(2022) 22:105
Fohringer et al. BMC Ecology and Evolution
https://doi.org/10.1186/s12862-022-02050-5
RESEARCH ARTICLE
BMC Ecology and Evolution
Open Access
Large mammal telomere length variation
across ecoregions
Christian Fohringer1* , Franz Hoelzl2, Andrew M. Allen3,4, Claire Cayol1, Göran Ericsson1, Göran Spong1,
Steven Smith2 and Navinder J. Singh1
Abstract
Background: Telomere length provides a physiological proxy for accumulated stress in animals. While there is a
growing consensus over how telomere dynamics and their patterns are linked to life history variation and individual
experience, knowledge on the impact of exposure to different stressors at a large spatial scale on telomere length is
still lacking. How exposure to different stressors at a regional scale interacts with individual differences in life history is
also poorly understood. To better understand large-scale regional influences, we investigated telomere length variation in moose (Alces alces) distributed across three ecoregions. We analyzed 153 samples of 106 moose representing
moose of both sexes and range of ages to measure relative telomere lengths (RTL) in white blood cells.
Results: We found that average RTL was significantly shorter in a northern (montane) and southern (sarmatic)
ecoregion where moose experience chronic stress related to severe summer and winter temperatures as well as high
anthropogenic land-use compared to the boreal region. Our study suggests that animals in the northern boreal forests, with relatively homogenous land use, are less disturbed by environmental and anthropogenic stressors. In contrast, animals in areas experiencing a higher rate of anthropogenic and environmental change experience increased
stress.
Conclusion: Although animals can often adapt to predictable stressors, our data suggest that some environmental
conditions, even though predictable and ubiquitous, can generate population level differences of long-term stress.
By measuring RTL in moose for the first time, we provide valuable insights towards our current understanding of
telomere biology in free-ranging wildlife in human-modified ecosystems.
Keywords: Alces alces, Biomarker, Chronic stress, Human modification, Life history, Telomere associations
Background
Human-induced rapid environmental change is creating novel stressors for animals and their populations
[1]. These external changes cascade via physiological
mechanisms affecting long-term survival and fitness in
wild animals. In particular, exposure to anthropogenic
perturbations (resource extraction, infrastructural
*Correspondence: fohringer.c@gmail.com
1
Department of Wildlife, Fish and Environmental Studies, Swedish University
of Agricultural Sciences, 90183 Umeå, Sweden
Full list of author information is available at the end of the article
developments, hunting, and pollution) combined with
environmental stressors (competition over resources,
disease, or thermal stress) may activate the hypothalamic–pituitary–adrenal (HPA) axis of animals resulting
in increased stress hormone levels [2–4]. Continued activation of the HPA axis beyond baseline levels can affect
the metabolic system of the organism via increased oxidative damage from reactive oxygen species (ROS), and
induce a state of chronic stress [5]. The (TTAGGG)n
repeats that constitute vertebrate telomeres are particularly vulnerable to oxidative attack [6]. Telomeres, i.e.,
the non-coding ends of linear chromosomes, are considered to play a fundamental role in the protection of the
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Fohringer et al. BMC Ecology and Evolution
(2022) 22:105
structural integrity of chromosomal DNA and in the regulation of cellular senescence [7, 8]. Thus, they have the
potential to serve as a molecular biomarker to determine
individual physiological state and past environmental
experiences [9, 10]. Shorter telomeres and elevated shortening rates are typically associated with stress and senescence [7, 9, 11, 12]. Angelier et al. [13] reviewed studies
determining the relationships between how different
stressors can influence telomere associations in wild vertebrates. Specifically, environmental factors such as water
temperature [14], weather [15], habitat quality [16–18] as
well as infectious diseases [19] were linked to altered telomere length in wildlife.
Ecoregions provide an ideal spatial scale to examine
differences in metabolic expenditure and chronic stress
expression as they offer a global categorization representing distinct units of biological diversity and its association with climatic conditions [20]. Distinct ecoregions
also encompass differences in anthropogenic pressures,
food availability and weather. Differences in the degree of
exposure to different environmental conditions (including an array of stressors) can potentially cause chronic
stress in organisms occupying ecoregions where they
experience repeated triggering of the HPA axis beyond
full recovery during the annual and seasonal cycles.
