inheritance of size and shape in a natural population of collared flycatchers, ficedula albicollis

J. evol. Biol. 6: 375-395 (1993) 1010-061X/93/030375-21 $ 1.50+0.20/O (7 1993 Birkhauser Verlag, Base1

Inheritance of size and shape in a natural population of collared flycatchers, Ficedzh albicollis

Juha Merila* and Lars Gustafsson

Depurtment of Zoology, Section of Animul Ecology, Uppsalu Univeristy, Box 561, S-751 22 Uppsulu, Sweden

Key words: Ficedulu alhicollis; body size; genetic covariance genetic correlation; heritability; metric traits.

Abstract

Four external skeletal and three feather dimensions were measured on adult collared flycatchers (Ficedulu ulbicollis) and their adult offspring. By using midoffspring-midparent regressions, all traits were found to be heritable with an arithmetic mean heritability of 0.46. Heritability estimates from full-sib analyses were about 1.5 times higher (mean 0.67) indicating that variation in traits was affected by shared nest environment among full-sibs. The overall body size as measured by principal component one (PCl) was found to be heritable (h2 = 0.40). However, this multivariate measure of heritability was not significant in offspring- father comparison, while highly so in offspring-mother comparison (h2 = 0.60). Low offspring-father resemblance was evident also in univariate estimates of heritability. Possible causes of this (extra-pair copulations, maternal effects, sex- linked variance) are discussed.

Genetic correlations among seven traits were estimated to be low (mean 0.22) and of similar magnitude or higher than phenotypic correlations (mean 0.18). All genetic correlations were positive. Genetic and phenotypic correlations as well as covariances were fairly similar to each other (Y = 0.85 and Y = 0.87, respectively). Environmental correlations did not follow the pattern of genetic correlations (v = 0.1 l), but were more similar to phenotypic correlations (r = 0.60). Given the low genetic correlations and moderate heritabilities, the overall conclusion is that the external morphology of collared flycatchers is largely under additive genetic control and that there is a strong potential for evolutionary change in morphology even under complex multivariate selection.

* Author for correspondence

375

376 Meriki and Gustafsson

The evolution of morphometric differences among members of a taxon is a function of their evolutionary history (genes inherited from a common ancestor) and the selective regime they currently encounter (e.g. Schluter, 1989). If we consider two closely related species hardly differing in their morphology, such as the two old world Aycatchers Ficedula hypoleuca and Ficrdula albicollis, two different explanations for their low degree of divergence can be offered (Merila et al., subm.). First, the low divergence could be explained by the hypothesis that these two species are ecologically very similar and share a common selective regime so that no selection for divergence has occurred. Second, low divergence may be explained by the shared developmental (genetic/epigenetic) constraints inherited from a common ancestor (Alberch, 1980; Arnold, 1981; Maynard Smith et al., 1985).

The role of constraints in evolutionary diversification has been clarified by Lande ( 1986) who argued that for developmental constraints to explain low divergence between related species, there must be no heritability in the traits, or genetic covariances among traits must be so strong that they cannot be independently altered (e.g. Arnold, 1981; Lofsvold, 1986). Thus, estimation of additive genetic variances and covariances provides a means of determining the magnitude of genetic constraints imposed by inheritance and pleiotropy (Arnold 1981; Clark, 1987; Leamy and Atchley, 1984; Lofsvold, 1988; Kingsolver and Wiernasz, 1991; Schluter, 1984).

