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\begin{document}

\title{Adult telomere length is positively correlated with survival and
lifetime reproductive success in a wild passerine}



\author[1]{Heung Ying Janet Chik}%
\author[2]{Maria-Elena Mannarelli}%
\author[2]{Natalie dos Remedios}%
\author[2]{Mirre Simons}%
\author[2]{Terry Burke}%
\author[3]{Julia Schroeder}%
\author[1]{Hannah Dugdale}%
\affil[1]{University of Groningen}%
\affil[2]{The University of Sheffield}%
\affil[3]{Imperial College London}%


\vspace{-1em}



  
  \date{May 29, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Explaining variation in individual fitness is a key goal in evolutionary
biology. Recently, telomeres, repeating DNA sequences capping the ends
of chromosomes, have gained attention as a biomarker for body state,
individual quality, and ageing. However, existing research has provided
mixed evidence for whether telomere length correlates with fitness
components, including survival and reproductive output. Moreover, few
studies have examined how telomere shortening correlates with fitness in
wild populations. Here, we intensively monitored an insular population
of house sparrows on Lundy Island, UK, and collected longitudinal
telomere and life history data spanning 16 years from 1,225 individuals.
We tested whether telomere length and/or shortening predict fitness
measures, namely survival, lifespan, as well as annual and lifetime
reproductive success. Telomere length positively predicted immediate
survival up to one year after measurement, independent of age, but did
not predict lifespan, suggesting either a diminishing telomere length --
survival correlation with age, or other extrinsic factors of mortality.
The positive effect of telomere length on survival translated to
reproductive benefits, as birds with longer telomeres produced more
genetic recruits over their lifetime, but not annually, suggesting
variation in individual quality. The rate of telomere shortening,
however, correlated with neither lifespan nor lifetime reproductive
success. Our results provided further evidence that telomere length
correlates with fitness, and they contributed to our understanding of
how telomere dynamics link with individual quality.%
\end{abstract}%



\sloppy


\emph{Original article}

\textbf{Adult telomere length is positively correlated with survival and
lifetime reproductive success in a wild passerine}

Running title: \emph{Telomere length is correlated with fitness}

Heung Ying Janet Chik\textsuperscript{1,2}, Maria-Elena
Mannarelli\textsuperscript{3,4}, Natalie dos
Remedios\textsuperscript{3,5}, Mirre J. P. Simons\textsuperscript{3},
Terry Burke\textsuperscript{3}, Julia Schroeder\textsuperscript{6}*,
Hannah L. Dugdale\textsuperscript{1}*

*H. L. Dugdale and J. Schroeder share last authorship

\textsuperscript{1}Groningen Institute for Evolutionary Life Sciences,
University of Groningen, Groningen 9747AG, the Netherlands.

\textsuperscript{2}School of Natural Sciences, Macquarie University, NSW
2109, Australia.

\textsuperscript{3}Ecology and Evolutionary Biology, School of
Biosciences, University of Sheffield, Sheffield S10 2TN, United Kingdom.

\textsuperscript{4}School of Biological Sciences, University of East
Anglia, Norfolk NR4 7TJ, United Kingdom.

\textsuperscript{5}School of Social Sciences, University of Auckland,
Auckland 1010, New Zealand.

\textsuperscript{6}Department of Life Sciences, Imperial College London
Silwood Park, Ascot SL5 7PY, United Kingdom.

Corresponding authors: Heung Ying Janet Chik (chikhyjanet@gmail.com),
Hannah L. Dugdale (h.l.dugdale@rug.nl)

\textbf{Funding statement}

HYJC was funded by a PhD scholarship from the University of Groningen
and Macquarie University. JS received funding from the European Research
Council (PCIG12-GA-2012-333096). TB, MJPS and JS were supported by the
Natural and Environmental Research Council (grant NE/J024567/1). HD was
supported by a Rosalind Franklin Fellowship from the University of
Groningen.

\textbf{Conflict of interest disclosure:} The authors declare no
conflicts of interest.

\textbf{Ethics approval:} All animal procedures are licensed and
approved by the British Trust for Ornithology and the UK Home Office
(Project License PP5873078).

\textbf{Abstract}

Explaining variation in individual fitness is a key goal in evolutionary
biology. Recently, telomeres, repeating DNA sequences capping the ends
of chromosomes, have gained attention as a biomarker for body state,
individual quality, and ageing. However, existing research has provided
mixed evidence for whether telomere length correlates with fitness
components, including survival and reproductive output. Moreover, few
studies have examined how telomere shortening correlates with fitness in
wild populations. Here, we intensively monitored an insular population
of house sparrows on Lundy Island, UK, and collected longitudinal
telomere and life history data spanning 16 years from 1,225 individuals.
We tested whether telomere length and/or shortening predict fitness
measures, namely survival, lifespan, as well as annual and lifetime
reproductive success. Telomere length positively predicted immediate
survival up to one year after measurement, independent of age, but did
not predict lifespan, suggesting either a diminishing telomere length --
survival correlation with age, or other extrinsic factors of mortality.
The positive effect of telomere length on survival translated to
reproductive benefits, as birds with longer telomeres produced more
genetic recruits over their lifetime, but not annually, suggesting
variation in individual quality. The rate of telomere shortening,
however, correlated with neither lifespan nor lifetime reproductive
success. Our results provided further evidence that telomere length
correlates with fitness, and they contributed to our understanding of
how telomere dynamics link with individual quality.

\textbf{Keywords:}

Telomere dynamics, survival, reproductive success, senescence,
individual quality

\textbf{Introduction}

Understanding how an organism's fitness is influenced by its traits is a
central tenet in evolutionary biology. While most measurable traits are
manifested at the organismal level, for example in reproduction,
survival, and behaviour, it is equally important to examine traits at
deeper levels of biological organization, including cell and body
physiology, as they underlie organismal performance. One of such traits
is telomere dynamics, which could reflect the cellular and body state of
the organism, bridging together physiology and fitness.

