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Fluctuating asymmetry in populations of British roe deer (Capreolus capreolus) following historical bottlenecks and founder events
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Short Communication
Fluctuating asymmetry in populations of British roe deer (Capreolus capreolus) following historical bottlenecks and founder events Karis H. Baker, A. Rus Hoelzel ∗ School of Biological and Biomedical Sciences, Durham University, South Road, Durham DH1 3LE, UK
a r t i c l e
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Article history: Received 7 November 2012 Accepted 3 February 2013 Available online xxx Keywords: Fluctuating asymmetry Genetic diversity Deer
a b s t r a c t The potential impact of population bottlenecks and founder events on genetic diversity and indirect measures of fitness (such as fluctuating asymmetry; FA) has important conservation implications. Here we take advantage of historical events that generated a remnant roe deer (Capreolus capreolus) population in the north of the British Isles that retained diversity, while populations in the south were apparently extirpated during the early mediaeval era. The southern population was later re-established from small founder populations of introduced European roe deer starting in the 19th century. We assess the impact of these events, using the northern remnant population as a reference, based on measures of FA at 16 bilateral cranial traits. Comparing the northern and southern populations we find evidence of differential impact on both the level of FA and the relationship between FA and levels of genetic diversity. © 2013 Published by Elsevier GmbH on behalf of Deutsche Gesellschaft für Säugetierkunde.
Loss of genetic diversity following a population bottleneck is frequently correlated with a loss of individual fitness in wild populations, often as a consequence of inbreeding depression (Crnokrak and Roff 1999; Coltman and Slate 2003). Genetically depauperate individuals have been shown to have a lowered reproductive output (e.g. red deer, Cervus elaphus, Slate et al. 2000) increased susceptibility to disease (e.g. Soay sheep, Ovis aries, Coltman et al. 1999; naked mole rat, Heterocephalus glaber, Ross-Gillespie et al. 2007) and reduced probability of survival (e.g. juvenile red deer, Coulson et al. 1999). Lowered genetic diversity has also been shown to reduce fitness at the population level (e.g. Newman and Pilson 1997). As a result, the overall effect of reduced genetic variability can be an increased risk of extinction (Saccheri et al. 1998; Ross-Gillespie et al. 2007). Comprehensive measures of fitness are difficult to collect data for and therefore rare, and so proxies are often employed. One choice has been an assessment of developmental homeostasis. This is the ability of an organism to withstand environmental and genetic disturbances encountered during development, to produce a genetically predetermined phenotype (Lerner 1954). The most commonly used method for measuring developmental stability or instability is fluctuating asymmetry (FA; see Palmer 1994). FA refers to the small random deviations from bilateral symmetry in a morphological trait, normally distributed around a mean of 0 (Van Valen 1962). It is based on the idea that both sides of an organism
∗ Corresponding author. Tel.: +44 191 334 1325; fax: +44 191 334 2001. E-mail address: [email protected] (A.R. Hoelzel).
presumably share the same genes and, under homogenous environments, external effects on development are also the same on both sides (Klingenberg 2003). During development, developmental noise acts locally to impact a small part of one body side. Therefore, any unsigned deviation from symmetry, to which the FA typically refers (Palmer and Strobeck 2003), can be interpreted as evidence of developmental instability. There are a number of advantages to this method, for example the ‘norm’ (perfect symmetry) is known (Palmer 1994) and the data relatively easy to acquire (Lens et al. 2002). However, there are problems as well. For example, there are questions about whether FA consistently correlates with genetic variability (e.g. Rasmuson 2002). Although a number of studies have shown the expected associations between fluctuating asymmetry and genetic stress (Leary et al. 1983; Mitton 1993), many have not (e.g. Mitton 1978; Gilligan et al. 2000; Kruuk et al. 2003; Fessehaye et al. 2007). A metaanalysis suggested that correlations between heterozygosity and FA were only very weak (Britten 1996; Vollestad et al. 1999). At the same time, studies based on mammalian populations have generally found the expected negative correlations between genetic variability and FA. Examples include those based on tamarin, Saguinus (Hutchison and Cheverud 1995); brown hare, Lepus europaeus (Hartl et al. 1995) German roe deer, Capreolus capreolus (Zachos et al. 2007) common shrew, Sorex araneus (White and Searle 2008) and reindeer, Rangifer tarandus (Lovatt and Hoelzel 2011). Although these examples support the use of FA as an indicator of fitness, the associations reported have tended to only be weak, consistent with the suggestions of Britten (1996) and Vollestad et al. (1999). British roe deer (Capreolus capreolus) have undergone historical population bottlenecks resulting from mediaeval deforestation and
1616-5047/$ – see front matter © 2013 Published by Elsevier GmbH on behalf of Deutsche Gesellschaft für Säugetierkunde. http://dx.doi.org/10.1016/j.mambio.2013.02.001
Please cite this article in press as: Baker, K.H., Hoelzel, A.R., Fluctuating asymmetry in populations of British roe deer (Capreolus capreolus) following historical bottlenecks and founder events. Mammal. Biol. (2013), http://dx.doi.org/10.1016/j.mambio.2013.02.001
