\documentclass[10pt]{article}

\usepackage{fullpage}
\usepackage{setspace}
\usepackage{parskip}
\usepackage{titlesec}
\usepackage[section]{placeins}
\usepackage{xcolor}
\usepackage{breakcites}
\usepackage{lineno}
\usepackage{hyphenat}





\PassOptionsToPackage{hyphens}{url}
\usepackage[colorlinks = true,
            linkcolor = blue,
            urlcolor  = blue,
            citecolor = blue,
            anchorcolor = blue]{hyperref}
\usepackage{etoolbox}
\makeatletter
\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{%
  \errmessage{\noexpand\@combinedblfloats could not be patched}%
}%
\makeatother


\usepackage{natbib}




\renewenvironment{abstract}
  {{\bfseries\noindent{\abstractname}\par\nobreak}\footnotesize}
  {\bigskip}

\titlespacing{\section}{0pt}{*3}{*1}
\titlespacing{\subsection}{0pt}{*2}{*0.5}
\titlespacing{\subsubsection}{0pt}{*1.5}{0pt}


\usepackage{authblk}


\usepackage{graphicx}
\usepackage[space]{grffile}
\usepackage{latexsym}
\usepackage{textcomp}
\usepackage{longtable}
\usepackage{tabulary}
\usepackage{booktabs,array,multirow}
\usepackage{amsfonts,amsmath,amssymb}
\providecommand\citet{\cite}
\providecommand\citep{\cite}
\providecommand\citealt{\cite}
% You can conditionalize code for latexml or normal latex using this.
\newif\iflatexml\latexmlfalse
\providecommand{\tightlist}{\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}%

\AtBeginDocument{\DeclareGraphicsExtensions{.pdf,.PDF,.eps,.EPS,.png,.PNG,.tif,.TIF,.jpg,.JPG,.jpeg,.JPEG}}

\usepackage[utf8]{inputenc}
\usepackage[T2A]{fontenc}
\usepackage[ngerman,greek,polish,english]{babel}



\usepackage{float}








\begin{document}

\title{Within-individual repeatability in telomere length: a meta-analysis in
non-mammalian vertebrates}



\author[1]{Tiia Kärkkäinen}%
\author[1]{Michael Briga}%
\author[1]{Toni Laaksonen}%
\author[1]{Antoine Stier}%
\affil[1]{University of Turku}%


\vspace{-1em}



  
  \date{June 01, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
Telomere length is increasingly used as a biomarker of long-term life
history costs, ageing and future survival prospects. Yet, to have the
potential to predict long-term outcomes, telomere length should exhibit
a relatively high within-individual repeatability over time, which has
been largely overlooked in past studies. To fill this gap, we conducted
a meta-analysis on 74 studies reporting longitudinal telomere length
assessment in non-mammalian vertebrates, with the aim to establish the
current pattern of within-individual repeatability in telomere length
and to identify the methodological (\emph{e.g}. qPCR/TRF, study length)
and biological factors (\emph{e.g.} taxon, wild/captive, age class,
species lifespan, phylogeny) that may affect it. While the median
within-individual repeatability of telomere length was moderate to high
(\emph{R} = 0.55; 95\% CI: 0.05-0.95; N = 82), marked heterogeneity
between studies was evident. Measurement method affected strongly
repeatability estimate, with TRF-based studies exhibiting high
repeatability (\emph{R} = 0.80; 95\% CI: 0.34-0.96; N = 25), while
repeatability of qPCR-based studies was only half of that and more
variable (\emph{R} = 0.46; 95\% CI: 0.04-0.82; N = 57). While phylogeny
explained some variance in repeatability, phylogenetic signal was not
significant (\selectlanguage{greek}\emph{λ} \selectlanguage{english}= 0.32; 95\% CI: 0.00-0.83). None of the
biological factors investigated here had a statistically significant
association with the repeatability of telomere length, being potentially
obscured by methodological noise. Our meta-analysis highlights the need
to carefully evaluate and consider within-individual repeatability in
telomere studies to ensure the robustness of using telomere length as a
biomarker of long-term survival and fitness prospects.%
\end{abstract}%



\sloppy


\textbf{Within-individual repeatability in telomere length: a
meta-analysis in non-mammalian vertebrates}

Running title: Within-individual repeatability in TL

Tiia K\selectlanguage{ngerman}ärkkäinen\textsuperscript{a}*\textsuperscript{\$}, Michael
Briga\textsuperscript{a}*, Toni Laaksonen\textsuperscript{a}, Antoine
Stier\textsuperscript{a\$}

\textsuperscript{a}Department of Biology, University of Turku, Turku,
Finland

* Equal contribution

\textsuperscript{\$} Corresponding authors:\emph{tmakark@gmail.com}
/\emph{antoine.stier@gmail.com}

0. Abstract

Telomere length is increasingly used as a biomarker of long-term life
history costs, ageing and future survival prospects. Yet, to have the
potential to predict long-term outcomes, telomere length should exhibit
a relatively high within-individual repeatability over time, which has
been largely overlooked in past studies. To fill this gap, we conducted
a meta-analysis on 74 studies reporting longitudinal telomere length
assessment in non-mammalian vertebrates, with the aim to establish the
current pattern of within-individual repeatability in telomere length
and to identify the methodological (\emph{e.g.} qPCR/TRF, study length)
and biological factors (\emph{e.g.} taxon, wild/captive, age class,
species lifespan, phylogeny) that may affect it. While the median
within-individual repeatability of telomere length was moderate to high
(\emph{R} = 0.55; 95\% CI: 0.05-0.95; N = 82), marked heterogeneity
between studies was evident. Measurement method affected strongly
repeatability estimate, with TRF-based studies exhibiting high
repeatability (\emph{R} = 0.80; 95\% CI: 0.34-0.96; N = 25), while
repeatability of qPCR-based studies was only half of that and more
variable (\emph{R} = 0.46; 95\% CI: 0.04-0.82; N = 57). While phylogeny
explained some variance in repeatability, phylogenetic signal was not
significant (\selectlanguage{greek}\emph{λ} \selectlanguage{english}=~0.32; 95\% CI: 0.00-0.83). None of the
biological factors investigated here had a statistically significant
association with the repeatability of telomere length, being potentially
obscured by methodological noise. Our meta-analysis highlights the need
to carefully evaluate and consider within-individual repeatability in
telomere studies to ensure the robustness of using telomere length as a
biomarker of long-term survival and fitness prospects.

\emph{Keywords} : ageing biomarker, qPCR, TRF, lifespan, phylogeny

1. Introduction

Telomeres are highly conserved repetitive sequences of non-coding DNA
that cap the ends of linear chromosomes of eukaryotic species and
contribute to genomic integrity maintenance (Blackburn, 1991). Telomeres
shorten with every cell division due to the end replication problem
(inability of DNA polymerase to copy terminal DNA) (Levy, Allsopp,
Futcher, Greider, \& Harley, 1992). Additionally, telomere shortening
can be accentuated by cellular stressors, such as oxidative stress
(Reichert \& Stier, 2017; von Zglinicki, 2002) or substantially
increased energy demands (Casagrande \& Hau, 2019; Ludlow et al., 2008).
When telomeres reach critically short length, they induce cell
senescence, apoptosis, or genomic instability, which in turn contribute
to ageing phenotypes (Campisi, 2005). Short telomeres have been
associated with increased risks of developing degenerative diseases in
humans (\emph{e.g.} cardiovascular and Alzheimer diseases), while long
telomeres could increase the risk of neoplastic diseases (Aviv \& Shay,
2018). Yet, short telomeres have been associated with increased
mortality risk in both humans and non-model vertebrates (Arbeev et al.,
2020; Boonekamp, Simons, Hemerik, \& Verhulst, 2013; Wilbourn et al.,
2018). While a causal role of telomeres in organismal ageing has been
questioned (Simons, 2015; Young, 2018), some recent evidence suggests
that experimentally increasing telomere length could extend lifespan in
laboratory mice (Mu\selectlanguage{ngerman}ñoz-Lorente, Cano-Martin, \& Blasco, 2019).
Irrespective of causality controversies, telomere shortening is
considered to be a hallmark of ageing (López-Otín, Blasco, Partridge,
Serrano, \& Kroemer, 2013) and telomere length has been suggested to act
as a biomarker of past stress exposure (Chatelain, Drobniak, \& Szulkin,
2020; Pepper, Bateson, \& Nettle, 2018), phenotypic quality (Angelier,
Weimerskirch, Barbraud, \& Chastel, 2019), future disease risk
(Fasching, 2018), survival probability (Wilbourn et al., 2018) and even
fitness prospects (Eastwood et al., 2019).

Making inferences about past stress exposure or predictions about future
long-term consequences based on a given telomere length requires that
past and future telomere length are reasonably correlated with the
current length. This correlation can be quantified as the
within-individual repeatability \emph{R} (Nakagawa \& Schielzeth, 2010).
The repeatability expresses the reproducible proportion of the total
variance among repeated measurements, while the non-repeatable
proportion consists of individual flexibility and measurement error
(Nakagawa \& Schielzeth, 2010). Because telomere length is
dynamic,\emph{i.e.} it changes over time, repeated telomere measurements
are not expected to be perfectly repeatable even in the absence of any
measurement error. \emph{R} is an important measure because it
quantifies the association between repeated telomere length measurements
and can vary from high (\emph{R} \textasciitilde{} {[}0.5 - 1.0{]}; Fig.
1a) to moderate (\emph{R} \textasciitilde{} {[}0.25 - 0.5{]} Fig. 1b) or
low (\emph{R} \textasciitilde{} {[}0.0 - 0.25{]} Fig. 1c), even when the
overall population telomere shortening is set at a fixed level (Fig
1a-1c). Benetos et al. (2019) evaluated within-individual repeatability
of telomere length in humans to be high (\emph{R} = {[}0.85-0.91{]}, but
see Martens et al. (2021) for somewhat lower correlation estimates), but
the few studies that reported \emph{R} of telomere length in other
species have provided more variable estimates ranging from 0.03 to 0.97
(Bichet et al., 2020; Boonekamp, Bauch, Mulder, \& Verhulst, 2017;
Fairlie et al., 2016; Nettle et al., 2016; Pérez-Rodríguez et al., 2019;
Spurgin et al., 2018; van Lieshout et al., 2019). Nonetheless,
longitudinal studies very rarely report within-individual repeatability,
while such information appears critical to evaluate the potential for
telomere length at a given time to make inferences about the past or the
future (Fig. 1).

While initial studies of telomeres were mostly cross-sectional and
measured telomere length only once per individual, the last decade has
been characterized by a marked increase in longitudinal studies
measuring telomere length at least twice from each individual. In such
longitudinal studies, telomeres are generally expected to shorten with
time/age, at least in most endotherm vertebrate species
(\emph{i.e.}mammals and birds; \emph{e.g.} Stier, Reichert, Criscuolo,
\& Bize, 2015 for a review in non-mammalian vertebrates). Indeed, while
the enzyme telomerase enabling telomere elongation is mainly suppressed
in somatic tissues of adult birds and mammals, this is not the case in
many ectotherm vertebrate species (\emph{i.e.} fish, amphibian and
reptiles; Gomes, Shay, \& Wright, 2010), which could explain the
diversity of telomere dynamics observed in such taxa (Olsson, Wapstra,
\& Friesen, 2018; Simide, Angelier, Gaillard, \& Stier, 2016). Yet, some
longitudinal studies in endotherms have also reported telomere
lengthening, which is suggested not to be explained by measurement error
alone (\emph{e.g.} Spurgin et al., 2018; van Lieshout et al., 2019). The
increasing availability of longitudinal studies now enables to get a
general picture of the within-individual repeatability in telomere
length in a variety of species and gives the opportunity to identify the
factors that could explain variation in such an important parameter.

Here, we aim at providing, to the best of our knowledge, the first
meta-analysis of within-individual repeatability of telomere length and
factors affecting it by focusing on non-mammalian vertebrates. We
address several methodological and biological factors that could create
variation in the within-individual repeatability of telomere length. 1)
As commonly used quantitative PCR method (qPCR) in telomere length
measurement is more prone to measurement error than telomere restriction
fragment assay (TRF) (Aviv et al., 2011), qPCR studies can be expected
to have lower within-individual repeatability estimates (Nettle, Seeker,
Nussey, Froy, \& Bateson, 2019; Nettle, Gadalla, Susser, Bateson, \&
Aviv 2020 preprint). 2) Studies measuring telomere length with a long
time interval between subsequent sampling occasions are expected to have
lower within-individual repeatability than studies using samples taken
only a few days from each other, due to both inter-individual
differences in telomere shortening rate, and potentially due to slight
differences in sample handling protocols, such as storage method
(Reichert et al., 2017). 3) As most ectotherms can maintain telomerase
activity in adulthood, allowing the possibility for telomere restoration
(Gomes et al., 2010), they are expected to have lower within-individual
repeatability than endotherms that mainly suppress their telomerase
activity. 4) Fast rate of telomere shortening could potentially lower
the estimates of within-individual repeatability. Consequently,
juveniles are expected to have lower \emph{R} than adults, as most of
the telomere shortening occurs in early life during growth (Spurgin et
al., 2018; Stier, Metcalfe, \& Monaghan, 2020). 5) Similarly, as there
is evidence that telomeres shorten slower in long-lived wild species
than in short-lived species (Dantzer \& Fletcher, 2015; Tricola et al.,
2018), species with short lifespan are expected to have lower
within-individual repeatability than species with long lifespans. 6)
Finally, the higher the between-individual variation in telomere
shortening rate is, due to, for example, environmental heterogeneity or
in sensitivities to the same stressor, the lower the within-individual
repeatability will be. Thus, studies on species living in the wild would
be expected to have lower \emph{R} than species that have been living in
stable captive conditions, sometimes for generations. By testing the
importance of these factors, we aim to increase knowledge about
within-individual repeatability of telomere length and factors
potentially affecting it. This should help to assess the validity of
telomere length as a biomarker for past stress exposure and future
long-term costs in particular study systems. Additionally, it may help
researchers that wish to estimate any past experiences or long-term
costs based on given telomere length to design their research to aim at
high within-individual repeatability.

