The effect of early life conditions on song traits in male dippers

(Cinclus cinclus)

Lucy Magoolagan1*, Peter J. Mawby2, Flora A. Whitehead2 and Stuart P. Sharp1

1Lancaster Environment Centre, Lancaster University, Lancaster, UK

2Unaffiliated, Lancaster, UK

*Correspondence:

L. Magoolagan, Lancaster Environment Centre, Lancaster University, Lancaster, LA1 4YQ

Email: lucy.magoolagan@hotmail.co.uk

Key words: birdsong, male song, developmental stress hypothesis, nutritional stress, parental

care, nestling development.

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Abstract

Song complexity and singing frequency in male birds are shaped by female choice; they

signal male quality because song is costly to develop and produce. The timing of song learning

and the development of the brain structures involved occur during a period when chicks are

exposed to a number of potential stressors. The quality and quantity of song produced by adults

may therefore reflect the level of stress experienced during early life, a theory known as the

‘developmental stress hypothesis’. We tested this hypothesis using song recordings and life-

history data from an individually marked, long-term study population of wild dippers (Cinclus

cinclus). The extent to which early life conditions predict adult song traits was investigated

using natal brood size as a measure of sibling competition; the rate of provisioning by parents as

a proxy for nutritional stress; and residuals of the linear regression between body mass and

tarsus length as a measure of nestling condition. The syllable diversity in the songs of adult

males was positively correlated with their body condition as nestlings, but there was no

significant correlation with either provisioning rate or brood size. Provisioning rate did,

however, predict song rate; males in relatively poor condition as nestlings or those raised in

smaller broods which were fed more frequently by their parents sang at a higher rate in

adulthood. These results support the developmental stress hypothesis and provide some of the

first evidence from a wild bird of how the conditions experienced during early life impact adult

song. Song traits may therefore provide females with information regarding both the current

condition and developmental history of males.

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Introduction

In many bird species, males produce complex songs to defend their territories and

attract a mate, and female choice is thought to be a major driver of the evolution of large song

repertoires (1,2). While several studies have shown that singing is not metabolically demanding

(3,4), the development of the brain structures necessary for learning complex songs is thought to

incur significant energetic costs (5). Furthermore, time spent learning and performing song is

time taken away from other essential activities such as foraging (6) and increases exposure to

predation (7). Females can therefore use song characteristics as measures of male quality

(2,8,9), thereby gaining benefits such as proficient paternal care, territory defence, and ‘good

genes’ for their offspring (2,10,11).

Songbirds have a specialised auditory-vocal area of the forebrain that is responsible for

song learning and production known as the “song system” (7,12). The development of this

region of the brain occurs during the nestling and fledgling period, a time when young birds are

most vulnerable to stress (10,12), and it has been shown that damage to the song system can be

detrimental to adult song production (13). Song learning can therefore be an indicator of early

ontogeny as individuals that have not encountered stress are more likely to have the resources

available to invest in optimal brain development (12). Adult birds may be constrained in the

quality and quantity of the song they are able to produce because the costs of stress experienced

during development cannot be compensated for later in life (8,10,14–17). Originally proposed

as the ‘nutritional stress hypothesis’ (12), this idea is now known as the ‘developmental stress

hypothesis’ to incorporate all of the different stressors which young birds are exposed to (8).

Many studies have found evidence to support the developmental stress hypothesis by

using dietary manipulation or the administration of the stress hormone corticosterone, and

testing for negative effects of these treatments on song characteristics in adulthood (18).

Examples include: European starlings (Sturnus vulgaris; 8,19), zebra finches (Taeniopygia

guttata; 9,20–22), swamp sparrows (Melospiza georgiana; 5) and song sparrows (Melospiza

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melodia; 23). However, most research has involved captive birds and there are very few reports

of a link between early life conditions and song traits in wild bird populations. One exception is

a study of male blue tits (Cyanistes caeruleus), in which individuals raised in experimentally

enlarged broods were shown to sing shorter strophe bouts than those from reduced broods (17).

Two further studies have used body size measurements as a proxy for early life stress; the

length of the innermost primary feather of nestling great reed warblers (Acrocephalus

arundinaceus) was positively correlated with adult repertoire size (10), whereas adult tarsus

length, a known correlate of nestling tarsus, was found to correlate with repertoire size in blue

tits (16).

In this paper, we provide one of the first tests of whether a wider range of early life

conditions predict adult song traits in a wild bird, the white-throated dipper (hereafter ‘dipper’;

Cinclus cinclus). Dippers are riverine passerines which sing highly complex songs (24). It has

previously been shown that unpaired males recorded early in the breeding season and assumed

to be attempting to attract a mate use a larger number of unique syllables within their songs and

sing at a higher rate than paired males (24). These song characteristics are likely to be favoured

by potential mates because female birds are known to prefer exaggerated acoustic traits in males

(15,25). Here, we test the developmental stress hypothesis by investigating whether these key

song characteristics, syllable diversity (as a proxy for repertoire size), song versatility and song

rate, are influenced by the conditions individuals experience as nestlings.

