Aggressive behavioural interactions between swans (Cygnus spp.) and other waterbirds during winter: A webcam-based study

Abstract

Background
Our understanding of any impacts of swans on other waterbirds (including other swans), and potential effects on waterbird community structure, remain limited by a paucity of fundamental behavioural and ecological data, including which species swans interact aggressively with and how frequently such interactions occur.

Methods
Behavioural observations of aggression by swans and other waterbirds in winters 2018/2019 and 2019/2020, were carried out via live-streaming webcams at two wintering sites in the UK. All occurrence sampling was used to identify all aggressive interactions between conspecific or heterospecifics individuals, whilst focal observations were used to record the total time spent by swans on aggressive interactions with other swans. Binomial tests were then used to assess whether the proportion of intraspecific aggressive interactions of each species differed from 0.5 (which would indicate equal numbers of intraspecific and interspecific interactions). Zero-inflated generalized linear mixed effects models (ZIGLMMs) were used to assess between-individual variation in the total time spent by swans on aggressive interactions with other swans.

Results
All three swan species were most frequently aggressive towards, and received most aggression from, their conspecifics. Our 10-min focal observations showed that Whooper ( Cygnus cygnus ) and Bewick’s Swans ( C. columbianus bewickii ) spent 13.8 ± 4.7 s (means ± 95% CI) and 1.4 ± 0.3 s, respectively, on aggression with other swans. These durations were equivalent to 2.3% and 0.2% of the Whooper and Bewick’s Swan time-activity budgets, respectively. Model selection indicated that the time spent in aggressive interactions with other swans was best-explained by the number of other swans present for Whooper Swans, and an interactive effect of time of day and winter of observation for Bewick’s Swans. However, the relationship between swan numbers and Whooper Swan aggression times was not strong ( R ² = 19.3%).

Conclusions
Whilst swans do exhibit some aggression towards smaller waterbirds, the majority of aggression by swans is directed towards other swans. Aggression focused on conspecifics likely reflects greater overlap in resource use, and hence higher potential for competition, between individuals of the same species. Our study provides an example of how questions relating to avian behaviour can be addressed using methods of remote data collection such as live-streaming webcams.

Full text

Woodetal. Avian Res (2020) 11:30
https://doi.org/10.1186/s40657-020-00216-7
RESEARCH
Aggressive behavioural interactions
betweenswans (Cygnus spp.) andother
waterbirds duringwinter: awebcam-based
study
Kevin A. Wood1, Phoebe Ham2, Jake Scales3, Eleanor Wyeth2 and Paul E. Rose1,2*
Abstract
Background: Our understanding of any impacts of swans on other waterbirds (including other swans), and potential
effects on waterbird community structure, remain limited by a paucity of fundamental behavioural and ecological
data, including which species swans interact aggressively with and how frequently such interactions occur.
Methods: Behavioural observations of aggression by swans and other waterbirds in winters 2018/2019 and
2019/2020, were carried out via live-streaming webcams at two wintering sites in the UK. All occurrence sampling
was used to identify all aggressive interactions between conspecific or heterospecifics individuals, whilst focal obser-
vations were used to record the total time spent by swans on aggressive interactions with other swans. Binomial tests
were then used to assess whether the proportion of intraspecific aggressive interactions of each species differed from
0.5 (which would indicate equal numbers of intraspecific and interspecific interactions). Zero-inflated generalized
linear mixed effects models (ZIGLMMs) were used to assess between-individual variation in the total time spent by
swans on aggressive interactions with other swans.
Results: All three swan species were most frequently aggressive towards, and received most aggression from, their
conspecifics. Our 10-min focal observations showed that Whooper (Cygnus cygnus) and Bewick’s Swans (C. columbi-
anus bewickii) spent 13.8 ± 4.7 s (means ± 95% CI) and 1.4 ± 0.3 s, respectively, on aggression with other swans. These
durations were equivalent to 2.3% and 0.2% of the Whooper and Bewick’s Swan time-activity budgets, respectively.
Model selection indicated that the time spent in aggressive interactions with other swans was best-explained by the
number of other swans present for Whooper Swans, and an interactive effect of time of day and winter of observation
for Bewick’s Swans. However, the relationship between swan numbers and Whooper Swan aggression times was not
strong (R2 = 19.3%).
Conclusions: Whilst swans do exhibit some aggression towards smaller waterbirds, the majority of aggression by
swans is directed towards other swans. Aggression focused on conspecifics likely reflects greater overlap in resource
use, and hence higher potential for competition, between individuals of the same species. Our study provides an
example of how questions relating to avian behaviour can be addressed using methods of remote data collection
such as live-streaming webcams.
© The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing,
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Open Access
Avian Research
*Correspondence: p.rose@exeter.ac.uk
2 Centre for Research in Animal Behaviour, Psychology, Washington
Singer, University of Exeter, Perry Road, Exeter, Devon EX4 4QG, UK
Full list of author information is available at the end of the article

