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\begin{document}

\title{Artificial size selection experiment reveals telomere length dynamics
and fitness consequences in a wild passerine}



\author[1]{Michael Pepke}%
\author[2]{Thomas Kvalnes}%
\author[2]{Bernt Rønning}%
\author[2]{Henrik Jensen}%
\author[3]{Winnie Boner}%
\author[2]{Bernt-Erik Sæther}%
\author[3]{Pat Monaghan}%
\author[2]{Thor Harald Ringsby}%
\affil[1]{Norges teknisk-naturvitenskapelige universitet}%
\affil[2]{Norwegian University of Science and Technology}%
\affil[3]{Glasgow University}%


\vspace{-1em}



  
  \date{May 26, 2024}


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\selectlanguage{english}
\begin{abstract}
Changes in telomere dynamics could underlie life-history trade-offs
among growth, size and longevity, but our ability to quantify such
mechanistic processes in natural, unmanipulated populations is limited.
We investigated how 4 years of artificial selection for either larger or
smaller body size affected early-life telomere length in two insular
populations of wild house sparrows. A negative correlation between
telomere length and structural size was evident under both selection
regimes. The study also revealed that male sparrows had longer telomeres
than females, after controlling for size, and there was a significant
negative effect of harsh weather conditions on telomere length. The
long-term fitness consequences of these changes in early-life telomere
length induced by the artificial size selection were explored over a
period of 11 years. These analyses indicated disruptive selection on
telomere length because both short and long early-life telomere length
tended to be associated with the lowest mortality rates and highest life
expectancy. There was also weak evidence for a negative association
between telomere length and annual reproductive success, but only in the
population where body size was increased experimentally. Our results
suggest that natural selection for optimal body size in wild animals
will affect early-life telomere length during growth, which is known to
be linked to longevity in birds, but also that the importance of
telomeres for long-term somatic maintenance and fitness is complex in a
wild bird species.%
\end{abstract}%



\sloppy


\textbf{INTRODUCTION}

Telomeres, the nucleoprotein complexes involving tandem repeats of a
non-coding DNA sequence, prevent the ends of linear chromosomes from
inappropriately activating the DNA repair machinery (Blackburn, 1991).
In the absence of restoration, telomeres shorten with each cell division
due to incomplete replication of DNA at the chromosome ends, and their
eventual dysfunction limits cell replicative potential (Hayflick, 1965).
Telomeres may be further eroded by other processes including oxidative
damage (von Zglinicki, 2002; Reichert \& Stier, 2017). Telomere length
(TL) changes might therefore reflect the cumulative costs associated
with acquiring and maintaining a particular body size, since this is
linked to cell replication levels (Monaghan \& Ozanne, 2018), as
outlined in Fig. 1. The functional relationships between size, growth
and telomere dynamics might thus play an important role in shaping the
optimal body size in wild species under natural selection (Ringsby et
al. 2015; Erten \& Kokko, 2020). Body size is a fundamental species
characteristic, which is intertwined with many aspects of species
ecology and evolution (Haldane, 1928; Peters, 1983; Woodward et al.,
2005; Sibly \& Brown, 2007), and is under directional selection in many
species (Kingsolver \& Pfennig, 2004). Both across and within species,
body size has been shown to correlate with important fitness-related
traits including survival, lifespan, fecundity and metabolic rate
(Bumpus, 1899; Gaillard et al., 1989; White et al., 2019). Life-history
theory predicts that organisms need to allocate their limited available
energy among different components of fitness, which leads to trade-offs
and selection for optimal solutions (Stearns, 1989). Trade-offs between
life-history traits may also occur due to antagonistic interactions; for
instance, if allocation of energy into developmental growth has negative
consequences due to long-term effects of telomere shortening (Fig. 1,
Monaghan et al., 2009; Young, 2018). For instance, Heidinger et al.
(2012) demonstrated a negative relationship between early-life TL and
lifespan in captive zebra finches (\emph{Taeniopygia guttata} ).
Negative correlations between fitness-related traits may conform to the
hypothesized life-history trade-offs (Futuyma, 2010), but they are
difficult to observe in the wild, for instance due to variation among
individuals in resource acquisition (van Noordwijk and de Jong 1986) and
random environmental variation (Pujol et al., 2018). Nonetheless,
physiological or genetic constraints generating life-history trade-offs
may be detected by comparing different phenotypes or genotypes (Reznick,
1985), or through experimental manipulations involving for example
natural selection in a controlled environment or artificial selection in
the traditional breeder's approach (Connor, 2003; Postma et al., 2007;
Pick et al., 2020). Correlated responses to artificial selection then
suggest additive genetic covariance between a trait and the selected
trait (Connor, 2003).

Several studies have investigated potential for telomere dynamics to
underpin individual variation in life-history strategies (Monaghan,
2010; 2014; Selman et al., 2012; Vedder et al., 2017). In the wild, many
of these studies, including long-lived bird species, mammals and
reptiles, suggest that most telomere loss occur during early life (Hall
et al., 2004; Spurgin et al., 2018) and that TL may be negatively
correlated with various developmental stress factors at this stage (Fig.
1), including brood competition (Boonekamp et al., 2014; Nettle et al.,
2016), poor nutrition and catch-up growth (Jennings et al., 1999; Geiger
et al., 2012). Such factors may result in the release of stress
hormones, which have been shown experimentally to increase early-life
telomere loss in the wild (Herborn et al., 2014), and oxidative stress
(Reichert \& Stier, 2017) that may directly increase the shortening of
telomeres. In addition, body size has been shown to negatively correlate
with TL within different tetrapod species (Scott et al., 2006; Pauliny
et al., 2006; Debes et al., 2016; Caprioli et al., 2013; Ringsby et al.,
2015; Spurgin et al., 2018), which is thought to be due to the
additional number of cell divisions required for acquiring larger size,
and the increased oxidative stress associated with maintaining larger
size (Monaghan \& Ozanne, 2018). The emerging field of telomere ecology
aims to identify factors that influence variation in individual TL and
their potential fitness consequences in free-living animals (Spurgin et
al., 2018). Whether there is a causal relationship between telomere
dynamics and individual variation in fitness in populations in the wild
and if TL is an indicator of individual quality are not yet fully
understood (Simons, 2015; Angelier et al., 2019). Experimental
approaches in both the field and the laboratory play an important role
in increasing our understanding of TL and life-history evolution.

In a large scale experimental study conducted in the wild, an artificial
directional selection regime on body size, as indicated by tarsus
length, was imposed annually and in opposite directions during four
consecutive years in two island populations of wild house sparrows
(\emph{Passer domesticus} ) in northern Norway (Fig. 2). Each winter,
adult sparrows with tarsus lengths smaller or larger than given
thresholds were removed from each population to produce a significant
bidirectional change in mean tarsus length across the adult breeding
populations (Kvalnes et al., 2017). Relatively high heritabilities for
tarsus length have been found in these house sparrow populations
(\emph{h} \textsuperscript{2}=0.3-0.4, Kvalnes et al., 2017), thus we
expected, based on the Breeder's equation (Lande, 1979), the artificial
selection to result in significant responses in offspring tarsus lengths
and growth rates. Here, we initially show how the artificial selection
on parental tarsus length affected the size of their offspring measured
during the nestling stage. Tarsus length is commonly used as a proxy for
structural body size in house sparrows (Rising \& Somers, 1989;
Araya-Ajoy et al. 2019). In a previous study based on a subsample of
chicks from the population undergoing artificial selection for larger
body size, Ringsby et al. (2015) showed that the selection regime had
indeed extended the range of chick body size at its upper end, and that
this was associated with a reduction in TL in red blood cells. In this
study, we examined whether the reciprocal effect, leading to longer TL,
occurred in the population in which body size was reduced, and also
examined the results in chicks whose parents were not subjected to the
selection regime. We constructed genetic pedigrees to identify nestlings
with parents that were subjected to artificial selection. We then
investigated how individual TL in nestling cohorts changed under the
different size selection regimes: We expected that increasing body size
through artificial selection led to shorter TLs through increased number
of cell divisions (Falconer et al., 1978) and oxidative stress
associated with increased energy expenditure (Geiger et al., 2012;
Pauliny et al. 2015; Smith et al., 2016; Monaghan \& Ozanne, 2018).
Similarly, we expected that smaller body size and thus decreased
nestling growth led to a slower rate of TL reduction (Vedder et al.,
2018). Since TL was measured in early-life (fledgling stage) we also
examined environmental factors previously shown to influence telomere
loss during this period (Chatelain et al., 2020), specifically brood
competition (Boonekamp et al., 2014) and weather conditions (Graham et
al., 2019).

The artificial selection was expected to shift the populations away from
their optimal body size and increase the phenotypic variance across the
populations (Kvalnes et al., 2017). Here, we investigated whether
changes in TL following the artificial size selection might
mechanistically underpin fitness effects due to the deviation from the
optimal body size. Thus, the survival and reproduction of all
individuals on both islands were monitored during and after the
selection events. Hypothesizing that shorter TL relative to body size
would be associated with lower survival and reduced lifespan (Heidinger
et al., 2012), because short telomeres reflect adverse early-life
conditions (Wilbourn et al., 2018; Eastwood et al, 2019), we tested the
effect of TL on short-term (first-year) survival under the two selection
regimes, and on long-term survival (lifespan) after the artificial
selection events. Similarly, we tested if longer early-life TL predicted
higher future reproductive success (e.g. Pauliny et al., 2006; Olsson et
al., 2011; Heidinger et al., 2012; Bauch et al. 2013) and if any of the
potential trade-offs (Fig. 1) were affected by the artificial selection.

\textbf{MATERIALS AND METHODS}

\textbf{Study system and sampling}

The study was performed on the islands of Vega (163
km\textsuperscript{2}, 65\selectlanguage{ngerman}°40'N, 11°55'E) and Leka (57
km\textsuperscript{2}, 65°06'N, 11°38'E) off the coast of northern
Norway (Fig. S1.1) in the years 2002-2012. The house sparrow is a small
sedentary passerine (Anderson, 2006), thus the geographical separation
of the islands (52 km) ensured no dispersal between populations (Kvalnes
et al., 2017). The average generation time in similar populations is
about 2 years (Jensen et al., 2008), and average lifespan is about 2
years (Jensen et al., 2004), but the maximum recorded lifespan is 13
years in the wild (Klimkiewicz \& Futcher, 1987). Both study islands are
dominated by cultivated land (silage production), heathland, mountains,
and sparse forest. The sparrows live closely associated with humans
mainly on dairy farms on these islands, where they nest in holes and
cavities and have access to food (grain) and shelter all year. During
the breeding season (mid-May to mid-August) from 2002-2006 all
accessible and active nests (they can lay up to 3 broods per season with
on average 4.6±0.06 eggs) were visited at least every
9\textsuperscript{th} day. Laying and hatching date were recorded for
each brood (207 broods from 158 nests) and chicks were marked as
fledglings (ca. 11 days old, ranging from 5-17 days) with a numbered
metal ring and plastic color rings for identification. Mist-netting
during the breeding season, in autumn (September-November), and in late
winter (February-March) ensured that around 90\% of the winter
population at both islands were marked at all times during the study
(see Kvalnes et al., 2017). For all fledglings and adults,
tarsometatarsus (tarsus) length was measured using calipers to the
nearest 0.01 mm. Because tarsus length increases with nestling age, we
estimated standardized tarsus length as the residuals of a linear
regression of tarsus length on age separately for each sex and
population. Age-corrected nestling tarsus length is a good predictor of
adult tarsus length (Fig. S1.2, \emph{r} =0.74, for \emph{n} =220 birds
that were recaptured as adults). To minimize disturbance, fledglings
were measured only once in the nest, thus, individual growth rates were
not directly monitored. However, within species in which growth is
seasonally constrained, larger individuals generally grow faster
(Metcalfe \& Monaghan, 2003). A small blood sample (25 \selectlanguage{greek}μ\selectlanguage{english}L) was collected
from all nestlings around 11 days (5-17 days) old by puncturing the
brachial vein. Blood was stored in 1 ml absolute ethanol at room
temperature in the field and subsequently in the laboratory at -20\selectlanguage{ngerman}°C
until DNA extraction.