Telomeres are nucleoprotein complexes at the ends of chromosome
consisting of repeating DNA sequences (TTAGGG\textsubscript{n} in
vertebrates; Blackburn, 1991). Telomeres are vulnerable to erosion due
to 1) the end-replication problem, where linear DNA is not fully
replicated during cell proliferation (Levy et al., 1992; Olovnikov,
1973); and 2) chemical damage from oxidative stress (Blackburn et al.,
2015; von Zglinicki, 2002). They therefore shorten over time. Shortened
telomeres can be restored, e.g. by telomerase, but telomerase activity
varies across life stages and species (Haussmann et al., 2007), and is
generally thought to be suppressed in adult somatic cells in humans and
mammals (Blackburn et al., 2015; Young, 2018). This creates a decline of
telomere length throughout lifespan, typically rapidly during early life
due to prominent cell proliferation, and more slowly in adulthood
(Heidinger et al., 2012; Spurgin et al., 2018; Stier et al., 2020),
though patterns vary across taxa (Remot et al., 2022). When telomeres
are critically short, cells enter a senescent state, and can undergo
apoptosis, leading to a decline in tissue function (Blackburn et al.,
2015; Campisi, 2005). Because of this, telomere length, and the rate of
telomere shortening, have gained attention in evolutionary biology and
epidemiology, as a biomarker of body state or individual quality (e.g.
Angelier et al., 2019; Bauch et al., 2013; Monaghan, 2010), a
measurement of physiological costs in life-history trade-offs (e.g.
Bauch et al., 2013), and a hallmark of ageing (e.g. L\selectlanguage{ngerman}ópez-Otín et al.,
2013).

Because telomeres link to cellular senescence, thereby tissue function,
and thus perhaps ultimately ageing, one would expect telomere dynamics
to be under selection, and therefore to be correlated with fitness.
However, studies examining the relationship between telomere dynamics
and survival and/or lifespan have provided mixed results. On average,
shorter telomeres are associated with higher mortality, but variation
exists (Wilbourn et al., 2018). Some studies found positive
relationships between early-life telomere length and survival or
lifespan (e.g. Eastwood et al., 2019; Fairlie et al., 2016; Heidinger et
al., 2012; Sheldon et al., 2022; van Lieshout et al., 2019); while
others found such a relationship also in adults (e.g. Bakaysa et al.,
2007; Bichet et al., 2020; Froy et al., 2021; Vedder et al., 2022), even
at a genetic level (Vedder et al., 2022). There has also been some
evidence that telomere shortening predicts survival and/or lifespan
(e.g. Boonekamp et al., 2014; Brown et al., 2022; Tricola et al., 2018;
Whittemore et al., 2019; Wood \& Young, 2019). To date, it remains
unclear whether, and how, telomere biology causally contribute to
organismal senescence (Simons, 2015; Young, 2018) and fitness variation.
This is particularly true for adult telomere dynamics, as most studies
focused on early-life telomere lengths.

The link between telomere dynamics and reproductive success, another
essential component of fitness, also demands attention (Sudyka, 2019).
Two main hypotheses link telomere dynamics with variation in
reproductive output: (1) the `individual quality hypothesis' suggests
that individuals with longer telomeres and/or slower telomere shortening
are of higher quality, either due to genetic differences (e.g. Pepke et
al., 2023), or environmental variation, e.g. better habitat that offers
more resources and less stress, such that these individuals both live
longer and have higher lifetime and annual reproductive output,
generating a positive relationship between telomere dynamics and
reproduction (e.g. Angelier et al., 2019; Heidinger et al., 2021). (2)
The `pace-of-life hypothesis' suggests that individuals differ in their
relative energetic investment in self maintenance versus reproductive
effort, such that individuals with a slower pace-of-life would exhibit a
longer lifespan, have longer telomeres and slower shortening, but
decreased annual reproductive success, resulting in a negative
relationship between telomere dynamics and reproduction (Bauch et al.,
2020; Bichet et al., 2020; Eastwood et al., 2019; Heidinger et al.,
2021; Ravindran et al., 2022). So far, research has largely focused on
early-life telomere length and its association with reproductive output,
and has provided mixed results: Support for the `individual quality
hypothesis' was found by e.g. Angelier et al., (2019); Eastwood et al.,
(2019) and Heidinger et al., (2021), whereas support for the
`pace-of-life hypothesis' was found by e.g. Bauch et al., (2013) and
Pepke et al., (2022). Additionally, it is still unclear how telomere
shortening relates to reproductive output. For example, Heidinger et al.
(2021) did not find an association between telomere shortening and
reproductive success, while Sudyka et al. (2019) found a negative
association. Further testing for fitness associations with telomere
length and shortening, especially in longitudinal, natural systems, can
thus enable us to better understand the evolutionary mechanism that
drives variation in telomere dynamics.

Here, we examined the links between telomere dynamics and fitness in a
free-living, insular population of house sparrows (\emph{Passer
domesticus} ), using longitudinal telomere measurements that span 16
years, and for which we have precise survival and lifetime reproductive
data. As there has been a relative lack of focus on telomere dynamics
beyond early life, we selected samples and quantified telomere lengths
from birds after they have fledged, and tested: 1) whether adult
telomere length predicts immediate survival up to 1 year
post-measurement; 2) whether average individual telomere length and rate
of telomere shortening across adulthood are associated with lifespan; 3)
whether adult telomere length is associated with annual reproductive
output; and 4) whether average telomere length and telomere shortening
are associated with lifetime reproductive output.

