DOI: 10.5061/dryad.msbcc2g3p
title: Data for: Early life conditions influence fledging success and subsequent local recruitment rates in a declining migratory songbird, the Whinchat Saxicola rubetra

description: Life history traits and environmental conditions influence reproductive
 success in animals, and consequences of these can influence subsequent
 survival and recruitment into breeding populations. Understanding
 influences on demographic rates is required to determine the causes of
 decline. Migratory species experience spatially and temporally variable
 conditions across their annual cycle, making identifying where the factors
 influencing demographic rates operate challenging. Here, we use the
 Whinchat Saxicola rubetra as a model declining long-distance migrant bird.
 We analyse 10 years of data from 247 nesting attempts and 2519
 post-fledging observations of 1193 uniquely marked nestlings to examine
 the influence of life history traits, habitat characteristics and weather
 on survival of young from the nestling stage to local recruitment into the
 natal population. We detected potential silver spoon effects where
 conditions during the breeding stage influence subsequent apparent local
 recruitment rates, with higher recruitment for fledglings from larger
 broods, and recruitment rate negatively related to rainfall that chicks
 experienced in-nest. Additionally, extreme temperatures experienced pre-
 and post-fledging increased fledging success and recruitment rate.
 However, we could not determine whether this was driven by temperature
 influencing mortality during the post-fledging period or later in the
 annual cycle. Brood size declined with hatching date. In-nest survival
 increased with brood size and was highest at local temperature extremes.
 Furthermore, nest survival was highest at nests surrounded with 40–60%
 vegetation cover of Bracken Pteridium aquilinum within 50m of the nest.
 Our results show that breeding phenology and environmental factors may
 influence fledging success and recruitment in songbird populations, with
 conditions experienced during the nestling stage influencing local
 recruitment rates in Whinchats (i.e. silver spoon effect). Recruitment
 rates are key drivers of songbird population dynamics. Our results help
 identify some of the likely breeding season mechanisms that could be
 important population drivers.
description: Study area Our study was conducted
 between 2013–2022 at RSPB Geltsdale nature reserve in the North Pennines
 in Cumbria, UK (54.9°N −2.6°S), which is jointly owned by the Royal
 Society for the Protection of Birds and the Weir Trust. The survey area is
 an ~11km<sup>2</sup> sub-section of the reserve comprising
 blanket bog, heathland and acid grassland, with an altitude of
 220–440m. Study species and field
 methods Whinchats are
 short-lived (&lt;8 years) Afro-Palearctic migrants, breeding in
 grassland habitats throughout Europe and Western Asia and migrating
 annually to sub-Saharan Africa for the northern winter. Whinchats are
 ground nesting and usually lay a single clutch of 4–7 eggs. The incubation
 period is 12–14 days with young provisioned by both parents for ~13 days
 before fledging. Young are capable of flight 3–5 days after fledging, with
 a further 9–15 days spent close to their natal nest while they are still
 dependent on their parents for food (Collar 2005, Tome & Denac
 2012). Post-independence, fledglings typically remain in their natal area
 for 1–2 months, when they undergo a partial moult prior to southerly
 migration (Collar 2005). We began searching for
 nests when Whinchats arrived at Geltsdale in May, with searches performed
 almost daily until nesting had ceased in July. Nests were located by
 observing adult behaviour (male singing, nest building, guarding, and
 incubating). Males are typically more conspicuous than females during the
 breeding season, so the male of a pair was usually identified first, but
 once a nest was located females were also identified. We visited each nest
 every 3–7 days, recording clutch size, brood size and to confirm number
 fledged (see Table 1 for definitions). We estimated first egg laying date
 through back-calculations from either observation of incomplete clutches
 assuming one egg is laid daily, or for nests found post-hatching, by
 back-calculating based on chick development stage assuming 14 days
 incubation and a clutch size equal to brood size plus the number of
 unhatched eggs. All chicks were ringed with a unique combination of three
 colour rings and a numbered metal ring 6–8 days post-hatching. Fledging
 success was usually determined from resighting of fledglings. Fledging
 date was estimated from chick development stage observed from nest visits.
