st kilda soay sheep project annual report...
TRANSCRIPT
ST. KILDA SOAY SHEEP PROJECT:
ANNUAL REPORT 2013
J.G. Pilkington1, S.D. Albon
2, C. Arthur
1, C. Berenos
1, D. Childs
6, L. Christensen
11,
D. Clements15
, T.H. Clutton-Brock
3, T. Coulson
21, M.J. Crawley
4, E. Damasceno
17,
J. Fairlie1, P. Ellis
1, R. Garnier
8, B. Godsall
4, A. Graham
8, B. Grenfell
8, A.
Hayward6, L. Harrington
13, J. Herman
16, R. Holland
1, S. Johnston
1, L. Kerr
18, C.
Klingenberg17
, L. Kruuk1, T. McNeilly
10, M. Morrissey
20, S. Murray
19, D. Nussey
1,
J.M. Pemberton1, C. Regan
1, P. Scott
15, C. Selman
14, J. Slate
6, I.R. Stevenson
7, Z.
Timmons16
, R. Watson1,9
, K. Watt1, A. Wilson
12, K. Wilson
5, R. Zamoyska
9.
1Institute of Evolutionary Biology, University of Edinburgh.
2James Hutton 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.
7Sunadal Data Solutions, Edinburgh.
8Dept. Ecology and Evolutonary BiologyPrinceton University, USA.
9Institute of Immunology and Infection Research, University of Edinburgh.
10Moredun Research Institute, Edinburgh.
11Institute of Biological and Environmental Sciences, University of Aberdeen.
12Centre for Ecology & Conservation, College of Life & Environmental
Sciences, University of Exeter Cornwall Campus. 13
Université de Montréal, Institute de Recherche en Immunologie et en
Cancérologie, Montréal, Canada. 14
IBAHCM, University of Glasgow 15
Royal (Dick) School of Veterinary Studies, the University of Edinburgh. 16
National Museums Scotland. 17
School of Life Sciences, University of Manchester. 18
SynthSys, School of Biological Sciences, University of Edinburgh. 19
Murray Survey, Dunkeld, Perthshire. 20
School of Biology, University of St. Andrews. 21
Department of Zoology, University of Oxford.
POPULATION OVERVIEW ..................................................................................................................................... 2
REPORTS ON COMPONENT STUDIES .................................................................................................................... 4
Vegetation...................................................................................................................................................... 4
Vegtation and ewe lifetime reproductive success .......................................................................................... 5
GPS tagging sheep ........................................................................................................................................ 6
Selection of skull morphology in Soay sheep ................................................................................................. 8
Nutrition and nutritional damage in the Soay sheep ..................................................................................... 9
Osteoarthritis of the jaw in Soay sheep on St Kilda .................................................................................... 11
Telomere dynamics in Soay sheep ............................................................................................................... 12
Estimating heritability for Soay sheep body size using genetic markers ..................................................... 13
Counting sheep on Boreray from aerial photograhs ................................................................................... 16
PUBLICATIONS. ................................................................................................................................................. 18
ACKNOWLEDGEMENTS.. ................................................................................................................................... 19
APPENDIX A: PERSONNEL NEWS & SCHEDULE OF WORK ................................................................................. 19
CIRCULATION LIST ........................................................................................................................................... 21
2
PO P UL A TI O N OVE R VI EW
The sheep population on Hirta entered 2013 at a moderate level and, as a result, there
was little mortality over winter. Lambing began on the 5th
of April with 89% of lambs
born surviving (Fig. 1).
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Figure 1. The temporal distribution of lamb births during 2013.
In December 2013, 759 tagged sheep were believed to be alive on Hirta, of which 545
regularly used the study area, an increase of 50.6% 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.
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0(CO) 1(CY) 2(BG) 3(BL) 4(BW) 5(BR) 6(BO) 7(BY) 8(AG) 9(AL) 10(AW) 11(AR) 12(AO) 13(AY) 14(YG) ?(OP)
Age (cohort tag) Males/females
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Figure 2. Age distribution of tagged Soay sheep presumed to be alive at the end of
2013.
3
Figure 3. The number of tagged sheep regularly using the study area since 1985.
Note that the island count for 2013 is a reconstructed figure (see below).
No island count was possible due to daily low mist over the August catch up fortnight
trip. There is a very close association between island count numbers and the number
of sheep present in Village Bay (r2 = 0.90, Fig. 4). We used the linear relationship
plotted in Fig. 4 (y = 21.659 + 3.1566x) to predict the number of sheep on Hirta in
2013 as 1742. The prediction is illustrated on Fig. 4 as the red square. Hence total
population probably increased by 34.8% since summer 2012 when it was at 1292.
This gives a delta (calculated as ln (Nt+1/Nt)) of 1.348.
Figure 4. Scatterplot of Village Bay population sizes versus Hirta population sizes
1985-2012 (open circles) with linear regression line also plotted. The estimated Hirta
population size for 2013 is presented as a red square.
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4
REPORTS ON COMPONENT STUDIES
Vegetation.
Mick Crawley.
