Fig 1.
Ecoregions of Bolivia and location of studies included in our analyses.
Limits of ecoregions follow the World Wildlife Fund [21]. The figure is similar but not identical to the original WWF figure, and is therefore for illustrative purposes only.
Table 1.
Bat assemblages included in our analyses. Data sets were extracted from published and unpublished sources.
Numbers in the first column correspond to locations shown in Fig 1. Effort is presented in mist net hours. N° of individuals refer to the number of captured bats in the Noctilionoidea superfamily only. SR = Species richness of noctilionoid bats, FR = Functional richness, the number of functional guilds in the assemblage, FR Chao = estimated functional richness using Chao non-parametric estimate.
Fig 2.
Results of the rarefaction procedure, for 100 individuals and 1000 runs for functional richness of bats in ten study sites across Bolivia.
Sites are YAlto = Yungas Alto, YMedio = Yungas Medio, PM-Vargas = Pie de Monte—Vargas, PM-Teran = Pie de Monte-Terán, AmIch = Amazonica Ichilo, AMdD = Amazonia Madre de Dios, SaIn = Sabana Inundable, Ch = Chaco, Ce = Cerrado. Sites belonging to the same ecoregion are underlined.
Fig 3.
Functional rank-richness distribution curves for bat assemblages at ten locations in Bolivia.
Curves were constructed for each study site by ranking functional groups from those with the greatest number of species to those with the least number of species. Functional groups are: CFRUG = Canopy frugivores, UFRUG = understory frugivores, NECT = nectarivores, CARN = carnivores, OMN = omnivores, PISCI = piscivores, SANG = sanguinivores, HCSGI = highly cluttered space gleaning insectivores, HCSAI = highly cluttered space aerial insectivores and BCSAI = background cluttered space aerial insectivores.
Fig 4.
UPGMA clustering dendrograms for (A) functional, (B) phylogenetic and (C) taxonomic diversity of bats in ten assemblages across ecoregions in Bolivia.
UPGMAs are calculated on relative abundance data and based on Sorensen’s dissimilarity indexes for functional and taxonomic diversity, and on the UniFrac metric for phylogenetic diversity.
Table 2.
Fit of cluster algorithms to distance matrices of functional, taxonomic and phylogenetic diversity, as explained by the cophenetic correlation and the agglomerative coefficient.
Fig 5.
Summary of the relative importance of species replacement (βrepl) and richness differences (βrich) as components of dissimilarity between assemblages from the same ecoregion (Yungas and SW Amazonia) and from sites of different ecoregions.
Pair-wise dissimilarity components are summarized in boxplots for functional, taxonomic and phylogenetic dissimilarities separately. Dissimilarity values used for calculations are presented in S1 Table.
Table 3.
Results of multiple regression on distance matrices (MRM) of functional diversity as a function of taxonomic and phylogenetic diversity of Bolivian bats.
MRM was calculated using a permutation method with the functional dissimilarity matrix used as the dependent matrix, and taxonomic distance and phylogenetic distance were independent matrices (F = 22.64, P = 0.002). The percentage of explained variation due to ‘pure’ effect corresponds to the contribution of each explanatory matrix to the model, as calculated by a hierarchical partitioning.