\textbf{Artificial selection on tarsus length}

In February and early March each year from 2002 to 2012 (11 years, Fig.
2) ca. 90\% of the house sparrows on Leka (\emph{n} =89-222 per year)
and Vega (\emph{n} =102-352 per year) were caught using mist-netting in
or around farms and then held in separate aviaries for up to 12 days
with\emph{ad libitum} water and food (sunflower seeds, cattle grain and
bread) to obtain morphological measurements. During 2002-2005, adult
sparrows with tarsi shorter (Leka, referred to as the
\emph{high}population) or longer (Vega, referred to as the \emph{low}
population) than the island population mean ±0.3 SD, within each sex,
were translocated to distant localities (Ranke et al., 2020) and thus
removed from the breeding populations (see Kvalnes et al., 2017 for
details of the selection procedure). Thus, both populations were subject
to the same artificial selection pressure, but in opposite directions.
On average, 56.4\% (\emph{high}population) and 62.9\%
(\emph{low}population) of the captured individuals were removed each
year, whereas the selected birds were released back at their capture
location. Thus, because ca. 10\% of the individuals present at the time
of selection were not captured, the artificially selected individuals
constituted ca. 78\% of the breeding populations (Kvalnes et al., 2017).
In the first year (2002) of the selection experiment, there was no
significant difference in average fledgling tarsus lengths between the
\emph{high}and \emph{low} populations (\emph{n}
=167,\selectlanguage{greek}\emph{β\textsubscript{low}}\selectlanguage{english} =0.22, lower and upper 95\% confidence
interval (CI)={[}-0.25, 0.70{]} accounting for age at sampling and sex).
In the years following the end of the selection experiment (from 2006)
all birds were released back at the original capture location (R\selectlanguage{ngerman}ønning
et al., 2016).

\textbf{Weather data}

For each nestling, local weather data retrieved from The Norwegian
Meteorological Institute (2018) were averaged over 30 days prior to
sampling (at around 11 days old) in order to reflect average
environmental conditions before and during the nestling growth phase
(see Appendix S1). Due to intercorrelation among weather data variables
(Fig. S1.3), we used principal component analysis (Fig. S1.4 and Table
S1.1) to explore relationships between local weather variables and the
regional daily North Atlantic Oscillation (NAO) index (retrieved from
the National Oceanic and Atmospheric Administration, 2019) averaged over
the same 30-day period (NAO\_30). In Appendix S1, we show that the
NAO\_30 index is positively correlated with temperature, humidity and
atmospheric pressure and negatively correlated with wind speed and
amount of precipitation in our study area and therefore might be used as
a simple index of overall harshness of weather conditions during the
nestling stage; negative and positive NAO\_30 indexes may indicate
``harsh'' and ``benign'' conditions, respectively (e.g. Stenseth et al.,
2003).

\textbf{Molecular analyses}

DNA extraction of the collected blood samples is described in Appendix
S1. Relative erythrocyte TLs were measured in all nestlings (\emph{n}
=566, average age 10.9±1.5 days) from 2002-2006 (5 cohorts from two
populations, Fig. 2) using the real-time quantitative polymerase chain
reaction (qPCR) amplification method as described by Cawthon et al.
(2002) and with modifications by Criscuolo et al. (2009). The qPCR
method measures the ratio of the telomere repeat copy number amount of
telomere sequence to a control single copy gene number (a non-variable
``housekeeping'' gene; GAPDH) relative to a reference sample. This ratio
is referred to as the relative telomere length (TL). DNA samples were
diluted with dH\textsubscript{2}O to yield 1.67 ng/\selectlanguage{greek}μ\selectlanguage{english}L (corresponding to
10 ng of DNA per well in the PCR assay) and subsequently stored at
-78\selectlanguage{ngerman}°C. All samples were randomized and run in triplicates on 96-well
plates, each plate including a 2-fold serial dilution (40-2.5 ng/well)
of a ``standard sample'' of DNA from a single individual used to produce
standard curves for each plate, a non-target control sample, and the
reference sample (all in triplicates). Following Ringsby et al. (2015),
PCR assays were prepared using the Absolute blue qPCR SYBR green Low Rox
master mix (ThermoFisher scientific) and run using a Stratagene Mx3005p
system and analyzed using the MxPro qPCR software (Agilent). Primers and
thermal profiles are specified in Appendix S1. Relative TLs were
calculated taking plate amplification efficiencies (all within 100±10\%,
mean telomere assay efficiency was 102.2±3.8\%, and 101.9±3.4\% for
GAPDH assays across 2x21 plates) into account using the Pfaffl (2001)
method. All telomere analyses were performed at the University of
Glasgow and included 507 individuals measured by MLP (this study) and 60
individuals (a subset of both male and female nestlings from both
populations from 2002 and 2005) measured by WB under identical
laboratory conditions and using the same reference sample (Ringsby et
al., 2015). The average of the reference sample cycle thresholds (Ct)
across all plates were 9.42±0.13 SD
(CV\textsubscript{interplate}=1.39\%,
CV\textsubscript{intraplate}=0.72\%) for telomere assays and 20.58±0.09
SD (CV\textsubscript{interplate}=0.46\%,
CV\textsubscript{intraplate}=0.40\%) for GAPDH assays.

Molecular sexing and pedigree reconstruction are described in Appendix
S1. Each nestling was classified into one of three selection categories
(sample sizes are shown in Table 1): \emph{Selected} (1): Both parents
had been subject to the selection regime, i.e. captured during a winter
selection event and allowed to stay in the population.
\emph{Intermediate} (0.5): one parent had been subject to artificial
selection. \emph{Unselected} (0): No parents had been artificially
selected (i.e. accidentally not captured during any winter selection
event) and these individuals are therefore the unselected controls.
Unknown genetic parents were assumed not to have been artificially
selected (i.e. not captured or immigrated after the selection event and
thus not included in the pedigree).

\textbf{Statistical analyses}

\emph{Temporal changes in telomere and tarsus lengths during artificial
selection}

In order to analyze how nestling tarsus length and TL were affected by
the artificial selection for longer (\emph{high} ) and shorter tarsi
(\emph{low} ) during the study from 2002-2006, we used linear mixed
effects models (R package \emph{lme4} , Bates et al. 2015) including
year (i.e. birth cohort 1 to 5) as a continuous predictor variable, as
well as the quadratic effect of year (year\textsuperscript{2}). Tarsus
length and TL are expected to change during development within
individual nestlings (Hall et al., 2004; Boonekamp et al., 2014) and
there might be sexual differences in morphology (Cordero et al., 2000)
and telomere dynamics (Barrett \& Richardson, 2011). Thus, nestling age
(number of days since hatching) and sex were included as explanatory
variables in all models. Selection status category (0, 0.5, or 1) was
included in addition to an interaction term between selection status and
year. All models assumed a Gaussian error distribution and included a
random intercept for brood identity to account for the non-independence
of nestlings from the same brood. We structured these analyses into four
sections, where we analyzed each selection regime (\emph{high} or
\emph{low} population) separately for each response variable (tarsus
length and TL). In order to identify the predictors most supported by
the empirical data we constructed and compared alternative candidate
models (Burnham \& Anderson, 2002) fitted with maximum likelihood within
each section using Akaike's information criterion (Akaike, 1973)
corrected for small sample sizes (AICc, Hurvich \& Tsai, 1989). All
models were validated visually by diagnostic plots and model parameters
are given from models refitted with restricted maximum likelihood
(REML). To reduce the problem of multicollinearity in multiple
regression analyses, we only included predictor variables with
intercorrelation Pearson's\emph{r} \textless{}0.5 for all relevant pairs
of explanatory variables. All statistical analyses were performed in R
version 3.5.2 (R Core Team, 2018).

\emph{Effects of body size and weather on telomere length}

We investigated factors affecting fledgling TL (response variable) by
constructing 30 biologically plausible linear mixed effects models with
combinations of the explanatory variables sex, age, standardized tarsus
length, brood size (number of chicks in the nest at the time of
sampling), and the NAO\_30 index. In addition, an interaction term
between island population identity (i.e. \emph{high} or
\emph{low}selection regime) and tarsus length was included in some
models to test for an effect of the artificial selection regimes on the
relationship between TL and tarsus length. Brood identity and year
(cohort) effects were accounted for by inclusion as random intercepts in
all models. Again, candidate models were compared using AICc.

\emph{Effects of telomere length on survival}

The effect of nestling TL on short- and long-term survival was analyzed
using two different approaches: First, we analyzed the effect of TL on
first-year survival (i.e. before removal of artificially selected
individuals) using generalized mixed effects linear models
(\emph{lme4}package) with a binomial error distribution and a logit link
function (logistic regression) using the bobyqa optimizer from the
`minca' package throughout to improve model convergence (Bates et al.,
2014). Birds that were never observed after the season in which they
were born (effectively after end of January when field work recommenced
the year after they were born) were considered to be dead. Thus,
mortality and emigration have the same effect on the local population.
The house sparrows show strong site fidelity and generally do not
display adult dispersal (Anderson, 2006; Pärn et al., 2009), such that
any emigrants do not return to the natal population. We used AICc to
compare 26 candidate models including TL, tarsus length, non-linear
effects of TL and tarsus length (TL\textsuperscript{2} and tarsus
length\textsuperscript{2}), the NAO\_30 index, and an interaction term
between tarsus length and population identity, and between TL and
population identity, respectively, as explanatory variables. Population
identity and sex were included as fixed factors, and brood identity and
year were included as random intercepts in all models.

Second, to test the effect of fledgling TL on survival throughout the
life of individuals we used multivariate Cox proportional hazards
regression to estimate hazard ratios (HR) relative to the baseline
sample mean mortality within each strata of all predictor variables. HR
is defined as the relative risk of death occurring at a given interval
of time compared to the total population (Cox, 1972). Thus, a
HR\textgreater{}1 indicates an increased mortality given an increase in
the trait. 26 candidate models were constructed using the\emph{survival}
package (Therneau, 2015) including TL, tarsus length, non-linear effects
of TL and tarsus length, the NAO\_30 index, and interaction terms
between population identity and tarsus length or TL, respectively. Sex
and population identity were included as fixed factors in all models,
which were then compared using AICc. Individuals that were removed
during the artificial selection were right-censored and the last
observation of an individual was used as an estimate of the (minimum)
lifespan measured in number of days since hatching. This procedure
appropriately accounts for the right-censoring caused by the artificial
removal of individuals, but underestimates absolute survival
probabilities, as recapture rates were not accounted for in this
approach. However, since both populations were carefully monitored each
year, we can assume that \textgreater{}90\% of the individuals that were
present in the study populations were recorded from year to year
(Kvalnes et al., 2017), and that the total observation interval
correlates with lifespan. No other birds were censored as observations
continued until 2012; two years after the last record of any sampled
individual (in 2010). The proportional hazards assumption was tested
using the correlation between scaled Schoenfeld residuals and time. We
accounted for possible non-independence of broods by including brood
identity as random factor (cluster).

\emph{Effects of telomere length on reproductive success}

Finally, the effect of fledgling TL on the total number of recruits
produced per individual (LRS, lifetime reproductive success) was
analyzed. We included only individuals that survived until breeding and
that were not removed during the artificial selection (and thus allowed
to reproduce). In the \emph{high} population, 10 out of 22 recruiting
individuals produced at least one recruit and 39 out of 80 individuals
did so in the \emph{low} population. While reproduction \emph{per se}may
accelerate telomere loss (Sudyka, 2019), we test here the predictive
value of early-life TL and/or tarsus length on subsequent reproductive
output (Eastwood et al., 2019). Since LRS and lifespan (measured in
years from first to last observation) were highly correlated
(\emph{high} population; Pearson's \emph{r} =0.83,\emph{p}
\textless{}0.0001, \emph{low} population; Pearson's\emph{r} =0.70,
\emph{p} \textless{}0.0001), we tested whether TL or tarsus length
predict LRS while controlling for lifespan (which is equivalent to the
individual average annual reproductive success, ARS). We fitted a set of
generalized linear mixed models with a Poisson error distribution
separately for each population to facilitate model convergence, using
the package \emph{glmmTMB} (Brooks et al. 2017). Sex was included as
fixed factor and brood identity and year were included as random
intercepts in all models. We compared the same 9 candidate models for
each population using AICc and models were validated using the DHARMa
package (Hartig, 2019).