2013 was a year of superlatives as far as the plants were concerned. We recorded the
highest numbers of flowering stems for many of the dominant grasses, the highest
total shoot biomass since 2003, second highest tussock density and second lowest gap
density since 1993. Cerastium fontanum, in particular, showed a huge increase in
biomass compared with the long-term average, producing the highest mean biomass
ever recorded from this species.
The fact that total sheep numbers were up substantially from their post-decline low of
2012, and that plant productivity (as measured by grass biomass inside temporary
sheep exclosures) was not particularly high in 2013, indicates that there is an
interesting time lag between reduced grazing pressure and increased above-ground
plant performance. It is plausible that immediately post-crash (during the growing
season of 2012), the plants were investing in below-ground storage and reserve-
replenishment following 8 years of continuously high grazing pressure. This, in turn,
allowed a mass flowering in 2013 for grasses like Holcus lanatus and Agrostis
capillaris and herbs like Potentilla erecta. As usual, the grazing pressure was
sufficiently high to prevent any flowering at all by the most palatable species like
Festuca rubra, Rumex acetosa and Achillea millefolium except in places like sea
cliffs and cleit roofs that are inaccessible to the sheep. Some of the species that flower
most conspicuously in a more typical year produced very small crops of flowers in
2013, perhaps as a result of competitive suppression by the dominant grasses.
In parallel with the long-term increase in sheep numbers, the long-term decline in the
mass of dead organic matter continues, despite an immediate post-crash peak in 2012
when grazing pressure was briefly quite low. This downward trend in dead organic
matter is mirrored by a long-term upward trend in the abundance of mosses and
liverworts, especially in the winter months. The steady long-term increase in heather
biomass continues, both in green shoots and woody tissue. Evidently, Calluna is not
suffering as a result of prolonged exposure to a food-limited sheep population on
Hirta.
5
Vegetation and ewe lifetime reproductive success.
Charlotte Regan and Mick Crawley.
Despite the fundamental importance of fitness measures such as lifetime reproductive
success (LRS) to wide ranging areas such as evolutionary biology and population
dynamics, studies of the relationship between such individually-based measures of
fitness and habitat use are limited. This is partially due to the need for long-term
studies, where data are collected from individuals throughout their lives. Such studies
are rare and therefore the large dataset available for the St. Kilda Soay sheep
population provides a relatively unique opportunity to carry out such work.
Despite this, until recently there was not the fine scale vegetation data available to
enable an examination of how individual sheep are affected by the vegetation they
use. However in 2012, information providing the species composition for each
hectare of the study area, as well as their percentage cover, was collected by Mick
Crawley. By combining this with home range data for each individual we have now
been able to show how the vegetation a ewe accesses during her lifetime relates to her
lifetime reproductive success (LRS), quantified as the number of lambs successfully
reared to weaning. Lifetime location data for individuals was used to calculate
weighted averages of the percentage cover of two plant species, a grass representing
high quality forage (Holcus lanatus) and heather (Calluna vulgaris) representing poor
quality forage. These values were then related to LRS and its components, namely
fecundity (the proportion of a female’s lifespan in which she produced a lamb),
lifespan and lamb survival.
We found that the vegetation utilised by females during their lifetime explained
significant variation in LRS, and that the two plant species largely associated with
LRS in opposing ways. Ewes experienced enhanced LRS as the percentage cover of
H. lanatus increased, with this at least partially due to the longer lifespan of ewes
inhabiting home ranges rich in H. lanatus and the improved survival of their
offspring. In contrast greater percentage cover of C. vulgaris was linked to lower LRS
with this mediated through the reduced fecundity and lifespan of females residing in
heather rich areas. These results could have arisen through the direct effect of forage
quality on ewe body weight and condition and thus their ability to provision
offspring, or we may see these trends as a result of intrinsic differences between
females. For example females of higher intrinsic quality may tend to inhabit areas of
higher quality forage due to factors such as superior competitive ability.
A somewhat surprising result was that increases in the percentage covers of both
species were associated with an increased likelihood of weaning a lamb. This
contrasted with our expectation that increasing percentage covers of heather would
consistently associate with reductions in LRS. This could be a legitimate trend,
brought about if females escape intense competition in high quality areas by utilising
heather dominated areas. The effect could however arise due to variation in the
number of times ewes are observed. If, for example females that die before
successfully producing a single offspring are observed fewer times, this could
systematically result in an underestimate of their use of heather and we may therefore
be undercounting the number of females living in heather rich areas that fail to wean
any lambs.
6
This is the first study to illustrate very fine-scale spatial differences in the vital rates
of female Soay sheep and work is currently underway to understand if male
reproductive success is associated in a similar way with home range characteristics.
GPS tagging sheep.
Tim Coulson, Ben Godsall, Ian Stevenson and Jill Pilkington.
In recent years there have been enormous advances in biodiversity technology,
including in the ability to accurately track wild animals in the field using high tech
tags. A company in the Netherlands (http://www.uva-bits.nl/) has developed tags that
incorporate accelerometers and a global positioning system (GPS) to monitor the
behaviour and whereabouts of birds. Although they have provided novel insight into
the ecology and life history of raptors and seabirds, the tags had not been used on
mammals. The Soay sheep project provided a unique opportunity to trial this
technology, because our detailed understanding of sheep on Hirta means we can
ground truth some of the data the tags generate.
