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ST. KILDA SOAY SHEEP & MOUSE PROJECTS:
ANNUAL REPORT 2011
J.G. Pilkington1, S.D. Albon
2, A. Bento
4, C. Berenos
1, T. Black
1, J.
Blount15
, E. Brown6, D. Childs
6, L. Christensen
14, T.H. Clutton-Brock
3,
T. Coulson4, M.J. Crawley
4, J. Dorrens
1, P. Ellis
1, A. Graham
10, J.
Gratten9, A. Hayward
6, L. Harrington
16, S. Johnston
6, P. Korsten
1, L.
Kruuk1, T. McNeilly
13, C. Mitchell
15, B. Morgan
7, K. Morriss
10, M.
Morrissey1, D. Nussey
1, M. Page
14, J.M. Pemberton
1, S. Robertson
1, C.
Selman14
, J. Slate6, I.R. Stevenson
8, K. Watt
1, A. Wilson
1, K. Wilson
5,
R. Zamoyska12
.
1Institute of Evolutionary Biology, University of Edinburgh.
2Macaulay Institute, Aberdeen.
3Department of Zoology, University of Cambridge.
4Department of Biological Sciences, Imperial College.
5Department of Biological Sciences, Lancaster University.
6Department of Animal and Plant Sciences, University of Sheffield.
7Institute of Maths and Statistics, University of Kent at Canterbury.
8Sunadal Data Solutions, Edinburgh.
9University of Queensland, Australia.
10Princeton University, USA.
11Roslin Institute, University of Edinburgh.
12Institute of Immunology and Infection Research, University of Edinburgh.
13Moredun Research Institute, Edinburgh.
14Institute of Biological and Environmental Sciences, University of Aberdeen.
15Centre for Ecology & Conservation, College of Life & Environmental
Sciences, University of Exeter Cornwall Campus. 16
Université de Montréal, Institute de Recherche en Immunologie et en
Cancérologie, Montréal, Canada.
POPULATION OVERVIEW .................................................................................................................................... 2
REPORTS ON COMPONENT STUDIES .................................................................................................................... 4
Vegetation ..................................................................................................................................................... 4
Weather effects on female August weight...................................................................................................... 6
Biomarkers of ageing in Soay sheep: oxidative stress and telomere length ................................................. 8
Explaining variation in immune responses in an evolutionary context....................................................... 11
Immunological and parasitological research: an update ........................................................................... 13
Changes in gene frequency: genetic drift or natural selection? ................................................................. 15
The population genetics of Soay sheep coat colour: a model-based approach .......................................... 19
Improving the pedigree of Village Bay sheep using superabundant genetic variation............................... 22
Genetic architecture of body size in Soay sheep ......................................................................................... 24
Population dynamics and genetic structuring of the St Kilda field mouse.................................................. 27
PUBLICATIONS.................................................................................................................................................. 30
ACKNOWLEDGEMENTS.. ................................................................................................................................... 31
APPENDIX A: PERSONNEL NEWS & SCHEDULE OF WORK ................................................................................ 31
CIRCULATION LIST ........................................................................................................................................... 33
2
PO P U L A T I O N OV E R V I E W
The sheep population on Hirta entered 2011 at a very high level and, as a result, there
was some mortality but not a population crash. 144 tagged sheep were found dead
within the study area between March and May of 2011. Lambing began on the 10th
of
April, much later than any previous year, with 78% of lambs born surviving (Fig. 1).
0
5
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04/2
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4/201
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/2011
01/05
/201
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/201
1
Date
Lam
bs
bo
rn
Figure 1. The temporal distribution of lamb births during 2011.
In December 2011, 887 tagged sheep were believed to be alive on Hirta, of which 649
regularly used the study area, a total decrease of 3.4% using the study area since the
previous year. The age distribution of the population is shown in Fig. 2 and changes
in sheep numbers in the study area over time are shown in Fig. 3.
0
20
40
60
80
100
120
0(BG) 1(BL) 2(B W) 3(BR) 4(BO) 5(BY ) 6(AG) 7(AL) 8(AW) 9(AR) 10(AO) 11(AY) 12(YG) ?(OP)
Age (cohort tag) Males/females
Nu
mb
ers
Figure 2. Age distribution of tagged Soay sheep presumed to be alive at the end of
2011.
3
0
500
1000
1500
2000
2500
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
2009
2011
Year
Sh
eep
Village bay
Island count
Figure 3. The number of tagged sheep regularly using the study area since 1985.
One whole-island count yielded 2147 tagged and untagged sheep, with the details
displayed in Table 1. The total population had increased by 4.3% since summer 2010
when it was at 2058. This gives a delta (calculated as ln (Nt+1/Nt)) of +0.04. The
whole island counts are also shown in Figure 3, where it can be seen that the 2011
count preserves the pattern that we have never seen two population declines in a row
since 1985.
Table 1. Demographic and geographic distribution of sheep observed during the count
of Hirta on August 17th 2011. Coat colours are DW = dark wild, DS = dark self, LW =
light wild, and LS = light self.
Location Females Males Lambs Total
Conachair/Oiseval
Mullach Bi/Cambir
Ruaival/Village
DW
209
304
291
DS
5
11
13
LW
62
82
106
LS
1
11
2
DW
59
75
94
DS
5
0
2
LW
27
17
21
LS
0
0
0
200
277
270
570
778
799
Total 804 29 250 14 228 7 65 0 750 2147
4
REPORTS ON COMPONENT STUDIES
Vegetation.
Mick Crawley.
Sheep numbers have been consistently high for the last 9 years, and this is reflected in
the vegetation. In 2011 we saw total biomass at its second-lowest ever in both March
and August: 7.2 g per 0.04 m2 in March (long-term mean = 10.7; only March 2009
was lower at 6.8) and 9.3 g per 0.04 m2 in August (long-term mean = 15.6; only
August 2008 was lower at 8.9).
The in-bye grassland on Hirta is the most productive plant community used by the
Soay sheep. At a scale of 10cm or so, the sward consists of a mosaic of gaps and
tussocks. The gaps are closely-grazed, remain bright green all year round, and
typically contain lots of clover (Trifolium repens). The tussocks are much taller (up to
20cm sward height), more lightly grazed, and in winter are made up largely of dead
organic matter (uneaten grass production left over from the previous summer). For
both gaps and tussocks, there is a very strong seasonal cycle in their biomass and
botanical composition. Green biomass is high in summer and low in winter, with
dead organic matter showing the opposite pattern. Because they can grow at very low
temperatures, bryophytes (mosses and liverworts) benefit from reduced light
competition resulting from die-back of the grasses, and peak in abundance in winter.
As is so often the case in studies of climate change, the size of these within-year
fluctuations is large compared to the long-term trends in average plant biomass (Fig.
4) There is a significant downward trend over time in green biomass in tussocks (top
right; p < 0.01) but not in gaps (top left).
Fluctuations in the sheep population are reflected by changes in the distribution,
abundance and botanical composition of gaps and tussocks. In the year following a
population crash, grazing pressure is relatively light, offtake is low, and tussocks are
larger and more numerous. The result is that dead organic matter builds up to a peak
in the winter following the crash. When sheep numbers are high (as they have been
for the last 9 years), then tussocks are rare (especially in winter) and gaps are
numerous and very closely grazed. Figure 5 shows the very clear long-term
downward trend in tussock cover. It will be interesting to see if tussock cover ever
returns to its pre-2005 levels.
5
Figure 4. Time series for March and August (1993 to 2011) green biomass (dry mass
of green grass plus herbs per 0.04m2; top row), dead organic matter (DOM mostly
dead grass; centre row), and bryophytes (mosses plus liverworts; bottom row), in
gaps (left column) and tussocks (right column) from the inbye grassland on Hirta (this
is the Agrostis-Holcus grassland between the Head Dyke and the shore; under the
National Vegetation classification, it is U4 Festuca ovina – Agrostis capillaris –
Galium saxatile grassland: Holcus lanatus – Trifolium repens sub-community). Note
the differences in scale on the y-axes between rows and columns (e.g. there is much
more green biomass and DOM in tussocks than in gaps, and more bryophyte biomass
in gaps than in tussocks).
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Figure 5. The clearest consequence of increasing sheep numbers (left; whole island
count in August showing more than 39 extra sheep per year on average; r2 = 0.48)
has been a decline in the mean cover of tussocks in the following March (right; p <
0.001). Tussock cover (scored on a scale of 1 to 5; right) is an average score out of 30
across 14 stratified random sampling stations within the Head Dyke each year. A
score of 5 equates to tussock cover of 5/30 = 16.7%. Clearly this linear trend cannot
continue (it predicts negative tussock cover by 2015), and we expect that tussock
cover would increase in the year following a crash (if we ever see one again).