\textbf{RESULTS}

\emph{Temporal changes in tarsus and telomere lengths during artificial
selection}

In the \emph{high} population, the tarsus of fledglings with both
parents artificially selected was on average longer than the tarsus of
fledglings produced by unselected individuals (i.e. with parents not
subjected to artificial selection; model ranked 1, selected vs.
unselected: \selectlanguage{greek}\emph{β\textsubscript{selected}}\selectlanguage{english} =0.52, CI={[}0.04, 1.02{]},
Fig. 3b, Tables S2.1 and S2.2), and tended to be shorter than tarsus of
fledglings produced by unselected individuals in the
\emph{low}population (model ranked 2, [?]AICc=0.6, selected vs.
unselected:\selectlanguage{greek}\emph{β\textsubscript{selected}}\selectlanguage{english} =-0.38, CI={[}-0.80,
0.03{]}, Fig. 4b, Table S2.1). Intermediate individuals with one
artificially selected parent showed a similar weak tendency when
compared to the unselected (\emph{high} :
\selectlanguage{greek}\emph{β\textsubscript{intermediate}}\selectlanguage{english} =0.25, CI={[}-0.21, 0.72{]};
\emph{low} : \selectlanguage{greek}\emph{β\textsubscript{intermediate}}\selectlanguage{english} =-0.31, CI={[}-0.69,
0.08{]}, Figs. 3b and 4b, Tables S2.1 and S2.2). Across the study period
(2002-2006), fledgling tarsus length increased linearly in the
\emph{high} population (\emph{n} =158, model ranked
1:\selectlanguage{greek}\emph{β\textsubscript{year}}\selectlanguage{english} =0.70, CI={[}0.01, 1.38{]}, Figs. 3a,
S2.1 and S2.2, Tables S2.1 and S2.2) and decreased linearly in the
\emph{low}population (\emph{n} =408, model ranked
1:\selectlanguage{greek}\emph{β\textsubscript{year}}\selectlanguage{english} =-0.63, CI={[}-1.23, -0.04{]}, Figs. 4a,
S2.1 and S2.2, Tables S2.1 and S2.2). In both populations, there was
weak evidence (i.e. marginally significant) for a curvilinear change
over the years, indicating that after the initial divergence the change
in tarsus length ceased (\emph{high} , model ranked
1:\selectlanguage{greek}\emph{β\textsubscript{year\^{}2}}\selectlanguage{english} =-0.09, CI={[}-0.20,
0.03{]};\emph{low} , model ranked 1: \selectlanguage{greek}\emph{β\textsubscript{year\^{}2}}\selectlanguage{english}
=0.10, CI={[}-0.00, 0.20{]}, Figs. 3a and 4a, Table S2.2).

TL of fledglings with both parents artificially selected was not
different from those of unselected individuals in the
\emph{high}population (model ranked 3, [?]AICc=0.7, selected vs.
unselected:\selectlanguage{greek}\emph{β\textsubscript{selected}}\selectlanguage{english} =0.02, CI={[}-0.18, 0.22{]},
Fig. 3d, Tables S2.1) nor in the \emph{low} population (model ranked 3,
[?]AICc=3.0, selected vs. unselected:\selectlanguage{greek}\emph{β\textsubscript{selected}}\selectlanguage{english}
=0.03, CI={[}-0.07, 0.13{]}, Fig. 4d, Table S2.1). However, intermediate
individuals with one artificially selected parent showed weak evidence
for a tendency towards shorter telomeres when compared to the unselected
in the \emph{high} population (\selectlanguage{greek}\emph{β\textsubscript{intermediate}}\selectlanguage{english}
=-0.13, CI={[}-0.32, 0.07{]}, Fig. 3d), and towards longer telomeres
compared to unselected individuals in the \emph{low} population
(\selectlanguage{greek}\emph{β\textsubscript{intermediate}}\selectlanguage{english} =0.05, CI={[}-0.05, 0.15{]}, Fig.
4d). Across the study period, fledgling TL decreased linearly in the
\emph{high} population (model ranked 2, [?]AICc=0.0:
\selectlanguage{greek}\emph{β\textsubscript{year}}\selectlanguage{english} =-0.26, CI={[}-0.50, -0.01{]}, Figs. 3c and
S2.1, Tables S2.1 and S2.2), but there was no change in the\emph{low}
population (model ranked 2, [?]AICc=1.9:\selectlanguage{greek}\emph{β\textsubscript{year}}\selectlanguage{english}
=0.01, CI={[}-0.02, 0.03{]}, Figs. 4c and S2.1, Table S2.1). In the
\emph{high} population there was weak evidence for a curvilinear change
over the years, indicating that after the initial increase the change in
TL ceased (model ranked 2:\selectlanguage{greek}\emph{β\textsubscript{year\^{}2}}\selectlanguage{english} =0.04,
CI={[}-0.00, 0.08{]}, Fig. 3c, Table S2.2), but there was no evidence
for any curvilinear change in the\emph{low} population (model ranked 4,
[?]AICc=0.7:\selectlanguage{greek}\emph{β\textsubscript{year\^{}2}}\selectlanguage{english} =0.01, CI={[}-0.01, 0.03{]},
Fig. 4c). The model ranked 1 in the \emph{low} population included only
sex and age (Table S2.1 and S2.2). Overall, our results show some
evidence for an inverse association between changes in tarsus length and
TL across cohorts in populations where body size is shifted away from
its optimum.

\emph{Effects of body size and weather on telomere length}

Combining data from both populations (\emph{n} =566), TL was found to be
negatively related to tarsus length (model ranked
1:\selectlanguage{greek}\emph{β\textsubscript{tarsus}}\selectlanguage{english} =-0.03, CI={[}-0.05,~-0.00{]}, Tables 2
and 3, Fig. 5a) and positively related to the NAO\_30 index
(\selectlanguage{greek}\emph{β\textsubscript{NAO\_30}}\selectlanguage{english} =0.10, CI={[}0.01, 0.19{]}, Fig. 5b).
This means that telomeres are shorter in larger individuals and when
overall weather conditions are harsh (Appendix S1). In addition, TLs
were shorter in females than males (\selectlanguage{greek}\emph{β\textsubscript{female}}\selectlanguage{english}
=-0.08, CI={[}-0.14,~-0.02{]}), and TLs were shorter in the \emph{low}
population compared to the \emph{high}population
(\selectlanguage{greek}\emph{β\textsubscript{low}}\selectlanguage{english} =-0.17, CI={[}-0.24, -0.10{]}). The
second-best model ([?]AICc=0.2) showed weak evidence that the negative
relationship between TL and tarsus length tended to be steeper in
the\emph{high} population compared to the \emph{low} population
(\selectlanguage{greek}\emph{β\textsubscript{low*tarsus}}\selectlanguage{english} =0.05, CI={[}-0.02, 0.11{]}),
suggesting that artificial selection for longer tarsus had a stronger
effect on TL compared to selecting for shorter tarsus. There was little
evidence for any effect of nestling age on TL (model ranked 3,
[?]AICc=1.4, \selectlanguage{greek}\emph{β\textsubscript{age}}\selectlanguage{english} =0.01, CI={[}-0.01, 0.03{]},
Table 2).

\emph{Effects of telomere length on survival}

Average first-year survival from fledging to recruitment was 29\% and
maximum recorded lifespan was 5.7 years with a mean of 152\selectlanguage{ngerman}±11 days
(\emph{n} =566). There was a positive effect of tarsus length on
first-year survival (i.e. recruitment probability) that was present in
four out of five models with \selectlanguage{english}[?]AICc\textless{}2 (model ranked 1: odds
ratio (\emph{OR} )\emph{\textsubscript{tarsus}} =1.27, CI={[}1.04,
1.57{]}, Table 4). In addition, there was weak evidence for a
curvilinear effect of tarsus length on first-year survival present in
the best model (\emph{OR\textsubscript{tarsus\^{}2}} =1.08, CI={[}0.99,
1.67{]}), indicating higher recruitment probability for individuals both
smaller and larger than the average. The third-best model (\selectlanguage{english}[?]AICc=1.5)
suggested that the positive effect of tarsus length on first-year
survival was stronger in the \emph{high} population compared to
the\emph{low} population (\emph{OR\textsubscript{low*tarsus}} =0.71,
CI={[}0.41, 1.18{]}), but the effect was uncertain. In addition, there
was an uncertain negative effect of TL present in one model (model
ranked 5, \selectlanguage{english}[?]AICc=1.9, \emph{OR\textsubscript{TL}} =0.89, CI={[}0.51,
1.53{]}, Table 4).

The Cox proportional hazards regression analyses of long-term survival
revealed a negative effect of tarsus length on the risk of death (hazard
ratio (\emph{HR} )\emph{\textsubscript{tarsus}} =0.91, CI={[}0.84,
0.99{]}, Tables S2.3 and S2.4, Fig. 6a). In addition, two out of four
models with \selectlanguage{english}[?]AICc\textless{}2 showed weak evidence for a curvilinear
effect of TL on mortality (\emph{HR\textsubscript{TL\^{}2}} =0.73,
CI={[}0.51, 1.04{]}), indicating a bimodal pattern, where fledglings
with both short and long TL have lower long-term mortality rates (Fig.
6b, Table S2.4). The curvilinear effect of tarsus length present in two
out of four models with \selectlanguage{english}[?]AICc\textless{}2, was uncertain and weak, i.e.
close to 1 (\emph{HR\textsubscript{tarsus\^{}2}} =0.98, CI={[} 0.95,
1.01{]}).

\emph{Effects of telomere length on reproductive success}

In the \emph{high} population, there was weak evidence for a negative
effect of TL on LRS while controlling for lifespan (model ranked 2,
\selectlanguage{english}[?]AICc=0.1, \selectlanguage{greek}\emph{β\textsubscript{TL}}\selectlanguage{english} =-1.24, CI={[}-2.52, 0.04{]},
Tables 5 and S2.5) suggesting that individuals with short telomeres in
early-life produced more recruits annually. In the \emph{low}population,
the best model was the simplest model including only the effects of sex
and lifespan (Tables 5 and S2.5).

\textbf{DISCUSSION}

Much of the theoretical and empirical research on the mechanisms that
shape variation in body size is based on life-history theory which
suggests that individuals allocate their acquired resources between
growth, reproduction, and self-maintenance (Stearns, 1989; Zera \&
Harshman, 2001; Roff \& Fairbairn, 2007). In this experimental study we
examined the consequences of artificial selection for larger and smaller
parental body size and how this influences variation in offspring TL at
early age, as well as the associations between TL and recruitment,
longevity and reproductive success in two wild house sparrow
populations. First, a negative correlation between nestling TL and
tarsus length was evident under the artificial selection for both larger
and smaller tarsi (Table 3). This link between TL and structural body
size suggests that telomere dynamics might mediate a trade-off between
investment in early-life growth and long-term somatic maintenance in the
wild (Metcalfe \& Monaghan 2003; Ringsby et al., 2015; Monaghan \&
Ozanne, 2018). Artificial selection for larger individuals in
the\emph{high} population caused TL to decrease significantly as tarsus
length increased during the four years of selection (Fig. 3).
Additionally, there was weak evidence that TL tended to increase as
tarsus length decreased in the \emph{low} population (Fig. 4). It is
possible that the artificial selection for smaller body size in adults
only caused a small change in offspring size because the proportion of
additive genetic variance may be lower for small compared to large
individuals (Charmantier et al., 2004). Thus, selecting for smaller body
size for multiple years, as in our experiment, may accumulate
individuals that are smaller than their predicted size due to for
instance malnutrition or disease caused by e.g. environmental or
parental effects (Angelier et al., 2015).

TL has been suggested as a biomarker monitoring health and stress
exposure of organisms (Monaghan, 2014; Pepper et al., 2018; Chatelain et
al., 2020), individual phenotypic quality (Bauch et al., 2013; Boonekamp
et al., 2013; Le Vaillant et al., 2015), and as an integrated
physiological marker of cumulative life-history costs (Monaghan \&
Haussmann, 2006). The prevailing negative correlation between TL and
body size documented in this study, indicates that TL is influenced by
structural growth in free-living birds, which confirms the observation
by Ringsby et al. (2015). The artificial selection pressure on body size
was accompanied by a reduction in TL that was probably not counteracted
within the nestling period by increased investment into telomere
maintenance (i.e. canalization). Early-life changes in TL have been
hypothesized to influence long-term somatic state (Eisenberg, 2011;
Boonekamp et al., 2013; Vedder et al., 2017; Criscuolo et al., 2018a).
The enzyme telomerase can elongate telomeres (Blackburn, 1991), but its
activity is assumed to be a physiologically costly process (Hatakeyama
et al., 2016; Criscuolo et al., 2018b) or with potential increased
cancer risk effects (Seluanov et al., 2018). Accordingly, somatic
telomerase activity is generally assumed to be repressed in birds (Gomes
et al., 2010), though more investigation of this is needed since some
somatic telomerase activity has been detected (Haussmann et al., 2007).
In common with other non-mammalian vertebrates, birds have nucleated
erythrocytes; thus, TLs derived from whole blood samples are mainly
measured in erythrocytes, which are normally produced in the bone
marrow. Compared to other tetrapods, avian erythrocytes have a
relatively short lifespan of 1 month with \textasciitilde{}3\% being
replaced each day (Glomski \& Pica, 2016). Early-life erythrocyte TLs in
house sparrows have been estimated to 15-20 kbp (Ringsby et al., 2015),
which is thought to reflect TLs in hematopoietic stem cells (Vaziri et
al., 1994). If 50-100 bp of telomeric DNA are lost with each cell
division (Lansdorp, 1995), these early cells would have the potential of
150-400 divisions, many more than is needed for growth and maturation of
the adult house sparrow (Sidorov et al., 2009). However, increased
oxidative stress associated with acquiring and maintaining a larger body
size (Alonso-Alvarez et al., 2007) could accelerate the shortening of
telomeres significantly (Reichert \& Stier, 2017) providing an
explanation for the observed negative association between size and TL
(see Fig. 1 and Fig. 5a).

