DOI: 10.5061/dryad.tx95x6b0t
title: Variety is the spice of life: flying-foxes exploit a variety of native and exotic food plants in an urban landscape mosaic

description: Generally, urbanization is a major threat to biodiversity; however, urban
 areas also provide habitats that some species can exploit. Flying-foxes
 (Pteropus spp.) are becoming increasingly urbanized; which is thought to
 be a result of increased availability and temporal stability of urban food
 resources, diminished natural food resources, or both. Previous research
 has shown that urban-roosting grey-headed flying-foxes (Pteropus
 poliocephalus) preferentially forage in human-modified landscapes.
 However, which land-use areas and food plants support its presence in
 urban areas is unknown. We tracked nine P. poliocephalus roosting in
 Adelaide, South Australia, between December 2019 and May 2020, using
 global positioning systems (GPS), to investigate how individuals used the
 urban landscape mosaic for feeding. The most frequently visited land-use
 category was “residential” (40% of fixes) followed by “road-side,”
 “reserves” and “primary production” (13–14% each). However, “reserves”
 were visited four times more frequently than expected from their areal
 availability, followed by the “residential” and “road-side” categories
 that were visited approximately twice more than expected each; in
 contrast, the “primary production” category was visited approximately five
 times less than expected. These results suggest that while residential
 areas provide most foraging resources supporting Adelaide’s flying-fox
 population, reserves contain foraging resources that are particularly
 attractive to P. poliocephalus. Primary production land was relatively
 less utilized, presumably because it contains few food resources.
 Throughout, flying-foxes visited an eclectic mixture of diet plants (49
 unique species), with a majority of feeding fixes (63%) to locally
 indigenous Australian native species; however, in residential areas 53% of
 feeding visits were to non-locally indigenous species, vs only 13% in
 reserves. Flowering and fruiting phenology records of the food plants
 visited further indicated that non-locally indigenous species increase the
 temporal availability of foraging resources for P. poliocephalus in urban
 Adelaide. Our findings demonstrate the importance of residential areas for
 urban-roosting P. poliocephalus, and suggest that the anthropogenic
 mixture of food resources available in the urban landscape mosaic supports
 the species’ year-round presence in urban areas. Our results further
 highlight the importance of conserving natural habitats within the urban
 landscape mosaic, and stress the need for accounting for wildlife
 responses to urban greening initiatives.
description: <em>Pteropus
 poliocephalus</em> individuals were captured at the Adelaide Botanic
 Park on 10 and 11 December 2019. Flying-foxes returning to the roost
 before dawn were captured using two double banked mist nets (18m × 5m and
 12m × 5m, 38mm mesh, Ecotone Telemetry, Poland) suspended 15 m high in the
 canopy of the colony. Nets were run on pulley systems that were
 continuously monitored by volunteers and two or three trained researchers.
 Each flying-fox was removed from the net by researchers and placed into a
 pillowcase hung from a horizontal pole. Females that were lactating,
 pregnant or carrying a pup were released immediately upon
 capture. Ten focal
 individuals (five adult males and five non-reproductively active adult
 females) were transported to Adelaide Zoo where they were processed.
 Individuals were anaesthetized using 5% vaporized isoflurane via facemask
 and maintained at 2% isoflurane until processing was complete. While
 anaesthetized, morphometrics were taken and all bats were banded with a
 single stainless-steel band (Australian Bird and Bat Banding Scheme). The
 ten individuals were fitted with a collar supporting a GPS and
 accelerometry unit (CREX GPS Logger, Ecotone Telemetry, Poland, hereafter:
 transmitter). The five females had a mass of 725 g ± 93.1 (568 - 806) and
 the five males had a body mass of 818 g ± 127.0 (685 - 954). The
 transmitter and collar weighed 10 g and 3 g, respectively, giving a total
 weight of 13 g, representing 1.6-2.3% and 1.4-1.9% of the body weight of
 females and males, respectively. After animals recovered from anesthesia,
 they were placed in animal holding facilities at the Adelaide Zoo for
 recovery. The five male flying-foxes required surgery to implant a
 temperature-sensitive VHF FM radio transmitter (model PD-2THX, 3.9 g;
 battery life: 5 months; Holohil) as part of another study (Walker,
 unpublished). Tracking devices represented &lt; 3.0 % of the body mass
 of the lightest individual. Individuals that did not require surgery (n =
 5 females) were released back into the colony within 6 h of capture; the
 five males were released back into the colony the next morning following
 an assessment by a wildlife veterinarian (Author WB). Research was conducted under
 Animal Research Authority no. A12217, issued by Western Sydney
 University. Data collection Global positioning systems and
 accelerometer data were collected for 5 months from 13 December 2019 to 23
 May 2020 (Austral Summer and Autumn). Accelerometer data were recorded on
 three orthogonal axes and were used to identify the GPS fixes associated
 with feeding, as opposed to other behaviors such as flying (see below).
