10.5061/DRYAD.XPNVX0KD5
Adorno, Bruno
0000-0003-1951-0863
Federal University of Alfenas
Barros, Fabio M.
Sao Paulo State University
Ribeiro, Milton Cezar
Sao Paulo State University
Silva, Vinicius X. da
Federal University of Alfenas
Hasui, Erica
Federal University of Alfenas
Landscape heterogeneity shapes bird phylogenetic responses at
forest-matrix interfaces in Atlantic Forest, Brazil
Dryad
dataset
2020
Compositional heterogeneity
Environmental sorting
landscape homogenization
São Paulo Research Foundation
https://ror.org/02ddkpn78
2013/19732-1,2013/50421-2
Coordenação de Aperfeicoamento de Pessoal de Nível Superior
https://ror.org/00x0ma614
2020-10-09T00:00:00Z
2020-10-09T00:00:00Z
en
https://doi.org/10.1007/s10980-019-00812-z
https://doi.org/10.1002/ecy.2119
106105 bytes
3
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
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.
1. Study sites and landscape metrics 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. 2. Bird sampling 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. 3. Bird phylogenetic trees 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). 4. Phylogenetic metrics 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: 1. Phylogenetic diversity (PD):
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); 2. Standard effect size of PD (SES.PD): 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; 3. Mean nearest taxon distance (MNTD)
abundance-weighted: 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); 4.
Standard effect size of MNTD (SES.MNTD) abundance-weighted: 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 > +1.5), while low values
indicate the co-occurrence of closely related ones (phylogenetic
clustering or SES.MNTD <-1.5). The SES.MNTD index is a better
metric for limiting similarity relationships (phylogenetic
over-dispersion); 5. Mean pairwise distance (MPD): 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); 6. Standard effect size of MPD
(SES.MPD): 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 > +1.5),
while low values indicate co-occurrence of closely related ones
(phylogenetic clustering or SES.MPD <-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). 5. Statistical analysis 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
< 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 < 2, wi >
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).