Weather effects on female August weight.
Ana Bento.
In the 2010 report, I wrote about the reconstructed weather time series for St. Kilda
and how it could be used to understand population crashes. The analysis showed that
are no clear environmental drivers that explain the crashes.
This past year, I have been focusing on weather effects on several aspects of sheep
demography and traits. Here, I analyse trends in female August weight for the time
period 1989 to 2010. Body weight is important for both fertility and survival, which
are components of fitness. In each age class we see clear interannual variation in the
distribution of body weight (Fig. 6).
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Figure 6. Female August weight trends for lambs (left) and adults (age 4+; right).
I set out to look for possible weather drivers that may explain this variation in body
weight against a background of past and current population densities, which have an
effect on food availability. In analysing the time series, despite the clear interannual
variability, significant trends emerge for some of the weather variables. For example,
for the period 1957 to 2010, mean maximum December temperature exhibits both
large variability between years, and a significant positive trend (Fig. 7). Furthermore,
recent data from the Met Office for 2011 shows that December maximum temperature
was the mildest since 2006, and over 5°C warmer than December 2010 (for the UK).
This additional data point would reinforce the already existing significant trend.
Figure 7. December daily maximum temperature for 1957 to 2010. The trend is
positive (p = 0.023). The solid and dashed lines depict the trend in the monthly mean
and median December maximum temperature across the years respectively.
8
I used a machine-learning algorithm, called random forests, to select the best
candidate weather variables affecting August weight for each of the age classes. I
collapsed females age four and above into one age class. I then regressed these
weather variables and past and current density dependence against body weight. As
was the case last year, the picture that emerges is complex (Table 2).
Table 2. Weather effects on the female August weight of different age classes.
Age class Significant weather variables Effect
Lambs Previous population density
March rain
Negative
Positive
Yearlings Population density two years prior
Previous population density
February rain
Previous summer rain
Spring day degrees
Negative
Negative
Positive
Positive
Negative
Age 2 Current population density Negative
Age 3 Previous population density
Current population density
Spring day degrees
Previous December wind
Negative
Negative
Positive
Negative
Age 4+ Previous population density
Previous December maximum temperature
March maximum temperature
Negative
Positive
Positive
Different weather variables affect weight at each age class. What this means is that
any climatic change is therefore likely to have complicated, and sometimes opposing,
effects on the dynamics of different demographic groups of the population. It is
interesting to point out that at age two, females appear not to be affected by weather
conditions. Furthermore, birth effects seem to be forgotten by this age too, since there
are no effects of weather or density in the year of birth at this age.
This analysis shows that the traditional way of integrating weather variables into
discrete calendar time periods may be insufficient to explain weather effects on a
population. We are now identifying critical time windows during which climatic
drivers affect traits, such as weight, to better understand the impact of climate change
and variability on population dynamics.
Biomarkers of ageing in Soay sheep: oxidative stress and telomere length.
Louise Christensen, Jen Dorrens, Colin Selman, Melissa Page, Jon Blount, Chris
Mitchell, Lea Harrington, Dan Nussey, and Kathryn Watt.
Ageing, or senescence, is the physiological deterioration with advancing age that is
accompanied by declining survival and reproductive performance in most species.
Studies of wild mammals and birds regularly document age-related declines in
survival and/or reproduction, and find among individual variation in the onset and rate
of the decline. However, the causes and consequences of this variation remain poorly
understood. Senescence is a physiological process, and may vary independently of
chronological age among individuals, so it is important to develop biomarkers that
9
can give some insight into the underlying process of physiological deterioration for
use in longitudinal studies of wild animals. Markers of oxidative stress and telomere
length are two prime candidate biomarkers of ageing that could give insight into the
processes of cellular damage accumulation underpinning senescence at the whole
organism level. Few detailed studies of wild vertebrates exist that have fully
investigated how and why such biomarkers might vary among individuals and within
individuals across their lifespan, and in turn test their capacity to predict future
survival and reproductive performance of individuals. Collaborative projects,
involving researchers from Aberdeen, Edinburgh, Exeter and Montreal are currently
under way to optimise robust measures of oxidative stress and telomere length using
blood samples from Soay sheep, and then determine the causes and life history
consequences of variation in these biomarkers.
Reactive oxygen species (ROS) are produced during normal metabolism but can also
cause damage to crucial cellular components, such as lipids, proteins and DNA,
unless their effects are neutralised by a variety of antioxidant molecules circulating in
our bodies. The accumulation of such oxidative damage to cells by ROS was
proposed as a key mechanism explaining why we age, although this hypothesis is not
standing up to close scrutiny from researchers working on short-lived organisms in
the lab. Increased oxidative damage has also been proposed as an important cost of
investment in metabolically expensive activities like growth and reproduction.
Studying oxidative processes in the field is challenging, as it requires the
measurement of highly unstable molecules and samples need to be kept at ultra-low
temperatures and measured fairly soon after collection. Using liquid nitrogen vapour
shippers and our new -80°C freezer on St Kilda (Fig. 8A) – transported with the kind
help of Angus Campbell of Kilda Cruises and Qinetiq Movement team – we were able
to bring back around 400 plasma samples kept at ultra-low temperatures from the
2010 and 2011 August catch-ups. Jon Blount’s (Exeter) and Colin Selman’s
(Aberdeen) labs have measured markers of oxidative damage to lipid
(malondealdehyde) and protein (carbonyls) molecules, and two measures of
antioxidant protection. As part of her honours project with Colin Selman at Aberdeen,
Louise Christensen has begun analysing this data. Louise has found interesting
associations among the markers measured in 2010, documenting negative associations
between one of the antioxidant markers (superoxide dismutase) and both markers of
damage (Fig. 8B), as well as positive associations among the two antioxidant markers.
However, the protein and lipid damage markers were not correlated, and there was
little evidence for consistent patterns of variation with age or sex among the different
markers. Further analysis on the full 2010 and 2011 data should illuminate similarities
and differences among markers in their associations with age and life history, and
follow-up sample collection and lab work in future will establish a longitudinal data
series to investigate within-individual variation in oxidative stress.
Telomeres are repetitive sequences of DNA that are found at the tips of the
chromosomes of many organisms, including all mammals. At each cell division, a
small amount of DNA at the chromosome ends is lost as the cell replicates its DNA.
Telomeres act to protect coding DNA from this attrition and are thus important for the
maintenance of cell replicative potential and the integrity of chromosomes within the
cell nucleus. Therefore, in the absence of telomere lengthening or repair mechanisms,
telomeres shorten at each cell division and are also thought to further shorten in
response to oxidative stress. Assays of telomere length from samples of cells in
10
humans, typically from white cells in the blood, suggest associations with life stress
and subsequent survival and health. Telomere length has also recently emerged as a
biomarker of some promise in ecological and evolutionary studies. We plan to use the
large freezer bank of stored white cell fractions (buffy coat) from Soay sheep
collected by Josephine Pemberton, Jill Pilkington and the respective August catch
teams going back at least a decade, to investigate variation in telomere length in a
wild mammal for the first time. Jen Dorrens has been working to optimise and
validate two different types of telomere length assay. First, telomere restriction
fragment analysis (TRF), often seen as the ‘gold standard’ measurement of telomere
length, is a low-throughput technique that measures the size range of the terminal
telomere restriction fragment present in a sample. An early successful TRF gel using
Soay samples from August 2011 suggests interesting patterns of change in the
variance of telomere lengths with age (Fig. 8C). The second technique is quantitative
real-time PCR (qPCR), a much higher-throughput and rapid technique which provides
an average measurement of telomere length relative to a single-copy reference gene.
So far, Jen has identified a suitable reference gene and has initiated a collaboration
with Systems Biology in Edinburgh to adapt high-throughput robotics to perform
qPCR assays on the sheep samples. Our plan is to complete optimisation of both
techniques, validate the qPCR assays against TRF assays run on approximately 30
Soay samples, and to utilise this qPCR assay to measure telomere lengths
longitudinally across the lives of many Soays. We hope to assess how genetic and
environmental variation generates among- and within-individual variation in telomere
length, and test whether telomere lengths predict subsequent fitness of sheep
independent of their age.
Figure 8. (A) The new -80°C freezer, installed on Kilda and used to collect samples
for oxidative stress in August 2011; (B) A negative association between a major
antioxidant molecule (SOD, superoxide dismutase) and a measure of damage to
proteins (“PC Concentration”: Protein carbonyls) from samples collected in August
2010; (C) A telomere restriction fragment analysis gel using white blood samples
collected in August 2011. The smears under the numbered lanes are telomeres from
different animals, with the darkest parts reflecting the ‘average’ telomere lengths in
the sample. The bands in the lanes either side are size ladders. Lanes 1-3 are females
7 years or older, lanes 4-7 are lambs.