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Methods

Study population

Data were collected between January 2014 and June 2016 in an individually marked

population of 40-50 pairs of dippers in the River Lune catchment near Sedbergh, Cumbria, UK

(54⁰323’N, 2⁰528’W). Individuals in this population are given a unique combination of three

plastic colour rings and a standard British Trust for Ornithology metal ring. Each year, all nests

in the study site are closely monitored to establish parental identity, the timing and outcome of

reproduction and a number of behavioural and life-history traits. Nestlings are ringed and

measured when they are nine days old. Measurements include tarsus length (± 0.1 mm) or

‘maximum tarsus’ which is the length of the tarsometatarsus, measured using dial callipers; and

body mass (± 0.1 g) taken using electronic scales. Adults are trapped for ringing and measuring

using mist nets or hand nets placed over the nest; any first-year birds which were ringed as

nestlings and have recruited into the population are also trapped so that they can be measured.

Fully grown birds can be sexed according to their wing length (± 0.5 mm), taken from the carpal

joint to the end of the longest primary on a closed wing using a capped wing rule (26,27); for

most individuals, sexing could be confirmed from observations of reproductive behaviour (e.g.

incubation is carried out by the female only; 28) and in no cases was there a discrepancy

between the two methods.

Song recording and analysis

Songs from 18 adult males born within the study site were recorded opportunistically

from distances of 10-15m using a Sennheiser ME66-K6 shotgun microphone with a Rycote

Softie windshield and a standard pistol grip connected to a Marantz PMD661 MKII solid state

recorder with a sampling frequency of 44.1 kHz; files were stored in WAV format. It was not

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possible to measure the true size of an individual’s repertoire of syllables due to the apparent

complexity and improvised nature of dipper song; the more recordings made, the more unique

syllables were identified. We therefore measured the number of unique syllables produced in a

standardised sample of 10 songs, hereafter ‘syllable diversity’, as a proxy for repertoire size.

Ten was the minimum number of songs recorded for each individual after those with low signal

to noise ratio had been excluded; if more songs were available, then the first 10 were selected so

that all songs from any given individual were recorded within the same breeding stage (see

below for the categorisation of stages). Spectrograms were produced using Avisoft SASLab

Pro, version 5.2.08 (29), with a 512-point fast Fourier transform length and Hamming window

function, 75% frame size, a 87.5% window overlap, 86 Hz frequency resolution and 1.45 ms

time resolution. All songs were also high pass filtered at 1 kHz to remove low frequency

background noise (e.g. the sound of the river); this threshold was chosen because preliminary

analyses revealed that a small number of Dipper songs contained elements as low as 1.03 kHz

(24).

Syllable diversity was measured using visual and auditory inspections of spectrograms

to count the number of unique syllables produced across 10 songs (24). ‘Versatility’ is another

measure of song complexity and was calculated by dividing the number of unique syllables

found within one song by the total number of syllables found in that song (30); this value was

then averaged across each of an individual’s 10 songs. ‘Song rate’ was calculated using the

number of complete songs produced within a 30-minute period of observation for each

individual during which the bird sang at least once, starting at the time when the bird was first

observed singing; if more than one observation was available, the first was used. Previous work

has shown that these three song traits vary between males recorded at different stages of the

reproductive cycle (24), and the same three mutually exclusive breeding stages were classified

here: (1) ‘solo’ songs were those of individuals recorded in January or February that had yet to

be seen with a partner; (2) ‘pre-breeding’ songs were given by individuals that had been seen

with the same partner on at least two occasions foraging, prospecting or nest-building together;

and (3) ‘breeding’ songs were those of individuals which had paired up and had nests at the

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laying, incubation or nestling stage. Insufficient recordings were available to include songs from

the same male recorded at different breeding stages. For more details of the song recording and

analysis procedures, see (24).

Provisioning rate

Offspring provisioning rates were measured during 1 h nest observations conducted at

least 15 m from the nest and using a portable hide if no natural hiding place was available. In a

small number of cases, manual observations were not possible (e.g. if the nest was inside a

culvert) and so recordings were made using a Panasonic HC-V160 video camera placed at least

5 m from the nest. For both manual observations and video recordings, the 1 h observation

period did not begin until after the first time an adult visited the nest. This minimised the effect

of observer or camera presence on provisioning rates, although very few birds ever seemed

aware of the observer or camera and in the vast majority of cases offspring provisioning took

place within a few minutes of the observer hiding or leaving the camera in place. Provisioning

rates were measured by simply counting the total number of feeds made by the two parents

during the hour. For each nest, observations were conducted between dawn and noon once in

each of the three weeks of the nestling period: on day 2, day 10 or 11, and day 17 or 18.

Statistical analyses

General or generalised linear models were used to test whether syllable diversity

(Gaussian error structure), versatility (Gaussian error structure) and song rate (Poisson error

structure) in adulthood were predicted by the conditions experienced during early life. These

explanatory variables included natal brood size (as a measure of sibling competition), parental

provisioning rate (as a measure of nutritional stress, whether mediated by parental investment or

food availability) and body condition when nine days old. The breeding stage of the male at the

time of song recording was also fitted to account for previously shown effects (24) and the age

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of the male was fitted to control for potential changes in song characteristics with age. ‘Brood

size’ was recorded as the highest number of nestlings within the individual’s natal nest at any

time during the nestling period, but then input into the models as a factor due to limited

variation: ‘small’ or ‘large’ according to whether it was above or below the mean (mean brood

size = 4.39; mean ± SD small brood size = 3.38 ± 0.74; large brood size = 5.17 ± 0.40).

‘Provisioning rate’ was the mean of the provisioning rates from the three observation periods.

