DOI: 10.5061/dryad.08kprr50d
title: Anthropogenic noise reduces avian feeding efficiency and increases vigilance along an urban-rural gradient regardless of species’ tolerances to urbanisation

description: Anthropogenic noise can adversely impact urban bird populations by
 interfering with vocal communication. Less research has addressed if
 anthropogenic noise masks the adventitious sounds that birds use to aid
 predator detection, which may lead to increased vigilance and reduced
 feeding efficiency. We test this hypothesis using a controlled playback
 experiment along an urban-rural gradient in Sheffield (UK). We also test
 the related predictions that anthropogenic noise has the greatest impacts
 on vigilance and feeding efficiency in rural populations, and on species
 that are more sensitive to urbanisation. We focus on six passerines, in
 order from most to least urbanised (based on how urbanisation influences
 population densities): blue tit Cyanistes caeruleus, robin Erithacus
 rubeculla, great tit Parus major, chaffinch Fringilla coelebs, coal tit
 Periparus ater and nuthatch Sitta europaea. We used play-back of
 anthropogenic urban noise and a control treatment at 46 feeding stations
 located along the urban-rural gradient. We assess impacts on willingness
 to visit feeders, feeding and vigilance rates. Exposure to anthropogenic
 noise reduced visit rates to supplementary feeding stations, reduced
 feeding rates and increased vigilance. Birds at more urban sites exhibit
 less marked treatment induced reductions in feeding rates, suggesting that
 urban populations may be partially habituated or adapted to noisy
 environments. There was no evidence, however, that more urbanised species
 were less sensitive to the impacts of noise on any response variable. Our
 results support the adventitious sound masking hypothesis. Urban noise may
 thus interfere with the ability of birds to detect predators, reducing
 their willingness to use food rich environments and increase vigilance
 rates resulting in reduced feeding rates. These adverse impacts may
 compromise the quality of otherwise suitable foraging habitats in noisy
 urban areas. They are likely to be widespread as they arise in a range of
 species including common urban birds.
description: The methodology used to collect these data is described in
 Merrall, E. S. and Evans, K. L. 2020. Anthropogenic noise reduces avian
 feeding efficiency and increases vig-ilance along an urban-rural gradient
 regardless of species’ tolerances to urbanisation. – J. Avian Biol. 2020:
 e02341 <i>Site selection and urbanisation
 metrics</i> Work was conducted in and around
 Sheffield (53°22′N, 1°20′W), which is England’s fifth largest city, with a
 population of c. 575,000.  We defined urban areas as 1km x 1km grid cells
 with more than 25% impervious surface (following Gaston et al. 2005) and
 selected 16 urban sites within 3 km of the city centre, 19 urban sites
 that were more than 3 km from the city centre and 11 rural sites that were
 between 1 and 3 kms away from the city’s outer limits (using the above
 definition of urban areas). This approach enabled us to haphazardly select
 sites that were spread across the urban to rural gradient (see below for
 quantification), with the fewest sites in rural areas due to their greater
 homogeneity in background noise levels. All sites within the urban area
 were public parks and green-spaces with some woody vegetation cover, and
 all rural sites were woodland (rather than the alternatives of moorland or
 farmland) to maximise the similarity of the focal bird community along the
 urban to rural gradient.  Urbanisation intensity was
 quantified at each site using the ‘Urbanisation Index’ software developed
 by Seress et al., 2014 (available at:
 https://keplab.mik.uni-pannon.hu/en/urbanization-index). This software
 uses a semi-automated method where it takes a 1km<sup>2</sup>
 area from google maps around the coordinates of each location, and then
 uses manually inputted training points to score each image for vegetation
 cover, forest, buildings and paved roads using 100x100m<sup>2
 </sup>cells. It then uses principle component analysis (PCA) to
 calculate an urbanisation intensity score for each area. <i>Experimental
 design</i> Field work
 took place between the 12<sup>th</sup> February and
 3<sup>nd</sup> April 2019. Our general approach was to assess
 avian feeding and vigilance rates at feeding stations under three
 experimental conditions: play back of urban noise and two control
 treatments (play back of natural noise and a silent control without
 playback).  The urban recording was created by splicing together 5-minute
 sound recordings from each of four locations in central urban Sheffield
 using a Zoom H4n sound recorder and Cubase LE AI Elements 10, with a
