1

ST. KILDA SOAY SHEEP PROJECT:

ANNUAL REPORT 2018

J.G. Pilkington1+5, B. Ashraf2, D. Childs2, J. Fairlie1, H. Froy1, J. Hadfield1, A. Hayward3,

W. Huang1, D. Hunter2+5, S. Johnston1, F. Kenyon3, D. McBean3, M. Morrissey5, D. Nussey1,

R. J. Pakeman7, J.M. Pemberton1, L. Seeker4, J. Slate2, I.R. Stevenson6, S. Underwood1, K.

Watt1, R. Wilbourn1.

1Institute of Evolutionary Biology, University of Edinburgh. 2Department of Animal and Plant Sciences, University of Sheffield. 3Moredun Research Institute, Edinburgh. 4Royal (Dick) School of Veterinary Studies, University of Edinburgh. 5School of Biology, University of St. Andrews. 6Sunadal Data Solutions, Penicuik. 7James Hutton Institute, Craigiebuckler, Aberdeen.

POPULATION OVERVIEW ......................................................................................................... 2

REPORTS ON COMPONENT STUDIES ........................................................................................ 4 Vegetation ...................................................................................................................................... 4

Nematode faecal egg counting on St Kilda: a comparison of methods and plan for the future ..... 4

Lifelong telomere dynamics ........................................................................................................... 7

Consequences of age structure on the efficiency of selection ........................................................ 9

Predicting phenotypes from genomes........................................................................................... 13

Selection on MHC class II haplotypes in a free-living ruminant ................................................. 16

PUBLICATIONS. ..................................................................................................................... 18

ACKNOWLEDGEMENTS.. ........................................................................................................ 19

APPENDIX A: PERSONNEL NEWS & SCHEDULE OF WORK ..................................................... 19

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Population Overview

The sheep population on Hirta entered 2018 at a moderate level and there was relatively high

mortality over winter with 228 tagged animals being found dead with in the study area.

Lambing began on the 2nd of April with 65.6% of lambs born surviving (Fig. 1).

Figure 1. The temporal distribution of lamb births during 2018.

In December 2018, 595 tagged sheep were believed to be alive on Hirta, of which 378 regularly

used the study area, a decrease of 28.5% using the study area since the previous year. The age

distribution of the population is shown in Figure 2 and changes in sheep numbers in the study

area over time are shown in Figure 3.

Figure 2. Age distribution of tagged Soay sheep presumed to be alive at the end of 2018.

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Figure 3. The number of sheep counted on the whole island and the number of tagged sheep

regularly using the study area since 1985.

One whole-island count yielded 1401 tagged and untagged sheep, with the details displayed in

Table 1. The total population had decreased by 22.7% since summer 2017 when it was

estimated as 1813. This gives a delta (calculated as ln (Nt+1/Nt)) of -0.258. The whole island

count is also shown in Figure 3.

Table 1. Demographic and geographic distribution of sheep observed during the count of Hirta

on August 10th 2018. Coat colours are DW = dark wild, DS = dark self, LW = light wild, and LS

= light self.

Location Females Males Lambs Total

DW DS LW LS DW DS LW LS

Conachair/Oiseval 161 8 34 1 29 0 11 0 96 340

Mullach Bi / Cambir 192 10 61 3 42 2 5 1 177 493

Ruaival/Village 224 10 66 6 45 2 17 0 198 568

Total 577 28 161 10 116 4 33 1 471 1401

4

Vegetation.

Robin Pakeman.

March 2018 saw the first vegetation survey without Mick Crawley (eek). At the same time, we

have instigated a change in methodology to take advantage of developments in technology.

Twice a year for 25 years, Mick and his team had sampled the vegetation by taking all the

above-ground plant material from 20 cm quadrats from two points (gap and tussock) in 30

locations around Village Bay. Each sample was sorted into species, dried and weighed to

estimate the composition and biomass of the plants present.

Instead, to estimate biomass we are using a portable NDVI meter to estimate the biomass

present. NDVI stands for Normalised Difference Vegetation Index and the meter takes

advantage of healthy vegetation reflecting more near-infrared light whilst it absorbs more red

light – the more vegetation present, the greater the difference between reflected infrared and

red. Thus, we can work out how much biomass is present at each of the locations at a larger

scale (100 m2 rather than 0.04 m2). Composition is still measured in the 20 cm quadrats to

maintain consistency, but it is estimated visually. To improve spatial coverage, six quadrats

per location are now sampled.

