Journal
FRESHWATER BIOLOGY
Volume 52, Issue 3, Pages 448-458Publisher
WILEY-BLACKWELL
DOI: 10.1111/j.1365-2427.2006.01704.x
Keywords
artificial neural network; Kohonen neural network; perch; pike; roach; rudd; self-organising map; temporal variation
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1. Although reservoirs are common aquatic habitats in Europe, there is little quantitative information on the spatial organisation of fish assemblages inhabiting their littoral zones. Consequently, we characterised fish assemblage structure in the littoral zone of a reservoir (Lake Pareloup) in SW France during late spring, summer and early autumn (the growing season). 2. We measured the relative abundance of fish weekly, from mid-May to mid-October, using point abundance sampling by electrofishing. We identified temporal patterns in assemblage structure using hierarchical cluster analysis, and then characterised the spatial distribution of 17 defined ecospecies using a Kohonen self-organising map (SOM, an unsupervised Artificial Neural Network). 3. Our analyses revealed three distinct faunal structures within the littoral zone. From mid-May to mid-July, adults and young-of-the-year (0+) occupied separate habitats, with most 0+ fish in vegetated habitats and adults in open water. From mid-July to late August, some 0+ co-occurred with adults, but most 0+ fishes remained in vegetated areas. Finally, from late August to mid-October, most fish (both 0+ and adults) left the vegetation for unvegetated littoral habitats, the exception being fish species known to be dependent on macrophytes. 4. Contrary to patterns for adult fishes, the 0+ fish assemblage was dynamic. These dynamics were driven by ontogenetic species-specific habitat changes. Consequently, there was little evidence of stable assemblages or strong assemblage-habitat relationships that would be expected of an 'interactive' assemblage. It is likely that the patterns observed are a result of species-specific response to habitat availability in the lake.
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