 mixture of traffic, pedestrian and construction noise. The urban noise
 treatment was played at approximately 80 decibels (dbc), i.e. the typical
 volume of anthropogenic noise in busy urban areas during the day (Maryland
 SHA, 2018). The natural control used a mixture of songs of summer migrants
 (barn swallow <i>Hirundo rustica</i>; whinchat
 <i>Saxicola rubetra</i>; redstart <i>Phoenicurus
 phoenicurus</i> and common whitethroat <i>Sylvia
 communis</i>). Recordings were obtained from Xeno-canto (2005-2019)
 and were vetted to ensure that they did not include alarm calls to ensure
 that this treatment did not include vocalisations to which birds using the
 feeders were likely to respond. The natural sound control treatment was
 played at approximately 40 decibels (dbc) that matched natural sound
 levels of avian vocalisations. The urban treatment lasted 40 minutes while
 each control lasted 20 minutes, with a 10-minute habituation period
 between equipment set-up and beginning playback, and between the urban
 treatment and the two control treatments (which each lasted 20 minutes). 
 We used two green, portable SONY SRS-XB10 Bluetooth controlled speakers
 (IPX rated 5) at each site located approximately a metre from the feeding
 station in a spatial configuration that created a surround sound
 effect. Each site’s feeding station was set up four to
 seven days before conducting the experiment to enable birds to habituate
 to the presence of the food source. Each station consisted of two
 standardised hanging feeders, each with two feeding ports, filled with
 sunflower hearts. These are a nutritious food source that has a negligible
 handling time and is thus widely used by a wide range of species. To
 reduce the risk of disturbance from grey squirrels <i>Sciurus
 carolinensis</i>, which can discourage birds from using feeders
 (Bonnington et al. 2014), poles were greased and sunflower hearts were
 coated in chilli powder (which squirrels avoid, whilst birds exhibit no
 adverse response). Feeders were placed in relatively open locations to
 allow easy observation, but close to vegetation cover to encourage birds
 to approach and use the feeders. Feeding stations were located away from
 footpaths to minimise effects of human disturbance. Treatments were applied in a
 randomised order at each site (with the three treatments being applied
 sequentially). Sites were visited in a haphazard manner with regard to the
 site’s urbanisation score. All data collection took place at least 1.5
 hours after civil dawn and before civil dusk, to avoid spikes in bird
 activity early and late in the day. Data were not collected when it was
 snowing or raining (except occasional light drizzle), or at high wind
 speeds as such conditions interfered with activity levels and noise
 transmission. Each treatment was filmed using a
 Panasonic (HC-X920) HD Camcorder and observed from an approximately 10m to
 15m distance. Videos recorded birds feeding on the feeder and those
 feeding on spilt food beneath the feeder. If disturbance events occurred
 during the treatment, such as a human or dog passing close to the feeder,
 and interrupted birds’ feeding behaviour, data collection was paused until
 birds resumed normal activity. Videos were analysed, and for each visit we
 recorded the species, visit duration (seconds), number of pecks (as a
 measure of feeding rate) and the amount of time (seconds) spent performing
 vigilance behaviour, defined following Quinn et al. (2006), as when the
 bird raised its head and scanned. For each site, the temperature
 (<sup>o</sup>C) and wind speed (kn) were also recorded using
 data from the nearest weather observation site (Met Office, 2019). The
 seven weather stations used ranged from 220 m to 6.1 km away from the
 study site. <i>Statistical
 analysis</i> All statistical analyses were
 conducted using R Studio (RStudio Team, 2016). Three response metrics were
 calculated from the videos for each species per treatment per site: i)
 visit rate, i.e. the rate at which the species visited the feeder per hour
 of treatment, ii) the peck rate per hour of treatment, and iii) the
 vigilance rate (in minutes) per hour of time spent on the feeder. A total
 of 19 species were observed using the feeders or feeding on fallen seed on
 the ground below, of which six occurred at ten or more sites and were
 included in data analysis (great tit <i>Parus major </i>46
 sites; blue tit <i>Cyanistes caeruleus</i> 44 sites; coal tit
 <i>Periparus ater</i> 33 sites; Eurasian robin
 <i>Erithacus rubeculla</i> 35 sites; nuthatch <i>Sitta
 europaea</i> 14 sites; and chaffinch <i>Fringilla
 coelebs</i> 13 sites). These species vary substantially in their
 ability to maintain high population densities in urban environments. This
 is indicated by their urbanisation scores as calculated by Evans et al.