Page 2 of 16
Woodetal. Avian Res (2020) 11:30
Background
Communities of birds that use aquatic habitats (here-
after ‘waterbirds’) typically contain individuals from
multiple species, many of which overlap in terms of the
resources that they use (Pöysä 1983; Davis etal. 2014).
Such overlaps in resource use can result in aggressive
interactions between individuals within waterbird assem-
blages (Wood et al. 2017; Marchowski and Neubauer
2019). Aggressive behaviours allow individuals to gain
and maintain access to valuable limited resources such
as food or preferred breeding or resting locations, and
to deny other individuals access to those resources (King
1973; Amat 1990; Pelligrini 2008).
Among waterbird assemblages, the true swans (Cygnus
spp.) are large-bodied, herbivorous waterbirds found on
all continents except Antarctica (Rees etal. 2019). Previ-
ous authors have highlighted that swans exhibit aggres-
sion towards both conspecifics and heterospecifics
(Johnsgard 1965). Indeed, there are numerous examples
in the literature of aggression by swans towards other
waterbirds (e.g. Johnsgard 1965; Tingay 1974; Ely etal.
1987; Burgess and Stickney 1994; Gurtovaya 2000). Swans
have been observed to threaten other birds with stylised
displays (Lind 1984), and to attack with their bill, wings
and body (Johnsgard 1965; Burgess and Stickney 1994),
which in some instances has resulted in the death of the
targeted individual (e.g. Stone and Marsters 1970; Dela-
cour 1973). For example, Ely etal. (1987) reported that
attacks by Whistling Swans (Cygnus columbianus colum-
bianus, a subspecies of Tundra Swan) killed two Greater
White-fronted Goose (Anser albifrons) goslings during
the breeding season. Similarly, Brazil (1983) observed a
Whooper Swan (Cygnus cygnus) attack and kill a juvenile
Eurasian Wigeon (Mareca penelope) in Iceland. Despite
such reports, recent studies have pointed out that the
extent of aggressive behaviours by swans, and the possi-
ble impacts that these may have on other waterbirds, are
poorly understood (Gayet etal. 2011; Wood etal. 2019a).
Moreover, two studies of breeding waterbirds found no
evidence that swans exclude other waterbirds from habi-
tat or reduce breeding densities (Gayet etal. 2011, 2016).
A recent meta-analysis cast further doubt, by show-
ing that swans spent no more time engaged in aggres-
sive behavioural interactions than other waterbird taxa
(Wood etal. 2017).
Nevertheless, aggression by swans towards other
waterbirds continues to be relevant for the management
and conservation of waterbirds and their habitats. In
North America, there have been attempts to eradicate
an invasive population of Mute Swans (Cygnus olor) due
to concerns about the effects of swans on native spe-
cies, including other waterbirds. For example, in Mary-
land, USA, a newly established Mute Swan moulting
flock displaced a mixed breeding colony of Least Tern
(Sterna antillarum) and Black Skimmer (Rynchops niger),
two species of local conservation concern (erres and
Brinkler 2004). Waterbird managers and conservation-
ists have also expressed concerns that the invasive Mute
Swans could out-compete the native Trumpeter (Cyg-
nus buccinator) or Whistling Swans for food and habi-
tat (Lumsden 2016). Similarly, conservationists have
questioned whether the observed c. 39% decline in win-
ter Bewick’s Swan (C. columbianus bewickii) numbers
in north-west Europe between 1995 and 2010 may be
at least partially attributable to competition with ris-
ing numbers of Whooper and Mute Swans in the region
(Rees et al. 2019). Even among conspecifics, interfer-
ence competition among foraging swans can reduce food
intake rates (Gyimesi etal. 2010). In the Russian Arctic,
concerns of local hunters regarding the impacts of swans
on other waterbirds have been identified as a motivation
for the illegal persecution of Bewick’s Swans (Newth etal.
in press). Similarly, legal hunting of Whistling Swans
in parts of the USA has been justified on the basis that
competition between the swans and other waterbirds
has been deemed excessive (Sladen 1991). However, our
understanding of any impacts of swans on other water-
birds (including other swans), and potential effects on
waterbird community structure, remain limited by a
paucity of fundamental behavioural and ecological data,
including which species swans interact aggressively with
and how frequently such interactions occur.
In this study we used repeated behavioural observa-
tions at two wintering sites to improve our understand-
ing of which species swans interact aggressively with
and how frequently such interactions occur. We used
the behavioural data that we obtained to test three
hypotheses. As Peiman and Robinson (2010) reported
that aggression is more likely between individuals with
the greatest overlap in resource use, our first hypoth-
esis (hereafter termed the ‘conspecific hypothesis’)
was that swans would devote more time to intraspe-
cific aggression compared with interspecific aggres-
sion. Aggressive behaviours among birds typically
become more common as the density of individuals
within a habitat increases (Metcalfe and Furness 1987;
Keywords: Aggression, Agonistic behaviour, Bewick’s Swans, Intraspecific versus interspecific competition, Remote
data collection, Waterfowl, Whooper Swans

Page 3 of 16
Woodetal. Avian Res (2020) 11:30
Wood etal. 2015), thus our second hypothesis (here-
after termed the ‘density hypothesis’) was that swans
would spend more time engaged in aggressive interac-
tions when present in higher density flocks. Finally, we
expected that as winter progressed, a dominance hier-
archy would establish among individual swans within
their flock, and so reduce further aggression; indeed,
Scott (1981) reported that the frequency of fights
between Bewick’s Swans declined progressively over
winter months. Therefore, our third hypothesis (here-
after termed the ‘winter decline hypothesis’) was that
swans would spend more time engaged in aggressive
interactions in early winter months compared with
later months.
Methods
Study systems
Our study focused on two wetlands used by wintering
waterbirds in the UK; the Wildfowl & Wetland Trust
(WWT) Centre reserves at Slimbridge (51°44ʹ29.3ʺ
N, 2°24ʹ21.52ʺ W) and Caerlaverock (54°59ʹ2.4ʺ N,
3°30ʹ0ʺ W), in southwest England and southwest Scot-
land, respectively. Both wetland sites feature a mosaic
of aquatic and terrestrial habitats, most notably small
lakes used by waterbirds for feeding and roosting. In
recent winters Slimbridge has supported both Bewick’s
Swans and Mute Swans, whilst Caerlaverock has sup-
ported Mute and Whooper Swans (Black and Rees
1984). e mean of the peak winter counts of individ-
ual swans between 2014/2015 and 2018/2019 was 149
Bewick’s Swans (range 116–212) and 441 Mute Swans
(range 374–500) in the Severn Estuary (which includes
Slimbridge), and 337 Whooper (range 257–487) and
74 Mute Swans (range 70–83) in the Solway Estuary
(which includes Caerlaverock) (Frost etal. 2020). Swans
at both sites share feeding and roosting habitat with a
range of smaller-bodied waterbird species, including
geese such as Canada Geese (Branta canadensis) and
Greylag Geese (Anser anser), dabbling ducks such as
Northern Mallard (Anas platyrhynchos), Eurasian Teal
(Anas crecca), and Northern Pintail (Anas acuta), div-
ing ducks such as Tufted Duck (Aythya fuligula) and
Common Pochard (Aythya ferina), Rallidae such as
Eurasian Coot (Fulica atra) and Common Moorhen
(Gallinula chloropus), as well as Common Shelduck
(Tadorna tadorna), and gull species (Larus spp.). e
non-migratory Mute Swans are resident at both Slim-
bridge and Caerlaverock throughout the year, and
Bewick’s Swans are typically present at Slimbridge
between November and February, whilst Whooper
Swans use Caerlaverock between October and March
(Black and Rees 1984; Rees 2006).
Data collection
We used two ways of collecting data concurrently from
observations made during periods of one hour in dura-
tion: one way was used to record aggression between
any two individuals of any waterbird species to address
our conspecific hypothesis, and the second way was used
to record swan-specific aggression to address our den-
sity and winter decline hypotheses. First we used all
occurrence sampling to record all incidents of aggres-
sion between individuals over the course of the hour-
long observation period (Altmann 1974), based on an
ethogram of the aggressive behaviours observed during
preliminary observations at both sites in October 2018.
ese preliminary observations indicated a number of
aggressive behaviours consistent with previous work
(Johnsgard 1965), including strikes made with the bill,
wings, or body, as well as chasing and lunging at another
individual. For each aggressive behaviour that was
observed, the species identities of both the aggressor and
its opponent were recorded; all species were identified on
the basis of size, body shape, and plumage characteris-
tics, with the aid of an online photo-identification guide
(RSPB 2018).
To address our density and winter decline hypotheses
regarding the variations in aggression with swan num-
bers and between months, we used focal sampling (Alt-
mann 1974) to quantify the total time that Bewick’s and
Whooper Swans spent engaged in aggressive interactions
with other swans. During each hour-long observation
period, an observer selected a swan at random and used a
stopwatch to record the duration of each aggressive inter-
action with another swan in a 10-min observation period;
hence, six individual swans could be observed during
each hour-long observation. A focal observation dura-
tion of 10min was selected in order to make our study
comparable with earlier time-activity budget of swans
that used an observation duration of 10min (e.g. O’Hare
et al. 2007; Tatu etal. 2007; Wood etal. 2019b). Both
immediately before and after each hour-long observation
period, the number of swans that could be observed was
counted, with the mean average taken as the number of
swans present during that observation.
All behavioural data were collected remotely via live-
streaming webcams (AXIS Q6035-E PTZ Dome Network
Camera), which were fixed in place at both sites. Both
webcams faced directly outwards over the study lake
from the shore, and each webcam maintained the same
zoom so that the field-of-view was standardised across
all observation periods (Additional file1: Figure S1). As
our study was conducted entirely via the webcams, we
do not know the precise numbers of birds outside of the
cameras field-of-view, but we suspect it to be low as the
cameras covered major proportions of the surface area

