Multi-scale assessment of human-induced changes to Amazonian instream habitats

Abstract

Context Land use change and forest degradation have myriad effects on tropical ecosystems. Yet their consequences for low-order streams remain very poorly understood, including in the worlds largest freshwater basin, the Amazon. Objectives Determine the degree to which physical and chemical characteristics of the instream habitat of low-order Amazonian streams change in response to past local-and catchment-level anthropogenic disturbances.Methods To do so, we collected field instream habitat (i.e., physical habitat and water quality) and landscape data from 99 stream sites in two eastern Brazilian Amazon regions. We used random forest regression trees to assess the relative importance of different predictor variables in determining changes in instream habitat response variables. Results Multiple drivers, operating at multiple spatial scales, were important in determining changes in the physical habitat and water quality of the sites. Although we found few similarities in modelled relationships between the two regions, we observed non-linear responses of specific instream Ecol (2016) 31:1725-1745 DOI 10.1007 characteristics to landscape change; for example 20 % of catchment deforestation resulted in consistently warmer streams.Conclusions Our results highlight the importance of local riparian and catchment-scale forest cover in shaping instream physical environments, but also underscore the importance of other land use changes and activities, such as road crossings and upstream agriculture intensification. In contrast to the propertyscale focus of the Brazilian Forest code, which governs environmental regulations on private land, our results reinforce the importance of catchment-wide management strategies to protect stream ecosystem integrity.

Metrics derived from fish assemblages as indicators of environmental degradation in Cerrado streams

“…In step two, streams were classified as reference (minimally disturbed), intermediate, or impacted, according to their environmental variables. Only variables that describe the gradient of environmental degradation in agroecosystems (variables 4-8 in sampling section), and which are useful to explain the variation in the structure and composition of fish assemblages were considered for stream classification (Casatti et al 2015, Leal et al 2016, Teresa and Casatti 2017. We generated a matrix of environmental dissimilarity among stream sites (Euclidian distance) based on a standardized environmental matrix (z-score) and performed a cluster analysis using the UPGMA algorithm.…”

“…These variables describe the natural environmental gradient (variables 1 to 3) and the level of stream degradation (variables 4 to 8) (Casatti et al 2006, Leal et al 2016, Teresa and Casatti 2017.…”

Disentangling the pathways of land use impacts on the functional structure of fish assemblages in Amazon streams

“…Once a center of pre-Columbian civilization, STM was founded in 1661 and has been densely settled by small-scale farmers for more than a century. Historical differences in land use changes, and hence the environmental legacies related to them, are recognized as critical factors that affect relationships among landscape, instream habitat and stream biodiversity, confounding interpretation of effects or hiding patterns and processes resulting from those differences (Allan et al 1997, Uriarte et al 2011, Leal et al 2016). Paragominas has also experienced a rapid recent expansion of silviculture (mostly Eucalyptus spp.…”

How well does random forest analysis model deforestation and forest fragmentation in the Brazilian Atlantic forest?

“…RF is used widely in bioinformatics (Cutler and Stevens 2006), for land cover classification (Gislason et al 2006) and analysis of medical experiments, for example. Although there were few ecological applications, it has recently gained popularity in this area (Prasad et al 2006;Fu et al 2010;Gilbert and Chakraborty 2011;Bonilla-Moheno et al 2012;Ellis et al 2012a;Leal et al 2016).…”