Abstract
The behavioural rhythms of organisms are thought to be under strong selection, influenced by the rhythmicity of the environment1,2,3,4. Such behavioural rhythms are well studied in isolated individuals under laboratory conditions1,5, but free-living individuals have to temporally synchronize their activities with those of others, including potential mates, competitors, prey and predators6,7,8,9,10. Individuals can temporally segregate their daily activities (for example, prey avoiding predators, subordinates avoiding dominants) or synchronize their activities (for example, group foraging, communal defence, pairs reproducing or caring for offspring)6,7,8,9,11. The behavioural rhythms that emerge from such social synchronization and the underlying evolutionary and ecological drivers that shape them remain poorly understood5,6,7,9. Here we investigate these rhythms in the context of biparental care, a particularly sensitive phase of social synchronization12 where pair members potentially compromise their individual rhythms. Using data from 729 nests of 91 populations of 32 biparentally incubating shorebird species, where parents synchronize to achieve continuous coverage of developing eggs, we report remarkable within- and between-species diversity in incubation rhythms. Between species, the median length of one parent’s incubation bout varied from 1–19 h, whereas period length—the time in which a parent’s probability to incubate cycles once between its highest and lowest value—varied from 6–43 h. The length of incubation bouts was unrelated to variables reflecting energetic demands, but species relying on crypsis (the ability to avoid detection by other animals) had longer incubation bouts than those that are readily visible or who actively protect their nest against predators. Rhythms entrainable to the 24-h light–dark cycle were less prevalent at high latitudes and absent in 18 species. Our results indicate that even under similar environmental conditions and despite 24-h environmental cues, social synchronization can generate far more diverse behavioural rhythms than expected from studies of individuals in captivity5,6,7,9. The risk of predation, not the risk of starvation, may be a key factor underlying the diversity in these rhythms.
This is a preview of subscription content, access via your institution
Access options
Subscribe to this journal
Receive 51 print issues and online access
$199.00 per year
only $3.90 per issue
Buy this article
- Purchase on SpringerLink
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
References
Dunlap, J. C., Loros, J. J. & DeCoursey, P. J. Chronobiology: Biological Timekeeping (Sinauer Associates, 2004)
Young, M. W. & Kay, S. A. Time zones: a comparative genetics of circadian clocks. Nat. Rev. Genet. 2, 702–715 (2001)
Helm, B. & Visser, M. E. Heritable circadian period length in a wild bird population. Proc. R. Soc. B 277, 3335–3342 (2010)
Koskenvuo, M., Hublin, C., Partinen, M., Heikkilä, K. & Kaprio, J. Heritability of diurnal type: a nationwide study of 8753 adult twin pairs. J. Sleep Res. 16, 156–162 (2007)
Kronfeld-Schor, N., Bloch, G. & Schwartz, W. J. Animal clocks: when science meets nature. Proc. R. Soc. B 280, 20131354 (2013)
Bloch, G., Herzog, E. D., Levine, J. D. & Schwartz, W. J. Socially synchronized circadian oscillators. Proc. R. Soc. B 280, 20130035 (2013)
Castillo-Ruiz, A., Paul, M. J. & Schwartz, W. J. In search of a temporal niche: social interactions. Prog. Brain Res . 199, 267–280 (2012)
Mistlberger, R. E. & Skene, D. J. Social influences on mammalian circadian rhythms: animal and human studies. Biol. Rev. Camb. Philos. Soc. 79, 533–556 (2004)
Davidson, A. J. & Menaker, M. Birds of a feather clock together–sometimes: social synchronization of circadian rhythms. Curr. Opin. Neurobiol. 13, 765–769 (2003)
Mrosovsky, N. Locomotor activity and non-photic influences on circadian clocks. Biol. Rev. Camb. Philos. Soc. 71, 343–372 (1996)
Regal, P. J. & Connolly, M. S. Social influences on biological rhythms. Behaviour 72, 171–198 (1980)
Emlen, S. T. & Oring, L. W. Ecology, sexual selection, and the evolution of mating systems. Science 197, 215–223 (1977)
Deeming, D. C. Avian Incubation: Behaviour, Environment and Evolution (Oxford Univ. Press, 2002)
Szekely, T. & Reynolds, J. D. Evolutionary transitions in parental care in shorebirds. Proc. R. Soc. B 262, 57–64 (1995)