(C)
(B)
(A)
11
Explaining variation in immune responses in an evolutionary context.
Adam Hayward, Andrea Graham, Jill Pilkington, Kathryn Watt and Andrea Graham.
In recent years, there has been increased interest in investigating the relationships
between the strength of immune responses and indicators of fitness, such as survival,
reproduction, and body condition. Knowing how immunity is related to fitness will
increase our understanding of how variation in immune responses between individuals
is maintained by natural selection. This has led to increased study of immunity in wild
populations, though ecological studies often make several key assumptions which
have been shown to be incorrect. For instance, higher immune responses do not
always lead to lower parasite levels, and lower parasite levels do not always maximise
health or fitness. Therefore, in order to understand how variation in immunity is
associated with fitness, measurement of immunity, parasite levels and fitness at the
same time is crucial. We used data collected from St Kilda from 1997 to 2007 to
examine associations between levels of an antibody produced in response to the most
important parasite (as a measure of immunity), faecal egg counts (FEC) of intestinal
worm eggs (as a measure of parasite burden), and body weight (as a measure of
condition or fitness).
Figure 9. Associations between measures of fitness (Weight), parasite burden (faecal
egg count, FEC), and immune response (Antibody) in A) lambs, and B) adult females.
The plots show schematic relationships from the results of linear mixed-effects
models.
We began by estimating the correlations between the three traits, in data collected
from lambs and adult females; the results are shown schematically in Figure 9. In
12
lambs, we found that sheep with higher body weight had lower faecal egg counts of
parasites (FEC), suggesting that heavier sheep had fewer parasites. We also found
some indication that heavier sheep produced higher antibody responses, though the
association was not as strong as it was between body weight and FEC. This suggested
that sheep with higher antibody responses should have lower FEC, but this was not
the case when the data were analysed. The analysis of adult females showed the same
results: heavier sheep had lower parasite levels, made stronger antibody responses
(though the association was stronger than in lambs), but there was no relationship
between antibody levels and FEC.
Next, we attempted to establish the genetic basis of the traits. Figure 10 shows the
variance in each of the three traits measured in lambs and adult females, and divides
this variance into contributions from genetic factors, temporal variation, and maternal
effects. As in previous studies, we found high genetic variance for body weight, but
low genetic variance for FEC. We also found, for the first time, high genetic variance
for the antibody response to the most important parasite. The genetic variance in the
trait indicates how strongly the trait can responds to natural selection - if there is high
genetic variance for a trait, and strong selection on it, the trait is expected to evolve.
Figure 10. The contribution of genetic variance to the total phenotypic variance in
each of body weight, faecal egg count (FEC), and antibody levels in lambs and adult
females. The total variation in each trait is divided into contributions from: annual
variation (purple); genetic variation (red-brown); non-genetic individual differences
(orange); maternal effects (yellow); variation between dates of antibody assays
(green); unexplained ‘residual’ or ‘environmental’ variation (grey). The proportion
of genetic variance is of interest since it is the heritability of the trait - e.g. the
antibody has a heritability of 30% in lambs and 20% in adults.
13
We next tested the basis of the correlations between the traits, by splitting them into
their genetic and environmental components. In lambs, we found that there was no
genetic basis for either the correlation between body weight and FEC, or for the
correlation between body weight and the antibody. In adult females, we found that the
association between body weight and FEC was due to differences between individuals
- those that were heavy had low parasite levels because of individual differences: for
instance they may have different grazing behaviour, causing them to avoid parasites
and feed on the most nutritious areas of pasture. The association between body weight
and antibody levels was not due to genetic effects, but to environmental sources.
This study is the first attempt to characterize the relationships between parasite
burden, a specific immune response, and a measure of fitness in a natural population.
The results illustrate the complexity of studying the immune system in such
populations, even when a large longitudinal data set is available. It also highlights the
fact that higher measures of immune responses are not necessarily associated with
lower parasite burdens. This is likely to be due in part to the complexity of the
immune response to infection - at early stages, higher antibody responses may be
associated with higher parasite levels in an attempt to counter them; late in infection,
high antibody levels may have reduced parasite numbers to a low level, leading to no
overall relationship. Alternatively, the strength of the immune response may not be a
function of the level of infection, but of individual condition - such a hypothesis is
consistent with the results described here. The results also highlight the need for a
multivariate approach for characterising immune responses in natural populations: by
measuring multiple aspects of immune phenotype simultaneously, richer insight into
the causes of variation in immune responses will be gained.
Immunological and parasitological research: an update
Dan Nussey, Kathryn Watt, Jill Pilkington, Josephine Pemberton, Rose Zamoyska,
Tom McNeilly, Adam Hayward, Andrea Graham, and Kathleen Morriss.
Individuals vary considerably in their susceptibility to infections and the fitness
consequences of those infections in natural populations. The ecological and
evolutionary drivers of this variation are likely to be underpinned by differences in
immune responses and in exposure to parasites associated with genes, environment,
age and sex. Previous work on the Soay sheep population has shown that the sheep
are exposed to a diversity of helminth and protozoan parasites and that levels of
infection vary with age and sex, while high parasite burdens are associated with lower
weight and survival prospects. We have also found that individuals with a high level
of a particular measure of immunity (anti-nuclear antibodies) have improved over-
winter survival during crashes but reduced fecundity, suggesting a trade-off that could
maintain variation in immunity. In August 2010 we preserved white blood cells from
a small number of ewes and assessed the proportions of different kinds of T cells,
which are a crucial aspect of the vertebrate immune response. Work in lab mice,
domestic mammals and humans suggests profound changes in the abundance of
different sorts of T cells as individuals age, and we found broadly similar patterns in
wild Soay sheep. Over the last 12 months, these findings have been followed up in
our labs at Edinburgh University, the Moredun Research Institute and Princeton
University.
14
Antibodies and fitness: We previously documented a positive association between
over-winter survival of a crash and levels of anti-nuclear antibodies (ANA) in the
plasma of adult Soay ewes. We are currently following up this finding, to try to
understand what kinds of antibodies are responsible for improved survival. ANA are
antibodies that bind to material commonly found in mammalian cells, and are
associated with both a normal, healthy immune response (e.g., indicative of the extent
of B cell stimulation and general immune responsiveness) but also, at high levels,
with some forms of autoimmune disease. We first hypothesized that the association of
ANA with adult survival might be driven by antibodies specific to Teladorsagia
circumcincta, the strongyle nematode species most prevalent and virulent in adults in
the population. We measured titres of the antibody in the 1997-2007 sample set but
found that T circumcincta-specific antibody did not explain the survival result. In
Edinburgh, we are currently optimising a variety of other antibody assays (including
total antibody levels, antibodies to a novel antigen, and different antibody isotypes)
and plan to measure these in ewe samples from pre-crash August catches and
determine which, if any, best explain the association between ANA and survival. We
also found a positive association between ANA in reproductive ewes and the
probability of their offspring surviving the first few months of life. Antibodies against
T. circumcincta have now been measured from 1997-2007 for lambs at birth and at 4-
5 months of age, to examine associations between lamb immunity, growth and
survival in relation to the antibody titres of their mothers. The associations are
complex, but we are finding that maternal antibody transfer affects survival of the first
year of life, via effects on growth and independent of birth weight. Finally, in
Princeton, we are developing plasma assays to quantify protein malnutrition. These
will allow us to assess the extent to which immunity and nutrition synergize to
determine whether an individual survives a crash.
T cell phenotype and function: In August 2011, we followed-up exciting results from
the 2010 pilot study which suggested variation in the proportions of different T cell
types with age in females. We separated and preserved white blood cells from around
200 sheep caught in August and are currently using flow cytometry to assess the same
T cell types as previously in a much larger sample. We hope soon to be able to use
this data to examine fitness correlates of T cell phenotypes, and compare these
measures among sexes and also individuals experiencing different levels of infection.
We also cryo-preserved white blood cells from around 150 individuals caught in
August 2011, and these samples are in long-term storage in liquid nitrogen in
Edinburgh. Preliminary examination of these samples suggests that the cryo-preserved
cells have remained viable and capable of proliferating following stimulation in the
laboratory. Our plans are to optimise proliferation assays using these cells, and to
examine the proliferative and cytokine response, and test whether these measures
relate to the levels and types of infection experienced by individuals. For example,
nematode infections are classically thought to elicit so-called Th2-type responses
from T cells, while protozoan parasites (such as Coccidea) elicit Th1-type responses.
We hope to test whether sheep with high nematode burdens but low Coccidea burdens
show stronger Th2 responses, and vice versa, and relate individuals’ infection history,
T cell function and subsequent survival and reproductive fitness.