\textbf{Materials and Methods}

\emph{Study population and life-history data collection}

We systematically monitored and collected life-history data from a
free-living, nest-box population of house sparrows (\emph{Passer
domesticus} ) on Lundy Island (51°10'N, 4°40'W), 19 km off the coast of
Devon, United Kingdom, starting in the year 2000 though genetic data
from as early as 1990 were also available, and used for pedigree
construction (see below). Annually, we monitored all nest boxes on the
island, and tagged \textgreater{}99\% of the population with uniquely
numbered metal rings from the British Trust for Ornithology and a unique
combination of three colour rings, either as nestlings for birds in nest
boxes, or during their first winter for birds fledged from wild nests.
We were thus able to obtain the hatch year and age of virtually all
individuals to the precision of one year, and the exact hatch dates for
birds hatched in nest boxes. Due to the geographical isolation of Lundy
Island, immigration and emigration is almost absent (four suspected
immigrants in 2000 - 2011, and three confirmed emigrants up to 2015;
Schroeder et al., 2015). We collected survival data through biannual
surveys where we recorded the presence/absence of each bird, with annual
re-sighting probability between 91-96\% (Schroeder et al., 2015). Except
for those with an explicit death record, birds that were not sighted for
two years consecutively since their last sighting were assumed dead, and
the year when they were last seen was assumed as the death year,
allowing us to calculate lifespan for each individual.

We repeatedly collected blood samples from individuals, typically at two
days of age, 12 days of age, and at most subsequent captures after
fledging. Blood samples were stored in 96\% ethanol at room temperature
until DNA extraction using the ammonium acetate method following
Richardson et al. (2001), and subsequently stored at -20°C until
analysis. We then assigned genetic parentage using up to 23 house
sparrow microsatellite markers from Dawson et al. (2012) for sparrows
hatched in 1990 -- 2019. Using the software CERVUS 3.0 we assigned the
genetic parents to \textgreater{}99\% recruits with 95\% confidence
(Kalinowski et al., 2007; Schroeder et al., 2015), totalling 10,731
birds in the pedigree used in this work. From this pedigree, we
calculated annual reproductive success (ARS) for each bird as the annual
number of genetic recruits, i.e. offspring that reproduced and appeared
in the pedigree as dams or sires themselves. We also calculated lifetime
reproductive success (LRS) as the sum of ARS across the lifespan of each
individual.

\emph{Telomere extraction and assay}

We measured relative telomere length (RTL) from blood samples of
sparrows after they fledged, collected from 2000 to 2015. DNA sample
concentration was first measured using a Nanodrop 8000 Spectrophotometer
(Thermo Fisher) and normalized to 20--30 ng/\selectlanguage{greek}µ\selectlanguage{english}l. Next we used a
monochrome multiplex quantitative polymerase chain reaction (MMqPCR)
method to quantify RTL (Cawthon, 2009) as described in Chik et al.
(2023). In brief, MMqPCR quantifies RTL as a ratio of telomeric signals
to that of a single-copy reference gene (GAPDH in our study), where
amplification of both target sequences occur within a single well to
eliminate error from sample loading. Samples were allocated to plates
using a slicing approach (van Lieshout et al., 2020), where each `slice'
contained samples obtained from the same year, and each plate contained
samples from three consecutive slices, to avoid confounding plate and
sample year effects. We measured samples in duplicates in adjacent
wells. Plates were run using two machines, a QuantStudio 12K Flex
Real-Time PCR System (Thermo Fisher Scientific, five plates) and a
StepOnePlus (Applied Biosystems, 77 plates), and by two technicians (MEM
ran 52 plates and NdR ran 30 plates), but machine identity did not have
an effect on RTL (Sibma, 2021). After MMqPCR, we removed samples with Ct
values \textgreater{} 25, and a between-duplicate relative difference
\textgreater{} 0.2. We averaged the RTL of the remaining duplicates as
the final measure for each sample. Mean qPCR amplification efficiencies
for telomeres and the reference gene were 90.7\% (range: 71 -- 109\%)
and 94.5\% (range 76 -- 119\%) respectively (Sibma, 2021). The
inter-plate repeatability was 0.49 (s.e. = 0.07), while the intra-plate
repeatability was 0.98 (Sibma, 2021). The final telomere dataset
consisted of 2,083 telomere length measurements from 1,225 birds, 476 of
which have telomere length measurements at multiple ages.

\emph{Statistical analysis}

We conducted all analyses in R 4.1.2 (R Core Team, 2021). To allow the
use of RTL as a predictor, we first corrected RTL measurements for
technical effects, including storage time, technician identity, and qPCR
plate effects (Chik et al., 2023; Sibma, 2021). We ran a linear mixed
model using the package \emph{lme4} 1.1-28 (Bates et al., 2015), with
RTL as the response variable, z-transformed such that effect sizes are
comparable between studies (Verhulst, 2020). We fitted the following
predictors: duration from when the sample was stored as a blood sample
until DNA extraction (`BloodAge', in years), duration from when the DNA
sample was stored until RTL measurement (`DNAAge', in years), the
squared terms for both storage durations to account for non-linear
effects, technician identity as a two-level fixed factor (A/B), and
plate identity as a random variable. We fitted this model assuming a
Gaussian error distribution. The residual RTL values (`corrected z-RTL')
were then extracted for the survival models.

We tested for the correlation between post-fledging telomere length and
short-term survival in two ways. First, we ran a generalized linear
mixed model (GLMM) using \emph{lme4} . We fitted annual survival
(whether an individual survived one more year after sampling as an adult
as a binomial response, 0/1) with a logit link function, and corrected
z-RTL as a continuous fixed predictor. As survival changes with age
non-linearly, we also fitted age at sampling (in years) and its squared
term as continuous fixed predictors, along with sex. Finally, we added
bird ID and year of capture as random variables to correct for
non-independence in observations from the same bird, and from yearly
stochasticity. We checked the variance inflation factor (VIF) of fixed
predictors, and concluded that there was minimal collinearity as all
VIFs were \textless{} 5 (Montgomery et al., 2013). Second, we fitted a
time-dependent Cox proportional hazard model (n = 1,211). In brief, at
the time of each death event, the model compares covariate values of
individuals who died, to all other individuals who were still alive and
therefore at risk of dying, to estimate the risk score associated with
the covariate value. To run this model, we coded time-to-event (death)
for each individual in days, in a step-wise manner, with the first step
being the time elapsed between the hatch date and first RTL measurement,
and subsequent steps being the time elapsed between two consecutive RTL
measurements, and the last step being the time elapsed between the last
RTL measurement and the date the bird was last seen, on which it was
assumed dead. We excluded birds without an exact hatch date. We then ran
the Cox model using the package \emph{coxme} 2.2-18.1 (Therneau, 2022),
right-censoring birds that were still alive at the time of the analysis,
and using the same fixed effect and random effect structures as the
binomial GLMM.

