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Molecular evidence for kin groups in the absence of large-scale genetic differentiation in a migratory bird.

Geographic substructuring of animal populations into kin groups (microgeographic differentiation) may have important consequences for evolution and behavior. For example, rates of morphological evolution and speciation seem to vary among classes of vertebrates (Wyles et al. 1983); the tempo of change is apparently higher in mammals and birds than in other vertebrates and greatest in primates and passerines, yet no satisfactory mechanism has been proposed. Because genetic structuring enables rapid fixation of new adaptations (Mayr 1970), differences in the extent of population genetic subdivision among taxa may explain variations in evolutionary rates. By accelerating the fixation of genes contributing to behavioral "innovations," microgeographic differentiation may also promote "behavioral drive" (evolutionary change induced by a behavioral shift to a new niche; Wyles et al. 1983), which may be a major evolutionary force in birds and mammals. Furthermore, subdivision of populations into kin groups may promote the evolution of social behavior through kin selection (Hamilton 1964). The potential for kin selection to occur in the absence of kin recognition in genetically subdivided populations is the subject of active debate, and new theoretical models are being developed (Queller 1992), yet no empirical support has been published.

Indirect evidence from long-term population studies and mark-and-recapture techniques indicates that a wide variety of vertebrates may form kin groups. Nonetheless, the genetics of microgeographic structuring have been studied in only a few species of mammals (Ferris et al. 1983; Aoki and Nozawa 1984; Plante et al. 1989; Pope 1992; Amos et al. 1993; Mathews and Porter 1993; Morin et al. 1994), fish (Toline and Baker 1994), and cooperatively breeding birds (e.g., Edwards 1993). The generally weak large-scale differentiation in birds (e.g., Ball et al. 1988; Barrowclough and Johnson 1988; Birt-Friesen et al. 1992; Zink and Dittmann 1993; Wenink et al. 1994; but see also Baker et al. 1990; Wenink et al. 1993; Zink 1994) may have lead to a presumption that birds do not generally form kin groups, yet studies of mammals indicate that micro- and macrogeographic differentiation are independent (e.g., Ferris et al. 1983; Pope 1992).

Murres (genus Uria) are colonial seabirds that breed on cliffs and offshore islands throughout the Northern Hemisphere (Nettleship and Evans 1985). They may fly thousands of kilometers during seasonal migrations (Brown 1985) but usually return to breed near natal sites (Noble et al. 1991; Gaston et al. 1994). Breeding pairs rear only one chick per year and rarely change nest sites or mates between years (Southern et al. 1965; Gaston et al. 1994). Distributions of color morphs of eggs, chicks, and adults are reportedly nonrandom within several colonies (Birkhead et al. 1980; Gaston and Nettleship 1981; Birkhead 1985), suggesting that colonies may be subdivided into kin groups. In the present study, we measured microgeographic differentiation in proteins and mitochondrial DNA (mtDNA) within a Norwegian colony of thick-billed murres (U. lomvia). We also conducted mathematical analyses to elucidate the conditions under which kin groups could evolve in the presence or absence of large-scale structuring.


In July 1989 and 1990, 85 thick-billed murres were sampled from six sites within the colony at Hornoya, Norway (72 [degrees] 22 [minutes] N, 31 [degrees] 10 [minutes] E; colony size = 430 breeding pairs, Vader et al. 1990; [ILLUSTRATION FOR FIGURE 1 OMITTED]). Two sites (2UE and 2UW) were at opposite ends of one cliff ledge within [approximately]10 m of each other, and a third (2B) was on a ledge directly below the first. Other sites (Ledges 1, 3, and 4) were distributed throughout the colony over [approximately]450 m. Substructuring could thus be measured: (1) within a ledge; (2) between ledges within an area; and (3) among areas. Adults were caught at their nests, and a small blood sample was taken (Birt-Friesen et al. 1992). Twenty protein loci were scored for electrophoretic variation on cellulose acetate using standard protocols (Richardson et al. 1986; Friesen 1992; Table 1), and a 253 base pair segment of the mitochondrial cytochrome b gene was sequenced via the polymerase chain reaction (Birt-Friesen et al. 1992; Friesen 1992).