Yet, comparative studies of chronic stress responses, or
its indicators, across biogeographic regions are largely
absent. This is especially true as data on multiple individuals and populations distributed across large spatial
scales are not often compared.
The main objective of this study is to compare relative
telomere length (RTL) across ecoregions and therefore
identify how levels of anthropogenic and environmental stress may correlate with RTL of individuals across
multiple populations. Our focal study species is the
moose (Alces alces) across the main three ecoregions in
Sweden. Shorter term stress in response to anthropogenic and environmental stressors have been demonstrated in moose previously [21] and their longevity (up
to ~ 20 years) makes them an ideal model species to also
evaluate accumulated stress across an individual’s life
span and to compare these across ecoregions. In addition,
moose are a cold-adapted species and are susceptible to
heat-stress at ambient temperatures above 14–17 °C [22,
23] during summer and above − 5–0 °C during winter
[22], meaning they may be particularly susceptible to
temperature changes brought about by climate change.
In combination with a known higher parasite burden
[24, 25], higher hunting pressure and higher inter-species
competition [26, 27] that moose are exposed to in their
southern range, we expect the chronic stress burden of
moose to decrease with increasing latitude. However,
moose at high altitudes, i.e., montane tundra habitat, may
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experience stress due to other factors, such as high snow
depth [28]. With this study we analyze RTL in moose for
the first time and examine how it reflects chronic stress
of individuals experiencing varying levels of environmental factors and anthropogenic impacts across large spatial
scales.
Results
Geographic variation in climate and land use
In line with our general hypothesis, we observed a
marked difference in GPS-collar recorded temperature [F(2,149) = 111.4, P-value < 0.001] and land-use
intensity [F(2,150) = 404.4, P-value < 0.001] that moose
experienced in each ecoregion based on their annual
movements (Fig. 1). Mean annual temperature (based
on GPS-collar temperature: Tc) was 5.44 ± 4.08 °C in the
montane, 9.80 ± 2.71 °C in the boreal and 14.16 ± 2.10 °C
in the sarmatic ecoregion (Fig. 1). Land use intensity follows a similar trend with low mean global Human Modification (gHM, [29]) values encountered by moose in the
montane (0.06 ± 0.05) and boreal (0.05 ± 0.02) but high
mean values (0.34 ± 0.08) encountered in the sarmatic
ecoregion (Fig. 1).
Relative telomere length
The ecoregions variable explained significant differences
in RTL as per the final model (Fig. 2; Table 1). Compared
to the boreal region, RTLs were significantly shorter
in the sarmatic study areas (1.42 [1.31, 1.53] 95% CI) in
southern Sweden. Additionally, shorter RTLs were also
observed in the northern montane area (1.35 [1.20, 1.44]
95% CI) compared to the boreal region (1.63 [1.49, 1.76]
95% CI). Sample storage time was negatively correlated
with RTL. Based on linear mixed effect model selection,
sex and age of animals did not influence RTL significantly
and were subsequently removed as explanatory variables
(Additional file 1: Table S1, Fig. S1–2). Pregnancy and the
number of calves at heel did not affect RTL (Additional
file 1: Fig. S3).
Discussion
Our results show how RTL, an indicator of chronic
stress, can vary across different ecoregions. Our study
provides the first assessment of telomere measurement
in moose, and after controlling for sample storage duration, we show that moose from the sarmatic and montane ecoregions had shorter RTL than moose from the
boreal ecoregion. These findings align with our hypothesis that moose in ecoregions encompassing higher levels
of anthropogenic and environmental stress would have
significantly shorter RTLs.