We then tested whether adult telomere dynamics are associated with
lifespan, using a bivariate model, which allows estimation of the
covariance among the two response variables, and allows fixed effects to
be fitted to only one of the two responses. We did so in a Bayesian
framework, fitted with \emph{MCMCglmm} 2.32 (Hadfield, 2010), following
a similar approach by Heidinger et al. (2021). In this model, we only
included the 1,214 birds that were dead at the time of analysis. We
fitted z-RTL, and individual lifespan as response variables, assuming
respectively a Gaussian and a Poisson distribution. For z-RTL only, we
fitted age at sampling in years, centred around the population mean, so
that the random individual intercept in z-RTL can be interpreted
relative to the population mean. We also fitted BloodAge, DNAAge, their
squared terms, and technician identity as fixed variables, to correct
for the age and technician effects on RTL. For the random effect
structure, we fitted a random slope function of RTL by age at the
individual level, along with the year of capture and plate ID as random
variables to RTL. We did not fit social parent identities as they were
found to explain minimal variation in RTL (Chik et al., 2023). Because
each individual had one lifespan value, a random effect of individual on
lifespan could be estimated as a part of the residual variation in the
model. The random-residual effects structure therefore allows the
estimation of the among-individual variance and covariance among RTL,
rate of RTL change, and lifespan:

\begin{equation}
\begin{bmatrix}\sigma_{\text{RTL}}^{2}&amp;\sigma_{RTL,\ RTL:Age}&amp;\sigma_{RTL,\ \ Lifespan}\\
\sigma_{RTL,\ RTL:Age}&amp;\sigma_{RTL:Age}^{2}&amp;\sigma_{RTL:Age,\ Lifespan}\\
\sigma_{RTL,\ \ Lifespan}&amp;\sigma_{RTL:Age,\ Lifespan}&amp;\sigma_{\text{Lifespan}}^{2}\\
\end{bmatrix}_{\text{ID}}\nonumber \\
 \end{equation}

To examine the correlations between telomere dynamics and reproductive
success, we fitted two bivariate mixed models, with LRS and ARS
respectively. The LRS model had the same framework as the lifespan
model, and included only the 1,214 individuals that were dead at the
time of analysis. For the ARS model, we used the whole dataset. We
paired ARS with the z-RTL measurement taken in the same year for each
bird. We retained the same fixed effects structure on RTL, and modelled
variance and covariance between z-RTL and ARS explained by bird ID and
capture year. We also estimated the residual covariance between z-RTL
and ARS in this model to examine the within-individual covariation
between the two variables.

For all three bivariate models, we used default priors for fixed
effects, inverse-Wishart priors for random effects, and adjusted the
number of iterations, burn-in, and thinning intervals for each model,
such that convergence is reached based on the following criteria: visual
inspection of posterior trace plots showed no distinguishable trend,
autocorrelation \textless{}0.1, and the effective sample size
\textgreater{}1000.

\textbf{Results}

\emph{Descriptive statistics}

In our dataset, the mean RTL was 1.29 (s.d. = 0.64, range = 0.14 --
6.61). 1,225 individuals were sampled between the age of 0--7, with a
mean of 1.7 samples per bird (range = 1 -- 9). Further summaries are in
Tables 1 and 2. At the time of analysis, 11 individuals were still
alive. Excluding these individuals, the mean lifespan was 1.7 years
(s.d. = 1.7, range = 0 -- 9, N = 1,214; 572 females and 637 males), and
the mean LRS was 1.5 (s.d. = 2.7, range = 0 -- 16). ARS of all birds in
the dataset had a mean of 0.6 (s.d. = 1.1, range = 0 -- 8, N = 1,225;
579 mothers and 641 fathers).

\emph{Telomere length and survival}

Both the binomial regression model and the Cox time-dependent
proportional hazard model indicated that adult RTL is positively
correlated with survival. In the binomial model, corrected z-RTL was
statistically significantly related to survival to the next year, with a
slope of 0.39 (Table 3, Fig. 1). Age also had a statistically
significant quadratic relationship with survival, with early-life and
late-life survival being lower than mid-life (Table 3, Fig. 2). There
was no difference in survival between the sexes (Table 3).

Similarly, the Cox model showed a statistically significant and negative
relationship between corrected z-RTL and mortality. Corrected z-RTL had
a negative coefficient of -0.15 on survival, meaning that for every unit
increase in corrected z-RTL the hazard ratio is multiplied by a factor
of 0.86, i.e. a 14\% decrease in mortality (Table 4). Age showed also a
quadratic, U-shaped effect on mortality (Table 4). There was no
significant effect of sex (Table 4).

\emph{Telomere dynamics and lifespan}

From the bivariate model, we found individual variation in RTL, the rate
of RTL change, and lifespan. We also found tendencies for RTL and the
rate of RTL change to positively covary with lifespan
(\(\sigma\) = 0.04 and 0.02 respectively), but the estimates
were small and did not reach statistical significance, as their 95\%
credible intervals overlapped zero (Table 5).

\emph{Telomere dynamics and reproductive success}

From the bivariate model with RTL and LRS, we found a statistically
significant and positive among-individual covariance between RTL and
LRS, indicating that individuals with longer mean telomere lengths
produce more genetic recruits over their lifetime (\(\sigma\) =
0.12, 95\% CI = 0.04 -- 0.22, Table 6). There was no among-individual
covariation between the rate of RTL change and LRS (Table 6). In
contrast, from the bivariate model with RTL and ARS, we did not find any
association between RTL and ARS among individuals (\(\sigma\) =
0.002, 95\% CI = -0.065 -- 0.054, Table 7), meaning that individuals
with longer telomeres on average did not produce more recruits from year
to year. There was also no statistically significant covariance between
RTL and ARS within an individual (residual \(\sigma\) = 0.04,
95\% CI = -0.032 -- 0.091, Table 7), meaning annual reproductive output
did not change as telomere shortens within a bird.