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over-hunting and from founder events during re-establishment (Whitehead 1964). Under theoretical expectations, populations that have undergone the most severe bottlenecks and lost the most genetic variability may show the lowest levels of fitness. Such populations generally occur in Southern UK where, following extirpation during the mediaeval period, modern populations were re-established from small founder events (see Baker and Hoelzel 2013). For this study, two populations from the south were examined. The first population, Dorset/Wiltshire, descended from several translocations of individuals involving native (e.g. Perthshire) stock (see Whitehead 1964). The second southern population, Norfolk, descended from the introduction of only 12 German roe introduced in 1884 (Whitehead 1964; Prior 1995). A previous study showed that southern populations exhibited the lowest genetic diversity and the strongest evidence for bottlenecking. In particular, for the Norfolk population the same population genetic study suggested low levels of mixing with other introduced populations or native populations in the north (Baker and Hoelzel 2013). The expectation is that even low levels of mixing may be able to increase fitness through ‘genetic rescue’, though the evidence for this is weak in this case. The expectation for the northern UK populations (which survived the mediaeval period of extirpation) is that they will have retained genetic diversity and exhibit higher fitness. Here we focus on a comparison between these two regions, and test the hypothesis that the relationship between genetic diversity and fitness as assessed by FA will vary between the north and south. Skulls were collected from female roe deer older than 2 years of age only. Females were selected to avoid possible inter-sex variation. In addition, individuals aged over 2 were chosen as this is when roe skulls are completely developed (Sokolov et al. 1985). Deer used in this study were collected from Moray, Perth, Carlisle, Durham and Lancashire in the north (N = 68) and from Norfolk, and Dorset and Wiltshire in the south (N = 54). Genetic analyses for 16 microsatellite DNA loci are for the same individuals (collected from tongue tissue) and from Baker and Hoelzel (2013) with details provided there. Genetic variability of individuals was measured as mean d2 , the squared difference in repeat units between two alleles at a locus averaged over all typed loci (Coulson et al. 1998); heterozygosity, the proportion of heterozygous loci within an individual; and, finally, internal relatedness, a measure based on allele sharing where the frequency of every allele counts towards the final score (Amos et al. 2001). The utility of each of these measures has been discussed extensively in the literature and each has their associated merits and problems (Pemberton et al. 1999; Amos et al. 2001; Tsitrone et al. 2001; Slate and Pemberton 2002). In general, mean d2 tends to be out performed by other measures of genetic variability, which is likely because the measure is based on long term mutational divergence between alleles and hence better suited to situations of population admixture (Pemberton et al. 1999). The three measures were used in this study to produce data directly comparable to previous analyses on genetic variability and FA (Zachos et al. 2007; Lovatt and Hoelzel 2011). A total of 16 bilateral metric skull and mandible characters were used to assess fluctuating asymmetry (see Fig. 1). Characters were chosen from among those used in previous studies investigating fluctuating asymmetry from cranial traits (e.g. Hartl et al. 1995; Zachos et al. 2007; Lovatt and Hoelzel 2011). Like FA, measurement error is often small and normally distributed around a mean of zero (Merilä and Björklund 1995). Measurement error can artificially inflate estimates of fluctuating asymmetry or completely obscure its detection (Palmer 1994) and so care was taken to control for it. All sets of measurements were taken using precision callipers and measured exclusively by one person (KHB) to avoid possible interobserver variability (Lee 1990). All measurements were replicated ‘blind’ and averaged to the nearest 0.01 mm. Several days elapsed between each complete set of measurements on each skull as this
Fig. 1. Traits measured in the skull of the roe deer: (a) lateral view of skull; (b) dorsal view of skull; (c) lateral view of mandible. Um3b-M, upper third molar to tip of maxillary; Um3b-Upm, upper tooth row length; Um3b-Um1, upper molar length; Um3a-3b, length of 3rd molar; J-Upm1, jugal to 1st premolar; N-Rh, nasal length; Ot-Ab Orb, otion to orbitale; Ot-Ent, otion to ectorbitale; Ot-Br, otion to bregma; Gov-C, dental height; Goc-Mt, mandibular length; Lm3-Lpm1, lower teeth row; Lpm1-Pa, Processus articularis to lower 1st premolar. The positions used for taking each measurement are indicated by arrows. (a) and (c) are adapted by permission from Macmillan Publishers Ltd: Heredity, Zachos et al. (2007), © 2007.
has been shown to give the most reliable estimate of measurement error (Palmer 1994). No measurements were attempted on missing or worn structures; therefore there are missing data. To assess the relative contribution of measurement error each measurement was repeated three times on each side in 40 individuals. Two-way mixed model ANOVAs were carried out (Palmer and Strobeck 1986) for each trait with the factors ‘sides’ (S) and ‘repeat’ (R) as fixed and ‘individual’ (I) as random. This tests for whether variation between sides is significantly greater than the variation attributable to measurement error. Data were also tested for alternative forms of asymmetry (directional and antisymmetry) and size dependency which can all inflate fluctuating asymmetry estimates (Palmer and Strobeck 2003). Directional asymmetry was tested for by two methods: a twotailed one-sample T-test against a mean of zero and the two way
Please cite this article in press as: Baker, K.H., Hoelzel, A.R., Fluctuating asymmetry in populations of British roe deer (Capreolus capreolus) following historical bottlenecks and founder events. Mammal. Biol. (2013), http://dx.doi.org/10.1016/j.mambio.2013.02.001
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Table 1 Basic statistics of fluctuating asymmetry represented by single FA-1 for each trait in each of the six designated populations in southern and northern UK. Trait
South
North
Population Norfolk
Um3b-Upm1 J-Upm1 Ni-P Vom-Po St-Po Ent-p Ot-Br Gov-Cr Lpm1-Pa Goc-Mt CFA-1
Dorset and Wiltshire
Durham and Carlisle
Perth
Moray
Lancashire
Mean FA1
±s.d.