Twelve tags were attached to sheep during the summer catch. During this time, base
stations communicated with each tag retrieving data from the accelerometers that
recorded how each tagged sheep moved its head and neck, and from the GPS that
beamed back the whereabouts of each tagged sheep. The tags use sunlight to
recharge, and we consequently also collected information on the level of charge in
each tag – could there really be sufficient light on St. Kilda to keep a GPS and
accelerometer working through the winter?
The tagging exercise was a pilot study to see whether tagging was a viable way to
monitor sheep – the study was not designed to answer novel scientific questions about
Figure 5. Ewe lifetime reproductive success (LRS) plotted against (a) the percentage cover of
Calluna vulgaris in the home range, and (b) the percentage cover of Holcus lanatus in the home
range. The regression lines come from generalised additive models of LRS against each
predictor in isolation.
7
their ecology or behaviour. The results of the pilot could help inform whether we can
ask interesting questions with a larger number of tagged animals.
We ended up with mixed results. First, the technology worked well. It recorded the
position of sheep quite accurately (Fig. 6) and the accelerometers also successfully
recorded data. It seems likely that the small-scale movement patterns the
accelerometers detected could be used to infer whether a sheep is eating, ruminating,
walking, running or sleeping. The tags did slowly lose power, which suggests that
were the tags to be deployed on a larger scale, the number of times per day that a
sheep’s position is recorded would need to less frequent than used in this pilot study.
The biggest issue we encountered was that most sheep (10/12) either lost or destroyed
their tags. The tags were attached to the fleece on the back of the neck using glue.
Although this method worked well in a short trial on sheep on a farm in the
Netherlands, it was clearly not ideal for Soay sheep on St. Kilda. Any future use of
the tags consequently requires a much better method of attachment.
We were able to adequately ground truth the general insights that the tags provided,
and demonstrate that the tags collect accurate movement and behavior data. They
could prove a useful tool in data collection on St. Kilda in the future.
Figure 6. The movement patterns of tagged sheep in the study area. Different colours
represent different tags.
8
Selection of Skull Morphology in Soay Sheep.
Elis Damasceno and Chris Klingenberg.
Food limitation and hard climatic conditions on St. Kilda, along with parasite burden,
are known to impose selective pressures on the Soay sheep population. Hard
environmental conditions such as these may favour some characteristics that increase
the individual's chances of survival and/or reproductive success (fitness). The aim of
this study is to measure the strength of natural selection acting on the skull shape of
Soay sheep. The analysis requires information on skull morphology and on individual
fitness. To capture skull shape in a numerical form, we used a method known as
geometric morphometrics. The method consists of the placement of landmarks on
consistent points on the skull. From each landmark we obtain spatial coordinates from
which we run the analysis. In this study we placed 57 landmarks (Fig. 7) on 522
skulls (236 females and 286 males). We used lifetime breeding success as our
measure of individual fitness.
Our preliminary analysis showed that male skull shape is under stronger selection
pressure than female skull shape, probably because males have to compete for access
to females, usually engaging in head-butting contests. The sexes also differ in the
aspects of skull shape under selection. Males were selected for a longer and more
posteriorly placed nasal bone, as well as longer tooth rows, larger braincase and more
frontally oriented orbits. Females were also selected for longer tooth rows, but were
selected for smaller braincases, and longer palate. It is hard to say exactly why some
skull shapes are being selected. We can only speculate when trying to find a
connection between skull shapes and higher breeding success. Larger braincases in
males may provide larger attachment areas for neck muscles, which could be directly
related to the ability to support bigger and heavier horns, as well as impact resistance
during head-butting. This may also explain why these characteristics are not under
direct selection in females, who do not engage in such contests. Selection for longer
tooth rows is most likely related to food intake rate, animals with bigger teeth can bite
and chew more efficiently which is essential, especially when food is scarce.
9
Figure 7. The location of where the 57 landmarks (red circles) were placed on the
skull.
Nutrition and nutritional damage in the Soay sheep.
Romain Garnier and Andrea L. Graham.
Understanding what drives population fluctuations in the wild is a long-standing
question in ecology, for which the Soay sheep are a perfect model. Some winters are
characterized by population ‘crashes’ in which up to 60% of the sheep die, and gross
pathology indicates a role for both gastro-intestinal nematodes and malnutrition.
These two factors are even suggested to act synergistically creating a negative spiral
mediated by several effects on the immune system that may lead to death. A
cornerstone of this hypothesis is that investment in immunity has nutritional costs.
Obtaining evidence of such costs has proved challenging, especially in natural
vertebrate populations where nutritional markers are not widely available.
We analysed plasma from females over two years old sampled in the summers prior
to three population crashes. We assessed individual nutritional status by measuring
albumin, total proteins, blood urea nitrogen and creatinine in plasma. To estimate
costs of immunity, we analysed nutritional data in relation to titres of antibodies
against different antigens of Teladorsagia circumcincta (the main nematode infecting
the Soay sheep) and against nuclear and cytoplasmic antigens that had already been
measured on the same set of individuals. Preliminary analysis indicates that antibody
markers are related to protein nutrition: more protein usually mean more antibodies in
adult females. Our preliminary analysis also indicates that, independently of
10
immunity markers, high levels of proteins in the blood dramatically increase winter
survival of adult females.