There is a growing amount of evidence from natural populations of birds that most morphological traits have large heritabilities (Boag and Grant, 1978; Smith and Zach, 1979; Boag and Grant, 1981; Dhondt, 1982; Boag, 1983; Grant, 1983; Smith and Dhondt, 1980; Alatalo and Lundberg, 1986; Schluter and Smith, 1986; Wiggins, 1989; Larsson and Forslund, 1992). However, many of these studies considered only one or a few characters, and consequently, few reliable estimates of genetic covariances and correlations are available. This is unfortunate, since the role of genetic constraints in evolution of metric traits still remains unclear partly because of the lack of empirical data from natural populations (Grant, 1983; Leamy and Atchley, 1984; Lofsvold, 1986; Turelli, 1988). To fully understand the role of genetic constraints in the evolution of metric traits, as well as other important empirical questions of quantitative genetics, such as maintenance of additive genetic variation in traits closely related to fitness, we need more estimates of genetic correlations for a whole range of species from natural populations (Turelli, 1988; Barton and Turelli, 1989; Stearns et al., 1991).

The present study was designed to provide heritability estimates for seven morphometric traits in a natural population of collared flycatchers, to see whether the population would respond genetically to current selection, or whether past episodes of selection had eliminated genetic variability. Genetic correlations and covariances between traits were estimated in order to evaluate possible constraints for further differentiation imposed by pleiotropy (Clark, 1987). Estimation of genetic correlations can also give insight into the action of selection in the past: traits which make an important joint contribution to fitness are predicted to be more tightly genetically correlated than functionally independent traits (Cheverud,

Inheritance of size and shape 377

1984). Finally, by comparing phenotypic and genetic correlations, we will also address the recent suggestion that when estimation of genetic correlations is not possible, phenotypic correlations can be used to replace them (Cheverud, 1988).

Material and methods

Relewnt naturul history

The collared flycatcher is a small (approx 13 g), hole-nesting migratory passerine breeding mainly in eastern and central Europe. Two isolated populations, however, inhabit the Baltic islands of eland and Gotland off the Swedish east coast. The data for this study derive from the population on Gotland (57’ IO’ N, 18’ 20’ E) where the number of breeding pairs has been recently estimated to be approx. 4000 (Gustafsson and Hogstrom, 1981). This population has been intensively studied since 1980 (e.g. Gustafsson, 1989a, 1989b), and the material analyzed here was collected during the nine breeding seasons from 1980 to 1988. A detailed description of the study area and breeding ecology of the species can be found in Gustafsson (op. cit.) and Part (1991).

The dutu

Family relationships were determined by capturing parents from nest-boxes while they were feeding the young (mainly males) or incubating the eggs (females). All fledglings were marked with aluminium rings at the age of 6 to 13 days, permitting individual recognition of birds recruiting to the population in subsequent years. Heritability estimates were thus based on measurements made upon fully grown offspring recruiting to the population as adults.

Since female flycatchers lay eggs only in their own nests, the mothers of young were always known with certainty. However, because extrapair copulations occur in this species (Alatalo et al., 1984, 1989; Gelter et al., 1992), some inaccuracy may affect father-offspring relationships. Using indirect reasoning, Alatalo et al. (op. pict.) estimated the cuckoldry frequency in this population to be about 21%.

A total of 774 families were included in the present analyses. Most of the families (71.3%) were represented by single offspring; there were 193 2-offspring families (24.9%) 24 3-offspring families (3.1%) and 5 4-offspring families (0.6%). In many tests, sample sizes varied considerably due to missing values, or due to the fact that only one parent of the offspring was known. Lower sample sizes of beak width and depth were due to the fact that measurement of these traits only began in 1985. For this reason, phenotypic correlations among the seven variables were calculated by using the subset of individuals (n = 1275, parents and offspring combined) measured for all traits.

The seven morphometric traits considered here include: length of folded right wing (WI) held flat and straight on a ruler; tail length (TL), measured from cloaca1

378 Merill and Gustafsson

notch to tip of the longest rectrices; length of first primary (PR) measured from metacarpal joint to tip of feather; tarsus length (TS), as described in Alatalo et al. ( 1984); beak length (BL) from angle of the skull to tip of upper mandible; beak width (BW), at the anterior edge of nostrils; and beak depth (BD), in a vertical plane at the anterior end of nostrils over both mandibles (all in mm). Feather lengths were measured with a ruler to an accuracy of 0.5 mm and skeletal dimensions with slide calibers to an accuracy of 0.1 mm. (The term skeletal will be used throughout this paper as a shortening to mean the skeletal structures covered with modified integument known as rhamphotheca; Stettenheim, 1972.) Although all traits were approximately normally distributed, they were In-transformed for calculations of phenotypic and genotypic correlations to improve linearity between measures. Since In-transformation did not affect heritability estimates, we present only estimates based on untransformed data. The majority of the measurements were taken by L. Gustafsson; special care was taken to reduce any inter-observed bias in measurements (repeated comparisons between measures).

