DOI: 10.5061/dryad.9s4mw6mcd
title: Spatial heterogeneity and habitat configuration overcome habitat composition influences on alpha and beta mammal diversity

description: The effects of habitat fragmentation on different taxa and ecosystems are
 subject to intense debate, and disentangling them is of utmost importance
 to support conservation and management strategies. We evaluated the
 importance of landscape composition and configuration, and spatial
 heterogeneity to explain α- and β-diversity of mammals across a gradient
 of percent woody cover and land use diversity. We expected species
 richness to be positively related to all predictive variables, with the
 strongest relationship with landscape composition and configuration, and
 spatial heterogeneity, respectively. We also expected landscape to
 influence β-diversity in the same order of importance expected for species
 richness, with a stronger influence on nestedness due to deterministic
 loss of species more sensitive to habitat disturbance. We analyzed
 landscape structure using: i) landscape metrics based on thematic maps and
 ii) image texture of a vegetation index. We compared a set of univariate
 explanatory models of species richness using AIC, and evaluated how
 dissimilarities in landscape composition and configuration and spatial
 heterogeneity affect β-diversity components using a Multiple Regression on
 distance Matrix. Contrary to our expectations, landscape configuration was
 the main driver of species richness, followed by spatial heterogeneity and
 last by landscape composition. Nestedness was explained, in order of
 importance, by spatial heterogeneity, landscape configuration, and
 landscape composition. Although conservation policies tend to focus mainly
 on habitat amount, we advocate that landscape management must include
 strategies to preserve and improve habitat quality and complexity in
 natural patches and the surrounding matrix, enabling landscapes to harbor
 high species diversity.
description: <b>Mammal diversity
 data</b> We performed
 four field expeditions in April 2009, August 2009, May and June 2010, and
 July and August 2010. This effort was carried over 20 landscapes, distant
 from each other between 20 km to 634 km, yielding 20 independent samples
 of terrestrial mammal occurrence with body sizes varying from small
 (&gt;1 kg) to large (Figure 2). On each expedition, we sampled mammals
 in five landscapes during five consecutive days and four nights using the
 following complementary methods: i) identification of vestiges, such as
 tracks (identified according to Angelo et al. 2008), feces, teeth, and
 others bones (bones were collected and compared to collection material for
 identification); ii) direct observation; iii) camera trapping; and iv)
 capture of small mammals with live traps. The sampling goal was not to
 estimate abundances, but to get a tally of species in each landscape for
 calculating species richness and composition. For the first
 two methods, we performed walks on foot or by car at different periods of
 day and night, covering the different environments within each landscape.
 For the third method, we installed between 11 and 16 camera traps
 (Tigrinus®, Timbó, Santa Catarina State, Brazil) at 30-40 cm above the
 ground, in tree trunks of forest or Cerrado patches in each landscape.
 Cameras were placed on transect lines of 110 m in length containing two
 cameras in each extremity (in the border and in the interior of each
 forest fragment), operating 24 hours a day, during four consecutive days
 and nights. Transect lines were distant at least 150 m from each other (in
 small areas), but usually a minimum distance of 300 m was set. The total
 sampling effort was of 1,128 traps-night, with the mean effort per
 landscape being 56 ± 7 traps-night. We captured rodents and marsupials
 (&lt;1 kg, Cricetidae, Echimyidae and Didelphidae families) using 65
 wire (33x12x12 cm) and Sherman live-traps (30x9x7 cm). Traps were
 installed in forest ground (wire) and understory (Sherman), between 1.5
 and 2 meters above the ground, during four consecutive nights, totaling
 6,800 trap-night overall and 340 traps-night per landscape. We baited the
 traps with a mixture of pumpkin, bacon, peanut butter and cod liver oil.
 In each landscape, we installed the traps along transects between the
 camera trap sampling points, 10 m apart from each other in the same
 transect, separated at least 150 m from each other transect line and at
 least 20 m from the nearest patch edge. Captured animals were identified
 and subsequently released. When necessary, we collected voucher specimens
 for identification, which were deposited in the mammalian collection of
 the Universidade Federal de Santa Maria (UFSM).  
 <b>Land use and land
 cover maps</b> We generated
 an 8-km buffer around the camera trap sampling points within each
 landscape to delimit landscape extent. We chose this extent based on
 previous studies reporting landscape structure effects on small-, medium-
 and large-sized mammal assemblage composition within the Atlantic Forest
 (e.g. Lyra-Jorge et al. 2010, Beca et al. 2017, Melo et al. 2017, Regolin
 et al. 2017), as well as to avoid spatial overlap (Jackson and Fahrig
 2015). We mapped land cover for each landscape using orthorectified images
 from the RapidEye satellite constellation, with 5m spatial resolution.
