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

Avian Research volume 11, Article number: 30 (2020) Cite this article

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 (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.

Background

Communities of birds that use aquatic habitats (hereafter ‘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 assemblages (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). Previous authors have highlighted that swans exhibit aggression 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; Delacour 1973). For example, Ely et al. (1987) reported that attacks by Whistling Swans (Cygnus columbianus columbianus, 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 possible 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 habitat or reduce breeding densities (Gayet et al. 2011, 2016). A recent meta-analysis cast further doubt, by showing that swans spent no more time engaged in aggressive 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 species, including other waterbirds. For example, in Maryland, 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 (Therres and Brinkler 2004). Waterbird managers and conservationists have also expressed concerns that the invasive Mute Swans could out-compete the native Trumpeter (Cygnus buccinator) or Whistling Swans for food and habitat (Lumsden 2016). Similarly, conservationists have questioned whether the observed c. 39% decline in winter Bewick’s Swan (C. columbianus bewickii) numbers in north-west Europe between 1995 and 2010 may be at least partially attributable to competition with rising numbers of Whooper and Mute Swans in the region (Rees et al. 2019). Even among conspecifics, interference 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 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.

In this study we used repeated behavioural observations at two wintering sites to improve our understanding 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 hypothesis (hereafter termed the ‘conspecific hypothesis’) was that swans would devote more time to intraspecific aggression compared with interspecific aggression. Aggressive behaviours among birds typically become more common as the density of individuals within a habitat increases (Metcalfe and Furness 1987; Wood et al. 2015), thus our second hypothesis (hereafter termed the ‘density hypothesis’) was that swans would spend more time engaged in aggressive interactions when present in higher density flocks. Finally, we expected that as winter progressed, a dominance hierarchy 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 (hereafter 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 Scotland, 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 supported Mute and Whooper Swans (Black and Rees 1984). The mean of the peak winter counts of individual 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), diving 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.). The non-migratory Mute Swans are resident at both Slimbridge 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 duration: 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 density and winter decline hypotheses. First we used all occurrence sampling to record all incidents of aggression 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. These 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 characteristics, 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 numbers and between months, we used focal sampling (Altmann 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 interaction with another swan in a 10-min observation period; hence, six individual swans could be observed during each hour-long observation. A focal observation duration 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 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 limited 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, inclusive) 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 webcam 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. Similar technical issues precluded data collection at WWT Caerlaverock during winter 2019/2020. Our sampling methodology of ten observations per hour, on an average 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 individuals were observed on multiple occasions, the aforementioned 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 version 3.6.3 (R Core Team 2020). To address our conspecific hypothesis that intraspecific interactions would be observed more frequently than interspecific interactions, we used two-tailed binomial tests to assess the statistical 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 species, separate tests were carried out for aggressive interactions (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 intraspecific 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 interspecific aggressive interactions recorded towards other birds; P values were again adjusted using Holm-Bonferroni corrections (Holm 1979).

To address our density and winter decline hypotheses regarding the variations in aggression with swan numbers and between months, we used zero-inflated generalized linear mixed effects models (ZIGLMMs), using the glmmTMB R package (Brooks et al. 2017). Zero-inflated models were required because of the relatively high proportions 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 during 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 different in terms of the sample size and the temporal replication. Therefore, we analysed the data for Whooper and Bewick’s Swans separately. For each species, we ran and compared candidate models that comprised all possible 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 observation, 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 observation, 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 ‘observation identity’), was fitted as a random intercept in each model to account for the non-independence of individual swans observed within the same hour-long observation 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 with the mean (ΔAICc = 4.31), or a Poisson distribution (ΔAICc = 169.40). For Bewick’s Swans there was little difference between models with either of the two negative binomial distributions (ΔAICc = 0.24), whilst the Poisson distribution did not converge. Therefore, in all subsequent models we used the negative binomial distribution in which the variance increased linearly with the mean.