The characteristics of the two ecoregions with shorter
RTLs vary substantially, and therefore reflect potentially
Fohringer et al. BMC Ecology and Evolution
(2022) 22:105
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Fig. 1 A Capture locations of 106 moose in three ecoregions (dark grey = montane birch forest and grasslands, grey = boreal forest, light
grey = sarmatic mixed forest). The map was created in Quantum GIS, version 6.10.6 (QGIS.org, 2020). Right: Mean annual GPS-collar temperature B
and corresponding mean global human modification (gHM) values extracted based on the annual GPS track C of all individuals distinguished by
ecoregion
Table 1 The best linear mixed effect model showing the
relationship between relative telomere length of moose
individuals (N = 106), the three considered ecoregions in
Sweden, and storage time
Predictor variable
Coefficient s.e
df
(intercept)
1.860
0.087 143.641 21.280
Montane
− 0.305
0.089 114.187 − 3.427 < 0.001
Sarmatic
− 0.208
0.086 110.247 − 2.403 0.018
Storage time
− 0.041
0.009 60.801
Random effect (Individual ID)
0.0940, Standard deviation: 0.307
Residuals
0.0350, Standard deviation: 0.187
t
p
< 0.001
− 4.349 < 0.001
Variable coefficients are presented along with their standard errors (s.e.), degree
of freedom (df ), test statistics (t), and p-value (p). Reference level is the ‘boreal’
ecoregion
Statistical significance levels were set to < 0.05
Fig. 2 Average observed relative telomere length (RTL) of 153
samples in three ecoregions (nmontane = 58, nboreal = 29, nsarmatic = 66)
from 106 animals. Animal age was included for each sample as black
dots, despite having been excluded from the final model
different stressors that drive variation in RTL. The montane ecoregion is characterized by relatively lower land
use intensity (Fig. 1), deep and extensive snow cover
Fohringer et al. BMC Ecology and Evolution
(2022) 22:105
limiting locomotion ability, and low forage availability
during winter [28]. All of which are likely responsible for
elevated metabolic expenditure resulting in shorter RTL
of animals in the montane ecoregion [29]. Conversely,
the sarmatic ecoregion is characterized by higher moose
hunting pressure from humans, competition with sympatric ungulate species and a higher land use intensity
through human population density, traffic infrastructure
(barriers and direct stress) and forestry activities [26–28].
The combined effect of these factors likely contributes to
the shorter RTLs in this ecoregion. In addition to these
anthropogenic stressors, mean annual Tc in the sarmatic
ecoregion is substantially higher than the suggested
upper critical temperature of 0 °C during winter, where
moose were observed to experience increased metabolic
rates and behavioural adaptation, such as altered habitat
use and activity patterns [22]. When Tc is corrected to
reflect actual ambient temperature experienced by moose
in the southern ecoregion (by a conservative mean of
7.2 °C [31]), animals are on average exposed to temperatures exceeding their thermoneutral zone by approximately 7 °C during winter. This finding emphasises the
concerns that moose in the southern limit of their range
are heat stressed during winter [32] (Singh N. J. personal
communication). Ultimately, chronic thermal stress [14]
and trade-offs influenced by selection of suboptimal habitats [33, 34] (Singh N. J. personal communication) may
therefore contribute towards determining RTL. Pathogen
prevalence is also higher at lower latitudes with warmer
climate [35, 36] and Beirne et al. [19] have demonstrated
that European badgers Meles meles exhibit higher telomere attrition rate post infection with bovine tuberculosis. In accordance with our results, Spong et al. [21] have
demonstrated that, moose hair cortisol levels—a shorterterm stress proxy than RTL—were higher in southern
Sweden than in north.
RTLs of animals in the boreal ecoregion were longer
compared to the other two regions. This can be attributed to a number of factors. First, the boreal region is
generally more homogenous in vegetation, dominated
by conifers that are interspersed with deciduous species.