2. Material and Methods

2.1 Literature search and data collection

We performed literature searches (last search on September 30, 2019)
using Web of Science search engine and following search terms: ``telome*
AND bird*'', ``telome* AND reptile*'', ``telome* AND ectotherm*'', and
``telomere dynamics''. We identified a total of 1292 records in these
searches (Fig. S1). In addition, we screened all the studies citing
Heidinger et al. (2012), and the reference list of Olsson et al. (2018)
to identify additional studies not found in the original searches (N =
6). We also included 4 unpublished datasets, of which one was provided
by M. Haussmann (Bucknell University) while others are authors' own
unpublished data. After duplicate removal, 1005 records remained, and
their titles and abstracts were screened for eligibility. 124 full text
articles were assessed using our inclusion criteria. We included studies
that (1) used a non-mammalian (bird or ectotherm) vertebrate study
species, (2) measured telomere length at least twice, (3) had at least
one day between the telomere measurements, and (4) provided the raw data
online/upon request. We obtained the raw datasets as many published
articles do not report the within-individual repeatability in telomere
length, which enabled us to calculate the within-individual
repeatabilities in a standardized way. Thus, if the raw data were not
available online, we contacted the corresponding authors with a request
to provide us with the raw data or to run standardized analyses using an
R script that we provided. We chose not to include mammals for two main
reasons: 1) human studies are mostly outside of our eco-evolutionary
scope, and 2) longitudinal telomere measurements in mammals are almost
exclusively measured from white blood cells, and the natural changes in
white-blood cell composition (\emph{e.g.} with season or age) have been
previously highlighted to seriously bias the estimation of telomere
length (Beaulieu, Benoit, Abaga, Kappeler, \& Charpentier, 2017). In
non-mammalian vertebrates, longitudinal telomere measurements are almost
exclusively measured from nucleated red blood cells, which represent a
more homogenous population of blood cells (Stier et al. 2015). We found
71 studies that met our inclusion criteria. Additionally, we were able
to include three studies using data that we extracted from scatter plots
using the METADIGITIZE package in R (Pick, Nakagawa, \& Noble, 2019),
adding up to 74 eligible studies (Fig. S1). We hence excluded 50 full
text articles for the following reasons: (i) In 27 studies, the data
were non-longitudinal; (ii) in 14 cases all or part of the data was used
in more than one publication, in which case we used the first
encountered article, or the most complete dataset; (iii) in seven cases
we were not able to obtain the raw data; and (iv) in two cases the data
were not comparable due to major methodological differences (Fig. S1).

From each eligible publication we recorded the following biological and
methodological factors: Taxon, Species, Sample size, Number of telomere
samples, Study length, Telomere measurement method, Age class of
individuals, and Environment (Fig. 2). Additionally, we obtained maximum
lifespan estimates for each species from the \emph{AnAge} database of
animal ageing and longevity (Tacutu et al., 2018). For three species
there was no lifespan estimate available, and we used the mean estimate
for the genus. We obtained all other predictor data directly from the
studies. If present, individuals with only one measurement were excluded
from the datasets. If the number of samples was unequal between
individuals, we used an average number of samples per study. Study
length was determined as time between successive telomere measurements.
If there was variation in time between different sampling points within
one study, we used the average time between the samples per study. Some
datasets included data for different levels of a categorical variable,
for example, some datasets included telomere measurements for both
juvenile and adult individuals, or for individuals that were sampled
only as juveniles and others that were sampled as both juveniles and
adults. Because we have reason to believe that within-individual
telomere length repeatability might differ between juveniles and adults
due to distinct growth patterns (\emph{i.e.} fast growth vs essentially
no growth at all), in these cases we split the data into two or three
according to the age class. In two datasets we did a similar split when
the same samples were measured with both qPCR and TRF methods. One
dataset included data for two different species and we thus split the
data into their respective species. Therefore, we obtained 82 effect
size estimates from 74 studies (Table 1).

2.2 Statistical analyses

We carried out all analyses in R (v. 4.0.1) (R Core Team, 2020). We
performed our analyses following (Holtmann, Lagisz, \& Nakagawa, 2017).
First, we checked the distribution of telomere length variable in all 82
datasets, and where needed, we transformed these data using log, square
root, or box-cox transformation to fulfill the assumptions of normality.
For each of these datasets, we estimated the within-individual
repeatability using a linear mixed model LMM approach (Nakagawa \&
Schielzeth, 2010), \emph{i.e.} repeatability is an intra-class
correlation coefficient that captures the between-individual variance
(by individual identity as a random intercept while controlling for the
time between successive measurements) relative to the total variance,
with the function `rpt' of the package `rptR' (Stoffel, Nakagawa, \&
Schielzeth, 2017). For one study with repeatability \textless{}0.005, we
estimated the within-individual repeatability using ANOVA approach as
the LMM approach biases very low repeatability values upwards (Holtmann
et al., 2017; Nakagawa \& Schielzeth, 2010). Confidence intervals (95\%
CI) around the repeatability were estimated based on 1,000 bootstraps.
These 95\% CI are similarly constrained between 0 and 1 thereby
underestimating the lower 95\% CI of studies with low repeatabilities
(upper 95\% CI were never close enough to 1 to be biased). To avoid this
bias, for studies with a lower 95\% CI \textless{} 0.005, we took the
symmetry of the (bootstrapped) upper 95\% CI using the standard error
and t-value of the t-distribution matching the study's sample size (1.96
when number of individuals \textgreater{}100). Performing this
estimation for the whole data range confirmed that the bias emerges when
lower 95\% CI \textless{} 0.005 (Fig. S2).

We performed mixed-model meta-analyses using general and generalized
linear models in R (R Core Team, 2020). In these models the
within-individual repeatability of telomere length is the dependent
variable, and we performed the analyses using two distributions. First,
we followed Holtmann et al. (2017) and standardized all the
repeatability estimates and their variance using Fisher's
Z-transformation. This transformation renders repeatability estimates
close to a normal distribution and after normalizing the heavy tail
using the Lambert W x F transformation (Goerg, 2011), we could perform
all analyses assuming a normal error distribution. In this approach, we
weighed each study according to their Fisher-z-transformed variance.
Second, we performed all analyses on untransformed repeatability
estimates, acknowledging that within-individual repeatability is a
continuous distribution of a proportion with 2 categories
(within\emph{vs.} between individual variance). As such, the
repeatability follows a beta distribution (Douma \& Weedon, 2019;
Ferrari \& Cribari-Neto, 2004). Hence, we also performed all analyses
using a beta distribution and logit link function and weighing each
study according to the inverse of their sample size. For both
approaches, model residuals fulfilled all assumptions, followed the
quantiles of the used distribution with variance homogeneity and without
influential datapoints, as checked with the functions 'influence' and
`testResiduals' of the packages `influence.ME' and `DHARMa' (Hartig,
2019; Nieuwenhuis, te Grotenhuis, \& Pelzer, 2012). Both approaches gave
consistent results and below we present the results of the first
approach using Fisher's Z-transformation and a normal error
distribution. For the ease of interpretation, we back-transformed Z
values to effect size (intra-class correlation coefficient ICC) values
and their 95\% CI following equation 6 in (Holtmann et al., 2017).

The models contained as fixed effects: (i) measurement method (qPCR or
TRF), (ii) study length (continuous variable), (iii) taxon (i.e.
ectotherm or endotherm), (iv) environment (captive, semi-wild or wild),
(v) age class (juveniles, adults or juveniles to adult) and (vi) species
maximum lifespan (continuous variable). We standardized continuous fixed
effects with a mean of 0 and a variance of 1. We analyzed whether there
were biases in predictor variables between measurement methods using
permutation tests with the function `independence\_test' of the package
`coin' (Hothorn, Van De Wiel, Hornik, \& Zeileis, 2008) based on 10,000
permutations. We used \selectlanguage{greek}χ\selectlanguage{english}\textsuperscript{2} tests to identify deviations
from 50/50. qPCR was used more than twice as often as TRF, accounting
for 70\% (N=57) of the estimates and 76\% (N=3757) of the individuals
(Fig. 2; \selectlanguage{greek}χ\selectlanguage{english}\textsuperscript{2}=5.4; p=0.02). There were no TRF
measurements in fish, but otherwise there was no taxon-specific or
system-specific bias between both methods (Table S1). Indeed, studies
from the wild were equally overrepresented in both methods, respectively
at 63\% (N=36) and 70\% (N=19) of the qPCR and TRF estimates (Z = 1.02;
p = 0.40). Species measured by qPCR lived somewhat shorter than those
measured by TRF, respectively with a median lifespan of 15.0 years (95\%
CI: 7.4 - 36.8) and 20.3 years (95\% CI : 6.0 - 34.4; Table S1), but
this difference was not statistically significant (Z = -1.29; p = 0.20).
Study length was somewhat shorter in qPCR than TRF studies with
respectively 3.3 months (95\% CI: 0.6 - 128) and 10.0 months (95\% CI:
0.8 -85; Table S1), but this difference was not statistically
significant (Z = -0.38; p = 0.70). When adjusting study length relative
to species lifespan the difference between qPCR and TRF became even
smaller at respectively 1.0\% (95\% CI: 0.13 - 19) and 3.0\% (95\% CI:
0.1 -15; Z = 0.05; p = 0.96). There was also no statistically
significant difference between both methods in monitored age classes
(juvenile versus adult or both; Table S1; Z = 1.59; p = 0.20). Hence, we
did not detect any species-specific bias in the data distribution
between TRF and qPCR measurement methods.

To account for the fact that several studies came from the same
laboratory (N = 22), we included laboratory identity as random
intercept. To account for multiple measurements of the same species (N =
42), we included species identity as a random intercept. Species are
however related by phylogeny (Fig. 3). We therefore also included
phylogeny as a random term in this analysis following Hadfield \&
Nakagawa (2010) and de Villemereuil \& Nakagawa (2014), in which the
phylogeny is captured in the variance-covariance matrix between species
in the mixed model. This model contains both species and phylogeny as
random intercepts because these terms capture different variances,
respectively the within-species variance, while the phylogeny accounts
for the relatedness between species (de Villemereuil \& Nakagawa, 2014).
To identify whether there was a role of phylogeny in the
within-individual repeatability of telomere length, we estimated the
phylogenetic signal lambda (\selectlanguage{greek}\emph{λ} \selectlanguage{english}), which is the ratio of the
variance explained by phylogeny relative to the total variance explained
by the model and hence its value ranges from 0 (no signal) to 1
(Freckleton, Harvey, \& Pagel, 2002; Hadfield \& Nakagawa, 2010). We
constructed a phylogeny of the 42 species in this study using the Open
Tree of Life (Hinchliff et al., 2015) with the package `rotl'
(Michonneau, Brown, \& Winter, 2016). We set the branch lengths
following Grafen (1989) with the function `compute.brlen' from the
package `ape' (Paradis \& Schliep, 2019).

We performed the mixed-model meta-analyses without and with phylogeny,
which gave consistent results (Table S2). Here we present the analyses
with phylogeny performed using a Bayesian approach with the function
`brm' from the package `brms' (B\selectlanguage{ngerman}ürkner, 2017), but note that the
conclusions were consistent with those based on a frequentist approach
with the functions `lmer' and `gls' of the packages `lme4' and `nlme'
respectively (results not shown). For the Bayesian models, we used
weakly informative priors and ran 4 chains each with 1,500,000
iterations, a burn-in of 100,000 and a thinning of 250, resulting in a
posterior effective sample size of \textgreater{}2000 and an Rhat of 1,
which together with pareto-k-diagnostics (k\textless{}0.7), visual
inspection of the trace plots and potential scale reduction factor
showed that simulations had ran properly (Bürkner, 2017). We evaluated
the relative fits of the Bayesian model on the data using the
leave-one-out cross-validation (LOO) approach (Vehtari, Gelman, \&
Gabry, 2017) and compared the models' relative weight with the functions
`loo' and `model\_weights ` of the package `brms'. In brief, a model's
weight is an estimate of the probability that the model will make the
best predictions on new data, conditional on the alternative models
considered with the weights of all models adding up to 1. We determined
the statistical `significance' of the fixed effects and random effects
based on their model fit (loo weights for Bayesian models) and the
overlap with 0 of the 95\% CI of coefficients or variance estimates.