To ensure that collinearity did not confound our modelling (Dormann et al. 2013), we tested for covariance among our explanatory variables. To our knowledge, Variance 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 Analysis of Variance (ANOVA) was used to test for covariance between our continuous variable (number of swans present) 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 statistically significant associations between the variables were found. Using these methods, we found for Whooper Swans a significant effect of time of day on the number 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. Therefore, in total we ran 3 and 11 candidate models for the Whooper Swan and Bewick’s Swan datasets, respectively, accounting for all possible non-collinear combinations of additive and two-way interactions between our explanatory variables.

We compared the relative support of each candidate 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 values < 6.0, following the advice of Richards (2008) for dealing with overdispersion. 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 further metrics for each model were used as indicators of the relative strength of support in the data, to facilitate 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 conditional 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

Intraspecific versus interspecific aggression

For 13 out of 14 focal waterbird species, the majority 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).

Table 1 The total numbers of aggressive interactions (n) given and received by each focal waterbird species
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The 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 interactions directed towards, and received from, conspecifics, 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 interactions were more frequent than interspecific interactions (Table 2; Fig. 1). Among the 11 species of smaller waterbirds, intraspecific interactions directed towards other individuals were significantly more frequent in 8 species, whilst intraspecific interactions received from other individuals were significantly more frequent in 7 species (Table 2; Fig. 1).

Table 2 The intra- and interspecific aggressive interactions given and received by each focal waterbird species
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Fig. 1
figure1

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

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Species comparisons of interspecific aggression

The 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, Northern Mallard, Northern Pintail, Eurasian Teal, Tufted Duck, and Common Moorhen (Table 3). The 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 significant (Table 3).

Table 3 Statistical comparison of the proportion of aggressive interactions that were interspecific
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Swan aggression times

Whooper Swans at WWT Caerlaverock spent a mean (± 95% CI) of 13.8 ± 4.7 s engaged in aggressive interactions with other swans, based on the sample of 42 focal observations collected during November 2018; this duration was equivalent to 2.3 ± 0.8% of their time-activity budget. A comparison of candidate models showed that the time spent by individual Whooper Swans in aggressive interactions with other swans was best explained by the mean number of swans present during the observation (Table 4). This 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 aggressive interactions by Whooper Swans increased with the mean number of swans present (Table 5; Fig. 2). Overall, the effect of swan numbers together with the random 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. The null model, comprised 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 aggression time varied with the time of day of the observation had an associated ΔAICc value of 3.42; however, the overall 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 evidence that Whooper Swan aggression time varied among the three times of day (Fig. 3).

Table 4 Comparison of models of the time spent in aggressive interactions between swans
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Table 5 Effect sizes for our best-supported models of Whooper and Bewick’s Swan time spent on aggression
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Fig. 2
figure2

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)

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Fig. 3
figure3

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

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Bewick’s Swans at WWT Slimbridge spent a mean (± 95% CI) of 1.4 ± 0.3 s engaged in aggressive interactions 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 candidate 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 observation 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 winter 2019/2020 (Table 6; Fig. 3); no other comparisons were significantly different. This 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 candidate models also had ΔAICc values within our threshold 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 additive effects contained within our best-supported model, time of day (ΔAICc = 5.94) and winter (ΔAICc = 5.31). The third comprised the numbers of swans present during the observation (ΔAICc = 4.74), but the support for this model was weak. The 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 considered that there was little evidence that the numbers of swans present had an effect on the time spent on aggression by Bewick’s Swans.

Table 6 Post-hoc contrasts associated with our best-supported model of Bewick’s Swan time spent on aggression
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Discussion

In accordance with our conspecific hypothesis, we found that aggressive interactions by swans were typically directed towards other swans rather than smaller waterbirds. Indeed, across all three swan species intraspecific 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 similarly 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 species. For Mute Swans and Whooper Swans, the proportion 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, conspecifics, 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 aggression observed in our study could carry the risk of serious injury or death, it may seem counter intuitive that smaller waterbirds are so often observed to share habitat with swans. Therefore, 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 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; Shimada 2012).