Page 4 of 16
Woodetal. Avian Res (2020) 11:30
of each lake (Additional file1: Figure S1). Webcams have
been shown previously to be useful tools in behavioural
studies, which allow remote collection of data with lim-
ited disturbance to the focal birds (e.g. Anderson etal.
2011; Schulwitz et al. 2018). During winter months
in 2018/2019 (November 2018–February 2019, inclu-
sive) and 2019/2020 (November 2019–December 2019,
inclusive), webcam footage from WWT Slimbridge was
watched for 1h on an average of 7.5days per month at
either 08:30 a.m. to 09:30 a.m. (hereafter “AM”), 11:30
a.m. to 12:30 p.m. (hereafter “MID”) or 14:30 p.m. to
15:30p.m. (hereafter “PM”); these times were selected to
achieve a balanced study design and to avoid coinciding
with the periods when food (wheat grains) is provided as
part of a public engagement programme. Winter storms
at the start of January 2020 regrettably damaged the web-
cam and prevented further data collection. We aimed
to use the same methodology for WWT Caerlaverock,
however, the webcam suffered a technical failure after
data for only November 2018 could be collected. Simi-
lar technical issues precluded data collection at WWT
Caerlaverock during winter 2019/2020. Our sampling
methodology of ten observations per hour, on an aver-
age of 7.5days per month, over 6months, yielded a total
of 450 observations at Slimbridge; however, swans were
only present during 282 of these. In contrast, swans were
present at Caerlaverock during every observation period.
In total, we therefore obtained 282 focal observations
(of 10min each) from WWT Slimbridge and 42 focal
observations (of 10min each) from WWT Caerlaverock.
While we cannot discount the possibility that some indi-
viduals were observed on multiple occasions, the afore-
mentioned large numbers of individual swans present at
both sites mean that this is unlikely to have been a major
issue.
Statistical analyses
All statistical analyses were carried out using R ver-
sion 3.6.3 (R Core Team 2020). To address our conspe-
cific hypothesis that intraspecific interactions would be
observed more frequently than interspecific interactions,
we used two-tailed binomial tests to assess the statis-
tical significance of the deviation of the proportion of
intraspecific aggressive interactions of each species from
0.5 (which would indicate equal numbers of intraspecific
and interspecific interactions). For each of our focal spe-
cies, separate tests were carried out for aggressive inter-
actions (i) targeted at other individuals, and (ii) received
from other individuals. Statistically significant differences
between proportions were attributed where P < 0.05,
after P values had been adjusted using Holm-Bonferroni
corrections to account for multiple comparisons (Holm
1979). Our binomial tests also allowed 95% confidence
intervals to be estimated for the proportions of intraspe-
cific and interspecific interactions (Clopper and Pearson
1934). In addition, we used two-sample binomial tests for
equality of proportions to assess the significance of the
differences between species in their proportion of inter-
specific aggressive interactions recorded towards other
birds; P values were again adjusted using Holm-Bonfer-
roni corrections (Holm 1979).
To address our density and winter decline hypotheses
regarding the variations in aggression with swan num-
bers and between months, we used zero-inflated general-
ized linear mixed effects models (ZIGLMMs), using the
glmmTMB R package (Brooks etal. 2017). Zero-inflated
models were required because of the relatively high pro-
portions of zeros in the data sets (i.e. observations during
which no aggression was recorded). In each model, our
response variable was the number of seconds spent in
aggressive interactions by each individual recorded dur-
ing our focal observation. As the aforementioned issues
with the webcam at WWT Caerlaverock prevented data
collection after November 2018, the resulting datasets
for Bewick’s and Whooper Swans were markedly differ-
ent in terms of the sample size and the temporal repli-
cation. erefore, we analysed the data for Whooper
and Bewick’s Swans separately. For each species, we ran
and compared candidate models that comprised all pos-
sible combinations of additive and two-way interactions
between our explanatory variables, as well as the null
model. For Whooper Swans, we considered the following
explanatory variables: (i) the time of day of the observa-
tion, a categorical factor with three levels (AM, MID, and
PM), and (ii) the mean number of swans present during
the observation. For Bewick’s Swans we considered the
following explanatory variables: (i) the time of day of the
observation, a categorical factor with three levels (AM,
MID, and PM), (ii) the mean number of swans present
during the observation, (iii) the month of observation, a
categorical factor with four levels (November, December,
January, and February), and (iv) the winter of observa-
tion, a categorical factor with two levels (“A” = 2018/2019
and “B” = 2019/2020). In addition, a categorical variable
unique to each observation block (termed the ‘observa-
tion identity’), was fitted as a random intercept in each
model to account for the non-independence of individ-
ual swans observed within the same hour-long observa-
tion period. Preliminary comparisons of global models
using second-order Akaike’s Information Criteria (AICc;
Burnham etal. 2011) showed that for the zero-inflated
generalized linear mixed effects models of the Whooper
Swan data, a negative binomial distribution in which the
variance increased linearly with the mean (Brooks etal.
2017) performed better than either a negative binomial
distribution in which the variance increased quadratically