We assessed publication bias based on visual inspection of funnel plots
of `meta-analytic' residuals of the model in Table 2 (Fig. S5), the
Egger's test on the residuals of this model (Egger, Smith, Schneider, \&
Minder, 1997; Nakagawa \& Santos, 2012) and using the trim and fill
method (Duval \& Tweedie, 2000) with the function 'trimfill' in the
package `metafor' (Viechtbauer, 2010). None of these approaches
indicated there was publication bias.

3. Results

We obtained 82 repeatability estimates from 74 studies on 42 species
measured in 22 laboratories based on a total of 4918 individuals.
Individuals were measured on average 2.3 times (95\% CI: 2.0 -- 3.0) and
were monitored for a median of 4.2 months (95\% CI: 0.6 -- 121) or 1.4
\% of their lifespan (95\% CI: 0.1 -- 19.0). Birds were overrepresented
relative to reptiles or fish, accounting for 87\% (N = 71, Fig. 2) of
the estimates and 90\% (N = 4439) of the individuals. Studies on wild
systems were twice as abundant as studies on semi-wild or captive
systems, accounting for 67\% (N = 55) of the estimates and 70\% (N =
3422) of the individuals (Fig. 2).

The within-individual repeatability of telomere length was overall
moderate to high, with a median value of \emph{R} = 0.55. Yet, there was
marked variation between studies, as exemplified by the large 95\% CI
around median \emph{R}value from 0.05 until 0.95. There was no clear
phylogenetic signal for the repeatability of telomere length (\selectlanguage{greek}\emph{λ R}\selectlanguage{english}
= 0.38; 95\%CI: 0.00-0.85; Fig. S4A; \selectlanguage{greek}\emph{λ} \selectlanguage{english}model table 2 = 0.32; 95\%
CI: 0.00-0.83, Fig. S4B), but phylogeny captured some variance (SD:
0.28; 95\%CI: 0.02-0.70; Table 2) and a model with phylogeny (Fig. 3)
was favored over one without phylogeny, albeit moderately (respective
loo-weights 0.57 vs. 0.43). As a control, we checked in the same
dataset, the phylogenetic signal of lifespan, which was moderate but
statistically significant (\selectlanguage{greek}\emph{λ} \selectlanguage{english}=0.38; 95\%CI: 0.03-0.77; Fig. S3;
Fig. S4C) and a model with phylogeny was strongly favored over a model
without phylogeny (loo-weights of 0.83 vs. 0.17).

There was a statistically significant effect of telomere length
measurement method (Table 2): the median within-individual repeatability
of TRF-based studies was high at \emph{R} = 0.80 (N = 25; 95\% CI: 0.34
- 0.96; Fig. 4; Fig. 5B), while that of qPCR-based studies was only
almost half of that and more variable at \emph{R} = 0.46 (N = 57; 95\%
CI: 0.04 - 0.82; Fig. 4; Fig. 5B). Within-individual repeatability of
telomere length also decreased with the length of the study (\selectlanguage{greek}β \selectlanguage{english}= -0.08;
-0.15 \textless{} 95\% CI \textless{} -0.01; Table 2; Fig. 5E). Once
these methodological variables were accounted for, none of the
biological variables we tested (\emph{i.e.} taxon, species lifespan,
environment, age class) had a statistically significant effect on the
repeatability of telomere length (Table 2; Fig. 5 A-G). There was a weak
negative association between the within-individual repeatability of
telomere length and species lifespan, but the 95\% CI of this
coefficient overlapped with zero (\selectlanguage{greek}β\selectlanguage{english}=-0.06; -0.16 \textless{} 95\% CI
\textless{} 0.05; Table 2; Fig. 5F), indicating little statistical
support for an association between species lifespan and the
repeatability of telomere length.

4. Discussion

The within-individual repeatability (\emph{R)} of measurements over time
is a key requirement to identify the dynamics of variables and the
factors driving these dynamics. Telomere length changes over time and
this change is known to be affected by various factors, \emph{e.g.} pre-
and postnatal environmental conditions (K\selectlanguage{ngerman}ärkkäinen, Teerikorpi, Schuett,
Stier, \& Laaksonen, 2021; Stier et al., 2020; Stier, Metcalfe, \&
Monaghan, 2020), stress exposure (Chatelain et al., 2020), and
reproductive effort (Reichert et al., 2014). Yet, to be informative of
future performances (\emph{e.g.} remaining lifespan), telomere length
also needs to be repeatable to some extent. Here, we performed a
meta-analysis of the within-individual repeatability of telomere length
and investigated some biological and methodological variables that might
affect this repeatability. Overall, we found telomere length to be
relatively repeatable at \emph{R} = 0.55 but the repeatability was
highly variable across studies varying from almost 0 to almost 1. The
repeatability was mainly driven by measurement method, with studies
using qPCR method showing a repeatability that was almost half and more
variable than those using TRF. The within-individual repeatability of
telomere length declined with the length of study and tended to decline
with species lifespan, although the latter was statistically
non-significant. Phylogeny explained a minor, but a statistically
significant part of the variance in within-individual repeatability. Any
other tested biological variable did not have a statistically
significant measurable effect on the repeatability estimates. Here we
discuss three major implications of our study.

First, while repeatabilities of physiological traits have been
investigated before, to the best of our knowledge this is the first
meta-analysis on the within-individual repeatability of telomere length.
The within-individual repeatability of glucocorticoid levels have been
reported to be about half of that of telomere length at \emph{R} \selectlanguage{english}[?] 0.3
(Schoenemann \& Bonier, 2018; Taff, Schoenle, \& Vitousek, 2018). This
is not surprising as repeatability is not expected to be really high for
labile traits (Bonier \& Martin, 2016), and most arguably
within-individual hormone levels are more variable in time than
within-individual telomere length. The repeatabilities of metabolic
rates (basal, standard and resting) were more similar to our estimates
of the repeatability of telomere length: \emph{R} \selectlanguage{english}[?] 0.60-0.80 in Nespolo
\& Franco (2007), \emph{R} \selectlanguage{english}[?] 0.45-0.55 in Holtmann et al.
(2017),\emph{R} \selectlanguage{english}[?] 0.42-0.65 in Auer et al. (2016), \emph{R} \selectlanguage{english}[?] 0.40-0.50,
but mass-adjusted repeatability of \emph{R} \selectlanguage{english}[?] 0.30-0.40 in Briga \&
Verhulst (2017). Phylogeny accounted for a large fraction of the
variation in the repeatability of metabolic rate between studies
(Holtmann et al., 2017). In contrast with metabolic rate, the
within-individual repeatability of telomere length was little affected
by phylogeny or the biological factors studied here. Contrary to our
prediction, we found a negative association between the
within-individual repeatability of telomere length and species lifespan,
albeit not statistically significant. There is some evidence that
long-lived bird species maintain telomerase activity throughout life
(Haussmann, Winkler, Huntington, Nisbet, \& Vleck, 2007), which could
explain this negative association. However, the lack of statistically
significant biological factors and the minor role of phylogeny in
explaining the variation in the within-individual repeatability of
telomere length indicate that current measurements of telomere length
are not able to detect much role of biology in the repeatability of
telomere length.

Second, methodological factors explained most of the variation in the
within-individual repeatability of telomere length. Consistent with our
expectation, the repeatability of telomere length declined with study
length. A similar association was also found for the repeatability of
metabolic rate (Auer et al., 2016; Briga \& Verhulst, 2017). However,
most of the variation in the repeatability was captured by the
measurement method. Consistent with our expectation, studies using TRF
method to measure telomere length yielded higher repeatability estimates
than studies using qPCR. The difference most probably arises from how
the methods quantify telomere length. While TRF measures the terminal
telomere length by the use of gel electrophoresis, qPCR is based on PCR
and amplification of the target sequences, and measures the relative
telomere length by calculating the amount of telomeric sequence (T) in
the sample in relation to the amount of non-variable copy-gene sequence
(S); this magnifies measurement errors in telomere and/or single copy
gene reactions in the resulting T/S ratio (Nettle et al., 2019).
Consequently, it has been shown that measurement error alone can
decrease within-individual repeatability in telomere length in
longitudinal qPCR studies (Nettle et al., 2019), even if cross-sectional
accuracy is confirmed with TRF (Nettle et al. 2020 preprint). An
extensive discussion on measurement reliability between these methods
has been had previously elsewhere (Martin-Ruiz et al., 2015; Martin-Ruiz
et al., 2015; Verhulst et al., 2016, 2015). It is also important to note
that both qPCR and TRF might over-estimate the within-individual
repeatability of telomere length. Indeed, qPCR also includes the
interstitial telomeric sequences (ITS) in the measure of relative
telomere length. Amount of ITS can vary between individuals of the same
species but is not considered to change in time (Foote, Vleck, \& Vleck,
2013), although this remains to be shown. This would artificially
increase the within-individual repeatability of telomere length if ITS
amount is indeed constant within an individual over time. Relatively
similarly, in addition to measuring the terminal telomeric sequences,
TRF also includes some amount of subtelomeric regions (Baird, 2005),
which, like ITS, can vary between individuals but are assumed to remain
stable within an individual over time. Thus, the occurrence of ITS and
of subtelomeric regions might inflate the repeatability of telomere
length, which might explain both the importance of measurement method
(effect size, Fig. 5G), and the large variation observed between qPCR
studies (\emph{i.e.} ITS amount is very variable between species; (Foote
et al., 2013).

Third, given the importance of measurement method, there are a number of
methodological practices that are known to affect the quality and
repeatability of telomere length measurements, especially in qPCR
telomere measurements: differences in sample storage (Eastwood, Mulder,
Verhulst, \& Peters, 2018; Reichert et al., 2017), DNA extraction method
(Dagnall et al., 2017; Seeker et al., 2016) and even the type of qPCR
master mix being used (Morinha, Magalhaes, \& Blanco, 2020a).
Furthermore, while DNA integrity is widely known to be important in TRF,
it is traditionally thought to be less crucial in qPCR (Aviv et al.,
2011). However, recent evidence suggests that DNA degradation can either
increase or decrease telomere length measured with qPCR (Ropio et al.,
2020; Tolios, Teupser, \& Holdt, 2015). Currently, DNA integrity is
rarely assessed before performing qPCR analyses, and standard agarose
gel electrophoresis might be insufficient to assess DNA integrity for
qPCR telomere length measurement (AS, \emph{pers. obs.} ). Practices
during the analytical phase can also potentially affect the
repeatability of telomere measurements. In longitudinal studies,
especially if using long-term data, data are often analyzed in batches
and/or clusters. Failure to consider the sample structure among the
batches and clusters can create variation from which it is impossible to
separate the biological variation from the confounding between
batch/cluster variation (van Lieshout et al., 2020). When analyzing
longitudinal samples, the samples from the same individual are often
analyzed on the same plate/gel to increase statistical power to detect
within-individual effects. However, doing so, but not controlling for
the plate/gel effect, can inadvertently increase the within-individual
repeatability, while analyzing the samples from the same individual on
different plates/gels will often decrease the within-individual
repeatability. Thus, there are a number of methodological practices that
need to be taken into account to improve the repeatability of telomere
length in both qPCR and TRF methods. Quantifying the relative importance
of these practices is beyond the scope of this meta-analysis but
warrants further investigation.

To conclude, telomere length is increasingly used as a biomarker for
past stress exposure (Chatelain et al., 2020) and future performance
(Eastwood et al., 2019; Heidinger et al., 2012). However, for telomere
length to be truly informative about the past or the future, telomere
length requires a reasonable within-individual repeatability over the
life course. For instance, inferring any long-lasting effects of
early-life environmental conditions or predicting future survival
probability when the within-individual repeatability is virtually close
to 0 (in \emph{ca.} 23\% of qPCR studies) is likely to lead to spurious
conclusions. Similarly, repeatability often sets an upper limit to
heritability (Falconer and Mackay, 1996; Lynch and Walsh, 1997, but see
Dohm, 2002) and low repeatability will conceal the heritable component
of telomere length. Accordingly, heritability estimates are usually
higher for TRF than qPCR studies (Bauch, Boonekamp, Korsten, Mulder, \&
Verhulst, 2020), which is in line with the strong method effect found
here for within-individual repeatability. Our study indeed indicates
that the within-individual repeatability of telomere length is mainly
driven by telomere measurement method, with the repeatability being
significantly lower with qPCR than with TRF. Unfortunately, the majority
of the longitudinal telomere studies to date have used qPCR, which may
partly mask the role of biological variation in telomere length and
dynamics. It might be worth reassessing whether biological factors drive
within-individual repeatability in telomere length when there will be
more TRF studies and/or when the repeatability of qPCR has improved.
Meanwhile, we encourage scientists to design their research and
laboratory practices to aim at high repeatability by reducing factors
that potentially create variation in telomere length measurements. It is
noteworthy, that while TRF studies regularly showed higher repeatability
estimates than qPCR studies, it is also possible to achieve high
repeatability using qPCR. In qPCR studies it is important to pay
particular attention to both sample handling and storage and optimizing
the qPCR protocol (see Morinha, Magalhaes, \& Blanco, 2020b for specific
guidelines). We particularly encourage scientists to estimate and report
the within-individual repeatability \emph{R} in their study systems.
Repeatabilities are driven by measurement error and/or biological
variables and to decompose the contribution of both components, studies
should report the within-individual repeatability of all longitudinal
samples along with the technical repeatability of their methodology
(\emph{e.g.} based on the repeated measurement of the same samples).
This should capture at least part of the loss of repeatability due to
measurement error. It would thus allow the estimation of a
biology-driven repeatability and hence testing some of the predictions
we proposed in this study. Longitudinal studies still rarely report
within-individual repeatability of telomere length, while it can be a
key statistic for the interpretation of both the reliability of the
methodology and the biology driving the dynamics of telomere length and
more generally individual traits.

