Methods
The Seychelles warbler model
system
The Seychelles warbler is a small insectivorous passerine currently
distributed across five islands in the Seychelles. The population on
Cousin Island (29 ha; 4°20’ S, 55°40’ E) – containing ca. 320
individuals – has been extensively monitored since 1986 (Komdeur, 1992;
Hammers et al. , 2015). Since 1997, nearly all individuals
(>96%) have been ringed with a unique combination of a
British Trust for Ornithology (BTO) metal ring and three colour rings
for identification (Raj Pant et al. , 2020) (Richardson et
al. , 2001). Individuals were usually first caught as nestlings, or as
dependent juveniles (<8 months old) with mist nets (see Kingmaet al. , 2016 for details). Juveniles were aged as fledglings
(1–3 months), old fledglings (3–5 months) or sub-adults (5–12 months)
based on behaviour and eye colour (Komdeur, 1992). Since the resighting
probability of individuals during the major breeding season is close to
one – 0.98 for individuals ≥2 years-old (Brouwer et al. , 2006)
– and dispersal from the island is virtually absent (Komdeur et
al. , 2004), individuals that were not observed during the major
breeding season were assumed dead. First year survival is 0.61 ± 0.09
SE, increasing to 0.84 ± 0.04 SE annual survival in adults (Brouweret al. , 2006). For individuals reaching fledgling age, the mean
life expectancy is 5.5 years (Komdeur, 1991), and the maximum recorded
lifespan is 19 years (Hammers and Brouwer, 2017).
The population is structured into ca 115 clearly defined
territories (Kingma et al. , 2016). The availability of the
warbler’s invertebrate prey (Komdeur, 1992) varies considerably due to
the interacting effects of defoliating salt spray (along coastal
territories), tree species abundance, elevation and rainfall (Van de
Crommenacker et al. , 2011). Territories are defended year-round
by a single dominant breeding pair, but ca 40% of territories
include an additional 1–5 sexually mature subordinates, often past
offspring of the same dominant pair (Richardson, Burke and Komdeur,
2002).
The majority of breeding activity (94% of territories) occurs from June
to August, but a minor breeding season also occurs from January to March
(Komdeur and Daan, 2005). Breeding attempts usually consist of one-egg
clutches (Komdeur, 1994). Only females incubate while both sexes
provision chicks and fledglings for ca three months
post-fledging. Around one third of subordinates also provide
alloparental care to group offspring, hereafter ‘helpers’ (Komdeur,
1994; Hammers et al. , 2019). About 44% of female helpers are
also co-breeders (Richardson et al. , 2001; Raj Pant et
al. , 2019). The offspring of cobreeders are jointly cared for by the
subordinate female and dominant pair (Richardson et al. , 2001;
Bebbington et al. , 2018). The frequency of extra-pair paternity
in the population is high (ca 41%; Raj Pant et al. , 2019)
and such paternity is nearly always gained by dominant males from other
territories (Richardson et al. , 2001), but males only provide
parental care in their own territory.
Data collection
Our study uses data collected from 1995 to 2015. Each year
(June–September), during the major breeding season, each territory was
visited at least every two weeks to determine the identity and status of
group individuals. During visits, the dominant female was followed for
at least 15 minutes to assess breeding activity (Richardson, Burke and
Komdeur, 2007). Territories with an active nest were visited every 3–4
days until the nestling(s) have fledged or the breeding attempt failed.
Observations of incubating and/or provisioning were used to estimate
hatching/fledging dates, and to determine whether any subordinates
present in the territory were helpers (Richardson, et al ., 2002;
van Boheemen et al. , 2019). For each territory, the availability
of food was calculated (following Komdeur, 1992). Briefly, the number of
insects (on the undersides of leaves) was multiplied by the percentage
cover of broad-leaf vegetation within territories. This number was then
divided by the number of adult territory occupants to give food
availability per individual (Brouwer et al. , 2006).