Because the liver is essential to protein metabolism, we are also in the process of
quantifying the damage suffered by this organ in individuals that died during the last
crash of winter 2011-2012. The winter field team collected and fixed necropsy
samples from 144 individuals, and at the laboratory we mounted them on slides and
stained them with a classical hematoxylin and eosin protocol. This protocol
differentially colors cells: cytoplasm appear in red while nuclei are stained in purple.
We then developed a computer-based image processing algorithm to quantitatively
describe liver degeneration based on high resolution pictures taken of these slides
(Fig. 8). Our preliminary analysis of a subset of 10 Soay sheep and 2 control
American domestic lambs show (i) differences between domestic and wild sheep in
the structure of the liver (ii) variation among wild individuals. Ultimately, we will be
able to use the quantitative data from the images, the nutritional data, and the
immunological data to look for associations between liver degeneration, nutritional
and immunological state. We aim to explain heterogeneity in the causes and timing of
death, and to tease apart the differential roles of nematode infection and malnutrition
as a cause of death.
Figure 8. Examples of pictures from histological slides of the livers of Soay sheep
and control domestic sheep, at two different magnifications (4x and 10x). At the 4x
magnification, quantitative traits likely to be of relevance to liver damage include
lacunarity (measuring the size of gaps and how they fill the space) and entropy (a
measure of the repeatability of patterns within an image) of such images. At the 10x
magnification, measures of interest will be related to the density and spatial
distribution of nuclei.
11
Osteoarthritis of the jaw in Soay sheep on St. Kilda.
Colin Arthur, Kathryn Watt, Dan Nussey, Josephine Pemberton, Jill Pilkington, Jerry
Herman, Zena Timmons, Dylan Clements, and Phil Scott.
Osteoarthritis (OA) is a degenerative disease of joints which, in livestock and
companion animals, can result in moderate to severe lameness when affecting limb
joints, and is associated with production losses. As ruminants, sheep spend up to 10
hours per day ruminating placing considerable wear and tear on their jaw (or
temporo-mandibular, TMJ) joints. Previous work has identified OA of the elbow joint
in domestic sheep, but the prevalence of OA of the jaw and in particular the TMJ has
not been previously reported. The only previous evidence comes from a single case
noted in a survey of skeletal material collected post-mortem from Soay sheep on St
Kilda in the 1970s and 1980s, which was led by Juliet Clutton-Brock. We followed
this up to determine the prevalence of this pathology using the collection of Soay
jaws archived at the National Museums Scotland facilities in Granton, Edinburgh.
Examination of 3062 sheep skulls collected since1984 from the Soay sheep on St
Kilda revealed OA of the TMJ on one side of the jaw in 15 adult sheep (10 right side;
5 left side) and involving both sides in 19 sheep with an overall prevalence of 2.3%
for females and 0.2% in males (see Fig. 9A for an example of the pathology). There
was a striking age-dependence in TMJ osteoarthritis incidence: 30 of the 35 cases
occurred in geriatric sheep (aged seven years or more, 11% prevalence within age
class), four in adults (2-6 years old, 1% prevalence), one in yearlings (0.3%
prevalence) and none in lambs (Fig. 9B). These results have stimulated investigations
into the prevalence of TMJ pathology in cull sheep on commercial farms in Scotland
especially in those sheep breeds with jaw mal-alignment, a surprisingly common
hereditary condition.
Figure 9. (A): Osteoarthritis of the right TMJ compared to the normal articular
surface of the left jaw in the same sheep; (B): The number of cases of osteoarthritic
pathology of the TMJ in Soay sheep against age in years at death. The bar plot and
right-hand y-axis show the number of cases of pathology in each age group and the
dot and line plot and the left-hand y-axis shows the total number of individual jaws
examined in each group in males (blue) and females (pink).
(A)
12
Telomere dynamics in Soay sheep.
Jen Fairlie, Rebecca Holland, Daniel Nussey, Josephine Pemberton, Jill Pilkington
Lorraine Kerr and Lea Harrington.
Telomeres are the specialized structures that cap the ends of all mammalian
chromosomes. Their purpose is to protect the integrity of coding DNA. A cell’s
telomeres get progressively shorter every time it divides, and on average an
individual’s telomeres get shorter as they age. Numerous studies looking at telomere
length (TL) in human white blood cells (WBC) have demonstrated links between
lifestyle stress and increased telomere loss, and suggest short average telomeres in
later life can predict subsequent health. TL has also recently been a focus of interest
in an ecological and evolutionary context, with longitudinal studies suggesting TL
can predict individual fitness and longevity in wild animals.