del Hoyo, J., Elliott, A. & Sargatal, J. Handbook of the Birds of the World. Vol. 3. Hoatzing to Auks . (Lynx Edicions, 1996)
Bulla, M. et al. Supporting Information for ‘Unexpected diversity in socially synchronized rhythms of shorebirds’. Open Science Frameworkhttp://doi.org/10.17605/OSF.IO/WXUFM (2016)
Williams, J. B. in Avian Energetics and Nutritional Ecology (ed. C. Carey ) Ch. 5, 375–416 (Chapman & Hall, 1996)
Bulla, M., Cresswell, W., Rutten, A. L., Valcu, M. & Kempenaers, B. Biparental incubation-scheduling: no experimental evidence for major energetic constraints. Behav. Ecol. 26, 30–37 (2015)
Martin, T. E., Scott, J. & Menge, C. Nest predation increases with parental activity: separating nest site and parental activity effects. Proc. R. Soc.B 267, 2287–2293 (2000)
Smith, P. A., Tulp, I., Schekkerman, H., Gilchrist, H. G. & Forbes, M. R. Shorebird incubation behaviour and its influence on the risk of nest predation. Anim. Behav. 84, 835–842 (2012)
McKinnon, L. et al. Lower predation risk for migratory birds at high latitudes. Science 327, 326–327 (2010)
Hut, R. A., Paolucci, S., Dor, R., Kyriacou, C. P. & Daan, S. Latitudinal clines: an evolutionary view on biological rhythms. Proc. R. Soc. B 280, 20130433 (2013)
van Oort, B. E. et al. Circadian organization in reindeer. Nature 438, 1095–1096 (2005)
Steiger, S. S. et al. When the sun never sets: diverse activity rhythms under continuous daylight in free-living arctic-breeding birds. Proc. R. Soc. B 280, 20131016 (2013)
Lesku, J. A. et al. Adaptive sleep loss in polygynous pectoral sandpipers. Science 337, 1654–1658 (2012)
Foster, R. G. & Wulff, K. The rhythm of rest and excess. Nat. Rev. Neurosci. 6, 407–414 (2005)
Silver, R. & Bittman, E. L. Reproductive mechanisms: interaction of circadian and interval timing. Ann. NY Acad. Sci . 423, 488–514 (1984)
Paul, M. J., Indic, P. & Schwartz, W. J. Social synchronization of circadian rhythmicity in female mice depends on the number of cohabiting animals. Biol. Lett. 11, 20150204 (2015)
Revell, L. J. Two new graphical methods for mapping trait evolution on phylogenies. Methods Ecol. Evol . 4, 754–759 (2013)
Felsenstein, J. Phylogenies and the comparative method. Am. Nat. 125, 1–15 (1985)
Bulla, M., Valcu, M., Rutten, A. L. & Kempenaers, B. Biparental incubation patterns in a high-Arctic breeding shorebird: how do pairs divide their duties? Behav. Ecol. 25, 152–164 (2014)
Reneerkens, J., Grond, K., Schekkerman, H., Tulp, I. & Piersma, T. Do uniparental sanderlings Calidris alba increase egg heat input to compensate for low nest attentiveness? PLoS One 6, e16834 (2011)
Kosztolányi, A. & Székely, T. Using a transponder system to monitor incubation routines of Snowy Plovers. J. Field Ornithol. 73, 199–205 (2002)
Conklin, J. R. & Battley, P. F. Attachment of geolocators to bar-tailed godwits: a tibia-mounted method with no survival effects or loss of units. Wader Study Group Bull . 117, 56–58 (2010)
Burger, J., Niles, L. J., Porter, R. R. & Dey, A. D. Using geolocator data to reveal incubation periods and breeding biology in Red Knots Calidris canutus rufa. Wader Study Group Bull . 119, 26–36 (2012)
Bouten, W., Baaij, E. W., Shamoun-Baranes, J. & Camphuysen, K. C. J. A flexible GPS tracking system for studying bird behaviour at multiple scales. J. Ornithol. 154, 571–580 (2012)
Bulla, M. R-SCRIPT and EXAMPLE DATA to extract incubation from temperature measurements. Version 1. figsharehttps://dx.doi.org/10.6084/m9.figshare.1037545.v1 (2014)
Bulla, M. R-SCRIPT and EXAMPLE DATA to extract incubation bouts from continuous RFID and video data. Version 1. figsharehttps://dx.doi.org/10.6084/m9.figshare.1533278.v1 (2015)
Bulla, M. Example of how to manually extract incubation bouts from interactive plots of raw data—R-CODE and DATA. Version 1. figsharehttps://dx.doi.org/10.6084/m9.figshare.2066784.v1 (2016)
Bulla, M. Procedure for manual extraction of incubation bouts from plots of raw data.pdf. Version 1. figsharehttps://dx.doi.org/10.6084/m9.figshare.2066709.v1 (2016)
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer 2009)
Sarkar, D. & Andrews, F. latticeExtra: Extra Graphical Utilities Based on Lattice. R package version 0.6-24 http://CRAN.R-project.org/package=latticeExtra (2012)
Sarkar, D. Lattice: Multivariate Data Visualization with R (Springer, 2008)