We found mixed support for our density hypothesis regarding the influence of swan numbers on the duration of aggressive behaviours. As expected, individual Whooper Swans spent longer in aggressive interactions when more swans were present. However, the relationship between swan numbers and Whooper Swan aggression 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. The reason for these divergent findings may be due to the differences in the ranges of swan numbers recorded at both sites. The mean swan numbers recorded during observations at WWT Caerlaverock, where large numbers of Whooper and Mute Swans overwinter, ranged between 9 and 46 individuals, whilst the mean recorded swan numbers at WWT Slimbridge 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 Slimbridge (Fig. 2). Aggressive behaviours among birds typically 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 would show increasing durations of aggression with rising 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. The 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 aggressive interactions between Bewick’s Swans at WWT Slimbridge declined progressively over winter months. The 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.

The 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. The more limited data collected 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 evidence that the time devoted to aggression varied both between times of day and between winters. The 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 aggressive 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. These 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 foraging). Types of food resources represent another potential 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 engagement 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). The distributions 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 competitors. Future research could also therefore assess how the frequency of aggressive behaviours responds to differences 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 associated with sampling) due to not having to undertake 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. However, 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 methods should consider carefully the reliability of both the cameras themselves as well as the stable internet connections 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 failure 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 investigations 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 interactions. Our findings that most aggression was intraspecific 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 aggressive interactions with other waterbirds and none with swans. Conservationists have questioned whether the observed c. 39% decline in winter Bewick’s Swan numbers in north-west Europe between 1995 and 2010 may have been at least partially attributable to competition 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, including migratory stopover sites and those in the breeding range.

Availability of data and materials

The datasets generated and analysed during the current study are available from the corresponding author upon reasonable request.

References

  1. Altmann J. Observational study of behavior: sampling methods. Behaviour. 1974;49:227–66.

    CAS  PubMed  Google Scholar 

  2. Amat JA. Food usurpation by waterfowl and waders. Wildfowl. 1990;41:107–16.

    Google Scholar 

  3. Anderson MJ, Urbine JL, Wilson C, Callabro L. Employment of web-based images and a live web cam in the examination of lateral neck-resting preferences in the American flamingo (Phoenicopterus ruber). J Caribb Ornithol. 2011;24:41–7.

    Google Scholar 

  4. Arnold TW. Uninformative parameters and model selection using Akaike’s Information Criterion. J Wildl Manage. 2010;74:1175–8.

    Google Scholar 

  5. Bailey RO, Batt BDJ. Hierarchy of waterfowl feeding with Whistling Swans. Auk. 1974;91:488–93.

    Google Scholar 

  6. Barton K. MuMIn: Multi-Model Inference. Version 1.43.15. 2019. https://CRAN.R-project.org/package=MuMIn.

  7. Beekman J, Koffijberg K, Wahl J, Kowallik C, Hall C, Devos K, et al. Long-term population trends and shifts in distribution for Bewick’s Swans Cygnus columbianus bewickii wintering in northwest Europe. Wildfowl. 2019; Special Issue 5:73–102.

    Google Scholar 

  8. Beven G. Coot feeding on weed disturbed by Mute Swans. Brit Birds. 1980;73:219–20.

    Google Scholar 

  9. Black JM, Rees EC. The structure and behaviour of the Whooper Swan population wintering at Caerlaverock, Dumfries and Galloway, Scotland: an introductory study. Wildfowl. 1984;35:21–36.

    Google Scholar 

  10. Bowler JM. Feeding strategies of Bewick’s Swans (Cygnus columbianus bewickii) in winter. PhD Thesis. Bristol: University of Bristol; 1996.

  11. Brazil MA. A case of unusual aggression by a Whooper Swan. Tori. 1983;32:155.

    Google Scholar 

  12. Brides K, Wood KA, Hearn RD, Fijen TPM. Changes in the sex ratio of the Common Pochard Aythya ferina in Europe and North Africa. Wildfowl. 2017;67:100–12.

    Google Scholar 

  13. Brooks ME, Kristensen K, van Benthem KJ, Magnusson A, Berg CW, Nielsen A, et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 2017;9:378–400.

    Google Scholar 

  14. Burgess RM, Stickney AA. Interspecific aggression by Tundra Swans towards Snow Geese on the Sagavanirktok River Delta, Alaska. Auk. 1994;111:204–7.