In addition, to help in the new project “The ecology within: The impact of gut ecosystem

dynamics on host fitness in the wild” we are also collecting vegetation samples across locations

to measure “quality” in terms of their digestibility by the sheep. This will allow us to look at

seasonal as well as yearly changes in quality and hence contribute to understanding how

interactions between diet and the gut ecosystem impact on fitness.

Finally, two new pairs of cages have been installed near the Big Burn to improve the precision

in how we measure productivity.

Nematode faecal egg counting on St Kilda: a comparison of methods and plan for the

future.

Adam Hayward, Fiona Kenyon, Jill Pilkington, Dave McBean & Dan Nussey.

The Soay sheep experience infections with a number of gastro-intestinal worm species. The

most common, and most damaging, are the strongyle nematodes. By counting strongyle eggs

in faecal sample, we can gain an estimate of an animal’s worm burden at a point in time:

animals carrying more adult worms should have higher faecal egg count (FEC).

Several egg counting methods are available to estimate FEC. The ‘McMaster’ technique has

been used to estimate FEC in all studies on St Kilda to date, while the newer ‘Cuvette’ method

is widely used in veterinary laboratories these days. Both methods involve mixing the faecal

sample with salt solution, in which nematode eggs float, enabling them to be counted. The key

difference between the techniques is that the McMaster technique takes a subsample of the

mixed faeces and salt solution for egg counting, and then multiplies the count by 100 to gain

an estimate of FEC in eggs per gram (epg). Meanwhile, the Cuvette method uses centrifugation

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to obtain all of the eggs in a 1g sample. Thus, the McMaster technique is accurate to 100epg,

while the Cuvette method is accurate to 1epg.

Faecal samples collected during the August catches in 2016-2018 (N = 564) were analysed

using both egg counting techniques. The linear association across all three years was strong (β

= 0.33±0.01; R2 = 0.72; P<0.001) and very consistent across the three years (Fig. 4). It was,

however, apparent that McMaster counts are generally higher than counts conducted using the

cuvette method (Fig. 5A). Conversely, the McMaster method is less sensitive than the cuvette

method and estimates many samples to have a FEC of zero, when in fact the cuvette method

shows that eggs are present (Fig. 5B).

Figure 4. Associations between cuvette and McMaster faecal egg counts in August faecal

samples, 2016-2018. Red dashed line shows a 1:1 relationship; blue lines show slopes of linear

regression (β), which are 0.31, 0.34 and 0.28 for 2016-2018, respectively.

The difference between the counts can be explained by the fact that eggs in salt solution behave

according to a Poisson process and so the observed eggs under the McMaster slide should have

a variance-mean ratio of 1:1. However, multiplying the observed counts by 100 inflates the

variance-mean ratio to 100:1 and skews the egg counts. We explored this inflation by

simulating observed and true egg counts using the ‘eggCounts’ R package. The simulations

show that when dilution factors are low (e.g. 1 or 10), the observed egg count closely matches

the true egg count; however, when the dilution factor is high (50 or 100), the association

between the observed and true counts weakens (Fig. 6). Thus, the simulations demonstrate that

the inflation of egg counts using the McMaster technique is due to the multiplication factor of

100 applied to the eggs observed under the slide.

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Figure 5. (A) Mean strongyle faecal egg counts (FEC) and (B) prevalence of infection in four

age categories estimated using the two methods. In B, small points show raw data and large

points show the mean prevalence of infection in each age group for each method ±1SE.

Figure 6. Simulation results showing that the relationship between the observed egg count and

the underlying ‘true’ egg count worsens with increasing dilution factor.

Future FEC data on St Kilda should be collected using the Cuvette method. However, how can

we link Cuvette data with existing McMaster data? One solution may be to multiply the

McMaster FEC data by 0.33 (the association between McMaster and Cuvette counts). Applying

this multiplication factor gives a 1:1 association between the McMaster and FEC counts (Fig.

7). It is apparent, however, that there is still considerable spread around the regression line.

