DOI: 10.5061/dryad.k0p2ngfgg
title: Continent-wide drivers of spatial synchrony in breeding demographic structure across wild great tit populations

description: Variation in age structure influences population dynamics, yet we have
 limited understanding of the spatial scale at which its fluctuations are
 synchronised between populations. Using 32 great tit populations, spanning
 4○W–33○E and 35–65○N involving &gt;130,000 birds across 67 years, we
 quantify spatial synchrony in breeding demographic structure (subadult vs.
 adult breeders) and its drivers. We show that larger clutch sizes, colder
 winters, and larger beech crops lead to younger populations. We report
 distance-dependent synchrony of demographic structure, maintained at
 approximately 650km. Despite covariation with demographic structure, we do
 not find evidence for environmental variables influencing the scale of
 synchrony, except for beech masting. We suggest that local ecological and
 density-dependent dynamics impact how environmental variation interacts
 with demographic structure, influencing estimates of the environment’s
 effect on synchrony. Our analyses demonstrate the operation of synchrony
 in demographic structure over large scales, with implications for
 age-dependent demography in populations.
description: <strong>Study systems and data
 collection</strong> The great tit <em>Parus
 major</em> is a passerine bird found in mixed woodlands across much
 of the Western Palearctic. Their reproductive lifespan ranges from 1–9,
 averaging 1.8 years (Bouwhuis <em>et al.</em> 2009; Woodman
 <em>et al.</em> 2022). Although there are some continuous
 changes with age (Bouwhuis <em>et al.</em> 2009), the main age
 effects on individual-level traits and population processes are captured
 by two age-classes: 1-year-olds (hereafter subadults) and older (hereafter
 adults, Gosler 1993; Harvey et al. 1979; Perrins 1979; Gamelon et al.
 2016, 2019; Woodman et al. 2022). Great tits generally undertake one
 breeding attempt during a single annual breeding season April–June (in
 some parts of their range second clutches can occur, Verhulst 1998; Visser
 et al. 2003). Data used here are from 32 populations, the geographical
 range of which represents a large part of the species’ breeding range
 (Sullivan <em>et al.</em> 2009). Generally, data collection at
 these sites included regular visits to nest-boxes during breeding to track
 reproductive attempts, individually mark chicks and breeding individuals,
 and record their morphometrics, sex and age. Age is based either on year
 of hatching for local birds, or plumage characteristics for immigrants,
 where subadults and adults are discriminated based on feather moult
 (Svensson 1992). Further details of data collection and metadata for
 populations can be found through the Studies of Populations of Individual
 Birds (www.spibirds.org,
 Culina et al. 2021). <strong>Breeding demographic
 structure</strong> We assigned age to all
 breeding great tits with known identity, across which exact year of
 hatching (birds first marked as chicks or subadults) was known for 82.5%
 of 135,967 captures. Birds first captured as adults (15.3% and 20.1% of
 breeding females and males, respectively) were assigned an age of 2, with
 subsequent age estimates based on this. Given annual mortality rates
 &gt;50% this assumption is likely to be accurate in most cases
 (Bouwhuis et al. 2009) – also, in these cases, individuals are still
 accurately designated as ‘subadult’ or ‘adult’. In total, age was assigned
 to 62.1% of parents where at least one egg was laid (due to some studies’
 protocols not always including parental identification, nests failing
 prior to capture, and unsuccessful trapping attempts, parental identity
 was unknown in some cases). For each year, we
 calculated the proportion of each breeding population consisting of
 subadults. While this is a univariate ratio that describes breeding
 demographic structure without fully capturing the multivariate nature of
 age structure, it is a relevant proxy for age structure and provides an
 interpretable measure of the relative abundance of young to old
 individuals. Moreover, the proportion of subadults has previously been
 shown to be important for population processes including density
 regulation and population-level breeding behaviour in this species
 (Gamelon et al. 2016, 2019; Woodman et al. 2022). However, we also
 considered alternative aspects of demographic structure by calculating
 five additional descriptors (mean population age; proportion of senescent
 individuals; and change in the three population demographic structure
  descriptors compared to a running mean, supporting information). We
 calculated these for every annual population, but only used data from
 years where the population included at least 20 individuals (mean, IQR:
 230, 60–356) and &gt;25% of the population was aged (mean, IQR: 56.0%,
 36.1–78.2%). In total, the study spanned 1956–2022, comprising 702 study
 years and 131,150 captures of 77,964 breeding individuals.
 <strong>Reproductive and environmental
 variables</strong> We assessed how reproductive
 and environmental variables that vary at different spatial scales relate
 to breeding demographic structure. First, we considered the influence of
 within-population average clutch size in year  on demographic
 structure in year . We would expect variation in mean clutch
 size to affect the demographic structure of the following breeding season,
 where large average clutch sizes would lead to more recruits (Ahola
 <em>et al.</em> 2009) and therefore a higher proportion of
 breeding subadults the next year, thus we test this prediction here. We
 calculated within-population average clutch size as the mean number of
 eggs produced per breeding attempt within a breeding season.