The evolutionary significance of the observed changes in TL induced by
the artificial size selection will depend on the heritability of TL,
which has been shown to vary considerably among species and populations:
Among bird species, TL heritability have been shown to range from 0 to 1
(reviewed in Dugdale \& Richardson, 2018), but may be relatively low in
house sparrows given the effects of growth and weather observed in this
study. We have refrained from estimating heritabilities of TL in the
present study, which would be biased by the non-random removal of
individuals during the artificial selection events (Steinsland et al.,
2014), but future studies may show whether the relationship between size
and TL is underpinned by genetic correlations (Monaghan \& Ozanne,
2018).

Like most altricial passerines, the growth and survival of house sparrow
nestlings depend on early-life conditions such as habitat quality and
insect food being supplied by the parents (Anderson, 2006). Larger
sparrows have higher juvenile and adult survival (Ringsby et al., 1998;
Jensen et al., 2008), and harsh weather during the nestling period
increases juvenile mortality (Ringsby et al., 2002). The associations
between TL and both body size and the weather proxy (NAO\_30) in
nestlings (Table 3) suggest that TL is determined by complex and
potentially counter-acting effects of growth, nutrition and external
factors (Angelier et al., 2015; Nettle et al., 2016). For instance,
malnutrition may lead to arrested growth, but also increased oxidative
stress and telomere attrition (Nettle et al., 2017). Also, indirect
effects of weather conditions may cause foraging stress or maternal
stress effects during breeding that negatively affect TL (Haussmann et
al., 2012; Mizutani et al. 2013), and direct effects of weather may
cause shortening of telomeres, such as thermal stress observed in e.g.
brown trout, \emph{Salmo trutta} (Debes et al., 2016), dark-eyed
juncos,\emph{Junco hyemalis} (Graham et al., 2019) and greater-eared
bats,\emph{Myotis myotis} (Foley et al., 2020). Thus, generally habitat
quality is important, with shorter telomeres in low-quality habitats
(Angelier et al., 2013; Watson et al., 2015; Wilbourn et al., 2017).
Spurgin et al. (2018) found a positive effect of seasonal insect prey
abundance on TL in Seychelles warblers (\emph{Acrocephalus sechellensis}
) when accounting for a negative correlation with tarsus length. In the
same population, the amount of reactive oxygen metabolites in the
territorial adult warblers, was shown to be higher in low quality
territories than in territories of higher quality (van de Crommenacker
et al., 2011), indicating that oxidative stress exposure is involved in
telomere shortening (von Zglinicki, 2002). The regional NAO\_30 index
must be interpreted with respect to local conditions along the northern
Norwegian coast but might be a better single proxy for the overall
weather conditions by reducing complexity and avoiding problems of model
variable selection (Stenseth et al. 2003; Hallett et al. 2004). Thus, a
low NAO\_30 index, which in our study area corresponds to a combination
of low temperatures, strong winds and rainfall during a 30-day interval
before TL sampling, was found to significantly reduce TL in nestlings,
when correcting for body size (Fig. 5b). This is consistent with studies
reporting shorter telomeres because of poor nutrition, competition, or
thermoregulation (reviewed in Chatelain et al., 2020).

Natural selection against shorter telomeres may be driven by their
negative effect on immune function and longevity (Wilbourn et al. 2018)
or reduced cell replicative potential (Blackburn, 1991), while selection
against longer telomeres is thought to be due to the high energetic
costs associated with increased somatic maintenance (Eisenberg, 2011;
Vedder et al., 2017) or increased cancer susceptibility (Aviv et al.,
2017; Pepke \& Eisenberg, 2020). Several ecological and epidemiological
studies have reported a negative association between TL and subsequent
mortality risk; mainly in birds (reviewed in Wilbourn et al., 2018) and
humans (reviewed in Boonekamp et al. 2013; Wang et al., 2018). This
association can be attributed to either the biomarker characteristic of
TL reflecting cumulative environmental stressors (Monaghan, 2014; Nettle
et al., 2017; Pepper et al., 2018; Angelier et al., 2018) or the direct
effect of having short telomeres leading to cellular senescence and
certain diseases (Blackburn et al., 2015; Young, 2018). However, this
correlation is not universal across tetrapods, with some studies finding
no correlation in birds (Boonekamp et al., 2014), mammals (Fairlie et
al., 2016), and reptiles (Olsson et al. 2011), or that shorter telomeres
correlate with higher survival in birds (Wood \& Young, 2019), snakes
(Ujvari \& Madsen, 2009), and fish (McLennan et al., 2017). Ringsby et
al. (2015) suggested that the changes in TL induced by the artificial
size selection could underpin a trade-off between body size and lifespan
if TL is related to lifespan (Heidinger et al., 2012). In this study, we
found little support for an effect of TL on short-term survival (i.e.
survival of juveniles until recruitment) after accounting for the
positive association between tarsus length and survival (Table 4). Body
size is likely to be an important component of juvenile mortality if the
mortality is mainly due to extrinsic factors (Wood \& Young, 2019;
Eastwood et al., 2020), as expected in juvenile house sparrows (Ringsby
et al., 1998). The artificial selection increased the range of body
sizes across the populations, which may more clearly reveal effects of
TL on fitness. The evidence for individuals with either short or long
telomeres to have reduced mortality risk through life, controlling for
the negative effect of tarsus length, was weak (Fig. 6b). While some
correlative studies may have overlooked such weak disruptive selection
on TL, such patterns can be confounded by (unmeasured) telomerase
expression in somatic cells with high proliferation rates (Klapper et
al., 1998; Ujvari \& Madsen, 2009; Cerchiara et al., 2017). However, if
TL is inversely related to telomere loss later in life (Verhulst et al.,
2013; Bauch et al., 2014), measuring TL at a later age may generate the
expected positive correlation between survival probability and TL (Wood
\& Young, 2019). Alternatively, in individuals with short telomeres, TL
may be traded off against some unmeasured component of individual
quality (Wilson \& Nussey, 2010). Yet, when controlling for lifespan,
short telomeres were associated with higher recruit production in
the\emph{high} population (Table 5). Telomere shortening rates in house
sparrows are unknown, but we found little evidence for directional
changes in TL across 5-17 days old nestlings (Table 2). The observation
in humans that short telomeres are associated with age-dependent
degenerative diseases and long telomeres with higher cancer incidence
rates (Aviv et al., 2017), suggests the opposite of our findings (i.e.
that both short and long TL is associated with higher mortality).
However, there is probably little or no constraints on TL imposed by
cancer or age-dependent diseases in free-living, short-lived sparrows
(M\selectlanguage{ngerman}øller et al., 2017). Combined, the adaptive significance of telomere
length dynamics may be complex in wild populations with high juvenile
mortality and no individuals surviving into very old age (the oldest
house sparrow in this study survived until its
6\textsuperscript{th}year).

There was a weak negative effect of TL on reproductive success within
individuals that survived until breeding in the population in which
selection for larger size was imposed (\emph{high} population, Table 5).
This might suggest that there are additional negative impacts on TL
associated with acquiring an artificially increased body size that
deviates from the optimal body size under the prevailing conditions.
Such impacts may act through increased competition when siblings are
larger (Nettle et al., 2016) and increased oxidative stress during
growth (Geiger et al., 2012). This indicates that in the
\emph{high}population, high fitness birds were bigger and therefore had
shorter telomeres.

Telomeres were longer in male than in female house sparrows, also when
correcting for size (Table 3). We also note that males tended to have
higher LRS (Table 5), but only in the \emph{high} population, where just
6 males managed to reproduce at least one recruit. There were no
sex-differences in survival in our study (Table S2.4), which has been
suggested to underlie sex-specific telomere dynamics in humans, mice,
and sand lizards (reviewed in Barrett \& Richardson 2011). In similar
Norwegian house sparrow populations, Holand et al. (2016) did not find
any general sex-biased mortality or senescence patterns among adults.
However, Cleasby et al. (2010) found females to have lower juvenile
mortality than males in an English house sparrow population. In birds
and mammals, adult mortality appears to be biased towards the
heterogametic sex (Liker \& Székely, 2005), which may be caused by the
potentially unmasked expression of deleterious sex-linked alleles
(Trivers, 1985; Hrdli\selectlanguage{polish}č\selectlanguage{english}kov\selectlanguage{ngerman}á et al., 2012). In birds, females are the
heterogametic sex, but sexual differences in telomere dynamics have only
rarely been observed among bird species (but see Foote et al. 2011;
Bauch et al., 2020). However, unmeasured sex-specific differences in
growth dynamics (in house sparrows, Cleasby et al., 2011) or
differential telomere loss under parasite infection (in blue
tits,\emph{Cyanistes caeruleus} , Sudyka et al., 2019) could also
generate the observed TL sex differences.

Our study demonstrates the differential impacts of artificial body size
selection on early-life TL during the important growth phase. TL was
influenced by growth and weather and varied between sexes and
populations. Body size was an important determinant of survival, but
both short and long telomeres tended to predict lower mortality across
the populations after the range of body sizes had been artificially
increased. In individuals larger than their optimal size in the wild, TL
was reduced, which may have been associated with an increased
reproductive output. When selecting for smaller adult body size, we
observed a smaller response in fledgling size and TL, and no
relationship between TL and reproductive success. Thus, this study shows
that the relationship between body size and fitness is complex, with
larger body size giving rise to shorter TL. The fitness consequences of
this interaction are not simple, and our experimental results suggest
that evolution will optimize TL alongside phenotypic parameters.

\textbf{ACKNOWLEDGEMENT}

We thank all fieldworkers who collected data for this study, the island
inhabitants, laboratory technician Randi Røsbak (NTNU, Trondheim) for
extracting DNA and genotyping procedures, and Håkon Holand for help with
parentage analyses. This work was funded by the Research Council of
Norway (Centre for Biodiversity Dynamics 223257 and 274930). MLP also
thanks ``\emph{Forsknings- og undervisningsfondet i Trondheim} '' for
financial support. The study design was subject to review by the
Norwegian Environment Agency (permits 2001/6427-ARTS/VI/ARE and
2004/1671-ARTS-VI-ID) and complied with the laws of animal welfare in
Norway (permits S-2603-01, S-204-8032-1, and S-2007/1482 from the
Norwegian Animal Research Authority and the Bird Ringing Centre at
Museum Stavanger, Norway). The authors have no conflicts of interest to
declare.

\textbf{REFERENCES}

Akaike, H. (1973). \emph{Information theory and an extension of the
maximum likelihood principle.} Paper presented at the Second
International Symposium on Information Theory, Akademiai Kiado,
Budapest.

Alonso-Alvarez, C., Bertrand, S., Faivre, B., \& Sorci, G. (2007).
Increased susceptibility to oxidative damage as a cost of accelerated
somatic growth in zebra finches. \emph{Functional Ecology, 21} (5),
873-879. doi:10.1111/j.1365-2435.2007.01300.x

Anderson, T. R. (2006). \emph{Biology of the ubiquitous house sparrow:
from genes to populations} . Oxford; New York: Oxford University Press.