Since some of the characters (WI, TL, PR) exhibited a considerable age-depen- dent variation (Merila et al., manus.), they were age-corrected before analysis. This was done by adding the average difference between 1 and 2 2 year old birds to the measurements of birds measured at the age of 1 year.

Repeatabilities

In order to assess measurement error for each trait (Boag and Noordwijk, 1987) we estimated repeatabilities for all traits by using variance components from single classification ANOVA (see Bailey and Byrnes, 1990; Lessels and Boag, 1987). Because of a slight size difference among sexes, separate repeatability analyses were made for females and males. For both sexes, we carried out an ANOVA which comprised all available repeated measures for all individuals measured for all traits. This estimate thus includes errors stemming from all possible sources, such as variation due to changes in measurement technique with time, growth and abrasion. A second type of repeatability estimate was obtained for males which were measured within the same breeding season (usually within a time interval of only few days). This analysis was performed in order to exclude the among year component of variation from measures and thus to obtain estimates of “pure” measurement error. This analysis was not made for females since too few repeated measures were available (the male data set consisted of polygamous males captured from different nest-boxes within a given breeding season).

Hrritddity estimutes

Heritability estimates were calculated by separately regressing mean values of offspring (i.e. mid-offspring values) on female and male parents (h2 = twice the


slope) and on mean values of both parents (i.e. mid-parent values (h’ = slope)). In addition, we performed a full-sib analysis by using single classification ANOVA (Sokal and Rohlf, 1981; 216) that partitioned the variance for within and among full-sib (litters) components. The heritability estimates thus obtained corresponded to twice the intra-class correlation coefficient among two or more sibs (Falconer, 1989). However, the estimates obtained from full-sib analyses are known to overestimate parametric heritabilities because twice the component of variance among full-sib families estimates the additive genetic variance plus half of the dominance variance (Falconer, 1989).

No family size weighting procedure was employed in any of the analyses since this would have had only a marginal effect on estimates (Falconer, 1989). By contrast, we performed a correction for a slight sexual dimorphism present in some of the characters by adjusting male values to female equivalents. This was done because failure to correct for sex differences can produce inflated estimates of phenotypic variance and correlation since the variance and correlation between male and female means are included (Falconer, 1989; Schluter and Smith, 1986). This correction was made by multiplying male values with a constant (BW 1.0083, BL 1.0026, TS 1.0035, WI 0.9814, TL 0.9813, PR 0.9947) reflecting female/male ratios in the sample used to calculate heritabilities. After these corrections, no sexual dimorphism was present in any character.

Significance tests and standard errors of heritability estimates were based on values uncorrected for measurement error. All regressions were inspected for outliers, but estimates did not change when single observations were excluded. Therefore, no outliers were deleted. Descriptive statistics for the samples used to calculate heritabilities are given in Appendix 1.

Confounding ,fac.tors

Several pitfalls have to be avoided when estimating heritabilities and genetic correlations. Inflated heritability estimates may arise for example as a result of assortative mating, unequal parental variances, and environmental correlations among offspring and parents (Falconer, 1989).

Assortative mating between parents may inflate heritabilities deduced from offspring on single parent regressions, but will not affect the estimates derived from offspring-midparent regressions (Falconer, 1989; 176). To check whether our h ‘-estimates could be inflated by assortative mating, we calculated Pearson product- moment correlations for each trait between mates involved in the midoffspring-midparent analysis. Significance levels for correlation coefficients were determined after Sokal and Rohlf (1981; 584).