Numerous statistical methods have been developed for estimating population genetic structure, gene flow, and genetic relatedness from molecular data. Several of these were explored with the present data. [Mathematical Expression Omitted] and [Mathematical Expression Omitted] for protein data were calculated as weighted means (Kirby 1975) using the computer package BIOSYS (Swofford and Selander 1981). These were checked for significance by Student's t test on estimates obtained by jackknifing across groups (Weir 1990). [G.sub.ST] for cytochrome b data was calculated as described previously (Takahata and Palumbi 1985; Birt-Friesen et al. 1992). Significance was determined using the equation [X.sup.2] = N (k - 1) [G.sub.ST], where N is sample size, k is number of genotypes, df = (k - 1) (n - 1), and n is number of groups (Pope 1992). This test assumes that sample sizes within groups are approximately equal; however, few birds were sampled from Ledge 1, and many were sampled from Ledge 2U, so significance was also checked using a computerized randomization procedure (A. Lynch, unpubl. program). Because [G.sub.ST] tends to decline as the number of genotypes increases, we also calculated the mutational divergence among groups, % defined as 1 - ([I.sub.B]/[I.sub.W]), where [I.sub.B] is the probability of identity of genotypes drawn from different subpopulations and [I.sub.W] is the probability of identity of genotypes drawn from a single subpopulation (Latter 1973; Lynch and Baker 1994). [Gamma] was tested for significance using a randomization procedure (A. Lynch, unpubl. program). Neither [G.sub.ST] nor [Gamma] incorporates information on sequence divergence among genotypes; we therefore also used an analysis of variance to calculate [Phi] or the extent to which sequence variation is partitioned among groups (Excoffier et al. 1992). [Phi] was also tested for significance using a randomization procedure (Excoffier et al. 1992). Other methods have also been developed specifically for calculating gene flow from sequence data (e.g., Slatkin and Maddison 1989; Neigel and Avise 1993). However, the present data were not amenable to these approaches due to the lack of clear phylogenetic relationships among genotypes (Birt-Friesen et al. 1992) and/or an inappropriate ("island" type) population structure.
TABLE 1. Allele frequencies for three variable protein loci for
murres sampled from different sites at Hornoya, Norway.

                                    Sampling site
Locus     Allele     1       2UE     2UW     2B     3      4
Alb         n       6        15      16      13     15     16
            A       0.67     0.87    0.90    0.77   0.73   0.66
            B       0.33     0.13    0.10    0.23   0.27   0.34

G6pd        n       6        6       6       6      6      6
            A       0.08     0.33    0.17    0.17   0.33   0.25
            B       0.92     0.58    0.83    0.83   0.67   0.75
            C       0.00     0.08    0.00    0.00   0.00   0.00

6Pgd        n       6        15      16      13     15     16
            A       1.00     1.00    0.97    0.96   1.00   1.00
            B       0.00     0.00    0.03    0.04   0.00   0.00

* Loci scored (E.C. number): Ada (, Ak (, Alb,
Ald (, Eap (, Est (, Est-D (,
G6pd (, Gpi (, Hb, Idh (, Ldh-1
(, Mdh-1 (, Mdh-2 (, Mpi
(, Pep-A (3.4.11/13), Pep-B (3.4.11/13),
6Pgd (, Pgm (, Tf.

The mean coefficient of relationship was calculated as [Mathematical Expression Omitted] for allozyme data (Crow and Aoki 1982; Aoki and Nozawa 1984), or r: 2 [G.sub.ST]/(1 + [G.sub.ST]) for mtDNA (Crow and Aoki 1984). Because r calculated from mtDNA data represents the matrilineal relationship among individuals, it was divided by 2 to obtain an estimate of overall r. The coefficient of relationship was also calculated from cytochrome b genotype frequencies using the method of Queller and Goodnight (1989): [Mathematical Expression Omitted], where [n.sub.ijk] is the number of alleles of type i in individual k in group j, [p.sub.ijk] is the frequency of allele i in individual k in group j, [p.sub.ij(-k)] is the mean frequency of allele i in group j excluding individual k, and [p.sub.i(-j)] is the mean frequency of allele i in the subpopulation excluding group j. This value of r was divided by 2 to obtain an estimate of overall relationship rather than matrilineal relationship.