 For our analyses, we used the nest visit data to determine how many chicks
 successfully hatched and whether a nest successfully fledged at least one
 chick. Nest visits and bird handling were undertaken by field workers with
 ringing permits granted by the British Trust for Ornithology, and to
 minimise nest disturbance, no active nest was intrusively monitored on
 more than four occasions. Colour-marked Whinchat
 fledglings were monitored in the year of fledging until autumn departure
 and were then searched for in the adult breeding population in subsequent
 years. Searches were made almost daily from late April until early
 September in all years 2013–2022. Typically, multiple observers
 independently surveyed the whole study area almost daily from May – July
 of each year. Late in the season fledglings and adults congregated to
 moult in certain areas, and these hotspots were surveyed more frequently
 in August and September. Despite rigorous and
 frequent searches of the field site, some nesting attempts would
 inevitably have been missed. Because failed nests are active for a shorter
 period than successful nests, they are more likely to be missed, so a
 direct estimate of nest success rates from failed vs fledged nests may
 overestimate fledging success. To account for this, we performed a nest
 survival analysis (Mayfield 1975, Dinsmore <em>et al.</em>
 2002), which estimated the probability of nest survival from the total
 number of days that each nest survived (<em>i.e. </em>exposure
 days). Using this approach, we estimated a nest survival rate of 73.3%,
 which is slightly lower than the direct estimate from our data (80.6%).
 However, this is unlikely to affect our assessment of the systematic
 factors influencing nest survival unless these factors likewise influenced
 the likelihood of observers finding a nest. Given that nests were usually
 found by locating calling adults we find it unlikely that any of the key
 factors we investigate (e.g. vegetation, weather) should affect the
 likelihood that a nest failed prior to being located. For further details
 on survival analysis see supplementary material. Vegetation and habitat sampling The vegetation substrate on which a nest was built and the
 vegetation within 5m, 50m and 200m radii of each nest were recorded
 between May – July of each year 2013–2014 and 2017–2019 after the nest had
 concluded. Radii boundaries were first marked, then observers measured the
 total area occupied by each individual vegetation type within this area,
 as follows: Bracken (<em>Pteridium sp.</em>), Tree scrub (e.g.
 <em>Crataegus sp.</em>), Tufted hair grass
 (<em>Deschampsia caespitosa</em>), Purple moor grass
 (<em>Molinia caerulea</em>), Rush (<em>Juncus
 sp.</em>), Bilberry etc. (<em>Vaccinium myrtillus</em>),
 Heather (<em>Calluna vulgaris</em>) and other grass (e.g.
 <em>Holcus lanatus</em>). These measures were then used to
 calculate the relative percentage cover by each vegetation type within
 each area. The number of trees and presence or absence of key features
 (e.g. fence post, wall) was also recorded within each radius; for the 5m
 radius the number of trees was counted exactly, whereas for 50m and 200m
 radii, the number of trees was estimated and binned. For further details
 on habitat sampling, see Table 1 and Table S1 (supplementary material).
 Additionally, the elevation at which each nest was located was
 recorded. Weather data
 To determine the effect of environmental conditions pre- and
 post-fledging we estimated relevant time windows for in-nest and
 post-fledging periods for each nest. The in-nest period, when chicks are
 flightless and dependent on parents for food, was defined as the period
 between the day of hatching and day of fledging. To account for variation
 of in-nest stage duration such as from the shorter periods of failed
 nests, this period was standardised as the median 13-days post-hatching in
 all cases (Fig. S1, supplementary material). The post-fledging period
 covers from when initially flightless chicks leave the nest until
 migratory dispersal. Upon fledging, Whinchat remain within 5 – 10m of
 their natal nest for 3 – 5 days when mortality risk is most acute (Tome
 & Denac 2012), then typically spend a further 10–12 days within
 50–75m of their nest before increasing their range to &gt;200m for the
 remainder of the post-fledging period (Tome & Denac 2012). To
 estimate dispersal date, an analysis of the final observation dates of
 fledglings at Geltsdale in the year of fledging indicated large drop-offs
 at 40- and 50-days post-fledging, with few observed after 50 days,
 suggesting most have either dispersed or died 50 days after fledging (Fig.