Mean FA1
±s.d.
Mean FA1
±s.d.
Mean FA1
±s.d.
Mean FA1
±s.d.
Mean FA1
±s.d.
0.061 0.067 0.044 0.125 0.133 0.064 0.064 0.118 0.065 0.089 0.083
0.054 0.059 0.043 0.082 0.093 0.053 0.058 0.172 0.063 0.119 0.035
0.051 0.093 0.078 0.147 0.174 0.070 0.073 0.064 0.095 0.089 0.093
0.040 0.065 0.055 0.161 0.163 0.059 0.051 0.059 0.087 0.063 0.039
0.049 0.076 0.049 0.111 0.122 0.046 0.049 0.080 0.077 0.106 0.077
0.046 0.049 0.036 0.098 0.103 0.073 0.053 0.071 0.070 0.089 0.029
0.047 0.079 0.065 0.129 0.173 0.059 0.084 0.048 0.051 0.092 0.076
0.036 0.062 0.070 0.139 0.169 0.041 0.090 0.044 0.050 0.087 0.022
0.045 0.072 0.053 0.099 0.144 0.040 0.037 0.082 0.120 0.080 0.077
0.035 0.047 0.047 0.093 0.130 0.041 0.038 0.054 0.116 0.058 0.652
0.019 0.111 0.092 0.130 0.123 0.063 0.083 0.048 0.072 0.085 0.083
0.016 0.068 0.046 0.147 0.126 0.119 0.049 0.027 0.078 0.080 0.034
mixed model ANOVA. Antisymmetry was tested by examining evidence for platykurtic or bimodal curves in R − L scatter plots and running a Kolomogorov–Smirnow test which looks for departures from normality (Palmer and Strobeck 1986). Size dependency, variation of the magnitude of asymmetry due to the difference in trait size (Palmer 1994), was tested for by a Spearman rank correlation between absolute FA (R − L) and the average of both sides (R + L/2). The FA-1 index defined by Palmer and Strobeck (1986) was applied and calculated as the absolute mean difference in length between right and left sides (mean [R − L]). As each pair of measurements was repeated twice, averages of the two estimates were used. FA-1 was calculated both for individuals within each of the populations and across all individuals. A composite measures of fluctuating asymmetry (CFA-1) (Leung et al. 2000) was calculated by summing the mean absolute values of FA in all traits for individuals within and across all populations. The two-way ANOVA, used to test whether between sides variation was significantly greater than variation due to measurement error, showed that in most cases results were significant (P < 0.001) suggesting traits to be repeatable. However, two traits (Um3a3b and Um3b-M) were not shown to be repeatable (P = 0.55 and P = 0.18 respectively) and were therefore excluded from further analyses. Other traits excluded from analyses included those showing evidence of being significantly influenced (after Bonferroni correction) by directional asymmetry (J-M, Lm3-Lpm1) or antisymmetry (Um3b-Um1, Lm3-Lpm1; see Table S1). One trait was affected by size dependence of fluctuating asymmetry (Um3bum1; see Table S1). N-Rh was additionally removed because of the possible influence of directional asymmetry on this trait, as identified by previous cervid studies (see Zachos et al. 2007; Lovatt and Hoelzel 2011). The remaining 10 traits used for further analyses of FA were: Um3b-Upm1, J-Upm1, Ni-P, Vom-Po, St-Po, Ent-P, Ot-Br, Gov-Cr, Lpm1-Pa, Goc-Mt. The amount of FA varied across single traits (measured from FA-1; Table 1). The highest levels of FA were found in the traits St-Po and Vom-Po, whilst lowest levels were found in the trait Um3b-Upm1. These results support previous studies which have suggested that some characters or traits may be better predictors of fluctuating asymmetry than others (Suchentrunk 1993; Palmer and Strobeck 1997). Karvonen et al. (2003) found that levels of FA in greenfinches differed depending on the character chosen in analysis; specifically, levels were lowest for functionally important traits and highest for less functionally important traits. In this study Um3b-Upm1 exhibited the lowest levels of FA (average across all populations = 0.045). This is a measure of the length of the lower row of teeth, a functional character associated with feeding, though the tolerance to variation is not known for this trait. One of the traits showing the highest FA,
Table 2 Basic statistics of genetic diversity (measured as heterozygosity, internal relatedness (IR) and mean d2 ) and CFA. T-tests asses differences between the northern and southern populations. South
Heterozygosity IR Mean d2 CFA
North
T test
Mean
±s.d.
Mean
±s.d.
T
Significance
0.518 0.319 0.105 0.092
0.134 0.175 0.051 0.045
0.630 0.156 0.142 0.078
0.136 0.178 0.050 0.025
4.529 4.999 3.977 2.115
P < 0.001 P < 0.001 P < 0.001 P < 0.05
Vom-po, also showed high FA for another cervid species (reindeer, Lovatt and Hoelzel, 2011). Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j. mambio.2013.02.001. Measures of genetic diversity were significantly different comparing the northern and southern pooled sample sets (Table 2). Heterozygosity and mean d2 were higher in the north while IR (a measure of homozygosity) was lower, as expected. A plot of CFA against heterozygosity illustrates how the highest CFA values are in the south and the highest heterozygosities in the north, but that there is also considerable overlap (Fig. 2). There are several outliers for the data from the south, and when these are removed, the difference between north and south is no longer significant for CFA (T = 1.2, P = 0.231). This could be due to either reduced power
Fig. 2. Correlation between CFA and heterozygosity for all individuals from southern and northern populations.
