DOI: 10.5061/dryad.z34tmpgbb
title: The community structure of bird assemblages on urban Strangler figs

description: Figs have been regarded as keystone plant resources that support diverse
 tropical vertebrate frugivore communities. Planting or conserving large
 fig trees, such as stranglers, has therefore been proposed for enhancing
 urban biodiversity. We compared the diversity and community structure of
 bird assemblages on strangler figs with non-fig urban trees as well as
 between the fruiting and non-fruiting fig trees in an urban setting in
 Singapore. The total bird abundance across all the fig trees when in fruit
 was 4.5-fold higher than on non-fig trees and 3.5-fold higher than when
 the same fig trees were not fruiting, but only attracted two more species.
 On individual trees, after accounting for the presence of mistletoes, tree
 height, the area covered by buildings and road lane density, and distance
 to natural vegetation, mean diversity was not different between non-fig
 trees and fig trees when they were not in fruit. On the other hand, when
 fruiting, each fig tree on average had 1.4 more species, 3 more counts of
 non-native birds, and 1.6 more counts of insectivorous birds than when not
 fruiting. There was significant compositional turnover between non-fig
 trees and non-fruiting fig trees, while community dispersion was
 significantly lower among fig trees in fruit. Our results demonstrate that
 fig trees provide fruit and non-fruit resources for birds in an urban
 landscape but do not necessarily support a more diverse total bird
 assemblages than non-fig trees. Instead, bird communities on fruiting
 urban figs would be highly homogeneous and dominated by a few species.
description: <strong>Tree selection</strong>
 Thirty pairs of fig [<i>Ficus benjamina</i> (Fb),
 <i>F. elastica</i> (Fe), <i>F.
 microcarpa </i>(Fm), <i>F. religiosa </i>(Fr), and
 <i>F. superba </i>(Fs)] and non-fig [<i>Albizia
 saman</i> (= <i>Samanea saman</i>; Ss),
 <i>Peltophorum pterocarpum</i> (Pp), <i>Swietenia
 macrophylla</i> (Sm), <i>Pterocarpus indicus</i> (Pi),
 and <i>Khaya senegalensis </i>(Ks)] focal trees were selected
 in this study. We first considered fig trees that were clearly in an urban
 landscape for our study, i.e., surrounded by built-up area and managed
 greenery but without natural vegetation within a 50-m radius from the
 tree’s trunk. The criteria for selecting fig focal trees included a
 minimum height (at least 10 m) which also ensures reproductive maturity. A
 non-fig focal tree was then identified near the potential fig focal tree.
 Non-fig focal trees were selected to match as closely as possible to its
 paired fig tree in terms of tree height as well as the urban landscape
 characteristics. Seven fig trees did not display
 sufficient fruiting to qualify for a <i>FIGF </i>survey, while
 one <i>F. elastica </i>was continuously in the edible phase as
 defined above and did not have a <i>FIGNF </i>survey. See
 below for more information on <em>FIGF</em>
 and <em>FIGNF</em> in addition to
 <em>NONFIG</em> surveys. <strong>Bird
 surveys</strong> Bird counts for each tree were
 conducted in every other 15-min interval from 7:30 am to 9:30 am, on days
 without strong wind or rain. All birds sighted (with the aid of an
 Opticron 8×42 binoculars) or heard within 20 m of the tree trunk by a
 single observer (Ng W.Q.) were identified and counted. Overflying birds
 were excluded. Birds seen or heard but could not be identified during the
 count were either photographed or the calls were recorded. These were
 later identified using appropriate references.Only one tree was surveyed
 at any one time. All bird counts were made from 19 August 2011 to 15
 February 2012, which encompasses the peak period for visits by migratory
 birds in Singapore. Bird counts were conducted once on
 each non-fig tree (<i>NONFIG</i>) and twice on each fig tree:
 once when the fig tree displayed edible (or ripe) fruit
 (<i>FIGF</i>) and once when it was not
 (<i>FIGNF</i>). <i>FIGF</i> bird counts were only
 conducted when edible figs on the tree were estimated to be at least 50%
 of the total crop produced. This was quantified by partitioning the tree
 crown into ten approximately equally-spaced wedges radiating from the
 trunk and estimating the percentage of ripe figs in each wedge, and then
 taking the average. As there are some non-focal fig
 trees near our focal trees, no counts were made for the
 <i>FIGNF</i> and <i>NONFIG</i> survey types if
 such fig trees present within 200 m of their vicinity were producing
 edible fruits. <b>Tree characteristics and urban
 landscape mapping</b> With an unobscured Google
 Earth image most recent to the survey date serving as base map, tree
 canopy cover (<i>CANOPY</i>; including both the focal tree
 crown as well as non-focal tree crowns), the area covered by buildings
 (<i>BUILT</i>) and by water bodies (<i>WATER</i>)
 were manually drawn at 1:1000 magnification. Most of the images were taken
 in the year of survey, i.e., 2011; however, older images from 2009 and
 2010 were used if<i> </i>the latest image was obstructed by
 cloud cover. This was then ground-truthed on the same
 day as the bird surveys, together with the data collection for other
 covariates: the heights of the focal tree (<i>TREE_HT</i>) and
 the tallest building within 50 m (<i>BUILD_HT</i>). In
 addition, the presence or absence of mistletoes, such as
 <i>Dendrophthoe pentandra</i> or <i>Macrosolen
 cochinchinensis </i>(Family: Loranthaceae), on the focal trees
 (<i>MISTLETOE</i>) was also recorded. The
 distance of the focal tree to the nearest natural vegetation patch
 (<i>NAT_DIST</i>) was extracted from the vegetation map of
 Singapore (Yee <i>et al.</i> 2011 Gardens' Bulletin
 ).
description: We have also provided the R script that produces the results in
 the paper. This R script uses each of the spreadsheets in the .XLSX file
 exported as .CSV files as inputs; the name of each spreadsheet is the name
 of the input .CSV file, e.g., "bird.csv". The
 .ZIP file contains all the shapefiles and associated files from the urban
 landscape mapping. These also need to be unzipped into a folder and the
 folder path modified accordingly in the R script to be read and the
 relevant landscape variables extracted and calculated.