    Google Scholar 

  15. Burnham KP, Anderson DR, Huyvaert KP. AIC model selection and multimodel inference in behavioral ecology: some background, observations, and comparisons. Behav Ecol Sociobiol. 2011;65:23–35.

    Google Scholar 

  16. Clopper CJ, Pearson ES. The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika. 1934;26:404–13.

    Google Scholar 

  17. Conover MR, Kania GS. Impact of interspecific aggression and herbivory by mute swans on native waterfowl and aquatic vegetation in New England. Auk. 1994;111:744–8.

    Google Scholar 

  18. Crawley MJ. The R Book. 2nd ed Chichester: Wiley; 2013.

    Google Scholar 

  19. Davis JB, Guillemain M, Kaminski RM, Arzel C, Eadie JM, Rees EC. Habitat and resource use by waterfowl in the northern hemisphere in autumn and winter. Wildfowl. 2014;4:17–69.

    Google Scholar 

  20. Delacour J. Waterfowl in large mixed collections. Int Zoo Yearb. 1973;13:15–9.

    Google Scholar 

  21. Dormann CF, Elith J, Bacher S, Buchmann C, Carl G, Carré G, et al. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography. 2013;36:27–46.

    Google Scholar 

  22. Eichorst B. Internet webcams provide opportunities for college student research on animal behavior and ecology: an example with birds. Am Biol Teacher. 2018;80:680–5.

    Google Scholar 

  23. Ely CR, Budeau DA, Swain UG. Aggressive encounters between Tundra Swans and Greater White-fronted Geese during brood rearing. Condor. 1987;89:420–2.

    Google Scholar 

  24. Frost TM, Calbrade NA, Birtles GA, Mellan HJ, Hall C, Robinson AE, et al. Waterbirds in the UK 2018/2019: The Wetland Bird Survey. Thetford: BTO/RSPB/JNCC; 2020.

    Google Scholar 

  25. Gayet G, Guillemain M, Mesléard F, Fritz H, Vaux V, Broyer J. Are Mute Swans (Cygnus olor) really limiting fishpond use by waterbirds in the Dombes, Eastern France? J Ornithol. 2011;152:45–53.

    Google Scholar 

  26. Gayet G, Calenge C, Broyer J, Mesléard F, Vaux V, Fritz H, et al. Analysis of spatial point pattern shows no desertion of breeding Mute Swan areas by the other waterbirds within fishpond. Acta Ornithol. 2016;5:151–63.

    Google Scholar 

  27. Gillham ME. Feeding habits and seasonal movements of mute swans on two south Devon estuaries. Bird Study. 1956;3:205–12.

    Google Scholar 

  28. Gurtovaya EN. Aggressive interactions between Bewick’s Swans and other Anseriformes in the breeding period. Casarca. 2000;6:167–76.

    Google Scholar 

  29. Gyimesi A, Stillman RA, Nolet BA. Cryptic interference competition in swans foraging on cryptic prey. Anim Behav. 2010;80:791–7.

    Google Scholar 

  30. Gyimesi A, van Lith B, Nolet BA. Commensal foraging with Bewick’s Swans Cygnus bewickii doubles instantaneous intake rate of Common Pochard Aythya ferina. Ardea. 2012;100:55–62.

    Google Scholar 

  31. Holm S. A simple sequentially rejective multiple test procedure. Scand J Stat. 1979;6:65–70.

    Google Scholar 

  32. Johnsgard PA. Handbook of waterfowl behavior. New York: Cornell University Press; 1965.

    Google Scholar 

  33. Källander H. Commensal association of waterfowl with feeding swans. Waterbirds. 2005;28:326–31.

    Google Scholar 

  34. King JA. The ecology of aggressive behavior. Annu Rev Ecol Syst. 1973;4:117–38.

    Google Scholar 

  35. Lenth R. Emmeans: Estimated Marginal Means, aka Least-Squares Means. Version 1.4.5. 2020. https://CRAN.R-project.org/package=emmeans.

  36. Lind H. The rotation display of the Mute Swan Cygnus olor: synchronised neighbour responses as instrument in the territorial defence strategy. Ornis Scand. 1984;15:98–104.