Page 5 of 16
Woodetal. Avian Res (2020) 11:30
with the mean (ΔAICc = 4.31), or a Poisson distribution
(ΔAICc = 169.40). For Bewick’s Swans there was little dif-
ference between models with either of the two negative
binomial distributions (ΔAICc = 0.24), whilst the Poisson
distribution did not converge. erefore, in all subse-
quent models we used the negative binomial distribution
in which the variance increased linearly with the mean.
To ensure that collinearity did not confound our mod-
elling (Dormann et al. 2013), we tested for covariance
among our explanatory variables. To our knowledge, Var-
iance Inflation Factors (VIFs), which are typically used to
identify collinear variables in linear models (Dormann
et al. 2013), are not currently available for ZIGLMMs,
and so alternative methods were used. One-way Analy-
sis of Variance (ANOVA) was used to test for covariance
between our continuous variable (number of swans pre-
sent) and each of our categorical variables: time of day,
month, and winter. Significant covariance was inferred
where statistically significant differences in the mean
number of swans present per categorical variable level
were detected. Values for the number of swans present
were square-root transformed so that model residuals
satisfied the assumptions of the ANOVA tests (Zuur etal.
2010). Associations between the frequencies of pairs of
categorical variables were tested using χ2 tests where all
frequencies were ≥ 5, or Fisher’s exact test where one or
more frequencies were < 5 (Crawley 2013). Significant
covariance between variables was inferred where statisti-
cally significant associations between the variables were
found. Using these methods, we found for Whooper
Swans a significant effect of time of day on the num-
ber of swans present (ANOVA: F2,39 = 47.27, P < 0.001).
For Bewick’s Swans we detected covariance between
the number of swans present and both the time of day
(ANOVA: F2,279 = 12.24, P < 0.001) and winter (ANOVA:
F1,280 = 22.81, P < 0.001), as well as between winter and
month (Fisher’s exact test: P < 0.001); all other tests were
non-significant (P > 0.05). Consequently, these collinear
variables were not permitted within the same candidate
models. erefore, in total we ran 3 and 11 candidate
models for the Whooper Swan and Bewick’s Swan data-
sets, respectively, accounting for all possible non-col-
linear combinations of additive and two-way interactions
between our explanatory variables.
We compared the relative support of each candi-
date model using AICc, calculated using the MuMIn
R package (Barton 2019). Typically the model with
the lowest AICc value is considered to be the best-
supported by the data, but we also considered models
to be competitive where AICc v alues < 6.0, following
the advice of Richards (2008) for dealing with over-
dispersion. Furthermore, to avoid selecting models
with uninformative parameters, we considered that a
model with one additional parameter was competitive
only if the associated AICc value was lower than the
more parsimonious model (Arnold 2010). Three fur-
ther metrics for each model were used as indicators of
the relative strength of support in the data, to facili-
tate more detailed comparisons among our candidate
models: (i) the probability of a model being the best-
fitting model compared with the best-supported model
shown by AICc (Relative Likelihood RL), (ii) the ratio
of ΔAICc values for each model relative to the whole
set of candidate models (Akaike weight wi), and (iii)
how many more times less likely a model is to be the
best-fitting model compared with the best-supported
model shown by AICc (Evidence Ratio ER) (Burnham
et al. 2011). In addition, to quantify the explanatory
power of each model (Mac Nally etal. 2018), the con-
ditional and marginal R2 values, which represented
the proportion of the between-swan variance in the
time spent on aggression that was accounted for both
the fixed and random effects combined and the fixed
effects alone, respectively (Nakagawa etal. 2017), were
calculated for each model using the sjstats R package
(Lüdecke 2020). Finally, Tukey’s post hoc comparisons
of the estimated marginal means of variables within
our best-supported models were carried out using the
emmeans R package (Lenth 2020).
Results
Intraspecic versusinterspecic aggression
For 13 out of 14 focal waterbird species, the major-
ity of aggressive behavioural interactions were given by,
and received from, conspecifics (Table 1). Overall, we
observed aggression by the three swan species towards
9 of the 11 smaller waterbird species, whilst in turn
the swans received aggression from 8 of the 11 species
(Table1).
e proportion of aggressive interactions directed by
swans towards conspecifics ranged from 0.589 (95% CI
0.540–0.637) among Bewick’s Swans to 0.801 (0.733–
0.858) among Whooper Swans (Table2; Fig.1). Similarly,
the proportion of aggressive interactions received by
swans from their conspecifics ranged from 0.623 (0.555–
0.687) for Whooper Swans up to 0.912 (0.880–0.938)
among Mute Swans (Table2; Fig.1). For both the inter-
actions directed towards, and received from, conspecif-
ics, the proportions of aggressive interactions that were
intraspecific were significantly greater for all three swan
species (P ≤ 0.003 in all cases); i.e. intraspecific interac-
tions were more frequent than interspecific interactions
(Table2; Fig.1). Among the 11 species of smaller water-
birds, intraspecific interactions directed towards other
individuals were significantly more frequent in 8 species,

Page 6 of 16
Woodetal. Avian Res (2020) 11:30
Table 1 The total numbers of aggressive interactions (n)
givenandreceived byeach focal waterbird species
Focal species Aggression
towards
nAggression from n
Bewick’s Swan Bewick’s Swan 245 Bewick’s Swan 245
Northern Pintail 37 Northern Pintail 11
Tufted Duck 32 Tufted Duck 3
Eurasian Coot 30 Eurasian Coot 4
Canada Goose 19 Canada Goose 16
Eurasian Teal 17 Eurasian Teal 1
Northern Mallard 11 Northern Mallard 2
Common Moorhen 9 Common Moor-
hen 1
Gull spp. 9 Gull spp. 0
Greylag Goose 7 Greylag Goose 7
Mute Swan 0 Mute Swan 23
Mute Swan Mute Swan 364 Mute Swan 364
Whooper Swan 80 Whooper Swan 27
Northern Mallard 31 Northern Mallard 3
Bewick’s Swan 23 Bewick’s Swan 0
Canada Goose 19 Canada Goose 5
Northern Pintail 1 Northern Pintail 0
Gull spp. 1 Gull spp. 0
Whooper Swan Whooper Swan 137 Whooper Swan 137
Mute Swan 27 Mute Swan 80
Northern Mallard 7 Northern Mallard 1
Canada Goose 0 Canada Goose 2
Canada Goose Canada Goose 190 Canada Goose 190
Northern Mallard 28 Northern Mallard 3
Bewick’s Swan 16 Bewick’s Swan 19
Mute Swan 5 Mute Swan 19
Northern Pintail 2 Northern Pintail 1
Whooper Swan 2 Whooper Swan 0
Tufted Duck 1 Tufted Duck 0
Eurasian Teal 1Eurasian Teal 0
Greylag Goose 0 Greylag Goose 3
Greylag Goose Greylag Goose 39 Greylag Goose 39
Bewick’s Swan 7 Bewick’s Swan 7
Canada Goose 3 Canada Goose 0
Northern Mallard 2 Northern Mallard 0
Northern Pintail 2 Northern Pintail 0
Tufted Duck 1 Tufted Duck 0
Common Shelduck Common Shelduck 2 Common Shelduck 2
Eurasian Coot 3 Eurasian Coot 0
Northern Mallard Northern Mallard 240 Northern Mallard 240
Northern Pintail 56 Northern Pintail 26
Tufted Duck 9 Tufted Duck 23
Eurasian Coot 7 Eurasian Coot 1
Eurasian Teal 6Eurasian Teal 4
Common Moorhen 6 Common Moor-
hen 3
Canada Goose 4 Canada Goose 28
Table 1 (continued)
Focal species Aggression
towards
nAggression from n
Mute Swan 3 Mute Swan 31
Bewick’s Swan 2 Bewick’s Swan 11
Whooper Swan 1 Whooper Swan 7
Common Pochard 1 Common Pochard 0
Gull spp. 1 Gull spp. 0
Greylag Goose 0 Greylag Goose 2
Northern Pintail Northern Pintail 356 Northern Pintail 356
Eurasian Teal 38 Eurasian Teal 24
Northern Mallard 26 Northern Mallard 56
Common Moorhen 22 Common Moor-
hen 18
Bewick’s Swan 11 Bewick’s Swan 37
Eurasian Coot 8 Eurasian Coot 21
Tufted Duck 4 Tufted Duck 10
Canada Goose 1 Canada Goose 2
Mute Swan 0 Mute Swan 1
Greylag Goose 0 Greylag Goose 2
Eurasian Teal Eurasian Teal 146 Eurasian Teal 146
Northern Pintail 24 Northern Pintail 38
Eurasian Coot 10 Eurasian Coot 12
Northern Mallard 4 Northern Mallard 6
Bewick’s Swan 1 Bewick’s Swan 17
Canada Goose 0 Canada Goose 1
Tufted Duck Tufted Duck 152 Tufted Duck 152
Northern Mallard 23 Northern Mallard 9
Northern Pintail 10 Northern Pintail 4
Bewick’s Swan 3 Bewick’s Swan 32
Eurasian Coot 3 Eurasian Coot 1
Common Pochard 1 Common Pochard 3
Canada Goose 0 Canada Goose 1
Greylag Goose 0 Greylag Goose 1
Common Pochard Common Pochard 10 Common Pochard 10
Tufted Duck 3 Tufted Duck 1
Eurasian Coot 2 Eurasian Coot 0
Northern Mallard 0 Northern Mallard 1
Eurasian Coot Eurasian Coot 61 Eurasian Coot 61
Northern Pintail 20 Northern Pintail 8
Eurasian Teal 12 Eurasian Teal 10
Common Moorhen 4 Common Moor-
hen 3
Bewick’s Swan 3 Bewick’s Swan 30
Northern Mallard 1 Northern Mallard 7
Tufted Duck 0 Tufted Duck 3
Common Pochard 0 Common Pochard 2
Common Shelduck 0 Common Shelduck 3
Common Moorhen Common Moorhen 102 Common Moor-
hen 102
Northern Pintail 18 Northern Pintail 22
Northern Mallard 3 Northern Mallard 6