‘Body condition’ was calculated using the residuals from a linear regression of the body mass

and tarsus length of nestlings when nine days old; this is a widely used measure of condition

(e.g. 31). ‘Age’ was measured as a factor as the bird’s age at the time of song recording, either

‘first-year’ or ‘adult’. ‘Breeding stage’ was also a factor relating to the time of recording: ‘solo’,

‘pre-breeding’ or ‘breeding’ as described above. Prior to analysis, all variables were centred and

standardised to improve interpretation of main effects (32). Collinearity between explanatory

variables was checked using pairwise scatterplots, correlation coefficients and variance inflation

factors (33). All variables were considered because there was no collinearity between them (r <

0.4 in all cases) and variance inflation factors were small (< 3; 31). These five explanatory

variables were fitted in the full model along with the interactions between all pairs of early life

variables. All models were constructed in R, version 3.2.2 (34), and the full model for each song

trait was subjected to the ‘dredge’ function in the package ‘MuMIn’ (35) to rank all sub-models

by Akaike's Information Criterion, with the Hurvich and Tsai correction for small sample size

(AICc; 34,35). If ∆AICc ≤ 2 between two or more of the most parsimonious models, model

averaging was performed using MuMIn. Models were checked for overdispersion and validated

by plotting the distribution of the residuals, the residuals versus the fitted values and the

residuals versus each of the covariates (38).

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Results

Syllable diversity

The syllable diversity of adult male dippers was predicted by their body condition as

nestlings; males in better condition when nine days old produced a greater number of unique

syllables within their adult songs (Table 1, Figure 1). No other variables were contained within

the best fitting models (Tables 1 and S1).

Figure 1. The relationship between nestling body condition and syllable diversity in adult

song.

The line shows the predicted relationship from averaging of the best fitting general linear

models; the raw data are plotted as points. The 95% confidence interval is shown in grey.

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Table 1. The best fitting general linear models of the factors associated with adult male syllable diversity:

lm(syllable diversity ~ age + body condition + brood size + breeding stage + provisioning rate + body condition : brood size + body condition : provisioning rate + brood size : provisioning rate)

Only those models with an AICc value within 2 of that of the best fitting model are shown. For the full list of models see Table S1.

Model #

(Intercept) Age Body condition

Brood size

Breeding stage

Provisioning rate

Body condition x Brood size

Body condition x Provisioning rate

Brood size x Provisioning rate

df loglik AICc Delta Weight

1 69.83 12.03 3 -81.07 169.8 0.00 0.28

2 91.00 2 -83.33 171.5 1.61 0.13

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Versatility

Breeding stage was an important predictor of versatility, supporting previous findings;

solo males produced songs with the highest versatility and breeding males the lowest (Table 2,

Supplementary figure 1). Provisioning rate was also contained within the best fitting models but

the effect size was negligible (Table 2). No other variables were present in the best fitting

models (Tables 2 and S2).

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Table 2. The best fitting general linear models of the factors associated with adult male versatility:

lm(versatility ~ age + body condition + brood size + breeding stage + provisioning rate + body condition : brood size + body condition : provisioning rate + brood size : provisioning rate)

Only those models with an AICc value within 2 of that of the best fitting model are shown, apart from the null model which is included by way of comparison. For the full list of models see Table S2.

Model #

(Intercept) Age Body condition

Brood size

Breeding stage

Provisioning rate

Body condition x Brood size

Body condition x Provisioning rate

Brood size x Provisioning rate

df loglik AICc delta weight

1 0.58 + 4 25.35 -39.6 0.00 0.38

2 0.57 + 0.02 5 26.54 -38.1 1.54 0.18

null 0.51 2 20.13 -35.5 4.15 0.05

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Song rate

The song rate of males was predicted by the rate at which they were provisioned as

nestlings; birds fed at a higher rate by their parents sang at a higher rate as adults (Table 3).

However, the best fitting models of song rate also contained the interactions between

provisioning rate and both body condition and brood size; the effect of provisioning rate on

song rate was greater for individuals in poorer condition and for those raised in small broods

(Table 3; Figs 2a and 2b). Song rate also varied with breeding stage, confirming previous

findings; solo males sang at a higher rate than pre-breeding and breeding males (Table 3). No

other terms were contained within the best fitting models (Tables 3 and S3).

Figure 2. The relationship between the rate at which nestlings were provisioned and their

song rate as adults.

The lines show the predicted relationships from averaging of the best fitting generalised linear

models, plotted for ‘solo’ breeding stage, with the 95% confidence intervals shown in grey. (a)

The dashed line shows the relationship for birds raised in small broods (< 4.39 nestlings, raw

data plotted as circles) and the solid line shows the same relationship for nestlings in large

broods (> 4.39 nestlings, raw data plotted as triangles), both plotted for mean nestling body

condition (-1.23 x 10-17). (b) The solid line shows the relationship for nestlings in good

condition on day 9 (mean condition + 1 SD) and the dashed line shows the same relationship for

nestlings in poor condition on day 9 (mean condition – 1 SD), both plotted for large broods

(>4.39 nestlings); the raw data are plotted as points.

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Table 3. The best fitting generalised linear models of the factors associated with adult male song rate:

glm(song rate ~ age + body condition + brood size + breeding stage + provisioning rate + body condition : brood size + body condition : provisioning rate + brood size + provisioning rate, family = “poisson”)

Only those models with an AICc value within 2 of that of the best fitting model are shown, apart from the null model which is included by way of comparison. For the full list of models see Table S3.