Please cite this article in press as: Baker, K.H., Hoelzel, A.R., Fluctuating asymmetry in populations of British roe deer (Capreolus capreolus) following historical bottlenecks and founder events. Mammal. Biol. (2013), http://dx.doi.org/10.1016/j.mambio.2013.02.001
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Fig. 3. Linear regression analyses (dark line for regression and grey lines for standard error) comparing heterozygosity and CFA in (a) in the south (R2 = 0.0186) and (b) in the north (R2 = 0.0796).
Only in the north, there was a significant correlation between genetic diversity and CFA at the individual level. This relationship may develop in undisturbed populations over time, and has been found even in large populations with high overall diversity (e.g. Heath et al. 2002; Vangestel et al. 2011). There is an expectation that the relationship should be stronger in disturbed environments (Siikamaki and Lammi 1998; Lens et al. 2000; Kark et al. 2001), though this does not always hold true. Vangestel et al. (2011) compared rural and urban house sparrow (Passer domesticus) populations, and found a stronger correlation between FA and diversity in the rural populations, contrary to expectations. However, they suggest that this may be due to low power, since the smaller sample sizes were from the urban populations, and this may be the case in our study as well, since the southern populations are represented by the smaller sample size. Alternatively, it may reflect the relatively recent introductions from independent sources and the small founder population sizes (representing a combination of separate sampling events), and the possibility of differential influence from non-genetic sources of stress (e.g. environmental stressors; temperature, nutrition or parasites) in those populations (for review see; Møller and Swaddle 1997). Whilst it is difficult to quantify variation in relevant environmental factors between the north and south, it is worth noting that southern populations may undergo more environmental stress as a result of higher deer population densities and thus increased inter and intraspecific resource competition (see Melis et al. 2009; Acevedo et al. 2010). Previous studies have reported that FA across roe deer populations significantly increases with density (Pelabon and van Breukelen 1998). Further work would be required to elucidate the role of this non-genetic source of stress in increasing FA and potentially disrupting relationships with genetic diversity in southern UK roe deer populations. Acknowledgements
(smaller sample size) or to the particular characteristics of these three skulls, but further data would be needed to resolve this. Linear regression analyses (using XLSTAT) were significant comparing heterozygosity and CFA in the north (R2 = 0.0796, F = 5.71, P = 0.0197), but not in the south (R2 = 0.0186, F = 0.969, P = 0.329; Fig. 3). The same pattern was seen for IR with the regression significant in the north (R2 = 0.0719, F = 5.11, P = 0.0271), but not in the south (R2 = 0.0158, F = 0.801, P = 0.375). There was no significant regression for mean d2 in either sample. An earlier study based on 105 individuals from five German roe deer populations genotyped at 8 loci also found a correlation between heterozygosity and CFA in roe deer, and not for mean d2 (Zachos et al. 2007). Both our results and those of Zachos et al. (2007) echo those of previous studies which have found that mean d2 has less power in detecting genotype-fitness correlations when compared to other indices (e.g. Coltman et al. 1998; Slate et al. 2000; Tsitrone et al. 2001; Slate and Pemberton 2002). According to Hedrick et al. (2001) mean d2 is an effective measure only when correlations with fitness are examined under a scenario whereby populations have arisen by admixture of two large, divergent subpopulations. Under virtually all other conditions, other measures of genetic diversity will outperform mean d2 (Hedrick et al. 2001). As expected based on the history of extirpation and small introduced founder populations in the south, and relatively less disturbance in the north, measures of FA were greater in the south and genetic diversity greater in the north. This pattern was consistent with the findings of previous studies that have demonstrated that following a bottleneck, populations of other mammalian species e.g. northern elephant seal, Mirounga angustirostris (Hoelzel et al. 2002), and reindeer, Rangifer tarundus (Lovatt and Hoelzel 2011) exhibit increased FA and decreased genetic diversity.
We sincerely thank roe deer stalkers and managers, including: John Hopkins, Trevor Banham, John Stubbs, Margaret Ralph, Derek Sealy, Douglas Brailey, Ian Smales, Steve Palmer, David Wain, John Wilson, Andrew Yool, James Johnston, Chris Dalton, Mike Cottam, Mike Hitchmough, John Bruce and Hugh Rose for collecting samples. We also thank Hugh Rose for his help in establishing many of the above contacts. Fiona Lovatt is thanked for her advice during the early stages of data analysis. We thank the Kenneth Whitehead Trust and The British Deer Society for funding this project and supporting K.B. with a Ph.D. studentship. References Acevedo, P., Ward, A.I., Real, R., Smith, G.C., 2010. Assessing biogeographical relationships of ecologically related species using favourability functions: a case study on British deer. Divers. Distrib. 16 (4), 1–14. Amos, W., Wilmer, J.W., Fullard, K., Burg, T.M., Croxall, J.P., Bloch, D., Coulson, T., 2001. The influence of parental relatedness on reproductive success. Proc. R. Soc. Lond. Ser. B: Biol. Sci. 268 (1480), 2021–2027. Baker, K.H., Hoelzel, A.R., 2013. Evolution of population genetic structure of the British roe deer by natural and anthropogenic processes (Capreolus capreolus). Ecol. Evol. 3 (1), 89–102. Britten, H.B., 1996. Meta-analyses of the association between multilocus heterozygosity and fitness. Evolution 50 (6), 2158–2164. Coltman, D.W., Bowen, W.D., Wright, J.M., 1998. Birth weight and neonatal survival of harbour seal pups ape positively correlated with genetic variation measured by microsatellites. Proc. R. Soc. Lond. Ser. B: Biol. Sci. 265 (1398), 803–809. Coltman, D.W., Pilkington, J.G., Smith, J.A., Pemberton, J.M., 1999. Parasite-mediated selection against inbred Soay sheep in a free-living, island population. Evolution 53 (4), 1259–1267. Coltman, D.W., Slate, J., 2003. Microsatellite measures of inbreeding: a metaanalysis. Evolution 57 (5), 971–983. Coulson, T., Albon, S., Slate, J., Pemberton, J., 1999. Microsatellite loci reveal sex-dependent responses to inbreeding and outbreeding in red deer calves. Evolution 53 (6), 1951–1960.