We have used the freezer bank of stored WBC samples collected in spring and
summer from Soay sheep on St Kilda to investigate the relationship between white
blood cell telomere length, ageing and health in a wild mammal. We have developed
an extremely high-throughput, low-cost protocol for TL measurement using a
quantitative real-time PCR approach and the robotics facility at SynthSys in
Edinburgh. We have measured TL across the lifetimes of females born in four years,
2002-2005, that differed markedly in environmental conditions and juvenile mortality
patterns. Our preliminary analyses of these data show that, as found in human follow-
up studies, there is considerable variation in year-to-year change in TL. Importantly,
as found in some human and in a growing number of avian studies, TL seems to be an
important predictor of survival in Soay sheep and individuals that die as lambs appear
to show rapid telomere loss in early life (Fig. 10).
13
Figure 10. Mean relative telomere length (with standard error bars) at different ages
for females born 2002-2005. Females are grouped according to their longevity,
illustrating that longer lived sheep have longer telomeres on average and that sheep
dying as lambs show more rapid telomere loss in the first few months of life.
Estimating heritability for Soay sheep body size using genetic markers.
Camillo Bérénos, Philip Ellis, Jill Pilkington and Josephine Pemberton.
Individuals of the same population often differ tremendously in phenotype (for
example, body size). In order to understand the evolutionary potential of this
diversity, we need to quantify the extent to which genes can explain the differences
between individuals. Statistical models are available to estimate the proportion of
within-population phenotypic variance which is explained by genes (heritability). As
this estimation relies on the assumption that related individuals are more similar in
phenotype than unrelated individuals, relatedness information is required to estimate
heritability. In wild populations, pedigrees are typically used as they most efficiently
use the complex relationship structure present in most natural systems. However,
even the best wild pedigrees have errors and missing links, leading to bias and
reduced precision of heritability estimates. In addition, the construction of pedigrees
is time-consuming and not realistically feasible in many study systems, limiting our
understanding of the genetics of phenotypic variation in natural populations. In recent
years, it has been shown that 1) relatedness can be estimated with high precision
using genetic markers, and 2) heritability can be estimated using this relatedness at
genetic markers instead of using a pedigree. So far, this approach has been
successfully applied to human and livestock datasets, but it is unknown whether
relatedness at genetic markers can be a substitute for pedigree-relatedness in the
estimation of heritability for traits in natural populations.
Age
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Died aged 1-5 yearsSurvived to 6 years +
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We compared heritability estimates obtained using relatedness at genetic markers
with heritability estimates obtained using a pedigree for five measures of adult body
size in the Soay sheep on St. Kilda. The pedigree used is the best pedigree currently
available for the Soays, which was constructed using a combination of observational
and genetic data. Relatedness at genetic markers was estimated using circa 37,000
different variable DNA segments called Single Nucleotide Polymorphisms, (SNP),
distributed throughout the sheep genome. The heritability estimates were very similar
(and not significantly different) between models using the pedigree and models using
marker relatedness (Fig. 11). In four traits pedigree estimates were slightly higher
than marker estimates, while this pattern was reversed for adult body weight (Fig.
11).
Figure 11. Comparison of phenotypic variance explained by genes using either
pedigree (green bars) or marker (orange bars) relatedness. Variance components
shown are maternal effect (VM), and heritability (VA). Error bars indicate the
standard error of the estimates.
One possible explanation for why marker-based estimates are generally lower than
the pedigree-based estimates is that the number of SNPs used to estimate relatedness
between phenotyped individuals is too low. As the genotyped SNP markers are most
likely not directly affecting the studied traits, accurate heritability estimates using
markers is dependent on how variants at genotyped SNPs predict variants at causal
genomic regions. It is expected that body size in Soay sheep, as in humans and cattle,
is a highly "polygenic" trait, i.e. influenced by many genomic regions each
contributing very little to trait variation. Therefore, we expect that relatedness at a
larger number of SNPs than we currently have available may reflect relatedness at the
causal gene variants better, and as a consequence lead to higher heritability estimates.
Conversely, lower heritability estimates are expected when relatedness is estimated
using a smaller number of markers. To examine the effect SNP number has on
heritability estimates we randomly sampled 2.5%, 5%, 10%, 30%, 50%, 70% and
90% of the available markers. Relatedness between individuals was estimated using
each subset of SNP markers, and heritability for the five body size traits was
estimating using the resulting relatedness estimates. This process was repeated fifty
times to account for sampling variation.
15
In all five traits we see that when very few markers are used to estimate relatedness,
heritability estimates are substantially lower than when all of the available markers
are used (Fig. 12). Interestingly, heritability estimates asymptote between 30% and
50% of the total number of available markers.
Figure 12. Estimated heritability (VA/VP) of adult body size as a function of
increasing marker number. Box and whiskers show the median and spread of 50
replicate sampled sets of SNPs. The solid and dashed lines represent pedigree
heritability and marker heritability estimates using all available markers respectivel.
This result suggests that (i) our heritability estimates are not downwards biased due to
a limited number of SNPs, and (ii) that estimating relatedness at an increased number
of SNPs (for example a sheep test with 600,000 SNPs has recently become available)
will not lead to increased heritability estimates.