Three of 20 allozyme loci showed electrophoretic variation: three alleles were identified for G6pd, and two each for [TABULAR DATA FOR TABLE 2 OMITTED] 6Pgd and Alb (Friesen 1992; Table 1). Cytochrome b genotypes of 65 birds are described in Birt-Friesen et al. (1992); 16 of 20 additional murres possessed genotype 1, two possessed genotype 6 and two possessed a new genotype (18) that differed from genotype 1 by a transition at nucleotide position 133.

Substructuring within Ledge 2U

Genetic substructuring in allozymes within Ledge 2U was low but could not be tested for significance due to the small number of sites sampled (Table 2). No differences in cytochrome b genotype frequencies were found among murres from sites 2UE and 2UW (Table 2). Data for these sites were therefore pooled.

Substructuring between Ledges in Area 2

Differentiation in allozymes between Ledges 2U and 2B was also low but could not be tested for significance (Table 2). However, cytochrome b genotype frequencies differed significantly between ledges (Table 2; [ILLUSTRATION FOR FIGURE 1 OMITTED]); whereas most murres on the upper ledge possessed genotype 1, Ledge 2B contained a broader representation of genotypes. This result indicates that substructuring can occur among ledges within areas. Data for Ledges 2U and 2B could not therefore be pooled.

Substructuring among Ledges

Although differentiation of allozymes among murres from the five ledges was low (Table 2), it was consistent and therefore significant both across loci (SE = 0.014, P [less than] 0.01) and among ledges (SE = 0.007, P [less than] 0.01). Significant differentiation also occurred in cytochrome b genotype frequencies (Table 2; [ILLUSTRATION FOR FIGURE 1 OMITTED]). Specifically, most murres on Ledges 2U and 4 possessed genotype 1, whereas only [approximately]50% of murres from Ledges 2B and 3, and 17% from Ledge 1 had this genotype. Genotype 2 occurred in 50% of birds on Ledge 1 but in essentially none from other ledges. Genotype 3 was recorded in [approximately]33% of murres on Ledges 1 and 3 but was essentially absent from other ledges. Substructuring of mtDNA first appeared with sample sizes of six birds from each ledge in 1989 and persisted when larger numbers of birds were sampled in 1990. Jackknife analysis, in which [G.sub.ST] was recalculated for each subset of four ledges, showed that substructuring in mtDNA was not due to any one ledge, i.e., elimination of individual ledges did not remove the significance of [G.sub.ST]. Bartlett's statistics were also significant for most pairwise comparisons (Excoffier et al. 1992), which further indicates that differentiation in mtDNA was not due to any one ledge. [F.sub.ST] for allozymes and [G.sub.ST] for mtDNA suggest a mean coefficient of relationship (r) within ledges of 0.11 and 0.13, respectively. The coefficient of relationship estimated using Queller and Goodnight's (1989) method was very similar, at 0.10.


Analyses of both allozymes and mtDNA indicate that thick-billed murres breeding on separate ledges within the colony at Hornoya, Norway, comprise extended family groups with a mean coefficient of relationship of [approximately]0.10. Previous reports of nonrandon distributions of egg, chick, and adult color morphs within several colonies (Birkhead et al. 1980; Gaston and Nettleship 1981; Birkhead 1985), suggest that kin groups may also occur in other colonies, although they may not be ubiquitous (Friesen 1992).

Although philopatry to natal sites can lead to inbreeding, murres at Hornoya did not appear to be inbred. Allozyme frequencies did not differ from Hardy-Weinberg expectations, and [F.sub.IS] did not differ from zero ([Mathematical Expression Omitted], SE = 0.086, P [greater than] 0.05). Similar observations have been reported for several species of mammals (Aoki and Nozawa 1984; Pope 1992; Amos et al. 1993). This finding may be explained by the fact that a single generation of outcrossing can restore normal levels of heterozygosity without appreciably reducing genetic substructuring.