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Please cite this article in press as: Baker, K.H., Hoelzel, A.R., Fluctuating asymmetry in populations of British roe deer (Capreolus capreolus) following historical bottlenecks and founder events. Mammal. Biol. (2013), http://dx.doi.org/10.1016/j.mambio.2013.02.001
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Short Communication
Fluctuating asymmetry in populations of British roe deer (Capreolus capreolus) following historical bottlenecks and founder events Karis H. Baker, A. Rus Hoelzel ∗ School of Biological and Biomedical Sciences, Durham University, South Road, Durham DH1 3LE, UK
a r t i c l e
i n f o
Article history: Received 7 November 2012 Accepted 3 February 2013 Available online xxx Keywords: Fluctuating asymmetry Genetic diversity Deer
a b s t r a c t The potential impact of population bottlenecks and founder events on genetic diversity and indirect measures of fitness (such as fluctuating asymmetry; FA) has important conservation implications. Here we take advantage of historical events that generated a remnant roe deer (Capreolus capreolus) population in the north of the British Isles that retained diversity, while populations in the south were apparently extirpated during the early mediaeval era. The southern population was later re-established from small founder populations of introduced European roe deer starting in the 19th century. We assess the impact of these events, using the northern remnant population as a reference, based on measures of FA at 16 bilateral cranial traits. Comparing the northern and southern populations we find evidence of differential impact on both the level of FA and the relationship between FA and levels of genetic diversity. © 2013 Published by Elsevier GmbH on behalf of Deutsche Gesellschaft für Säugetierkunde.
Loss of genetic diversity following a population bottleneck is frequently correlated with a loss of individual fitness in wild populations, often as a consequence of inbreeding depression (Crnokrak and Roff 1999; Coltman and Slate 2003). Genetically depauperate individuals have been shown to have a lowered reproductive output (e.g. red deer, Cervus elaphus, Slate et al. 2000) increased susceptibility to disease (e.g. Soay sheep, Ovis aries, Coltman et al. 1999; naked mole rat, Heterocephalus glaber, Ross-Gillespie et al. 2007) and reduced probability of survival (e.g. juvenile red deer, Coulson et al. 1999). Lowered genetic diversity has also been shown to reduce fitness at the population level (e.g. Newman and Pilson 1997). As a result, the overall effect of reduced genetic variability can be an increased risk of extinction (Saccheri et al. 1998; Ross-Gillespie et al. 2007). Comprehensive measures of fitness are difficult to collect data for and therefore rare, and so proxies are often employed. One choice has been an assessment of developmental homeostasis. This is the ability of an organism to withstand environmental and genetic disturbances encountered during development, to produce a genetically predetermined phenotype (Lerner 1954). The most commonly used method for measuring developmental stability or instability is fluctuating asymmetry (FA; see Palmer 1994). FA refers to the small random deviations from bilateral symmetry in a morphological trait, normally distributed around a mean of 0 (Van Valen 1962). It is based on the idea that both sides of an organism
∗ Corresponding author. Tel.: +44 191 334 1325; fax: +44 191 334 2001. E-mail address: [email protected] (A.R. Hoelzel).