Page 7 of 16
Woodetal. Avian Res (2020) 11:30
whilst intraspecific interactions received from other
individuals were significantly more frequent in 7species
(Table2; Fig.1).
Species comparisons ofinterspecic aggression
e proportion of interspecific aggressive interactions
towards other birds was significantly greater in Bewick’s
Swans (mean = 0.411, 95% CI 0.363–0.460) than in
Mute Swans, Whooper Swans, Canada Geese, North-
ern Mallard, Northern Pintail, Eurasian Teal, Tufted
Duck, and Common Moorhen (Table3). e difference
in the proportions of interspecific aggression towards
other species in Whooper Swans (mean = 0.199, 95%
CI 0.142–0.267) and Eurasian Coots (mean = 0.396,
95% CI 0.300–0.498) was close to significance, with
an adjusted P value of 0.058. No other differences
between species were found to be statistically signifi-
cant (Table3).
Swan aggression times
Whooper Swans at WWT Caerlaverock spent a mean
(± 95% CI) of 13.8 ± 4.7 s engaged in aggressive interac-
tions with other swans, based on the sample of 42 focal
observations collected during November 2018; this dura-
tion was equivalent to 2.3 ± 0.8% of their time-activity
Table 1 (continued)
Focal species Aggression
towards
nAggression from n
Eurasian Coot 3 Eurasian Coot 4
Bewick’s Swan 1 Bewick’s Swan 9
Gull spp. Gull spp. 19 Gull spp. 19
Bewick’s Swan 0 Bewick’s Swan 9
Mute Swan 0 Mute Swan 1
Northern Mallard 0 Northern Mallard 1
Table 2 The intra- andinterspecic aggressive interactions givenandreceived byeach focal waterbird species
A comparison of the proportions of intraspecic (PIntra) and interspecic (PInter) aggressive interactions given and received by each focal waterbird species. All P values
associated with our binomial tests were adjusted using Holm-Bonferroni corrections for multiple comparisons. Total numbers of interactions (n) are also indicated
Species Interaction nIntra nInter nTotal PIntra PIntra 95% CI PInter PInter 95% CI P value
Bewick’s Swan To other 245 171 416 0.589 0.540–0.637 0.411 0.363–0.460 0.003
From other 245 68 313 0.783 0.733–0.827 0.217 0.173–0.267 < 0.001
Mute Swan To other 364 155 519 0.701 0.660–0.740 0.299 0.260–0.340 < 0.001
From other 364 35 399 0.912 0.880–0.938 0.088 0.062–0.120 < 0.001
Whooper Swan To other 137 34 171 0.801 0.733–0.858 0.199 0.142–0.267 < 0.001
From other 137 83 220 0.623 0.555–0.687 0.377 0.313–0.445 0.003
Canada Goose To other 190 55 245 0.776 0.718–0.826 0.224 0.174–0.282 < 0.001
From other 190 45 235 0.809 0.752–0.857 0.191 0.143–0.248 < 0.001
Greylag Goose To other 39 15 54 0.722 0.584–0.835 0.278 0.165–0.416 0.012
From other 39 7 46 0.848 0.711–0.937 0.152 0.063–0.289 < 0.001
Common Shelduck To other 2 3 5 0.400 0.053–0.853 0.600 0.147–0.947 1.000
From other 2 0 2 1.000 0.158–1.000 0.000 0.000–0.842 1.000
Northern Mallard To other 240 96 336 0.714 0.663–0.762 0.286 0.238–0.337 < 0.001
From other 240 136 376 0.638 0.587–0.687 0.362 0.313–0.413 < 0.001
Northern Pintail To other 356 110 466 0.764 0.723–0.802 0.236 0.198–0.277 < 0.001
From other 356 171 527 0.676 0.634–0.715 0.324 0.285–0.366 < 0.001
Eurasian Teal To other 146 39 185 0.789 0.723–0.846 0.211 0.154–0.277 < 0.001
From other 146 74 220 0.664 0.597–0.726 0.336 0.274–0.403 < 0.001
Tufted Duck To other 152 40 192 0.792 0.727–0.847 0.208 0.153–0.273 < 0.001
From other 152 51 203 0.749 0.683–0.807 0.251 0.193–0.317 < 0.001
Common Pochard To other 10 5 15 0.667 0.384–0.882 0.333 0.118–0.616 1.000
From other 10 2 12 0.833 0.516–0.979 0.167 0.021–0.484 0.270
Eurasian Coot To other 61 40 101 0.604 0.502–0.700 0.396 0.300–0.498 0.276
From other 61 66 127 0.480 0.391–0.571 0.520 0.429–0.609 1.000
Common Moorhen To other 102 25 127 0.803 0.723–0.868 0.197 0.132–0.277 < 0.001
From other 102 41 143 0.713 0.632–0.786 0.287 0.214–0.368 < 0.001
Gull spp. To other 19 0 19 1.000 0.824–1.000 0.000 0.000–0.176 < 0.001
From other 19 11 30 0.633 0.439–0.801 0.367 0.199–0.561 1.000