Further work will explore the role of Poisson variation in creating this variation and will aim

to account for it statistically in order to improve the relationship. The ultimate aim is to devise

a robust method which will enable analysis of the combined McMaster and Cuvette FEC data,

without the method used biasing results.

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Figure 7. The association between McMaster and Cuvette faecal egg counts collected 2016-

2018 after multiplying McMaster counts by 0.33 (β = 0.01; R² = 0.72)

Lifelong telomere dynamics.

Hannah Froy, Sarah Underwood, Rachael Wilbourn, Luise Seeker, Jen Fairlie, Kathryn Watt,

Jill Pilkington, Josephine Pemberton & Dan Nussey.

Telomeres are sections of non-coding DNA that cap linear chromosomes, and they are

important for maintaining DNA stability and integrity. Telomeres play an important role in

ageing: in the absence of the enzyme telomerase, telomere length decreases with each cell

division, and they also decrease in the presence of oxidative stress. When telomeres shorten

beyond a critical threshold length, this triggers cellular senescence and death. Telomeres are

also thought to be closely linked to whole-organism senescence, and a large body of evidence

links telomere length with ageing, morbidity and mortality in humans and laboratory animals.

Interest in telomeres has increased dramatically in the fields of ecology and evolution in recent

years, due to the suggestion that telomere length may act as a biomarker of individual

physiological state, potentially mediating trade-offs across the lifespan, and linking lifestyle

with longevity. Interesting associations between telomere length and aspects of life-history are

increasingly being documented in a range of bird and mammal species.

A number of studies have found a positive association between telomere length and subsequent

survival probability. This could be because individuals with longer telomeres on average tend

to have longer lifespans, for example because of the genes they possess, or because they

experienced a favourable environment during early life. However, it could also be that

individuals who invest heavily in growth or reproduction, or who experience harsh conditions

such as food limitation, suffer from more rapid telomere shortening, and this is associated with

an increased risk of mortality.

The Soay sheep study offers a great opportunity to test whether the association between

telomere length and survival is driven by consistent differences between individuals, or within-

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individual telomere shortening. It can also provide important insights into the causes of

variation in telomere length in the wild, in particular to reveal the relative contribution of

genetics vs environment using the population pedigree.

We used quantitative PCR to measure Relative Telomere Length (RTL) in the white cell

fraction of blood samples collected in August between 1998 and 2016. After quality control,

our dataset consisted of 3722 measurements of RTL from 1612 male and female sheep from

across the age range. We found that on average, telomere length decreased with age as

expected, but there was a lot of variation within each age class (Fig. 8). The most rapid decline

occurred between lambs measured at 4 months and yearlings (Fig. 8), again as expected during

this period of physical growth.

The repeatability of telomere length across the entire lifespan was 0.25 (95%CI: 0.20–0.28),

indicating that individuals do show consistent differences in RTL. Using information from the

population pedigree, we found that these consistent differences in telomere length were almost

entirely explained by genetics: relatives tended to have more similar telomere lengths. The

heritability of telomere length measured in adults was 0.23 (0.17–0.27) (Fig. 9). Telomere

length measured in 4 month-old lambs was even more consistent among relatives, with a

heritability of 0.42 (0.28–0.5) (Fig. 9).

Finally, we found that adult telomere length measured in August was positively associated with

survival over the subsequent winter. This association was driven by consistent differences

between individuals, rather than within-individual telomere shortening: individuals with

consistently longer telomeres throughout life tended to have higher survival probabilities and

longer lifespans (Fig. 10). Further work will test whether there is a genetic correlation between

telomere length and survival in Soay sheep.

Figure 8. The relationship between telomere length and age in Soay sheep. The raw data are

shown on the left, and means and standard errors on the right. Females are shown in green,

and males in grey.

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Figure 9. The proportion of phenotypic variance in telomere length explained by different

random effects, shown separately for lambs and adults.

Figure 10. The relationship between average telomere length measured in August and

overwinter survival probability for adults. Points show raw data, and the green line shows

predictions from a bivariate model.

Consequences of age structure on the efficiency of selection.

Darren C. Hunter & Michael B. Morrissey.

The age structure of populations of wild long-lived organisms is likely to vary through time.