Microparasite survey: To understand how immunity affects fitness in the sheep, we
are seeking comprehensive information on the infections that they carry. Helminths,
coccidia, and ectoparasitic infections have been well characterized over the years, but
15
whether those are the only infections circulating among the sheep is unknown. In
2000, Ken Wilson spearheaded a small survey to assess the seroprevalence of
microparasites (bacteria and viruses) that are common in sheep on the Scottish
mainland (see Annual Report from 2001): Mycobacterium paratuberculosis,
Chlamydophila abortus (formerly known as Chlamydia psittaci), Mycoplasma
ovipneumoniae, and the pestivirus that causes Border disease. Ken sent 50 plasma
samples to a testing service at Scottish Agricultural College (SAC) and found no
evidence that these microparasites affect the St Kilda population: only one animal was
weakly positive for one of these antigens (C. abortus), and with such a low titre that it
is likely to have been a false positive. In 1986, Josephine Pemberton submitted
samples for a similar serosurvey for Maedi-Visna virus, with similarly negative
results. We decided to undertake a larger serosurvey (in terms of the number of sheep
as well as years tested) for these microparasites plus Leptospira spp., parainfluenza
virus, and orfpoxvirus (with advice and cooperation of Dr. Franz Brulisauer and
colleagues at SAC). Analysis of 400 adults suggests that prevalences are indeed very
low, though positive responses to Leptospira antigens were detectable in 7% of
samples. We are currently verifying this result with a serosurvey of 350 yearling
sheep, the age class most likely to reflect exposure to leptospirosis. We are also
planning to test 2010-11 blood samples for ovine herpesvirus (OHV-2) by PCR. We
hope to have results to report by mid-2012.
Changes in gene frequency: genetic drift or natural selection?
Jon Slate, Jake Gratten and Susan Johnston.
In previous reports we have described the discovery of the genes responsible for the
coat colour, coat pattern and horn type polymorphisms observed in Soay sheep. The
genes are called TYRP1, ASIP and RXFP2 respectively. Because we have obtained
DNA profiles of over 1000 sheep at all three genes, it is possible to ask whether (and
by how much) the frequency of the different variants (alleles) at these genes has
changed over the course of the long-term study? Identifying a significant temporal
change in allele frequency is exciting, because it could mean that the gene is
responding to natural selection (i.e. we are observing microevolution). However,
natural selection is not the only evolutionary process that can lead to changes in allele
frequency. Every generation, allele frequencies can change by random sampling
events in a process known as genetic drift. Genetic drift affects all genes, but in a less
predictable way than natural selection does. Furthermore, genetic drift is strongest in
small, isolated populations and therefore could be important in Soay sheep. We were
keen to establish whether temporal trends in allele frequency at TYRP1, ASIP and
RXFP2 could be explained by genetic drift alone or were large enough to be due to
natural selection.
To test the two alternatives, we utilised a simulation method known as gene dropping.
The idea behind gene dropping is very simple. Using the exact same pedigree as the
Soay sheep pedigree that has been reconstructed by 25 years worth of DNA profiling,
we simulated the inheritance of genes with similar starting allele frequencies as the
genes of interest. We ensured that the inheritance pattern was ‘random’ with respect
to which allele was passed from parent to offspring, and we counted allele frequencies
each year (see Fig. 11). This process mimics genetic drift in that Mendelian
inheritance is random, and there is no selection on the gene. By doing this many times
16
1. Assign alleles in Gen 0 using frequencies at start of study
2. Offspring in Gen 1 inherit an allele at random from parents
3. Individuals in Gen t, with unknown parents, are allocated alleles based on
frequency in Gen t-1.
4. The process is continued until everyone has been assigned a genotype
5. Perform regression of allele frequency against year
6. Repeat 10000 times to obtain distribution of regression coefficients if no
selection
(e.g. 10,000), and recording the change in gene frequency over time it is possible to
determine the amount of change in allele frequency expected by drift alone. By
comparing the distribution of allele frequency changes at the simulated genes with
that seen at our three genes of interest we can ask whether the observed results are
within the boundaries expected by genetic drift. If TYRP1, ASIP or RXFP2 show
greater changes in allele frequency than expected by chance, then genetic drift is
unlikely to be the only mechanism involved, and instead there is evidence of
microevolution due to natural selection.
Figure 11. Cartoon illustrating the gene-dropping simulation.
Coat colour (TYRP1)
The light coat colour has
increased in frequency over
the course of the long term
study (regression of light
colour frequency on year, r2
= 0.33; p < 0.01). However,
the gene dropping
simulations show that
genetic drift alone could
generate a change as great
as this with probability
0.13. Therefore, there is no
overwhelming evidence
that the trend is attributable
to anything other than
genetic drift.
Figure 12. Temporal changes in the light coat colour (L) and the distribution of gene-
dropping predicted regression coefficients (R). The observed temporal change is
within the distribution expected by drift alone.
17
Coat pattern (ASIP)
The frequency of the self coat pattern has declined over the course of the long-term
study (linear regression: r2 = 0.30, F1,22 = 9.38, p = 0.006), but again gene-dropping
shows the trend is within the limits expected by genetic drift alone (p = 0.21).
However, gene-dropping did reveal one unusual pattern at ASIP. Over the course of
the study the frequency of individuals that are heterozygous has increased, going from
a deficit (relative to the number
expected, given allele frequencies) to
an excess. This trend is stronger than
expected by drift (p = 0.019). The
reason for this trend is not certain, but
it could be because homozygous self
individuals have low juvenile survival
but high reproductive success (among
those that do survive). Although self
homozygous adults are rare, they tend
to have a greater than average number
of offspring, and most of those
offspring are heterozygous. Note
however, that this explanation, which
is a form of frequency-dependent
selection, can only result in an excess
of heterozygous individuals
ephemerally, and once the allele
frequencies reach equilibrium, the
pattern should disappear.
Figure 13. Temporal changes and gene-dropping results for the self coat pattern (L)
and the frequency of ASIP heterozygotes (R) at ASIP.
18
Horn type (RXFP2)
The polymorphism in horns type (normal or scurred in males; normal, scurred or
polled in females) is caused by unknown mutations in the RXFP2 gene. Two genetic
markers within RXFP2 are good, but imperfect, predictors of horn type. At both of
these markers, the alleles associated with
the polled allele (which causes scurs in
males and polledness in females) have
increased in frequency. In both
instances, the temporal trends are
stronger than expected by genetic drift,
and therefore it seems likely that RXFP2
is undergoing microevolutionary
changes driven by natural selection. The
precise mechanism for this is unknown
although there is evidence that
heterozygous males, who have relatively
small, but normal, horns have relatively
high survival and
reproductive success
compared to males
with either
homozygous
genotype (Susan
Johnston, PhD thesis
2010). If so, it is
expected that the
temporal change in
allele frequency will
level off and RXFP2
will be in Hardy-
Weinberg Equilibrium, once the polymorphism becomes balanced and reaches
equilibrium frequency.
Figure 14. Temporal changes (top) and the distribution of gene-dropping predicted
regression coefficients (bottom) at RXFP2 markers SNP10 and SNP11
Summary
Gene-dropping is a useful procedure for determining what magnitude of change in
allele frequency can occur by genetic drift alone. Some quite strong trends can be
generated by drift, and so some of the more striking changes in gene frequency, such
as that seen in TYRP1 (the coat colour gene), need not be caused by natural selection.
Of course, gene-dropping only provides clues, not a definitive answer, into what is
causing evolutionary change, because genetic drift and natural selection are not
mutually exclusive. However, in the absence of temporal trends that cannot be
explained by genetic drift, it is hard to make a case that gene frequencies have
changed as a result of natural (or sexual) selection.
P = 0.035 P =0.001
19
The population genetics of Soay sheep coat colour: a model-based approach.
Dylan Childs and Jon Slate.
Understanding the role that natural selection plays in shaping observed patterns of
genetic variation remains an important challenge for evolutionary biologists. The
development of effective molecular genetics tools has made it possible to identify
many of the genes associated with adaptively significant traits in natural populations.
For example, recent work with Soay sheep has identified loci associated with
morphological traits such as coat colour and pattern, as well as horn morphology in
males and females. By analyzing the association between the genotype of an
individual and components of their fitness, we can begin to explain the observed
patterns. However, such relationships can be difficult to synthesize when genotype
effects are sex-specific, or environmentally dependent.
