DOI: 10.5061/dryad.sn02v6x1p
title: Data from: Functional diversity of phyllostomid bats in an urban-rural landscape: a scale-dependent analysis

description: Urbanization is one of the most pervasive landscape transformational
 processes responsible for novel selection agents promoting functional
 community homogenization. Bats may persist in those human environments,
 but the mechanisms responsible for their adaptability and the spatial
 scales in which landscape imposes environmental filtering remain poorly
 studied in the Neotropics. We tested the hypothesis that landscape
 composition interacts with the spatial scale to affect the functional
 diversity of phyllostomids in an urban-rural gradient. Based on functional
 traits, we calculated indices of functional richness, divergence,
 evenness, community-weighted means of morphological traits, and classified
 species into functional groups. We evaluated the changes of those
 variables in response to forest, grassland, and urbanized areas at 0.5,
 1.25 and, 2 km scales. The number of functional groups, functional
 richness, and functional evenness tended to be higher in areas far from
 cities and with higher forest cover, whereas functional divergence
 increased in more urbanized areas. Our results show that the mean value of
 wing loading in the assemblage was negatively associated to landscape
 transformation at several spatial scales. However, environmental filtering
 driven by grass cover was particularly robust at the 500 m scale,
 affecting big-sized species with long pointed wings. Retaining natural
 forest in cattle ranging systems at ~12 km2 appears to favor bat abundance
 evenness among functional types in the urban-rural landscape. Recognizing
 the scale of the effect on phyllostomid functional responses appears to be
 a fundamental issue for elucidating the spatial extent to which
 phyllostomid conservation planning in urban-rural landscapes should be
 addressed.
description: We selected five sampling
 sites with different landscape compositions and structures, hereafter
 named landscape units (LU). An LU refers to the spatial extent from which
 we measured landscape metrics, whereas a site refers to the centroid of
 each LU in which phyllostomid bats were surveyed. Sites were selected
 based on the following criteria: (a) enough tree cover to place mist-nets
 with canopy cover above 80% and (b) a separation between any two sites of
 at least 4.3 km to reduce spatial autocorrelation bias. At each site, we
 set two to three mist-nets of 12×3 m, separated at least 50 m from each
 other. Each mist-net remained open from 1730 h until 0100 h for every
 field survey. We visited each site five times at similar times during the
 extent of the study, five replicates of observations per site, selecting
 nights with moon phase levels lower than 60% . Sampling effort at each
 site ranged from 288 to 300 m<sup>2</sup>×nights, and the
 total sampling effort was 1,484
 m<sup>2</sup>×nights. To avoid bias in the
 quantification of species abundance and to assess the independence of bat
 captures among mist-nets at each site, we marked all captured individuals
 with a unique numerical code using a rabbit tattoo. This mark was placed
 in the right wing at the lower part of the plagiopatagium. We identified
 all individuals to species level using taxonomic references based on
 external characters. Those individuals that we could not identify in the
 field were collected for accurate taxonomic identification using dental
 and cranial characters, and by comparison with specimens in the Museo de
 Historia Natural – Universidad de Los Llanos, MHN-U. Bat functional traits
 — We used one categorical (trophic guild -
 <i>Tg</i>) and four morphological traits related to body size:
 (body mass – <i>Bm</i>) and wing morphology (wingspan -
 <i>Ws</i>; aspect ratio - <i>Ar</i>; wing loading
 - <i>Wl</i>). We used information from the literature to
 assign trophic categories to each bat species. This categorical assignment
 was confirmed by collecting samples of feces from captured
 individuals. We
 placed the bats in a paperboard tube and measured <i>Bm</i>
 using a digital balance (0.1g precision). Wing morphological traits were
 measured as follow: (a) we took a photo of the left wing fully extended in
 a ventral position against a paper with millimeter scale. Then, (b) those
 images were processed using ImageJ 1.6 (Abramoff et
 al<i>.,</i> 2004) to measure <i>Ws</i> and wing
 area as described by Norberg & Rayner (1987). Finally, (c)
 <i>Ar</i> was calculated as the quotient of the
 <i>Ws</i> squared and wing area, and Wl was estimated by
 dividing Bm into the product of gravitational acceleration, 9.81
 m/s<sup>2</sup>, and wing area. Land use classification and
 predictor variables — We processed DigitalGlobe satellite images from
 Google Earth taken between 2014 and 2015, with a resolution of 2
 m<sup>2</sup> per pixel, to characterize landscape composition
 and structure. We used a supervised classification method of ArcGIS 10.1
 to generate maps with three land uses: built-up areas, grasslands, and
 forest. We did not include in the analysis other land uses and landscape
 elements such as crops, water bodies, or bare ground because they covered
 less than 5% of each area at a 2 km radius. We defined concentric buffers
 around the sampling sites to delimit landscape units (LU) at three spatial
 scales, 0.5, 1.25 and 2 km of radius. At each spatial scale we measured
 the percentage of built area, grassland, and forest cover, number of
 forest patches, mean forest patch size, forest patch size standard
 deviation, and forest patch densities.  We used LINKTREE analysis to
 estimate the number of functional groups for the whole assemblage. We
 calculated indices of functional richness, evenness, divergence and CWM
 using phyllostomid numerical traits. Then, using GLMs, we modeled the
 changes in those variables in response to landscape composition and
 structure at each spatial scale.