Some variation in age structure is likely to be driven by, and consequently correlated with,

variation in environmental conditions. This situation is particularly true in the Soay sheep

population where winter conditions on the island greatly influence the composition of the

population the following year. While the number of individuals in most age groups remain

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reasonably constant, the number of yearlings (individuals surviving their first winter) can vary

substantially (Fig. 11).

A key aim in evolutionary biology is predicting how a heritable trait will change over different

periods of time due to selection pressures. This genetic component of any such phenotypic

change is the overall response to cumulative periods of selection. In general, observed rates of

phenotypic and genetic change are often much less than would be predicted from widespread

observations of (i) strong selection in nature and (ii) abundant genetic variation. The primary

reason that this would be the case is an inability to fully characterise selection. Although many

explanations have been proposed that may partly explain the paradox of evolutionary stasis, a

potential component which has received little attention is the consequences of age structure.

When a new cohort of individuals is born the overall success of that cohort, in terms of its

genetic representation in the future, will partly depend on the composition of the current

population along with many other environmental factors. One way of encapsulating this

concept is through the theoretical idea of a reproductive value. The reproductive value can be

estimated for both individuals and groups of individuals and can be thought of as comparable

to a fitness measure, taking into account an individual’s survival and fecundity, as well as that

of its descendants. When cohort reproductive values differ between years the resulting

phenotypic change caused by a period of selection is not just determined by the strength of the

selection that acted, but also by the ability of the remaining individuals to contribute to the

future population.

To explore variation in the strength of selection on first year August mass in the Soay sheep,

and also the expected cumulative response to this selection, accounting for fluctuations in age

structure, we estimated the selection differential on August lamb mass via first year survival,

for the 1995 to 2014 cohorts. The total cohort reproductive values were estimated using the

high quality pedigree and life history data available. There is a positive relationship between

population size and strength of selection (Fig. 12, A and B), with stronger selection at larger

population sizes. In contrast the cohort reproductive values tend to be higher at lower

population sizes (Fig. 12, C and D). We can assess the effect that this has on predictions of

phenotypic change over the 19 year period by comparing the average selection differential with

an average weighted by the estimated cohort reproductive values. The overall predicted change

in weight is reduced by around 30% when the reproductive values are taken into account. This

is a reduction of about 200g in the effective selection differential in both males and females,

and is a substantial reduction given that the average weight for a female individual in their first

August is 12kg while males are slightly heavier at 13.4kg. These results show that considering

age structure can alter predictions of the degree of longer term phenotypic change. This finding

may be relevant to other wild populations as it is likely that the strongest periods of selection

in many populations could involve high juvenile mortality and/or reduced reproductive

success.

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Figure 11. Age structure of males (A) and females (B) in the population from 1995 to 2014.

The relative numbers in most age classes do not vary greatly between years. However, the

number of yearlings (orange) present can change dramatically, showing varying selection on

newborn (red) individuals over their first winter.

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Figure 12. Regressions of selection differentials, population size and total cohort reproductive

values in both sexes. Plots (A) & (B) show the change in the selection differentials for males

and females for lamb mass in August with Village Bay population size. Plots (C) & (D) show

the regression of cohort total reproductive value on population size. The size of the points is

representative of the number of individuals born that year relative to the other years.

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Predicting phenotypes from genomes.

Jon Slate, Bilal Ashraf, Darren Hunter, Susan Johnston, Jill Pilkington & Josephine Pemberton.

In the last 10-20 years the cost of DNA sequencing and related technologies has fallen

dramatically. For example, the human genome project is estimated to have cost $10 Billion,

yet today a person’s entire DNA code can be sequenced for well under $1000. This means that

entire populations of most species can now be genetically screened. In animal and plant

breeding, and in human medicine, this affordable technology has been transformative. Many

breeding programs now employ a technique known as genomic prediction or genomic

selection. The idea is that an individual’s phenotype can be predicted entirely from thousands

of genetic markers dispersed across the genome. This can be especially useful if the phenotype

is hard to measure, such as when it is only expressed in one sex (e.g. milk yield), or under

certain environmental conditions (e.g. drought tolerance) or after an individual has died (e.g.

longevity). In human medicine, a similar idea underpins ‘personalised medicine’. Here, an

individual’s risk of a particular disease can be predicted by screening their genome. If

necessary, preventative medicine or lifestyle changes can be introduced to stop the disease from

progressing/developing. It should be stressed that genomic prediction can only work if the

phenotype is heritable, but most phenotypes have some genetic component.