 Transmitters were programmed to collect accelerometer data in three burst
 types, dependent on battery voltage (solar recharge): 12 s at 5 Hz every
 15 mins, 2 s at 30 Hz every 30 mins, or 3 s at 10 Hz every 30 mins.
 Transmitters were set to record GPS data every 30 mins during the night
 when there was sufficient solar recharge. Accelerometer data were linked
 with the GPS duty cycle. The duty cycles of the transmitters were
 monitored and changed remotely via Global System for Mobile (GSM) network
 using the web-panel depending upon the solar recharge of the
 batteries. Data were
 collected via a GSM link from GPS trackers to 3G-enabled mobile phone
 towers that then reported the data to a File Transfer Protocol (FTP)
 server, accessed through the GPS data processing software package “NGA
 Analyzer” (Ecotone Telemetry, Poland). The amount of time that the trackers
 produced usable data varied from 3 to 154 days (Mean = 78.4; Supplementary
 Table 1). Some of the collared individuals left the Adelaide region
 (defined as ≥75 km from the center of the Adelaide Botanic Garden roost)
 during our study (Supplementary Table 1). One female (FFOX05) left the
 Adelaide region before the transmitter began collecting data and hence her
 data were excluded from analyses herein. Another female (FFOX02) left the
 Adelaide region on 18 January 2020, and returned on 2 April 2020. Two
 males (FFOX07 and FFOX09) left the Adelaide region on 31 December 2019 and
 on 19 March 2020, respectively, and did not return to the region for the
 duration of the study. We removed GPS fixes associated with travel outside
 of the Adelaide region (GPS fixes greater than 75 km from the colony). GPS
 fixes less than 500 m from the center of the colony were also removed to
 exclude resting time within the colony. As the boundary of the roost
 varies with the number of flying-foxes present, this conservative approach
 means that we are potentially excluding some nearby foraging fixes. For
 those individuals that left the Adelaide region, GPS fixes recorded on the
 day of departure or day of return were excluded as any food resources
 associated with these fixes were likely supporting the individual’s
 journey to or from another roost rather than their stay in
 Adelaide. Identifying
 feeding fixes Global
 positioning systems locations were designated “feeding fixes” if they were
 temporally aligned with moderate levels of activity, as determined by
 variance in the acceleration data. We applied a principal component
 analysis (PCA) using the “prcomp” function from the “stats” package in R
 to the three recorded axes of acceleration forces to maximize the amount
 of variation caused by movement that is expressed in a single vector
 (i.e., principal component 1). We did this also to account for variation
 in the spatial orientation of the transmitter between individuals, which
 could have influenced the distribution of acceleration forces caused by
 movement across the three axes. We plotted a frequency histogram of the
 standard deviation (SD) of the PC1 scores over each burst of acceleration
 data for each individual and used troughs between primary modes in the
 distribution of SD values as thresholds for designating among broad
 categories of acceleration intensity (Collins et al., 2015). Firstly, we
 identified a mode of greatest SD values, most likely associated with wing
 flapping during flight, and assigned data above a trough threshold
 defining the lower limit of this mode to high-level activity
 (Supplementary Figure 1A). The remaining data were subject to another PCA.
 We identified two modes in the frequency distribution of SD values of the
 new PC1 vector: a lower mode clearly associated with inactivity (i.e.,
 little, if any, and body movement), and an upper mode that included values
 of greater acceleration intensity, but excluded the highest values
 previously assigned to high-level activity. We assigned data above the
 trough between these two modes as moderate activity that likely pertains
 to tree-based movements, including feeding (i.e., the behavior of eating a
 dietary component and any associated movement within a foraging tree;
 Supplementary Figure 1B). Date-time stamps associated with moderate
 activity were rounded to the nearest second. We then aligned the closest
 activity date-time stamps to the GPS date-time stamp. To minimize the
 possibility of misinterpreting the level of activity at each GPS
 coordinate, we calculated the time discrepancy between each pair of
 activity and GPS data, using the “difftime” function from the “lubridate”
 package in R. We excluded the data to include only data pairs that were
 within a ±60 s discrepancy buffer. We used the “suncalc” package in R to
 calculate local sunrise and sunset times and define night (after sunset
 and before sunrise) and included GPS data for the moderate level of
 activity during the night only, which resulted in 489 GPS fixes associated
 with moderate activity during the night. Land-use categories To investigate which land-use categories
 <em>P</em>. <em>poliocephalus</em> used for
 feeding we used the South Australian Government’s “Generalized Land Use
 2020” shapefile of the land-use categories of South Australia (Government
 of South Australia, 2020). Land-use polygons were grouped into 10
 categories: “primary production” (i.e., areas that are used for
 agriculture, horticulture, forestry, and livestock), “residential” (i.e.,
 urban and rural residential, hotel/motel accommodation, institutional
 accommodation, and orchards nestled within peri-urban residential areas),
 “reserve” (i.e., national park, median strips, and road reserves),
 “vacant” (i.e., urban and non-urban vacant land, and steep/rocky land),
 “utilities” (i.e., gas, electricity, water/sewage/waste disposal, public
 transportation, and telecommunications), “institution” (i.e., government,
 and education), “mining” (i.e., mines, open workings, wells, and
 quarries), “recreation” (i.e., ovals, golf courses, camping grounds, and
 stadiums), “commercial” (i.e., wholesale trade, retail, and finance), and
 “industrial” (i.e., food manufacture). The shapefile has gaps between
 polygons that pertain to linear landscape features including roads and
 watercourses and thus, we assigned these areas a new land-use category
 called “road/river.” Land-use by foraging <em>P.