Model #

(Intercept) Age Body condition

Brood size

Breeding stage

Provisioning rate

Body condition x Brood size

Body condition x Provisioning

rate

Brood size x Provisioning

rate

df loglik AICc delta weight

1 2.09 -0.09 + 1.16 -0.28 + 6 -59.17 138.0 0.00 0.42

2 2.63 -0.25 + + 1.00 -0.35 + 8 -53.21 138.4 0.45 0.34

null 2.32 1 -84.6 171.5 33.5 0

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Discussion

The conditions individuals experience during the early part of their lives have been shown to

impact a range of life history variables and phenotypic traits in adulthood (39,40), including

behavioural traits such as birdsong (19,41). Our results demonstrate that song traits in male dippers

are correlated with early life conditions and provide some of the first evidence from a wild bird

population to support the developmental stress hypothesis (8,12). This hypothesis proposes that song

production and complexity may be compromised by exposure to stress during the nestling period as

this is when the brain structures responsible develop (5,8,12). Nestling body condition, which likely

reflects nutritional stress, was positively correlated with syllable diversity, a proxy for repertoire size;

this supports the findings of several experimental studies of captive birds in which the quality of

nestling diet was manipulated (9,19,21). The nestling phase is a critical period of growth for birds

(42), and individuals in poorer condition at this time are presumably unable to invest as heavily in the

development of the brain structures associated with song learning (5,43). Such birds would be unable

to learn and produce as many different syllables, hence syllable diversity may provide females with an

honest signal of male quality (2,8,9). Interestingly, versatility was found to vary with breeding stage

but was not correlated with any of the measures of early life conditions. Solo and pre-breeding males

sing with higher versatility than breeding males (24), suggesting that individuals are able to vary the

use of their repertoires according to context even if the size of their repertoire is constrained by

developmental stress. However, further research is needed to explore these ideas, in particular the

analysis of songs recorded from the same males at different breeding stages.

The rate at which male dippers were fed as nestlings was an important predictor of their song

rate as adults. In contrast to syllable diversity, song rate is usually considered a plastic trait associated

with the current condition of an individual, rather than developmental history (41,44,45, but see 8).

However, it is often reported that developmental stress can cause long-term damage and have a

negative effect on adult phenotypes, thereby affecting an individual’s ability to cope in their current

environment (40). For example, zebra finch chicks that were raised on a poor quality diet exhibited

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increased resting metabolic rates as adults (46); this leads to an increase in daily food demand, which

in turn may increase the time an individual spends foraging and decrease the time spent performing

other behaviours such as singing (46). Individual song sparrows experiencing less stress during

development were found to have better body condition as adults (47), and condition directly affects

song output because only individuals with sufficient energy reserves are able to sustain high song

rates (45).

As with all altricial species, dipper chicks are completely dependent upon their parents for

food for the duration of the nestling period (28,48). An increase in foraging time and the associated

decrease in foraging success has been shown to cause reductions in clutch size, brood size, egg mass,

nestling mass, growth rates and survival rates (49–51). Provisioning rate is therefore likely to have a

major effect on the growth and development of each individual, and this may be greater in smaller

broods where the per capita effect of a change in food availability is higher. Our results show that

individuals raised in small broods which were fed more frequently sang at a higher rate as adults,

presumably because the additional resources available to these birds as chicks allowed them to

maintain better body condition later in life (5,43). The apparent absence of this effect in larger broods

may be due to sibling competition offsetting the benefits of an increase in provisioning rate. Similarly,

the positive correlation between provisioning rate and song rate in individuals whose nestling body

condition was relatively poor suggests that nutritional stress in early life impacts the condition-

dependent investment in singing behaviour of adults. While some studies have suggested that singing

is energetically inexpensive (3,4), adult condition has been shown to be positively correlated with

song rate in several species (2,45,52). Our study is the first to provide evidence that early life

conditions can influence song rate as well as complexity in wild birds, and further work is now

required to investigate the mechanisms behind these relationships.

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Acknowledgements

We would like to thank the many local landowners and Paul Wilkinson at Yorkshire Dales

National Park for providing access and supporting our work; all the students and volunteers who

provided assistance in the field, especially Tess Moore; Robert Lachlan for advising on acoustic

analyses; and Mark Mainwaring, Rupert Marshall and Jos Barlow for comments on an earlier draft.

This work was approved by the relevant ethics committee at Lancaster University and complied with

the current laws of the UK.

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References

1. Searcy WA, Yasukawa K. Song and Female Choice. Ecology and Evolution of Acoustic

Communication in Birds. 1996.

2. Catchpole CK, Slater PJB. Bird song-biological themes and variations. Cambridge Universty

Press; 2008.

3. Franz M, Goller F. Respiratory patterns and oxygen consumption in singing zebra finches. J

Exp Biol. 2003;206(Pt 6):967–78.

4. Ward S, Speakman JR, Slater PJB. The energy cost of song in the canary, Serinus canaria.

Anim Behav. 2003;66:893–902.

5. Nowicki S, Searcy WA, Peters S. Brain development, song learning and mate choice in birds:

a review and experimental test of the “nutritional stress hypothesis”. J Comp Physiol A. 2002

Dec;188(11–12):1003–14.

