DOI: 10.5061/dryad.80gb5mkng
title: Patch size and vegetation structure drive changes to mixed-species flock diversity and composition across a gradient of fragment sizes in the Western Andes of Colombia

description: This data set represents a series of 502 mixed-species bird flock
 compositions, and derived taxonomic, functional, and phylogenetic
 diversity indices, that were gathered along a gradient of forest fragment
 sizes (range = 10-173 ha) in the Colombian Western Andes. We sampled
 mixed-species flocks using transect surveys along 14 transects in 8
 fragments and a continuous forest reference site in the same
 landscape and at the same elevation (~1900-2200 m.a.s.l.). We also used
 buffer analysis to quantify the proportion of forest cover and forest edge
 within 1 km of each transect, and calculated local vegetation density and
 complexity, as well as distance from edge, for each 100-meter transect
 segment (n = 70 segments). Flock composition data observed on a
 transect were used to calculate overall species richness and flock size as
 well as two indices of functional and phylogenetic diversity; we
 calculated the stadardized effect size (SES) of each measure to account
 for the correlation between these measures and species richness. We also
 provide the raw counts of each species for each flock composition. These
 data were used for the analyses in Jones and Robinson (2020). 
description: <b>Study System and
 Sites</b> We conducted all fieldwork in
 subtropical humid forests located within the municipality of El Cairo,
 Valle del Cauca department in Colombia. The study region is part of the
 Serrania de los Paraguas in the Western Andes mountain range, a center of
 avian threatened species diversity and endemism within Colombia. The study
 landscape in this municipality consists of a patchwork of forest fragments
 embedded in a matrix of cattle pasture, regenerating scrub, and coffee
 farms. Within this landscape, we selected eight fragments representing a
 gradient in patch sizes (range 10 to 170 ha). Sites are in the same
 altitudinal belt (1900-2200 m.a.s.l.) and matrix type (cattle pasture) to
 control for effects of altitude and matrix type on flock size and
 composition. Within-patch
 disturbance is common in fragmented Andean forests in Colombia,
 particularly illegal selective logging, which in our landscape typically
 occurred as removal of select old-growth trees for lumber by landowners;
 logging histories varied considerably from historical to ongoing, and
 extensive to limited, both within and between patches. We
 established 500-meter transects through forest interior
 (<i>n</i> = 14 total transects) which were opportunistically
 placed on existing trails, at variable distances from the edge of the
 fragments. We further divided each transect into 100-meter segments to
 account for heterogeneity in vegetation structure within transects. We
 accounted for edge effects by measuring the distance to forest edge of
 each transect segment.  We stratified forest fragments into large
 (≥ 100 ha), medium (~30-50 ha), and small (≤ 20 ha) size categories and
 selected a minimum of two replicates of each; these represent the range of
 fragment sizes available in our study landscape. We also included a non-fragmented
 reference site (Reserva Natural Comunitária Cerro El Inglés, ~750 ha)
 connected to over 10,000 ha of continuous forest to the north and west
 along the spine of the Serranía de los Paraguas.  We only
 selected fragments with primary or late-successional secondary forest;
 vegetation structure and canopy height varied substantially between
 patches based on intensities of selective logging and land-use histories
 (see above). Fragments were all separated by ≥ 100 meters to minimize
 among-patch movement of birds, and all transects in different fragments
 were at least 250 meters apart. <b>Transect Surveys for
 Mixed-species Flocks </b> We
 performed transect surveys for mixed-species flocks, adapted from Goodale
 et al. (2014), in forest fragments from June-August 2017 (boreal migrants
 absent) and January-March 2018 (boreal migrants present). Both sampling
 periods corresponded to a dry season in the Western Andes, which has a
 bimodal two-dry, two-wet seasonality pattern. For each transect, we spent
 two and a half sequential field days performing continuous transect
 surveys; we conducted surveys in small fragments, large fragments, and
 continuous forest sites in random order to avoid a temporal bias in
 sampling. Surveys were distributed across the morning (7:30-11:30) and
 evening (15:00-17:30) hours. Transects were walked slowly and
 continuously by 2-3 observers, including local birdwatchers familiar with