\textbf{Discussion}

Using longitudinal telomere measurements from the Lundy Island house
sparrow population, where precise ages, death status and reproductive
success are known, we estimated the relationships between telomere
dynamics and fitness measures, including survival and reproductive
success. We found that in post-fledging birds, independent of age,
longer telomeres were associated with higher chance to survive to the
next year. This finding was consistent with existing literature on adult
telomere length (e.g. Angelier et al., 2013; Barrett et al., 2013) and
meta-analytic results (Wilbourn et al., 2018). It also agrees with the
speculation of the selective disappearance of older birds with short
telomeres in the Lundy sparrows (Chik et al., 2023). The link between
telomere length and survival/mortality could be explained by two
mechanisms: Telomeres could play a causal and active role, by inducing
cell senescence and cell death at a critically short length. The
accumulation of senescent cells could hinder tissue functions, lead to
organ failure, and eventual death (Barrett et al., 2013; Monaghan, 2010;
Sahin et al., 2011). Alternatively, telomere length could also not
participate directly in causing death, but serve as an indicator of the
accumulative damage received by the body, or as a measure of `frailty',
the capacity of the body to withstand and/or recover from damage
(Monaghan, 2010). Regardless of causality, our finding supports that
telomere length could serve as a biomarker of immediate survival.

Nevertheless, the demonstrated association between adult telomere length
and survival in our study contradicts others. In another insular house
sparrow study in Norway, authors found no correlation between early-life
telomere length and adult survival (Pepke et al., 2022). This could be a
result of habitat differences -- in the Norwegian population, some
sparrows resided on islands with limited food and shelter, leading to
higher competition and increased juvenile mortality, ultimately the
decoupling of early-life telomere length and adult survival (Pepke et
al., 2022); whereas in the Lundy population, food and shelter is
available to sparrows year round, and mortality was less dependent on
resources availability and population density (Simons et al., 2019),
thus revealing a stronger effect of telomere length. As telomere
dynamics are influenced by environmentally-induced oxidative stress
(Monaghan \& Ozanne, 2018), it is perhaps not surprising that the
telomere-mortality link would be context-dependant, necessitating
further studies using different ages, populations, and taxa (Wilbourn et
al., 2018).

Compared with survival, the link between telomere dynamics and lifespan
was much weaker, though still in the expected direction. This weaker
link could be the result of the more removed nature of lifespan as an
indicator of survival, or extrinsic factors. Independent of telomere
length, age was linked with mortality: the youngest and oldest birds had
a higher probability of dying. This could mean that other age-specific
factors, such as predation, became the main cause of death in the
shortest and longest living birds. This would weaken the link between
lifespan and telomere length at the extreme ages, and drive down sample
sizes, especially of long-lived birds, such that we could no longer
detect an effect of telomere length on lifespan. In the Lundy sparrows,
predation pressure was stronger in adults than in juveniles (Simons et
al., 2019), but we do not know the main cause of death in each age
class, nor have we tested for age dependency in TL-mortality
association. Further studies should address these topics. Nevertheless,
the effect found here agreed with the positive link we found between
telomere length and immediate survival.

If telomere length acts as an indicator of somatic redundancy/frailty,
then the TL-mortality link would be weaker at older ages, and the rate
of telomere shortening could emerge as a better predictor of lifespan
(Boonekamp et al., 2013; Monaghan, 2010). However, we did not find such
association here, as covariance between the rate of RTL change and
lifespan was not statistically significant, despite finding individual
variation in the rate of telomere shortening (Chik et al., 2023). This
could be a result of not having enough statistical power: In our
dataset, only 270 birds were sampled three times or more, and few
individuals lived to old ages of 9 and above.

In addition to survival, we also found a link between telomere length
and reproductive success, such that individuals with longer telomeres on
average, produce more genetic recruits over their lifetime, which in our
population, predicts expected genetic contribution and fitness (Alif et
al., 2022). In contrast, there was no evidence of any relationship
between annual telomere length and reproductive output. Our results
indicated that the link between telomere length and fitness is primarily
through higher survival, where individuals with longer telomeres survive
longer and as a result reproduced more, similar to the finding by
Heidinger et al. (2021), and consistent with the `individual quality
hypothesis', i.e. individuals with a higher quality will have better
body conditions, and hence survival and/or reproductive prospects, than
poorer quality individuals, a trend found also in classical brood size
manipulation studies testing for survival-reproduction trade-offs
(Winder et al., 2022). One important contributor to variation in
individual quality is parental age at conception -- previously we
detected such Lansing effect in the Lundy sparrows, where birds whose
biological parents were older when they hatched, produced fewer recruits
annually and over a lifetime, suggesting epigenetic detrimental effects
that were carried down generations (Schroeder et al., 2015). Further
studies should test for a similar Lansing effect in telomere dynamics to
better elucidate the intrinsic and extrinsic contributors to variation
in individual quality (e.g. Drake \& Simons, 2023), and how telomere
dynamics is mechanistically linked to quality and reproduction.

In contrast, there was no relationship between annual telomere length
and reproductive output among individuals. This did not align with the
`pace-of-life hypothesis', under which individuals with a faster
pace-of-life are expected to sacrifice somatic maintenance for
reproduction, trading higher output for shorter telomeres. This could
mean that there is little variation in the pace-of-life in the Lundy
population, which is in line with another finding by Heidinger et al.
(2021). Alternatively, our result could also indicate that the
physiological costs of reproduction was not reflected on telomere
dynamics, or that the trade-off between reproduction and ageing is not
as strong within-species as previously considered, and masked by quality
effects (Winder et al., 2022). Indeed, we did not find an association
between the rate of telomere shortening and lifetime reproductive
output, nor a trade-off between telomere length and reproductive output
within an individual, suggesting that the lack of association could not
be attributed solely to differences in individual quality, e.g. in
resource acquisition or stress resistance. Note, however, that
reproductive success need not equate to reproductive effort. For
example, previous experiments have shown parents of enlarged broods had
shorter telomeres and faster shortening than those with unmanipulated or
reduced broods (Reichert et al., 2014; Sudyka et al., 2014). Further
studies should therefore examine the effect of e.g. parental care on
telomere dynamics, to determine whether the latter is an indicator of
the costs of reproduction in the Lundy sparrows.

