800(01)00304-0

We analysed the regional distribution of 283 lotic macroinvertebrate species from four insect orders (Ephemeroptera, Plecoptera, Trichoptera, Coleoptera=EPTC) in the Adour-Garonne drainage basin (South–Western France, surface=116 000 km 2). The aim of this work was to provide a stream classification based on characteristic species assemblages. The faunistic data corresponded to the occurrence (presence or absence) of 283 species at 252 sampling sites. These data were computed with the Kohonen self organised map algorithm (SOM) (Kohonen, Self-Organizing Maps, volume30 of Springer Series in Information Sciences. Springer, Berlin, Heidelberg. (Second Extended Edition 1997)). This neural network algorithm has already been successfully used in ecology (Giraudel et al., Artificial neural networks, applications to ecology and evolution. Springer-Verlag, (in press); Chon et al., Ecol. Model., 90, 1996, 69–78) for communities patternizing. SOM enable visualisation of the complex species assemblage in a two-dimensional space, preserving the topology of the input data. Then, using the U-matrix method, it was possible to classify the data without prior knowledge. Four major EPTC regions were characterised within the drainage basin (Massif Central mountains, Pyrénées mountains, Piedmont and plains, Toulouse city agglomeration), along with their theoretical species assemblage. The number of species characterising each region ranged from 45 to 159, underlining the spatial (i.e. longitudinal and geographical) differences in EPTC assemblages. The main interest of our results is that the stability of these theoretical assemblages may be used to define representative and/or reference sites for biological surveillance, as any change in species composition within a given EPTC region can be considered as a biological indicator of environmental changes.