Commercial forestry is the main form of land use in this
region, characterized by large tracts of monoculture and
clear cutting being the most common method of timber
harvest. Moose are known to prefer clear cuts and young
pine forest < 5 m in height [37, 38]. Secondly, the proportion of migratory moose is higher in this region [28, 39],
which allows the population to evade stressful periods of
low food availability and deep snow, and provides food
access all year round. Thirdly, the year round- availability of food through conifers being green, reduces starvation related stress. Fohringer et al. [30] identified several
metabolites linked to high metabolic expenditure (e.g.,
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several amino acids and ketone bodies) in moose in the
corresponding montane area, while animals in the boreal
region did not show elevated concentrations of such biomarkers that indicated starvation responses due to limiting winter diets. Moose in the montane region were
observed to have a lower propensity to migrate, move
shorter distances and have smaller seasonal home ranges
compared to those in the boreal region [28]. This reduced
migratory propensity and relatively higher and prolonged exposure to environmental stressors and a lack
of abundant winter forage likely causes a higher chronic
stress. The fact that all our captures were carried out
during peak winter suggests that animals do not evade
the environmental stressors experienced in this region
at least during this period of limited browse availability
[30]. Shorter RTL was also determined for roe deer [18]
experiencing poor environmental conditions compared
to a population in less harsh environments. Similarly,
Hoelzl et al. [40] detected shorter RTL in edible dormice
Glis glis that were not provided food ad libitum compared to individuals that were, and suggested that forage
availability could be a major factor in determining telomere length in a wild species subject to highly variable
resource availability.
The lack of significant results in relation to moose
RTL and age in this study could be related to the fact
that only animals in good body condition with the vast
majority past their developmental phase, i.e., adults, were
captured (Additional file 1: Figs. S1 and S2). Adult vertebrates (beyond significant additional growth) were shown
to exhibit less variation in RTL than during the developmental (growing) phase [9, 13, 41]. Changes of RTL with
age might, therefore, be less pronounced in adult individuals, such as those included in this study (aligning with
the results of Wilbourn et al. [18] and Fairlie et al. [41]
who reasoned that a selective disappearance of individuals with short telomeres increases average RTL with age
in wild mammals). The onset of cellular and reproductive
senescence effects in moose has been observed after the
age of 10 for males [42] and 12 for females [43], however
the management strategy of maintaining a moose population in prime condition, to maximise the number of
individuals that can be hunted, means that few(er) individuals achieve ages at which senescence occurs. Moose
management strategies in Sweden may therefore also
partly explain the absence of a relationship between age
and RTL. To better understand the role of animal age in
telomere dynamics, individuals of all age groups would
have to be examined, ideally in a longitudinal experiment
[41]. Despite having observed insignificant changes of
RTL with age, variation of RTL withing age groups was
high and could be driven by regional effects, that may be
attributed to differing degrees of environmental stress
Fohringer et al. BMC Ecology and Evolution
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exposure and/or genetic differences. Moreover, sex was
shown to not be a significant predictor for RTL in our
study, which is also in line with other studies performed
on free-ranging mammals (reviewed by [18, 19, 44]), but
see, for instance, Watson et al. having found sex-differences in wild Soay sheep Ovis aries [45].
Despite the known caveats in using mammalian blood
as a source material for RTL quantification, most notably
due to potential immune responses causing shifts of the
leukocyte profile (see [46]), we were able to rely on this
sample type by streamlining lab work and careful statistical examination of potential bias-inducing variables. We
were therefore able to produce comparable results in line
with several other studies that relied on leukocyte DNA
[e.g., 11, 18, 19, 41, 45, 47]. The strong effect of storage time highlights that telomere studies should always
control for this issue if varying storage periods cannot
be avoided. Reichert et al. [48] found that the storage
method of blood affected RTL, indicating that storage
duration will also have an effect on RTL. The effect of
storage duration did not impact our study as storage time
was randomly distributed throughout ecoregions and
the other variables. Our study was not able to investigate
whether RTL is a suitable biomarker for age (in this species and in the developmental stage tested). Future studies may benefit from the inclusion of telomerase activity
estimates as suggested by several authors (e.g., [49, 50])
in order to better understand the associations of telomere
length with environmental variables in the examined
study system and beyond.
Due to known genetic differences between moose in
southern and northern Sweden [51–53], we cannot rule
out potential population effects that might contribute to
differing telomere length between northern and southern ecoregions. Our finding that differences in RTL were
not consistent over a latitudinal gradient is in line with
Kärkkäinen et al. [16], suggesting that regional variation
of telomere length may mirror local environmental conditions and/or genetic differences. By measuring heritability and including more (known) populations in their
analysis, future studies should account for the effects of
population pedigree [15, 17] and between-population differences on RTL [54], thereby enabling the disentanglement of potential genetic differences from environmental
conditions.