We set out to develop a framework for studying the population genetics of
morphological traits in Soay sheep using a recently developed population modelling
tool, the general Integral Projection Model (IPM). The IPM has gained prominence in
recent years as an efficient tool for modelling the population dynamics of
demographically structured populations, where individual performance varies as a
consequence of variables such as size, age, sex, and of course, genotype. Such models
are valuable because they can incorporate a great deal of information about Soay
sheep demography in order to generate precise quantitative predictions. Most
importantly, with such a model in hand, it becomes possible to perform in silico
experiments to understand how different factors influence the dynamics of genes
within the St Kilda population.
-2 -1 0 1 2
0.0
0.2
0.4
0.6
0.8
1.0
density environment (standardised)
P(s
urv
ival)
F
M
-2 -1 0 1 2
0.5
1.0
1.5
2.0
2.5
density environment (standardised)
rela
tive fecun
dity (
lam
bs s
ired)
Figure 15. (left panel) Adult survival probability as a function of density,
sex, and genotype. Dashed and continuous lines: males and females,
respectively. Dark, grey, and light lines: GG, GT, TT genotypes,
respectively. (right panel) Relative male reproductive success as a
function of density and genotype. Dark, grey, and light lines: GG, GT, TT
genotypes, respectively.
20
Our first “proof of concept” model has been constructed to understand the
polymorphism in coat colour. Coat colour in Soay sheep is determined by a single
locus with two alleles, a dominant “wild type” dark coat (labeled G) and light coat
allele (labeled T). The frequency of the G allele has fluctuated over the course of the
St Kilda study, but little directional change has occurred. Gene-dropping studies (see
previous report) indicate that the observed variation is consistent with random genetic
drift, suggesting that fitness differences between genotypes are weak or nonexistent.
However, such studies do not exclude the possibility that the selection regime has
changed over the course of the study, or that the polymorphism is actively maintained
via a classical mechanism such as heterozygote advantage. We set out to examine
these possibilities.
In order to construct the model we statistically characterised the relationship between
individual state (genotype, sex, age and size) and demographic rates (probability of
survival, reproductive success, offspring size and growth). We compared a range of
different models for the effect of genotype and found that those in which the coat
colour alleles impact demographic performance additively generally performed best.
The fitted models also include terms that capture variation among years (“year
effects”) and total population density. Interestingly, we found clear evidence for an
interaction between genotype and population density (Fig. 15). Such interactions are
interesting because they favour the maintenance of a genetic polymorphism.
With a complete set of demographic functions in hand, it is straightforward to
construct an IPM for the St Kilda population. The resultant model (not shown)
describes the stochastic, density dependent dynamics of the size-age-sex-genotype
distribution. By running the model for many decades we were able to establish
whether the polymorphism is indeed stable and if not, to estimate the rate at which the
successful allele is expected to reach fixation. The model predicts that over the long
term, the wild type dark colour allele will move toward fixation at a rate of
approximately 2% per year. Preliminary analysis suggests that this is largely due to
increased reproductive success of males carrying at least one copy of the dark coat
allele (GG and GT) relative to the light homozygotes (TT).
The density of Soay sheep on the island of Hirta has been increasing over the course
of the study, and the presence of genotype-by-density interactions in the demographic
models suggests that this change may have altered selection on the polymorphism. In
order to understand whether the relative fitness of the two alleles has remained
approximately constant over the course of the study we analysed a slightly modified
version of the model. The statistical functions describing the demographic rates were
refitted with an additional term capturing temporal trends in vital rates. We then took
each year of the study in turn and constructed a “year-specific” model for the
population. However, note that each of the resultant models is still stochastic since it
incorporates temporal variation that cannot be explained by the trend effect.
We then determined whether the polymorphism was stable in each year assuming the
mean environment did not change by calculating the estimated growth rate (fitness) of
each allele when rare (i.e. T invading a homozygous GG population, and vice versa)
in the environment defined by that year. These quantities – the marginal growth rates
– are used to establish conditions for a “protected polymorphism”, corresponding to
the case where each allele has positive fitness when rare. The absence of a protected
21
polymorphism implies that the fittest allele will eventually reach fixation. By plotting
the marginal growth rate of each allele as a function of each year-specific model, we
were able to establish how the selective environment has changed over time (Fig. 16).
This analysis showed that the selective regime operating on the coat colour allele has
reversed over the course of the study, with selection for the light coat colour occurring
early on in the study.
1990 1995 2000 2005
-0.1
0-0
.05
0.0
00
.05
0.1
0
Environment (year type)
Ma
rgin
al G
row
th R
ate
T invading G
G invading T
Figure 16. Marginal growth rates (MGR) of each coat colour allele as a
function of the “year-type”. When one allele has a positive MGR and the
other a negative MGR, the former is predicted to reach fixation. The
environment favoured the light allele until ~1991. The model predicts that
the dark coat allele will eventually reach fixation.
These simple in silico experiments indicate that selection on the coat colour alleles
has shifted over the course of the study, and that the polymorphism is probably not
neutral. Though it initially appears contradictory, this result is not entirely at odds
with gene-dropping studies in Soay sheep, as the predicted reversal of selection in
such a short time may well generate changes in allele frequencies that resemble
genetic drift. In future we aim to validate the predictive properties of the model (by
hindcasting the observed genotype frequency changes, for example) and use
perturbation analyses to establish which aspects of the model most influence our
predictions. Ultimately, the same baseline model can be used to understand selection
on those other morphological traits (e.g. coat pattern, horn type) with well-
characterised genetics.
22
Improving the pedigree of Village Bay sheep using superabundant genetic
variation.
Josephine Pemberton, Camillo Berenos, Phil Ellis and Michael Morrissey
The pedigree of sheep in Village Bay is one of the largest wild pedigrees derived
using genetics in the world, especially among populations in which many individual
males compete to fertilise females. The paternity data and pedigree have been used to
investigate male lifetime reproductive success, inbreeding and its consequences, the
inheritance of visible traits like coat colour and horns and the inheritance of more
subtly-varying traits like body size.
However, the current pedigree is by no means perfect. On the maternal side, some
ewes are known to lamb, but their lamb is never identified, while other ewes are never
seen to have lambed, even though they are prime-aged and it is likely they did.
Meanwhile, dead lambs are found and sampled, but our genetic system to date (a
panel of 18 microsatellite markers) was not powerful enough to make all the
connections between them and their mothers. Our paternity identification system
draws power from knowing the identity of the mother of each lamb, and is under-
powered relative to the paternity identification task at hand (often 2-300 candidate
fathers per lamb), meaning that for some lambs it does not find a statistically
supported father, and for a few it almost certainly identifies a wrong father.
In a new project starting in late 2010, we are genotyping as many as possible of the
archived Soay samples with a sheep chip which probes about 51,000 places (loci) in
the sheep genome for a single nucleotide polymorphism (SNP) known to exist in
sheep. About 38,000 of these loci are polymorphic in the Soays, giving
overwhelming power to determine parentage. Indeed, 38,000 loci is altogether too
much information, and in this report we work with a panel of 348 loci chosen to be
strongly polymorphic (minor allele frequency � 0.4) and independent of each other
(linkage disequilibrium R2 � 0.0099).
As an exercise, we compared the results of paternity analysis using microsatellites
versus SNPs for the 2009 cohort of lambs, which was the last cohort analysed with
microsatellites. We compared the previously ‘best’ pedigree generated by Michael
Morrissey using microsatellites with one generated using the SNPs. In both cases we
used the parentage inference software MasterBayes (written by Jarrod Hadfield).
In 2009, according to our database, 250 individual lambs were born and identified, of
which 214 had a known mother and 36 no known mother. Meanwhile 91 ewes, of
which 46 were older than yearlings, were alive but did not have a known lamb. 213 of
the identified lambs have been genotyped at microsatellites, and of these 163 or 77%
have a father identified by microsatellites.
Of the 2009 lambs, 209 have so far been genotyped at the SNPs. We first checked
whether we could confirm the field-identified mothers and find mothers for lambs
with no recorded mothers. The results are shown in Table 3. Although not all lambs
or their mothers have yet been SNP genotyped, where they both have, 100% of the
mothers were correct. This is a tribute to both the field identification system and the
record-keeping and sampling labeling procedures in place. Even better, 14 new
mother-lamb relationships were established, involving 12 ewes which had no
23
recorded lamb in that year and two which were recorded with a singleton but actually
had twins, of which one (the newly attributed one) died. These 14 lambs consisted of
seven which were found dead soon after birth and seven which were caught in
summer catches after the time at which they could be mothered-up. When applied
across the whole dataset, these adjustments will change ewe breeding records and
estimates of ewe fecundity and lifetime breeding success.
Table 3. Outcome of maternity analysis of 2009 cohort using 384 SNP loci and
MasterBayes.