 all species (Harrison Jones present for all surveys); flocking birds were
 identified by both sight and sound. When we encountered a flock, we noted
 the time of day and transect segment in which it was observed and spent up
 to a maximum of 45 minutes characterizing it with 10x binoculars. At least
 5 minutes were spent with each flock, following it if possible. Because
 detection of species in flocks was imperfect, we only included a flock
 observation in the analysis if we felt that at least 80% of the
 individuals were observed (e.g. after spending several minutes of
 continuous observation at the end of the survey period without observing a
 new species or individual); incomplete flock observations were not
 included in analyses. We feel that our survey methodology accurately
 described flock composition because birds moved and called frequently in
 flocks, leading to high detectability. We noted the start and end time of each
 survey, and the presence of incomplete flocks to calculate flock encounter
 rate. We also supplemented the transect surveys with data from
 flocks opportunistically observed on a transect while performing other
 fieldwork. Some flocks in the data set represent flock compositions
 recorded near but not on a transect; these compositions have no associated
 transect segment.   <b>Calculation of Landscape-level
 Variables </b>             We obtained landscape-level
 variables for analyses using geographic information software (GIS)
 analysis in ArcGIS (ArcMap 10.3.1; Esri; Redlands, CA). To quantify
 landscape composition and configuration, we buffered each transect
 (<i>n</i> = 14) by 1 km; buffers extended from the entire
 length of the transect. We then calculated measures of landscape
 composition and configuration using a recent land-cover/use categorization
 made by the Corporación Autónoma Regional del Valle del Cauca, converted
 to a 25-m cell-size raster. To quantify landscape composition, we
 calculated percentages of the forest-cover type within each buffer using
 the ‘isectpolyrst’ tool in Geospatial Modelling Environment (version
 0.7.4.0). We measured landscape configuration for each transect as edge
 density, or length of all forest edges (in meters) divided by total buffer
 area (in hectares). The distance to
 edge was calculated in meters for each 100-meter transect segment
 (<i>n</i> = 70) as the shortest straight-line distance between
 the center point of the segment and the nearest edge of the
 fragment.  <b>Vegetation Measurements and
 Principal Component Analysis</b>             We measured vegetation
 structure in each 100-m transect segment used for flock sampling.
 Vegetation measurements were made from June-August 2017; based on our
 observations of vegetation, we assumed variation between the two sampling
 periods was minimal. We used the sampling methodology of James and Shugart
 (1970), following the modifications made by Stratford and Stouffer (2013),
 and further modified to be used with belt transects. Broadly, the
 methodology comprises two components for every 100-meter transect segment:
 (1) the quantification of canopy cover, ground cover, canopy height, and
 foliage height diversity of vegetation using point sampling every 10
 meters and (2) the quantification of shrub, vine, fern, palm, and tree
 fern and tree density using 3 meter-wide belt sampling. For the point sampling,
 we measured eight variables at ten-meter intervals, for 10 points per
 100-meter segment. As a measure of foliage height diversity along the
 transect, we noted the presence or absence of live vegetation at five
 heights: <i>&lt;</i>0.5 m,
 <i>&gt;</i>0.5–3 m, <i>&gt;</i>3–10 m,
 <i>&gt;</i>10–20 m, and <i>&gt;</i>20 m.
 Above 3 meters, we used a rangefinder to determine heights while sighting
 through a tube with crosshairs. Canopy and ground cover were calculated to
 the nearest 1/8<sup>th</sup> of the field of view by sighting
 through a vertical canopy densiometer (GRS Densiometer, Geographic
 Resource Solutions, Arcata, CA). For each segment, we averaged values for
 canopy cover, and ground cover, and calculated the proportion of points at
 which vegetation was present for each height category. For the belt
 transect sampling, we surveyed vegetation along the same transects and
 calculated densities for each 100-m transect interval. We counted all
 shrubs, vines, ferns, tree ferns, and palms encountered on 1.5 meters to
 either side. Secondly, we counted all trees (woody vegetation &gt; 2 m
 in height) within 1.5 meters of the transect and measured their diameter
 at breast height (DBH). Trees were later categorized into six DBH size
 classes for analysis: 1-7 cm, 8-15 cm, 16-23 cm, 24-30 cm, 31-50 cm, and
 &gt; 50 cm. We additionally recorded the largest tree’s DBH.
