Occurrence Prediction of Riffle Beetles (Coleoptera: Elmidae) in a Tropical Andean Basin of Ecuador Using Species Distribution Models.

Genera and species of Elmidae (riffle beetles) are sensitive to water pollution; however, in tropical freshwater ecosystems, their requirements regarding environmental factors need to be investigated. Species distribution models (SDMs) were established for five elmid genera in the Paute river basin (southern Ecuador) using the Random Forest (RF) algorithm considering environmental variables, i.e., meteorology, land use, hydrology, and topography. Each RF-based model was trained and optimised using cross-validation. Environmental variables that explained most of the Elmidae spatial variability were land use (i.e., riparian vegetation alteration and presence/absence of canopy), precipitation, and topography, mainly elevation and slope. The highest probability of occurrence for elmids genera was predicted in streams located within well-preserved zones. Moreover, specific ecological niches were spatially predicted for each genus. Macrelmis was predicted in the lower and forested areas, with high precipitation levels, towards the Amazon basin. Austrelmis was predicted to be in the upper parts of the basin, i.e., páramo ecosystems, with an excellent level of conservation of their riparian ecosystems. Austrolimnius and Heterelmis were also predicted in the upper parts of the basin but in more widespread elevation ranges, in the Heterelmis case, and even in some areas with a medium level of anthropisation. Neoelmis was predicted to be in the mid-region of the study basin in high altitudinal streams with a high degree of meandering. The main findings of this research are likely to contribute significantly to local conservation and restoration efforts being implemented in the study basin and could be extrapolated to similar eco-hydrological systems.

Water quality assessment with emphasis in parameter optimisation using pattern recognition methods and genetic algorithm.

A non-supervised (k-means) and a supervised (k-Nearest Neighbour in combination with genetic algorithm optimisation, k-NN/GA) pattern recognition algorithms were applied for evaluating and interpreting a large complex matrix of water quality (WQ) data collected during five years (2008, 2010-2013) in the Paute river basin (southern Ecuador). 21 physical, chemical and microbiological parameters collected at 80 different WQ sampling stations were examined. At first, the k-means algorithm was carried out to identify classes of sampling stations regarding their associated WQ status by considering three internal validation indexes, i.e., Silhouette coefficient, Davies-Bouldin and Calinski-Harabasz. As a result, two WQ classes were identified, representing low (C1) and high (C2) pollution. The k-NN/GA algorithm was applied on the available data to construct a classification model with the two WQ classes, previously defined by the k-means algorithm, as the dependent variables and the 21 physical, chemical and microbiological parameters being the independent ones. This algorithm led to a significant reduction of the multidimensional space of independent variables to only nine, which are likely to explain most of the structure of the two identified WQ classes. These parameters are, namely, electric conductivity, faecal coliforms, dissolved oxygen, chlorides, total hardness, nitrate, total alkalinity, biochemical oxygen demand and turbidity. Further, the land use cover of the study basin revealed a very good agreement with the WQ spatial distribution suggested by the k-means algorithm, confirming the credibility of the main results of the used WQ data mining approach.