Category No of lambs % of genotyped
lambs
Database mother confirmed 161 77.0
SNP genotyped but no mother found. BUT database mother
not yet SNP genotyped
29 13.9
Mother unknown in database but found 14 6.7
Mother unknown in database and not found (yet) 5 2.4
Total genotyped at SNPs 209 100
Next we tested the paternity of the 2009 lambs. Of the 209 SNP genotyped lambs a
father was found for 169 or 81%. This may not seem like a drastic improvement over
the 77% reported above, but two points should be noted. First, not all candidate
fathers have yet been genotyped at the SNPs, so this proportion is likely to rise.
Second, the individual confidence of these paternities is in every case 100 rather than
averaging 98% as in the microsatellite paternities.
Finally, we inspected differences between the two paternity analyses. 199 lambs have
been genotyped by both methods and can be compared. Results are shown in Table 4.
Given that not all candidate sires have yet been SNP-genotyped, the results are most
encouraging. Not only do the SNPs identify a father for an additional 20% of lambs,
but there is every chance they will confirm another 10% of paternities once all
candidate sires are genotyped. It looks as if 95% or more of all lambs may have a
known sire once all the genotypes are in.
Table 4. Comparison of paternity analysis of 2009 cohort by the microsatellite and
SNP methods.
Category No of lambs % of genotyped
lambs
Same sire called 114 57.3
Sire called by SNPs but not microsatellites 40 20.1
Sire called by microsatellites but not by SNPs – in every
case the microsatellite sire has not been SNP genotyped
yet
19 9.6
Neither method calls a sire 17 8.5
Different sire called 9 4.5
Total genotyped lambs 199 100
24
In summary, once the SNP genotyping and parentage analysis is complete, it is going
to greatly increase the completeness, accuracy and confidence of the pedigree. In
particular, the enhanced power to identify mothers will help to resolve the current lack
of information on the maternity of the 2001 lambs, which we were unable to tag at
birth due to foot-and-mouth restrictions in that year.
Genetic architecture of body size in Soay sheep.
Camillo Berenos, Phil Ellis and Josephine Pemberton.
Body size has a strong genetic basis in Soay sheep, as genes are estimated to explain
nearly 50% of the variation in body size. Previous work showing positive selection on
body size suggests that it should be increasing in size over time, but in fact it has
decreased during the course of the study. Ecological processes can explain much of
this change, hence it is uncertain if the sheep have responded genetically to selection.
Ideally we would like to 1) find genomic regions that underlie variation in body size
(called Quantitative Trait Loci or QTL) 2) study selection on these regions and 3)
track allele frequencies of variants at QTL over time.
To investigate this issue, we are genotyping the Soay sample collection at 38,000
variable DNA markers called single nucleotide polymorphisms, (SNP), which are
uniformly distributed across the genome. Here we present a preliminary analysis of a
body size trait (adult hindleg length) at three hierarchical levels, First, we examine
how much of the phenotypic variance can be explained by all screened SNP. Second
we partition phenotypic variance between chromosomes. Finally, we examine
whether we can identify specific genomic locations that explain significant amounts
of variation in body size.
The total variance explained by all SNPs is very similar to the variance explained
using a pedigree-based relationship (Fig. 17), and both methods attribute 45-48% of
phenotypic variance to genes. More than 90% of genetic variance (as estimated using
pedigree relatedness) can be explained by the scored SNPs (Fig. 17). This is in
contrast with results from human genetics studies, where only 50% of additive genetic
variance can be explained by SNPs. This can partly be explained by the higher
amounts of linkage disequilibrium in the Soay sheep, due to possible admixture
events and a historically low effective population size.
25
Figure 17. Comparison of proportion of phenotypic variance explained for adult
hindleg length between models using either pedigree relatedness (dark bars; N = 798
individuals) or realised relatedness (calculated using 38,000 SNPs, light bars;
(N=598 individuals). VA, (additive genetic variance), calculated using pedigree
relatedness reflects heritability, while VA calculated using realised relatedness
reflects the proportion of phenotypic variance explained by all SNPs. Other estimated
variance components are VPE (Permanent environment, among-individual variance
attributed to repeated measures); Capyear, (Year of capture) and Birthyear, (Year of
Birth). Estimates obtained are very similar between the two models, and most of the
heritable variation can be explained by SNPs.
Several chromosomes contribute to variation in body size, while other chromosomes
explain very little (Fig. 18). The sum of variance explained for all chromosome is
50% which is very similar to the variance estimated using all SNPs (Fig. 17).
Figure 18. Variance in adult hindleg length explained by each chromosome. There
are substantial differences between chromosomes in how much they contribute to
phenotypic variation, as several chromosomes explain no variation, while others
explain up to 7 percent of total phenotypic variance (N = 598 sheep).
26
We also examined whether specific SNPs showed significant association with hindleg
length using genome-wide association (GWAS). Some regions show SNPs which
suggest a possible association with adult hindleg length, but no single SNP reached
genome-wide significance after correcting for multiple testing (Fig. 19). A reassuring
finding is that the cluster of SNP suggestive of an association with adult hindleg
length which appears at the end of chromosome 16 seems to confirm previous
findings obtained with the same panel of markers, but using a highly selected sample
of Soays.
Figure 19. Results of genome-wide association of adult hindleg length. (N = 598
sheep).
In summary, we have found that SNPs explain most of the genetic variance in a
quantitative trait, and that this is likely due to the joint effect of many genes with
(relatively) small effect. The results presented here are highly preliminary, as
approximately half the projected number of individuals have been successfully
genotyped thus far. As our available sample size increases we expect more power to
detect statistical significance of chromosome contributions to trait variation and
associations between SNP genotypes and trait values. Despite the lack of statistical
power, our results are very promising, and among the first of their kind in any natural
population.
27
Population dynamics and genetic structuring of the St Kilda field mouse.
Tom Black and Shaun Robertson.
An island subspecies endemic to the St Kilda archipelago, the field mouse Apodemus
sylvaticus hirtensis is being studied in order to provide a comprehensive overview of
its ecology and evolutionary history. Intensive and ongoing live trapping over the last
two years at three sites (Village Bay, Glen Bay and Carn Mor) on Hirta has provided
data and samples that can be used to examine the population dynamics, breeding
ecology, diet, size and genetics of these mice. Some preliminary findings are
discussed below.
Population dynamics:
A core part of the project is to determine the density of mice living at each site, and
how this varies over time. Intensive live trapping of individually marked mice at three
sites has provided data with several possibilities for estimating population densities.
However, this is made complex by the fact that animals are wont to move into and out
of a trapping area. Traditional mark-recapture methods can measure the abundance of
mice within an area, but defining the limits of that area can be problematic. To
overcome this, we have used spatially explicit capture-recapture (SECR) analyses
which provide pure estimates of density without the need to define an effective
trapping area.
The population of St Kilda field mice has fluctuated over the last two years in a
manner typical of small mammals in seasonal habitats (Fig. 20), in which densities are
greatest in late autumn, with as many as 55 mice/ha following summer breeding,
followed by a decline in numbers during winter and spring to between 2 and 10
mice/ha. There are also significant differences between years and between the three
trapping sites, with Carn Mor standing out as having more pronounced swings in
population size. It is hypothesized that this may be due to greater summer food
availability provided by the large numbers of seabirds nesting at the site, but results
from the dietary analysis are not yet available to confirm this.
The models underlying the SECR analysis have also highlighted some quirks of
mouse behaviour, such as a behavioural response to trapping in which individuals are
more likely to be caught when they have encountered a trap already (‘trap happy’
mice), and that range size varies according to season and sex, largely as a result of
increased ranging behaviour in males during the mating season.
28
Gle
n B
ay
0
10
20
30
40
50
60
70
80
90 N
ov-0
9
Jan-
10
Mar
-10
May
-10
Jul-1
0
Sep-1
0
Nov
-10
Jan-
11
Mar
-11
May
-11
Jul-1
1
Sep-1
1
Nov
-11
Number caught (solid line)
Density (mice/ha, dashed line)
Ca
rn M
or
0
10
20
30
40
50
60
70
80
90 N
ov-0
9
Jan-
10
Mar
-10
May
-10
Jul-1
0
Sep-1
0
Nov
-10
Jan-
11
Mar
-11
May
-11
Jul-1
1
Sep-1
1
Nov
-11
Number caught (solid line)
Density (mice/ha, dashed line)
F
igu
re 2
0.
Abundance a
nd d
ensi
ty o
f m
ice o
ver
tim
e,
for
thre
e t
rappin
g s
ites
(Vil
lage B
ay,
Gle
n B
ay &
Carn
Mor)
. G
raphs
show
num
ber
of
indiv
idual
mic
e caught
per
trappin
g s
ess
ion (
soli
d l
ine),
alo
ng w
ith S
EC
R -
reve
ale
d d
ensi
ty e
stim
ate
s (d
ash
ed l
ine)
and 9
5%
confi
den
ce
inte
rvals
(dott
ed l
ine).