 Images were selected preferably from the dry season, due to lesser cloud
 cover and greater contrast between land use classes (47 images acquired
 between January 2011 and August 2013). Image processing was performed over
 all five spectral bands (blue, green, red, red edge and near infrared) and
 included: i) atmospheric correction using the ‘Quick Atmospheric
 Correction – QUAC’ algorithm implemented in the ENVI 5.0 software and ii)
 unsupervised classification using the ‘Auto Class’ software
 (github.com/JohnWRRC). Auto Class uses the GRASS function ‘i.segment’ to
 generate image segments and the K-means Clustering function of the
 ‘foreign’ R package (R Core Team 2017) to group the segments into classes
 according to the mean and standard deviation of pixel values. We then
 converted this unsupervised map into a thematic classification by
 supervised visual interpretation and manual editing, based on image
 visualization at 1:2,500 cartographic scale, generating a final map with
 11 classes (Figure 2).   <b>Landscape structure
 metrics</b> The produced
 land cover maps in raster format were used as inputs for landscape
 structure metric calculations. We used the ‘raster’ R package (Hijmans et
 al. 2017) to load the raster data and define custom functions to calculate
 the following landscape structure metrics: (i) woody cover — percent woody
 (forest plus cerrado) cover in the landscape, (ii) patch density — ratio
 between the number of woody patches and total landscape area, (iii) edge
 density — ratio between area of woody patch edges and landscape area, and
 (iv) landscape diversity — Shannon index for mosaic of patches including
 all cover types. Woody cover and landscape diversity are used as measures
 of woody habitat composition, whereas edge density and patch density are
 measures of woody habitat configuration (Villard and Metzger
 2014).  
 <b>Within-habitat</b><b> </b><b>spatial heterogeneity</b> We estimated within-habitat spatial heterogeneity by calculating image texture measures from the normalized difference vegetation index (NDVI). NDVI is a spectral index sensitive to photosynthetically active vegetation, which is related to plant biomass productivity (Justice et al. 1998). We calculated NDVI using the red and near-infrared spectral bands of RapidEye images (5-m spatial resolution) using the ‘spatial.tools’ R package (Greenberg 2018). Image textures are statistical descriptors of the spatial relationship among pixel values within an image region, thus capturing spatial heterogeneity (St-Louis et al. 2009, 2014). When calculated using NDVI, texture therefore represents spatial variability in photosynthetically active vegetation within a given area (Wood et al. 2012). Texture measures calculated from high resolution images have been related with descriptors of vegetation heterogeneity such as leaf-area index and foliage height diversity (Colombo<i> </i>et al<i>.</i> 2003, Wood<i> </i>et al<i>.</i> 2012). Particularly, textures can yield larger explanatory power for species richness than classified images because it captures fine-scale variability within coarse habitat classes in areas of gradual transition between vegetation types (St-Louis et al. 2009, Wood et al. 2013). We calculated 12 texture measurements from NDVI, using the ‘r.texture’ GRASS GIS function, being seven first order metrics: (i) sum average, (ii) entropy, (iii) difference entropy, (iv) sum entropy, (v) variance, (vi) difference variance, (vii) sum variance; and five second-order metrics based on a pairwise matrix of spatial relationships among pixels (grey-level co-occurrence matrix; Haralick 1979),  (viii) angular second moment, (ix) inverse difference moment, (x) contrast, (xi) correlation, and (xii) information measures of correlation. Each texture was calculated in four directions (0, 45, 90 and 135 degrees) considering a central pixel and its neighbors within the specified window, and then average of texture metrics were calculated to summarize all directions. We derived textures using four different moving window sizes on each pixel (3x3, 5x5, 7x7 and 9x9 pixels of 5m).
description: <strong>Table columns description</strong>
 Landscape_name: Name of the landscapes. 42 levels. 
 x_utm: X value of the landscape centroid in the Universal
 Transverse Mercator coordinate system (UTM), zone 21S. 42
 values.  y_utm: Y value of the landscape centroid in
 the Universal Transverse Mercator coordinate system (UTM), zone 21S. 42
 values.  Columns 4 to 51: Occurrence of each species in
 the landscapes. 2 values: 1 presence and 0 absence. 
 Columns 52 to 100: Within-habitat spatial heterogeneity
 measurements.  Columns 101 to 105: Landscape
 metrics.