Conclusions
Animals that are highly adaptable to land use change
likely face environmental constraints beyond high land
use intensity that lead to an accumulation of stressors
driving chronic stress and ultimately RTL. Increased
encroachment via the accumulation and extension of
different forms of land use and impacts of accelerated
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climate change at northern latitudes can limit the
potential of animals to evade stressful environmental
conditions via, for example, migration and will likely
exacerbate metabolic demand and negative consequences
on animal health. Our study emphasises that it is crucial
to consider distinct biogeographic scales that encompass cumulative impacts affecting organisms holistically.
Future analysis of chronic stress effects in free-ranging
species should focus on the continuous resampling of
cohorts of animals to understand inter and intra-individual telomere dynamics in wild animals at the life history
scale.
Methods
Study area
The study area covers the three major ecoregions in
Sweden, i.e. montane birch forest and grasslands (‘montane’), boreal forest (‘boreal’) and sarmatic mixed forest (‘sarmatic’) (Fig. 1; [55]). Moose were captured in all
three ecoregions. Ecoregion assignment was based on
the winter capture location. The ‘montane’ ecoregion is
characterized by high-elevation tundra vegetation and
mountain birch Betula pubescens belt. Duration of snow
cover in the capture area within the montane ecoregion
lasts approximately 210 days and mean snow depth is
approximately 45 cm. Accordingly, the duration of the
vegetation-growing season lasts less than 100 days in this
capture area. The ‘boreal’ ecoregion occupies the largest
portion of Sweden’s biomes and is dominated by coniferous trees, interspersed with patches of deciduous forest. Despite mean snow depths in capture area within
the boreal being similar to the montane ecoregion, snow
cover lasts less than 190 days and the growing season
is extended to approximately 120 days. The ‘sarmatic’
ecoregion in southern Sweden consists of a mixed conifer-broadleaf plant association. The climate in the two
capture areas within the sarmatic ecoregions is comparably mild, ranging between 90 and 200 days of snow cover,
10–15 cm snow depth and a vegetation growing period
of 180–220 days. For detailed habitat characterization of
moose capture areas see [28].
Forestry is the prevailing form of land use occurring
throughout northern Sweden except for the montane
ecoregion, where forestry is unfeasible. Generally, forestry is expected to be more intensive in the southern
study area, where more commercial tree species occur
and turn-over rate is higher [56]. While the landscape in
the south is forest dominated, it is also highly fragmented
with clear cuts, settlements and agriculture. In contrast,
agriculture and settlements occur only sporadically in the
boreal capture area, and are virtually absent in the montane region.
Fohringer et al. BMC Ecology and Evolution
(2022) 22:105
While moose and roe deer Capreolus capreolus occur
throughout Sweden, the distribution of red deer Cervus elaphus, fallow deer Dama dama and wild boar Sus
scrofa is limited to southern Sweden. Hunting pressure
remained relatively stable for moose and roe deer in
recent decades [57] but southern latitudes are experiencing higher hunting pressure due to the higher diversity
of sympatric game species [26, 27]. Prevalence of disease
and parasites affecting moose health was also shown to
be higher in southern Sweden moose populations compared to those in the north [24, 25].