Angelier, F., Costantini, D., Blevin, P., \& Chastel, O. (2018). Do
glucocorticoids mediate the link between environmental conditions and
telomere dynamics in wild vertebrates? A review. \emph{Gen Comp
Endocrinol, 256} , 99-111. doi:10.1016/j.ygcen.2017.07.007

Angelier, F., Vleck, C. M., Holberton, R. L., \& Marra, P. P. (2015).
Bill size correlates with telomere length in male American
Redstarts.\emph{Journal of Ornithology, 156} (2), 525-531.
doi:10.1007/s10336-015-1158-9

Angelier, F., Vleck, C. M., Holberton, R. L., Marra, P. P., \& Blount,
J. (2013). Telomere length, non\selectlanguage{english}-breeding habitat and return rate in
maleAmerican redstarts. \emph{Functional Ecology, 27} (2), 342-350.
doi:10.1111/1365-2435.12041

Angelier, F., Weimerskirch, H., Barbraud, C., Chastel, O., \& Hopkins,
W. (2019). Is telomere length a molecular marker of individual quality?
Insights from a long-lived bird. \emph{Functional Ecology, 33} (6),
1076-1087. doi:10.1111/1365-2435.13307

Araya-Ajoy, Y. G., Ranke, P. S., Kvalnes, T., Ronning, B., Holand, H.,
Myhre, A. M., . . . Wright, J. (2019). Characterizing morphological
(co)variation using structural equation models: Body size, allometric
relationships and evolvability in a house sparrow
metapopulation.\emph{Evolution, 73} (3), 452-466. doi:10.1111/evo.13668

Aviv, A., Anderson, J. J., \& Shay, J. W. (2017). Mutations, cancer and
the telomere length paradox. \emph{Trends Cancer, 3} (4), 253-258.
doi:10.1016/j.trecan.2017.02.005

Barrett, E. L., \& Richardson, D. S. (2011). Sex differences in
telomeres and lifespan. \emph{Aging Cell, 10} (6), 913-921.
doi:10.1111/j.1474-9726.2011.00741.x

Bates, D., Mullen, K. M., Nash, J. C., \& Varadhan, R. (2014). minqa:
Derivative-free optimization algorithms by quadratic approximation. R
package version 1.2.4. Retrieved from
https://CRAN.R-project.org/package=minqa

Bates, D., Machler, M., Bolker, B., \& Walker, S. (2015). Fitting linear
mixed-effects models using lme4. \emph{Journal of Statistical Software,
67} (1), 1-48. doi:10.18637/jss.v067.i01

Bauch, C., Becker, P. H., \& Verhulst, S. (2013). Telomere length
reflects phenotypic quality and costs of reproduction in a long-lived
seabird. \emph{Proc Biol Sci, 280} (1752), 20122540.
doi:10.1098/rspb.2012.2540

Bauch, C., Becker, P. H., \& Verhulst, S. (2014). Within the genome,
long telomeres are more informative than short telomeres with respect to
fitness components in a long-lived seabird. \emph{Mol Ecol, 23} (2),
300-310. doi:10.1111/mec.12602

Bauch, C., Gatt, M. C., Granadeiro, J. P., Verhulst, S., \& Catry, P.
(2020). Sex-specific telomere length and dynamics in relation to age and
reproductive success in Cory's shearwaters. \emph{Mol Ecol, 29} (7),
1344-1357. doi:10.1111/mec.15399

Blackburn, E. H. (1991). Structure and function of
telomeres.\emph{Nature, 350} (6319), 569-573. doi:10.1038/350569a0

Blackburn, E. H., Epel, E. S., \& Lin, J. (2015). Human telomere
biology: A contributory and interactive factor in aging, disease risks,
and protection. \emph{Science, 350} (6265), 1193-1198.
doi:10.1126/science.aab3389

Blanckenhorn, W. U. (2000). The evolution of body size: What keeps
organisms small? \emph{The Quarterly Review of Biology, 75} (4),
385-407. doi:10.1086/393620

Boonekamp, J. J., Mulder, G. A., Salomons, H. M., Dijkstra, C., \&
Verhulst, S. (2014). Nestling telomere shortening, but not telomere
length, reflects developmental stress and predicts survival in wild
birds. \emph{Proc Biol Sci, 281} (1785), 20133287.
doi:10.1098/rspb.2013.3287

Boonekamp, J. J., Simons, M. J., Hemerik, L., \& Verhulst, S. (2013).
Telomere length behaves as biomarker of somatic redundancy rather than
biological age. \emph{Aging Cell, 12} (2), 330-332.
doi:10.1111/acel.12050

Brooks, M. E., Kristensen, K., Benthem, K. J. v., Magnusson, A., Berg,
C. W., Nielsen, A., . . . Bolker, B. M. (2017). glmmTMB balances speed
and flexibility among packages for zero-inflated generalized linear
mixed modeling. \emph{The R Journal, 9} (2), 378-400.
doi:10.32614/rj-2017-066

Bumpus, H. C., \& Eugenics, G. L. f. N. (1899). \emph{The elimination of
the unfit as illustrated by the introduced sparrow, passer domesticus:
(a fourth contribution to the study of variation)} : Gin.

Burnham, K. P., \& Anderson, D. R. (2002). \emph{Model selection and
multimodel inference. A practical information-theoretic approach} (2
ed.). New York, U.S.A.: Springer-Verlag.

Caprioli, M., Romano, M., Romano, A., Rubolini, D., Motta, R., Folini,
M., \& Saino, N. (2013). Nestling telomere length does not predict
longevity, but covaries with adult body size in wild barn
swallows.\emph{Biol Lett, 9} (5), 20130340. doi:10.1098/rsbl.2013.0340

Cawthon, R. M. (2002). Telomere measurement by quantitative
PCR.\emph{Nucleic Acids Research, 30} (10), e47.
doi:10.1093/nar/30.10.e47

Cerchiara, J. A., Risques, R. A., Prunkard, D., Smith, J. R., Kane, O.
J., \& Boersma, P. D. (2017). Telomeres shorten and then lengthen before
fledging in Magellanic penguins (\emph{Spheniscus magellanicus}
).\emph{Aging (Albany NY), 9} (2), 487-493. doi:10.18632/aging.101172

Charmantier, A., Kruuk, L. E., Blondel, J., \& Lambrechts, M. M. (2004).
Testing for microevolution in body size in three blue tit
populations.\emph{J Evol Biol, 17} (4), 732-743.
doi:10.1111/j.1420-9101.2004.00734.x

Chatelain, M., Drobniak, S. M., \& Szulkin, M. (2020). The association
between stressors and telomeres in non-human vertebrates: a
meta-analysis. \emph{Ecol Lett, 23} (2), 381-398. doi:10.1111/ele.13426

Cleasby, I. R., Burke, T., Schroeder, J., \& Nakagawa, S. (2011). Food
supplements increase adult tarsus length, but not growth rate, in an
island population of house sparrows (\emph{Passer domesticus}
).\emph{BMC Res Notes, 4} , 431. doi:10.1186/1756-0500-4-431

Cleasby, I. R., Nakagawa, S., Gillespie, D. O. S., \& Burke, T. (2010).
The influence of sex and body size on nestling survival and recruitment
in the house sparrow. \emph{Biological Journal of the Linnean Society,
101} (3), 680-688. doi:10.1111/j.1095-8312.2010.01515.x

Conner, J. K. (2003). Artificial selection: A powerful tool for
ecologists. \emph{Ecology, 84} (7), 1650-1660.
doi:10.1890/0012-9658(2003)084{[}1650:Asaptf{]}2.0.Co;2

Cordero, P. J., Griffith, S. C., Aparicio, J. M., \& Parkin, D. T.
(2000). Sexual dimorphism in house sparrow eggs. \emph{Behavioral
Ecology and Sociobiology, 48} (5), 353-357. doi:10.1007/s002650000252

Cox, D. R. (1972). Regression models and life-tables. \emph{Journal of
the Royal Statistical Society. Series B (Methodological), 34} (2),
187-220. Retrieved from www.jstor.org/stable/2985181

Criscuolo, F., Bize, P., Nasir, L., Metcalfe, N. B., Foote, C. G.,
Griffiths, K., . . . Monaghan, P. (2009). Real-time quantitative PCR
assay for measurement of avian telomeres. \emph{Journal of Avian
Biology, 40} (3), 342-347. doi:10.1111/j.1600-048X.2008.04623.x

Criscuolo, F., Smith, S., Zahn, S., Heidinger, B. J., \& Haussmann, M.
F. (2018). Experimental manipulation of telomere length: does it reveal
a corner-stone role for telomerase in the natural variability of
individual fitness? \emph{Philos Trans R Soc Lond B Biol Sci, 373}
(1741), 20160440. doi:10.1098/rstb.2016.0440

Criscuolo, F., Sorci, G., Behaim-Delarbre, M., Zahn, S., Faivre, B., \&
Bertile, F. (2018). Age-related response to an acute innate immune
challenge in mice: proteomics reveals a telomere maintenance-related
cost. \emph{Proc Biol Sci, 285} (1892), 20181877.
doi:10.1098/rspb.2018.1877

Debes, P. V., Visse, M., Panda, B., Ilmonen, P., \& Vasemagi, A. (2016).
Is telomere length a molecular marker of past thermal stress in wild
fish? \emph{Mol Ecol, 25} (21), 5412-5424. doi:10.1111/mec.13856

Dugdale, H. L., \& Richardson, D. S. (2018). Heritability of telomere
variation: it is all about the environment! \emph{Philos Trans R Soc
Lond B Biol Sci, 373} (1741), 20160450. doi:10.1098/rstb.2016.0450

Eastwood, J. R., Hall, M. L., Teunissen, N., Kingma, S. A., Hidalgo
Aranzamendi, N., Fan, M., . . . Peters, A. (2019). Early-life telomere
length predicts lifespan and lifetime reproductive success in a wild
bird. \emph{Mol Ecol, 28} (5), 1127-1137. doi:10.1111/mec.15002

Eisenberg, D. T. (2011). An evolutionary review of human telomere
biology: the thrifty telomere hypothesis and notes on potential adaptive
paternal effects. \emph{Am J Hum Biol, 23} (2), 149-167.
doi:10.1002/ajhb.21127

Erten, E. Y., \& Kokko, H. (2020). From zygote to a multicellular soma:
Body size affects optimal growth strategies under cancer
risk.\emph{Evolutionary applications, 13} (7), 1593-1604.
doi:10.1111/eva.12969

Fairlie, J., Holland, R., Pilkington, J. G., Pemberton, J. M.,
Harrington, L., \& Nussey, D. H. (2016). Lifelong leukocyte telomere
dynamics and survival in a free-living mammal. \emph{Aging Cell, 15}
(1), 140-148. doi:10.1111/acel.12417

Falconer, D. S., Gauld, I. K., \& Roberts, R. C. (1978). Cell numbers
and cell sizes in organs of mice selected for large and small body
size.\emph{Genet Res, 31} (3), 287-301. doi:10.1017/s0016672300018061

Foley, N. M., Petit, E. J., Brazier, T., Finarelli, J. A., Hughes, G.
M., Touzalin, F., . . . Teeling, E. C. (2020). Drivers of longitudinal
telomere dynamics in a long-lived bat species, \emph{Myotis myotis}
.\emph{Mol Ecol, 29} (16), 2963-2977. doi:10.1111/mec.15395

Foote, C. G., Daunt, F., Gonzalez-Solis, J., Nasir, L., Phillips, R. A.,
\& Monaghan, P. (2011). Individual state and survival prospects: age,
sex, and telomere length in a long-lived seabird. \emph{Behavioral
Ecology, 22} (1), 156-161. doi:10.1093/beheco/arq178

Futuyma, D. J. (2010). Evolutionary constraint and ecological
consequences. \emph{Evolution, 64} (7), 1865-1884.
doi:10.1111/j.1558-5646.2010.00960.x

Gaillard, J. M., Pontier, D., Allaine, D., Lebreton, J. D., Trouvilliez,
J., Clobert, J., \& Allaine, D. (1989). An analysis of demographic
tactics in birds and mammals. \emph{Oikos, 56} (1), 59-76.
doi:10.2307/3566088

Geiger, S., Le Vaillant, M., Lebard, T., Reichert, S., Stier, A., Y, L.
E. M., \& Criscuolo, F. (2012). Catching-up but telomere loss:
half-opening the black box of growth and ageing trade-off in wild king
penguin chicks. \emph{Mol Ecol, 21} (6), 1500-1510.
doi:10.1111/j.1365-294X.2011.05331.x

Glomski, C. A., \& Pica, A. (2016). \emph{The avian erythrocyte: Its
phylogenetic odyssey} : CRC Press.

Gomes, N. M., Shay, J. W., \& Wright, W. E. (2010). Telomere biology in
Metazoa. \emph{Febs Letters, 584} (17), 3741-3751.
doi:10.1016/j.febslet.2010.07.031

Graham, J. L., Bauer, C. M., Heidinger, B. J., Ketterson, E. D., \&
Greives, T. J. (2019). Early-breeding females experience greater
telomere loss. \emph{Mol Ecol, 28} (1), 114-126. doi:10.1111/mec.14952

Haldane, J. B. S. (1928). \emph{On being the right size. Possible
worlds} . London: Chatto and Windus.

