DOI: 10.5061/dryad.xpnvx0kd5
title: Landscape heterogeneity shapes bird phylogenetic responses at forest-matrix interfaces in Atlantic Forest, Brazil

description: Agricultural intensification is one of the major factors driving
 biodiversity loss. However, most studies in human-dominated landscapes
 have used taxonomic diversity in their analysis, ignoring evolutionary
 relationships. Consequently, the relationship between landscape structure
 and phylogenetic diversity is not well understood. Here, we tested the
 hypothesis that landscape heterogeneity is positively related to bird
 phylogenetic indexes of diversity and structure, leading to over-dispersed
 phylogenies in very heterogeneous landscapes. We analyzed phylogenetic
 responses in interfaces between forest edges and anthropogenic matrices
 (forest-pasture and forest-eucalyptus) using generalized linear mixed
 models. We also compared these indexes between land covers to assess which
 one best preserves the phylogenetic history of communities. We used both
 traditional phylogenetic indexes and those corrected for species richness.
 Our results showed that phylogenetic diversity varied significantly
 between land cover types, but this did not occur when we removed effects
 associated with species richness, suggesting that all land covers preserve
 similar levels of evolutionary history. Additionally, our best models
 showed a positive relationship between landscape heterogeneity and bird
 phylogenetic indexes of diversity and structure, but the strength of these
 relationships may be land-cover dependent. In summary, our work highlights
 the influence of landscape heterogeneity on the phylogenetic diversity and
 structure of bird communities, reinforcing the need for its incorporation
 into conservation-based studies.
description: <b>1. Study sites and
 landscape metrics</b> We created land-use maps for this
 study area using satellite images of high resolution (ArcGIS 10.3 base map
 imagery, Digital Globe satellites 2010–2011, 1:4,000; Figure S2). We made
 this map with manual digitization based on visual interpretation of patch
 differences in color, texture and shapes. We considered 14 different land
 covers or human land uses: old-growth forest, pasture, eucalyptus
 plantations, second-growth, wetland, cropland (mainly maze), sugarcane,
 water bodies, urban areas, rural homesteads, urban or suburban homesteads,
 paved roads, buildings, and bare soil (for more details, see Barros et
 al., 2019). Based on this map, we chose 34 sample landscapes to represent
 the regional gradient of compositional landscape heterogeneity. Of these,
 16 had their points located across forest-eucalyptus interfaces and 16
 across forest-pasture interfaces. The eucalyptus plantations were, in
 general, homogenous, lacking a native vegetation understory, while the
 pastures were not exposed to intensive grazing. In addition, we used two
 control landscapes in continuous forest areas so as to include forest
 specialist species in the phylogenetic tree. This allowed the inclusion of
 forest species that originally inhabited our study area but which are now
 only seen in large forest blocks. Landscape sites were defined
 using 1.2-km buffers around the centroid of the two sampling sites (one at
 forest edge, the other in pasture or at eucalyptus edge). We chose this
 spatial scale based on previous evidence from multiscale analysis of bird
 responses to landscape structure (Barros et al., 2019). We also defined a
 2-km minimum distance between sampling landscapes to avoid recounting the
 same individuals in different landscapes. For each landscape, we calculated
 the compositional landscape heterogeneity (Fahrig et al., 2010) via a
 Shannon diversity index using Fragstat v.4 software (Mcgarigal et al.,
 2012). This index is based on the number of physical land cover types or
 human land uses and their evenness within the landscape. When the value of
 the Shannon index is zero, the landscape contains only one patch (i.e.,
 homogeneous landscape). Landscape heterogeneity increases as the number of
 different patch types increases and/or the proportional distribution of
 area among patch types becomes more equitable.     <b>2. Bird
 sampling</b> We used point counts, with a 50-m
 survey radius, to sample bird communities (Sutherland et al., 2006). We
 selected paired forest-matrix ecotones as sample sites in each landscape,
 one in forest edge and other in the adjacent pasture or eucalyptus
 plantation. The paired sampling sites were located around 70-100 m from
 the edge (140-200 m from each other, Figure S2), while control sampling
 sites consisted of a single point at each location, situated in the
 interior of continuous forests, and at least 1 km from the edge. We
 sampled each site for ten minutes, three times, on different days during
 the first three hours after sunrise, during two consecutive breeding
 seasons, totaling 30 minutes per site. We sampled half of sampling sites
 from September 2014 to January 2015, and the remainder from October to
 December 2015. Only birds obviously using the habitats were recorded (e.g.
 birds flying overhead were not included). For each sampling site, data
 recorded from the three replicates were combined into a single community
 database, except for the abundance data. We set the abundance data of each
 sampling site as the highest value recorded in one single day. This is a
 conservative value adopted to avoid overestimated data. <b>3. Bird phylogenetic
 trees</b> We considered all species
 recorded in the 34 landscapes as the regional pool of species. Then, to
 construct the phylogenetic tree, we used the phylogeny database of bird
 species built by Jetz et al. (2014), available for download at
 http://birdtree.org/, using the Hackett All Species option as the source
 of trees. From the 1,000 trees pruned for our full set of species, we
 created a consensus tree using the Mesquite 2.75 program (Maddison
 & Maddison, 2010). <b>4. Phylogenetic
 metrics</b> To evaluate the phylogenetic
 metrics for each community, we used the “picante” package (Kembel et al.,
 2010) in R statistical software (R Core Team 2018). We chose six
 phylogenetic metrics to represent the phylogenetic diversity and structure
 of sampled bird communities: <b>1.