Evolution of Kin Groups

Whereas murres often nest near natal sites, band returns indicate that up to 25% may breed on nonnatal ledges (Noble et al. 1991; Gaston et al. 1994). The evolution of kin groups may seem incompatible with these high migration rates but may arise in several ways. First, gene flow requires successful reproduction of migrants; failed breeding attempts by immigrants will reduce effective gene flow below apparent rates and consequently will increase genetic structuring. The pattern of dispersal is also important: preferential recruitment of native over foreign young to established ledges will promote formation of extended family groups, whereas even high emigration rates will not disrupt substructuring if recruits tend to settle in new areas. Finally, population genetic structure depends on long-term effective population size ([N.sub.e]) as well as on migration rate (m). Under an island model of dispersal, [F.sub.ST] = 1 / (4[N.sub.e]m + 1) for nuclear genes (Wright 1965), and [G.sub.ST] = 1 / (2[N.sub.f]m + 1) for mtDNA, where [N.sub.f] is the effective number of females (Birky et al. 1983). Thus, small effective population sizes can result in genetic subdivision of populations despite moderate to high migration rates. Cliff ledges at murre colonies typically contain on the order of [10.sup.1] to [10.sup.2] breeding pairs, but effective population sizes are probably at least an order of magnitude smaller than census sizes due to temporal fluctuations in numbers and differential reproductive success (Barrowclough and Johnson 1988). A mean effective population size for cliff ledges on the order of 10 females, combined with an estimated migration rate of [approximately]0.25, equates to an [F.sub.ST] of [approximately]0.048 for nuclear genes or [approximately]0.17 for mtDNA. These numbers are very close to observed values.

Genetic substructuring within Hornoya contrasts with genetic homogeneity among Atlantic colonies of thick-billed murres ([G.sub.ST] = 0.001; Birt-Friesen et al. 1992). To gain insight into this apparent discrepancy, we conducted some simple mathematical analyses. Specifically, we wanted to know the circumstances under which [F.sub.ST] or [G.sub.ST] within subpopu-lations (denoted [G.sub.W], in which subpopulations of murres are defined as breeding colonies) could exceed [F.sub.ST] or [G.sub.ST] among subpopulations (denoted [G.sub.A]). First we investigated the genetic model for a population that conforms to an island pattern of dispersal. We then probed the effects of violations of major assumptions of this model. Results are discussed for mtDNA, but conclusions for nuclear DNA are analogous.

If [N.sub.W] is the effective population size of breeding groups (number of females, in which breeding groups at murre colonies are considered to be cliff ledges), [m.sub.W] is the migration rate among breeding groups within a subpopulation (proportion of females migrating), [N.sub.A] is the effective size of subpopulations, and [m.sub.A] is the migration rate among subpopulations, then [G.sub.W] = 1 / (2[N.sub.W][m.sub.W] + 1), and [G.sub.A] = 1 / (2[N.sub.A][m.sub.A] + 1) (adapted from Birky et al. 1983). Because the effective population size of breeding groups ([N.sub.W]) will always be less than the effective size of subpopulations ([N.sub.A]), substructuring within subpopulations will always be greater than structuring among subpopulations as long as migration rates within sub-populations are equal to rates among subpopulations [ILLUSTRATION FOR FIGURE 2A OMITTED]. However, migration rates within and among subpopulations of vertebrates are probably rarely equal. It can be shown algebraically that [G.sub.W] will exceed [G.sub.A] as long as [N.sub.A] / [N.sub.W] [greater than] [m.sub.W] / [m.sub.A]. For example, if subpopulations are 10 x larger than breeding groups ([N.sub.A] = 10 [N.sub.W]), substructuring within subpopulations will exceed structuring among subpopulations as long as the migration rate among breeding groups does not exceed 10 x the migration rate among subpopulations [ILLUSTRATION FOR FIGURE 2B OMITTED].