 <i>            </i>To
 quantify foliage height diversity, we calculated the Shannon Diversity
 Index of the proportion of points with vegetation present in each of the
 five height bands for each segment (<i>n</i> = 70 segments).
 To reduce redundancy and minimize correlation between variables, we
 (separately) ordinated our tree DBH and understory plant density data
 using principal component analysis (PCA: McGarigal et al. 2000) for each
 100-meter transect segment. We column (Z score) standardized data prior to
 ordination to account for differences in the units of measurement and used
 the covariance matrix to run the PCA. The principal components were
 interpreted using the significance of the principal component loadings.
 The PCA was run in R (version 3.5.1) using the <i>princomp</i>
 function in the <i>stats</i> package. The Shannon Index was
 calculated using the <i>diversity</i> function of the
 <i>vegan</i> package (Oksanen et al. 2019). The results of the
 vegetation data ordination are reported in the Supplementary Materials for
 Jones and Robinson (2020).  <b>Calculation and Standardization
 of Functional and Phylogenetic Diversity Metrics </b>
             We define functional
 diversity as the diversity of foraging niches and behaviors present within
 a given flock. We calculated two measures of functional diversity:
 functional richness (Villéger et al. 2008) and functional dispersion
 (Laliberté and Legendre 2010). These are multivariate measures calculated
 using a distance framework from a matrix of quantitative and categorical
 traits of all species observed (Laliberté and Legendre 2010). We built a
 trait matrix for all species observed in flocks based on information from
 the <i>Handbook of the Birds of the World Alive </i>website
 (del Hoyo et al. 2020; also uploaded on Dryad), supplemented with
 family-specific references where appropriate (full reference list provided
 in Supplementary Materials of Jones and Robinson [2020]). We included body
 mass, diet (degree of insectivory, frugivory, nectivory, and granivory),
 foraging maneuvers, foraging substrates, foraging strata (ground,
 understory, midstory, sub-canopy, and canopy), and habitat preferences
 (forest interior, forest edge, secondary forest, open habitat) as
 functional traits. Categories were not mutually exclusive; species’ diet
 preferences were classified on a 0 to 3 scale based on reported frequency
 of consumption, while use of foraging maneuvers, substrates, strata and
 habitat types were keyed as present (1) or absent (0). Foraging maneuvers
 and substrates were classified according to the Remsen and Robinson (1990)
 typology. Functional diversity metrics were calculated from a matrix of
 species-by-flock abundances using the <i>dbFD</i> function of
 the <i>FD</i> package (Laliberté et al. 2014). Functional
 dispersion was weighted by the abundance of each species in each flock. We
 standardized functional richness by the ‘global’ functional richness to
 constrain it between 0 and 1.             We also selected
 phylogenetic diversity as a measure of the quantity of phylogenetic
 differences present in each flock. This is measured as the summed branch
 lengths of all species present on a phylogeny of all species observed
 (Faith 1992). The divergence of phylogenetic lineages present in each
 flock was measured using the mean pairwise distance measure, which
 calculates the mean branch lengths between all species pairs in a given
 flock (Webb et al. 2002). We created a phylogeny of all species observed
 in flocks by sub-setting the global Jetz (2012) phylogeny. We downloaded
 1000 trees from the Bird Tree website (www.birdtree.org)
 using the Hackett (2008) backbone phylogeny. We calculated a 50%
 majority-rule consensus tree using mean edge lengths with the
 <i>consensus.edge</i> function of the
 <i>phytools</i> package (Revell 2019); the observed metrics
 were then calculated using the <i>pd</i> and
 <i>mpd</i> functions of the <i>picante</i> package
 (Kembel et al. 2019), using the same species-by-flock abundances matrix as
 for the functional diversity measures.             Because measures of
 functional and phylogenetic diversity are often correlated with species
 richness (Vellend et al. 2014; Weiher 2014), we used standardized effect
 sizes (SES) to control for species richness when calculating these
 metrics. To create a null model of functional and phylogenetic diversity
 we used 999 iterations of the tip swapping method (Webb et al. 2008) to
 randomly shuffle taxa labels across the tips of the functional trait
 matrix and flocking species phylogeny, respectively. We then calculated
 the relevant diversity metrics on each of the null communities and
 compared it to the observed value. We standardized SES values for each
 metric by subtracting the mean of the null values from the observed value
 and then dividing this by the standard deviation of the null values. We
 used the <i>ses.pd</i> and <i>ses.mpd</i>
 functions of the <i>picante</i> package to calculate
 phylogenetic diversity SES values; functional diversity values were
 calculated based on novel code developed by Dr. Ben Baiser (all R code
 available as an electronic supplement in Jones and Robinson
 [2020]).
