DOI: 10.5061/dryad.fn2z34tr8
title: Data from: Beta diversity patterns of bats in the Atlantic Forest: how does the scale of analysis affect the importance of spatial and environmental factors?

description: Aim: Environmental and spatial factors are broadly recognized as important
 predictors of beta diversity patterns. However, the scale at which beta
 diversity patterns are evaluated will affect the outcoming results. For
 example, studies at larger scales will usually find spatial processes as
 the main predictor of beta diversity patterns. In this study we evaluate
 how beta diversity patterns change when analyses are conducted at
 different scales by reducing the scale of analysis in a hierarchical
 manner. Taxon: Chiroptera. Location: Atlantic Forest biome. Methods:
 Information on the occurrence of 59 bat species were obtained from the
 Atlantic Bats and Species Link database. We partitioned beta diversity
 into its two components (nestedness and turnover), and calculated these
 indexes hierarchically: the biome in its entirety (all ecoregions);
 between larger regions (north, central and south); and between ecoregions
 within each region. We performed a Generalized Dissimilarity Model (GDM)
 to identify and predict the turnover of bat species in the Atlantic Forest
 based on geo-climatic predictors. We obtained 19 geo-climatic data from
 AMBDATA, an environmental dataset based on different data sources commonly
 used in species distribution modeling. Results: We found that turnover was
 the main component influencing a latitudinal gradient when the biome was
 analysed in its entirety. However, when the scale of the analysis was
 reduced, we found that species loss (nestedness component) had a large
 effect in determining beta diversity dissimilarity. We also found that
 nestedness was the main pattern explaining beta diversity dissimilarity
 along a longitudinal gradient. Main conclusions: Beta diversity patterns
 changed with the scale of analysis, which indicates that bat species
 composition does not follow the same pattern throughout the Atlantic
 Forest. This corroborates the importance of analysing beta diversity
 patterns at different scales in order to understand how environmental
 dissimilarity across geographic space can influence species distribution
 patterns.
description: Occurrence and geo-climatic
 data            
 Our study was based on 2,626 bat occurrence data points for 525 sites
 (coordinates) within the Atlantic Forest. Our data came from two sources.
 We extracted 1,795 occurrence data points for 160 sites from Muylaert et
 al. (2017). This dataset was compiled by bat specialists who also reviewed
 the taxonomy of the species and the coordinates of sampling sites. We then
 used the species list provided in Muylaert et al. (2017) to search for
 other occurrence records in speciesLink (data downloaded from
 http://splink.cria.org.br/). We obtained 830 occurrence records of bat
 species for 364 sites. We reviewed the dataset obtained from speciesLink
 according to reliability of information regarding: i) coordinates and site
 correspondence (we used google maps to check if the coordinates were
 referring to the places indicated), ii) correct taxonomy (we excluded
 species with “sp”, “ssp”, “cf” and “aff”), and iii) voucher specimens (we
 only considered records with specimens that were deposited in a museum).
 We also included a single occurrence record of <i>Natalus
 macrourus</i> (Trajano, 1984) from Parque Estadual Turístico do Alto
 Ribeira (PETAR), which was not considered by Muylaert et al. (2017) or
 SpeciesLink. Occurrence records belonging to the bat families Molossidae,
 Vespertilionidae and Embalonuridae were not included in our study because
 they are seldomly captured in mist-nets (Nogueira, Pol &,
 Peracchi, 1999; Nogueira, Pol, Monteiro &, Peracchi, 2008), which
 was the predominant method used for sampling bat species represented in
 our data sources.             We obtained
 geo-climatic data from AMBDATA (available at
 http://www.dpi.inpe.br/Ambdata/index.php). The AMBDATA is an environmental
 dataset systematized from different data sources and commonly used in
 species distribution modelling.  It consists of 19 bioclimatic variables
 at 30 arc-sec resolution (approx. 1 km). These are: 1) annual mean
 temperature (ºC); 2) mean diurnal range (ºC); 3) isothermality (mean
 diurnal range divided by annual temperature range, and multiplied by 100);
 4) temperature seasonality (standard deviation *100); 5) maximum
 temperature of warmest month (ºC); 6) minimum temperature of coldest month
 (ºC); 7) temperature annual range (ºC); 8) mean temperature of wettest
 quarter (ºC); 9) mean temperature of driest quarter (ºC); 10) mean