Long-term effective population sizes of thick-billed murre colonies appear to be on the order of 104 pairs (see Appendix), whereas breeding ledges within colonies probably contain on the order of 10s of murres (see above). Thus, genetic structuring among colonies of murres will only exceed substructuring within colonies if migration rates within colonies are more than 1000 x migration rates among colonies. Band returns indicate that migration rates within colonies of murres are on the order of [10.sup.-1] (Noble et al. 1991; Gaston et al. 1994), whereas migration among colonies appears to be on the order of [10.sup.-3] (Noble et al. 1991; R. T. B. unpubl. data); the observation that subtructuring within Hornoya exceeds structuring among Atlantic colonies therefore fits predictions of basic population genetic theory.

The above calculations involve several assumptions, the most important of which are (1) an island model of dispersal, and (2) genetic equilibrium both among and within subpopulations. The extent to which a population conforms to these assumptions will affect the extent to which substructuring within subpopulations exceeds structuring among subpopulations.

Dispersal Pattern

Crow and Aoki (1982) argued that gene flow is two to four times less effective at homogenizing genotype frequencies under a stepping-stone model of dispersal than under an island model. Differences in dispersal patterns within versus among subpopulations will therefore influence genetic differentiation within versus among subpopulations. For example, an island pattern of dispersal among subpopulations and a stepping-stone pattern within subpopulations will accentuate genetic substructuring within subpopulations relative to structuring among subpopulations. Molecular data indicate that migration among colonies of thick-billed murres is independent of distance (V.L.F., unpubl. data), suggesting an island model of dispersal among colonies. In contrast, band returns indicate that dispersal within colonies may approximate a stepping-stone pattern, with most dispersal occurring between neighboring ledges (A. J. G., unpubl. data).

Genetic Equilibrium

Birky et al. (1989) showed that for mtDNA, the time (in generations) for [G.sub.ST] to go half-way to equilibrium, [t.sub.1/2], is equal to ln (2) / (2m + 1 / [N.sub.e]). Thus, the time required for a population to attain genetic equilibrium increases with effective population size and decreases with migration rate [ILLUSTRATION FOR FIGURE 2C OMITTED]. Because long-term effective population sizes will be smaller for breeding groups than for subpopulations, equilibrium will be attained more quickly within than among subpopulations. Lower migration rates among than within subpopulations will further decrease the rate at which equilibrium is reached among subpopulations [ILLUSTRATION FOR FIGURE 2C OMITTED]. Genetic disequilibrium may either increase or decrease genetic structuring within versus among subpopulations, depending on founder conditions. Founder effects may result in a temporarily high [G.sub.ST], whereas large numbers of colonizers may produce a transiently low [G.sub.ST] in a subpopulation with little or no ongoing gene flow.

Assuming that migration rates within colonies of thick-billed tourres are on the order of 0.1, and that effective population sizes of breeding ledges are on the order of 10 (see above), [G.sub.ST] among breeding groups will go half-way to equilibrium in only 2-3 generations, or 20-30 years. In contrast, effective population sizes of murre colonies appear to be on the order of 10,000 (see Appendix), and most colonies occur in areas that were glaciated during the Pleistocene, so are probably less than 10,000 years or 1000 generations old. Thus, colonies have not had sufficient time to attain genetic equilibrium [ILLUSTRATION FOR FIGURE 2C OMITTED]. Given evidence from band returns of strong natal philopatry in murres (Noble et al. 1991; Gaston et al. 1994), the low [G.sub.ST] for the Atlantic population of thick-billed murres (0.001; Birt-Friesen et al. 1992) probably reflects historical association of colonies. Genetic substructuring within colonies will therefore be accentuated relative to structuring among colonies due to genetic disequilibrium among colonies.