\textbf{Acknowledgements}

We are grateful to Frederic Angelier, Muhammad Asghar, Els Atema,
Michael Beaulieu, Staffan Bensch, Pierre Bize, Pierre Blevin, Olivier
Chastel, Francois Criscuolo, Adriana Dorado-Correa, Mathieu Giraudeau,
Dennis Hasselquist, Britt Heidinger, Lisa Jacquin, Alexander Kitaysky,
Sophie Lardy, Jimena Lopez-Arrabe, Pat Monaghan, Jose Noguera, Joacim
Naslund, Mats Olsson, Marco Parolini, Lorenzo Perez-Rodrigues, Sophie
Reichert, Nicky Rollings, Pablo Salmon, Manrico Sebastiano, Joanna
Sudyka, Mathilde Tissier, Oscar Vedder, Simon Verhulst, Hannah Watson,
Tony Williams, and Rebecca Young for sharing raw data of their published
work. We especially thank Mark Haussmann and Barbara Tschirren for
sharing their unpublished data on the Japanese quail. This study was
financially supported by the Finnish Cultural Foundation Varsinais-Suomi
Regional Fund (TK), Turku University Foundation, Finnish Cultural
Foundation (TK), the Ella \& Georg Ehrnrooth Foundation (MB) and the
Turku Collegium for Science and Medicine (AS).

\textbf{Author contribution}

AS and TK conceived the study and collected data from the literature, MB
and TK analyzed the data. All authors contributed to data
interpretation. TK, MB and AS wrote the manuscript with input from TL.

\textbf{Conflict of interest}

The authors declare no conflicts of interest.

\textbf{Data accessibility}

Data used in this study will be made publicly available in Figshare upon
acceptance.

5. References

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

Arbeev, K. G., Verhulst, S., Steenstrup, T., Kark, J. D., Bagley, O.,
Kooperberg, C., \ldots{} Aviv, A. (2020). Association of Leukocyte
Telomere Length With Mortality Among Adult Participants in 3
Longitudinal Studies. \emph{JAMA Network Open} , \emph{3} (2),
e200023--e200023. doi: 10.1001/jamanetworkopen.2020.0023

Asghar, M., Hasselquist, D., Hansson, B., Zehtindjiev, P., Westerdahl,
H., \& Bensch, S. (2015). Hidden costs of infection: Chronic malaria
accelerates telomere degradation and senescence in wild
birds.\emph{Science} , \emph{347} (6220), 436--438. doi:
10.1126/science.1261121

Asghar, M., Palinauskas, V., Zaghdoudi-Allan, N., Valki\selectlanguage{polish}ū\selectlanguage{english}nas, G., Mukhin,
A., Platonova, E., \ldots{} Hasselquist, D. (2016). Parallel telomere
shortening in multiple body tissues owing to malaria
infection.\emph{Proceedings of the Royal Society B: Biological Sciences}
,\emph{283} (1836), 20161184. doi: 10.1098/rspb.2016.1184

Atema, E., Mulder, E., Dugdale, H. L., Briga, M., van Noordwijk, A. J.,
\& Verhulst, S. (2015). Heritability of telomere length in the Zebra
Finch. \emph{Journal of Ornithology} , \emph{156} (4), 1113--1123. doi:
10.1007/s10336-015-1212-7

Atema, E., Mulder, E., Noordwijk, A. J. van, \& Verhulst, S. (2019).
Ultralong telomeres shorten with age in nestling great tits but are
static in adults and mask attrition of short telomeres. \emph{Molecular
Ecology Resources} , \emph{19} (3), 648--658. doi:
10.1111/1755-0998.12996

Auer, S. K., Bassar, R. D., Salin, K., \& Metcalfe, N. B. (2016).
Repeatability of metabolic rate is lower for animals living under field
versus laboratory conditions. \emph{Journal of Experimental Biology}
,\emph{219} (5), 631--634. doi: 10.1242/jeb.133678

Aviv, A., Hunt, S. C., Lin, J., Cao, X., Kimura, M., \& Blackburn, E.
(2011). Impartial comparative analysis of measurement of leukocyte
telomere length/DNA content by Southern blots and qPCR. \emph{Nucleic
Acids Research} , \emph{39} (20), e134--e134. doi: 10.1093/nar/gkr634

Aviv, A., \& Shay, J. W. (2018). Reflections on telomere dynamics and
ageing-related diseases in humans. \emph{Philosophical Transactions of
the Royal Society B: Biological Sciences} , \emph{373} (1741), 20160436.
doi: 10.1098/rstb.2016.0436

Baird, D. M. (2005). New developments in telomere length
analysis.\emph{Experimental Gerontology} , \emph{40} (5), 363--368. doi:
10.1016/j.exger.2005.02.008

Barrett, E. L. B., Burke, T. A., Hammers, M., Komdeur, J., \&
Richardson, D. S. (2013). Telomere length and dynamics predict mortality
in a wild longitudinal study. \emph{Molecular Ecology} , \emph{22} (1),
249--259. doi: 10.1111/mec.12110

Bateson, M., Brilot, B. O., Gillespie, R., Monaghan, P., \& Nettle, D.
(2015). Developmental telomere attrition predicts impulsive
decision-making in adult starlings. \emph{Proceedings of the Royal
Society of London B: Biological Sciences} , \emph{282} (1799), 20142140.
doi: 10.1098/rspb.2014.2140

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

Bauch, C., Boonekamp, J. J., Korsten, P., Mulder, E., \& Verhulst, S.
(2020). High heritability of telomere length, but low evolvability, and
no significant heritability of telomere shortening in wild
jackdaws.\emph{BioRxiv} , 2020.12.16.423128. doi:
10.1101/2020.12.16.423128

Beaulieu, M., Benoit, L., Abaga, S., Kappeler, P. M., \& Charpentier, M.
J. E. (2017). Mind the cell: Seasonal variation in telomere length
mirrors changes in leucocyte profile. \emph{Molecular Ecology}
,\emph{26} (20), 5603--5613. doi: https://doi.org/10.1111/mec.14329

Beaulieu, M., Reichert, S., Le Maho, Y., Ancel, A., \& Criscuolo, F.
(2011). Oxidative status and telomere length in a long-lived bird facing
a costly reproductive event. \emph{Functional Ecology} , \emph{25} (3),
577--585. doi: 10.1111/j.1365-2435.2010.01825.x

Benetos, A., Verhulst, S., Labat, C., Lai, T.-P., Girerd, N., Toupance,
S., \ldots{} Aviv, A. (2019). Telomere length tracking in children and
their parents: Implications for adult onset diseases. \emph{The FASEB
Journal} , \emph{33} (12), 14248--14253. doi: 10.1096/fj.201901275R

Bichet, C., Bouwhuis, S., Bauch, C., Verhulst, S., Becker, P. H., \&
Vedder, O. (2020). Telomere length is repeatable, shortens with age and
reproductive success, and predicts remaining lifespan in a long-lived
seabird. \emph{Molecular Ecology} , \emph{29} (2), 429--441. doi:
10.1111/mec.15331

Bize, P., Criscuolo, F., Metcalfe, N. B., Nasir, L., \& Monaghan, P.
(2009). Telomere dynamics rather than age predict life expectancy in the
wild. \emph{Proceedings of the Royal Society of London B: Biological
Sciences} , \emph{276} (1662), 1679--1683. doi: 10.1098/rspb.2008.1817

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

Bl\selectlanguage{ngerman}évin, P., Angelier, F., Tartu, S., Bustamante, P., Herzke, D., Moe,
B., \ldots{} Chastel, O. (2017). Perfluorinated substances and telomeres
in an Arctic seabird: Cross-sectional and longitudinal
approaches.\emph{Environmental Pollution} , \emph{230} , 360--367. doi:
10.1016/j.envpol.2017.06.060

Bonier, F., \& Martin, P. R. (2016). How can we estimate natural
selection on endocrine traits? Lessons from evolutionary
biology.\emph{Proceedings of the Royal Society B: Biological Sciences}
,\emph{283} (1843), 20161887. doi: 10.1098/rspb.2016.1887

Boonekamp, J. J., Bauch, C., Mulder, E., \& Verhulst, S. (2017). Does
oxidative stress shorten telomeres? \emph{Biology Letters} ,\emph{13}
(5), 20170164. doi: 10.1098/rsbl.2017.0164

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{Proceedings of the Royal Society of London B: Biological
Sciences} , \emph{281} (1785), 20133287. doi: 10.1098/rspb.2013.3287

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

Briga, M., \& Verhulst, S. (2017). Individual variation in metabolic
reaction norms over ambient temperature causes low correlation between
basal and standard metabolic rate. \emph{Journal of Experimental
Biology} , \emph{220} (18), 3280--3289. doi: 10.1242/jeb.160069

Bürkner, P.-C. (2017). brms: An R Package for Bayesian Multilevel Models
Using Stan. \emph{Journal of Statistical Software} , \emph{80} (1),
1--28. doi: 10.18637/jss.v080.i01

Campisi, J. (2005). Senescent Cells, Tumor Suppression, and Organismal
Aging: Good Citizens, Bad Neighbors. \emph{Cell} , \emph{120} (4),
513--522. doi: 10.1016/j.cell.2005.02.003

Casagrande, S., \& Hau, M. (2019). Telomere attrition: Metabolic
regulation and signalling function? \emph{Biology Letters} ,\emph{15}
(3), 20180885. doi: 10.1098/rsbl.2018.0885

Cerchiara, J. A., Risques, R. A., Prunkard, D., Smith, J. R., Kane, O.
J., \& Boersma, P. D. (2017). Magellanic penguin telomeres do not
shorten with age with increased reproductive effort, investment, and
basal corticosterone. \emph{Ecology and Evolution} , \emph{7} (15),
5682--5691. doi: https://doi.org/10.1002/ece3.3128

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

Criscuolo, F., Fowler, M. F., Fuhrer, V. A., Zahn, S., \& Williams, T.
D. (2018). Telomere length, individual quality and fitness in female
European starlings (Sturnus vulgaris) during breeding. \emph{BioRxiv} ,
416438. doi: 10.1101/416438

Dagnall, C. L., Hicks, B., Teshome, K., Hutchinson, A. A., Gadalla, S.
M., Khincha, P. P., \ldots{} Savage, S. A. (2017). Effect of
pre-analytic variables on the reproducibility of qPCR relative telomere
length measurement. \emph{PLOS ONE} , \emph{12} (9), e0184098. doi:
10.1371/journal.pone.0184098

Dantzer, B., \& Fletcher, Q. E. (2015). Telomeres shorten more slowly in
slow-aging wild animals than in fast-aging ones. \emph{Experimental
Gerontology} , \emph{71} , 38--47. doi: 10.1016/j.exger.2015.08.012

de Villemereuil, P., \& Nakagawa, S. (2014). General Quantitative
Genetic Methods for Comparative Biology. In \emph{Modern Phylogenetic
Comparative Methods and their Application in Evolutionary Biology} (pp.
287--303). doi: 10.1007/978-3-662-43550-2

Dohm, M. R. (2002). Repeatability estimates do not always set an upper
limit to heritability. \emph{Functional Ecology} , \emph{16} (2),
273--280. doi: https://doi.org/10.1046/j.1365-2435.2002.00621.x

Dorado-Correa, A. M., Zollinger, S. A., Heidinger, B., \& Brumm, H.
(2018). Timing matters: Traffic noise accelerates telomere loss rate
differently across developmental stages. \emph{Frontiers in Zoology}
,\emph{15} (1). Scopus. doi: 10.1186/s12983-018-0275-8

Douma, J. C., \& Weedon, J. T. (2019). Analysing continuous proportions
in ecology and evolution: A practical introduction to beta and Dirichlet
regression. \emph{Methods in Ecology and Evolution} , \emph{10} (9),
1412--1430. doi: 10.1111/2041-210X.13234

Duval, S., \& Tweedie, R. (2000). Trim and Fill: A Simple
Funnel-Plot--Based Method of Testing and Adjusting for Publication Bias
in Meta-Analysis. \emph{Biometrics} , \emph{56} (2), 455--463. doi:
https://doi.org/10.1111/j.0006-341X.2000.00455.x