The equality of parental phenotypic variances for each trait was tested by using F-statistics. Among parents included in midoffspring-midparent comparison unequal parental variances were only detected for break depth [J;c,,298) = 1.44, p = 0.002; all others F < 1.20, p > 0.05). This difference remains significant after sequential Bonferroni correction. In the case of midoffspring-single parent regres-

380 Merili and Gustaf’sson

sions, none of parental variances was found to be unequal (in all F < 1.19). Thus, the difference in beak depth variances may be due to sampling error, since it was not found in the latter comparison. Moreover, since the F-ratio was fairly low, this difference is not likely to impose any strong bias upon heritability estimates.

If genotypes are not randomly distributed among environments, resemblance between offspring and parents may be inflated due to shared environment, i.e. resemblance between parents and offspring may be mainly environmental (Feldman and Lewontin, 1975; van Noordwijk, 1984; Larsson and Forslund, 1992). One way to test the importance of this cause of parent-offspring resemblance is to exchange young among broods and then compare resulting offspring values with both true and foster parent values (e.g. Smith and Dhondt, 1980; Alatalo and Lundberg, 1986; Alatalo and Gustafsson, 1988; Wiggins, 1989). If environmental causes of resemblance are strong, young should resemble their foster parents as much as, or more than, their true parents; if they are weak, young should resemble only their true parents. However, previously collected data from cross-fostering experiments with the collared flycatcher showed no such effects on tarsus length (Alatalo et al., 1990). For the other six traits, we found no indication that environmental correlations confound heritability estimates (Gustafsson and Merilh, subm.).

Phmot~~pic and gemtypic cnrrclutions

Genetic correlations were computed from midparent-midoffspring covariances (Falconer, 1989). Two estimates of genetic correlations were obtained for each pair of traits X and Y from the following equations:

r(;(,) = cov(X’, Y)/J{cov(X’, X)cov( Y’, Y)}, and

r(;(2) = cov(X, Y’>/J{cov(xr, X)cov( Y’, Y)},

where ’ refers to the mid-parent value. Since the two estimates should be the same, their similarity can be used to assess their robustness (van Noordwijk, 1984; Schluter and Smith, 1986). The arithmetic mean of these two estimates is the final estimate for the genetic correlations rC;, which was used to calculate environmental correlations:

r t = (r,, - rc;hxhy )/JC 1 - hZ, )( 1 - h’Y h

where r). is the phenotypic correlation between X and Y, and h is the square root of the heritability (Falconer, 1989). Approximate standard errors for genetic correlations were calculated from Reeve’s (1955) formula:

SE,, = (1 - r~;/J2)(SEh$SEh$/h$h$)

where SE is standard error. Phenotypic correlations were calculated using standard procedures (Sokal and

Rohlf, 1981) with individual data. Principal components were extracted from an r’p matrix (unrotated eigenvalue solution) to summarize linear trends in the correlated


data set. Component selection was carried out using the method described by Muirhead (1982: 406-420). In short, the t - 1, t - 2, . , f - n, eigenvalues were tested sequentially until we found the number, (I = t -k, of eigenvalues that were equal. The k eigenvalues are thus distinct. By these means we found four distinct eigenvalues. By using midoffspring-midparent regressions, component scores for parents and offspring were used to suggest which overall phenotypic qualities were most strongly inherited.

To compare similarity between genetic, phenotypic and environmental correlations, we applied Mantel tests, a randomization test free from distributional assumptions. 1000 randomly selected permutations were used to assess the validity of the null hypothesis of no matrix correlation. The P value reported is the proportion of the randomizations with correlation coefficients as high or higher than the observed value (Cheverud et al., 1989).

To analyze the proportionality of correlation or variance-covariance matrices, we first calculated eigenvectors and then used pairwise vector correlations (rV) to measure degree of orthogonality among vectors (Lofsvold, 1986). A correlation coefficient of I.0 corresponds to total collinearity, whereas a correlation coefficient of 0.0 corresponds to total orthogonality (Morrison, 1976).