6. Oberweger K, Goller F. The metabolic cost of birdsong production. J Exp Biol.

2001;204(19):3379–88.

7. Gil D, Gahr M. The honesty of bird song: multiple constraints for multiple traits. Trends Ecol

Evol. 2002 Mar;17(3):133–41.

8. Buchanan KL, Spencer K a, Goldsmith AR, Catchpole CK. Song as an honest signal of past

developmental stress in the European starling (Sturnus vulgaris). Proc Biol Sci. 2003 Jun

7;270(1520):1149–56.

9. Spencer KA, Buchanan KL, Goldsmith AR, Catchpole CK. Song as an honest signal of

developmental stress in the zebra finch (Taeniopygia guttata). Horm Behav. 2003

Aug;44(2):132–9.

10. Nowicki S, Hasselquist D, Bensch S, Peters S. Nestling growth and song repertoire size in

298

299

300

301

302

303

304

305

306

307

308

309

310

311

312

313

314

315

316

317

318

319

320

great reed warblers: evidence for song learning as an indicator mechanism in mate choice.

Proc Biol Sci. 2000 Dec 7;267(1460):2419–24.

11. Jones AG, Ratterman NL. Mate choice and sexual selection: What have we learned since

Darwin? Proc Natl Acad Sci. 2009;106(Supplement_1):10001–8.

12. Nowicki S, Peters S, Podos J. Song Learning, Early Nutrition and Sexual Selection in

Songbirds. Integr Comp Biol. 1998;38(1):179–90.

13. Nottebohm F, Stokes TM, Leonard CM. Central control of song in the canary. J Comp Neurol.

1976;165:457–86.

14. Buchanan KL, Catchpole CK. Song as an indicator of male parental effort in the sedge

warbler. Proc Biol Sci. 2000 Feb 22;267(1441):321–6.

15. Catchpole CK. Song and female choice: good genes and big brains? Trends Ecol Evol.

1996;11(9):358–60.

16. Doutrelant C, Blondel J, Perret P, Lambrechts MM. Blue Tit song repertoire size, male quality

and interspecific competition. J Avian Biol. 2000 Sep;31(3):360–6.

17. Dreiss A, Richard M, Moyen F, White J, Møller AP, Danchin E. Sex ratio and male sexual

characters in a population of blue tits, Parus caeruleus. Behav Ecol. 2006;17(1):13–9.

18. Drummond H, Ancona S. Observational field studies reveal wild birds responding to early-life

stresses with resilience, plasticity, and intergenerational effects. Auk. 2015;132(3):563–76.

19. Spencer KA, Buchanan KL, Goldsmith AR, Catchpole CK. Developmental stress, social rank

and song complexity in the European starling (Sturnus vulgaris). Proc R Soc B Biol Sci.

2004;271(Suppl 3):S121-3.

20. De Kogel CH, Prijs HJ. Effects of brood size manipulations on sexual attractiveness of

offspring in the zebra finch. Anim Behav. 1996;51(3):699–708.

321

322

323

324

325

326

327

328

329

330

331

332

333

334

335

336

337

338

339

340

341

342

343

21. Buchanan KL, Leitner S, Spencer K a, Goldsmith AR, Catchpole CK. Developmental stress

selectively affects the song control nucleus HVC in the zebra finch. Proc Biol Sci.

2004;271(1555):2381–6.

22. Brumm H, Zollinger SA, Slater PJB. Developmental stress affects song learning but not song

complexity and vocal amplitude in zebra finches. Behav Ecol Sociobiol. 2009 Jul;63(9):1387–

95.

23. Schmidt KL, Moore SD, MacDougall-Shackleton EA, MacDougall-Shackleton SA. Early-life

stress affects song complexity, song learning and volume of the brain nucleus RA in adult

male song sparrows. Anim Behav. Elsevier Ltd; 2013 Jul;86(1):25–35.

24. Magoolagan L, Mawby PJ, Whitehead F, Sharp SP. The structure and context of male and

female song in dippers (In Review). J Ornithol. 2018;

25. Nowicki S, Searcy WA, Peters S. Quality of song learning affects female response to male bird

song. Proc Biol Sci. 2002 Sep 22;269(1503):1949–54.

26. Andersson JS, Wester SAL. Length of wing, bill, and tarsus as a character of sex in the Dipper

Cinclus cinclus. Ornis Scand. 1971;2(1):75–9.

27. Svensson L. Identification Guide to European Passerines. Fourth. British Trust for

Ornithology; 1992.

28. Tyler SJ, Ormerod SJ. The Dippers. First. T & AD Poyser Ltd; 1994.

29. Specht R. Avisoft-SASLab Pro. 1993.

30. Järvi T. The evolution of song versatility in the willow warbler Phylloscopus trochilus: A case

of evolution by intersexual selection explained by the “female’s choice of the best mate”.

Ornis Scand. 1983;14(2):123–8.

31. Schulte-Hostedde AI, Zinner B, Millar JS, Hickling GJ. Restitution of Mass – Size Residuals :

Validating Body Condition Indices. Ecology. 2005;86(1):155–63.

344

345

346

347

348

349

350

351

352

353

354

355

356

357

358

359

360

361

362

363

364

365

366

367

32. Schielzeth H. Simple means to improve the interpretability ofregression coefficients. Methods

Ecol Evol. 2010;1:103–13.