Data collection and sampling
From 2009 to 2018, 153 samples of free-ranging adult
moose were collected during winter (Jan–April) within
the framework of the national moose research. Animals were immobilized from a helicopter via dart injection [58] with a CO2-powered rifle (Dan-Inject, Børkop,
Denmark) with the drug combination of 4.5 mg etorphine (Captivon® 98 Etorphine HCl 9.8 mg/ml, Wildlife
Pharmaceuticals (PTY) Ltd., 38 Wilkens St., Rocky Drift,
White River, South Africa) and 50 mg xylazine (Xylased®
500 mg, Bioveta, a.s., Komenského 212, 68,323 Ivanovice
na Hané, Česká Republica) [59–61]. During immobilization, all animals were fitted with GPS-collars including
a temperature receiver (Vectronic-Aerospace, Berlin,
Germany). Pregnancy status was determined by a veterinarian via rectal palpation in sarmatic and montane
areas [62]. Age was estimated based on tooth wear [42,
63]. The number of calves at heel was determined visually
from the helicopter. Blood samples were collected into
9 ml S-Monovette® Z-Gel dry collection tubes (Sarstedt,
Germany) by jugular venipuncture of the fully immobilized animals. Collection tubes were processed according
to the manufacturer’s instructions and stored at − 20 °C
until DNA extraction. Data on GPS positions, ancillary
Tc, sex, pregnancy status, and number of calves at heel
was stored and accessed via the Wireless Remote Animal
Monitoring (WRAM) database [64].
Since RTL was compared across ecoregions to evaluate chronic stress, we estimated the ambient temperature and level of human impacts experienced by moose
in each of our sample areas based on their GPS tracks.
Anthropogenic impacts on the landscape were measured
using the global Human Modification map (gHM), which
provides a cumulative measure of human modification
of terrestrial lands across the globe at a 1-km resolution
[29]. The mean gHM value was estimated for each individual based on one year of movement post (re-)capture.
The individual movement track was standardized to eight
locations per day and used to estimate the mean gHM
value from the underlying raster. Moose generally show
fidelity to their winter and summer ranges [28, 65] and
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we therefore assume that movements post-capture also
reflect environmental conditions pre-capture. Similarly,
mean annual Tc (as a proxy for ambient temperature;
[31]) was based on GPS-locations post capture. We used
R packages amt [66], SDLfilter [67], trajr [68], adehabitatLT and adehabitatHR [69] for GPS- and Tc-data preparation as well as raster [70] and rgdal [71] for gHM value
extraction.
DNA extraction
Prior to DNA extraction, blood samples were thawed
simultaneously at 4 °C for 4 h and the serum fraction and
the gel layer were discarded. Per sample, approximately
40 mg of the coagulated blood fraction was incubated at
56 °C with 30 µl proteinase K (20 mg/ml; Qiagen, Germany) for one hour with repeated inverting and shaking
of samples. A liquid state of the sample was attained by
subsequent addition of 190 ml PBS pH 7.4 (2.7 mM KCl,
140 mM NaCl, 10 mM Phosphate), pipetting up and
down and vortexing for 30 s. DNA extraction and purification were carried-out on a QIASymphony SP platform
using the DSP DNA minikit (Qiagen, Germany) according to the manufacturer’s instructions. DNA yield and
quality were quantified using a NanoDrop 2000 spectrometer (Thermo Fisher Scientific, USA; Additional
file 2). Purified DNA was stored at − 20 °C for up to one
month until further processing via qPCR, wherefore
DNA was refrigerated at 4 °C for up to two days.
Relative telomere length (RTL) assessment
For measuring RTL, we used the real-time PCR approach
[72] adapted for moose for the first time. A 54 bp fraction of the beta-lactoglobulin (BLG) gene was used as
non-variable copy number (non-VCN) gene (tested for
non-variability as described by Cawthon [73], Smith
et al. [74] and Turbill et al. [75]). Primer sequences for
the non-VCN gene were 5′- GCA GCT GTC TTT CAG
GGA GAA TG -3′ (rt_BLG F) and 5′- CCC GAC ACT
TAC CAT CGA TCT TG -3′ (rt_BLG R). Telomeric
primer sequences were 5′-CGG TTT GTT TGG GTT
TGG GTT TGG GTT TGG GTT TGG GTT-3′ (tel 1b)
and 5′-GGC TTG CCT TAC CCT TAC CCT TAC CCT
TAC CCT TAC CCT-3′ (tel 2b). Telomere and non-VCN
gene PCRs were carried out in 9 separate runs with 20 ng
DNA per reaction, 400 nmol l−1 of each primer combination (Tel1b/Tel2b or rt_BLG F/ rt_BLG R) in a final volume of 20 μl containing 10 μl of GoTaq® qPCR Master
Mix (Promega). Samples were randomized per run based
on sex, capture area, and capture year (see Additional
file 2). PCR conditions for the telomere runs were 2 min
at 95 °C followed by 40 cycles of 15 s at 95 °C, 20 s at
58 °C and 20 s at 72 °C. For non-VCN runs, PCR conditions were 2 min at 95 °C followed by 45 cycles of 15 s at
Fohringer et al. BMC Ecology and Evolution
(2022) 22:105
95 °C, 20 s at 58 °C and 20 s at 72 °C. A final melting step
was included in each run with the temperature ramping
from 65 to 95 °C in 1 °C steps. Each run contained a negative (non-template) control and two DNA extracts from
moose livers as standard samples (to assess inter-run variability). All samples and controls were run in triplicates.