Phenotypic and genetic rlclriLln~e-~or~arian~e rnutrices

Genetic correlations cannot be directly incorporated into evolutionary models which make use of variance-covariance matrices. For each particular correlation matrix there may exist an infinite number of covariance matrices, and different covariance matrices with identical correlation structures under identical selective regime may yield evolutionary trajectories that arc qualitatively different (Kohn and Atchley, 1988; Cheverud et al., 1989). For this reason we also calculated phenotypic (P) and genetic (C) variance-covariance matrices from the data used to estimate corresponding correlations (In-transformed; see above). Genetic variances were calculated as twice the covariance among the same trait in offspring and parent, given that the pure covariance term estimates only half of the trait additive genetic variance (Falconer, 1989). Genetic covariance among traits was calculated as the sum of two reciprocal covariances COVE; = cov(X”, Y’) + cov(Y”, X’), where ” denotes the trait value of mid-parents and ’ that of mid-offspring.

Results

Repeutuhilities

Repeatabilities of seven metric traits are given in Table 1. Short term repeatabilities, made for same individuals within a given breeding season, were about 12.5% higher than long-term repeatabilities (Wilcoxon signed-rank; z = - 2.20, p < 0.05).

382 Merila and Gustafsson

Table I. Repeatability estimates for seven morphometric traits in the collared hycatcher (Ficedulu ulhic~ollis) based on variance components of one-way ANOVAs. (I) repeated measurements made within a given breeding season (i.e. measurement error), (II) analyses where all individual measures were included (i.e. includes variance due to growth and wear). Feather measures are age-corrected. N = number of individuals in sample. For each ANOVA. table-wide p < 0.05.

Trait Male

(1)

Female Male N N N

(11) (11)

Beak width (BW) 65.4 54.5 Beak depth (BD) 67.9 51.6 Beak length (BL) 69.7 66.7 Tarsus length (TS) 90.7 91.5 Wing length (WI) 88.4 73.6 Tail length (TL) 65.5 49.1 I st prim. length (PR) 82.5 52.0

Mean 75.7 62.8

58.7 24 282 208 51.7 25 282 208 66.9 69 282 208 92. I 71 282 208 72.0 71 282 208 64.4 65 282 208 60.6 64 282 208

66.6

Since these repeatabilities were measured within a short time span, they express the accuracy with which each trait was measured in our study. They suggest that about 25% of the variation in a given trait is attributable to measurement error.

Long term repeatabilities for these characters averaged 64.7%, indicating that about 35.3% of the variation in traits are due to measurement error, as well as due to growth, regrowth, and wear in these traits. These figures are similar to those usually obtained in quantitative genetic studies of birds (see Boag and Nordwijk, 1987, for a review).

Since assortative mating among parents might inflate heritability estimates obtained from midoffspring-single parent regressions, we checked for this possibility by calculating correlation coefficients between mates in our sample (Tab. 2). Two

Table 2. Pearson product-moment correlations (T) between mates. Underlined P-value indicates assorta- tivc mating with respect to trait. Other P-values did not reach significance after table-wide (sequential) Bonferroni correction (a-level = 0.05). Feather measures were age-corrected prior to analysis. n = number of pairs. Variable abbreviations as in Table I.

Trait I n P

BW 0.067 300 0.25 BD 0.015 298 0.x0 BL 0.051 681 0.19 TS 0.083 691 0.03 WI 0.144 680 m TL 0.038 61 I 0.35 PR 0.069 5x5 0.10


of the seven correlations were significant, indicating assortative mating by wing and tarsus length. However, after a sequential Bonferonni correction (Rice, 1989) only the correlation of wing length among mates remained significant. Since this correlation was fairly low (v = 0.114; r* = 0.021) and corrections made for heritability estimates (Falconer, 1989; 404) did not differ qualitatively from uncorrected values, we conclude that assortative mating has very little influence upon the single-parent heritability estimates.