Page 8 of 16
Woodetal. Avian Res (2020) 11:30
budget. A comparison of candidate models showed that
the time spent by individual Whooper Swans in aggres-
sive interactions with other swans was best explained by
the mean number of swans present during the observa-
tion (Table4). is model had the lowest AICc value and
accounted for approximately 51% of the total Akaike
weights (Table4). In this model, the time spent in aggres-
sive interactions by Whooper Swans increased with the
mean number of swans present (Table5; Fig.2). Over-
all, the effect of swan numbers together with the ran-
dom effect accounted for 35.1% of the variance in swan
aggression times in total, with the effect of swan numbers
accounting for 19.3% of the variance. However, two other
models had associated AICc values within our threshold
of 6.0 of this best-supported model. e null model, com-
prised of only an intercept and random effect term, had a
ΔAICc value of 0.45 and accounted for c. 40% of the total
Akaike weights (Table4). As the model containing the
effect of swan numbers performed only marginally better
than the null model, this suggests that the effect of swan
numbers was not strong and had limited explanatory
power, and hence an effect of swan numbers should be
interpreted cautiously. Finally, a model in which aggres-
sion time varied with the time of day of the observation
had an associated ΔAICc value of 3.42; however, the over-
all support for this model was weak, as it accounted for
only c. 9% of the total Akaike weights and the evidence
ratio value indicated that it was > 5.5 times less likely to
be best-fitting model compared with the lowest AICc
model (Table4). Moreover, the model containing time
of day performed less well than the null model (Table4),
and so overall we considered that there was little evi-
dence that Whooper Swan aggression time varied among
the three times of day (Fig.3).
Bewick’s Swans at WWT Slimbridge spent a mean
(± 95% CI) of 1.4 ± 0.3s engaged in aggressive interac-
tions with other swans, based on the sample of 282 focal
observations collected during winters 2018/2019 and
2019/2020; this duration was equivalent to 0.2 ± 0.1%
of their time-activity budget. Comparison of our candi-
date models revealed that the time spent by individual
Bewick’s Swans in aggressive interactions with other
swans was best explained by an interaction between the
time of day and the winter of observation (Tables4, 5).
Post-hoc testing indicated that swan aggression times
varied between time of day and the winter of obser-
vation such that swans spent less time on aggression
during observations made during the afternoons in
winter 2019/2020 than during either the afternoons
of winter 2018/2019 or midday observations in win-
ter 2019/2020 (Table6; Fig. 3); no other comparisons
were significantly different. is model had the lowest
AICc value and accounted for > 66% of the total Akaike
weights (Table4). Overall, the effects of time of day and
winter together with the random effect accounted for
34.3% of the variance in swan aggression times in total,
with the effect of time of day and winter accounting
for 23.3% of the variance (Table4). Crucially, this best-
supported model had a lower AICc value than the null
model (ΔAICc = 3.24; Table 4). A further three candi-
date models also had ΔAICc values within our thresh-
old of 6.0 of the minimum AICc value, although none of
these performed better than the null model (Table4).
Two of these three models comprised the single addi-
tive effects contained within our best-supported model,
time of day (ΔAICc = 5.94) and winter (ΔAICc = 5.31).
e third comprised the numbers of swans present dur-
ing the observation (ΔAICc = 4.74), but the support
for this model was weak. e model of the numbers
of swans accounted for only c. 6% of the total Akaike
weights and the evidence ratio value indicated that
it was > 10.68 times less likely to be best-fitting model
compared with the lowest AICc model (Table4). As a
fixed effect the number of swans accounted for only
0.7% of the variance in the time spent in aggressive
interactions (Table 4; Fig. 2), and so overall we con-
sidered that there was little evidence that the numbers
of swans present had an effect on the time spent on
aggression by Bewick’s Swans.
Discussion
In accordance with our conspecific hypothesis, we found
that aggressive interactions by swans were typically
directed towards other swans rather than smaller water-
birds. Indeed, across all three swan species intraspe-
cific aggression accounted for between 59 and 80% of
all aggressive interactions directed at other individuals.
Previous studies of Mute Swans by Conover and Kania
(1994) and Włodarczyk and Minias (2015) found simi-
larly that 47% and 80%, respectively, of all aggressive
behaviours were directed towards conspecifics. Our data
showed that all three swan species received the greatest
proportion of interspecific aggression from another swan
Fig. 1 The proportions of intraspecific and interspecific aggressive interactions, directed towards or received from, other individuals. Error bars
represent the ± 95% binomial CIs. Common Shelduck and Gull spp. are not shown due to low sample sizes. Statistical significance of differences
between intraspecific and interspecific proportions is shown in Table 2
(See figure on next page.)

Page 9 of 16
Woodetal. Avian Res (2020) 11:30

Page 10 of 16
Woodetal. Avian Res (2020) 11:30
Table 3 Statistical comparison oftheproportion ofaggressive interactions thatwere interspecic
Binomial tests conducted on interspecic proportions towards other species, as reported in Table2. Values in the lower left triangle represent the χ2 test statistic, while the values in the upper right triangle represent the
adjusted P values
Bewick’s
Swan Mute Swan Whooper
Swan Canada
Goose Greylag
Goose Common
Shelduck Northern
Mallard Northern
Pintail Eurasian
Teal Tufted
Duck Common
Pochard Eurasian
Coot Common
Moorhen Gull spp.
Bewick’s
Swan – 0.037 0.000 0.000 1.000 1.000 0.040 0.000 0.000 0.000 1.000 1.000 0.002 0.067
Mute Swan 12.36 – 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.763
Whooper
Swan 23.09 5.95 – 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.058 1.000 1.000
Canada
Goose 23.03 4.23 0.26 – 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.140 1.000 1.000
Greylag
Goose 3.02 0.03 1.07 0.44 – 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
Common
Shelduck 0.16 0.94 2.60 2.06 0.98 – 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.328
Northern
Mallard 12.21 0.11 4.04 2.45 < 0.01 1.08 – 1.000 1.000 1.000 1.000 1.000 1.000 1.000
Northern
Pintail 30.21 4.58 0.79 0.06 0.26 1.87 2.27 – 1.000 1.000 1.000 0.116 1.000 1.000
Eurasian
Teal 21.72 4.84 0.02 0.05 0.72 2.32 3.11 0.35 – 1.000 1.000 0.106 1.000 1.000
Tufted Duck 22.94 5.30 0.01 0.08 0.80 2.39 3.43 0.45 < 0.01 – 1.000 0.081 1.000 1.000
Common
Pochard 0.11 < 0.01 0.80 0.43 0.01 0.28 0.01 0.32 0.60 0.65 – 1.000 1.000 1.000
Eurasian
Coot 0.03 3.28 11.49 9.72 1.66 0.19 3.91 10.11 10.31 10.82 0.79 – 0.122 0.150
Common
Moorhen 18.44 4.77 < 0.01 0.23 1.01 2.58 3.32 0.66 0.02 0.01 0.03 10.00 – 1.000
Gull spp. 11.20 6.58 3.35 4.11 5.05 8.12 6.06 4.53 3.68 3.62 5.01 9.58 3.23 –