Densi
ty e
stim
ate
s are
base
d o
n d
ata
fro
m N
ovem
ber
2009 u
nti
l June
2011 o
nly
.
Villa
ge
Ba
y
0
10
20
30
40
50
60
70
80
90 N
ov-0
9
Jan-
10
Mar
-10
May
-10
Jul-1
0
Sep-1
0
Nov
-10
Jan-
11
Mar
-11
May
-11
Jul-1
1
Sep-1
1
Nov
-11
Number caught (solid line)
Density (mice/ha, dashed line)
Population genetic structuring
Knowing the genetic structure of a population can be important for conservation
planning and provides an insight into the broader ecology of a species. Field mouse
populations have been known to demonstrate considerable genetic subdivision over
relatively small spatial scales despite their propensity for dispersal, and this is also
true of the St Kilda mice. Genotyping of a total of around a hundred individuals from
Dun and the three trapping sites on Hirta at six microsatellite loci found a high level
of genetic differentiation between the two islands (Fig. 21), suggesting that there is
little or no gene flow across the narrow sea gap between them. Furthermore, there is
some evidence of genetic subdivision within the Hirta population; mice from Carn
Mor appear to be divergent from those at the Village and Glen Bay sites. Man-made
obstacles have been cited as a likely cause of genetic structuring in mainland field
mice, but here it seems that the precipitous terrain of St Kilda may also be sufficient
to prevent the population from homogenising.
Figure 21. Estimated population structure of mice from Dun and Hirta, including
prior population sampling information. According to the population genetics software
STRUCTURE, three genetic clusters are present, each represented by a different
colour. Each individual is represented by a vertical line coloured according to the
probability of assignment to each cluster. Individuals are arranged by sample
location (1, Village Bay; 2, Glen Bay; 3, Carn Mor; 4, Dun).
The other major finding of this preliminary genetics work is that there is clearly a
very low level of genetic diversity in the St Kilda field mouse population, presumably
as a result of it being founded by a small number of individuals. In fact, with a
maximum of 6 alleles (mean 3.2) at any one locus it is possible that a single pregnant
female could have founded the entire population, given that multiple paternity litters
are common in field mice.
30
Publications
Childs, D. Z., Coulson, T. N., Pemberton J., Clutton-Brock T. & Rees, M. (2011)
Predicting trait values and measuring selection in complex life histories:
reproductive allocation decisions in Soay sheep. Ecology Letters 14, 985-992.
Colchero F. & Clark, J. S. (2011) Bayesian inference on age-specific survival for
censored and truncated data. Journal of Animal Ecology, 81, 139-149.
Di Fonzo, M. M. I., Pelletier, F., Clutton-Brock, T. H., Pemberton, J. M., & Coulson,
T. (2011) The population growth consequences of variation in individual
heterozygosity. PloS one 6:e19667.
Hayward, A. D., Wilson, A. J., Pilkington, J. G., Clutton-Brock, T. H., Pemberton, J.
M., & Kruuk, L. E. B. (2011) Natural selection on a measure of parasite
resistance varies across ages and environmental conditions in a wild mammal.
Journal of Evolutionary Biology 24:1664-1676.
Johnston, S. E., McEwan, J. C., Pickering, N. K., Kijas, J. W., Beraldi, D., Pilkington,
J. G., Pemberton, J. M., & Slate, J. (2011) Genome-wide association mapping
identifies the genetic basis of discrete and quantitative variation in sexual
weaponry in a wild sheep population. Molecular Ecology 20: 2555-2566.
Nussey, D. H., Watt, K., Pilkington, J. G., Zamoyska, R. & McNeilly, T. N. (2012)
Age-related variation in immunity in a wild mammal population. Aging Cell
11, 178-180.
Preston, B. T., Stevenson, I. R., Lincoln, G. A., Monfort, S. L., Pilkington, J. G., &
Wilson, K. (2012) Testes size, testosterone production and reproductive
behaviour in a natural mammalian mating system. Journal of Animal Ecology:
296-305.
In press:
Morrissey. M. B., Parker, D., Korsten, P., Clutton-Brock, T. H., Pemberton, J. M.,
Kruuk, L. E. B., & Wilson, A. J. The prediction of adaptive evolution:
empirical application of the secondary theorem of selection and comparison to
the breeder’s equation. Evolution.
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A C K N O W L E D G E M E N T S
We are grateful to the National Trust for Scotland and to Scottish Natural Heritage for
permission to work on St Kilda, and for their assistance in many aspects of the work.
The project would not be possible without the generous assistance and support of MOD,
QinetiQ and E.S.S. staff stationed on St Kilda and Benbecula and servicing the island.
We are particularly grateful to Susan Bain, the Western Isles Manager for the NTS, Paul
Sharman the NTS Ranger for St. Kilda, to Ian McHardy and Carol Knott the
Archaeologists on the island, and to Gina Prior the Seabird and Marine Ranger.
We are also grateful for the help of volunteers without whom the fieldwork for 2011
would not have been possible: Gebre Asefa, Billy Craigens, Dean Dowden, Katie
Hatton, Rebecca Hewitt, Linda Kehoe, Deborah Leigh, Ruari Macleod, Oliver Moore,
Anne O’Callaghan and Federico Tettamanti. Thank you.
Our research is supported by grants and studentships from the Natural Environment
Research Council, the Biotechnology and Biological Sciences Research Council, the
Royal Society and the European Research Council.
AP P E N D I X A: PE R S O N N E L NEWS & SC H E D U L E O F WO R K
Personnel News
Jill Pilkington was awarded an MBE for her services to Science. This was conferred
by HRH Prince Charles on November 18th
at Buckingham Palace.
In late 2010 Camillo Berenos and Phil Ellis joined the Project, working on the
genomics of Soay sheep with Josephine Pemberton, funded by the European Research
Council.
Jen Dorrens has joined the project as a post-graduate research assistant, and will be
studying telomere length as part of a project in collaboration with Lea Harringon, Dan
Nussey and Josephine Pemberton.
Louise Christensen has completed her undergraduate honours thesis at the University
of Aberdeen investigating oxidative stress and ageing, as part of an ongoing
collaboration involving Colin Selman, Jon Blount and Dan Nussey.
Schedule of work on St Kilda
Winter - Spring
Jill Pilkington monitored mortality during February and with volunteers, throughout
lambing. During this period, detailed data were collected on individual sheep found
dead, and samples were taken for genetic and parasitological study.
32
From March 18th
until May 6th
, Jill Pilkington and 3 volunteers carried out ten
population censuses and tagged and sampled lambs, with assistance during the peak
of lambing from Dan Nussey. 270 lambs were born to 260 ewes; these figures include
10 sets of twins (7 ewes held both lambs, 2 lost one lamb and 1 ewe lost both lambs).
161 lambs (80 male and 81 female) were caught and tagged; a further 59 lambs died
before any tagging attempt. Mick Crawley and two assistants collected vegetation data.
Tom Black and one volunteer conducted trapping sessions in March and May on Hirta.
Summer
Jill Pilkington and two volunteers returned to Hirta on July 12th to carry out ten
population censuses, conduct mortality searches (yielding 11 tagged dead animals),
and prepare for the main catch-up of study area sheep. The catch-up took place from
August 5th
– 19th, was led by Josephine Pemberton, and conducted by a team of 11
additional project members and volunteers. 288 sheep were caught and processed, of
which 100 were lambs (50 males and 50 females), 23 were yearlings (5 males and 18
females), 44 were adult males, and 121 were adult females. All animals were weighed
and measured to monitor growth, and sampled for parasite and genetic analyses. 25
Sheep were retagged because of damaged or missing tags. 17 previously untagged
lambs and 1 yearling were caught and processed. Mick Crawley and two assistants
collected vegetation data. Jill Pilkington and two volunteers remained on Hirta until
3rd
September to complete parasite counts and pasture larvae counts.
Tom Black and one volunteer conducted trapping sessions in August/September.
Autumn
From October 18th
to December 6th
Jill Pilkington, Alastair Wilson and one volunteer
carried out ten population censuses, monitored the mating period, capturing and
processing 3 incoming tups and 1 resident tup and ewes. 44 previously darted, non-
resident tups were seen in the study area during this rut. 4 dead sheep were found.
Tom Black and one volunteer conducted trapping sessions in November/December.
33
C I R C U L A T I O N L I S T - (Please advise J.Pilkington of any changes or additions)
Prof. S. Albon Macaulay Institute, Craigiebuckler, Aberdeen, AB15 8QH.
Ms. S. Bain NTS, Balnain House, 40 Huntly St., Inverness, IV3 5HR.