Heritabilitirs

Heritability estimates obtained from midparent-midoffspring regressions were all significantly different from zero (Tab. 3). Values ranged from 0.38 to 0.54 (average 0.46) indicating a substantial additive genetic component to phenotypic variance. Although the magnitude of the heritability estimates tended to be higher among

Table 3. Narrow-sense heritability estimates (*SE) for seven morphometric traits and for four first principal components (PCI 4: extracted from the phenotypic correlation matrix) in the collared

flycatcher. Sample size (=27X for PCI -4) and significance level (indicated by *s) is given below h2

cstimatc. Trait abbreviations as in Table I.

Midoff-Midp h2z/,

Midoff-Father h'=2h

Midoff-Mother h2=2h

BW

BD

BL

TS

WI

TL

PR

Mean

PCI PC2

PC3 PC4

0.399 & 0.07x***

0.496 i 0.073*** 0.526 i 0.075*** 0.296 f 0.067***

Mean 0.430

0.396 + 0.071 300***

0.378 + 0.072

298*** 0.41 I + 0.045

681*** 0.534 * 0.044

691***

0.514 f 0.038 681***

0.454 * 0.05 I 6ll***

0.541 * 0.051 585***

0.461

0.313 * 0.102 305***

0.258 k 0. I02

305* 0.442 i 0.064

695*** 0.527 i 0.066

710***

0.579 i 0.064 692***

0.427 f 0.074 628***

0.542 f 0.074 616***

0.441

0.203 f 0.104

0.574 f 0.1 lo*** 0.489 + 0.114*** 0.175 f 0.098

0.360

0.567 & 0.104 336***

0.468+0.112 335***

0.430 + 0.068 748***

0.65 I If- 0.070 755***

0.566 & 0.056

74x*** 0.538 f 0.076

677+**

0.634 & 0.074 655***

0.551

0.608 k 0.1 l6***

0.521 i 0.1 l6*** 0.648 F 0.1 l6*** 0.429 & 0.094***

0.551

* , **, *** indicate the respective probabilities (p < 0.5, p <O.OI, p < 0.001) that h2 estimates are different from zero.


Table 4. Heritability estimates (&SE) for seven morphometric traits obtained from full-sib analyses. Heritability was calculated as twice the intra-class correlation coefficient among two or more siblmgs. N = number of individuals, A’,,, = number of families. Diff = difference between heritability estimates

from midoffspring-midparent regression and full-sib analyses.

Trait N N,,, h2 SE Diff

BW 2x0 128 0.716 0.07 I +0.32

BD 280 128 0.577 0.074 +0.20 BL 489 221 0.647 0.055 +0.24 TS 491 222 1.046 0.045 f0.51

WI 437 I98 0.678 0.057 +0.16 TL 480 217 0.429 0.058 -0.03

PR 471 213 0.581 0.057 +0.04

feather (average 0.50) than skeletal traits (average 0.43; Wilcoxon, sign-rank, ;= -2.37, p < 0.05) estimates for different traits were fairly similar.

Heritability estimates from full-sib analysis were higher than estimates obtained from regression analysis, except for tail length (Tab. 4, average diff. = 0.21, wilcoxon sign-rank, z = -2.197, p < 0.05). Proportionality among estimates obtained from full-sib and regression analysis, however, was not similar (Y = 0.36, r’ = 0.13). Heritability estimates from offspring-father regressions (average 0.44 i: 0.05) were lower than estimates obtained from offsprng-mother regressions (average 0.55 + 0.03; Wilcoxon sign-rank, z = 3.29, p = 0.001). These differences seem to be mainly due to lowered additive genetic variance among father-offspring comparisons since phenotypic variances did not differ among parents, except in the case of beak depth (see methods).

