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 EphemeropteraPlecoptera-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.

A review of macroinvertebrate‐ and fish‐based stream health modelling techniques

“…Linear statistical models have also been continuously applied as benchmarks for comparison with other statistical methods and machine learning approaches. For macroinvertebrate communities, traditional regression models are still used to relate abiotic stressors with species occurrence in order to explore habitat and water quality preferences, especially in headwaters (Pond, Krock, Cruz, & Ettema, 2017) and tropical regions (Damanik-Ambarita et al, 2016;Everaert et al, 2014;Jerves-Cobo et al, 2017). However, prediction of the biological condition under different spatial and temporal domains and scales have been also addressed in some studies for both macroinvertebrate and fish assemblages (Frimpong, Sutton, Engel, & Simon, 2005;Johnson & Host, 2010;Van Sickle & Burch Johnson, 2008).…”

“…• Low predictive power • Model structure (distributions selection) must be defined a priori Low Low Damanik-Ambarita et al, 2016;Death et al, 2015;Donohue et al, 2006;Everaert et al, 2014;Gieswein et al, 2017;Holguin-Gonzalez, Everaert, et al, 2013;Jerves-Cobo et al, 2017;Kuemmerlen et al, 2014;Moya et al, 2011;Pont et al, 2009;Sauer, Domisch, Nowak, & Haase, 2011;Van Sickle et al, 2004Fukuda et al, 2013Gieswein et al, 2017;Grenouillet et al, 2011;Hermoso, Linke, Prenda, & Possingham, 2011;Kwon, Bae, Hwang, Kim, & Park, 2015;Leclere et al, 2011;Patrick & Yuan, 2017;Sui et al, 2014 Generalized additive models • Suitable for modelling nonlinear relationships • Uses nonparametric basis functions • Prone t...…”

“…Pressures on freshwater ecosystems comprise hydrologic and hydraulic stress, sediment intake, and substrate stability, among others, which can be represented by process-based models developed in Merriam et al, 2013Merriam et al, , 2015Pond et al, 2017;Waite et al, 2010, 2012Frimpong et al, 2005Van Sickle & Burch Johnson, 2008 Generalized linear models Death et al, 2015;Donohue et al, 2006;Everaert et al, 2014;Gieswein et al, 2017;Holguin-Gonzalez, Everaert, et al, 2013;Jerves-Cobo et al, 2017;Kuemmerlen et al, 2014;Moya et al, 2011;Pont et al, 2009;Sauer, Domisch, Nowak, & Haase, 2011;Van Sickle et al, 2004Fukuda et al, 2013Gieswein et al, 2017;Grenouillet et al, 2011;…”

Model-Based Analysis of the Potential of Macroinvertebrates as Indicators for Microbial Pathogens in Rivers

“…On the other hand, the assessment of habitats and the determination of the relation between the presence of an organism and environmental variables has been done through the modeling of running waters based on ecological, physicochemical and microbiological parameters. These modeling techniques have allowed for the handling of the non-linear behavior of the ecosystem, obtaining models with a high reliability [20][21][22]. In this way, the FC has been associated as one of the explanatory variables describing the presence or absence of some taxa of macroinvertebrates [22][23][24].…”

“…These modeling techniques have allowed for the handling of the non-linear behavior of the ecosystem, obtaining models with a high reliability [20][21][22]. In this way, the FC has been associated as one of the explanatory variables describing the presence or absence of some taxa of macroinvertebrates [22][23][24]. Machine learning with different modeling techniques, such as classification trees (CTs) combine reliable classification predictions with transparency, and have been proven to be effective to assess running waters [25,26].…”