A predictive index based on environmental filters for the bioassessment of river basins without reference areas in Atlantic Forest biome, Brazil

Abstract

Biological assessments that use the reference condition approach are based on the concept of comparing a site's observed biology to sites where disturbance is minimal or absent. However, in many regions of the world, such areas are scarce or nonexistent. In this study, an alternative approach proposed by Chessman and Royal for bioassessment without reference areas based on environmental filters was tested in Brazil. This approach assumes that key environmental features act in the selection of potential colonists, from a regional pool of taxa, based on the ecological traits (tolerances) possessed by each taxon. We developed the approach by: 1) determining the regional pool, based on a large Atlantic Forest biome database; 2) selecting environmental filters (elevation, original vegetation and soil type); and 3) including information on the tolerance and preferences of aquatic insects to these filters. With this information we were able to determine the expected taxon under natural conditions and compare with observed taxon, developing a predictive index (Observed/Expected). Although the model was intended to predict the fauna in regions without reference sites, we included reference areas to test the model responsiveness, precision and sensitivity. Our results indicated that the index was able to discriminate impairment classes (F=56.9; p<0,001), it has high precision due to low standard deviation across reference sites values (SD=0.098) and high sensitivity due the correlation with environmental variables that are sensitive to human alteration (r=0.74, p<0.01). Also, it was strongly correlated with multimetric indices developed for multiple watersheds in the state, showing agreement between the methods in relation to ecological quality classification. Even though the predictive index had performed well in our study, we make some considerations that may help to improve its sensitivity of similar methods that are being tested using the environmental filters approach.