 poliocephalus</em> The land-use shapefile was clipped by a
 circle with a 75 km radius (Figure 1), and a geoBoundaries shapefile of
 Australia (Runfola et al., 2020), using the function “st_buffer” in the R
 package “st,” to obtain the total area where <em>P</em>.
 <em>poliocephalus</em> foraging could have occurred. A 75 km
 radius was selected since the maximum distance any of the tracked
 individuals traveled and returned to the Botanic Park colony was 70.8
 km. Land-use categories
 were extracted for all feeding fixes in the study area (n = 489). The
 areal extent of each land-use category within the study area was
 calculated. To calculate the area pertaining to the “road/river” land-use
 category, the sum of the area of all the land-use categories was
 subtracted from the clipped study area. To examine whether <em>P</em>.
 <em>poliocephalus</em> foraged at random across the landscape,
 we compared the proportion of feeding fixes in each land-use category to
 the proportion that would be expected based on the area of each land-use
 category in the study area, using a Chi-squared test for given
 probabilities. Food plant
 species To investigate the
 food plant species visited by foraging individuals, we first read fixes
 pertaining to moderate levels of activity at night into Google Earth Pro
 and confirmed that all likely feeding fixes were in trees. This resulted
 in 489 feeding tree locations. A random sample (n = 322) of the total 489
 feeding tree locations, were approached in person. Of these, some (n = 35)
 could not be visited due to dangerous terrain or formal access
 restrictions; of which 60% were in residential areas, 20% in reserves, 9%
 in agricultural areas, 6% in mining and road/river areas, and 3% in
 utilities (Supplementary Table 2). The frequency of inaccessible fixes in
 each land-use category was not significantly different to the observed
 frequency of feeding fixes in each land-use category overall (Pearson’s
 Chi-squared test n = 489; χ 2 = 55, df = 50, p = 0.29), and thus,
 exclusion of these fixes from the dataset is unlikely to bias the
 results. Each accessible
 feeding tree (n = 287) was photographed, and samples of bark, leaves,
 buds, flowers and fruit were taken, where possible, to aid in species
 identification. In addition, close-up photographs of plant characteristics
 using a telephoto zoom lens were captured, particularly where direct
 samples were unable to be obtained (see Figure 2A-F). Tree characteristics
 including details on the bark, leaves, buds, fruits and inflorescences
 were recorded for each tree following the EUCLID key, and an attempt at
 identifying the species was made using tree identification guides
 (Nicolle, 2013; Centre for Australian National Biodiversity Research
 [CANBR], 2015; Brooker and Kleinig, 2016; Lucid, 2016). Author DN provided
 subsequent validations on each food plant species from photographs and
 detailed field notes. Of the 287 food plants assessed, 22 lacked
 sufficient detail and thus could not be confidently identified. Therefore,
 a total of 265 individual food plants were identified. We used known distributions from plant
 identification guides (Nicolle, 2013; Centre for Australian National
 Biodiversity Research [CANBR], 2015; Brooker and Kleinig, 2016; Lucid,
 2016) to classify each identified tree (n = 265) as “locally indigenous”
 to the study region, “non-indigenous Australian native,” or
 “non-Australian” species, this is referred to as their “geographic
 origin.” Where plants could not be identified to species level, their
 origin was conservatively classified as “unknown.” Here, two Pearson’s Chi-squared tests were
 conducted, respectively, to examine (i) whether the relative number of
 unique species that occurred in each of the four “geographic origin”
 categories (i.e., locally indigenous, non-indigenous Australian native,
 non-Australian, and unknown) varied across land-use categories
 (Supplementary Table 3); and (ii) whether the relative number of visits
 (i.e., feeding fixes) to trees of varying “geographic origin” (i.e.,
 locally indigenous, non-indigenous Australian native, non-Australian, and
 unknown) varied across the visited land-use categories (Supplementary
 Table 4). Food plant
 species flowering and fruiting phenologies Phenology tables were constructed by
 compiling flowering and fruiting data collected from a range of published
 articles, online databases, and apiary flowering records. Where data were
 available, the peak time of flowering/fruiting, duration of
 flowering/fruiting in months, and annual reliability of
 flowering/fruiting, was recorded and standardized. Where records from the
 Northern Hemisphere were used, flowering/fruiting phenology was
 standardized by stating in which season flowering was recorded. 
 Data filtering, compilation and
 analyses were performed in the R environment for statistical computing
 (version 4.1.2; R Core Team, 2017) interfaced with RStudio (version
 2021.09.0; RStudio Team, 2015).