 Second, we considered two climatic variables: temperature and
 precipitation. We calculated the average mean daily temperature (°C) and
 the average daily precipitation sum (mm) from the E-OBS dataset
 (Cornes <em>et al.</em> 2018) across four periods preceding
 the focal breeding season for each population: June–August (hereafter
 summer); September–November (autumn); December–February (winter); and
 March–May (spring). We also considered the frequency of extreme climatic
 events (ECEs) by calculating the number of ‘cold ECEs’ and ‘hot ECEs’
 June–May. We define ECEs as events with an observed occurrence in the
 extreme 5% of the tail of the relevant distribution across the entire
 study period (1956–2022) in each population separately (Marrot
 <em>et al.</em> 2017; Moreno & Møller 2011). Thus, a
 cold ECE occurred when minimum daily temperature was less than the 5%
 threshold; and a hot ECE occurred when maximum daily temperature exceeded
 the 95% threshold. Third, we considered European
 beech <em>Fagus sylvatica</em> masting, an environmental
 variable which is generally understood to vary at a larger spatial scale
 than variation in temperature and precipitation. Beech masting is the
 annual production of seeds (Kelly 1994), which constitute part of the
 winter diet of great tits, thus influencing survival, particularly in the
 first-year of life (Perdeck et al. 2000). The distribution of beech does
 not cover the entire range of populations assessed here, and in southern
 Europe is restricted to higher altitudes (Bolte <em>et
 al.</em> 2007). However, masting-related demographic fluctuations in
 tits are synchronised across regions with and without beech, suggesting
 that beech masting is correlated with fruiting of other tree species, such
 that years with a large beech crop are rich in other food resources,
 promoting survival across different habitats (Klomp 1980; Perrins 1966).
 Thus, for each annual population, we obtained a masting value from a
 continental-scale dataset of beech masting up to 2017 (MASTREE+,
 Hacket-Pain et al. 2022), using the masting value from a data collection
 site closest to that of each population in the year preceding breeding.
 The central coordinates for all sites were less than 1500km from the focal
 breeding population, which is the spatial scale at which masting remains
 synchronised (Bogdziewicz <em>et al.</em> 2021), and most were
 much closer (median, IQR: 143km, 88–297km). To assess the influence of
 masting at a more local scale, we created a subset of populations within
 the distribution of beech and where data was collected within 100km of the
 population (12 populations, n = 188 population-years).
 <strong>References</strong> Ahola,
 M.P., Laaksonen, T., Eeva, T. & Lehikoinen, E. (2009). Great tits
 lay increasingly smaller clutches than selected for: A study of climate-
 and density-related changes in reproductive traits. Journal of Animal
 Ecology, 78, 1298–1306. Bogdziewicz, M., Hacket-Pain,
 A., Ascoli, D. & Szymkowiak, J. (2021). Environmental variation
 drives continental-scale synchrony of European beech reproduction.
 Ecology, 102, 1–10. Bolte, A., Czajkowski, T. &
 Kompa, T. (2007). The north-eastern distribution range of European beech -
 a review. Forestry, 80, 413–429. Bouwhuis, S., Sheldon,
 B.C., Verhulst, S. & Charmantier, A. (2009). Great tits growing
 old: Selective disappearance and the partitioning of senescence to stages
 within the breeding cycle. Proceedings of the Royal Society B: Biological
 Sciences, 276, 2769–2777. Culina, A., Adriaensen, F.,
 Bailey, L.D., Burgess, M.D., Charmantier, A., Cole, E.F., et al. (2021).
 Connecting the data landscape of long-term ecological studies: The
 SPI-Birds data hub. Journal of Animal Ecology, 90, 2147–2160.
 Gosler, Andrew. (1993). The great tit. The great tit, Hamlyn
 species guides. Hamlyn, London. Hacket-Pain, A., Foest,
 J.J., Pearse, I.S., LaMontagne, J.M., Koenig, W.D., Vacchiano, G., et al.
 (2022). MASTREE+: Time-series of plant reproductive effort from six
 continents. Glob Chang Biol, 28, 3066–3082. Harvey,
 P.H., Greenwood, P.J., Perrins, C.M. & Martin, A.R. (1979).
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 female parent. Ibis, 121, 216–219. Källander, H.
 (1981). The Effects of Provision of Food in Winter on a Population of the
 Great Tit Parus major and the Blue Tit P. caeruleus. Ornis Scandinavica,
 12, 244–248. Kelly, D. (1994). The evolutionary ecology
 of mast seeding. Trends Ecol Evol, 9, 465–470. Klomp,
 H. (1980). Fluctuations and stability in great tit populations. Ardea, 68,
 205–224. Marrot, P., Garant, D. & Charmantier,
 A. (2017). Multiple extreme climatic events strengthen selection for
 earlier breeding in a wild passerine. Philosophical Transactions of the
 Royal Society B: Biological Sciences, 372. Moreno, J.