    Google Scholar 

  37. Lüdecke D. Sjstats: Statistical Functions for Regression Models. Version 0.17.9. 2020. https://CRAN.R-project.org/package=sjstats.

  38. Lumsden HG. Trumpeter Swans and Mute Swans compete for space in Ontario. Ontario Birds. 2016;34:14–23.

    Google Scholar 

  39. Mac Nally R, Duncan RP, Thomson JR, Yen JD. Model selection using information criteria, but is the “best” model any good? J Appl Ecol. 2018;55:1441–4.

    Google Scholar 

  40. Marchowski D, Neubauer G. Kleptoparasitic strategies of Mallards towards conspecifics and Eurasian Coots. Ardea. 2019;107:110–4.

    Google Scholar 

  41. Metcalfe NB, Furness RW. Aggression in shorebirds in relation to flock density and composition. Ibis. 1987;129:553–63.

    Google Scholar 

  42. Nakagawa S, Johnson PC, Schielzeth H. The coefficient of determination R2 and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded. J R Soc Int. 2017;14:20170213.

    Google Scholar 

  43. Newth JL, McDonald RA, Wood KA, Rees EC, Semenov I, Chistyakov A, et al. Predicting intention to hunt protected wildlife: a case study of Bewick’s swans in the European Russian Arctic. Oryx. (in press).

  44. O’Hare MT, Stillman RA, McDonnell JO, Wood LR. Effects of mute swan grazing on a keystone macrophyte. Freshwater Biol. 2007;52:2463–75.

    Google Scholar 

  45. Peiman K, Robinson B. Ecology and evolution of resource-related heterospecific aggression. Quart Rev Biol. 2010;85:133–58.

    PubMed  Google Scholar 

  46. Pelligrini AD. The roles of aggressive and affiliative behaviors in resource control: a behavioral ecological perspective. Dev Rev. 2008;28:461–87.

    Google Scholar 

  47. Peluso AI, Royer EA, Wall MJ, Anderson MJ. The relationship between environmental factors and flamingo aggression examined with internet resources. Avian Biol Res. 2013;6:215–20.

    Google Scholar 

  48. Pöysä H. Resource utilization pattern and guild structure in a waterfowl community. Oikos. 1983;40:295–307.

    Google Scholar 

  49. R Core Team. R: A language and environment for statistical computing. [3.6.3]. Vienna, Austria: R Foundation for Statistical Computing; 2020.

  50. Rees EC. Bewick’s Swan. London: T & AD Poyser; 2006.

    Google Scholar 

  51. Rees EC, Cao L, Clausen P, Coleman JT, Cornely J, Einarsson O, et al. Conservation status of the world’s swan populations, Cygnus sp. and Coscoroba sp.: a review of current trends and gaps in knowledge. Wildfowl. 2019; Special Issue 5:35–72.

    Google Scholar 

  52. Richards SA. Dealing with overdispersed count data in applied ecology. J Appl Ecol. 2008;45:218–27.

    Google Scholar 

  53. RSPB. Birds A- Z: Bird Guides. Royal Society for the Protection of Birds. https://www.rspb.org.uk/birds-and-wildlife/wildlife-guides/bird-a-z/. Accessed October 2018.

  54. Schulwitz SE, Spurling DP, Davis TS, McClure CJW. Webcams as an untapped opportunity to conduct citizen science: siz years of the American Kestrel Partnership’s KestrelCam. Glob Ecol Conserv. 2018;15:e00434.

    Google Scholar 

  55. Scott DK. Social behaviour of wintering Cygnus columbianus bewickii. In: Matthews GVT, Smart M, editors. Proceedings of the Second International IWRB Swan Symposium, Sapporo, Japan, 1980. Slimbridge: International Waterfowl Research Bureau; 1981. p. 211–25.

  56. Shimada T. Ducks foraging on swan faeces. Wildfowl. 2012;62:224–7.

    Google Scholar 

  57. Sladen WJL. Swans should not be hunted. In: Sears J, Bacon PJ, editors. Proceedings of the Third International IWRB Swan Symposium, Oxford, 1989. Slimbridge: International Waterfowl Research Bureau; 1991. p. 368–75.