presumably share the same genes and, under homogenous environments, external effects on development are also the same on both sides (Klingenberg 2003). During development, developmental noise acts locally to impact a small part of one body side. Therefore, any unsigned deviation from symmetry, to which the FA typically refers (Palmer and Strobeck 2003), can be interpreted as evidence of developmental instability. There are a number of advantages to this method, for example the ‘norm’ (perfect symmetry) is known (Palmer 1994) and the data relatively easy to acquire (Lens et al. 2002). However, there are problems as well. For example, there are questions about whether FA consistently correlates with genetic variability (e.g. Rasmuson 2002). Although a number of studies have shown the expected associations between fluctuating asymmetry and genetic stress (Leary et al. 1983; Mitton 1993), many have not (e.g. Mitton 1978; Gilligan et al. 2000; Kruuk et al. 2003; Fessehaye et al. 2007). A metaanalysis suggested that correlations between heterozygosity and FA were only very weak (Britten 1996; Vollestad et al. 1999). At the same time, studies based on mammalian populations have generally found the expected negative correlations between genetic variability and FA. Examples include those based on tamarin, Saguinus (Hutchison and Cheverud 1995); brown hare, Lepus europaeus (Hartl et al. 1995) German roe deer, Capreolus capreolus (Zachos et al. 2007) common shrew, Sorex araneus (White and Searle 2008) and reindeer, Rangifer tarandus (Lovatt and Hoelzel 2011). Although these examples support the use of FA as an indicator of fitness, the associations reported have tended to only be weak, consistent with the suggestions of Britten (1996) and Vollestad et al. (1999). British roe deer (Capreolus capreolus) have undergone historical population bottlenecks resulting from mediaeval deforestation and
1616-5047/$ – see front matter © 2013 Published by Elsevier GmbH on behalf of Deutsche Gesellschaft für Säugetierkunde. http://dx.doi.org/10.1016/j.mambio.2013.02.001
Please cite this article in press as: Baker, K.H., Hoelzel, A.R., Fluctuating asymmetry in populations of British roe deer (Capreolus capreolus) following historical bottlenecks and founder events. Mammal. Biol. (2013), http://dx.doi.org/10.1016/j.mambio.2013.02.001
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over-hunting and from founder events during re-establishment (Whitehead 1964). Under theoretical expectations, populations that have undergone the most severe bottlenecks and lost the most genetic variability may show the lowest levels of fitness. Such populations generally occur in Southern UK where, following extirpation during the mediaeval period, modern populations were re-established from small founder events (see Baker and Hoelzel 2013). For this study, two populations from the south were examined. The first population, Dorset/Wiltshire, descended from several translocations of individuals involving native (e.g. Perthshire) stock (see Whitehead 1964). The second southern population, Norfolk, descended from the introduction of only 12 German roe introduced in 1884 (Whitehead 1964; Prior 1995). A previous study showed that southern populations exhibited the lowest genetic diversity and the strongest evidence for bottlenecking. In particular, for the Norfolk population the same population genetic study suggested low levels of mixing with other introduced populations or native populations in the north (Baker and Hoelzel 2013). The expectation is that even low levels of mixing may be able to increase fitness through ‘genetic rescue’, though the evidence for this is weak in this case. The expectation for the northern UK populations (which survived the mediaeval period of extirpation) is that they will have retained genetic diversity and exhibit higher fitness. Here we focus on a comparison between these two regions, and test the hypothesis that the relationship between genetic diversity and fitness as assessed by FA will vary between the north and south. Skulls were collected from female roe deer older than 2 years of age only. Females were selected to avoid possible inter-sex variation. In addition, individuals aged over 2 were chosen as this is when roe skulls are completely developed (Sokolov et al. 1985). Deer used in this study were collected from Moray, Perth, Carlisle, Durham and Lancashire in the north (N = 68) and from Norfolk, and Dorset and Wiltshire in the south (N = 54). Genetic analyses for 16 microsatellite DNA loci are for the same individuals (collected from tongue tissue) and from Baker and Hoelzel (2013) with details provided there. Genetic variability of individuals was measured as mean d2 , the squared difference in repeat units between two alleles at a locus averaged over all typed loci (Coulson et al. 1998); heterozygosity, the proportion of heterozygous loci within an individual; and, finally, internal relatedness, a measure based on allele sharing where the frequency of every allele counts towards the final score (Amos et al. 2001). The utility of each of these measures has been discussed extensively in the literature and each has their associated merits and problems (Pemberton et al. 1999; Amos et al. 2001; Tsitrone et al. 2001; Slate and Pemberton 2002). In general, mean d2 tends to be out performed by other measures of genetic variability, which is likely because the measure is based on long term mutational divergence between alleles and hence better suited to situations of population admixture (Pemberton et al. 1999). The three measures were used in this study to produce data directly comparable to previous analyses on genetic variability and FA (Zachos et al. 2007; Lovatt and Hoelzel 2011). A total of 16 bilateral metric skull and mandible characters were used to assess fluctuating asymmetry (see Fig. 1). Characters were chosen from among those used in previous studies investigating fluctuating asymmetry from cranial traits (e.g. Hartl et al. 1995; Zachos et al. 2007; Lovatt and Hoelzel 2011). Like FA, measurement error is often small and normally distributed around a mean of zero (Merilä and Björklund 1995). Measurement error can artificially inflate estimates of fluctuating asymmetry or completely obscure its detection (Palmer 1994) and so care was taken to control for it. All sets of measurements were taken using precision callipers and measured exclusively by one person (KHB) to avoid possible interobserver variability (Lee 1990). All measurements were replicated ‘blind’ and averaged to the nearest 0.01 mm. Several days elapsed between each complete set of measurements on each skull as this
Fig. 1. Traits measured in the skull of the roe deer: (a) lateral view of skull; (b) dorsal view of skull; (c) lateral view of mandible. Um3b-M, upper third molar to tip of maxillary; Um3b-Upm, upper tooth row length; Um3b-Um1, upper molar length; Um3a-3b, length of 3rd molar; J-Upm1, jugal to 1st premolar; N-Rh, nasal length; Ot-Ab Orb, otion to orbitale; Ot-Ent, otion to ectorbitale; Ot-Br, otion to bregma; Gov-C, dental height; Goc-Mt, mandibular length; Lm3-Lpm1, lower teeth row; Lpm1-Pa, Processus articularis to lower 1st premolar. The positions used for taking each measurement are indicated by arrows. (a) and (c) are adapted by permission from Macmillan Publishers Ltd: Heredity, Zachos et al. (2007), © 2007.
