Continent-wide drivers of spatial synchrony in breeding demographic structure across wild great tit populations
Data files
Jan 22, 2025 version files 63 KB
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bred_dem_synchrony.RData
58.26 KB
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README.md
4.74 KB
Abstract
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 >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.
README: Continent-wide drivers of spatial synchrony in breeding demographic structure across wild great tit populations
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.
- "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)
- "locs" = Location information
Columns:
-- pop.id: location code for population (character)
-- lat: latitude in decimal degrees (numeric)
-- lon: longitude in decimal degrees (numeric)
- "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.
Methods
Study systems and data collection
The great tit Parus major 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 et al. 2009; Woodman et al. 2022). Although there are some continuous changes with age (Bouwhuis et al. 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 et al. 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).
Breeding demographic structure
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 >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 >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.
Reproductive and environmental variables
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 et al. 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 et al. 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 et al. 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 Fagus sylvatica 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 et al. 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 et al. 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).
References
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Bogdziewicz, M., Hacket-Pain, A., Ascoli, D. & Szymkowiak, J. (2021). Environmental variation drives continental-scale synchrony of European beech reproduction. Ecology, 102, 1–10.
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