A methodology to model environmental preferences of EPT taxa in the Machangara River Basin (Ecuador)


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.

Año de publicación:

2017

Keywords:

  • Decision support in water management;generalized linear modeling
  • pbkp_redictive models
  • Generalized linear models

Fuente:

scopusscopus

Tipo de documento:

Article

Estado:

Acceso abierto

Áreas de conocimiento:

  • Ecología
  • Ecología
  • Ecología

Áreas temáticas de Dewey:

  • Ecología
  • Naturaleza, paisajes urbanos y otros temas
  • Economía de la tierra y la energía
Procesado con IAProcesado con IA

Objetivos de Desarrollo Sostenible:

  • ODS 6: Agua limpia y saneamiento
  • ODS 14: Vida submarina
  • ODS 15: Vida de ecosistemas terrestres
Procesado con IAProcesado con IA