Page 11 of 16
Woodetal. Avian Res (2020) 11:30
species. For Mute Swans and Whooper Swans, the pro-
portion of aggressive interactions directed towards other
birds did not differ from the values observed for smaller
waterbirds, although Bewick’s Swans showed higher
values than 8 of the 13 other species. Taken together,
our results were consistent with previous findings that
aggression is more likely between individuals with greater
overlap in resource use (Peiman and Robinson 2010).
Although the majority of aggressive interactions by
swans were directed towards, and received from, conspe-
cifics, we observed some aggression towards 9 of the 11
smaller waterbird species present at the two sites. Only
Common Pochard and Common Shelduck were not
observed in aggressive interactions with swans, which
may have been due to their low relative abundance at
the sites. Given that even the low incidences of aggres-
sion observed in our study could carry the risk of seri-
ous injury or death, it may seem counter intuitive that
smaller waterbirds are so often observed to share habitat
with swans. erefore, smaller waterbirds must balance
potential risks of aggression with the possible benefits of
sharing habitat or even of associating more closely with
swans. Previous studies have found that some smaller
waterbird species associate with swans for improved
access to food resources, such as submerged aquatic
plants that swans bring to the surface (e.g. Bailey and
Table 4 Comparison ofmodels ofthetime spent inaggressive interactions betweenswans
A summary of the relative support for each of our candidate models of the time spent by swans in aggressive interactions with other swans. Model parameters:
intercept (i), number of swans present (N), month (M), winter (W), time of day (T), and observation identity (D). k refers to the number of xed eects in the model
Species Model kAICcΔAICcRL wiER
R2
c
R2
m
Whooper Swans i + N + (i|D) 2 295.87 0.00 1.00 0.505 1.00 0.351 0.193
i + (i|D) 1 296.31 0.45 0.80 0.404 1.25 0.215 0.000
i + T + (i|D) 4 299.29 3.42 0.18 0.091 5.53 0.142 0.129
Bewick’s Swans i + T * W + (i|D) 12 802.43 0.00 1.00 0.664 1.00 0.343 0.233
i + (i|D) 1 805.67 3.24 0.20 0.131 5.06 0.226 0.000
i + N + (i|D) 2 807.16 4.74 0.09 0.062 10.68 0.236 0.007
i + W + (i|D) 3 807.74 5.31 0.07 0.047 14.26 0.226 0.000
i + T + (i|D) 4 808.37 5.94 0.05 0.034 19.51 0.211 0.017
i + M + (i|D) 5 808.80 6.37 0.04 0.027 24.16 0.224 0.030
i + T + W + (i|D) 6 810.44 8.01 0.02 0.012 54.83 0.213 0.019
i + N + M + (i|D) 6 810.74 8.31 0.02 0.010 63.75 0.231 0.030
i + T + M + (i|D) 8 811.00 8.57 0.01 0.009 72.75 0.198 0.070
i + N * M + (i|D) 10 813.40 10.97 0.00 0.003 241.39 0.195 0.071
i + T * M + (i|D) 20 820.81 18.38 0.00 0.000 9786.90 0.216 0.091
Table 5 Eect sizes forour best-supported models ofWhooper andBewick’s Swan time spent onaggression
The mean and SE estimated eect sizes associated with each of the parameters (as dened in Table4) in our best-supported models of Whooper and Bewick’s Swan
time spent on aggression. Additionally, the variance (and SD) associated with the random eect of observation identity is also shown
Species Model Parameter Estimate SE Variance SD
Whooper Swans Conditional i1.68 0.70 – –
N0.04 0.02 – –
(i|D) – – 0.17 0.41
Zero-inflation i–1.79 0.82 – –
Bewick’s Swans Conditional i1.09 0.19 – –
T(Mid) –0.29 0.29 – –
T(P.M.) 0.32 0.24 – –
W(2019/2020) 0.27 0.66 – –
T(Mid):W(2019/2020) 0.66 0.82 – –
T(P.M.):W(2019/2020) –2.59 1.03 – –
(i|D) – – 0.15 0.38
Zero-inflation i0.33 0.16 – –

Page 12 of 16
Woodetal. Avian Res (2020) 11:30
Batt 1974; Beven 1980; Källander 2005). Foraging swans
typically bring to the surface more food than they ingest
(Gillham 1956), which consequently provides foraging
opportunities for other species. Gyimesi et al. (2012)
found that commensal foraging with Bewick’s Swans
doubled the instantaneous food intake rate of Common
Pochard. In addition to commensal feeding associations,
some smaller waterbird species such as Eurasian Coot
may also feed directly on swan faeces (Vogrin 1997; Shi-
mada 2012).
We found mixed support for our density hypothesis
regarding the influence of swan numbers on the dura-
tion of aggressive behaviours. As expected, individual
Whooper Swans spent longer in aggressive interactions
when more swans were present. However, the relation-
ship between swan numbers and Whooper Swan aggres-
sion times was not strong, as the marginal R2 value
indicated that swan numbers accounted for only 19.3%
of the variance in Whooper Swan aggression times.
Yet in contrast, Bewick’s Swan aggression showed no
effect of swan numbers. e reason for these divergent
findings may be due to the differences in the ranges of
swan numbers recorded at both sites. e mean swan
numbers recorded during observations at WWT Caer-
laverock, where large numbers of Whooper and Mute
Swans overwinter, ranged between 9 and 46 individuals,
whilst the mean recorded swan numbers at WWT Slim-
bridge ranged between 1 and 13 individuals; hence, the
number of potential competitors faced by the Whooper
Swans at WWT Caerlaverock was markedly higher
than that faced by the Bewick’s Swans at WWT Slim-
bridge (Fig.2). Aggressive behaviours among birds typi-
cally become more common as the density of individuals
within a habitat increases (Metcalfe and Furness 1987;
Wood et al. 2015). It is possible that Bewick’s Swans
Fig. 2 The relationships between the number of swans present and
the time spent in aggressive interactions with other swans. The data
points are indicated by the solid circles, whilst the solid and dashed
lines represent the mean and 95% CI relationships estimated by our
best-supported model, where evidence for such an effect was found
(where no such effect was detected, no line is presented)
Fig. 3 The mean (± 95% CI) duration that swans spent engaged in
aggressive interactions with other swans for each a winter month
and b time of day. Data for winters 2018/2019 and 2019/2020 are
presented separately