 (2011), i.e. the ratio of urban to rural population densities obtained
 from Breeding Bird Survey data from approximately 3,000 randomly selected
 1 km x 1 km squares located across the UK. These scores, from most to
 least urbanised species, are: blue tit 1.46; robin 0.99; great tit 0.74;
 chaffinch 0.25; coal tit 0.23, nuthatch 0.17. Matched paired t-tests
 demonstrated that each of these six species’ visit, peck and vigilance
 rates did not differ between the two forms of control (<i>P
 </i>ranges from  0.110 to 0.877) and these data were thus merged to
 form a single control treatment.  We modelled visit, peck and
 vigilance rates using mixed effect models (lme4 package; Bates et al,
 2015). These models pool data across species and include the following
 main effects as predictors: species (fixed effect), treatment (fixed
 effect), the site’s urbanisation intensity, date (number of days from the
 1<sup>st</sup> of January), time of day (hours since sunrise),
 temperature (°C), wind-speed (kn), and site (random effect). We also
 included the following interaction terms: species*treatment*urbanisation
 intensity (to test the predictions that treatment impacts vary with
 species and along the urbanisation gradient); species*treatment (to test
 the prediction that species vary in their responses to treatment);
 treatment*urbanisation intensity (to test the prediction that, regardless
 of species identity, urban populations are less sensitive to treatment
 effects); and species*urbanisation intensity (to take into account the
 potential that species’ vary in how their visit, peck and vigilance rates
 change along the urbanisation gradient, although such patterns are not
 associated with our objectives of assessing impacts of urban noise and are
 not explored further). A significant species*treatment interaction term
 would indicate that species varied in their responses to the urban noise
 treatment. Consideration of the correlations between each species’
 parameter estimates from this interaction term and their urbanisation
 scores (from Evans et al. 2011; see above) enable us to assess if species
 with greater tolerance to urban development are less sensitive to
 treatment effects than species that are more impacted by urban
 development. We start by building a full model that
 includes all main effects, the three-way interaction term and the three
 two-way interaction terms. We simplify this full model by removing
 non-significant interaction terms in a step-wise manner according to their
 P values, with significance being determined using the car package (Fox
 and Weisberg, 2019) to calculate Wald chi-square statistics (Type III). We
 use this approach as the alternative of using information theoretic
 approaches based on criteria such as change in Aikaike Information
 Criteria (AIC) is not recommended when models include interaction terms
 (Cade, 2015).  
 References Bates, D.,
 Maechler, M., Bolker, B., Walker, S. 2015. Fitting Linear Mixed-Effects
 Models Using lme4. J. Stat. Software, 67: 1-48. Bonnington, C.,
 Gaston, K. J. and Evans, K. L. 2014. Assessing the potential for Grey
 Squirrels <i>Sciurus carolinensis</i> to compete with birds at
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description: Both files contain metadata sheets that explain each
 variable.