In summary, our results indicate that it is now possible to estimate quantitative
genetic parameters in the Soay sheep on St. Kilda with the genomic tools currently
available to us. This has important implications for addressing evolutionary puzzles in
both the Soay sheep population and other unmanaged populations. First, for traits
where maternal effects are not important, we are no longer limited by lack of
parentage data when estimating quantitative genetic parameters such as heritability
and genetic correlations between traits. This could lead to increased sample sizes and
thus increased power to detect such effects. Second, it implies that estimation of
heritability in natural populations is no longer limited by the ability to construct a
pedigree, which could advance our understanding of (micro-) evolution in the wild.
16
Counting sheep on Boreray from aerial photographs.
Stuart Murray.
Date : 6/09/2013
Time : 13.45
Count standard : good
Sections counted : all nine
No. of counts : one
No. of counters : one
Counted by : S Murray
Land count : no
Sea count : no
Aerial survey : yes
Section number and name
1. Clagan na Rusgachan 0
2. Clais na Runaich 24
3. Na Roachan 5
4. Udraclete 7
5. Sunadal 106
6. Mullach an Tuamail 3
7. Creagan na Rubhaig Bana 38
8. Coinneag 2
9. South Slope 328
Total sheep 513
Remarks
This is the first sheep count made from aerial photographs. Five were used from a
series taken by D. Cowley of RCAHMS on 6/09/2013, chosen for area coverage,
height over the ground and sharpness of resolution. The coverage is not complete and
there is missed ground in sections 8, 5, 4, & 3 mainly due to foreshortening caused by
the oblique angle the photos were taken from. Sections 5 & 9 are overhead shots,
leaving very little obscured or missed ground. The count of 513 is considered a
minimum.
The late date meant that lambs were close to adult size and could not be differentiated
from tups or ewes the total therefore is for sheep only. Two all dark sheep were
found, but only in Section 9, although others could have been present in hidden
ground. Numbers of this Soay 'dark wild' type vary between years, with 11 the
maximum recorded between 1996 and 1998 (Murray 1998). Bullock (1981) describes
four grades of fleece, his 'blackish' may approximate 'dark wild' used by SM, if so, his
frequency rates are c 4% compared with c 2% for SM.
Of 33 published counts made between 1950 & 2002 (NTS 2003) only three exceed
500 animals. Only one of these is a whole island count, made from the sea by SM in
September 1998, totaling 541 sheep.
17
Earlier counts of 703 in 1980 (Bullock 1981) and 532 in 1996 (Murray 1998) were
land counts of Sunadal (Section 5) and South Slope (Section 9) combined, with in
1980 sheep from adjacent Sections 2, 6 & 7. Based on two land counts and one sea
count Bullock concluded that sea counts underestimate the population by up to 33%.
(With previous offshore count experience and good conditions, i.e. calm sea, good
light and 2 or more counters, I believe the difference can be much less).
So far as is known the last year a whole island count of all nine sections was made,
from the sea, was in 2003 when SM & M. Harman counted a mean of 505 sheep
(report to NTS Ranger).
References cited
Bullock D (1981) 1980 Boreray Expedition Report.
Murray S (1998) St Kilda Warden's Report November 1998.
NTS (2003) World Heritage Site submission.
18
Publications on the shelf in 2013
Brown, E. A., Pilkington, J. G., Nussey, D. H., Watt, K.A. Hayward, A.D., Tucker,
R., Graham, A. L., Paterson, S., Beraldi, D., Pemberton, J. M. and Slate, J. (2013)
Detecting genes for variation in parasite burden and immunological traits in a wild
population: testing the candidate gene approach. Molecular Ecology 22: 757-773.
Feulner, P.G.D., Gratten, J., Kijas, J.W., Visscher, P.M., Pemberton, J.M. and Slate,
J. (2013) Introgression and the fate of domesticated genes in a wild mammal
population. Molecular Ecology 16: 4210-4221.
Hayward, A.D., Wilson, A.J., Pilkington, J.G., Clutton-Brock, T.H., Pemberton, J.M.
and Kruuk, L.E.B. (2013) Reproductive senescence in female Soay sheep: variation
across traits and contributions of individual ageing and selective disappearance.
Functional Ecology 27: 184-195.
Johnston, S.E., Gratten, J., Berenos, C., Pilkington, J.G., Clutton-Brock, T.H.,
Pemberton J.M. and Slate, J. (2013) Life history trade-offs at a single locus maintain
sexually-selected genetic variation. Nature 502: 93-93.
Jones, O.R. et al (2014) Diversity of ageing across the tree of life. Nature, 505: 169-
173.
Langrock, R. and King, R. (2013) Maximum likelihood estimation of mark-recapture-
recovery models in the presence of continuous covariates. Annals of Applied
Statistics 7:1709-1732
Nussey, D.H., Watt K.W., Clark, A., Pilkington, J.G., Pemberton, J.M., Graham, A.L.
& McNeilly, T.N. (2014) Multivariate immune defences and fitness in the wild:
complex but ecologically important associations among plasma antibodies, health and
survival. Proceedings of the Royal Society of London: Series B. 281: 20132931.