description: <strong>Variable Descriptions </strong>
 Flock_ID: A unique identifier for each flock
 composition.  Julian_Date: The Julian (numeric) date on
 which the flock was observed.  Season: Indicates
 whether the flock was observed during the boreal summer sampling period
 (0; June-August 2018) or the boreal winter sampling period (1;
 January-March 2019).  Site_Segment: Indicates the name
 of the transect on which the flock was observed followed by the
 100-meter transect segment (1-5 for each transect). Where no value is
 given the flock was observed off of the transect but within 100 meters of
 the transect; these compositions were not used for analyses. Site
 characteristics and coordinates for each transect are described in Table
 1, Jones and Robinson (2020). Transect: The name of the
 transect that the mixed-species flock was observed on or near. 
 Site: Corresponds to the forest fragment number that the flock
 was observed in. In some cases, fragments had multiple transects present
 in them. Site numbers are ordered by increasing area; site 9 corresponds
 to a continuous forest reference site (RNC Cerro El Ingles). Site
 characteristics are listed in Table 1, Jones and Robinson
 (2020).  Minute and Minute_2: Numeric minute and minute
 squared of the day at which the flock was first detected. Zero corresponds
 to midnight.  Chlorospingus: This variable indicates
 whether <em>Chlorospingus canigularis</em>, a nuclear species
 in this Andean flocking system, was present in the flock
 composition.  Species: The total species richness of
 species observed in the flock during the survey.  Size:
 The total number of individuals of all bird species observed in the flock
 during the survey.  Furnariid_Sp: The total observed
 species richness of ovenbirds (Furnariidae) in the flock during the
 survey.  Tyrannid_Sp: The total observed species
 richness of tyrant flycatchers (Tyrannidae) in the flock during the
 survey.  Migrant_Sp: The total observed species
 richness of boreal migrant species observed in the flock during the
 survey.  Thraupid_Sp: The total observed species
 richness of tanager (Thraupidae) species observed in the flock during the
 survey.  SESFRic: The standardized effect size of the
 functional richness index of functional diversity present in the
 flock.  SESFDis: The standardized effect size of the
 functional dispersion index of functional diversity present in the
 flock.  SESPD: The standardized effect size of the PD
 measure of phylogenetic diversity present in the flock. 
 SESMPD: The standardized effect size of the mean pairwise
 distance measure of phylogenetic diversity present in the
 flock.  Dist_Edge: The straight-line distance in meters
 from the midpoint of the transect segment in which the flock was obsserved
 to the nearest fragment edge.  1km_forest: The
 percentage of forest land use in a 1 km buffer centered along the entire
 length of the transect. This variable was calculated at the transect (and
 not transect segment) level.  1km_edge: The edge
 density, calculated as the distance of forest edge in meters divided by
 the total area in hectares, for a 1 km buffer centered along the entire
 length of the transect. Calculated at the transect level. 
 Canopy_Cover: The average canopy cover calculated using a canopy
 densiometer at ten points for each transect segment. 
 Understory_Density: A principal component axis measuring the
 density of understory shrubs, ferns, vines, tree ferns, and palms, as
 measured using belt transects, for each transect segment. More negative
 values correspond to higher densities of each category of
 plant.  Tree_Size: A principal component axis measuring
 the density of large-diameter trees, as measured by belt transects, for
 each transect segment. More positive values correspond to greater
 densities of large-diameter trees.  
 Vertical_Sturcture: A measure of vertical vegetation complexity.
 We calculated the proportion of ten point surveys that contained
 vegetation in five height bands for each transect segment. We then used
 these proportion data to calculate the Shannon Diversity Index of
 vegetation complexity for each transect segment. This variable was closely
 correlated with canopy height at our sites.   Species
 abundances: The observed abundance of each species in the flock. Species
 names use the latin binomial.