Hall, M. E., Nasir, L., Daunt, F., Gault, E. A., Croxall, J. P.,
Wanless, S., \& Monaghan, P. (2004). Telomere loss in relation to age
and early environment in long-lived birds. \emph{Proc Biol Sci, 271}
(1548), 1571-1576. doi:10.1098/rspb.2004.2768

Hallett, T. B., Coulson, T., Pilkington, J. G., Clutton-Brock, T. H.,
Pemberton, J. M., \& Grenfell, B. T. (2004). Why large-scale climate
indices seem to predict ecological processes better than local
weather.\emph{Nature, 430} (6995), 71-75. doi:10.1038/nature02708

Hatakeyama, H., Yamazaki, H., Nakamura, K., Izumiyama-Shimomura, N.,
Aida, J., Suzuki, H., . . . Ishikawa, N. (2016). Telomere attrition and
restoration in the normal teleost Oryzias latipes are linked to growth
rate and telomerase activity at each life stage. \emph{Aging (Albany
NY), 8} (1), 62-76. doi:10.18632/aging.100873

Haussmann, M. F., Longenecker, A. S., Marchetto, N. M., Juliano, S. A.,
\& Bowden, R. M. (2012). Embryonic exposure to corticosterone modifies
the juvenile stress response, oxidative stress and telomere
length.\emph{Proc Biol Sci, 279} (1732), 1447-1456.
doi:10.1098/rspb.2011.1913

Haussmann, M. F., Winkler, D. W., Huntington, C. E., Nisbet, I. C., \&
Vleck, C. M. (2007). Telomerase activity is maintained throughout the
lifespan of long-lived birds. \emph{Experimental gerontology, 42} (7),
610-618. doi:10.1016/j.exger.2007.03.004

Heidinger, B. J., Blount, J. D., Boner, W., Griffiths, K., Metcalfe, N.
B., \& Monaghan, P. (2012). Telomere length in early life predicts
lifespan. \emph{Proc Natl Acad Sci U S A, 109} (5), 1743-1748.
doi:10.1073/pnas.1113306109

Holand, H., Kvalnes, T., Gamelon, M., Tufto, J., Jensen, H., Parn, H., .
. . Saether, B. E. (2016). Spatial variation in senescence rates in a
bird metapopulation. \emph{Oecologia, 181} (3), 865-871.
doi:10.1007/s00442-016-3615-4

Hrdli\selectlanguage{polish}č\selectlanguage{english}kov\selectlanguage{ngerman}á, R., Nehyba, J., Lim, S. L., Grützner, F., \& Bose, H. R.,
Jr. (2012). Insights into the evolution of mammalian telomerase:
platypus TERT shares similarities with genes of birds and other reptiles
and localizes on sex chromosomes. \emph{BMC Genomics, 13} , 216-216.
doi:10.1186/1471-2164-13-216

Hurvich, C. M., \& Tsai, C.-L. (1989). Regression and time series model
selection in small samples. \emph{Biometrika, 76} (2), 297-307.
doi:10.1093/biomet/76.2.297

Jennings, B. J., Ozanne, S. E., Dorling, M. W., \& Hales, C. N. (1999).
Early growth determines longevity in male rats and may be related to
telomere shortening in the kidney. \emph{Febs Letters, 448} (1), 4-8.
doi:10.1016/s0014-5793(99)00336-1

Jensen, H., Sæther, B.-E., Ringsby, T. H., Tufto, J., Griffith, S. C.,
\& Ellegren, H. (2004). Lifetime reproductive success in relation to
morphology in the house sparrow \emph{Passer domesticus} . \emph{Journal
of Animal Ecology, 73} (4), 599-611.
doi:10.1111/j.0021-8790.2004.00837.x

Jensen, H., Steinsland, I., Ringsby, T. H., \& Saether, B. E. (2008).
Evolutionary dynamics of a sexual ornament in the house sparrow (Passer
domesticus): the role of indirect selection within and between
sexes.\emph{Evolution, 62} (6), 1275-1293.
doi:10.1111/j.1558-5646.2008.00395.x

Kingsolver, J. G., \& Pfennig, D. W. (2004). Individual-level selection
as a cause of Cope's rule of phyletic size increase. \emph{Evolution,
58} (7), 1608-1612. doi:10.1111/j.0014-3820.2004.tb01740.x

Klapper, W., Heidorn, K., Kühne, K., Parwaresch, R., \& Guido, K.
(1998). Telomerase activity in `immortal' fish. \emph{Febs Letters, 434}
(3), 409-412. doi:10.1016/s0014-5793(98)01020-5

Klimkiewicz, M. K., \& Futcher, A. G. (1987). Longevity records of North
American birds: Coerebinae through Estrildidae. \emph{Journal of Field
Ornithology, 58} (3), 318-333. Retrieved from

\emph{//WOS:A1987J520600011}

Kvalnes, T., Ringsby, T. H., Jensen, H., Hagen, I. J., Rønning, B.,
Parn, H., . . . Sæther, B. E. (2017). Reversal of response to artificial
selection on body size in a wild passerine. \emph{Evolution, 71} (8),
2062-2079. doi:10.1111/evo.13277

Lande, R. (1979). Quantitative genetic analysis of multivariate
evolution, applied to brain:body size allometry. \emph{Evolution, 33}
(1Part2), 402-416. doi:10.1111/j.1558-5646.1979.tb04694.x

Lansdorp, P. M. (1995). Telomere length and proliferation potential of
hematopoietic stem cells. \emph{J Cell Sci, 108 ( Pt 1)} (1), 1-6.
Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/7738087

Le Vaillant, M., Viblanc, V. A., Saraux, C., Le Bohec, C., Le Maho, Y.,
Kato, A., . . . Ropert-Coudert, Y. (2015). Telomere length reflects
individual quality in free-living adult king penguins. \emph{Polar
Biology, 38} (12), 2059-2067. doi:10.1007/s00300-015-1766-0

Liker, A., \& Szekely, T. (2005). Mortality costs of sexual selection
and parental care in natural populations of birds. \emph{Evolution, 59}
(4), 890-897. doi:10.1111/j.0014-3820.2005.tb01762.x

McLennan, D., Armstrong, J. D., Stewart, D. C., McKelvey, S., Boner, W.,
Monaghan, P., . . . Williams, T. (2017). Shorter juvenile telomere
length is associated with higher survival to spawning in migratory
Atlantic salmon. \emph{Functional Ecology, 31} (11), 2070-2079.
doi:10.1111/1365-2435.12939

Metcalfe, N. B., \& Monaghan, P. (2003). Growth versus lifespan:
perspectives from evolutionary ecology. \emph{Exp Gerontol, 38} (9),
935-940. doi:10.1016/s0531-5565(03)00159-1

Mizutani, Y., Tomita, N., Niizuma, Y., \& Yoda, K. (2013). Environmental
perturbations influence telomere dynamics in long-lived birds in their
natural habitat. \emph{Biol Lett, 9} (5), 20130511.
doi:10.1098/rsbl.2013.0511

Monaghan, P. (2010). Telomeres and life histories: the long and the
short of it. \emph{Ann N Y Acad Sci, 1206} , 130-142.
doi:10.1111/j.1749-6632.2010.05705.x

Monaghan, P. (2014). Organismal stress, telomeres and life
histories.\emph{J Exp Biol, 217} (Pt 1), 57-66. doi:10.1242/jeb.090043

Monaghan, P., \& Haussmann, M. F. (2006). Do telomere dynamics link
lifestyle and lifespan? \emph{Trends Ecol Evol, 21} (1), 47-53.
doi:10.1016/j.tree.2005.11.007

Monaghan, P., Metcalfe, N. B., \& Torres, R. (2009). Oxidative stress as
a mediator of life history trade-offs: mechanisms, measurements and
interpretation. \emph{Ecol Lett, 12} (1), 75-92.
doi:10.1111/j.1461-0248.2008.01258.x

Monaghan, P., \& Ozanne, S. E. (2018). Somatic growth and telomere
dynamics in vertebrates: relationships, mechanisms and
consequences.\emph{Philos Trans R Soc Lond B Biol Sci, 373} (1741),
20160446. doi:10.1098/rstb.2016.0446

Møller, A. P., Erritzoe, J., \& Soler, J. J. (2017). Life history,
immunity, Peto's paradox and tumours in birds. \emph{J Evol Biol, 30}
(5), 960-967. doi:10.1111/jeb.13060

National Oceanic and Atmospheric Administration (NOAA). (2018). Climate
prediction center: North Atlantic Oscillation (NAO). Retrieved
{[}01.12.2018{]} from
https://www.cpc.ncep.noaa.gov/data/teledoc/nao.shtml.

Nettle, D., Andrews, C., Reichert, S., Bedford, T., Gott, A., Parker,
C., . . . Bateson, M. (2016). Brood size moderates associations between
relative size, telomere length, and immune development in European
starling nestlings. \emph{Ecology and Evolution, 6} (22), 8138-8148.
doi:10.1002/ece3.2551

Nettle, D., Andrews, C., Reichert, S., Bedford, T., Kolenda, C., Parker,
C., . . . Bateson, M. (2017). Early-life adversity accelerates cellular
ageing and affects adult inflammation: Experimental evidence from the
European starling. \emph{Sci Rep, 7} , 40794. doi:10.1038/srep40794

Olsson, M., Pauliny, A., Wapstra, E., Uller, T., Schwartz, T., \&
Blomqvist, D. (2011). Sex differences in sand lizard telomere
inheritance: paternal epigenetic effects increases telomere heritability
and offspring survival. \emph{PLoS One, 6} (4), e17473.
doi:10.1371/journal.pone.0017473

Pauliny, A., Devlin, R. H., Johnsson, J. I., \& Blomqvist, D. (2015).
Rapid growth accelerates telomere attrition in a transgenic
fish.\emph{BMC Evol Biol, 15} (1), 159. doi:10.1186/s12862-015-0436-8

Pauliny, A., Wagner, R. H., Augustin, J., Szep, T., \& Blomqvist, D.
(2006). Age-independent telomere length predicts fitness in two bird
species. \emph{Mol Ecol, 15} (6), 1681-1687.
doi:10.1111/j.1365-294X.2006.02862.x

Pepke, M. L., \& Eisenberg, D. T. A. (2020). On the comparative biology
of mammalian telomeres: Telomere length co-evolves with body mass,
lifespan and cancer risk. \emph{Authorea} .
doi:10.22541/au.159682209.91318403

Pepper, G. V., Bateson, M., \& Nettle, D. (2018). Telomeres as
integrative markers of exposure to stress and adversity: a systematic
review and meta-analysis. \emph{R Soc Open Sci, 5} (8), 180744.
doi:10.1098/rsos.180744

Peters, R. H. (1983). \emph{The ecological implications of body size} .
Cambridge Cambridgeshire ; New York: Cambridge University Press.

Pfaffl, M. W. (2001). A new mathematical model for relative
quantification in real-time RT-PCR. \emph{Nucleic Acids Research, 29}
(9), e45. doi:10.1093/nar/29.9.e45

Pick, J. L., Hatakeyama, M., Ihle, K. E., Gasparini, J., Haussy, C.,
Ishishita, S., . . . Tschirren, B. (2020). Artificial selection reveals
the role of transcriptional constraints in the maintenance of life
history variation. \emph{Evol Lett, 4} (3), 200-211.
doi:10.1002/evl3.166

Postma, E., Visser, J., \& Van Noordwijk, A. J. (2007). Strong
artificial selection in the wild results in predicted small evolutionary
change. \emph{J Evol Biol, 20} (5), 1823-1832.
doi:10.1111/j.1420-9101.2007.01379.x

Pujol, B., Blanchet, S., Charmantier, A., Danchin, E., Facon, B.,
Marrot, P., . . . Winney, I. (2018). The missing response to selection
in the wild. \emph{Trends Ecol Evol, 33} (5), 337-346.
doi:10.1016/j.tree.2018.02.007

Pärn, H., Jensen, H., Ringsby, T. H., \& Sæther, B.-E. (2009).
Sex-specific fitness correlates of dispersal in a house sparrow
metapopulation. \emph{Journal of Animal Ecology, 78} (6), 1216-1225.
doi:https://doi.org/10.1111/j.1365-2656.2009.01597.x

R Core Team. (2020). R: A language and environment for statistical
computing. (Version 3.6.3). Vienna, Austria.: R Foundation for
Statistical Computing. Retrieved from https://www.R-project.org/

Ranke, P. S., Skjelseth, S., Hagen, I. J., Billing, A. M., Pedersen, Å.
A. B., Pärn, H., . . . Jensen, H. (2020). Multi-generational genetic
consequences of reinforcement in a bird
metapopulation.\emph{Conservation Genetics, 21} (3), 603-612.
doi:10.1007/s10592-020-01273-7

Reichert, S., \& Stier, A. (2017). Does oxidative stress shorten
telomeres in vivo? A review. \emph{Biology Letters} , 13(12), 20170463.
doi:10.1098/rsbl.2017.0463

Reznick, D. (1985). Costs of reproduction: An evaluation of the
empirical evidence. \emph{Oikos, 44} (2), 257-267. doi:10.2307/3544698

Ringsby, T. H., Jensen, H., Pärn, H., Kvalnes, T., Boner, W., Gillespie,
R., . . . Monaghan, P. (2015). On being the right size: increased body
size is associated with reduced telomere length under natural
conditions. \emph{Proc Biol Sci, 282} (1820), 20152331.
doi:10.1098/rspb.2015.2331

Ringsby, T. H., Sæther, B.-E., \& Solberg, E. J. (1998). Factors
affecting juvenile survival in house sparrow \emph{Passer domesticus}
.\emph{Journal of Avian Biology, 29} (3), 241-247. doi:10.2307/3677106