Lisovski, S. Geolocator-ArcticWader-BreedingSiteEstimation. Version 2015-08-05. GitHub repositoryhttps://github.com/slisovski/Geolocator-ArcticWader-BreedingSiteEstimation (2015)
Lisovski, S. & Hahn, S. GeoLight—processing and analysing light-based geolocator data in R. Methods Ecol. Evol . 3, 1055–1059 (2012)
Lisovski, S. et al. Geolocation by light: accuracy and precision affected by environmental factors. Methods Ecol. Evol . 3, 603–612 (2012)
Conklin, J. R., Battley, P. F., Potter, M. A. & Fox, J. W. Breeding latitude drives individual schedules in a trans-hemispheric migrant bird. Nat. Commun. 1, 67 (2010)
Poole, A. The Birds of North America (Cornell Laboratory of Ornithology, 2005)
Lappo, E., Tomkovich, P. & Syroechkovskiy, E. Atlas of Breeding Waders in the Russian Arctic (UF Ofsetnaya Pecha, 2012)
Chandler, R. J. Shorebirds of the Northern Hemisphere (Christopher Helm, 2009)
Brazil, M. Birds of East Asia: Eastern China, Taiwan, Korea, Japan, and Eastern Russia (Christopher Helm, 2009)
Dale, J. et al. Sexual selection explains Rensch’s rule of allometry for sexual size dimorphism. Proc. R. Soc.B 274, 2971–2979 (2007)
Bulla, M. et al. Supplementary Data 3—Study sites: location, population wing length, monitoring method, tide. Version 11. figsharehttps://dx.doi.org/10.6084/m9.figshare.1536260.v11 (2016)
Cramp, S. Handbook of the Birds of Europe, the Middle East, and North Africa: The Birds of the Western Palearctic Volume III: Waders to Gulls (Oxford Univ. Press, 1990)
Freckleton, R. P., Harvey, P. H. & Pagel, M. Phylogenetic analysis and comparative data: a test and review of evidence. Am. Nat. 160, 712–726 (2002)
Pagel, M. Inferring evolutionary processes from phylogenies. Zool. Scr. 26, 331–348 (1997)
Martins, E. P. & Hansen, T. F. Phylogenies and the comparative method: a general approach to incorporating phylogenetic information into the analysis of interspecific data. Am. Nat. 149, 646–667 (1997)
Pagel, M. Inferring the historical patterns of biological evolution. Nature 401, 877–884 (1999)
Hackett, S. J. et al. A phylogenomic study of birds reveals their evolutionary history. Science 320, 1763–1768 (2008)
Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. & Mooers, A. O. The global diversity of birds in space and time. Nature 491, 444–448 (2012)
Küpper, C. et al. Kentish versus snowy plover: phenotypic and genetic analyses of Charadrius alexandrinus reveal divergence of Eurasian and American subspecies. Auk 126, 839–852 (2009)
Hadfield, J. D. MCMC methods for multi-response generalized linear mixed models: the MCMCglmm R package. J. Stat. Softw. 33, 1–22 (2010)
R-Core-Team. R: A Language and Environment for Statistical Computing. Version 3.1.1. R Foundation for Statistical Computinghttp://www.R-project.org/ (2014)
Anderson, D. R. Model Based Inference in the Life Sciences: A Primer on Evidence (Springer, 2008)
Hijmans, R. J. raster: Geographic data analysis and modeling. R package version 2.3-24. http://CRAN.R-project.org/package=raster (2015)
Bivand, R. & Lewin-Koh, N. maptools: Tools for reading and handling spatial objects. R package version 0.8-30. http://CRAN.R-project.org/package=maptools (2014)
Revell, L. J. in Modern Phylogenetic Comparative Methods and Their Application in Evolutionary Biology (ed. L. Z. Garamszegi ) Ch. 4, 77–103 (Springer, 2014)
Johnson, O. W. et al. Tracking Pacific golden-plovers Pluvialis fulva: transoceanic migrations between non-breeding grounds in Kwajalein, Japan and Hawaii and breeding grounds in Alaska and Chukotka. Wader Study 122, 13–20 (2015)
Kosztolanyi, A., Cuthill, I. C. & Szekely, T. Negotiation between parents over care: reversible compensation during incubation. Behav. Ecol. 20, 446–452 (2009)
St Clair, J. J. H., Herrmann, P., Woods, R. W. & Székely, T. Female-biased incubation and strong diel sex-roles in the two-banded plover Charadrius falklandicus. J. Ornithol. 151, 811–816 (2010)
Spiegel, C. S., Haig, S. M., Goldstein, M. I. & Huso, M. Factors affecting incubation patterns and sex roles of black oystercatchers in Alaska. Condor 114, 123–134 (2012)
Praus, L. & Weidinger, K. Predators and nest success of sky larks Alauda arvensis in large arable fields in the Czech Republic. Bird Study 57, 525–530 (2010)
Orme, D. et al. caper: Comparative Analyses of Phylogenetics and Evolution in R. R package version 0.5.2. http://CRAN.R-project.org/package=caper (2013)
Acknowledgements