During each major breeding season, as much of the adult population as
possible (normally around 30%) was caught and re-sampled: ca 25
ul of blood was taken from the brachial vein and stored in 100% ethanol
(Richardson et al. , 2001). DNA extracted from the blood samples
(following Richardson et al. , 2001) was used to confirm sex and
assign parentage using MasterBayes 2.52 based on genotypes derived from
30 microsatellite loci (for details see Sparks et al. , 2020). The
presence of haemosporidian infection (Haemoproteus
nucleocondensus ; hereafter referred to as malaria) - the only known
parasite in the Seychelles warbler (Hutchings, 2009) - was screened for
following Hellgren et al . (2004). In the Seychelles warbler,
nearly all individuals become infected with malaria in their first year
(Hammers et al. , 2016). Thus, in our study (of individuals
>1 year of age), birds in which we did not detect malaria
may be in the latent infection stage – where parasites persist in
organs - or have cleared infection, while infected individuals are
either in the later chronic stage of infection, or in a subsequent
relapse or reinfection. Infection with malaria does not appear to have
an impact on annual survival in the Seychelles warbler (Hammers et
al. , 2016) but has been linked to telomere attrition in the great reed
warbler (Acrocephalus arundinaceus : Asghar et al. , 2015).
Relative Telomere Length (RTL; the concentration of amplified telomeric
DNA relative to that amplified at GAPDH – a single copy gene)
had previously been measured using qPCR as part of another study
(Spurgin et al. , 2018). Since avian erythrocytes are nucleated
and vastly outnumber other blood cell types, blood RTL is effectively a
measure of erythrocyte RTL (Stier et al. , 2015). Individuals with
two or more RTL measurements were used in the current study, with the
difference between consecutive pairs of RTL measurements (ΔRTL) as the
response variable. We excluded RTL measurements from young individuals
(< 1 year), as previous work in this population has shown that
within-individual rate of attrition per annum is an order of magnitude
greater in the first year compared to adult life (Spurgin et al. ,
2018). For consistency, we focused on RTL measurements from catches only
within the major breeding season, since inter-seasonal ∆RTL could
reflect seasonal effects on RTL. Individuals are caught
opportunistically, meaning that the follow-up period between RTL
measurements (hereafter ∆RTL period) ranged from one year (i.e.
consecutive seasons) to 9 years (Fig. S1). ∆RTL did not exhibit a
significant relationship with the ∆RTL period. The final dataset
comprised 359 ΔRTL measures from 227 adults.
Reproductive effort was measured as the number of offspring raised by an
individual in the ∆RTL period; specifically, offspring that had hatched
after time 1 and had reached independence (3 months old) before time 2.
Social offspring – those for which a dominant breeder provides parental
care – are determined from behavioral observations during nest
attempts. Offspring are genotyped to identify genetic parentage. This is
an underestimation of total offspring produced, since we excluded
offspring for which parents could not be assigned (ca 15% of
offspring ) and some offspring are likely to have died before being
sampled (Edwards, Burke and Dugdale, 2017). We used the number of social
offspring as our estimate of reproductive output in males (since males
do not care for offspring sired in other territories). Females
(dominants or co-breeder) always contribute to the care of any offspring
in the nest; (Richardson, Burke and Komdeur, 2002). Thus, female
reproductive output was the number of co-bred offspring. For both sexes,
the majority of individuals had 0–2 offspring within each ∆RTL period
(Fig. S1). Offspring number was positively correlated with the ∆RTL
period (Pearson’s r = 0.69, df = 357, P <
0.001, Fig. 1S), meaning that individuals typically produced one
offspring every two years.
During the ∆RTL period, we averaged food availability (insect abundance
per individual per field season) across field seasons. Reproductive
effort - in terms of time spent incubating and provisioning - of
dominant breeders is reduced by the presence of helpers, including
co-breeders (Hammers et al. , 2019; van Boheemen et al. ,
2019). Reduced telomere attrition in dominant females has been
associated with the presence of helpers in a previous study (Hammerset al. , 2019). Therefore, we determined whether nest helpers
(including co-breeders) were present in the territories of individuals
that produced offspring.