Reactions were prepared using the Qiagility PCR robot
(Qiagen, Germany) to minimize pipetting errors, and
cycling was performed on a Rotorgene Q quantitative
thermocycler (Qiagen, Germany). We used the software
LinRegPCR (2012.0) [76] for analysis of non-baselinecorrected raw qPCR data, exported from the instrument.
RTL was calculated using the method described by Ruijter et al. [77], modified by Hoelzl et al. [78].
The mean qPCR efficiency was calculated via the
amplification plot method [76] which gives lower but
more accurate estimates of efficiency than standard curve
based methods [79, 80]. The estimates were 76.9% and
86.7% for the non-VCN gene and telomere reactions,
respectively.
The intraclass correlation coefficient (ICC) was calculated as a measure of reliability within and between the
runs, as suggested by Koo and Li [81]. ICC estimates and
their 95% confident intervals for sample triplicates were
calculated in R Version 3.5.2 [82]. Intra-rater ICC was
calculated on all included data points based on a singlerating, absolute-agreement, 2-way mixed-effects model
(ICC in library ‘irr’, [83]). Intra-assay ICC for Ct values for
telomere assay was 0.85 [p < 0.0001, 95% (CI 0.82–0.88)]
and for BLG 0.96 [p < 0.0001, 95% (CI 0.94–0.97)] showing a good and an excellent degree of reliability respectively. The ICC for inter-assay reliability was calculated
for the standard samples based on a mean rating (k = 3),
agreement, 2-way mixed-effects model. Interrater ICC
for Ct values for the telomere assay was 0.94 [p < 0.0001,
95% (CI 0.54–1.0)] and for BLG 0.99 [p < 0.0001, 95% (CI
0.97–1.0)] showing an excellent degree of reliability for
both. As all samples per individual were run on the same
plate, inter-assay variability should have minimal effect
on our longitudinal results.
The intra-assay coefficient of variation among replicates (intra-assay variation), an estimate of system precision, was further used to assess reproducibility. Mean
intra-assay CV for Ct values of the non-VCN gene and
telomere assay were 0.35 and 0.86%, respectively. The
mean coefficient of variation among replicates (intraassay variation) for Ct values of the non-VCN gene and
telomere assay were 0.35 and 0.86%, respectively. Among
runs (inter-assay variation), the mean coefficient of variation for Ct values of the non-VCN gene was 0.94%, and
this was 2.76% for the telomere reaction.
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Statistics
All statistical analyses were carried out using R 3.5.2
[82]. To explain variation among individuals in RTL, linear mixed effects models and postHoc test with Tukey
adjustment for multiple comparisons were used (library
lme4; [84], library emmeans). The initial model contained
the two-way interaction between animal age (continuous)
and sex, ecoregion, as well as storage time (to control for
potential effects of sample storage duration, since time of
storage have been associated with change in RTL [79]) as
explanatory variables. To account for potential pseudoreplication among samples from recaptured individuals,
individual ID was included as a random effect. Capture
location was not included in the models as they highly
correlate with ecoregions that animals were captured in.
Due to the limited number of samples from recaptured
individuals (n = 39) and the absence of recaptures in the
boreal ecoregion, intra-individual telomere dynamics
were not considered in our analysis. Additionally, we ran
a model on a subset of the data containing only females
(n = 68), accounting for the explanatory variables mentioned above, and we also included pregnancy status and
number of calves at heel as additional variables. Model
selection was carried out using the R function dredge
(library MuMIn; [85]) which evaluates all possible candidate models, from which the best-fit model was selected
based on AICc. Coefficients, their standard errors (s.e.),
degrees of freedom (df ), t and corresponding P-values of
the models are reported using the lmerTest package [86].
