Sovan Lek

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A counterpropagation neural network (CPN) was applied to predict species richness (SR) and Shannon diversity index (SH) of benthic macroinvertebrate communities using 34 environmental variables. The data were collected at 664 sites at 23 different water types such as springs, streams, rivers, canals, ditches, lakes, and pools in The Netherlands. By training(More)
Habitat fragmentation affects the integrity of many species, but little is known about species-specific sensitivity to fragmentation. Here, we compared the genetic structure of four freshwater fish species differing in their body size (Leuciscus cephalus; Leuciscus leuciscus; Gobio gobio and Phoxinus phoxinus) between a fragmented and a continuous(More)
Benthic macroinvertebrate communities in stream ecosystems were assessed hierarchically through two-level classification methods of unsupervised learning. Two artificial neural networks were implemented in combination. Firstly, the self-organizing map (SOM) was used to reduce the dimension of community data, and secondly, the adaptive resonance theory (ART)(More)
Investigations on the functional niche of organisms have primarily focused on differences among species and tended to neglect the potential effects of intraspecific variability despite the fact that its potential ecological and evolutionary importance is now widely recognized. In this study, we measured the distribution of functional traits in an entire(More)
A full understanding of life history characteristics of invasive species is a fundamental prerequisite for the development of management strategies. Two introduced goby species (Rhinogobius cliffordpopei and Rhinogobius giurinus) have established highly abundant populations in Lake Erhai (China). In the present study, we examined the reproductive biology of(More)
This study aimed at analysing the relationship between river characteristics and abundance of Gammarus pulex. To this end, four methods which can identify the relative contribution and/or the contribution profile of the input variables in neural networks describing the habitat preferences of this species were compared: (i) the "PaD" ("Partial Derivatives")(More)
To reach a better understanding of the spatial variability of water quality in the Lower Mekong Basin (LMB), the Self-Organizing Map (SOM) was used to classify 117 monitoring sites and hotspots of pollution within the basin identified according to water quality indicators and US-EPA guidelines. Four different clusters were identified based on their similar(More)