Evolution of Alloparenting in Murres

Subdivision of populations into kin groups may promote the evolution of altruistic behavior through kin selection (Hamilton 1964). These groups do not need to be widespread in either time or space for a new behavior to become established. Murres often brood and protect neighboring chicks and sometimes feed them (Tuck 1961; Tschanz 1979; Birkhead and Nettleship 1984; Wanless and Harris 1984; V.L.F. and W.A.M., pers. obs.). They have also been reported to adopt a nearby egg (Gaston et al. 1993) or chick (Gaston et al. 1995) if they lose their own. This type of behavior is rare among seabirds (Birkhead and Nettleship 1984). Alloparenting or "helping" may improve the fitness of recipient murres through earlier fledging and/or higher fledging success (Birkhead and Nettleship 1984; Wanless and Harris 1984). Conversely, chick-rearing has been shown to reduce adult survivorship in black guillemots (Cepphus grylle; Asbirk 1979). Thus, alloparenting may decrease the donors' fitness. Helping in murres apparently does not involve reciprocation (Birkhead and Nettleship 1984; Wanless and Harris 1985; see Trivers 1971), and adoption is prone to "cheaters" because neighbors may change annually. However, if ledges contain extended family groups, then alloparenting in murres may have evolved through kin selection. Hamilton (1964) argued that kin selection can occur, even in the absence of kin recognition, if c / b [less than] r, where c is the fitness cost to the benefactor, b is the fitness increase for the recipient, and r is the coefficient of relationship between the benefactor and recipient. Although no cost/benefit data are available for alloparenting in thick-billed murres, Hamilton's equation gives a quantitative framework for further molecular investigations into the evolution of kin selection. For example, if alloparenting in murres has evolved through kin selection, then the total fitness benefit to the alloparent should exceed 10 times the fitness cost of the behavior. Rigorous assessment of the fitness consequences of alloparenting will require detailed behavioral observations.

Genetic substructuring, in the presence or absence of macrogeographic differentiation, has been reported in cyprinid fish (Toline and Baker 1994), cooperatively breeding birds (Edwards 1993), rodents (Ferris et al. 1983; Plante et al. 1989), deer (Mathews and Porter 1993), whales (Amos et al. 1993), and primates (Aoki and Nozawa 1984; Pope 1992). Results of the present study indicate that kin groups may evolve under a broad range of conditions, and underline the need for more extensive investigations into the prevalence of microgeographic structuring, especially in birds. If genetic substructuring can accelerate the rate of evolution as predicted by Wyles et al. (1983), then microgeographic differentiation should be generally greater in birds and mammals than in other vertebrates and greatest in passerines and primates; this prediction needs to be tested. The present findings also highlight the importance of systematic collection regimes for large-scale surveys; haphazard sampling within a substructured population may increase apparent structuring among subpopulations. Furthermore, because genetic substructuring may increase the vulnerability of a population to extinction, these results have important conservation implications.


We thank A. J. Baker, S. E. Bartlett, S. M. Carr, R. W. Furness, D. Innes, and M. Peck for field and/or laboratory assistance and advice; and A. J. Baker, G. F. Barrowclough, T. R. Birkhead, T. P. Birt, J. Brown, S. M. Carr, J. F. Crow, K. Egger, P. G. H. Evans, M. P. Harris, D. Houle, D. Innes, A. Lynch, R. Myers, K. Ritland, D. S. Schneider, D. T. Stewart, C. A. Toline, and S. Wanless for helpful discussions and comments on earlier versions of the manuscript. Funding was provided by the Department of Supply and Service, the Canadian Wildlife Service, the Natural Sciences and Engineering Research Council (Canada), the Cooper Foundation, Sigma Xi, the Norwegian Institute for Nature Research and the Norwegian Research Council of Science and the Humanities.


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Long-term effective population sizes (number of breeding pairs) can be estimated from sequence data using the equation [N.sub.e] = [Pi]/rg, where [Pi] is the nucleotide diversity index, r is the substitution rate and g is the mean generation time (Nei and Li 1979). Birt-Friesen et al. (1992) estimated nucleotide diversities in cytochrome b for several colonies of thick-billed murres. The substitution rate for cytochrome b has not been calibrated for birds, but an approximate rate of [approximately]2%/million yr may be assumed given that the substitution rate for cytochrome b is approximately average for the mitochondrial genome (Whittam et al. 1986), and that the mean substitution rate for mtDNA is [approximately]2%/million yr (Shields and Wilson 1987). Southern et al. (1965) estimated a mean generation time of 8.8 yr for murres. Because of potential errors in all these values, estimates of [N.sub.e] provide indications of order of magnitude only.
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Title Annotation:Brief Communications
Author:Friesen, V.L.; Montevecchi, W.A.; Gaston, A.J.; Barrett, R.T.; Davidson, W.S.
Date:Apr 1, 1996
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