In conclusion, in this study we examined the fitness consequences of
telomere dynamics in a longitudinal, closed house sparrow population,
and found evidence that indeed telomere length was correlated with
fitness. Our results provide additional support that telomere length is
linked with survival and therefore in turn with lifetime reproductive
success, but also add to the debate of the role of telomere shortening
as an indicator of senescence, somatic resilience, and fitness. It is
important as a next step to determine whether the associations we found
are only at the phenotypic level, or occur also at the genetic level,
which coupled with heritable variation in telomere dynamics (Chik et
al., 2023), would inform how telomere dynamics evolve in the wild.

\textbf{Acknowledgements}

We thank the Lundy Company and the Lundy Field Society for their
continuous support in the Lundy Sparrow Project, and numerous field
assistants and master students for field data collection. HYJC was
funded by a PhD scholarship from the University of Groningen and
Macquarie University. JS received funding from the European Research
Council (PCIG12-GA-2012-333096). TB, MJPS and JS were supported by the
Natural and Environmental Research Council (grant NE/J024567/1). HD was
supported by a Rosalind Franklin Fellowship from the University of
Groningen.

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\textbf{Data Accessibility and Benefit-Sharing}

\emph{Data Accessibility Statement}

The telomere and life history datasets used in this study, along with
the R script used for analysis, will become publicly available on a data
repository e.g. Figshare, upon acceptance for publication.

\textbf{Authors contribution}

HYJC, HLD and JS conceptualized the study. NdR and MEM conducted the
telomere measurements, and JS and TB curated the telomere and life
history datasets. HYJC compiled the datasets used for this study,
conducted the statistical analysis and wrote the initial draft of the
manuscript, with input from HLD and JS. All authors contributed to the
revision of the manuscript and agreed on the final version of the
manuscript to be published.

\textbf{Tables and Figures}

Table 1. Summary of the number of repeated RTL measurements and
associated number of individuals in the Lundy house sparrow dataset, for
blood samples collected in 2000 -- 2015.\selectlanguage{english}
\begin{longtable}[]{@{}ll@{}}
\toprule
\emph{Number of samples} & \emph{Number of individuals}\tabularnewline
\midrule
\endhead
1 & 749\tabularnewline
2 & 256\tabularnewline
3 & 126\tabularnewline
4 & 53\tabularnewline
5 & 22\tabularnewline
6 & 14\tabularnewline
7 & 3\tabularnewline
8 & 1\tabularnewline
9 & 1\tabularnewline
Total number of birds & 1225\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 2.} Summary of the number of birds and samples across age
classes in the Lundy house sparrow telomere dataset, for blood samples
collected in 2000 -- 2015.\selectlanguage{english}
\begin{longtable}[]{@{}lll@{}}
\toprule
\emph{Age in years} & \emph{Number of birds} & \emph{Number of
samples}\tabularnewline
\midrule
\endhead
0 & 703 & 800\tabularnewline
1 & 535 & 669\tabularnewline
2 & 248 & 298\tabularnewline
3 & 144 & 175\tabularnewline
4 & 64 & 78\tabularnewline
5 & 35 & 40\tabularnewline
6 & 15 & 16\tabularnewline
7 & 5 & 7\tabularnewline
Total number of samples & Total number of samples & 2083\tabularnewline
\bottomrule
\end{longtable}

Table 3. Summary of the generalized linear mixed model (GLMM) testing
for the effects of corrected, z-transformed relative telomere length
(corrected z-RTL), age, and sex on survival as a post-fledgling to one
year after sampling in the Lundy house sparrows. Statistically
significant effects (excluding intercept) are highlighted in bold. N =
2,078.\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
& \emph{Estimate} & \emph{Estimate} & \emph{Std. err.} & \emph{z-value}
& \emph{p-value}\tabularnewline
\midrule
\endhead
Fixed effects & Fixed effects & Fixed effects & Fixed effects & Fixed
effects & Fixed effects\tabularnewline
(Intercept) & -1.220 & -1.220 & 0.496 & -2.458 & 0.013\tabularnewline
\textbf{Corrected z-RTL} & \textbf{0.393} & \textbf{0.393} &
\textbf{0.118} & \textbf{3.319} &
\textbf{\textless{}0.001}\tabularnewline
Sex & -0.024 & -0.024 & 0.200 & -0.118 & 0.906\tabularnewline
\textbf{Age} & \textbf{0.909} & \textbf{0.909} & \textbf{0.164} &
\textbf{5.558} & \textbf{\textless{}0.001}\tabularnewline
\textbf{Age\textsuperscript{2}} & \textbf{-0.226} & \textbf{-0.226} &
\textbf{0.037} & \textbf{-6.053} &
\textbf{\textless{}0.001}\tabularnewline
Random effects & Random effects & Random effects & Random effects &
Random effects & Random effects\tabularnewline
& & \emph{Variance} & \emph{Variance} & \emph{Number of levels} &
\emph{Number of levels}\tabularnewline
Bird ID & Bird ID & 4.737 & 4.737 & 1220 &\tabularnewline
Capture year & Capture year & 2.975 & 2.975 & 15 &\tabularnewline
\bottomrule
\end{longtable}

Table 4. Summary of the time-dependent Cox proportional hazards model
testing for the relationship between corrected, z-transformed relative
telomere length (RTL), age, sex, genetic maternal age at conception
(MAC), genetic paternal age at conception (PAC) and mortality risk.
Statistically significant effects are highlighted in bold. N = 1,211.