Dr. D. Bancroft GPC AG, Lochhamer Str. 29D-82152, Munich, Germany.
Mr. A. Bennett NTS, Balnain House, 40 Huntly St., Inverness, IV3 5HR.
Ms. A. Bento Dept. Biological Sciences, Imperial College, Silwood Park, Ascot, SL5 7PY.
Dr D. Beraldi Roslin Institute Edinburgh Univ., Roslin, Edinburgh EH25 9PS.
Dr. C. Berenos Inst. Evol. Biol., Edinburgh Univ., West Mains Rd, Edinburgh EH9 3JT.
Mr. T. Black Inst. Evol. Biol., Edinburgh Univ., West Mains Rd, Edinburgh EH9 3JT.
Mr. J. Blount Centre for Ecology and Conservation, Univ. Exeter, Cornwall Campus, TR10 9EZ
Ms. E. Brown Dept. of Animal and Plant Sciences, Univ. Sheffield, S10 2TN.
Dr. D. Childs Dept. of Animal and Plant Sciences, Univ. Sheffield, S10 2TN.
Dr. D. Clements Royal (Dick) School of Veterinary Sciences, Edinburgh Univ., Easter Bush, EH25 9RG
Prof. T. Clutton-Brock Dept. Zoology, Cambridge Univ., Downing St., CB2 3EJ.
Dr. D. Coltman Dept. Biol. Sci., Univ. Alberta, Edmonton AB, T6G 2E9, Canada.
Dr. T. Coulson Dept. Biological Sciences, Imperial College, Silwood Park, Ascot, SL5 7PY.
Dr. B. Craig Wildlife, Ecology and Management Group, Central Sc. Lab., York, YO41 1LZ.
Prof. M. Crawley Dept. Biological Sciences, Imperial College, Silwood Park, Ascot, SL5 7PY.
Dr. S. Davies SNH, Fraser Darling House, 9 Culduthel Road, IV2 4AG.
Ms. J. Dorrens Inst. Evol. Biol., Edinburgh Univ., West Mains Rd, Edinburgh EH9 3JT.
Mr. P. Ellis Inst. Evol. Biol., Edinburgh Univ., West Mains Rd, Edinburgh EH9 3JT.
Dr. T. Ezard Dept. Biological Sciences, Imperial College, Silwood Park, Ascot, SL5 7PY.
Ms. J. Ferguson SNH, Stilligarry, South Uist, HS8 5RS.
Dr. P. Feulner Westfälische Wilhelms Univ., Inst. Evol. and Biodiv., Hüfferstrasse,
148149 Münster, Germany.
Dr. A. Graham Dept. Ecol. Evol. Biol., Guyot Hall, Princeton Univ., NJ 08544 2016, U.S.A. Dr. J. Gratten Queensland Inst. Med. Res., PO Royal Brisbane Hospital, Q4029, Australia.
Prof. B. Grenfell Dept. Ecol. Evol. Biol., Guyot Hall, Princeton Univ., NJ 08544 2016, U.S.A. Dr. F. Gulland TMMC, Marin Headlands, Sausalito, CA 94965, USA.
Dr. J. Hadfield Inst. Evol. Biol., Edinburgh Univ., West Mains Rd, Edinburgh, EH9 3JT.
Ms. J. Harden NTS, Balnain House, 40 Huntly St., Inverness, IV3 5HR.
Mr. A. Hayward Inst. Evol. Biol., Edinburgh Univ., West Mains Rd, Edinburgh, EH9 3JT.
Prof. L. Harrington Université de Montréal, Institute de Recherche en Immunologie et en Cancérologie,
Montréal, Canada
Prof. A. Illius Inst. Evol. Biol., Edinburgh Univ., West Mains Rd, Edinburgh, EH9 3JT.
Dr. S. Johnston Dept. of Animal and Plant Sciences, Univ. Sheffield, S10 2TN.
Dr. O. Jones Inst. Zoology, ZSL, Regent’s Park, London NW1 4RY..
Dr. P. Korsten Inst. Evol. Biol., Edinburgh Univ., West Mains Rd, Edinburgh, EH9 3JT.
Dr. L. Kruuk Inst. Evol. Biol., Edinburgh Univ., West Mains Rd, Edinburgh, EH9 3JT.
Dr. G. Lincoln MRC Centre for Rep. Biol., 49 Little France Cres., Edinburgh, EH3 9EW.
Mr. J. Love The Watchers Cottage, Snishival, South Uist, HS8 5RW.
Dr. R Luxmoore NTS, 28 Charlotte Square, Edinburgh, EH2 4DU.
Dr. A. MacColl School of Biology, Univ. of Nottingham, NG7 2RD.
Mr. D. MacLennan SNH, 17 Frances St., Stornoway. Lewis, Outer Hebrides.
Prof. J Matthews Moredun Research Institute, Edinburgh.
Dr T. McNeilly Moredun Research Institute, Edinburgh.
Mr. A. McRae Queensland Inst. Med. Res., PO Royal Brisbane Hospital, Q4029, Australia.
Dr. R. Mellanby Royal (Dick) School of Veterinary Sciences, Edinburgh Univ., Easter Bush, EH25 9RG
Dr. J. Milner Hogskolen i Hedmark, Evenstad, NO2480, Koppang, Norway.
Prof. B. Morgan Inst. Maths.& Stats., Univ. Kent., Canterbury, Kent, CT2 7NF.
Ms. K. Morriss Dept. Ecol. Evol. Biol., Guyot Hall, Princeton Univ., NJ 08544 2016, U.S.A. Dr. M. Morrissey Inst. Evol. Biol., Edinburgh Univ., West Mains Rd, Edinburgh, EH9 3JT.
Mr. S. Murray Craigie Dhu, Cardney, Dunkeld, Perthshire, PH8 0EY.
Dr A. Nisbet Moredun Research Institute, Edinburgh.
Dr. D. Nussey Inst. Evol. Biol., Edinburgh Univ., West Mains Rd, Edinburgh EH9 3JT.
Dr. A. Ozgul Dept. Biological Sciences, Imperial College, Silwood Park, Ascot, SL5 7PY.
Prof. S. Paterson School of Biological Sciences, Univ. of Liverpool, L69 7ZB.
Dr. F. Pelletier Dept. Biologie, Univ. of Sherbrooke, Quebec, Canada, J1K 2RI.
Prof. J. Pemberton Inst. Evol. Biol., Edinburgh Univ., West Mains Rd, Edinburgh EH9 3JT.
Mrs J. Pilkington Inst. Evol. Biol., Edinburgh Univ., West Mains Rd, Edinburgh EH9 3JT.
Dr. B. Preston Max Planck Inst. Evol. Anthropology, 04103 Leipzig, Germany.
Dr. G. Prior Macaulay Institute, Craigiebuckler, Aberdeen, AB15 8QH.
Dr. M Rees Dept. of Animal and Plant Sciences, Univ. Sheffield, S10 2TN.
Mr. S. Robertson Inst. Evol. Biol., Edinburgh Univ., West Mains Rd, Edinburgh, EH9 3JT.
34
Dr. M. Robinson Dept. of Animal and Plant Sciences, Univ. Sheffield, S10 2TN.
Dr. P. Scott Royal (Dick) School of Veterinary Sciences, Edinburgh Univ., Easter Bush, EH25 9RG
Dr. C. Selman Inst. Of Biological and Environmental Sciences. Univ., Aberdeen, AB24 2TZ
Prof. J. Slate Dept. of Animal and Plant Sciences, Univ. Sheffield, S10 2TN.
Dr. R. Stevens Dept. of Archaeology, University of Cambridge, Downing St., CB2 3ER.
Dr. I. Stevenson Sunadal Data Solutions, Midlothian, Innovation Centre, Roslin, EH25 9RE.
Dr. G. Tavecchia Imedea-CSIC/UIB, c. M. Marques 21, 07190 – Esporles, Mallorca, Spain.
Dr. L. Tempest 7 Mandrake Road, London, SW17 7PZ.
Dr. P. Visscher Queensland Inst. Med. Res., PO Royal Brisbane Hospital, Q4029, Australia.
Dr. S. Votier Sch. of Biomed. & Biol. Sci., Davy Building, Plymouth, Devon, PL4 8AA.
Ms. K. Watt Inst. Evol. Biol., Edinburgh Univ., West Mains Rd, Edinburgh EH9 3JT.
Dr. A. Wilson Inst. Evol. Biol., Edinburgh Univ., West Mains Rd, Edinburgh EH9 3JT.
Dr. K. Wilson Dept. of Biological Sciences, Lancaster University, LA1 4YQ.
Prof R. Zamoyska Institute of Immunological and Infection Research, University of Edinburgh
