Generalized Linear Models to Identify Key Hydromorphological and Chemical Variables Determining the Occurrence of Macroinvertebrates in the Guayas River Basin (Ecuador)

Abstract

Abstract:The biotic integrity of the Guayas River basin in Ecuador is at environmental risk due to extensive anthropogenic activities. We investigated the potential impacts of hydromorphological and chemical variables on biotic integrity using macroinvertebrate-based bioassessments. The bioassessment methods utilized included the Biological Monitoring Working Party adapted for Colombia (BMWP-Col) and the average score per taxon (ASPT), via an extensive sampling campaign that was completed throughout the river basin at 120 sampling sites. The BMWP-Col classification ranged from very bad to good, and from probable severe pollution to clean water based on the ASPT scores. Generalized linear models (GLMs) and sensitivity analysis were used to relate the bioassessment index to hydromorphological and chemical variables. It was found that elevation, nitrate-N, sediment angularity, logs, presence of macrophytes, flow velocity, turbidity, bank shape, land use and chlorophyll were the key environmental variables affecting the BMWP-Col. From the analyses, it was observed that the rivers at the upstream higher elevations of the river basin were in better condition compared to lowland systems and that a higher flow velocity was linked to a better BMWP-Col score. The nitrate concentrations were very low in the entire river basin and did not relate to a negative impact on the macroinvertebrate communities. Although the results of the models provided insights into the ecosystem, cross fold model development and validation also showed that there was a level of uncertainty in the outcomes. However, the results of the models and sensitivity analysis can support water management actions to determine and focus on alterable variables, such as the land use at different elevations, monitoring of nitrate and chlorophyll concentrations, macrophyte presence, sediment transport and bank stability.

A Methodology to Model Environmental Preferences of EPT Taxa in the Machangara River Basin (Ecuador)

“…GLMs have the advantage of directly establishing and reporting the relative importance of each variable in searching for the biotic integrity [68]. Furthermore, a stepwise discriminant procedure to select the most significant variables based on the AIC selection criteria applied in this research, has been shown to be effective in the prediction of species distribution [69].…”

Using the Soil and Water Assessment Tool to Simulate the Pesticide Dynamics in the Data Scarce Guayas River Basin, Ecuador

“…An example of this is the Guayas River basin in Ecuador wherein key ecosystems are at risk due to an increased application of pesticides. The current intensification of the human activities within the basin (agriculture, fishery, hydropower and industry) is a severe threat to the aquatic ecosystem and causes the impairment of many important ecosystem services, including habitat provision (affecting biodiversity), water provision and the safety of potable water [4][5][6]. Moreover, the changes in tillage practices negatively affected pesticide adsorption and have caused an increasing degradation of soils.…”

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...…”