Fixed effects\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
& \emph{Coefficient} & \emph{s.e.} & \emph{Hazard ratio} &
\emph{z-value} & \emph{p-value}\tabularnewline
\textbf{Corrected z-RTL} & \textbf{-0.154} & \textbf{0.056} &
\textbf{0.858} & \textbf{-2.67} & \textbf{0.008}\tabularnewline
\textbf{Age} & \textbf{-0.284} & \textbf{0.106} & \textbf{0.752} &
\textbf{-2.68} & \textbf{0.007}\tabularnewline
\textbf{Age\textsuperscript{2}} & \textbf{0.051} & \textbf{0.024} &
\textbf{1.052} & \textbf{2.17} & \textbf{0.030}\tabularnewline
Sex & -0.001 & 0.078 & 0.999 & -0.01 & 0.990\tabularnewline
Random effects & Random effects & Random effects & Random effects &
Random effects & Random effects\tabularnewline
& \emph{Variance} & \emph{Variance} & \emph{Number of levels} &
\emph{Number of levels} & \emph{Number of levels}\tabularnewline
Capture year & 0.269 & 0.269 & 15 & 15 & 15\tabularnewline
\bottomrule
\end{longtable}

Table 5. Summary of the bivariate mixed model, with a random intercept
and slope function of relative telomere length (RTL) with age, and a
random intercept of lifespan, at the individual level. This model
estimates the variance and covariance in RTL, rate of RTL change with
age, and lifespan among individuals in the Lundy house sparrows.
Significant effects are highlighted in bold, excluding fixed intercepts.

Fixed effects\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
& & \emph{Post. mode} & \emph{95\% CI} & \emph{Effective sample size} &
\emph{p-MCMC}\tabularnewline
Intercept (Lifespan) & Intercept (Lifespan) & 0.324 & 0.256 -- 0.394 &
13051 & \textless{}0.001\tabularnewline
Intercept (RTL) & Intercept (RTL) & 0.373 & -0.039 -- 0.848 & 13521 &
0.067\tabularnewline
Mean-centred age* & Mean-centred age* & -0.070 & -0.129 -- 0.007 & 13067
& 0.083\tabularnewline
\textbf{BloodAge*} & \textbf{BloodAge*} & \textbf{-0.155} &
\textbf{-0.232 -- -0.087} & \textbf{13500} &
\textbf{\textless{}0.001}\tabularnewline
\textbf{BloodAge\textsuperscript{2}*} &
\textbf{BloodAge\textsuperscript{2}*} & \textbf{0.005} & \textbf{0.001
-- 0.009} & \textbf{13500} & \textbf{0.010}\tabularnewline
DNAAge* & DNAAge* & 0.034 & -0.056 -- 0.110 & 13500 &
0.494\tabularnewline
\textbf{DNAAge\textsuperscript{2}*} &
\textbf{DNAAge\textsuperscript{2}*} & \textbf{-0.010} & \textbf{-0.018
-- -0.004} & \textbf{13500} & \textbf{0.002}\tabularnewline
Technician (B)* & Technician (B)* & 0.025 & -0.213 -- 0.222 & 13500 &
0.976\tabularnewline
Random effects & Random effects & Random effects & Random effects &
Random effects & Random effects\tabularnewline
Capture year* & Capture year* & 0.142 & 0.067 -- 0.349 & 13500
&\tabularnewline
Plate* & Plate* & 0.101 & 0.073 -- 0.155 & 13500 &\tabularnewline
Bird ID & Bird ID & & & &\tabularnewline
& Var(RTL) & 0.120 & 0.092 -- 0.151 & 12510 &\tabularnewline
& Var(RTL:Age) & 0.071 & 0.052 -- 0.097 & 13500 &\tabularnewline
& Var(Lifespan) & 0.416 & 0.337 -- 0.512 & 13134 &\tabularnewline
& Cov(RTL, RTL:Age) & -0.007 & -0.027 -- 0.012 & 12714 &\tabularnewline
& Cov(RTL, Lifespan) & 0.040 & -0.016 -- 0.085 & 12953 &\tabularnewline
& Cov(RTL:Age, Lifespan) & 0.021 & -0.016 -- 0.059 & 13500
&\tabularnewline
Residuals & Residuals & 0.394 & 0.361 -- 0.431 & 13143 &\tabularnewline
Effects fitted on RTL only & *Effects fitted on RTL only & *Effects
fitted on RTL only & *Effects fitted on RTL only & *Effects fitted on
RTL only & *Effects fitted on RTL only\tabularnewline
\bottomrule
\end{longtable}

Table 6. Summary of the bivariate mixed model, with a random intercept
and slope function of relative telomere length (RTL) with age, and a
random intercept of lifetime reproductive success (LRS), at the
individual level. This model estimates the variance and covariance in
RTL, rate of RTL change with age, and LRS among individuals in the Lundy
house sparrows. Significant effects are highlighted in bold, excluding
fixed intercepts.

