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A methodology to model environmental preferences of EPT taxa in the Machangara river basin (Ecuador)
Jerves-Cobo, R.; Everaert, G.; Iñiguez-Vela, X.; Córdova-Vela, G.; Díaz-Granda, C.; Cisneros, F.; Nopens, I.; Goethals, P.L.M. (2017). A methodology to model environmental preferences of EPT taxa in the Machangara river basin (Ecuador). Water 9(3): 195. https://hdl.handle.net/10.3390/w9030195
In: Water. MDPI AG: Basel. ISSN 2073-4441, more
Peer reviewed article  

Available in  Authors 

Keyword
    Fresh water
Author keywords
    generalized linear models; predictive models; decision support in water management; generalized linear modeling

Authors  Top 
  • Jerves-Cobo, R., more
  • Everaert, G., more
  • Iñiguez-Vela, X.
  • Córdova-Vela, G.
  • Díaz-Granda, C.
  • Cisneros, F.
  • Nopens, I., more
  • Goethals, P.L.M., more

Abstract
    Rivers have been frequently assessed based on the presence of the Ephemeroptera— Plecoptera—Trichoptera (EPT) taxa in order to determine the water quality status and develop conservation programs. This research evaluates the abiotic preferences of three families of the EPT taxa Baetidae, Leptoceridae and Perlidae in the Machangara River Basin located in the southern Andes of Ecuador. With this objective, using generalized linear models (GLMs), we analyzed the relation between the probability of occurrence of these pollution-sensitive macroinvertebrates families and physicochemical water quality conditions. The explanatory variables of the constructed GLMs differed substantially among the taxa, as did the preference range of the common predictors. In total, eight variables had a substantial influence on the outcomes of the three models. For choosing the best predictors of each studied taxa and for evaluation of the accuracy of its models, the Akaike information criterion (AIC) was used. The results indicated that the GLMs can be applied to predict either the presence or the absence of the invertebrate taxa and moreover, to clarify the relation to the environmental conditions of the stream. In this manner, these modeling tools can help to determine key variables for river restoration and protection management.

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