 & Møller, A.P. (2011). Extreme climatic events in relation to
 global change and their impact on life histories. Curr Zool, 57,
 375–389. Perdeck, A.C., Visser, M.E. & Balen,
 J.H. van. (2000). Great Tit Parus major survival, and the beech-crop
 cycle. Ardea, 88, 99–106. Perrins, C.M. (1966). The
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 1st edn. Collins, London. Sullivan, B.L., Wood, C.L.,
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 Svensson ; British Trust for Ornithology, Stockholm : Thetford.
 Verhulst, S. (1998). Multiple breeding in the Great Tit, II. The
 costs of rearing a second clutch. Funct Ecol, 12, 132–140.
 Visser, M.E., Adriaensen, F., Van Balen, J.H., Blondel, J.,
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description: # Continent-wide drivers of spatial synchrony in breeding demographic
 structure across wild great tit populations [Access this dataset on
 Dryad](https://doi.org/10.5061/dryad.k0p2ngfgg) Presented here is the raw
 data ("bred_dem_synchrony.RData") and annotated R Script
 ("Continent-wide drivers of spatial synchrony in breeding demographic
 structure across wild great tit populations.R") needed to run
 analyses for the project "Continent-wide drivers of spatial synchrony
 in breeding demographic structure across wild great tit populations".
 ## Description of the data and file structure
 "bred_dem_synchrony.RData": R-data file needed to run analyses.
 Descriptions of the three datasets within this file are found in the R
 script, and also below. 1. "bred_dem_variables" = Base data for
 annual breeding demographic structure variables of great tit populations.
 Only includes populations with annual population size equal or greater
 than 20 individuals and where 25% or more of individuals have been aged.
 Columns: -- year: year (numeric) -- prop.juv: age-structure measure;
 proportion of breeding population consisting of subadults (i.e. yearlings)
 (numeric) -- prop.sen: age-structure measure; proportion of breeding
 population consisting of senescent individuals (i.e. 3-yo or older;
 https://doi.org/10.1098/rspb.2009.0457) (numeric) -- mean.age:
 age-structure measure; mean breeding age (numeric) -- prop.aged:
 proportion of individuals in breeding population that have been aged
 (numeric) -- pop.size: number of breeding individuals in the breeding
 population (numeric) -- pop.id: location code for population (character)
 -- n.aged: number of individuals in breeding population that have been
 aged (numeric) -- time.id: time indicator, where 1 is the first year in
 the data (numeric) 2. "locs" = Location information Columns: --
 pop.id: location code for population (character) -- lat: latitude in
 decimal degrees (numeric) -- lon: longitude in decimal degrees (numeric)
 3. "pred_variables" = Reproductive and environmental predictor
 variables Columns: -- year: year (numeric) -- pop.id: location code for
 population (character) -- mean.temp.aut: mean daily temperature
 September-November in year prior to breeding (numeric) -- mean.temp.spr:
 mean daily temperature March-May in year of breeding (numeric) --
 mean.temp.sum: mean daily temperature June-August in year prior to
 breeding (numeric) -- mean.temp.win: mean daily temperature
 December-February in year prior to/year of breeding (numeric) --
 mean.precip.aut: mean daily precipitation sum September-November in year
 prior to breeding (numeric) -- mean.precip.spr: mean daily precipitation
 sum March-May in year of breeding (numeric) -- mean.precip.sum: mean daily
 precipitation sum June-August in year prior to breeding (numeric) --
 mean.precip.win: mean daily precipitation sum December-February in year
 prior to/year of breeding (numeric) -- mast.value: ordinal masting value
 of annual reproductive output 1-5 (numeric) -- nao: winter nao value
 December-March in year prior to/year of breeding (numeric) --
 num.cold.ECEs: number of cold extreme climatic events* in year prior
 to/year of breeding (numeric) -- num.hot.ECEs: number of hot extreme
 climatic events* in year prior to/year of breeding (numeric) --
 mean.clutch.size: mean clutch size of breeding pairs in previous
 year's breeding season (numeric) ## Sharing/Access information Data
 was derived from the following sources: - "bred_dem_variables":
 derived from European populations of great tits, requested and accessed
 through SPI-Birds [https://spibirds.org/en]. - "locs": derived
 from European populations of great tits, requested and accessed through
 SPI-Birds [https://spibirds.org/en]. - "pred_variables":
 temperature and precipitation data derived from the E-OBS dataset version
 27.0e ( https://doi.org/10.1029/2017JD028200); masting data from MASTREE+
 (a long-term continental-scale dataset of masting time series up to 2017;
 https://doi.org/10.1111/gcb.16130); annual winter North Atlantic
 Oscillation (NAO) index value from The Climate Data Guide
 (https://doi.org/10.1126/science.269.5224.676;
 https://doi.org/10.1002/2013EO130001); and mean clutch size derived from
 European populations of great tits, requested and accessed through
 SPI-Birds [https://spibirds.org/en]. ## Code/Software R is required to run
 "Continent-wide drivers of spatial synchrony in breeding demographic
 structure across wild great tit populations.R"; the script was
 created and run using version 4.2.2. Annotations are provided throughout
 the script.