Fixed effects\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
& & \emph{Post. mode} & \emph{95\% CI} & \emph{Effective sample size} &
\emph{p-MCMC}\tabularnewline
Intercept (LRS) & Intercept (LRS) & -0.890 & -0.078 -- -0.731 & 9000 &
\textless{}0.001\tabularnewline
Intercept (RTL) & Intercept (RTL) & 0.438 & -0.052 -- 0.838 & 9000 &
0.082\tabularnewline
\textbf{Mean-centred age*} & \textbf{Mean-centred age*} &
\textbf{-0.061} & \textbf{-0.130 -- -0.008} & \textbf{9000} &
\textbf{0.026}\tabularnewline
\textbf{BloodAge*} & \textbf{BloodAge*} & \textbf{-0.164} &
\textbf{-0.226 -- -0.083} & \textbf{8605} &
\textbf{\textless{}0.001}\tabularnewline
\textbf{BloodAge\textsuperscript{2}*} &
\textbf{BloodAge\textsuperscript{2}*} & \textbf{0.005} & \textbf{0.001
-- 0.009} & \textbf{9000} & \textbf{0.018}\tabularnewline
DNAAge* & DNAAge* & 0.042 & -0.046 -- 0.123 & 9000 &
0.412\tabularnewline
\textbf{DNAAge\textsuperscript{2}*} &
\textbf{DNAAge\textsuperscript{2}*} & \textbf{-0.012} & \textbf{-0.018
-- -0.005} & \textbf{9298} & \textbf{0.001}\tabularnewline
Technician (B)* & Technician (B)* & -0.013 & -0.216 -- 0.211 & 9229 &
0.990\tabularnewline
Random effects & Random effects & Random effects & Random effects &
Random effects & Random effects\tabularnewline
Capture year* & Capture year* & 0.142 & 0.070 -- 0.355 & 9000
&\tabularnewline
Plate* & Plate* & 0.101 & 0.074 -- 0.153 & 9000 &\tabularnewline
Bird ID & Bird ID & & & &\tabularnewline
& Var(RTL) & 0.120 & 0.094 -- 0.153 & 9000 &\tabularnewline
& Var(RTL:Age) & 0.070 & 0.052 -- 0.096 & 9000 &\tabularnewline
& Var(LRS) & 3.110 & 2.666 -- 3.744 & 9000 &\tabularnewline
& Cov(RTL, RTL:Age) & -0.010 & -0.026 -- 0.012 & 9000 &\tabularnewline
& \textbf{Cov(RTL, LRS)} & \textbf{0.117} & \textbf{0.035 -- 0.224} &
\textbf{9000} &\tabularnewline
& Cov(RTL:Age, LRS) & -0.010 & -0.034 -- 0.150 & 8093 &\tabularnewline
Residuals & Residuals & 0.393 & 0.363 -- 0.431 & 9430 &\tabularnewline
Effects fitted on RTL only & *Effects fitted on RTL only & *Effects
fitted on RTL only & *Effects fitted on RTL only & *Effects fitted on
RTL only & *Effects fitted on RTL only\tabularnewline
\bottomrule
\end{longtable}

Table 7. Summary of the bivariate mixed model estimating the variance
and covariance among relative telomere length (RTL) and annual
reproductive success (ARS) among individuals in the Lundy house
sparrows. Significant effects are highlighted in bold, excluding fixed
intercepts.

Fixed effects\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
& & \emph{Post. mode} & \emph{95\% CI} & \emph{Effective sample size} &
\emph{p-MCMC}\tabularnewline
Intercept (ARS) & Intercept (ARS) & -1.518 & -2.070 -- -1.033 & 15525 &
\textless{}0.001\tabularnewline
Intercept (RTL) & Intercept (RTL) & 0.434 & -0.093 -- 0.946 & 19800 &
0.114\tabularnewline
RTL: Mean-centred age & RTL: Mean-centred age & -0.010 & -0.049 -- 0.022
& 19800 & 0.482\tabularnewline
\textbf{ARS: Mean-centred age} & \textbf{ARS: Mean-centred age} &
\textbf{0.746} & \textbf{0.665 -- 0.846} & \textbf{10768} &
\textbf{\textless{}0.001}\tabularnewline
\textbf{BloodAge*} & \textbf{BloodAge*} & \textbf{-0.146} &
\textbf{-0.229 -- -0.074} & \textbf{19800} &
\textbf{\textless{}0.001}\tabularnewline
\textbf{BloodAge\textsuperscript{2}*} &
\textbf{BloodAge\textsuperscript{2}*} & \textbf{0.004} & \textbf{0.001
-- 0.009} & \textbf{19800} & \textbf{0.015}\tabularnewline
DNAAge* & DNAAge* & 0.042 & -0.058 -- 0.119 & 19800 &
0.472\tabularnewline
\textbf{DNAAge\textsuperscript{2}*} &
\textbf{DNAAge\textsuperscript{2}*} & \textbf{-0.011} & \textbf{-0.018
-- -0.004} & \textbf{19800} & \textbf{0.003}\tabularnewline
Technician (B)* & Technician (B)* & 0.041 & -0.207 -- 0.248 & 19800 &
0.802\tabularnewline
Random effects & Random effects & Random effects & Random effects &
Random effects & Random effects\tabularnewline
Capture year & Capture year & & & &\tabularnewline
& Var(RTL) & 0.214 & 0.074 -- 0.403 & 19800 &\tabularnewline
& Var(ARS) & 0.678 & 0.324 -- 1.718 & 18742 &\tabularnewline
& Cov(RTL, ARS) & 0.008 & -0.267 -- 0.359 & 20766 &\tabularnewline
Plate & Plate & & & &\tabularnewline
& Var(RTL) & 0.116 & 0.084 -- 0.174 & 19800 &\tabularnewline
& Var(ARS) & 0.132 & 0.075 -- 0.207 & 16787 &\tabularnewline
Bird ID & Bird ID & & & &\tabularnewline
& Var(RTL) & 0.104 & 0.076 -- 0.134 & 19800 &\tabularnewline
& Var(ARS) & 0.798 & 0.572 -- 1.051 & 6655 &\tabularnewline
& Cov(RTL, ARS) & 0.002 & -0.065 -- 0.054 & 15619 &\tabularnewline
Residuals & Residuals & & & &\tabularnewline
& Var(RTL) & 0.431 & 0.402 -- 0.472 & 19800 &\tabularnewline
& Var(ARS) & 0.222 & 0.125 -- 0.369 & 7386 &\tabularnewline
& Cov(RTL, ARS) & 0.039 & -0.032 -- 0.091 & 16503 &\tabularnewline
Effects fitted on RTL only & *Effects fitted on RTL only & *Effects
fitted on RTL only & *Effects fitted on RTL only & *Effects fitted on
RTL only & *Effects fitted on RTL only\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

Fig. 1. The positive relationship between relative telomere length
(corrected for technical effects) and survival to one year after
sampling (0/1) in the Lundy house sparrows. Solid black line indicates
predicted relationship, shaded area indicates 95\% confidence interval,
and black dots indicates raw data points.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

Fig. 2. The quadratic relationship between age at sampling (in years)
and survival to one year after sampling (0/1) in the Lundy house
sparrows. Solid black line indicates predicted relationship, shaded area
indicates 95\% confidence interval, and black dots (jittered) indicate
raw data points.

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