33. Zuur AF, Ieno EN, Elphick CS. A protocol for data exploration to avoid common statistical

problems. Methods Ecol Evol. 2010 Mar 13;1(1):3–14.

34. R Core Team, R Development Core Team R. R: A Language and Environment for Statistical

Computing. Team RDC, editor. R Foundation for Statistical Computing. Vienna, Austria: R

Foundation for Statistical Computing; 2015. p. 409.

35. Barton K. MuMIn: Multi-Model Inference. 2016.

36. Hurvich CM, Tsai C. Biometrika Trust Regression and Time Series Model Selection in Small

Samples. Biometrika. 1989;76(2):297–307.

37. Burnham KP, Anderson DR. Model Selection and Multimodel Inference A Practical

Information-Theoretic Approach. Berlin, Germany: Springer; 2002.

38. Zuur AF, Ieno EN, Walker N, Saveliev AA, Smith GM. Mixed Effects Models and Extensions

in Ecology with R. 2009.

39. Krause ET, Honarmand M, Wetzel J, Naguib M. Early fasting is long lasting: differences in

early nutritional conditions reappear under stressful conditions in adult female zebra finches.

PLoS One. 2009 Jan;4(3):e5015.

40. Spencer KA, MacDougall-Shackleton SA. Indicators of development as sexually selected

traits: the developmental stress hypothesis in context. Behav Ecol. 2011 Mar 4;22(1):1–9.

41. Schmidt KL, MacDougall-Shackleton EA, Kubli SP, MacDougall-Shackleton SA.

Developmental stress, condition, and birdsong: A case study in song sparrows. Integr Comp

Biol. 2014;54(4):568–77.

42. Aldredge RA. Using non-linear mixed effects models to identify patterns of chick growth in

House Sparrows Passer domesticus. Ibis (Lond 1859). 2016;16–27.

368

369

370

371

372

373

374

375

376

377

378

379

380

381

382

383

384

385

386

387

388

389

390

391

43. Nowicki S, Searcy WA. Song function and the evolution of female preferences: why birds

sing, why brains matter. Ann N Y Acad Sci. 2004 Jun;1016:704–23.

44. Møller A, Thornhill R. Male parental care, differential parental investment by females and

sexual selection. Anim Behav. 1998 Jun;55(6):1507–15.

45. Grunst ML, Grunst AS. Song complexity, song rate, and variation in the adrenocortical stress

response in song sparrows (Melospiza melodia). Gen Comp Endocrinol. 2014;200:67–76.

46. Criscuolo F, Monaghan P, Nasir L, Metcalfe NB. Early nutrition and phenotypic development:

“catch-up” growth leads to elevated metabolic rate in adulthood. Proc R Soc B Biol Sci.

2008;275(1642):1565–70.

47. Pfaff JA, Zanette L, MacDougall-Shackleton SA, MacDougall-Shackleton EA. Song repertoire

size varies with HVC volume and is indicative of male quality in song sparrows (Melospiza

melodia). Proc R Soc London B Biol Sci. 2007;274(1621):2035–40.

48. O’Connor RJ. The Growth and Development of Birds. Chichester: J. Wiley; 1984.

49. Ormerod SJ, Tyler SJ. Long-term change in the suitability of welsh streams for dippers

Cinclus cinclus as a result of acidification and recovery: a modelling study. Environ Pollut.

1989;62(2–3):171–82.

50. Ormerod SJ, O’Halloran J, Gribbin SD, Tyler SJ. The ecology of dippers Cinclus cinclus in

relation to stream acidity in upland Wales: breeding performance, calcium physiology and

nestling growth. J Appl Ecol. 1991;28(2):419–33.

51. Vickery J. The reproductive success of the dipper Cinclus cinclus in relation to the acidity of

streams in south‐west Scotland. Freshw Biol. 1992;28(2):195–205.

52. Berg ML, Beintema NH, Welbergen JA, Komdeur J. Singing as a Handicap : The Effects of

Food Availability and Weather on Song Output in the Australian Reed Warbler Acrocephalus

australis. J Avian Biol. 2005;36:102–9.

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SUPPORTING INFORMATION

Table S1. Model selection table for global model:

lm(Repertoire ~ Context + Age + Provisioning rate + Brood size + Tarsus length + Provisioning rate * Tarsus length + Brood size * Tarsus length)