has been shown to give the most reliable estimate of measurement error (Palmer 1994). No measurements were attempted on missing or worn structures; therefore there are missing data. To assess the relative contribution of measurement error each measurement was repeated three times on each side in 40 individuals. Two-way mixed model ANOVAs were carried out (Palmer and Strobeck 1986) for each trait with the factors ‘sides’ (S) and ‘repeat’ (R) as fixed and ‘individual’ (I) as random. This tests for whether variation between sides is significantly greater than the variation attributable to measurement error. Data were also tested for alternative forms of asymmetry (directional and antisymmetry) and size dependency which can all inflate fluctuating asymmetry estimates (Palmer and Strobeck 2003). Directional asymmetry was tested for by two methods: a twotailed one-sample T-test against a mean of zero and the two way
Please cite this article in press as: Baker, K.H., Hoelzel, A.R., Fluctuating asymmetry in populations of British roe deer (Capreolus capreolus) following historical bottlenecks and founder events. Mammal. Biol. (2013), http://dx.doi.org/10.1016/j.mambio.2013.02.001
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Table 1 Basic statistics of fluctuating asymmetry represented by single FA-1 for each trait in each of the six designated populations in southern and northern UK. Trait
South
North
Population Norfolk
Um3b-Upm1 J-Upm1 Ni-P Vom-Po St-Po Ent-p Ot-Br Gov-Cr Lpm1-Pa Goc-Mt CFA-1
Dorset and Wiltshire
Durham and Carlisle
Perth
Moray
Lancashire
Mean FA1
±s.d.
Mean FA1
±s.d.
Mean FA1
±s.d.
Mean FA1
±s.d.
Mean FA1
±s.d.
Mean FA1
±s.d.
0.061 0.067 0.044 0.125 0.133 0.064 0.064 0.118 0.065 0.089 0.083
0.054 0.059 0.043 0.082 0.093 0.053 0.058 0.172 0.063 0.119 0.035
0.051 0.093 0.078 0.147 0.174 0.070 0.073 0.064 0.095 0.089 0.093
0.040 0.065 0.055 0.161 0.163 0.059 0.051 0.059 0.087 0.063 0.039
0.049 0.076 0.049 0.111 0.122 0.046 0.049 0.080 0.077 0.106 0.077
0.046 0.049 0.036 0.098 0.103 0.073 0.053 0.071 0.070 0.089 0.029
0.047 0.079 0.065 0.129 0.173 0.059 0.084 0.048 0.051 0.092 0.076
0.036 0.062 0.070 0.139 0.169 0.041 0.090 0.044 0.050 0.087 0.022
0.045 0.072 0.053 0.099 0.144 0.040 0.037 0.082 0.120 0.080 0.077
0.035 0.047 0.047 0.093 0.130 0.041 0.038 0.054 0.116 0.058 0.652
0.019 0.111 0.092 0.130 0.123 0.063 0.083 0.048 0.072 0.085 0.083
0.016 0.068 0.046 0.147 0.126 0.119 0.049 0.027 0.078 0.080 0.034
mixed model ANOVA. Antisymmetry was tested by examining evidence for platykurtic or bimodal curves in R − L scatter plots and running a Kolomogorov–Smirnow test which looks for departures from normality (Palmer and Strobeck 1986). Size dependency, variation of the magnitude of asymmetry due to the difference in trait size (Palmer 1994), was tested for by a Spearman rank correlation between absolute FA (R − L) and the average of both sides (R + L/2). The FA-1 index defined by Palmer and Strobeck (1986) was applied and calculated as the absolute mean difference in length between right and left sides (mean [R − L]). As each pair of measurements was repeated twice, averages of the two estimates were used. FA-1 was calculated both for individuals within each of the populations and across all individuals. A composite measures of fluctuating asymmetry (CFA-1) (Leung et al. 2000) was calculated by summing the mean absolute values of FA in all traits for individuals within and across all populations. The two-way ANOVA, used to test whether between sides variation was significantly greater than variation due to measurement error, showed that in most cases results were significant (P < 0.001) suggesting traits to be repeatable. However, two traits (Um3a3b and Um3b-M) were not shown to be repeatable (P = 0.55 and P = 0.18 respectively) and were therefore excluded from further analyses. Other traits excluded from analyses included those showing evidence of being significantly influenced (after Bonferroni correction) by directional asymmetry (J-M, Lm3-Lpm1) or antisymmetry (Um3b-Um1, Lm3-Lpm1; see Table S1). One trait was affected by size dependence of fluctuating asymmetry (Um3bum1; see Table S1). N-Rh was additionally removed because of the possible influence of directional asymmetry on this trait, as identified by previous cervid studies (see Zachos et al. 2007; Lovatt and Hoelzel 2011). The remaining 10 traits used for further analyses of FA were: Um3b-Upm1, J-Upm1, Ni-P, Vom-Po, St-Po, Ent-P, Ot-Br, Gov-Cr, Lpm1-Pa, Goc-Mt. The amount of FA varied across single traits (measured from FA-1; Table 1). The highest levels of FA were found in the traits St-Po and Vom-Po, whilst lowest levels were found in the trait Um3b-Upm1. These results support previous studies which have suggested that some characters or traits may be better predictors of fluctuating asymmetry than others (Suchentrunk 1993; Palmer and Strobeck 1997). Karvonen et al. (2003) found that levels of FA in greenfinches differed depending on the character chosen in analysis; specifically, levels were lowest for functionally important traits and highest for less functionally important traits. In this study Um3b-Upm1 exhibited the lowest levels of FA (average across all populations = 0.045). This is a measure of the length of the lower row of teeth, a functional character associated with feeding, though the tolerance to variation is not known for this trait. One of the traits showing the highest FA,
Table 2 Basic statistics of genetic diversity (measured as heterozygosity, internal relatedness (IR) and mean d2 ) and CFA. T-tests asses differences between the northern and southern populations. South
Heterozygosity IR Mean d2 CFA
North
T test
Mean
±s.d.