Statistical analysis
Using RStudio (v1.2.5033, Rstudio Team 2020) we tested whether food
availability, reproductive output, helper presence and malaria status
predicted ∆RTL, with the prediction that high food availability, low
reproductive output, helper presence and no malaria infection - or a
combination of these factors - would result in telomere lengthening. We
adjusted ∆RTL following Verhulst et al. (2013a); this method
subtracts the mean difference between successive samples expected from
the regression-to-mean effect, estimated by the correlation between
successive samples. In our dataset, this correlation was very weak
(Peason’s r = 0.06, df = 357 P = 0.22), as expected
given the low within-individual consistency of RTL in this system
(Spurgin et al. , 2018). This results in an adjusted ∆RTL
(hereafter ∆RTL) which is equivalent to RTL at time 2; positive values
indicate longer RTL and negative values indicate shorter RTL, relative
to the population mean RTL. As expected, ∆RTL was strongly correlated
with unadjusted ∆RTL (Pearson’s r = 0.71, df = 357,P < 0.001), meaning that individuals with lengthened or
shortened RTL (relative to their initial RTL) tended to have more
positive or more negative ∆RTL, respectively.
The association between factors and ∆RTL was tested using Linear Mixed
Models (package lme4 v1.1-25; Bates et al. , 2015). We
deliberately focused on a restricted set of fixed effects – chosen a
priori based on logic and evidence of influencing telomere dynamics –
to avoid data dredging, which could generate false-positive
associations. Chosen fixed effects included mean food availability
(continuous), number of offspring (continuous), malaria status (infected
or uninfected), helper presence (yes or no). We also included age at
time 1 (continuous), ∆RTL period and logical two-way interactions; for
example, the effect of offspring production on telomere maintenance may
depend on food availability and/or helper presence. Effects were likely
to differ between the sexes due to differing investments in
reproduction, so to investigate sex-specific differences, while also
avoiding the need for complex three-way interactions, separate models
were created for males and females. We included the sample year of the
first RTL measurement as a random factor. Variation in RTL between qPCR
plates (Sparks et al. , 2020) could contribute to variation in
∆RTL, since RTL measurements from longitudinal samples were run on
separate plates. Therefore, the plate identities of both RTL
measurements per ∆RTL were included as random factors. Since individuals
with three or more RTL measurements had multiple measures of ∆RTL,
individual identity was also included as a random factor. Full models
were reported after removing non-significant interactions. Offspring
number, food availability and ∆RTL period were log10 transformed (for
normality) and mean centered to remove collinearity between their main
effects and interaction (Schielzeth, 2010). Since most individuals had
0–2 offspring, offspring number was reduced to a categorical variable
(zero, one or ≥2) for graphical interpretation of interactions.
To test whether ∆RTL influenced subsequent survival, we performed a Cox
proportional hazards regression analysis (package survivalv3.2-7; Therneau, 2014). The response variable was the number of years
an individual lived beyond the sampling date of its last RTL
measurement. We included 16 individuals that were still alive in 2020 as
right-censored data points. 13 individuals translocated to other islands
post-sampling were excluded, leaving 214 individuals. Predictor
variables were ∆RTL, sex and age at last RTL measurement (since older
individuals are expected to have shorter remaining lifespans). Where
multiple measures of ∆RTL were available per individual, we used only
the last measurement, meaning that each ∆RTL value represents the last
known change in telomere length before the individual died. We were
interested in whether predicted hazard ratios exhibited a proportional
change across the range of ∆RTL values, or whether the association was
nonlinear. Therefore, we modelled ∆RTL as both a linear and quadratic
function. In this model, positive hazard coefficients would indicate a
decreased probability of survival with increasing values of the
predictor variable. Hazard ratios represent the effect size of
predictors; for example, a hazard ratio of 2 indicates that the risk of
death is twice as high for the corresponding change in a predictor
variable.