  58. Stone WB, Marsters AD. Aggression among captive Mute Swans. New York Fish Game J. 1970;17:51–3.

    Google Scholar 

  59. Tatu KS, Anderson JT, Hindman LJ, Seidel G. Diurnal foraging activities of mute swans in Chesapeake Bay, Maryland. Waterbirds. 2007;30:121–9.

    Google Scholar 

  60. Therres GD, Brinkler DF. Mute Swan interactions with other birds in Chesapeake Bay. In: Perry MC, editor. Mute Swans and Their Chesapeake Bay Habitats: Proceedings of a Symposium. Virginia: US Geological Survey; 2004. p. 43–6.

  61. Tingay A. Aggression in the Black Swan. Emu. 1974;74:35–8.

    Google Scholar 

  62. Vogrin M. A Coot Fulica atra eating waterfowl droppings. Butll GCA. 1997;14:63–4.

    Google Scholar 

  63. Włodarczyk R, Minias P. Division of parental duties confirms a need for bi-parental care in a precocial bird, the mute swan Cygnus olor. Anim Biol. 2015;65:163–76.

    Google Scholar 

  64. Wood KA, Stillman RA, Goss-Custard JD. The effect of kleptoparasite and host numbers on the risk of food-stealing in an avian assemblage. J Avian Biol. 2015;46:589–96.

    Google Scholar 

  65. Wood KA, Ponting J, D’Costa N, Newth JL, Rose PE, Glazov P, et al. Understanding intrinsic and extrinsic drivers of aggressive behaviour in waterbird assemblages: a meta-analysis. Anim Behav. 2017;126:209–16.

    Google Scholar 

  66. Wood KA, Cao L, Clausen P, Ely CR, Luigujõe L, Rees EC, et al. Current trends and future directions in swan research: insights from the 6th International Swan Symposium. Wildfowl. 2019a; Special Issue 5:1–34.

    Google Scholar 

  67. Wood KA, Hilton GM, Newth JL, Rees EC. Seasonal variation in energy gain explains patterns of resource use by avian herbivores in an agricultural landscape: insights from a mechanistic model. Ecol Model. 2019b;409:108762.

    Google Scholar 

  68. Zuur AF, Ieno EN, Elphick CS. A protocol for data exploration to avoid common statistical problems. Methods Ecol Evol. 2010;1:3–14.

    Google Scholar 

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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.

Funding

This work was supported by the Wildfowl & Wetlands Trust and the University of Exeter.

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Affiliations

  1. Wildfowl & Wetlands Trust, Slimbridge Wetland Centre, Slimbridge, Gloucestershire, GL2 7BT, UK

    Kevin A. Wood & Paul E. Rose

  2. Centre for Research in Animal Behaviour, Psychology, Washington Singer, University of Exeter, Perry Road, Exeter, Devon, EX4 4QG, UK

    Phoebe Ham, Eleanor Wyeth & Paul E. Rose

  3. University Centre Sparsholt College, Sparsholt, Hampshire, SO21 2NF, UK

    Jake Scales

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  1. Kevin A. Wood
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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.

Corresponding author

Correspondence to Paul E. Rose.

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Ethics approval and consent to participate

The procedures in this study comply with the current laws of the United Kingdom, 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).

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The authors declare that they have no competing interests.

Supplementary information

Additional file 1: Figure S1.

Screenshots of the live webcam footage, illustrating the field-of-view of the webcams at (a) WWT Slimbridge and (b) WWT Caerlaverock.

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Wood, K.A., Ham, P., Scales, J. et al. Aggressive behavioural interactions between swans (Cygnus spp.) and other waterbirds during winter: a webcam-based study. Avian Res 11, 30 (2020). https://doi.org/10.1186/s40657-020-00216-7

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Keywords

  • Aggression
  • Agonistic behaviour
  • Bewick’s Swans
  • Intraspecific versus interspecific competition
  • Remote data collection
  • Waterfowl
  • Whooper Swans

Avian Research

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