All means are given together with their standard error.
Abbreviations
BLG: Beta-lactoglobulin; DNA: Deoxyribonucleic acid; gHM: Global human
modification; GPS: Global positioning system; HPA: Hypothalamo-pituitaryadrenal; ICC: Intraclass correlation coefficient; PCR: Polymerase chain reaction;
qPCR: Quantitative polymerase chain reaction; RTL: Relative telomere length;
VCN: Variable copy number.
Supplementary Information
The online version contains supplementary material available at https://doi.
org/10.1186/s12862-022-02050-5.
Additional file 1: Table S1 is the model selection table for models
included in this manuscript. Figures S1–3 are showing variables that did
not pass model selection.
Additional file 2. DNA quality and sample distribution per qPCR run.
[https://doi.org/10.5061/dryad.44j0zpcd0].
Additional file 3. RTL dataframe. [https://doi.org/10.5061/dryad.44j0z
pcd0].
Acknowledgements
We thank Fredrik Stenbacka for assistance during fieldwork and in accessing the biobank. Moreover, we thank Helena Königsson at the genetics lab
at Department of Wildlife, Fish, and Environmental Studies at the Swedish
University of Agricultural Sciences and staff at the genetics lab Evolution at
Fohringer et al. BMC Ecology and Evolution
(2022) 22:105
Page 8 of 10
University of Veterinary Medicine Vienna for facilitating DNA extraction and
RTL quantification, respectively.
5.
Author contributions
CF, GS, and SS conceived the ideas and designed methodology; GE authorised
sample acquisition from the in-house biobank; CF collected additional samples; CF extracted DNA; CF and FH ran the qPCR assay and assessed telomere
length under supervision of SS; CF and FH compiled data and analysed data
and together with CC, AMA and NJS; CF and NJS led the writing of the manuscript; AMA, NJS and GE contributed with information regarding the study
species and movement ecology; SS and FH contributed with information on
telomere dynamics. All authors read and approved the final manuscript.
6.
Funding
Open access funding provided by Swedish University of Agricultural Sciences.
This study was financed by the project “Resource Extraction and Sustainable
Arctic Communities (REXSAC), which is funded by Nordforsk—a “Nordic Centre of Excellence”—(project number 76938) and NJS was partially supported
by another Nordforsk programme REIGN—Reindeer Husbandry in a globalizing north. The Seth M Kempe Foundation funded travel and accommodation costs to carry out lab work at the Konrad Lorenz Institute of Ethology,
University of Veterinary Medicine, Vienna. Consumables and running costs
for lab work was covered by a grant from the Helge Ax:son Johnsons stiftelse
(Grant number F18-0363).
9.
Availability of data and materials
The datasets generated and analysed during the current study are available in
the Dryad repository, https://doi.org/10.5061/dryad.44j0zpcd0
14.
7.
8.
10.
11.
12.
13.
15.
Declarations
Ethics approval and consent to participate
All moose captures and handling are in line with the ethical permits: A124-05,
A77-06, A116-09, A50-12 and A14-15, granted by the Swedish Animal Ethics
Committee.
16.
17.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Author details
1
Department of Wildlife, Fish and Environmental Studies, Swedish University
of Agricultural Sciences, 90183 Umeå, Sweden. 2 Konrad Lorenz Institute
of Ethology, University of Veterinary Medicine, Savoyenstraße 1, 1160 Vienna,
Austria. 3 Department of Animal Ecology, Netherlands Institute of Ecology
(NIOO-KNAW), Droevendaalsesteeg 10, 6708PB Wageningen, The Netherlands. 4 Department of Animal Ecology and Physiology, Radboud University,
6500GL Nijmegen, The Netherlands.
Received: 1 July 2021 Accepted: 22 July 2022
18.
19.
20.
21.
22.
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