We have been exploring whether genomic prediction can be used to address more ecological

types of question. Soay sheep are the ideal system in which to test the efficacy of genomic

prediction in the wild. To test whether it will work, the following ingredients are needed: (i) A

large sample of individuals, for whom phenotypic data of a number of traits has been collected,

(ii) Knowledge that those phenotypes are heritable, and ideally knowledge about how many

genes affect those phenotypes (iii) DNA data from those same individuals, with many

thousands of places in the genome profiled, (iv) A family tree, or ‘pedigree’ of the same

individuals, so that the genetic basis of those traits can be estimated using more traditional

quantitative genetic approaches. The last ingredient is useful because it means comparisons

between pedigree-based and genome-based approaches to estimating genetic parameters can

be made. The Soay sheep project database contains all of these ingredients.

In this study we looked at eight different phenotypes, all of which are heritable in Soay sheep,

but with a different underlying genetic basis (or ‘genetic architecture’). Traits like coat colour

and coat pattern are very simple, with just a single gene responsible for each of these

polymorphisms. Traits such as adult weight and skeletal measurements are much more

complex. Previous studies have shown that it is very hard, if not impossible, to find most of the

underlying genes, and that there must be many genes of tiny effect involved. The horn length

of males with normal horns is somewhere between the two. A single gene explains a lot of the

variation between males, but many other unknown genes also contribute. We compared 5

different methods for performing genomic prediction. To measure the accuracy of genomic

prediction we calculated two key parameters. From the genomic data we estimated each

individual’s Genomic Estimated Breeding Value (GEBV). From pedigree data we used a more

traditional quantitative genetic approach to obtain Estimated Breeding Values (EBVs) for the

same individual sheep. The correlation between the GEBVs and the EBVs is a widely adopted

measure of the accuracy of genomic prediction.The results for the quantitative traits (adult

weight, foreleg length, hindleg length, metacarpal length, jawlength and hornlength) are

provided in Figure 13. The figure summarises many thousands of hours of computer runtime,

and a more detailed description is provided in the Figure legend. The key points to take from

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the analysis are (i) For all of the traits, the accuracy of genomic prediction is typically between

0.50 and 0.75 when something called the ‘training size’ is 95%. The training size simply

reflects the proportion of available phenotyped and genotyped individuals that were used in the

analysis. These levels of accuracy would be regarded as high in the animal and plant breeding

literature. They mean that using genetic markers, we can reliably measure an individual’s

genetic merit. (ii) The accuracy declines with smaller training sizes. (iii) The five different

methods that we tested perform very similarly, with BayesR and BayesB marginally

outperforming the other methods.

Figure 13. Accuracy of genomic prediction for 6 quantitative traits. The vertical axis shows

the mean correlation between GEBVs and EBVs, averaged over 20 replicates. The three

columns represent three different training sizes (the percentage of available individuals that

were used in the analysis. The six rows represent different traits. Within each panel, five

different methods (BayesR, BayesB, BayesA, BayesL and GBLUP) are shown.

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The results for the two Mendelian (single gene) traits, coat colour and coat pattern are even

more impressive. Using genetic markers we can predict an individual’s coat with almost perfect

accuracy. Notably, the method employed seems to matter more for single gene traits. Here

BayesR and BayesB quite clearly outperform the other methods.

Figure 14. Accuracy of genomic prediction for coat colour and coat pattern. Training sizes

and methods are as described in Figure 13. Accuracy was determined by a Receiver Operating

Curve (ROC) appropriate for a phenotype with a binary outcome.

Summary.

We think this is the first comprehensive test of whether genomic prediction can be used to

predict phenotypes in wild populations. The accuracy obtained is impressive (applied breeding

programs of animals and plants routinely make good genetic progress on traits with lower

accuracies). If the results are replicable in other systems, then it should become feasible to

perform quantitative genetic studies of wild populations in the absence of multi-generational

pedigrees. This could open up the way to studying the genetics of populations that lack the

decades’ worth of data that have been collected on the Soay sheep. For example, when a new

ecological challenge or problem arises such as the ash dieback outbreak in the UK, an intensive

data collection effort over one or a few field seasons could be a viable approach to genetic

studies.