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

Eastwood, J. R., Mulder, E., Verhulst, S., \& Peters, A. (2018).
Increasing the accuracy and precision of relative telomere length
estimates by RT qPCR. \emph{Molecular Ecology Resources} , \emph{18}
(1), 68--78. doi: https://doi.org/10.1111/1755-0998.12711

Egger, M., Smith, G. D., Schneider, M., \& Minder, C. (1997). Bias in
meta-analysis detected by a simple, graphical test. \emph{BMJ}
,\emph{315} (7109), 629--634. doi: 10.1136/bmj.315.7109.629

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}
,\emph{15} (1), 140--148. doi: 10.1111/acel.12417

Falconer, D. S., \& Mackay, T. F. C. (1996). \emph{Introduction to
quantitative genetics.} Oliver \& Boyd, Edinburgh \& London. Retrieved
from https://www.cabdirect.org/cabdirect/abstract/19601603365

Fasching, C. L. (2018). Telomere length measurement as a clinical
biomarker of aging and disease. \emph{Critical Reviews in Clinical
Laboratory Sciences} , \emph{55} (7), 443--465. doi:
10.1080/10408363.2018.1504274

Ferrari, S. L. P., \& Cribari-Neto, F. (2004). Beta regression for
modelling rates and proportions. \emph{Journal of Applied Statistics}
,\emph{31} (7), 799--815. doi: 10.1080/0266476042000214501

Fitzpatrick, L. J., Olsson, M., Parsley, L. M., Pauliny, A., While, G.
M., \& Wapstra, E. (2019). Tail loss and telomeres: Consequences of
large-scale tissue regeneration in a terrestrial ectotherm.\emph{Biology
Letters} , \emph{15} (7), 20190151. doi: 10.1098/rsbl.2019.0151

Foote, C. G., Gault, E. A., Nasir, L., \& Monaghan, P. (2011). Telomere
dynamics in relation to early growth conditions in the wild in the
lesser black-backed gull. \emph{Journal of Zoology} , \emph{283} (3),
203--209. doi: 10.1111/j.1469-7998.2010.00774.x

Foote, C., Vleck, D., \& Vleck, C. M. (2013). Extent and variability of
interstitial telomeric sequences and their effects on estimates of
telomere length. \emph{Molecular Ecology Resources} , \emph{13} (3),
417--428. doi: 10.1111/1755-0998.12079

Fragueira, R., Verhulst, S., \& Beaulieu, M. (2019). Morph- and
sex-specific effects of challenging conditions on maintenance parameters
in the Gouldian finch. \emph{Journal of Experimental Biology}
,\emph{222} (7). doi: 10.1242/jeb.196030

Freckleton, R. P., Harvey, P. H., \& Pagel, M. (2002). Phylogenetic
analysis and comparative data: A test and review of
evidence.\emph{American Naturalist} , \emph{160} (6), 712--726. doi:
10.1086/343873

Geiger, S., Vaillant, M. L., Lebard, T., Reichert, S., Stier, A., Maho,
Y. L., \& 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{Molecular Ecology} , \emph{21} (6), 1500--1510.
doi: https://doi.org/10.1111/j.1365-294X.2011.05331.x

Goerg, G. M. (2011). Lambert W random variables-a new family of
generalized skewed distributions with applications to risk
estimation.\emph{Annals of Applied Statistics} , \emph{5} (3),
2197--2230. doi: 10.1214/11-AOAS457

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

Grafen, A. (1989). The phylogenetic regression. \emph{Philosophical
Transactions of the Royal Society of London. Series B, Biological
Sciences} , \emph{326} (1233), 119--157. doi: 10.1098/rstb.1989.0106

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

Grunst, M. L., Raap, T., Grunst, A. S., Pinxten, R., \& Eens, M. (2019).
Artificial light at night does not affect telomere shortening in a
developing free-living songbird: A field experiment: Artificial light at
night and telomere dynamics. \emph{Science of The Total Environment}
,\emph{662} , 266--275. doi: 10.1016/j.scitotenv.2018.12.469

Hadfield, J. D., \& Nakagawa, S. (2010). General quantitative genetic
methods for comparative biology: Phylogenies, taxonomies and multi-trait
models for continuous and categorical characters. \emph{Journal of
Evolutionary Biology} , \emph{23} (3), 494--508. doi:
10.1111/j.1420-9101.2009.01915.x

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{Proceedings of the
Royal Society of London B: Biological Sciences} , \emph{271} (1548),
1571--1576. doi: 10.1098/rspb.2004.2768

Hartig, F. (2019). \emph{DHARMa: Residual diagnostics for hierarchical
(multi-level/mixed) regression models. URL:
https://CRAN.R-project.org/package=DHARMa} .

Hau, M., Haussmann, M. F., Greives, T. J., Matlack, C., Costantini, D.,
Quetting, M., \ldots{} Partecke, J. (2015). Repeated stressors in
adulthood increase the rate of biological ageing. \emph{Frontiers in
Zoology} , \emph{12} , 4. doi: 10.1186/s12983-015-0095-z

Haussmann, M. F., Winkler, D. W., Huntington, C. E., Nisbet, I. C. T.,
\& Vleck, C. M. (2007). Telomerase activity is maintained throughout the
lifespan of long-lived birds. \emph{Experimental Gerontology} ,\emph{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{Proceedings of the National Academy of Sciences}
,\emph{109} (5), 1743--1748. doi: 10.1073/pnas.1113306109

Heidinger, B. J., Herborn, K. A., Granroth-Wilding, H. M. V., Boner, W.,
Burthe, S., Newell, M., \ldots{} Monaghan, P. (2016). Parental age
influences offspring telomere loss. \emph{Functional Ecology} ,\emph{30}
(9), 1531--1538. doi: 10.1111/1365-2435.12630

Herborn, K. A., Heidinger, B. J., Boner, W., Noguera, J. C., Adam, A.,
Daunt, F., \& Monaghan, P. (2014). Stress exposure in early post-natal
life reduces telomere length: An experimental demonstration in a
long-lived seabird. \emph{Proc. R. Soc. B} , \emph{281} (1782),
20133151. doi: 10.1098/rspb.2013.3151

Hinchliff, C. E., Smith, S. A., Allman, J. F., Burleigh, J. G.,
Chaudhary, R., Coghill, L. M., \ldots{} Cranston, K. A. (2015).
Synthesis of phylogeny and taxonomy into a comprehensive tree of
life.\emph{Proceedings of the National Academy of Sciences of the United
States of America} , \emph{112} (41), 12764--12769. doi:
10.1073/pnas.1423041112

Holtmann, B., Lagisz, M., \& Nakagawa, S. (2017). Metabolic rates, and
not hormone levels, are a likely mediator of between-individual
differences in behaviour: A meta-analysis. \emph{Functional Ecology}
,\emph{31} (3), 685--696. doi: https://doi.org/10.1111/1365-2435.12779

Hothorn, T., Van De Wiel, M. A., Hornik, K., \& Zeileis, A. (2008).
Implementing a class of permutation tests: The coin
package.\emph{Journal of Statistical Software} , \emph{28} (8), 1--23.
doi: 10.18637/jss.v028.i08

Karell, P., Bensch, S., Ahola, K., \& Asghar, M. (2017). Pale and dark
morphs of tawny owls show different patterns of telomere dynamics in
relation to disease status. \emph{Proc. R. Soc. B} , \emph{284} (1859),
20171127. doi: 10.1098/rspb.2017.1127

Kärkkäinen, T., Teerikorpi, P., Panda, B., Helle, S., Stier, A., \&
Laaksonen, T. (2019). Impact of continuous predator threat on telomere
dynamics in parent and nestling pied flycatchers. \emph{Oecologia}
,\emph{191} (4), 757--766. doi: 10.1007/s00442-019-04529-3

Kärkkäinen, T., Teerikorpi, P., Schuett, W., Stier, A., \& Laaksonen, T.
(2021). Interplays between pre- and post-natal environments affect
early-life mortality, body mass and telomere dynamics in the
wild.\emph{Journal of Experimental Biology} , \emph{224} (1). doi:
10.1242/jeb.231290

Lardy, S., Gasparini, J., Corbel, H., Frantz, A., Perret, S., Zahn, S.,
\ldots{} Jacquin, L. (2017). Telomere erosion varies with sex and age at
immune challenge but not with maternal antibodies in
pigeons.\emph{Journal of Experimental Zoology Part A: Ecological and
Integrative Physiology} , \emph{327} (9), 562--569. doi:
https://doi.org/10.1002/jez.2142

Levy, M. Z., Allsopp, R. C., Futcher, A. B., Greider, C. W., \& Harley,
C. B. (1992). Telomere end-replication problem and cell
aging.\emph{Journal of Molecular Biology} , \emph{225} (4), 951--960.
doi: 10.1016/0022-2836(92)90096-3

López-Arrabé, J., Monaghan, P., Cantarero, A., Boner, W.,
Pérez-Rodríguez, L., \& Moreno, J. (2018). Sex-specific Associations
between Telomere Dynamics and Oxidative Status in Adult and Nestling
Pied Flycatchers. \emph{Physiological and Biochemical Zoology} . doi:
10.1086/697294

López-Otín, C., Blasco, M. A., Partridge, L., Serrano, M., \& Kroemer,
G. (2013). The Hallmarks of Aging. \emph{Cell} , \emph{153} (6),
1194--1217. doi: 10.1016/j.cell.2013.05.039

Ludlow, A. T., Zimmerman, J. B., Witkowski, S., Hearn, J. W., Hatfield,
B. D., \& Roth, S. M. (2008). Relationship between Physical Activity
Level, Telomere Length, and Telomerase Activity. \emph{Medicine and
Science in Sports and Exercise} , \emph{40} (10), 1764--1771. doi:
10.1249/MSS.0b013e31817c92aa

Lynch, M., \& Walsh, B. (1997). Genetics and analysis of quantitative
traits. \emph{Genetics and Analysis of Quantitative Traits.} Retrieved
from https://www.cabdirect.org/cabdirect/abstract/19980108045

Martens, D. S., Van Der Stukken, C., Derom, C., Thiery, E., Bijnens, E.
M., \& Nawrot, T. S. (2021). Newborn telomere length predicts later life
telomere length: Tracking telomere length from birth to child- and
adulthood. \emph{EBioMedicine} , \emph{63} , 103164. doi:
10.1016/j.ebiom.2020.103164

Martin-Ruiz, C. M., Baird, D., Roger, L., Boukamp, P., Krunic, D.,
Cawthon, R., \ldots{} Von Zglinicki, T. (2015). Is Southern blotting
necessary to measure telomere length reproducibly? Authors' Response to:
Commentary: The reliability of telomere length
measurements.\emph{International Journal of Epidemiology} , \emph{44}
(5), 1686--1687. doi: 10.1093/ije/dyv169

Martin-Ruiz, C. M., Baird, D., Roger, L., Boukamp, P., Krunic, D.,
Cawthon, R., \ldots{} von Zglinicki, T. (2015). Reproducibility of
telomere length assessment: An international collaborative
study.\emph{International Journal of Epidemiology} , \emph{44} (5),
1673--1683. doi: 10.1093/ije/dyu191

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

Michonneau, F., Brown, J. W., \& Winter, D. J. (2016). rotl: An R
package to interact with the Open Tree of Life data. \emph{Methods in
Ecology and Evolution} , \emph{7} (12), 1476--1481. doi:
10.1111/2041-210X.12593

Mizutani, Y., Niizuma, Y., \& Yoda, K. (2016). How Do Growth and Sibling
Competition Affect Telomere Dynamics in the First Month of Life of
Long-Lived Seabird? \emph{PLOS ONE} , \emph{11} (11), e0167261. doi:
10.1371/journal.pone.0167261

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

Morinha, F., Magalhães, P., \& Blanco, G. (2020a). Different qPCR master
mixes influence telomere primer binding within and between bird
species.\emph{Journal of Avian Biology} , \emph{51} (2). doi:
https://doi.org/10.1111/jav.02352

Morinha, F., Magalhães, P., \& Blanco, G. (2020b). Standard guidelines
for the publication of telomere qPCR results in evolutionary
ecology.\emph{Molecular Ecology Resources} , \emph{20} (3), 635--648.
doi: https://doi.org/10.1111/1755-0998.13152

Muñoz-Lorente, M. A., Cano-Martin, A. C., \& Blasco, M. A. (2019). Mice
with hyper-long telomeres show less metabolic aging and longer
lifespans. \emph{Nature Communications} , \emph{10} (1), 4723. doi:
10.1038/s41467-019-12664-x

Nakagawa, S., \& Santos, E. S. A. (2012). Methodological issues and
advances in biological meta-analysis. \emph{Evolutionary Ecology}
,\emph{26} (5), 1253--1274. doi: 10.1007/s10682-012-9555-5