Mean
±s.d.
T
Significance
0.518 0.319 0.105 0.092
0.134 0.175 0.051 0.045
0.630 0.156 0.142 0.078
0.136 0.178 0.050 0.025
4.529 4.999 3.977 2.115
P < 0.001 P < 0.001 P < 0.001 P < 0.05
Vom-po, also showed high FA for another cervid species (reindeer, Lovatt and Hoelzel, 2011). Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j. mambio.2013.02.001. Measures of genetic diversity were significantly different comparing the northern and southern pooled sample sets (Table 2). Heterozygosity and mean d2 were higher in the north while IR (a measure of homozygosity) was lower, as expected. A plot of CFA against heterozygosity illustrates how the highest CFA values are in the south and the highest heterozygosities in the north, but that there is also considerable overlap (Fig. 2). There are several outliers for the data from the south, and when these are removed, the difference between north and south is no longer significant for CFA (T = 1.2, P = 0.231). This could be due to either reduced power
Fig. 2. Correlation between CFA and heterozygosity for all individuals from southern and northern populations.
Please cite this article in press as: Baker, K.H., Hoelzel, A.R., Fluctuating asymmetry in populations of British roe deer (Capreolus capreolus) following historical bottlenecks and founder events. Mammal. Biol. (2013), http://dx.doi.org/10.1016/j.mambio.2013.02.001
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Fig. 3. Linear regression analyses (dark line for regression and grey lines for standard error) comparing heterozygosity and CFA in (a) in the south (R2 = 0.0186) and (b) in the north (R2 = 0.0796).
Only in the north, there was a significant correlation between genetic diversity and CFA at the individual level. This relationship may develop in undisturbed populations over time, and has been found even in large populations with high overall diversity (e.g. Heath et al. 2002; Vangestel et al. 2011). There is an expectation that the relationship should be stronger in disturbed environments (Siikamaki and Lammi 1998; Lens et al. 2000; Kark et al. 2001), though this does not always hold true. Vangestel et al. (2011) compared rural and urban house sparrow (Passer domesticus) populations, and found a stronger correlation between FA and diversity in the rural populations, contrary to expectations. However, they suggest that this may be due to low power, since the smaller sample sizes were from the urban populations, and this may be the case in our study as well, since the southern populations are represented by the smaller sample size. Alternatively, it may reflect the relatively recent introductions from independent sources and the small founder population sizes (representing a combination of separate sampling events), and the possibility of differential influence from non-genetic sources of stress (e.g. environmental stressors; temperature, nutrition or parasites) in those populations (for review see; Møller and Swaddle 1997). Whilst it is difficult to quantify variation in relevant environmental factors between the north and south, it is worth noting that southern populations may undergo more environmental stress as a result of higher deer population densities and thus increased inter and intraspecific resource competition (see Melis et al. 2009; Acevedo et al. 2010). Previous studies have reported that FA across roe deer populations significantly increases with density (Pelabon and van Breukelen 1998). Further work would be required to elucidate the role of this non-genetic source of stress in increasing FA and potentially disrupting relationships with genetic diversity in southern UK roe deer populations. Acknowledgements
(smaller sample size) or to the particular characteristics of these three skulls, but further data would be needed to resolve this. Linear regression analyses (using XLSTAT) were significant comparing heterozygosity and CFA in the north (R2 = 0.0796, F = 5.71, P = 0.0197), but not in the south (R2 = 0.0186, F = 0.969, P = 0.329; Fig. 3). The same pattern was seen for IR with the regression significant in the north (R2 = 0.0719, F = 5.11, P = 0.0271), but not in the south (R2 = 0.0158, F = 0.801, P = 0.375). There was no significant regression for mean d2 in either sample. An earlier study based on 105 individuals from five German roe deer populations genotyped at 8 loci also found a correlation between heterozygosity and CFA in roe deer, and not for mean d2 (Zachos et al. 2007). Both our results and those of Zachos et al. (2007) echo those of previous studies which have found that mean d2 has less power in detecting genotype-fitness correlations when compared to other indices (e.g. Coltman et al. 1998; Slate et al. 2000; Tsitrone et al. 2001; Slate and Pemberton 2002). According to Hedrick et al. (2001) mean d2 is an effective measure only when correlations with fitness are examined under a scenario whereby populations have arisen by admixture of two large, divergent subpopulations. Under virtually all other conditions, other measures of genetic diversity will outperform mean d2 (Hedrick et al. 2001). As expected based on the history of extirpation and small introduced founder populations in the south, and relatively less disturbance in the north, measures of FA were greater in the south and genetic diversity greater in the north. This pattern was consistent with the findings of previous studies that have demonstrated that following a bottleneck, populations of other mammalian species e.g. northern elephant seal, Mirounga angustirostris (Hoelzel et al. 2002), and reindeer, Rangifer tarundus (Lovatt and Hoelzel 2011) exhibit increased FA and decreased genetic diversity.
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Please cite this article in press as: Baker, K.H., Hoelzel, A.R., Fluctuating asymmetry in populations of British roe deer (Capreolus capreolus) following historical bottlenecks and founder events. Mammal. Biol. (2013), http://dx.doi.org/10.1016/j.mambio.2013.02.001