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Selection on MHC class II haplotypes in a free-living ruminant.

Wei Huang, Jarrod Hadfield & Josephine Pemberton.

The major histocompatibility complex (MHC) is one of the most variable gene families in

vertebrates. MHC molecules can recognize antigens from pathogens and parasites and signal

immune cells to invoke an adaptive immune response. Parasite-mediated selection (PMS) is

believed to be the main force maintaining diversity at MHC genes but it has proven hard to

demonstrate the exact selection regime that maintains variation in natural populations.

An effective method to examine selection on MHC genes is to test for association between

MHC gene variation and fitness. However, many previous studies suffer from poor genetic

tools, low sample size, short time scale and anticonservative statistical approaches. For more

than three decades, Soay sheep living in the study area have been followed from birth, through

all breeding attempts, to death. With a genetically-inferred multigenerational pedigree, we can

measure individual fitness of Soay sheep directly. In addition, in a previous PhD study Kara

Dicks worked out MHC class II gene variation in 5349 sheep alive between 1985 to 2012 and

identified a total of eight MHC types (called A to H). The availability of accurate fitness

measurements and known MHC types makes the Soay sheep a good system to study selection

on MHC genes.

We examined the associations between MHC variation and five fitness components including

juvenile survival (JS), annual survival (AS), lifespan (LS), annual breeding success (ABS) and

lifetime breeding success (LBS) in 3288 Soay sheep (1911 males and 1917 females) born

between 1989 and 2012. First we used sex-specific models accounting for relatedness to test

for associations between each fitness component and MHC heterozygosity. Second, we used

models accounting for relatedness to test associations between each fitness component and

specific MHC types by fitting MHC type both as dosage (0, 1, or 2 copies of the relevant type)

and as presence/absence models (0 or 1). Finally we conducted a ‘gene drop’ analysis to test

whether any of the MHC types changed frequency more than expected by chance over time

from the pedigree.

We found MHC heterozygosity was associated with increased female JS. For specific MHC

types, we found a number of associations with fitness components, which were very similar

between the dosage and the presence/absence models (Fig. 15). The most striking result was

that type C was associated with decreased male ABS in both the dosage models and

presence/absence models, while D was associated with increased female AS and LS in dosage

models. In the gene drop analysis we found that MHC type C declined more than expected by

chance and MHC types D and F increased more than expected by chance. Together these results

strongly suggest haplotype C and D are under sex –specific directional selection. The most

likely interpretation of these results is that we are observing a period of directional selection at

the MHC, which is compatible with the population moving to a new equilibrium under

frequency-dependent selection or with selective change in a fluctuating selection regime.

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Figure 15. Associations between MHC (here labelled “haplotypes”) and fitness components

in Soay sheep. For the fitness measures, F represents female while M represents male. The left

hand panel shows the results of dosage models while the right hand panel shows the results of

presence/absence models (see text for explanation). Only significant results are presented here

(p<0.05) with colours showing the effect sizes (coefficients).

18

PUBLICATIONS

Douhard, M., Festa-Bianchet, M., Hamel, S., Nussey, D.H., Côté, S.D., Pemberton,

J.M. and Pelletier, F. (2019) Maternal longevity and offspring sex in wild ungulates.

Proceedings of the Royal Society B 286. doi.org/10.1098/rspb.2018.1968

Froy, H., L. Börger, C. E. Regan, A. Morris, S. Morris, J. G. Pilkington, M. J. Crawley,

T. H. Clutton‐Brock, J. M. Pemberton and D. H. Nussey (2018). Declining home range

area predicts reduced late‐life survival in two wild ungulate populations. Ecology

Letters 21: 1001-1009. doi.org/10.1111/ele.12965

Hunter, D.C., Pemberton, J.M., Pilkington, J.G. and Morrissey, M.B. (2018)

Quantification and decomposition of environment-selection relationships. Evolution

72: 851-866. doi.org/10.1111/evo.13461

Sargison, N.D., Herman, J., Pilkington, J., Buckland, P., Watt, K.,, Chambers, A.,

Chaudhry, U. (2018) Resolution of a host-parasite relationship by the molecular

confirmation of Hymenolepis hibernia in field mice (Apodemus sylvaticus) from St

Kilda. International Journal for Parasitology: Parasites and Wildlife 7, 364-368.

doi.org/10.1016/j.ijppaw.2018.09.007

Sparks, A.M., Watt, K., Sinclair, R., Pilkington, J.G., Pemberton, J.M., Johnston, S.E.,

McNeilly, T.N. and Nussey, D.H. (2018) Natural selection on antihelminth antibodies

in a wild mammal population. The American Naturalist 192: 745-

760. doi.org/10.1086/700115

Vicari, M., Puentes, A., Granath, G., Georgeff, J., Strathdee, F. and Bazely, D.R.