 Phylogenetic diversity (PD):</b> described as the total sum of
 phylogenetic history, this is measured through the total branch length of
 a phylogeny representing the species in a community (Faith,
 1992); <b>2.
 Standard effect size of PD (SES.PD):</b> PD of communities may be
 correlated with their species richness (SR, or number of species; Swenson,
 2014). Thus, the effect of SR can be removed by comparing the PD values of
 studied communities with that of communities of equal species richness
 generated by null models drawn randomly from the regional species
 pool; <b>3. Mean
 nearest taxon distance (MNTD) abundance-weighted</b>: MNTD is
 weighted by abundance, and represents the average phylogenetic distance
 between an individual and the most closely related non-conspecific
 individual (Webb, 2000; Webb, 2002); high MNTD indexes indicate the
 co-occurrence of distantly related species within communities
 (phylogenetic over-dispersion), while low levels indicate the
 co-occurrence of closely related species (phylogenetic
 clustering); <b>4.
 Standard effect size of MNTD (SES.MNTD) abundance-weighted: </b>MNTD
 may also be correlated with SR. Communities with higher than expected MNTD
 values for a given SR indicate the co-occurrence of distantly related
 species (phylogenetic over-dispersion or SES.MNTD &gt; +1.5), while
 low values indicate the co-occurrence of closely related ones
 (phylogenetic clustering or SES.MNTD &lt;-1.5). The SES.MNTD index is
 a better metric for limiting similarity relationships (phylogenetic
 over-dispersion); <b>5. Mean
 pairwise distance (MPD):</b> represents the average phylogenetic
 distance between all pairwise species combinations present in a community,
 and it is influenced by relationships in deep evolutionary time (Webb,
 2002). High MPD indexes indicate the co-occurrence of distantly related
 species within communities (phylogenetic over-dispersion), while low
 levels indicate the co-occurrence of closely related species (phylogenetic
 clustering); <b>6.
 Standard effect size of MPD (SES.MPD):</b> MPD may be correlated
 with SR. Communities with higher than expected MPD values for a given SR
 indicate co-occurrence of distantly related species (phylogenetic
 over-dispersion or SES.MPD &gt; +1.5), while low values indicate
 co-occurrence of closely related ones (phylogenetic clustering or SES.MPD
 &lt;-1.5). The SES.MPD index is a better indicator of environmental
 filter relationships (phylogenetic clustering; Webb, 2000; Kraft,
 Cornwell, Webb, & Ackerly, 2007); To determine whether SES values
 differed from the community expected by chance, we compared observed
 values between individuals to expected SES values for 999 communities
 using an independent swap algorithm (Gotelli, 2000). Additionally, to
 assess whether such traits were conserved across the phylogeny (Data S1),
 we calculated the phylogenetic signal (Blomberg, Garland, & Ives,
 2003) for six bird functional traits. For this, we conducted a
 phylogenetically independent contrast analysis using the “aotf” function
 in Phylocom software (Webb, Ackerly, & Kembel, 2008).  
 <b>5. Statistical
 analysis</b> To assess whether the
 phylogenetic diversity indexes and species richness differed among all
 land covers, we used one-way ANOVA, and to compare differences among
 matrices and forest edges, we used paired-sample t-tests (Rezende
 & Diniz-Filho, 2012). To compare only matrices (pasture and
 eucalyptus plantations), we performed simple t-tests. Results were
 interpreted via P-values where P &lt; 0.05 indicates significant
 differences between land covers. Statistical analyses were performed using
 R Statistical Software (R Core Team, 2018).  We used generalized linear mixed
 models (Table 1) to analyze the relationship between phylogenetic and
 landscape metrics (Glmm; function “lmer”, package “lme4”; Bates, Mächler,
 Bolker, & Walker, 2015; Bolker et al., 2009).  For each
 phylogenetic metric of diversity (PD and SES.PD) and structure (MPD, MNTD,
 SES.MPD and SES.MNTD), we produced simple and additive models using
 spatial heterogeneity and land cover as predictive variables. We fitted
 these models using the landscape identification (a code for each
 landscape) as a random effect to account for spatial dependence of the
 sampling points present in the same landscape. In some models, we also
 tested, as random effects, the landscape heterogeneity and land cover.
 Next, we ranked these models and, using the Akaike Information Criterion
 corrected for small samples (AICc), estimated which ones best predicted
 the phylogenetic diversity and structure of bird communities. We
 considered best models to be those with the lowest AICc values. Models
 with ΔAICc differences less than two were considered as equally plausible
 to explain observed patterns (Martensen, Ribeiro, Bankes-Leite, Prado,
 & Metzger, 2012). For each model, we also calculated the Akaike
 weights wi (range from 0 to 1; the highest values were considered the most
 plausible models). We calculated all these indexes using the AICctab
 function of the “bbmle” package (Bolker, 2017). After selecting the most
 plausible models (ΔAICc &lt; 2, wi &gt; 0.1), we tested for
 spatial autocorrelation in the residual distribution using Moran's I
 test (“DHARMa” package in R). Because of spatial dependence, we used
 spatial regression models that included the spatial effect (function
 “fitme”, package “spam”; Rousset & Ferdy, 2014; Table 2). In these
 models, we excluded landscape identification as a random effect as these
 regression models already took into account the spatial pattern present in
 the data. Finally, we visually checked the model fits and residual
 distributions and selected the best ones that controlled the spatial
 effect (Figure S3). All these analyses were performed using the R
 statistical software package (R Core Team 2018).