Ringsby, T. H., Sæther, B.-E., Tufto, J., Jensen, H., \& Solberg, E. J.
(2002). Asynchronous spatiotemporal demography of a house sparrow
metapopulation in a correlated environment. \emph{Ecology, 83} (2),
561-569. doi:10.1890/0012-9658(2002)083{[}0561:Asdoah{]}2.0.Co;2

Rising, J. D., \& Somers, K. M. (1989). The measurement of overall body
size in birds. \emph{The Auk, 106} (4), 666-674. Retrieved from
//WOS:A1989AY24300016

Roff, D. A., \& Fairbairn, D. J. (2007). The evolution of trade-offs:
where are we? \emph{Journal of Evolutionary Biology, 20} (2), 433-447.
doi:https://doi.org/10.1111/j.1420-9101.2006.01255.x

Rønning, B., Broggi, J., Bech, C., Moe, B., Ringsby, T. H., Pärn, H., .
. . Jensen, H. (2016). Is basal metabolic rate associated with recruit
production and survival in free\selectlanguage{english}-living house sparrows? \emph{Functional
Ecology, 30} (7), 1140-1148. doi:10.1111/1365-2435.12597

Schulte-Hostedde, A. I., Zinner, B., Millar, J. S., \& Hickling, G. J.
(2005). Restitution of mass--size residuals: Validating body condition
indices. \emph{Ecology, 86} (1), 155-163. doi:10.1890/04-0232

Scott, N. M., Haussmann, M. F., Elsey, R. M., Trosclair, P. L., \&
Vleck, C. M. (2006). Telomere length shortens with body length
in\emph{Alligator mississippiensis} . \emph{Southeastern Naturalist, 5}
(4), 685-692. doi:10.1656/1528-7092(2006)5{[}685:Tlswbl{]}2.0.Co;2

Selman, C., Blount, J. D., Nussey, D. H., \& Speakman, J. R. (2012).
Oxidative damage, ageing, and life-history evolution: where
now?\emph{Trends Ecol Evol, 27} (10), 570-577.
doi:10.1016/j.tree.2012.06.006

Seluanov, A., Gladyshev, V. N., Vijg, J., \& Gorbunova, V. (2018).
Mechanisms of cancer resistance in long-lived mammals. \emph{Nat Rev
Cancer, 18} (7), 433-441. doi:10.1038/s41568-018-0004-9

Sibly, R. M., \& Brown, J. H. (2007). Effects of body size and lifestyle
on evolution of mammal life histories. \emph{Proceedings of the National
Academy of Sciences, 104} (45), 17707. doi:10.1073/pnas.0707725104

Sidorov, I., Kimura, M., Yashin, A., \& Aviv, A. (2009). Leukocyte
telomere dynamics and human hematopoietic stem cell kinetics during
somatic growth. \emph{Exp Hematol, 37} (4), 514-524.
doi:10.1016/j.exphem.2008.11.009

Simons, M. J. (2015). Questioning causal involvement of telomeres in
aging. \emph{Ageing Res Rev, 24} (Pt B), 191-196.
doi:10.1016/j.arr.2015.08.002

Smith, S. M., Nager, R. G., \& Costantini, D. (2016). Meta-analysis
indicates that oxidative stress is both a constraint on and a cost of
growth. \emph{Ecology and Evolution, 6} (9), 2833-2842.
doi:10.1002/ece3.2080

Spurgin, L. G., Bebbington, K., Fairfield, E. A., Hammers, M., Komdeur,
J., Burke, T., . . . Richardson, D. S. (2018). Spatio-temporal variation
in lifelong telomere dynamics in a long-term ecological study. \emph{J
Anim Ecol, 87} (1), 187-198. doi:10.1111/1365-2656.12741

Stearns, S. C. (1989). Trade-offs in life-history
evolution.\emph{Functional Ecology, 3} (3), 259-268. doi:10.2307/2389364

Steinsland, I., Larsen, C. T., Roulin, A., \& Jensen, H. (2014).
Quantitative genetic modeling and inference in the presence of
nonignorable missing data. \emph{Evolution, 68} (6), 1735-1747.
doi:10.1111/evo.12380

Stenseth, N. C., Ottersen, G., Hurrell, J. W., Mysterud, A., Lima, M.,
Chan, K. S., . . . Adlandsvik, B. (2003). Review article. Studying
climate effects on ecology through the use of climate indices: the North
Atlantic Oscillation, El Nino Southern Oscillation and beyond.\emph{Proc
Biol Sci, 270} (1529), 2087-2096. doi:10.1098/rspb.2003.2415

Sudyka, J. (2019). Does reproduction shorten telomeres? Towards
integrating individual quality with life-history strategies in telomere
biology. \emph{Bioessays, 41} (11), e1900095. doi:10.1002/bies.201900095

Sudyka, J., Podmokla, E., Drobniak, S. M., Dubiec, A., Arct, A.,
Gustafsson, L., \& Cichon, M. (2019). Sex-specific effects of parasites
on telomere dynamics in a short-lived passerine-the blue tit. \emph{Die
Naturwissenschaften, 106} (1-2), 6. doi:10.1007/s00114-019-1601-5

The Norwegian Meteorological Institute. (2018). eKlima. Retrieved from
http://eklima.met.no. Retrieved 01.12.2018 http://eklima.met.no

Therneau, T. (2015). A package for survival analysis in S.
doi:https://CRAN.R-project.org/package=survival

Trivers, R. (1985). \emph{Social evolution} : Benjamin/Cummings Pub. Co.

Ujvari, B., \& Madsen, T. (2009). Short telomeres in hatchling snakes:
erythrocyte telomere dynamics and longevity in tropical
pythons.\emph{PLoS One, 4} (10), e7493. doi:10.1371/journal.pone.0007493

van de Crommenacker, J., Komdeur, J., Burke, T., \& Richardson, D. S.
(2011). Spatio-temporal variation in territory quality and oxidative
status: a natural experiment in the Seychelles warbler (Acrocephalus
sechellensis). \emph{J Anim Ecol, 80} (3), 668-680.
doi:10.1111/j.1365-2656.2010.01792.x

van Noordwijk, A. J., \& de Jong, G. (1986). Acquisition and allocation
of resources: Their influence on variation in life history
tactics.\emph{The American Naturalist, 128} (1), 137-142.
doi:10.1086/284547

Vaziri, H., Dragowska, W., Allsopp, R. C., Thomas, T. E., Harley, C. B.,
\& Lansdorp, P. M. (1994). Evidence for a mitotic clock in human
hematopoietic stem cells: loss of telomeric DNA with age. \emph{Proc
Natl Acad Sci U S A, 91} (21), 9857-9860. doi:10.1073/pnas.91.21.9857

Vedder, O., Verhulst, S., Bauch, C., \& Bouwhuis, S. (2017). Telomere
attrition and growth: a life-history framework and case study in common
terns. \emph{J Evol Biol, 30} (7), 1409-1419. doi:10.1111/jeb.13119

Vedder, O., Verhulst, S., Zuidersma, E., \& Bouwhuis, S. (2018).
Embryonic growth rate affects telomere attrition: an experiment in a
wild bird. \emph{J Exp Biol, 221} (Pt 15), jeb181586.
doi:10.1242/jeb.181586

Verhulst, S., Aviv, A., Benetos, A., Berenson, G. S., \& Kark, J. D.
(2013). Do leukocyte telomere length dynamics depend on baseline
telomere length? An analysis that corrects for 'regression to the
mean'.\emph{Eur J Epidemiol, 28} (11), 859-866.
doi:10.1007/s10654-013-9845-4

von Zglinicki, T. (2002). Oxidative stress shortens
telomeres.\emph{Trends Biochem Sci, 27} (7), 339-344.
doi:10.1016/s0968-0004(02)02110-2

Wang, Q., Zhan, Y., Pedersen, N. L., Fang, F., \& Hagg, S. (2018).
Telomere Length and All-Cause Mortality: A Meta-analysis. \emph{Ageing
Res Rev, 48} , 11-20. doi:10.1016/j.arr.2018.09.002

Watson, H., Bolton, M., \& Monaghan, P. (2015). Variation in early-life
telomere dynamics in a long-lived bird: links to environmental
conditions and survival. \emph{J Exp Biol, 218} (Pt 5), 668-674.
doi:10.1242/jeb.104265

White, C. R., Marshall, D. J., Alton, L. A., Arnold, P. A., Beaman, J.
E., Bywater, C. L., . . . Ortiz-Barrientos, D. (2019). The origin and
maintenance of metabolic allometry in animals. \emph{Nat Ecol Evol, 3}
(4), 598-603. doi:10.1038/s41559-019-0839-9

Wilbourn, R. V., Froy, H., McManus, M. C., Cheynel, L., Gaillard, J. M.,
Gilot-Fromont, E., . . . Nussey, D. H. (2017). Age-dependent
associations between telomere length and environmental conditions in roe
deer. \emph{Biol Lett, 13} (9), 20170434. doi:10.1098/rsbl.2017.0434

Wilbourn, R. V., Moatt, J. P., Froy, H., Walling, C. A., Nussey, D. H.,
\& Boonekamp, J. J. (2018). The relationship between telomere length and
mortality risk in non-model vertebrate systems: a
meta-analysis.\emph{Philos Trans R Soc Lond B Biol Sci, 373} (1741),
20160447. doi:10.1098/rstb.2016.0447

Wilson, A. J., \& Nussey, D. H. (2010). What is individual quality? An
evolutionary perspective. \emph{Trends Ecol Evol, 25} (4), 207-214.
doi:10.1016/j.tree.2009.10.002

Wood, E. M., \& Young, A. J. (2019). Telomere attrition predicts reduced
survival in a wild social bird, but short telomeres do not. \emph{Mol
Ecol, 28} (16), 3669-3680. doi:10.1111/mec.15181

Woodward, G., Ebenman, B., Emmerson, M., Montoya, J. M., Olesen, J. M.,
Valido, A., \& Warren, P. H. (2005). Body size in ecological
networks.\emph{Trends Ecol Evol, 20} (7), 402-409.
doi:10.1016/j.tree.2005.04.005

Young, A. J. (2018). The role of telomeres in the mechanisms and
evolution of life-history trade-offs and ageing. \emph{Philos Trans R
Soc Lond B Biol Sci, 373} (1741), 20160452. doi:10.1098/rstb.2016.0452

Zera, A. J., \& Harshman, L. G. (2001). The physiology of life history
trade-offs in animals. \emph{Annual Review of Ecology and Systematics,
32} (1), 95-126. doi:10.1146/annurev.ecolsys.32.081501.114006

\textbf{DATA ACCESSIBILITY}

All data will be made available on the Dryad Digital Repository or
another open access channel upon acceptance of the manuscript.

\textbf{AUTHOR CONTRIBUTIONS}

MLP analyzed the data and wrote the manuscript with input from all
authors. MLP and WB measured telomere lengths. MLP and BR constructed
pedigrees. THR, HJ and B-ES designed the artificial selection
experiment. THR, HJ \& BR contributed to the fieldwork and TK curated
field data.

\textbf{FIGURES AND TABLES}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Fig. 1:} Hypotheses of expected positive (solid lines) and
negative (dotted lines) effects shaping variation in early-life telomere
length during the nestling stage, and later-life fitness consequences.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Fig. 2:} Timeline of the artificial size selection experiment on
free-living house sparrows conducted in parallel on two islands in
Norway starting in year 2002 (1\textsuperscript{st} year). Adult birds
were captured each winter for 4 years and selected for either longer or
shorter tarsus length (each selection event showed with a solid black
arrow). Telomere lengths (TL) were measured on nestling cohorts produced
in the subsequent summer breeding season (showed as dotted grey arrows).
Birds that were not removed during the artificially selection stayed in
their populations, and survival and recruit production were monitored
until all sampled birds were assumed to had died.