We thank all that made the data collection possible. We are grateful to W. Schwartz, E. Schlicht, W. Forstmeier, M. Baldwin, H. Fried Petersen, D. Starr-Glass and B. Bulla for comments on the manuscript and to F. Korner-Nievergelt, J. D. Hadfield, L. Z. Garamszegi, S. Nakagawa, T. Roth, N. Dochtermann, Y. Araya, E. Miller and H. Schielzeth for advice on data analysis. Data collection was supported by various institutions and people listed in supplementary data 1 at https://osf.io/sq8gk (ref. 16). The study was supported by the Max Planck Society (to B.K.). M.B. is a PhD student in the International Max Planck Research School for Organismal Biology.
Author information
Authors and Affiliations
Contributions
M.B. and B.K. conceived the study. All authors except B.H. collected the primary data (see https://osf.io/sq8gk, ref. 16). M.B. coordinated the study and managed the data. M.B. and M.V. developed the methods to extract incubation. M.B. extracted bout lengths and with help from A.R. and M.V. created actograms. M.B. analysed the data with help from M.V. M.B. prepared the supporting information. M.B. and B.K. wrote the paper with input from the other authors. Except for the first, second and last author, the authors are listed alphabetically by their first name.
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing financial interests.
Additional information
Reviewer Information Nature thanks P. Bartell, C. Buck and M. Visser for their contribution to the peer review of this work.
Extended data figures and tables
Extended Data Figure 1 Extracting period length of incubation rhythms.
a–c, Each column represents an example for a specific nest with long, intermediate and short incubation bouts. a, From the extracted bout lengths we created a time series that indicated—for each nest and for every 10 min interval—whether a specific parent (female, if sex was known) incubated or not. Exchange gaps (no parent on the nest) had to be <6 h to be included (for treatment of exchange gaps >6 h see d, e). b, We then estimated the autocorrelation for each 10-min time-lag up to 4 days (R ‘acf’ function63). Positive values indicate a high probability that the female was incubating, negative values indicate that it was more likely that the male was incubating. We used only nests that had enough data to estimate the autocorrelation pattern (n = 584 nests from 88 populations of 30 species). The visualized autocorrelation time series never resembled white or random noise indicative of an arrhythmic incubation pattern. To determine the period (that is, cycle of high and low probability for a parent to incubate) that dominated the incubation rhythm, we fitted to the autocorrelation estimates a series of periodic logistic regressions. In each regression, the time lag (in hours) transformed to radians was represented by a sine and cosine function
where f(t) is the autocorrelation at time-lag t; a0 is the intercept; b is the slope for sine and c the slope for cosine, T represents the length of the fitted period (in hours), and e is an error term. We allowed the period length to vary from 0.5 h to 48 h (in 15 min intervals, giving 191 regressions). c, By comparing the Akaike’s information criterion64 (AIC) of all regressions, we estimated, for each nest, the length of the dominant period in the actual incubation data (best fit). Regressions with ΔAIC (AICmodel−AICmin) close to 0 are considered as having strong empirical support, while models with ΔAIC values ranging from 4–7 have less support64. In 73% of all nests, we determined a single best model with ΔAIC ≤ 3 (c, middle ΔAIC graph), in 20% of nests two best models emerged and in 6% of nests 3 or 4 models had ΔAIC ≤ 3 (c, left and right ΔAIC graphs). However, in all but three nests, the models with the second-, third- and so on best ΔAIC were those with period lengths closest to the period length of the best model (c, left and right ΔAIC graphs). This suggests that multiple periodicities are uncommon. d, e, The extraction of the period length (described in a–c) requires continuous data sets, but some nests had long (>6 h) gaps between two consecutive incubation bouts, for example because of equipment failure or because of unusual parental behaviour. In such cases, we excluded the data from the end of the last bout until the same time the following day, if data were then available again (d), or we excluded the entire day (e).