(Intercept) Age Brood size

Context Provisioning rate

Tarsus length

Brood size x Tarsus length

Provisioning rate x Tarsus length

df LogLik AICc Delta Weight

1 69.83 2 -83.33 171.46 0.00 0.25

5 91.00 + 4 -80.87 172.82 1.36 0.13

3 76.75 + 3 -82.75 173.22 1.76 0.10

17 69.83 5.22 3 -82.94 173.60 2.14 0.08

9 69.83 2.33 3 -83.25 174.22 2.76 0.06

2 70.00 + 3 -83.33 174.37 2.91 0.06

51 78.24 + 35.36 + 5 -79.93 174.86 3.40 0.05

6 91.00 + + 5 -80.20 175.41 3.95 0.03

7 96.56 + + 5 -80.26 175.53 4.07 0.03

19 76.98 + 5.48 4 -82.30 175.67 4.21 0.03

11 77.32 + 3.39 4 -82.58 176.24 4.79 0.02

4 78.32 + + 4 -82.72 176.51 5.05 0.02

425

426

427

428

21 90.55 + 1.61 5 -80.84 176.67 5.22 0.02

13 91.18 + 0.66 5 -80.86 176.73 5.27 0.02

18 67.90 + 6.29 4 -82.87 176.82 5.36 0.02

25 69.83 0.42 5.06 4 -82.94 176.96 5.50 0.02

10 69.71 + 2.36 4 -83.25 177.58 6.13 0.01

8 95.36 + + + 6 -79.83 179.31 7.85 0.00

22 89.55 + + 5.16 6 -79.88 179.40 7.94 0.00

52 78.97 + + 35.27 + 6 -79.92 179.48 8.02 0.00

59 78.34 + 0.66 35.01 + 6 -79.92 179.48 8.02 0.00

27 77.21 + 1.52 4.91 5 -82.27 179.54 8.08 0.00

20 75.98 + + 5.91 5 -82.29 179.57 8.11 0.00

14 91.06 + + 0.21 6 -80.20 180.04 8.58 0.00

23 96.10 + + 1.99 6 -80.21 180.05 8.60 0.00

12 78.43 + + 3.24 5 -82.57 180.13 8.67 0.00

15 96.52 + + 0.37 6 -80.26 180.16 8.70 0.00

26 67.91 + 0.41 6.13 5 -82.87 180.74 9.28 0.00

89 69.66 0.47 4.99 0.48 5 -82.94 180.88 9.42 0.00

55 94.87 + + 29.17 + 7 -77.91 181.02 9.56 0.00

29 90.77 + 1.14 1.96 6 -80.82 181.27 9.81 0.00

91 76.86 + 1.86 4.51 2.54 6 -82.20 184.04 12.58 0.00

28 76.26 + + 1.49 5.33 6 -82.26 184.15 12.69 0.00

24 93.79 + + + 4.89 7 -79.53 184.27 12.81 0.00

16 95.29 + + + 0.51 7 -79.83 184.86 13.40 0.00

30 89.82 + + 1.47 5.64 7 -79.84 184.89 13.43 0.00

60 79.08 + + 0.68 34.91 + 7 -79.91 185.02 13.56 0.00

123 78.25 + 0.74 34.75 + 0.57 7 -79.92 185.03 13.57 0.00

90 66.82 + 0.59 6.12 1.73 6 -82.84 185.32 13.86 0.00

93 101.02 + 2.30 4.14 8.97 7 -80.17 185.53 14.07 0.00

31 96.10 + + 0.13 2.03 7 -80.21 185.62 14.16 0.00

56 93.41 + + + 29.03 + 8 -77.54 187.08 15.62 0.00

63 94.90 + + 0.67 29.45 + 8 -77.90 187.80 16.34 0.00

92 74.79 + + 1.89 5.25 3.28 7 -82.16 189.52 18.06 0.00

94 98.17 + + 2.36 6.94 7.20 8 -79.40 190.80 19.34 0.00

32 93.80 + + + 0.69 5.13 8 -79.52 191.05 19.59 0.00

95 102.43 + + 1.28 3.66 6.79 8 -79.86 191.73 20.27 0.00

124 78.94 + + 0.72 34.79 + 0.30 8 -79.91 191.82 20.36 0.00

127 103.14 + + 2.22 33.37 + 8.92 9 -77.12 194.74 23.28 0.00

64 93.43 + + + 1.01 29.46 + 9 -77.52 195.53 24.08 0.00

96 99.41 + + + 1.65 6.34 5.86 9 -79.25 199.00 27.54 0.00

128 101.28 + + + 2.38 33.07 + 8.23 10 -76.84 205.11 33.65 0.00

1 69.83 2 -83.33 171.46 0.00 0.25

5 91.00 + 4 -80.87 172.82 1.36 0.13

429

Table S2. Model selection table for global model:

glm(song rate ~ context + age + provisioning rate + brood size + tarsus length + brood*provisioning rate + brood size * tarsus length + tarsus length * provisioning rate )