\textbf{Table 1:} Number of sampled offspring (\emph{n} =566) in each
artificial selection category (1: both parents artificially selected;
0.5: one parent subject to artificial selection; 0: no parents
artificially selected, i.e. unselected) from year 2002-2006 in two
island populations selected for larger (\emph{high} ) and smaller
(\emph{low} ) tarsus length, respectively. Unknown genetic parents were
assumed not to have been artificially selected.\selectlanguage{english}
\begin{longtable}[]{@{}lllllllllllll@{}}
\toprule
Population: & \emph{High} (Leka) & \emph{High} (Leka) & \emph{High}
(Leka) & \emph{High} (Leka) & \emph{High} (Leka) & \emph{High} (Leka) &
\emph{Low} (Vega) & \emph{Low} (Vega) & \emph{Low} (Vega) & \emph{Low}
(Vega) & \emph{Low} (Vega) & \emph{Low} (Vega)\tabularnewline
\midrule
\endhead
Selection category: & \textbf{2002} & \textbf{2003} & \textbf{2004} &
\textbf{2005} & \textbf{2006} & \textbf{Sum:} & \textbf{2002} &
\textbf{2003} & \textbf{2004} & \textbf{2005} & \textbf{2006} &
\textbf{Sum:}\tabularnewline
\emph{Selected} (1) & 5 & 18 & 16 & 21 & 14 & \textbf{75} & 45 & 48 & 25
& 40 & 14 & \textbf{172}\tabularnewline
\emph{Intermediate} (0.5) & 26 & 5 & 3 & 12 & 13 & \textbf{59} & 54 & 33
& 16 & 19 & 41 & \textbf{163}\tabularnewline
\emph{Unselected} (0) & 16 & 3 & 3 & 2 & 1 & \textbf{25} & 21 & 8 & 18 &
5 & 21 & \textbf{73}\tabularnewline
Sum: & \textbf{47} & \textbf{26} & \textbf{22} & \textbf{35} &
\textbf{28} & \textbf{158} & \textbf{120} & \textbf{89} & \textbf{59} &
\textbf{64} & \textbf{76} & \textbf{408}\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{Fig. 3:} \emph{High} population; effect plots of trends in
nestling tarsus (a) and telomere lengths (c) during artificial selection
for larger tarsus length in parents. The artificial selection ended
after cohort 4 was born. The effect of selection category (\emph{0} : no
parents selected (unselected), \emph{0.5} : one parent selected,
and\emph{1} : both parents selected) on tarsus (b) and telomere length
(d) is shown.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\textbf{Fig. 4:} \emph{Low} population; effect plots of trends in
nestling tarsus (a) and telomere lengths (c) during artificial selection
for smaller tarsus length in parents. The artificial selection ended
after cohort 4 was born. The effect of selection category (\emph{0} : no
parents selected (unselected), \emph{0.5} : one parent selected,
and\emph{1} : both parents selected) on tarsus (b) and telomere length
(d) is shown.

\textbf{Table 2:} Linear mixed effects models with [?]AICc\textless{}5 of
variation in early-life telomere length in house sparrow nestlings
(\emph{n} =566) in two island populations. All models included random
intercepts for brood identity and year. The models are ranked by AICc,
and number of degrees of freedom (df) and model weights (w) are shown.\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
Model & Model & [?]AICc & df & w\tabularnewline
\midrule
\endhead
1 & \emph{TL = sex + population + tarsus + NAO\_30} & 0.0 & 8 &
0.203\tabularnewline
2 & \emph{TL = sex + population + tarsus + NAO\_30 + population*tarsus}
& 0.2 & 9 & 0.180\tabularnewline
3 & \emph{TL = sex + population + age + tarsus + NAO\_30} & 1.4 & 9 &
0.099\tabularnewline
4 & \emph{TL = sex + population + age + tarsus + NAO\_30 +
population*tarsus} & 1.7 & 10 & 0.088\tabularnewline
5 & \emph{TL = sex + population + tarsus + NAO\_30 + brood size} & 2.1 &
9 & 0.072\tabularnewline
6 & \emph{TL = sex + population + tarsus + NAO\_30 + population*tarsus +
brood size} & 2.3 & 10 & 0.064\tabularnewline
7 & \emph{TL = sex + population + NAO\_30} & 2.3 & 7 &
0.064\tabularnewline
8 & \emph{TL = sex + population + tarsus} & 2.9 & 7 &
0.048\tabularnewline
9 & \emph{TL = sex + population + tarsus + population*tarsus} & 3.2 & 8
& 0.041\tabularnewline
10 & \emph{TL = sex + population + age + tarsus + NAO\_30 + brood size}
& 3.5 & 10 & 0.035\tabularnewline
11 & \emph{TL = sex + population + age + tarsus + NAO\_30 + brood size +
population*tarsus} & 3.7 & 11 & 0.031\tabularnewline
12 & \emph{TL = sex + population + age + NAO\_30} & 3.8 & 8 &
0.031\tabularnewline
13 & \emph{TL = sex + population + age + tarsus} & 4.3 & 8 &
0.024\tabularnewline
14 & \emph{TL = sex + population + age + tarsus + population*tarsus} &
4.7 & 9 & 0.020\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 3:} Estimates (\selectlanguage{greek}\emph{β} \selectlanguage{english}) with standard errors (SE) and
lower and upper 95\% confidence intervals (CI) from a linear mixed
effects model of variation in telomere length (TL, \emph{n} =566).
Random intercepts for brood identity and year were included in the
model.\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
Response variable: TL & \selectlanguage{greek}\emph{β} & SE & Lower CI & Upper
CI\tabularnewline
\midrule
\endhead
intercept & 1.19 & 0.04 & 1.12 & 1.25\tabularnewline
\emph{tarsus} & \emph{-0.03} & \emph{0.01} & \emph{-0.05} &
\emph{-0.00}\tabularnewline
\emph{sex (female)} & \emph{-0.08} & \emph{0.03} & \emph{-0.14} &
\emph{-0.02}\tabularnewline
\emph{population (low)} & \emph{-0.17} & \emph{0.04} & \emph{-0.24} &
\emph{-0.10}\tabularnewline
\emph{NAO\_30} & \emph{0.10} & \emph{0.05} & \emph{0.01} &
\emph{0.19}\tabularnewline
\selectlanguage{greek}\emph{σ\textsuperscript{2}\textsubscript{brood ID}}\selectlanguage{english} & 0.00\selectlanguage{ngerman}±0.05 SD &
0.00\selectlanguage{ngerman}±0.05 SD & 0.00 & 0.12\tabularnewline
\selectlanguage{greek}\emph{σ\textsuperscript{2}\textsubscript{year}}\selectlanguage{english} & 0.00\selectlanguage{ngerman}±0.02 SD &
0.00\selectlanguage{ngerman}±0.02 SD & 0.00 & 0.08\tabularnewline
Marginal R\textsuperscript{2} / Conditional R\textsuperscript{2}: 0.070
/ 0.092 & Marginal R\textsuperscript{2} / Conditional
R\textsuperscript{2}: 0.070 / 0.092 & Marginal R\textsuperscript{2} /
Conditional R\textsuperscript{2}: 0.070 / 0.092 & Marginal
R\textsuperscript{2} / Conditional R\textsuperscript{2}: 0.070 / 0.092 &
Marginal R\textsuperscript{2} / Conditional R\textsuperscript{2}: 0.070
/ 0.092\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

\textbf{Fig. 5:} The relationship between nestling telomere length (TL)
and (a) age-corrected tarsus length and (b) the NAO\_30 index (the
average NAO index across 30 days prior to TL measurement) with
regression lines based on the highest ranked model according to the AICc
(Tables 2 and 3). Although there is considerable variation in TLs among
individuals of similar size, tarsus length and the NAO\_30 index
significantly correlate with TL. Males (black) have longer telomeres
than females (grey) and individuals in the\emph{high} population
(circles, full line) have longer telomeres than those in the \emph{low}
population (triangles, dotted line).

\textbf{Table 4:} Binomial generalized linear mixed models with
[?]AICc\textless{}5 of variation in first-year survival in house sparrows
in two island populations. All models included random intercepts for
brood identity and year. The models are ranked by AICc, and number of
degrees of freedom (df) and model weights (w) are shown.\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
& Model & [?]AICc & df & w\tabularnewline
\midrule
\endhead
1 & \emph{Survival = sex + population + tarsus +
tarsus\textsuperscript{2}} & 0.0 & 7 & 0.179\tabularnewline
2 & \emph{Survival = sex + population + tarsus} & 1.2 & 6 &
0.010\tabularnewline
3 & \emph{Survival = sex + population + tarsus + population*tarsus} &
1.5 & 7 & 0.083\tabularnewline
4 & \emph{Survival = sex + population} & 1.7 & 5 & 0.075\tabularnewline
5 & \emph{Survival = sex + population + tarsus +
tarsus\textsuperscript{2} + TL} & 1.9 & 8 & 0.070\tabularnewline
6 & \emph{Survival = sex + population + tarsus +
tarsus\textsuperscript{2} + NAO\_30} & 2.0 & 8 & 0.065\tabularnewline
7 & \emph{Survival = sex + population + tarsus +
tarsus\textsuperscript{2} + TL + TL\textsuperscript{2}} & 2.5 & 9 &
0.052\tabularnewline
8 & \emph{Survival = sex + population + tarsus + TL} & 3.0 & 7 &
0.040\tabularnewline
9 & \emph{Survival = sex + population + tarsus + NAO\_30} & 3.2 & 7 &
0.036\tabularnewline
10 & \emph{Survival = sex + population + tarsus + population*tarsus +
TL} & 3.4 & 8 & 0.032\tabularnewline
11 & \emph{Survival = sex + population + TL} & 3.5 & 6 &
0.032\tabularnewline
12 & \emph{Survival = sex + population + tarsus + population*tarsus +
NAO\_30} & 3.6 & 8 & 0.030\tabularnewline
13 & \emph{Survival = sex + population + NAO\_30} & 3.7 & 6 &
0.028\tabularnewline
14 & \emph{Survival = sex + population + tarsus + TL +
TL\textsuperscript{2}} & 3.7 & 8 & 0.028\tabularnewline
15 & \emph{Survival = sex + population + tarsus +
tarsus\textsuperscript{2} + TL + NAO\_30} & 3.9 & 9 &
0.025\tabularnewline
16 & \emph{Survival = sex + population + TL + TL\textsuperscript{2}} &
4.2 & 7 & 0.022\tabularnewline
17 & \emph{Survival = sex + population + tarsus +
tarsus\textsuperscript{2} + TL + TL\textsuperscript{2} + NAO\_30} & 4.5
& 10 & 0.019\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}

\textbf{Fig. 6:} Cox proportional hazards regression models showing
predicted values of hazard ratios (i.e. mortality risk) with 95\%
confidence intervals as a function of a) tarsus length, and b) telomere
length for each sex. Differences between sexes were not significant.
Larger individuals had lower long-term mortality, and model selection
indicated a tendency for both short and long telomeres to be associated
with lower mortality.

\textbf{Table 5:} Estimates with 95\% confidence intervals (CI) and
standard errors (SE) from the highest ranked generalized linear models
(see Table S2.5) fitted using a Poisson distribution of variation in
lifetime reproductive success (LRS) corrected for lifespan for each
population (\emph{high} /\emph{low} ). Random intercepts for brood
identity and year were included as random factors in all models. Only
individuals that were not removed during the artificial selection and
that survived until breeding were included in these analyses.\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
\emph{High} (\emph{n}=22): LRS & \selectlanguage{greek}\emph{β} & SE & Lower CI & Upper
CI\tabularnewline
\midrule
\endhead
intercept & 0.03 & 0.72 & -1.39 & 1.45\tabularnewline
\emph{lifespan} & \emph{0.72} & \emph{0.15} & \emph{0.43} &
\emph{1.00}\tabularnewline
sex (female) & -1.11 & 0.58 & -2.25 & 0.03\tabularnewline
TL & -1.24 & 0.65 & -2.52 & 0.04\tabularnewline
\selectlanguage{greek}\emph{σ\textsuperscript{2}\textsubscript{brood ID}}\selectlanguage{english} & 0.00\selectlanguage{ngerman}±0.00 &
0.00\selectlanguage{ngerman}±0.00 & 0.00 & 0.00\tabularnewline
\selectlanguage{greek}\emph{σ\textsuperscript{2}\textsubscript{year}}\selectlanguage{english} & 0.00\selectlanguage{ngerman}±0.00 & 0.00\selectlanguage{ngerman}±0.00
& 0.00 & 0.00\tabularnewline
\emph{Low} (\emph{n}=80): LRS & & & &\tabularnewline
intercept & -2.01 & 0.43 & -2.86 & -1.16\tabularnewline
\emph{lifespan} & \emph{0.93} & \emph{0.14} & \emph{0.66} &
\emph{1.20}\tabularnewline
sex (female) & -0.03 & 0.35 & -0.71 & 0.65\tabularnewline
\selectlanguage{greek}\emph{σ\textsuperscript{2}\textsubscript{brood ID}}\selectlanguage{english} & 0.56\selectlanguage{ngerman}±0.75 &
0.56\selectlanguage{ngerman}±0.75 & 0.45 & 1.26\tabularnewline
\selectlanguage{greek}\emph{σ\textsuperscript{2}\textsubscript{year}}\selectlanguage{english} & 0.05\selectlanguage{ngerman}±0.23 & 0.05\selectlanguage{ngerman}±0.23
& 0.01 & 4.12\tabularnewline
\bottomrule
\end{longtable}

\textbf{SUPPORTING INFORMATION}

Additional Supporting Information may be found in the online version of
this article.

Appendix S1: Notes on methods.

Appendix S2: Notes on results.

\selectlanguage{english}
\FloatBarrier
\end{document}