Nakagawa, S., \& Schielzeth, H. (2010). Repeatability for Gaussian and
non-Gaussian data: A practical guide for biologists. \emph{Biological
Reviews of the Cambridge Philosophical Society} , \emph{85} (4),
935--956. doi: 10.1111/j.1469-185X.2010.00141.x

Näslund, J., Pauliny, A., Blomqvist, D., \& Johnsson, J. I. (2015).
Telomere dynamics in wild brown trout: Effects of compensatory growth
and early growth investment. \emph{Oecologia} , \emph{177} (4),
1221--1230. doi: 10.1007/s00442-015-3263-0

Nespolo, R. F., \& Franco, M. (2007). Whole-animal metabolic rate is a
repeatable trait: A meta-analysis. \emph{Journal of Experimental
Biology} , \emph{210} (11), 2000--2005. doi: 10.1242/jeb.02780

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

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

Nettle, D., Monaghan, P., Boner, W., Gillespie, R., \& Bateson, M.
(2013). Bottom of the Heap: Having Heavier Competitors Accelerates
Early-Life Telomere Loss in the European Starling, Sturnus
vulgaris.\emph{PLOS ONE} , \emph{8} (12), e83617. doi:
10.1371/journal.pone.0083617

Nettle, D., Monaghan, P., Gillespie, R., Brilot, B., Bedford, T., \&
Bateson, M. (2015). An experimental demonstration that early-life
competitive disadvantage accelerates telomere loss. \emph{Proceedings of
the Royal Society of London B: Biological Sciences} , \emph{282} (1798),
20141610. doi: 10.1098/rspb.2014.1610

Nettle, D., Seeker, L., Nussey, D., Froy, H., \& Bateson, M. (2019).
Consequences of measurement error in qPCR telomere data: A simulation
study. \emph{PLOS ONE} , \emph{14} (5), e0216118. doi:
10.1371/journal.pone.0216118

Nieuwenhuis, R., te Grotenhuis, M., \& Pelzer, B. (2012). Influence.ME:
tools for detecting influential data in mixed effects models., 4(2),
38-47. \emph{R Journal} , \emph{4} (2), 38--47.

Noguera, J. C., Metcalfe, N. B., Boner, W., \& Monaghan, P. (2015).
Sex-dependent effects of nutrition on telomere dynamics in zebra finches
(Taeniopygia guttata). \emph{Biology Letters} , \emph{11} (2), 20140938.
doi: 10.1098/rsbl.2014.0938

Noguera, J. C. (2017). Interacting effects of early dietary conditions
and reproductive effort on the oxidative costs of
reproduction.\emph{PeerJ} , \emph{5} , e3094. doi: 10.7717/peerj.3094

Olsson, M., Wapstra, E., \& Friesen, C. (2018). Ectothermic telomeres:
It's time they came in from the cold. \emph{Philosophical Transactions
of the Royal Society B: Biological Sciences} , \emph{373} (1741).
Scopus. doi: 10.1098/rstb.2016.0449

Olsson, M., Friesen, C. R., Rollings, N., Sudyka, J., Lindsay, W.,
Whittington, C. M., \& Wilson, M. (2018). Long-term effects of
superoxide and DNA repair on lizard telomeres. \emph{Molecular Ecology}
,\emph{27} (24), 5154--5164. doi: 10.1111/mec.14913

Olsson, M., Pauliny, A., Wapstra, E., \& Blomqvist, D. (2010). Proximate
determinants of telomere length in sand lizards (Lacerta
agilis).\emph{Biology Letters} , \emph{6} (5), 651--653. doi:
10.1098/rsbl.2010.0126

Paradis, E., \& Schliep, K. (2019). Ape 5.0: An environment for modern
phylogenetics and evolutionary analyses in R. \emph{Bioinformatics}
,\emph{35} (3), 526--528. doi: 10.1093/bioinformatics/bty633

Parolini, M., Possenti, C. D., Romano, A., Caprioli, M., Rubolini, D.,
\& Saino, N. 2019. Perinatal variation and covariation of oxidative
status and telomere length in yellow-legged gull chicks. \emph{Current
Zoology} . doi: 10.1093/cz/zoy084

Parolini, M., Romano, A., Khoriauli, L., Nergadze, S. G., Caprioli, M.,
Rubolini, D., \ldots{} Giulotto, E. (2015). Early-Life Telomere Dynamics
Differ between the Sexes and Predict Growth in the Barn Swallow (Hirundo
rustica). \emph{PLOS ONE} , \emph{10} (11), e0142530. doi:
10.1371/journal.pone.0142530

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

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{BioRxiv} , 320150. doi: 10.1101/320150

Pérez-Rodríguez, L., Redondo, T., Ruiz-Mata, R., Camacho, C.,
Moreno-Rueda, G., \& Potti, J. (2019). Vitamin E Supplementation---But
Not Induced Oxidative Stress---Influences Telomere Dynamics During Early
Development in Wild Passerines. \emph{Frontiers in Ecology and
Evolution} , \emph{7} . doi: 10.3389/fevo.2019.00173

Pick, J. L., Nakagawa, S., \& Noble, D. W. A. (2019). Reproducible,
flexible and high-throughput data extraction from primary literature:
The metaDigitise r package. \emph{Methods in Ecology and Evolution}
,\emph{10} (3), 426--431. doi: https://doi.org/10.1111/2041-210X.13118

R Core Team. (2020). \emph{R: A language and environment for statistical
computing. R Foundation for Statistical Computing, Vienna, Austria. URL:
https://www.R-project.org/} .

Reichert, S., Rojas, E. R., Zahn, S., Robin, J.-P., Criscuolo, F., \&
Massemin, S. (2015). Maternal telomere length inheritance in the king
penguin. \emph{Heredity} , \emph{114} (1), 10--16. doi:
10.1038/hdy.2014.60

Reichert, S., Bize, P., Arrivé, M., Zahn, S., Massemin, S., \&
Criscuolo, F. (2014). Experimental increase in telomere length leads to
faster feather regeneration. \emph{Experimental Gerontology} , \emph{52}
, 36--38. doi: 10.1016/j.exger.2014.01.019

Reichert, S., Criscuolo, F., Zahn, S., Arrive, M., Bize, P., \&
Massemin, S. (2015). Immediate and delayed effects of growth conditions
on ageing parameters in nestling zebra finches. \emph{Journal of
Experimental Biology} , \emph{218} (3), 491--499. doi:
10.1242/jeb.109942

Reichert, S., Froy, H., Boner, W., Burg, T. M., Daunt, F., Gillespie,
R., \ldots{} Monaghan, P. (2017). Telomere length measurement by qPCR in
birds is affected by storage method of blood samples. \emph{Oecologia} ,
1--10. doi: 10.1007/s00442-017-3887-3

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

Reichert, S., Stier, A., Zahn, S., Arrivé, M., Bize, P., Massemin, S.,
\& Criscuolo, F. (2014). Increased brood size leads to persistent eroded
telomeres. \emph{Frontiers in Ecology and Evolution} , \emph{2} . doi:
10.3389/fevo.2014.00009

Rollings, N., Friesen, C. R., Sudyka, J., Whittington, C., Giraudeau,
M., Wilson, M., \& Olsson, M. (2017). Telomere dynamics in a lizard with
morph-specific reproductive investment and
self-maintenance.\emph{Ecology and Evolution} , \emph{7} (14),
5163--5169. doi: https://doi.org/10.1002/ece3.2712

Ropio, J., Chebly, A., Ferrer, J., Prochazkova\selectlanguage{english}-Carlotti, M., Idrissi,
Y., Azzi\selectlanguage{english}-Martin, L., \ldots{} Chevret, E. (2020). Reliable blood cancer
cells' telomere length evaluation by qPCR. \emph{Cancer Medicine}
,\emph{9} (9), 3153--3162. doi: https://doi.org/10.1002/cam4.2816

Salmon, P., Nilsson, J. F., Watson, H., Bensch, S., \& Isaksson, C.
(2017). Selective disappearance of great tits with short telomeres in
urban areas. \emph{Proceedings of the Royal Society B: Biological
Sciences} , \emph{284} (1862), 20171349. doi: 10.1098/rspb.2017.1349

Salomons, H. M., Mulder, G. A., van de Zande, L., Haussmann, M. F.,
Linskens, M. H. K., \& Verhulst, S. (2009). Telomere Shortening and
Survival in Free-Living Corvids. \emph{Proceedings: Biological Sciences}
, \emph{276} (1670), 3157--3165.

Schoenemann, K. L., \& Bonier, F. (2018). Repeatability of
glucocorticoid hormones in vertebrates: A meta-analysis. \emph{PeerJ}
,\emph{6} , e4398. doi: 10.7717/peerj.4398

Schultner, J., Moe, B., Chastel, O., Bech, C., \& Kitaysky, A. S.
(2014). Migration and stress during reproduction govern telomere
dynamics in a seabird. \emph{Biology Letters} , \emph{10} (1), 20130889.
doi: 10.1098/rsbl.2013.0889

Sebastiano, M., Eens, M., Angelier, F., Pineau, K., Chastel, O., \&
Costantini, D. (2017). Corticosterone, inflammation, immune status and
telomere length in frigatebird nestlings facing a severe herpesvirus
infection. \emph{Conservation Physiology} , \emph{5} (cow073). doi:
10.1093/conphys/cow073

Seeker, L. A., Holland, R., Underwood, S., Fairlie, J., Psifidi, A.,
Ilska, J. J., \ldots{} Nussey, D. H. (2016). Method Specific Calibration
Corrects for DNA Extraction Method Effects on Relative Telomere Length
Measurements by Quantitative PCR. \emph{PLOS ONE} , \emph{11} (10),
e0164046. doi: 10.1371/journal.pone.0164046

Simide, R., Angelier, F., Gaillard, S., \& Stier, A. (2016). Age and
Heat Stress as Determinants of Telomere Length in a Long-Lived Fish, the
Siberian Sturgeon. \emph{Physiological and Biochemical Zoology}
,\emph{89} (5), 441--447. doi: 10.1086/687378

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

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

Stier, A., Delestrade, A., Bize, P., Zahn, S., Criscuolo, F., \&
Massemin, S. (2016). Investigating how telomere dynamics, growth and
life history covary along an elevation gradient in two passerine
species. \emph{Journal of Avian Biology} , \emph{47} (1), 134--140. doi:
10.1111/jav.00714

Stier, A., Massemin, S., Zahn, S., Tissier, M. L., \& Criscuolo, F.
(2015). Starting with a handicap: Effects of asynchronous hatching on
growth rate, oxidative stress and telomere dynamics in free-living great
tits. \emph{Oecologia} , \emph{179} (4), 999--1010. doi:
10.1007/s00442-015-3429-9

Stier, A., Metcalfe, N. B., \& Monaghan, P. (2020). Pace and stability
of embryonic development affect telomere dynamics: An experimental study
in a precocial bird model. \emph{Proceedings of the Royal Society B:
Biological Sciences} , \emph{287} (1933), 20201378. doi:
10.1098/rspb.2020.1378

Stier, A., Reichert, S., Criscuolo, F., \& Bize, P. (2015). Red blood
cells open promising avenues for longitudinal studies of ageing in
laboratory, non-model and wild animals. \emph{Experimental Gerontology}
,\emph{71} (Supplement C), 118--134. doi: 10.1016/j.exger.2015.09.001

Stoffel, M. A., Nakagawa, S., \& Schielzeth, H. (2017). RptR:
repeatability estimation and variance decomposition by generalized
linear mixed-effects models. \emph{Methods in Ecology and Evolution}
,\emph{8} (11), 1639--1644. doi: 10.1111/2041-210X.12797

Sudyka, J., Arct, A., Drobniak, S., Dubiec, A., Gustafsson, L., \&
Cicho\selectlanguage{polish}ń, \selectlanguage{english}M. (2014). Experimentally increased reproductive effort alters
telomere length in the blue tit (Cyanistes caeruleus). \emph{Journal of
Evolutionary Biology} , \emph{27} (10), 2258--2264. doi:
https://doi.org/10.1111/jeb.12479

Sudyka, J., Casasole, G., Rutkowska, J., \& Cicho\selectlanguage{polish}ń, \selectlanguage{english}M. (2016). Elevated
reproduction does not affect telomere dynamics and oxidative
stress.\emph{Behavioral Ecology and Sociobiology} , \emph{70} (12),
2223--2233. doi: 10.1007/s00265-016-2226-8

Sudyka, J., Podmok\selectlanguage{polish}ł\selectlanguage{english}a, E., Drobniak, S. M., Dubiec, A., Arct, A.,
Gustafsson, L., \& Cicho\selectlanguage{polish}ń, \selectlanguage{english}M. (2019). Sex-specific effects of parasites
on telomere dynamics in a short-lived passerine---The blue tit.\emph{Die
Naturwissenschaften} , \emph{106} (1). doi: 10.1007/s00114-019-1601-5

Tacutu, R., Thornton, D., Johnson, E., Budovsky, A., Barardo, Di.,
Craig, T., \ldots{} De Magalh\selectlanguage{ngerman}ães, J. P. (2018). Human Ageing Genomic
Resources: New and updated databases. \emph{Nucleic Acids Research}
,\emph{46} (D1), D1083--D1090. doi: 10.1093/nar/gkx1042