In press:
Hayward, A.D., Garnier, R., Watt, K.A., Pilkington, J.G., Grenfell, B.T., Matthews,
J.B., Pemberton, J.M., Nussey, D.H. & Graham, A.L.. (2014) Heritable,
heterogeneous and costly resistance of sheep against nematodes and potential
feedbacks to epidemiological dynamics. American Naturalist.
19
ACK NO W LE D GEM E N TS
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 Kevin Grant the Archaeologist 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 2013
would not have been possible: Ben Godsall, Katie Hatton, Olivia Hicks, Helen
Hipperson, Daniel Hobson, and Annelies Leeuw. Thank you.
Our research is supported by grants and studentships from the Natural Environment
Research Council, the Biotechnology and Biological Sciences Research Council and
the European Research Council.
AP P E N DI X A: PER S O NN E L NE WS & SCH ED U LE O F WO RK
Personnel News
Charlotte Regan started a PhD on mother-offspring interactions, supervised by Per
Smiseth at the University of Edinburgh.
Kara Dicks started a PhD on the Soay sheep major histocompatibility complex,
supervised by Josephine Pemberton at the University of Edinburgh.
Becky Holland started work with Dan Nussey, Jen Fairlie, Josephine Pemberton and
Lea Harrington on the sheep telomere project.
Phil Ellis left his position at the University of Edinburgh. We wish Phil all the best in
his new job and thank him for all this hard work in the lab and the field during his
time with the project.
20
Schedule of work on St Kilda
Winter - Spring
From March 5th
until May 11th
, Jill Pilkington, Becky Watson and Louise Christensen
carried out ten population censuses and tagged and sampled lambs, with assistance
during the peak of lambing from Romain Garnier and one volunteer. 208 lambs were
born to 181 ewes; these figures include 27 sets of twins (25 ewes held both lambs 2
ewes held one lamb and lost one). 169 lambs (86 male and 83 female) were caught and
tagged; a further 23 lambs died before any tagging attempt. Mick Crawley and two
assistants collected vegetation data.
Summer
Jill Pilkington, Louise Christensen and Daniel Hobson returned to Hirta on July 17th
to carry out ten population censuses, conduct mortality searches (yielding 15 tagged
dead animals), and prepare for the main catch-up of study area sheep. The catch-up
took place from August 9th
– 24th
and was conducted by a team of 12 additional
project members and volunteers. 271 sheep were caught and processed, of which 116
were lambs (56 males and 60 females), 55 were yearlings (20 males and 35 females),
16 were adult males, and 84 were adult females. All animals were weighed and
measured to monitor growth, and sampled for parasite and genetic analyses. 22 Sheep
were retagged because of damaged or missing tags. 28 previously untagged lambs, 6
yearlings and 4 adults were caught and processed. Mick Crawley and two assistants
collected vegetation data. Jill Pilkington and two volunteers remained on Hirta until 6th
September to complete parasite counts and pasture larvae counts.
Autumn
From October 22nd
to December 10th
Jill Pilkington, Katie Hatton and Rebecca
Holland carried out ten population censuses, monitored the mating period, capturing
and processing 41 incoming tups, 20 resident tups and 1 ewe. 29 previously darted,
non-resident tups were seen in the study area during this rut. Three dead sheep were
found.
21
C I RC U LA TI O N L I S T - (Please advise J.Pilkington of any changes or additions)
Prof. S. Albon James Hutton, Craigiebuckler, Aberdeen, AB15 8QH.
Mr. C. Arthur Inst. Evol. Biol., Edinburgh Univ., West Mains Rd, Edinburgh EH9 3JT.
Ms. S. Bain NTS, Balnain House, 40 Huntly St., Inverness, IV3 5HR.
Dr. K. Ballinghall Moredun Research Institute, Penicuik, Midlothian, EH260PZ
Dr. D. Bancroft GPC AG, Lochhamer Str. 29D-82152, Munich, Germany.
Dr. D. Bartley Moredun Research Institute, Penicuik, Midlothian, EH260PZ
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 Dept. Chemistry, University of Cambridge.
Dr. C. Bérénos 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
Dr. 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.
Ms. L. Christensen School of Biological Sciences, Aberdeen Univ., Tillydrone Ave., Aberdeen, AB24 2TZ
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.
Prof. D. Coltman Dept. Biol. Sci., Univ. Alberta, Edmonton AB, T6G 2E9, Canada.
Prof. T. Coulson Dept. Zoology, University of Oxford, South Parks Road, OX13PS.
Dr. B. Craig Glasgow.
Prof. M. Crawley Dept. Biological Sciences, Imperial College, Silwood Park, Ascot, SL5 7PY.
Ms. E. Damasceno Faculty of Life Science, University of Manchester, Manchester M13 9PT.
Dr. S. Davies SNH, Fraser Darling House, 9 Culduthel Road, IV2 4AG.
Ms. K. Dicks Inst. Evol. Biol., Edinburgh Univ., West Mains Rd, Edinburgh EH9 3JT.
Mrs J. Fairlie 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.
Ms. J. Ferguson SNH, Stilligarry, South Uist, HS8 5RS.