Page 13 of 16
Woodetal. Avian Res (2020) 11:30
would show increasing durations of aggression with ris-
ing swan numbers if observations could be made over a
greater range of swan numbers; although with ongoing
declines in Bewick’s Swan numbers at wintering sites in
the UK (Beekman etal. 2019), obtaining such data may
prove challenging.
Further comparisons of our models of the time spent
in aggressive interactions with other swans found no
support for our winter decline hypothesis, namely that
swans would spend more time engaged in aggressive
interactions in early winter months compared with later
months. e more limited data collected for Whooper
Swans did not allow between-month effects to be tested
for, and so only our Bewick’s Swan data could be used to
test this hypothesis. Our results contrasted with those
of Scott (1981), who found that the frequency of aggres-
sive interactions between Bewick’s Swans at WWT
Slimbridge declined progressively over winter months.
e number of Bewick’s Swans overwintering at WWT
Slimbridge was lower in our study winters than in the late
1970s and early 1980s (Beekman etal. 2019), and so the
divergent findings may reflect lower levels of competition
in recent winters.
e data that we collected showed that the times spent
by swans in aggressive interactions with other swans
showed some variation between time of day and between
winters for Bewick’s Swans. e more limited data col-
lected for Whooper Swans did not allow between-winter
effects to be tested for, although no consistent variation
between the three times of day was observed. For our
focal Bewick’s Swans, for which data could be collected
over multiple months in two winters, we found evi-
dence that the time devoted to aggression varied both
between times of day and between winters. e tendency
for Bewick’s Swans to spend less time on aggression
with other swans during the afternoon observations in
2019/2020 could be due to the swans spending less time
foraging, and hence less aggression linked to competition
for food resources. Previous studies of swan behaviour
have shown that the most intensive foraging periods for
swans typically occur in early morning and before dusk
(e.g. Bowler 1996), and thus the afternoon represents a
period of low foraging activity. Future research could test
for a correlation between foraging activity and aggres-
sive interactions by collecting data on all behaviours as
part of a time-activity budget. Our repeated 10-min focal
observations showed that Whooper and Bewick’s Swans
spent means (± 95% CI) of 13.8 ± 4.7 s and 1.4 ± 0.3 s ,
respectively, engaged in aggression with other swans.
ese durations were equivalent to 2.3% and 0.2% of
the Whooper and Bewick’s Swan time-activity budgets,
respectively. In this study we focused our explanatory
modelling on the interactions between swans, as for all
swan species the interactions between swans were more
frequent than interactions between swans and smaller
waterbirds. However, future research could extend our
methodology to examine the time spent by swans on
interactions with all waterbird species, as well as the
behavioural context of aggression (for example, whether
the aggression occurred while both individuals were for-
aging). Types of food resources represent another poten-
tial area for further investigations. At our sites the swans
fed on natural vegetation, supplemented by some wheat
grains which were provided as part of a public engage-
ment programme (Black and Rees 1984). At other sites
swans are known to feed on food items that are buried
in aquatic and terrestrial sediment, including pond weed
tubers (Potamogeton spp.) and root crops such as Sugar
Beet (Beta vulgaris) (Wood etal. 2019b). e distribu-
tions of such cryptic food items are more difficult for
the birds to predict, and there may be a higher perceived
value of defending profitable feeding patches from com-
petitors. Future research could also therefore assess how
the frequency of aggressive behaviours responds to dif-
ferences in the types of food resources that are available.
Our study provided an example of how questions
relating to avian behaviour could be answered using
data that were collected remotely via live-streaming
webcams. Such remote data collection offers several
advantages to researchers, including less disturbance
to the focal birds (once the camera has been installed),
lower environmental costs (i.e. carbon footprint
Table 6 Post-hoc contrasts associated with our
best-supported model of Bewick’s Swan time spent
onaggression
Parameters are as dened in Table4. Statistically signicant contrasts are in
italics
Contrast Estimate SE t ratio P value
T(A.M.):W(2018/2019) − T(Mid):W(2018/2019) 0.29 0.29 1.02 0.912
T(A.M.):W(2018/2019) − T(P.M.):W(2018/2019) – 0.32 0.24 – 1.35 0.756
T(A.M.):W(2018/2019) − T(A.M.):W(2019/2020) – 0.27 0.66 – 0.41 0.999
T(A.M.):W(2018/2019) − T(Mid):W(2019/2020) – 0.63 0.46 – 1.38 0.739
T(A.M.):W(2018/2019) − T(P.M.):W(2019/2020) 2.01 0.81 2.49 0.131
T(Mid):W(2018/2019) − T(P.M.):W(2018/2019) – 0.61 0.28 – 2.21 0.239
T(Mid):W(2018/2019) − T(A.M.):W(2019/2020) – 0.56 0.67 – 0.84 0.961
T(Mid):W(2018/2019) − T(Mid):W(2019/2020) – 0.93 0.48 – 1.93 0.388
T(Mid):W(2018/2019) − T(P.M.):W(2019/2020) 1.71 0.82 2.10 0.293
T(P.M.):W(2018/2019) − T(A.M.):W(2019/2020) 0.05 0.65 0.08 1.000
T(P.M.):W(2018/2019) − T(Mid):W(2019/2020) – 0.31 0.45 – 0.70 0.982
T(P.M.):W(2018/2019) − T(P.M.):W(2019/2020) 2.33 0.80 2.91 0.045
T(A.M.):W(2019/2020) − T(Mid):W(2019/2020) – 0.37 0.76 – 0.48 0.997
T(A.M.):W(2019/2020) − T(P.M.):W(2019/2020) 2.27 1.01 2.25 0.217
T(Mid):W(2019/2020) − T(P.M.):W(2019/2020) 2.64 0.90 2.95 0.040

Page 14 of 16
Woodetal. Avian Res (2020) 11:30
associated with sampling) due to not having to under-
take visits to study sites, greater accessibility of research
to scientists who cannot physically travel to study sites
(either due to logistical difficulties or disability), and
the facilitation of citizen science programmes (Eichorst
2018; Schulwitz et al. 2018). Given these advantages,
we expect that remote data collection methods will
become increasingly popular with researchers. How-
ever, our study also highlights a key drawback of such
remote data collection, namely the reliability of the
technology involved. Malfunctions and environmental
damage to the webcams limited the collection of data at
both of our study sites, although this did not prevent us
from addressing the key questions of our study. Future
studies that aim to use remote data collection meth-
ods should consider carefully the reliability of both the
cameras themselves as well as the stable internet con-
nections required to stream the camera videos. For
example, the use of multiple webcams at a single site
would provide a buffer against the impacts of the fail-
ure of a single camera. We also believe that waterbirds
are useful focal species with which to test remote data
collection methods, as the birds typically have relatively
large body sizes and use open habitat which provides
researchers with the unobstructed views required to
make reliable identifications of species and accurate
assessments of behaviour (Anderson etal. 2011; Peluso
etal. 2013).
Conclusions
Our study illustrates how detailed behavioural inves-
tigations can help to improve our understanding of
the prevalence of aggressive interactions within and
between species. Spatially- and temporally-replicated
data can allow researchers to identify which species are
most commonly involved in aggressive interactions, as
well as the frequency and direction of these interac-
tions. Our findings that most aggression was intraspe-
cific and accounted for a low proportion of the total
time-budget, together with the lack of strong density-
dependence, suggest an absence of any conservation
or management issues related to aggression between
waterbirds at either site. For example, the behavioural
data show that Common Pochard, a species listed as
Vulnerable that is undergoing declining population
size (Brides et al. 2017), show relatively few aggres-
sive interactions with other waterbirds and none with
swans. Conservationists have questioned whether the
observed c. 39% decline in winter Bewick’s Swan num-
bers in north-west Europe between 1995 and 2010 may
have been at least partially attributable to competi-
tion with rising numbers of Whooper and Mute Swans
(Rees et al. 2019). However, our findings show that
aggressive interactions from Mute Swans accounted for
only 7% of all of the aggressive interactions received,
whereas intraspecific aggression from other Bewick’s
Swans represented 78% of aggression received. Given
these findings, it appears unlikely that aggression from
Mute Swans has contributed to the observed decline in
Bewick’s Swan winter numbers. Similar research is now
needed at other sites used by Bewick’s Swans, includ-
ing migratory stopover sites and those in the breeding
range.
Supplementary information
Supplementary information accompanies this paper at https ://doi.
org/10.1186/s4065 7-020-00216 -7.
Additional le1: Figure S1. Screenshots of the live webcam footage,
illustrating the field-of-view of the webcams at (a) WWT Slimbridge and
(b) WWT Caer laverock.
Acknowledgements
We are grateful for the contributions of Anna Allis and Esenya Mathews in
observing the swans and helping to collect the final dataset. The editor and
two anonymous reviewers provided valuable feedback that helped us to
improve this manuscript. We thank Dave Paynter, Paul Marshall, John Herivel,
and Samantha Luxa for assistance with the webcams.
Authors’ contributions
KAW and PER conceived and designed the study. PH, JS and EW collected the
data. KAW carried out the analyses. All authors contributed to the writing of
the manuscript. All authors read and approved the final manuscript.
Funding
This work was supported by the Wildfowl & Wetlands Trust and the University
of Exeter.
Availability of data and materials
The datasets generated and analysed during the current study are available
from the corresponding author upon reasonable request.
Ethics approval and consent to participate
The procedures in this study comply with the current laws of the United King-
dom, where they were performed. This study was carried out with the prior
approval of the ethics committee of the College of Life and Environmental
Sciences of the University of Exeter (eCLESPsy000890v3.3).
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Author details
1 Wildfowl & Wetlands Trust, Slimbridge Wetland Centre, Slimbridge, Glouces-
tershire GL2 7BT, UK. 2 Centre for Research in Animal Behaviour, Psychology,
Washington Singer, University of Exeter, Perry Road, Exeter, Devon EX4 4QG,
UK. 3 University Centre Sparsholt College, Sparsholt, Hampshire SO21 2NF, UK.
Received: 29 June 2020 Accepted: 2 August 2020

Page 15 of 16
Woodetal. Avian Res (2020) 11:30
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