Taff, C. C., Schoenle, L. A., \& Vitousek, M. N. (2018). The
repeatability of glucocorticoids: A review and
meta-analysis.\emph{General and Comparative Endocrinology} , \emph{260}
, 136--145. doi: 10.1016/j.ygcen.2018.01.011

Taff, C. C. \& Freeman\selectlanguage{english}-Gallant, C. R. (2017). Sexual signals reflect
telomere dynamics in a wild bird. \emph{Ecology and Evolution} ,\emph{7}
(10), 3436--3442. doi: https://doi.org/10.1002/ece3.2948

Tissier, M. L., Williams, T. D., \& Criscuolo, F. (2014). Maternal
Effects Underlie Ageing Costs of Growth in the Zebra Finch (Taeniopygia
guttata). \emph{PLOS ONE} , \emph{9} (5), e97705. doi:
10.1371/journal.pone.0097705

Tolios, A., Teupser, D., \& Holdt, L. M. (2015). Preanalytical
Conditions and DNA Isolation Methods Affect Telomere Length
Quantification in Whole Blood. \emph{PLOS ONE} , \emph{10} (12),
e0143889. doi: 10.1371/journal.pone.0143889

Tricola, G., Simons, M. J. P., Atema, E., Boughton, R. K., Brown, J. L.,
Dearborn Donald C., \ldots{} Haussmann Mark F. (2018). The rate of
telomere loss is related to maximum lifespan in
birds.\emph{Philosophical Transactions of the Royal Society B:
Biological Sciences} , \emph{373} (1741), 20160445. doi:
10.1098/rstb.2016.0445

Ujvari, B., Biro, P. A., Charters, J. E., Brown, G., Heasman, K.,
Beckmann, C., \& Madsen, T. (2017). Curvilinear telomere length dynamics
in a squamate reptile. \emph{Functional Ecology} , \emph{31} (3),
753--759. doi: https://doi.org/10.1111/1365-2435.12764

Ujvari, B., \& Madsen, T. (2009). Short Telomeres in Hatchling Snakes:
Erythrocyte Telomere Dynamics and Longevity in Tropical
Pythons.\emph{PLOS ONE} , \emph{4} (10), e7493. doi:
10.1371/journal.pone.0007493

van Lieshout, S. H. J., Bretman, A., Newman, C., Buesching, C. D.,
Macdonald, D. W., \& Dugdale, H. L. (2019). Individual variation in
early-life telomere length and survival in a wild mammal.\emph{Molecular
Ecology} , \emph{28} (18), 4152--4165. doi: 10.1111/mec.15212

van Lieshout, S. H. J., Froy, H., Schroeder, J., Burke, T., Simons, M.
J. P., \& Dugdale, H. L. (2020). Slicing: A sustainable approach to
structuring samples for analysis in long-term studies. \emph{Methods in
Ecology and Evolution} , \emph{11} (3), 418--430. doi:
https://doi.org/10.1111/2041-210X.13352

Vedder, O., Verhulst, S., Zuidersma, E., \& Bouwhuis, S. (2018).
Embryonic growth rate affects telomere attrition: An experiment in a
wild bird. \emph{Journal of Experimental Biology} , jeb.181586. doi:
10.1242/jeb.181586

Vehtari, A., Gelman, A., \& Gabry, J. (2017). Practical Bayesian model
evaluation using leave-one-out cross-validation and
WAIC.\emph{Statistics and Computing} , \emph{27} (5), 1413--1432. doi:
10.1007/s11222-016-9696-4

Verhulst, S., Susser, E., Factor-Litvak, P. R., Simons, M., Benetos, A.,
Steenstrup, T., \ldots{} Aviv, A. (2016). Response to: Reliability and
validity of telomere length measurements. \emph{International Journal of
Epidemiology} , \emph{45} (4), 1298--1301. doi: 10.1093/ije/dyw194

Verhulst, S., Susser, E., Factor-Litvak, P. R., Simons, M. J., Benetos,
A., Steenstrup, T., \ldots{} Aviv, A. (2015). Commentary: The
reliability of telomere length measurements. \emph{International Journal
of Epidemiology} , \emph{44} (5), 1683--1686. doi: 10.1093/ije/dyv166

Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor
package. \emph{Journal of Statistical Software} , \emph{36} (3), 1--48.

von Zglinicki, T. (2002). Oxidative stress shortens
telomeres.\emph{Trends in Biochemical Sciences} , \emph{27} (7),
339--344. doi: 10.1016/S0968-0004(02)02110-2

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{Journal of Experimental Biology}
,\emph{218} (5), 668--674. doi: 10.1242/jeb.104265

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{Phil. Trans. R. Soc. B} , \emph{373} (1741),
20160447. doi: 10.1098/rstb.2016.0447

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

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

Young, R. C., Kitaysky, A. S., Haussmann, M. F., Descamps, S., Orben, R.
A., Elliott, K. H., \& Gaston, A. J. (2013). Age, Sex, and Telomere
Dynamics in a Long-Lived Seabird with Male-Biased Parental
Care.\emph{PLOS ONE} , \emph{8} (9), e74931. doi:
10.1371/journal.pone.0074931

Young, R. C., Welcker, J., Barger, C. P., Hatch, S. A., Merkling, T.,
Kitaiskaia, E. V., \ldots{} Kitaysky, A. S. (2017). Effects of
developmental conditions on growth, stress and telomeres in black-legged
kittiwake chicks. \emph{Molecular Ecology} , \emph{26} (13), 3572--3584.
doi: https://doi.org/10.1111/mec.14121