 temperature of warmest quarter (ºC); 11) mean temperature of coldest
 quarter (ºC); 12) annual precipitation (mm); 13) precipitation of wettest
 month (mm); 14) precipitation of driest month (mm); 15) precipitation
 seasonality (coefficient of variation); 16) precipitation of wettest
 quarter (mm); 17) precipitation of driest quarter (mm); 18) precipitation
 of warmest quarter (mm); and 19) precipitation of coldest quarter (mm). We
 also included three non-climatic environmental variables from AMBDATA: 1)
 tree cover at a 500 m resolution (percentage); 2) elevation (m) at 3
 arc-sec horizontal resolution (about 90 m) and a vertical resolution of 1
 m and lastly, 3) declivity (degrees) generated from the elevation
 grid. Beta
 diversity and the Generalized Dissimilarity Model (GDM) <i>           
 </i>There are various dissimilarity indices to measure changes in
 species composition between assemblages. We used the Sorensen index (βsØr)
 as implemented in ‘Betapart package’ – ‘R-project’ (Baselga &
 Orme, 2012). The input data table consists of the presence and absence of
 bat species for each study site (latitude and longitude). The package
 computes the total dissimilarity across all sites, and calculates turnover
 (Simpson’s index, βsim) and nestedness (the difference between the
 Sorensen and Simpson index, βsne) components. ‘Betapart’ returns cluster
 and dissimilarity matrices (between pairwise sites, and pairwise matrices
 of shared and non-shared species between sites) of turnover and
 nestedness. First, we
 computed total beta diversity and its two components, nestedness and
 turnover, among the ten Atlantic Forest ecoregions proposed by Olson et
 al. (2001). Then we split the Atlantic Forest into three larger regions
 (southern, central and northern). Lastly, nestedness and turnover were
 calculated among the ecoregions making up each of the three regions. Each
 region was treated separately.             We used a species
 presence data frame with the coordinates of the occurrence sites to
 perform Generalized Dissimilarity Modeling (GDM), which analyses spatial
 patterns of pairwise dissimilarity in species composition between sites,
 using a nonlinear regression matrix. GDM quantifies dissimilarity using
 the Soresen Index (total beta diversity), then associates the turnover
 component (βsim) with biological distance (predictor variables) between
 sites (Fitzpatrick et al., 2013). The GDM procedure was used to predict
 bat species turnover across the Atlantic Forest based on environmental
 data. We used the ‘R packageGDM’ (Fitzpatrick & Lisk, 2016) to fit
 a GDM with the 22 environmental variables and the geographical distance
 (decimal degrees) between occurrence sites. The latter was calculated
 using the option ‘geo=T’ in the function ‘gdm’ of the GDM package. We used
 the parameter ‘weightType= richness’ to weight sites relative to the
 number of species to minimize sampling bias. We chose not to exclude sites
 with few species (i.e. less than five) because in our database over 360
 occurrence sites had five or fewer species, and 90 sites had 10 species or
 fewer. Less than 11 sites recorded 50% or more of the total number of
 species (59), so that excluding sites with few species would lead to a
 significant loss of data. In addition, the average
 number of species per site was equal to five, and sites with few species
 are evenly distributed throughout the biome. Therefore, maintaining all
 sites while correcting for species richness, even with a low number of
 species, does not weaken our model (see Fig.
 S1.2). Patterns of
 species turnover can be visualized on a raster with RGB colour standards;
 areas with similar colours contain similar assemblages. The GDM matrix
 regression used was I-spline with three basic functions, meaning that we
 used three points (the minimum) to form the I-spline curve (Fitzpatrick
 & Lisk, 2016). I-Splines can be visualized in a graph showing the
 relationship of predicted biological distance versus observed biological
 distance, providing an indication of how species composition changes along
 each environmental gradient (Fitzpatrick & Lisk, 2016). The
 selection of the best subset of predictors for our model followed
 Williams, Belbin, Austin, Stein, & Ferrier (2012): the initial
 model included all predictors; variables that contributed less than 2% to
 model explanation were iteratively removed. Variable removal was done on a
 stepwise basis beginning with the elimination of the variable that
 contributed the least to model explanation. Variables were reassessed
 regarding their importance and significance during each step of model
 reduction (i.e., backward elimination). Our model started with 23
 predictor variables and ended with 11.