 S2). To minimise estimation error, we used two different period lengths,
 40 days and 50 days after fledging; however, due to high correlation
 between these data, no final model included both post-fledging period
 lengths. Whilst our analysis cannot distinguish post-fledging mortality
 from mortality during subsequent stages of the annual cycle, we aimed to
 investigate links between post-fledging conditions and survival to
 recruitment as these conditions may impact recruitment directly via
 mortality during the post-fledging period, and indirectly by influencing
 subsequent survival in later stages of the annual cycle. We downloaded data on daily interpolated maximum and minimum
 temperature and total precipitation for the 5x5 km square encompassing
 Geltsdale (Eastings: 360000–365000; Northings: 555000–560000) for
 June-September each year from CEDA (Hollis <em>et al.</em>
 2018). We then averaged the daily maximum and minimum temperature and
 total rainfall values for the two respective survival stage periods.
 Descriptions of all fixed effects (Table 1a), response variables (Table
 1b) and random effects (Table 1c) are available in Table 1.
 Analysis All analyses were
 performed using R 4.0.2 (R Core Development Team, 2020). Weather data were
 extracted and analysed using the packages raster (Hijmans 2022), ncdf4
 (Pierce 2021) and rgdal (Bivand, Keitt & Rowlingson 2022). Data
 were visualised using the ggplot2 (Wickham 2016) and cowplot (Wilke 2020)
 packages. All generalised linear mixed effects models (GLMMs) were built
 using the lme4 package (Bates <em>et al.</em> 2015) and
 analysed using lmerTest (Kuznetsova <em>et al.</em>
 2017). Because vegetation data
 was sampled during a subset of years (2013 – 2014 & 2017 – 2019),
 but weather and life history trait data were available for all years (2013
 – 2021), we performed two analyses for each response variable, one using
 all 9 years data without vegetation variables (‘all years’), and another
 for the 5-year subset with all variables including vegetation (‘vegetation
 years’). Vegetation sampling
 produced &gt;40 individual variables, therefore, to avoid over-fitting
 we performed a two-stage modelling approach first used and validated, by
 Pearce-Higgins <em>et al.</em> (2009)<strong>
 </strong>(used also in: Dormann <em>et al.</em> 2013,
 Stanbury <em>et al.</em> 2022) to determine which explanatory
 variables to include in the final stage of analysis for ‘vegetation years’
 models. First, a series of single-term GLMMs were run and the effect of
 each term on a given response variable was assessed individually, with
 explanatory variables P ≤ 0.10 considered for inclusion in subsequent
 models (see Tables S2 – 4, supplementary material). Secondly, two-term
 GLMMs were used to assess which continuous variables should be included as
 quadratic variables. Any quadratic variables with P ≤ 0.10 were considered
 for inclusion in subsequent models as both quadratic and linear variables.
 Finally, these variables were checked for correlations, and where two
 variables were highly correlated with each other (Spearman rank
 correlation coefficient, |r<sub>s</sub>| ≥ 0.70, Dormann
 <em>et al.</em> 2013) the more significant predictor
 (<em>i.e.</em> the smaller <em>P</em>-value) was
 included in the full model (Tables S5 – 6). This process was repeated for
 each response variable. For the analysis of data from all years, all
 weather and life history variables were included in the full model as both
 linear and quadratic terms (Tables S7 – 9) unless highly correlated
 (|r<sub>s</sub>| ≥ 0.70) with a more significant term (Tables
 S10–11). Once the number of
 explanatory variables was reduced, we fitted GLMMs by performing stepwise
 elimination by iteratively removing the term with the
 largest <em>P</em>-value and then refitted the model until the
 Minimum Adequate Model (MAM) remained (all variables P &lt; 0.05). In
 total, we ran GLMMs for four response variables with ‘Year’ as a random
 effect for both the full sample of all years and for the vegetation years,
 all using the reduced set of predictors as fixed effects and including
 year as a random effect. Full details of all model structures, their
 response variables, fixed effects (full model and MAM), error
 distributions and sample sizes are available in Table 2, full model
 outputs are available in Tables S12–17.
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