\textbf{Table 1.} All the studies, and effect size groups derived from
those studies with associated \emph{R} values included in the
meta-analysis.\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
\textbf{Species} & \textbf{Study} & \textbf{Effect size group} & R &
\textbf{Reference}\tabularnewline
\midrule
\endhead
Great reed warbler (\emph{Acrocephalus arundinaceus}) & Hidden costs of
infection: Chronic malaria accelerates telomere degradation and
senescence in wild birds & Juveniles to adults & 0,723 & (Asghar et al.,
2015)\tabularnewline
Siskin (\emph{Spinus spinus}) & Parallel telomere shortening in multiple
body tissues owing to malaria infection & Juveniles & 0,288 & (Asghar et
al., 2016)\tabularnewline
Zebra finch (\emph{Taeniopygia guttata}) & Heritability of telomere
length in zebra finch & Juveniles to adults & 0,814 & (Atema et al.,
2015)\tabularnewline
& & Adults & 0,819 &\tabularnewline
Great tit (\emph{Parus major}) & Ultra-long telomeres shorten with age
in nestling great tits but are static in adults and mask attrition of
short telomeres & Juveniles & 0,491 & (Atema et al.,
2019)\tabularnewline
Seychelles warbler (\emph{Acrocephalus sechellensis}) & Telomere length
and dynamics predict mortality in a wild longitudinal study & Adults &
0,191 & (Barrett et al., 2013)\tabularnewline
European starling (\emph{Sturnus vulgaris}) & Developmental telomere
attrition predicts impulsive decision-making in adult starlings &
Juveniles to adults & 0,52 & (Bateson et al., 2015)\tabularnewline
Common tern (\emph{Sterna hirundo}) & Telomere length reflects
phenotypic quality and costs of reproduction in a long-lived seabird &
Adults & 0,787 & (Bauch et al., 2013)\tabularnewline
Ad\selectlanguage{ngerman}élie penguin (\emph{Pygoscelis adeliae}) & Oxidative status and
telomere length in a long-lived bird facing a costly reproductive event
& Adults & 0,528 & (Beaulieu et al., 2011)\tabularnewline
Alpine swift (\emph{Apus melba}) & Telomere dynamics rather than age
predict life expectancy in the wild & Adults & 0,285 & (Bize et al.,
2009)\tabularnewline
Black-legged kittiwakes (\emph{Rissa tridactyla}) & Perfluorinated
substances and telomeres in an Arctic seabird: Cross-sectional and
longitudinal approaches & Adults & 0,749 & (Bl\selectlanguage{ngerman}évin et al.,
2017)\tabularnewline
Jackdaw (\emph{Coloeus monedula}) & Nestling telomere shortening, but
not telomere length, reflects developmental stress and predicts survival
in wild birds & Juveniles & 0,953 & (Boonekamp et al.,
2014)\tabularnewline
Jackdaw (\emph{Coloeus monedula}) & Does oxidative stress shorten
telomeres in vivo? & Juveniles & 0,969 & (Boonekamp et al.,
2017)\tabularnewline
Magellanic penguin (\emph{Spheniscus magellanicus}) & Magellanic penguin
telomeres do not shorten with age with increased reproductive effort,
investment, and basal corticosterone & Adults & 0,109 & (Cerchiara et
al., 2017)\tabularnewline
European starling (\emph{Sturnus vulgaris}) & Telomere length,
individual quality and fitness in female European starlings
(\emph{Sturnus vulgaris}) during breeding & Adults & 0,259 & (Criscuolo
et al., 2018)\tabularnewline
Zebra finch (\emph{Taeniopygia guttata}) & Timing matters: traffic noise
accelerates telomere loss rate differently across developmental stages &
Juveniles to adults & 0,435 & (Dorado-Correa et al.,
2018)\tabularnewline
Spotted snow skink (\emph{Niveoscincus ocellatus}) & Tail loss and
telomeres: consequences of large-scale tissue regeneration in a
terrestrial ectotherm & Adults & 0,19 & (Fitzpatrick et al.,
2019)\tabularnewline
Lesser black-backed gull (\emph{Larus fuscus}) & Telomere dynamics in
relation to early growth conditions in the wild in the lesser
black-backed gull & Juveniles & 0,542 & (Foote et al.,
2011)\tabularnewline
Gouldian finch (\emph{Erythrura gouldiae}) & Morph- and sex-specific
effects of challenging conditions on maintenance parameters in the
Gouldian finch & Adults & 0,818 & (Fragueira et al.,
2019)\tabularnewline
King penguin (\emph{Aptenodytes patagonicus}) & Catching-up but telomere
loss: half-opening the black box of growth and ageing trade-off in wild
king penguin chicks & Juveniles & 0,166 & (Geiger et al.,
2012)\tabularnewline
Dark-eyed junco (\emph{Junco hyemalis}) & Early breeding females
experience greater telomere loss & Adults & 0,356 & (Graham et al,
2019)\tabularnewline
Great tit (\emph{Parus major}) & Artificial light at night does not
affect telomere shortening in a developing free-living songbird: A field
experiment Artificial light at night and telomere dynamics & Juveniles &
0,131 & (Grunst et al., 2019)\tabularnewline
European shag (\emph{Phalacrocorax aristotelis}) & Telomere loss in
relation to age and early environment in long-lived birds & Juveniles to
adults & 0,586 & (Hall et al., 2004)\tabularnewline
Eurasian blackbird (\emph{Turdus merula}) & Repeated stressors in
adulthood increase the rate of biological ageing & Adults & 0,871 & (Hau
et al., 2015)\tabularnewline
Japanese quail (\emph{Coturnix japonica}) & Effect of egg size selection
on telomere length in Japanese quail & Juveniles & 0,645 & Haussmann \&
Tschirren unpublished\tabularnewline
Zebra finch (\emph{Taeniopygia guttata}) & Telomere length in early life
predicts lifespan & Juveniles to adults & 0,468 & (Heidinger et al.,
2012)\tabularnewline
European shag (\emph{Phalacrocorax aristotelis}) & Parental age
influences offspring telomere loss & Juveniles & 0,697 & (Heidinger et
al., 2016)\tabularnewline
European shag (\emph{Phalacrocorax aristotelis}) & Stress exposure in
early post-natal life reduces telomere length: an experimental
demonstration in a long-lived seabird & Juveniles & 0,55 & (Herborn et
al., 2014)\tabularnewline
Tawny owl (\emph{Strix aluco}) & Pale and dark morphs of tawny owls show
different patterns of telomere dynamics in relation to disease status &
Adults & 0,113 & (Karell et al., 2017)\tabularnewline
Pied flycatcher (\emph{Ficedula hypoleuca}) & Impact of continuous
predator threat on telomere dynamics in parent and nestling pied
flycatchers & Juveniles & 0,196 & (K\selectlanguage{ngerman}ärkkäinen et al.,
2019)\tabularnewline
& & Adults & 0,375 &\tabularnewline
Pied flycatcher (\emph{Ficedula hypoleuca}) & Interplays between pre-
and post-natal environments affect early-life mortality, body mass and
telomere dynamics in the wild & Juveniles & 0,321 & (K\selectlanguage{ngerman}ärkkäinen et al.,
2021)\tabularnewline
Feral pigeon (\emph{Columbia livia}) & Telomere erosion varies with sex
and age at immune challenge but not with maternal antibodies in pigeons
& Juveniles to adults & 0,307 & (Lardy et al., 2017)\tabularnewline
Pied flycatcher (\emph{Ficedula hypoleuca}) & Sex-specific Associations
between Telomere Dynamics and Oxidative Status in Adult and Nestling
Pied Flycatchers & Adults & 5,44E-18 & (L\selectlanguage{ngerman}ópez-Arrabé et al.,
2018)\tabularnewline
Atlantic salmon (\emph{Salmo salar}) & Shorter juvenile telomere length
is associated with higher survival to spawning in migratory Atlantic
salmon & Juveniles to adults & 0,264 & (McLennan et al.,
2017)\tabularnewline
Blacktailed gull (\emph{Larus crassirostris}) & Environmental
perturbations influence telomere dynamics in long-lived birds in their
natural habitat & Adults & 0,803 & (Mizutani et al.,
2013)\tabularnewline
Blacktailed gull (\emph{Larus crassirostris}) & How Do Growth and
Sibling Competition Affect Telomere Dynamics in the First Month of Life
of Long-Lived Seabird? & Juveniles & 0,607 & (Mizutani et al.,
2016)\tabularnewline
European starling (\emph{Sturnus vulgaris}) & Bottom of the Heap: Having
Heavier Competitors Accelerates Early-Life Telomere Loss in the European
Starling, \emph{Sturnus vulgaris} & Juveniles & 0,702 & (Nettle et al.,
2013)\tabularnewline
European starling (\emph{Sturnus vulgaris}) & An experimental
demonstration that early-life competitive disadvantage accelerates
telomere loss & Juveniles & 0,917 & (Nettle et al., 2015)\tabularnewline
European starling (\emph{Sturnus vulgaris}) & Brood size moderates
associations between relative size, telomere length, and immune
development in European starling nestlings & Juveniles & 0,73 & (Nettle
et al., 2016)\tabularnewline
European starling (\emph{Sturnus vulgaris}) & Early-life adversity
accelerates cellular ageing and affects adult inflammation: Experimental
evidence from the European starling & Juveniles & 0,869 & (Nettle et
al., 2017)\tabularnewline
Zebra finch (\emph{Taeniopygia guttata}) & Sex-dependent effects of
nutrition on telomere dynamics in zebra finches (\emph{Taeniopygia
guttata}) & Juveniles to adults & 0,536 & (Noguera et al.,
2015)\tabularnewline
Zebra finch (\emph{Taeniopygia guttata}) & Interacting effects of early
dietary conditions and reproductive effort on the oxidative costs of
reproduction & Adults & 0,0526 & (Noguera, 2017)\tabularnewline
Brown trout (\emph{Salmo trutta}) & Telomere dynamics in wild brown
trout: effects of compensatory growth and early growth investment &
Juveniles & 0,455 & (N\selectlanguage{ngerman}äslund et al., 2015)\tabularnewline
Sand lizard (\emph{Lacerta agilis}) & Proximate determinants of telomere
length in sand lizards (\emph{Lacerta agilis}) & Adults & 0,609 &
(Olsson et al., 2010)\tabularnewline
Painted ground-dragon (\emph{Ctenophorus pictus}) & Long term effects of
superoxide and DNA repair on lizard telomeres & Adults & 0,523 & (Olsson
et al., 2018)\tabularnewline
Barn swallow (\emph{Hirundo rustica}) & Early-Life Telomere Dynamics
Differ between the Sexes and Predict Growth in the Barn Swallow
(\emph{Hirundo rustica}) & Juveniles & 0,662 & (Parolini et al.,
2015)\tabularnewline
Yellow-legged gull (\emph{Larus michahellis}) & Perinatal variation and
covariation of oxidative status and telomere length in yellow-legged
gull chicks & Juveniles & 0,379 & (Parolini et al., 2019)\tabularnewline
Coho salmon (\emph{Oncorhynchus kisutch}) & Rapid growth accelerates
telomere attrition in a transgenic fish & Juveniles & 0,487 & (Pauliny
et al., 2015)\tabularnewline
Pied flycatcher (\emph{Ficedula hypoleuca}) & Vitamin E
Supplementation---But Not Induced Oxidative Stress---Influences Telomere
Dynamics During Early Development in Wild Passerines & Juveniles & 0,603
& (P\selectlanguage{ngerman}érez-Rodríguez et al., 2019)\tabularnewline
Zebra finch (\emph{Taeniopygia guttata}) & Experimental increase in
telomere length leads to faster feather regeneration & Adults & 0,418 &
(Reichert et al., 2014)\tabularnewline
Zebra finch (\emph{Taeniopygia guttata}) & Increased brood size leads to
persistent eroded telomeres & Adults & 0,518 & (Reichert et al.,
2014)\tabularnewline
King penguin (\emph{Aptenodytes patagonicus}) & Maternal telomere length
inheritance in the king penguin & Juveniles & 0,283 & (Reichert et al.,
2015)\tabularnewline
Zebra finch (\emph{Taeniopygia guttata}) & Immediate and delayed effects
of growth conditions on ageing parameters in nestling zebra finches &
Juveniles & 0,713 & (Reichert et al., 2015)\tabularnewline
& & Juveniles to adults & 0,32 &\tabularnewline
Australian painted dragon (\emph{Ctenophorus pictus}) & Telomere
dynamics in a lizard with morph-specific reproductive investment and
self-maintenance & Adults & 0,711 & (Rollings et al.,
2017)\tabularnewline
Great tit (\emph{Parus major}) & Selective disappearance of great tits
with short telomeres in urban areas & Juveniles to adults & 0,231 &
(Salm\selectlanguage{ngerman}ón et al., 2017)\tabularnewline
& & Adults & 0,398 &\tabularnewline
Jackdaw (\emph{Coloeus monedula}) & Telomere shortening and survival in
free-living corvids & Juveniles & 0,929 & (Salomons et al.,
2009)\tabularnewline
Black-legged kittiwake (\emph{Rissa tridactyla}) & Migration and stress
during reproduction govern telomere dynamics in a seabird & Adults &
0,928 & (Schultner et al., 2014)\tabularnewline
Magnificent frigatebird (\emph{Fregata magnificens}) & Corticosterone,
inflammation, immune status and telomere length in frigatebird nestlings
facing a severe herpesvirus infection & Juveniles & 0,63 & (Sebastiano
et al., 2017)\tabularnewline
Siberian sturgeon (\emph{Acipenser baerii}) & Age and Heat Stress as
Determinants of Telomere Length in a Long-Lived Fish, the Siberian
Sturgeon & Juveniles & 0,0265 & (Simide et al., 2016)\tabularnewline
Great tit (\emph{Parus major}) & Starting with a handicap: effects of
asynchronous hatching on growth rate, oxidative stress and telomere
dynamics in free-living great tits & Juveniles & 0,597 & (Stier et al.,
2015)\tabularnewline
Great tit (\emph{Parus major}) & Investigating how telomere dynamics,
growth and life history covary along an elevation gradient in two
passerine species & Great tit & 0,738 & (Stier et al.,
2016)\tabularnewline
Coal tit (\emph{Periparus ater}) & & Coal tit & 0,743 &\tabularnewline
Collared flycatcher (\emph{Ficedula albicollis}) & Effect of prenatal TH
on telomere length in Collared flycatcher & qPCR & 0,653 & Stier et al.
unpublished\tabularnewline
& & TRF & 0,935 &\tabularnewline
Japanese quail (\emph{Coturnix japonica}) & Prenatal temperature and TL
in Japanese quail & qPCR & 0,662 & Stier at al.
unpublished\tabularnewline
& & TRF & 0,934 &\tabularnewline
Blue tit (\emph{Cyanistes caeruleus}) & Experimentally increased
reproductive effort alters telomere length in the blue tit
(\emph{Cyanistes caeruleus}) & Adults & 0,479 & (Sudyka et al.,
2014)\tabularnewline
Zebra finch (\emph{Taeniopygia guttata}) & Elevated reproduction does
not affect telomere dynamics and oxidative stress & Adults & 0,386 &
(Sudyka et al., 2016)\tabularnewline
Blue tit (\emph{Cyanistes caeruleus}) & Sex-specific effects of
parasites on telomere dynamics in a short lived passerine - a blue tit &
Adults & 0,398 & (Sudyka et al., 2019)\tabularnewline
Common yellowthroat (\emph{Geothlypis trichas}) & Sexual signals reflect
telomere dynamics in a wild bird & Adults & 0,531 & (Taff \&
Freeman-Gallant, 2017)\tabularnewline
Zebra finch (\emph{Taeniopygia guttata}) & Maternal Effects Underlie
Ageing Costs of Growth in the Zebra Finch (\emph{Taeniopygia guttata}) &
Juveniles & 0,724 & (Tissier et al., 2014)\tabularnewline
Water python (\emph{Liasis fuscus}) & Short Telomeres in Hatchling
Snakes: Erythrocyte Telomere Dynamics and Longevity in Tropical Pythons
& Juveniles & 0,104 & (Ujvari \& Madsen, 2009)\tabularnewline
& & Adults & 0,955 &\tabularnewline
Frillneck lizard (\emph{Chlamydosaurus kingii}) & Curvilinear telomere
length dynamics in a squamate reptile & Adults & 0,562 & (Ujvari et al.,
2017)\tabularnewline
Common tern (\emph{Sterna hirundo}) & Telomere attrition and growth: a
life-history framework and case study in common terns & Juveniles &
0,871 & (Vedder et al., 2018)\tabularnewline
European storm petrel (\emph{Hydrobates pelagicus}) & Variation in
early-life telomere dynamics in a long-lived bird: links to
environmental conditions and survival & Juveniles & 0,0683 & (Watson et
al., 2015)\tabularnewline
White-browed sparrow-weaver (\emph{Plocepasser mahali}) & Telomere
attrition predicts reduced survival in a wild social bird but short
telomeres do not & Juveniles & 0,657 & (Wood \& Young,
2019)\tabularnewline
Thick-billed murre (\emph{Uria lomvia}) & Age, Sex and Telomere Dynamics
in a Long-Lived Seabird with Male-Biased Parental Care & Juveniles to
adults & 0,692 & (Young et al., 2013)\tabularnewline
Black-legged kittiwake (\emph{Rissa tridactyla}) & Effects of
developmental conditions on growth stress and telomeres in black-legged
kittiwake chicks & Juveniles & 0,74 & (Young et al.,
2017)\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 2.} The results of a phylogenetic model including all
tested predictor variables. Coefficients show \emph{R} values and are
the result of back-transformed standardized Z-values. Species accounts
for the repeated measures per species, phylogeny for the fact that these
species are related, and laboratory identity for the fact that protocols
are more similar within a laboratory. Fixed effects in italics show the
variables for which the 95\% CI did not overlap with zero. Intercept
shows qPCR for average study length in a captive adult population of
endotherm for a species with an average maximum lifespan. This table
shows the model with all terms; a model without phylogeny or that
contained only the terms in which 95\% CI did not overlap with zero,
gave consistent results (Table S1 A-D). l-95\% CI and u-95\% CI indicate
lower and upper 95\% confidence intervals, respectively.

\textbf{Figure 1.} Illustrating the variation in within-individual
repeatability of telomere length. Simulated data is presented where
population-level telomere shortening is constant (\emph{i.e.} 1kb loss
between time 1 and time 2) but within-individual repeatability is either
(a) high (b) moderate or (c) low. Data has been simulated based on
collared flycatcher telomere characteristics: mean \selectlanguage{ngerman}± SD = 18.6 ± 1.6kb,
1kb shortening occurring approximately over 2.5 years of life, N = 30,
normal distribution (Stier et al. 2020). Black diagonals show x =
y,\emph{i.e.} when the telomere length at first and second measurement
are the same, while dotted lines show regression lines for each dataset.
Values above the diagonal illustrate telomere elongation (which can for
example arise due to measurement error) and below the diagonal telomere
shortening.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Figure 2.} Distribution of data for all categorical variables in
the study. (A) measurement method, (B) taxon, (C) environment (semi-wild
refers to individuals from wild populations held in captivity during the
study), and (D) age classes (juvenile \& adults refers to individuals
that were measured both as juveniles and as adults).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{Figure 3.} Distribution of the within-individual repeatability
of telomere length along the phylogeny of the 42 species in this study.
Although the phylogenetic signal was not significantly different from 0
(lambda: 0.32; 95\% CI: 0.00-0.83), phylogeny still captured some
variance (Table 2) and a model with phylogeny was favored over one
without phylogeny. The phylogenetic distributions of the other traits in
this study are shown in figure S3.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\textbf{Figure 4.} Distribution of within-individual repeatability
estimates (and 95\% CI) of telomere length for studies using (A) TRF and
(B) qPCR and (C) their boxplots with median, quartiles and 95\% CI.
Vertical dashed line denotes a repeatability of 0.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

\textbf{Figure 5.} Effects of biological and methodological factors on
the repeatability of telomere length. Measurement method (B) and study
length (E) affected clearly the repeatability of telomere length, while
the effect of the biological variables (A: taxon, C: environment, D: age
class, and F: species lifespan) were small and their effect size
overlapped with zero (G). For illustration purposes, fits on the
continuous predictors are the result of frequentist models on the best
fitting model in table 2, but are consistent with the conclusions of the
model in table 2.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}

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