(2018) Unpacking multi-trophic herbivore-grass-endophyte interactions: feedbacks

across different scales in vegetation responses to Soay sheep herbivory. The Science

of Nature 105: 66. doi.org/10.1007/s00114-018-1590-9

IN PRESS

Colchero, Fernando, et al. (2019) The diversity of population responses to

environmental change. Ecology Letters 22: 342-353. doi.org/10.1111/ele.13195

Hayward, A., Pilkington, J.G., Wilson, K., McNeilly T. and Watt, K. (2019)

Reproductive effort influences intra-seasonal variation in parasite-specific antibody

responses in wild Soay sheep. Functional Ecology

19

ACKNOWLEDGEMENTS

We are very grateful to the National Trust for Scotland 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 Elior 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, Jonathan Grant, John Sikorski and Pete Holden the NTS relief Rangers

for St. Kilda, Craig Stanford the Archaeologist on the island and Ciaran Hatsell the Seabird and

Marine Ranger.

We are also grateful for the help of volunteers without whom the fieldwork for 2018 would

not have been possible: Xavier Bal, Cameron Fox-Clarke, George Geddes, Penny Jack, Anett

Kiss, Edward Lavender, Emma Moreland, Harry Porter, Judith Risse and Megan Stamp.

Thank you.

Our research is supported by grants, fellowships and studentships from the Natural

Environment Research Council, the Biotechnology and Biological Sciences Research Council,

the European Research Council, the Royal Society and the Leverhulme Trust.

APPENDIX A: PERSONNEL NEWS & SCHEDULE OF WORK

Personnel News

In Sheffield, postdoc Weixuan Zhu left and was replaced by Alison Parton funded by the

Leverhulme Trust.

Schedule of work on St Kilda

Winter - Spring

From the 18th January till the 2nd February Jill Pilkington collected repeat samples from every

study area female lamb and yearling for pregnancy testing.

From March 20th until May 10th, Jill Pilkington, Emma Moreland and Cameron Fox-Clarke

carried out ten population censuses and tagged and sampled lambs, with assistance during the

peak of lambing from Michael Morrissey and Darren Hunter. 192 lambs were born to 179 ewes;

these figures include 13 sets of twins (7 ewes held both lambs, 3 ewes held one lamb and lost one

and 3 ewes lost both lambs). 126 lambs (55 male and 71 female) were caught and tagged; a further

66 lambs died before any tagging attempt. Robin Pakeman collected vegetation data.

Summer

Jill Pilkington, Emma Moreland and Cameron Fox-Clarke returned to Hirta on July 17th to

carry out ten population censuses, conduct mortality searches (yielding 4 tagged dead animals),

and prepare for the main catch-up of study area sheep. The catch-up took place from August

8th – 22nd and was conducted by a team of 12 additional project members and volunteers. 190

sheep were caught and processed, of which 81 were lambs (37 males and 44 females), 4 were

yearlings (1 male and 3 females), 9 were adult males, and 96 were adult females. All animals

were weighed and measured to monitor growth, and sampled for parasite and genetic analyses.

20

25 sheep were retagged because of damaged or missing tags. 11 previously untagged lambs, 1

yearling and 3 adults were caught and processed. Robin Pakeman collected vegetation data. Jill

Pilkington and two volunteers remained on Hirta until 30th August to complete parasite counts

and pasture larvae counts.

Autumn

From October 28th to December 4th Jill Pilkington, Xavier Bal and Emma Moreland carried out

ten population censuses, monitored the mating period, capturing and processing 33 incoming

tups, 5 resident tups and 13 resident ewes. 25 previously darted, non-resident tups were seen

in the study area during this rut. 3 dead sheep were found.