Extended Data Figure 2 Extracting incubation bouts from light-logger data.
a, An example of a nest with a light intensity signal from both parents (yellow line, female; blue line, male. The incubation bouts for a given parent reflect periods dominated by lower light values compared to those of the partner. Note the sharp drop in the light levels at the beginning of each incubation bout and the sharp increase in the light levels at the end. Change-overs between partners occur when the light signal lines cross. Such pronounced changes in light intensity detected by the logger were used to assign incubation even when only a single parent was tagged. Note that after the chicks hatch and leave the nest (9 July, vertical bar), the light intensity signals from both parents remain similar. b, An example of a nest where one incubating parent was simultaneously equipped with a light-logger and with a GPS tag. The yellow line indicates light levels, red dots indicate the distance of the bird to the nest. As expected, low light levels co-occur with close proximity to the nest, and therefore reflect periods of incubation. Although light levels decrease during twilight (light grey horizontal bar), the recordings were still sensitive enough to reflect periods of incubation, that is, the light signal matches the distance (for example 25 May: female incubated during dawn, but was off the nest during dusk). a, b, Rectangles in the background indicate incubation bouts (female, light yellow polygon; male, light blue polygon).
Extended Data Figure 3 Relationship between bout and period length for 30 shorebird species.
Each dot represents one nest (n = 584 nests), colours indicate the genus.
Extended Data Figure 4 Ecological correlates of latitude.
a, Variation in minimum temperature across the globe represented by mean minimum June temperature for the Northern Hemisphere and mean minimum December temperature for the Southern Hemisphere. b, Correlation between absolute latitude and the mean minimum temperature of the month (n = 729 nests). For each nest we used the month that contained most of the incubation data. For maximum temperature the correlation was the same (r = −0.91, n = 729 nests). c, Daily variation in sun elevation (that is, in light conditions) are represented as the difference between the noon and midnight sun elevation for the summer solstice in the Northern Hemisphere and the winter solstice in the Southern Hemisphere. d, Correlation between absolute latitude and daily variation in sun elevation for mid-day of incubation data for each nest (n = 729 nests). The points are jittered, as otherwise they form a straight line. a, c, Red points indicate the breeding site for each population (n = 91). a, b, The minimum and maximum monthly temperature data were obtained from http:\\www.worldclim.org using the raster R-package65. c, d, Sun-elevation was obtained by the ‘solarpos’ function from the maptools R-package66.
Extended Data Figure 5 Between-species variation in parental crypsis during incubation.
a, b, Shorebirds vary in how visible they are on the nest while incubating. The nearly invisible great knot (Calidris tenuirostris; a; central and facing right) sits tight on the nest when approached by a human until nearly stepped upon. In contrast, the conspicuous Eurasian oystercatcher (Haematopus ostralegus; b) is visible on the nest from afar and when approached by a human leaves the nest about 100 m in advance (Credits: a, M. Šálek; b, J. van de Kam).
Rights and permissions
About this article
Cite this article
Bulla, M., Valcu, M., Dokter, A. et al. Unexpected diversity in socially synchronized rhythms of shorebirds. Nature 540, 109–113 (2016). https://doi.org/10.1038/nature20563
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/nature20563
This article is cited by
-
Acoustic monitoring reveals a diel rhythm of an arctic seabird colony (little auk, Alle alle)
Communications Biology (2024)
-
Food web interactions of two breeding Arctic shorebird species, little stint Calidris minuta and red knot Calidris canutus, are shaped by their elevational distribution
Polar Biology (2024)
-
Avian migration clocks in a changing world
Journal of Comparative Physiology A (2024)
-
Offspring thermal demands and parental brooding efficiency differ for precocial birds living in contrasting climates
Frontiers in Zoology (2023)
-
Incubation behaviour of the Common Ringed Plover Charadrius hiaticula at different latitudes
Journal of Ornithology (2023)
Comments
By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.