(Intercept) Age Brood Context Provisioning rate

Tarsus length

Brood size x

Provisioning rate

Brood size x Tarsus length

Provisioning rate x Tarsus

length

df logLik AICc delta weight

187 1.97 + 0.75 0.04 + -0.30 6 -60.50 140.63 0.00 0.43

43 2.03 + 0.90 + 4 -65.10 141.28 0.65 0.31

47 2.32 + + 0.73 + 6 -62.27 144.17 3.54 0.07

59 2.03 + 0.88 -0.03 + 5 -65.01 145.02 4.39 0.05

44 2.04 + + 0.89 + 5 -65.08 145.16 4.53 0.04

188 1.89 + + 0.76 -0.01 + -0.32 7 -60.14 145.47 4.84 0.04

251 1.97 + 0.77 0.07 + + -0.30 7 -60.49 146.18 5.55 0.03

48 2.29 + + + 0.76 + 7 -61.90 149.00 8.37 0.01

123 2.03 + 0.83 -0.12 + + 6 -64.93 149.50 8.87 0.01

60 2.03 + + 0.88 -0.04 + 6 -65.01 149.66 9.03 0.00

63 2.31 + + 0.73 -0.01 + 7 -62.26 149.72 9.09 0.00

153 2.07 0.21 0.15 -0.47 4 -70.02 151.13 10.50 0.00

191 2.05 + + 0.75 0.07 + -0.28 8 -60.00 152.01 11.38 0.00

430

431432

252 1.88 + + 0.80 0.05 + + -0.32 8 -60.10 152.21 11.57 0.00

219 2.00 + 0.16 -0.38 + -0.40 6 -66.74 153.12 12.49 0.00

154 2.11 + 0.21 0.16 -0.46 5 -69.92 154.83 14.20 0.00

155 2.04 + 0.20 0.14 -0.46 5 -69.94 154.88 14.25 0.00

124 2.03 + + 0.83 -0.12 + + 7 -64.93 155.06 14.43 0.00

64 2.27 + + + 0.73 -0.06 + 8 -61.74 155.48 14.85 0.00

87 2.46 + + -0.60 + 6 -68.07 155.79 15.16 0.00

7 2.59 + + 4 -72.51 156.09 15.46 0.00

127 2.31 + + 0.73 -0.02 + + 8 -62.26 156.52 15.89 0.00

15 2.56 + + 0.12 5 -71.34 157.67 17.04 0.00

157 2.29 + 0.18 0.12 -0.38 6 -69.25 158.13 17.50 0.00

220 2.05 + + 0.16 -0.37 + -0.38 7 -66.58 158.36 17.73 0.00

5 2.79 + 3 -75.42 158.54 17.91 0.00

192 1.97 + + + 0.77 0.02 + -0.30 9 -59.16 158.82 18.19 0.00

13 2.74 + 0.14 4 -73.95 158.98 18.35 0.00

23 2.57 + + -0.08 5 -72.03 159.06 18.43 0.00

156 2.08 + + 0.20 0.16 -0.44 6 -69.84 159.32 18.68 0.00

8 2.59 + + + 5 -72.48 159.96 19.33 0.00

255 2.05 + + 0.81 0.19 + + -0.29 9 -59.92 160.33 19.70 0.00

95 2.45 + + 0.08 -0.55 + 7 -67.69 160.59 19.96 0.00

21 2.76 + -0.09 4 -74.80 160.67 20.04 0.00

88 2.47 + + + -0.60 + 7 -68.04 161.29 20.66 0.00

6 2.79 + + 4 -75.14 161.35 20.72 0.00

31 2.55 + + 0.11 -0.03 6 -71.27 162.18 21.55 0.00

16 2.56 + + + 0.12 6 -71.30 162.24 21.61 0.00

14 2.74 + + 0.14 5 -73.72 162.44 21.81 0.00

83 2.07 + -0.76 + 4 -75.71 162.50 21.87 0.00

84 2.23 + + -0.70 + 5 -73.76 162.52 21.89 0.00

159 2.28 + + 0.16 0.09 -0.31 7 -68.77 162.74 22.11 0.00

29 2.73 + 0.12 -0.04 5 -73.87 162.75 22.12 0.00

158 2.30 + + 0.19 0.13 -0.37 7 -69.23 163.65 23.02 0.00

24 2.56 + + + -0.08 6 -72.02 163.68 23.05 0.00

223 2.21 + + 0.13 -0.40 + -0.27 8 -65.85 163.70 23.07 0.00

128 2.27 + + + 0.77 0.00 + + 9 -61.71 163.92 23.29 0.00

22 2.76 + + -0.08 5 -74.71 164.42 23.79 0.00

91 2.08 + 0.08 -0.70 + 5 -75.23 165.45 24.82 0.00

92 2.25 + + 0.10 -0.62 + 6 -73.09 165.81 25.18 0.00

30 2.74 + + 0.13 -0.02 6 -73.71 167.05 26.42 0.00

96 2.47 + + + 0.08 -0.54 + 8 -67.62 167.23 26.60 0.00

32 2.56 + + + 0.11 -0.03 7 -71.27 167.73 27.10 0.00

4 2.27 + + 3 -80.41 168.53 27.90 0.00

2 2.45 + 2 -81.88 168.56 27.93 0.00

10 2.42 + 0.13 3 -80.49 168.69 28.06 0.00

256 1.94 + + + 0.91 0.26 + + -0.33 10 -58.78 169.00 28.37 0.00

3 2.13 + 2 -82.30 169.39 28.76 0.00

160 2.28 + + + 0.16 0.09 -0.31 8 -68.77 169.54 28.91 0.00

11 2.14 + 0.12 3 -80.94 169.60 28.97 0.00

12 2.27 + + 0.11 4 -79.30 169.67 29.04 0.00

9 2.31 0.15 2 -82.64 170.07 29.44 0.00

19 2.13 + -0.07 3 -81.84 171.40 30.76 0.00

18 2.44 + -0.01 3 -81.87 171.46 30.83 0.00

1 2.32 1 -84.61 171.47 30.84 0.00

26 2.44 + 0.15 0.06 4 -80.29 171.66 31.03 0.00

20 2.27 + + -0.01 4 -80.40 171.88 31.25 0.00

224 2.22 + + + 0.13 -0.40 + -0.27 9 -65.82 172.13 31.50 0.00

27 2.14 + 0.11 -0.03 4 -80.87 172.82 32.19 0.00

25 2.31 0.14 -0.03 3 -82.56 172.84 32.21 0.00

17 2.32 -0.08 2 -84.02 172.85 32.21 0.00

28 2.29 + + 0.13 0.05 5 -79.13 173.26 32.62 0.00

187 1.97 + 0.75 0.04 + -0.30 6 -60.50 140.63 0.00 0.43

433

434

Supplementary Figure 1

Supplementary Figure 2

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