Dr. P. Feulner Westfälische Wilhelms Univ., Inst. Evol. and Biodiv., Hüfferstrasse,
148149 Münster, Germany.
Dr. R. Garnier Dept. Ecol. Evol. Biol., Guyot Hall, Princeton Univ., NJ 08544 2016, U.S.A. Dr. B. Godsall Dept. Biological Sciences, Imperial College, Silwood Park, Ascot, SL5 7PY.
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.
Dr. A. Hayward Dept. of Animal and Plant Sciences, Univ. Sheffield, S10 2TN.
Prof. L. Harrington Université de Montréal, Montréal, Canada
Dr. J. Herman National Museum of Scotland, Chambers Street, Edinburgh, EH1 1JF
Ms. R. Holland Inst. Evol. Biol., Edinburgh Univ., West Mains Rd, Edinburgh EH9 3JT.
Prof. A. Illius Raperlaw, Lilliesleaf, Melrose TD6 9EP.
Dr. S. Johnston Inst. Evol. Biol., Edinburgh Univ., West Mains Rd, Edinburgh EH9 3JT.
Dr. O. Jones Institute of Biology, University of Southern Denmark. Dr .F. Kenyon Moredun Research Institute, Penicuik, Midlothian, EH260PZ
Dr. L. Kerr SynthSys, School of Biological Sciences, University of Edinburgh.
Dr. C. Klingenberg Faculty of Life Science, University of Manchester, Manchester M13 9PT
Dr. P. Korsten University of Groningen, The Netherlands. . Dr. L. Kruuk Inst. Evol. Biol., Edinburgh Univ., West Mains Rd, Edinburgh, EH9 3JT.
Dr. G. Lincoln Auchtertool, Fife, KY2 5XQ Mr. J. Love The Watchers Cottage, Snishival, South Uist, HS8 5RW.
Dr. R Luxmoore NTS, Hermiston Quay, 5 Cultins Rd, Edinburgh, EH11 4DF.
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.
Dr. 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 School of Biological Sciences, University of Aberdeen
Prof. B. Morgan Inst. Maths.& Stats., Univ. Kent., Canterbury, Kent, CT2 7NF.
Dr. M. Morrissey School of Biology, University of St Andrews, Fife, KY16 9TH.. Mr. S. Murray Craigie Dhu, Cardney, Dunkeld, Perthshire, PH8 0EY.
22
Dr A. Nisbet Moredun Research Institute, Edinburgh.
Dr. D. Nussey Inst. Evol. Biol., Edinburgh Univ., West Mains Rd, Edinburgh EH9 3JT.
Dr. A. Ozgul University of Zurich, Switzerland. Prof. S. Paterson School of Biological Sciences, Univ. of Liverpool, L69 7ZB.
Ms. A. Pavitt Inst. Evol. Biol., Edinburgh Univ., West Mains Rd, Edinburgh EH9 3JT.
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 Natural Science & Psychology, Liverpool John Moores University, Liverpool.
Dr. G. Prior NTS, Balnain House, 40 Huntly St., Inverness, IV3 5HR.
Prof. M. Rees Dept. of Animal and Plant Sciences, Univ. Sheffield, S10 2TN.
Ms. C. Regan Inst. Evol. Biol., Edinburgh Univ., West Mains Rd, Edinburgh EH9 3JT.
Dr. J. Reid School of Biological Sciences, Aberdeen Univ., Tillydrone Ave., Aberdeen, AB24 2TZ
Dr. M. Robinson Dept. of Animal and Plant Sciences, Univ. Sheffield, S10 2TN.
Prof. N. Sargison Royal (Dick) School of Veterinary Sciences, Edinburgh Univ., Easter Bush, EH25 9RG
Dr. P. Scott Royal (Dick) School of Veterinary Sciences, Edinburgh Univ., Easter Bush, EH25 9RG
Prof. C. Selman IBAHCM, University of Glasgow, G12 8QQ. Ms. R. Sinclair Achindarroch, Duror, Argyll.
Prof. J. Slate Dept. of Animal and Plant Sciences, Univ. Sheffield, S10 2TN.
Dr. P. Smiseth Inst. Evol. Biol., Edinburgh Univ., West Mains Rd, Edinburgh EH9 3JT.
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.
Ms. Z. Timmons National Museum of Scotland, Chambers Street, Edinburgh, EH1 1JF
Prof. P. Visscher Queensland Inst. Med. Res., PO Royal Brisbane Hospital, Q4029, Australia.
Dr. C. Walling Inst. Evol. Biol., Edinburgh Univ., West Mains Rd, Edinburgh EH9 3JT.
Ms. R. Watson Inst. Evol. Biol., Edinburgh Univ., West Mains Rd, Edinburgh EH9 3JT.
Ms. K. Watt Inst. Evol. Biol., Edinburgh Univ., West Mains Rd, Edinburgh EH9 3JT.
Dr. A. Wilson Centre for Ecology and Conservation, Univ. Exeter, Cornwall Campus, TR10 9EZ.Dr. K.
Prof. K. Wilson Dept. of Biological Sciences, Lancaster University, LA1 4YQ.
Prof R. Zamoyska Institute of Immunological and Infection Research, University of Edinburgh