European Freshwater Fish Species Research Articles

Life-history Patterns of 25 species from European Freshwater Fish Communities

Fish life-history patterns were evaluated in relation to the trilateral continuum model by analyzing data from 25 species inhabiting European freshwaters. Multivariate tests identified a trend between later-maturing fishes with higher fecundity, larger size, and few spawning bouts per year and the opposite suite of traits with small fishes. A second trend contrasted fishes having parental care, smaller eggs, and longer breeding seasons against fishes with the opposite suite of traits. As a result, two extreme life-history patterns could be identified among European freshwater fish species: opportunistic and periodic. Nevertheless, intermediate patterns were also present. A true equilibrium life-history pattern was not represented among 25 fish species from European freshwaters. The high concordance of basic life-history patterns among distantly related taxa is probably caused by some universal trade-offs among life-history variables. As a consequence, only a limited life-history patterns may be recognizable among fish species, independently of the origin of fish communities.

Genetic structure and phylogeography of European catfish (Silurus glanis) populations.

The genetic structure of Silurus glanis (Europe's largest freshwater fish species) across most of its natural distribution was investigated using 10 microsatellite loci. The revealed levels of genetic diversity were much higher than previous allozyme and restriction fragment length polymorphism mitochondrial DNA analyses had shown; relative levels of variability among populations were however, in good agreement with the previous studies. Populations from large basins (Volga and Danube rivers) were the most polymorphic, while samples from the smaller Greek rivers, which are more prone to genetic bottleneck, exhibited the lowest levels of genetic diversity. Microsatellite multilocus genotyping permitted the assignment of individual fish to their population of origin with a score as high as 98.3%. Despite the great genetic differentiation of S. glanis populations, no consistent pattern of geographical structuring was revealed, in contrast to previous studies of European freshwater fish species. A model of isolation by distance seems more probable and a hypothesis of recent dispersion from only one glacial refugium is proposed. The discovery of the highest levels of microsatellite and mitochondrial diversity in the Volga sample and the presence of river connections, during the Pleistocene, between this area and all major areas of the present catfish distribution, place this refugium around the Ponto-Caspian region. Combining these data with those from previous studies, a number of markers are now available to monitor wild and hatchery populations even at the individual level.

Utilisation of non-supervised neural networks and principal component analysis to study fish assemblages

Kohonen self-organizing maps (SOM) belong to the non-supervised artificial neural network modelling methods. It typically displays a high dimensional data set in a lower dimensional space. In this way, that method can be considered as a non-linear surrogate to the principal component analysis (PCA). In order to test the efficiency of SOM on complex ecological data gathered in the natural environment, we made a comparison between PCA and SOM capabilities to analyse the spatial occupancy of several European freshwater fish species in the littoral zone of a large French lake. The same data matrix consisting of 710 samples and 15 species was analysed using PCA and SOM. Both methods provided insights on the major trends in fish spatial occupancy. However, a more detailed analysis showed that only SOM was able to reliably visualise the entire fish assemblage in a two dimensional space (i.e. both dominant and scarce species). On the contrary PCA provided irrelevant ecological information for some species. These drawbacks were afforded to data heterogeneity, scarce species being poorly represented on the PCA plane. These results led us to conclude that SOM constitute a more reliable data representation method than PCA when complex ecological data sets are used.