Responses of Aquatic Insect Functional Diversity to Landscape Changes in Atlantic Forest

Abstract

The Atlantic Forest domain, one of the 25 world's hotspots for biodiversity, has experienced dramatic changes in its landscape. While the loss of species diversity is well documented, functional diversity has not received the same amount of attention. In this study, we evaluated functional diversity of insects in streams utilizing three indices: functional diversity (FD), functional dispersion (FDis), and functional divergence (FDiv), seeking to understand the roles of three predictor sets in explaining functional patterns: (1) bioclimatic and landscape variables; (2) spatial variables; and (3) local environmental variables. We determined the amount of variation in different measures of functional diversity that was explained by each predictor set and their interplays using variation partitioning. Our study showed that variation in functional diversity is better explained by a set of variables linked to different scales dependent on spatial structures, indicating the importance of landscape and mainly environmental variables in the functional organization of aquatic insect communities, and that the relative importance of predictor sets depends on the indices considered. Variation in FD was better explained by the interplay among the three predictor sets and by local environmental variables, whereas variation in FDis was better explained by spatial variables and by the interplay between environmental and spatial variables. Variation in FDiv was not significantly explained by any predictors. Our study adds more evidence on the harmful effects caused by landscape changes on biodiversity in the Atlantic Forest, suggesting that these effects also influence the functional organization of stream insect communities.

The taxonomic distinctness of macroinvertebrate communities of Atlantic Forest streams cannot be predicted by landscape and climate variables, but traditional biodiversity indices can

“…Thus, although we estimated both pure environmental and spatial components in variation partitioning, our main intention was to use spatial variables as a way to control for inflated Type I error in assessing the environmental component. Together with previous studies about Atlantic Forest streams, these results indicate that by using local, landscape and spatial predictors (or a combination of them), we are usually able to explain around 40% or less of the of variation in macroinvertebrate abundance and distribution (Siqueira et al, 2009), taxa associations (Roque et al, 2010), diversity metrics ), common and rare taxa (Siqueira et al, 2012b) and functional diversity metrics (Colzani et al, 2013). Recent studies have tested the performance of taxonomic diversity and taxonomic distinctness in several systems and taxonomic groups.…”

Functional diversity: a review of methodology and current knowledge in freshwater macroinvertebrate research

“…Ecological traits included the above set of Grinnellian traits (Usseglio-Polatera et al, 2000; Colas et al, 2013); rheophily and thermal preference (Poff et al, 2006;Milner et al, 2011;Brown & Milner, 2012); rheophily alone (Colzani et al, 2013); temperature, pH, trophic status, longitudinal distribution, microhabitat and current velocity preferences (Martinez et al, 2013) and substrate preferences (Vaz et al, 2014). Although we do not state that the conclusions drawn from these studies are incorrect, we argue following Verberk et al (2013) that ecological traits describing habitat preferences of macroinvertebrates should not be used for assessing functional diversity because ecological traits should be regarded as response traits (Violle et al, 2007), and response traits are not directly linked to ecosystem functions (Fig.…”

“…The dendrogram-based measure includes the following three steps: (1) calculating the functional trait dissimilarity matrix of taxa, (2) obtaining the dendrogram from this dissimilarity matrix by cluster analysis, and (3) quantifying functional diversity of the community as the total branch length of the dendrogram. Although the original idea provoked intensive debate on how dendrograms should be used for quantifying functional diversity (Podani & Schmera, 2006Petchey & Gaston, 2007, 2009, several papers have used them for quantifying functional diversity of freshwater assemblages (Vidakovic & Palijan, 2010;Kadoya et al, 2011;Patrick & Swan, 2011;Brown & Milner, 2012;Colzani et al, 2013;Martinez et al, 2013). According to Mason et al (2005), dendrograms quantify the functional richness aspect of functional diversity.…”

“…Quantifying the functional diversity of aquatic insects in the Atlantic Forest (Brazil) with dendrograms, Colzani et al (2013) considered functional divergence and functional dispersion. The authors defined functional divergence as an aspect of functional diversity that enumerates the degree to which an abundance distribution maximizes divergence (or differences) in functional characters within the community (Mason et al, 2005;Mouillot et al, 2005).…”

Assessing the spatial variation of functional diversity estimates based on dendrograms in phytoplankton communities

“…The Functional Diversity (FD) index is the sum of dendrogram branch lengths (Petchey & Gaston 2002), generated from a functional traits distance matrix, while the Mean Pairwise Distance (MPD) index is the average distance between pairs of species that compose a community (Webb 2000) and the Mean Nearest Taxon Distance (MNTD) index is equal to the average dendrogram lengths between the functionally most similar pairs of species in the community (Webb 2000). Although there are other continuous indices of functional diversity based on dendrograms (e.g., GFD Mouchet et al 2008;NMDS Cadotte et al 2009), the above mentioned indices are some of the most frequently used to describe functional richness and divergence in aquatic (Colzani et al 2013;Carvalho & Tejerina-Garro 2015;Dunck et al 2015) and terrestrial communities (e.g., Bihn et al 2010;Cianciaruso et al 2012;Hidasi-Neto et al 2012).…”