Genetic correlations between traits were generally low and positive ( I8 out of 2 I), with an arithmetic mean of 0.22; Table 5. The two estimates obtained for each pair of traits were moderately correlated (intra-class correlation = 0.60, n = 21, 17 < 0.002) and the average difference between pairwise estimates was low (0.032; Wilcoxon sign-rank, z = -0.89, p > 0.3).

Phenotypic correlations

Phenotypic correlations between traits were low (X = 0.18) and all significant correlations were positive (Tab. 5). Correlations including only feather or only skeletal traits were higher than correlations where one trait was skeletal and the other feather (.u = 0.26 ver. x = 0.12, respectively). However, since estimates of rp are not independent, statistical testing of these values was not possible.


Table 5. Genetic, phenotypic and environmental correlations among seven morphometric traits in the

collared flycatcher. Genetic correlations are tabulated from midoffspring-midparent covariances as described in methods. a) Genetic correlations beneath diagonal, respective standard errors above. b)

Phenotypic correlations (N = 1275) beneath diagonal, environmental correlations above. Correlations that are underlined are significantly different from zero at a matrix-wide P-value of 0.05 (sequential

Bonferroni test).

BW BD BL TS WI TL PR

a) rcI matrix (below) and SEs (above)

BW 0.077 0.092 0.086 0.081 0.096 0.092 BD 0.638 0.092 0.089 0.073 0.103 0.093 BL 0.267 0.314 0.060 0.060 0.076 0.062

TS -0.026 -0.000 0.315 0.048 0.066 0.054

WI 0.063 0.083 0.199 0.363 0.041 0.048

TL 0.211 0.075 0.168 0.159 0.605 0.063

PR 0.030 -0.138 0.038 0.439 0.428 0.369

b) I,, (below) and rt. matrices (above) BW 0.064 0.153 0.298 0.179 0.017 0.218

BD 0.286 0.123 0.187 -0.027 0.097 0.069 BL g.l.l 0. I99 0.192 0.152 0.056 0.263

TS 0.170 0.101 0.248 0. I75 0.068 0.029 WI 0.126 0.022 0.173 0.273 0.327 0.397 TL 0.099 0.088 0.104 0.113 0.461 0.082

PR 0.129 -0.026 0.155 0.250 0.413 0.224

The implication of significant heritabilities for the seven traits is that overall size is heritable in collared flycatchers. This is also borne out by the results of principal component analysis made for phenotypic correlation matrix (Tab. 3). The first principal component (PCl) is heritable and can be interpreted as a composite size factor because all variables contribute to it in a similar manner (Appendix 2). Offspring-midparent regressions further show that additional composite, shape variables (PC22PC4) are also largely heritable (Tab. 3). In addition, heritability of principal component scores (X = 0.43) was close to univariate estimates obtained from regression analysis. As in the univariate analysis, there was a clear tendency for lower offspring-father resemblance as compared to offspring-mother resemblance. The heritability of PCI and PC4 scores was not significantly different from zero when estimated using father-offspring regression.

lk~ironmental correlations

While environmental correlations among characters were low (X = 0.15; Tab. 5) there was a tendency for correlations among traits of closer developmental proximity (i.e. feather v. skeletal trait) to be higher (x = 0.20) than correlations among developmentally more distant traits (X = 0.1 1). Not surprisingly, the highest


environmental correlations occurred among feather traits which simultaneously achieve their dimensions during moult.

Similarity of genetic, phenotypic and environmental correlations

The similarity between genetic and phenotypic correlations, as measured by matrix correlation, was high (r = 0.85, n = 21, p < 0.001). On the other hand, while there was no similarity between genetic and environmental correlations (r = 0.11, n = 21, p = 0.33) the fit between phenotypic and environmental correlations was moderate (r = 0.60, y1 = 21, p < 0.006). However, as pointed out by Willis et al. ( 1991) this kind of comparison does not directly test for disparities between correlations. Large positive matrix correlations merely indicate that correlations vary in similar directions, but not necessarily that the actual values are identical. In order to acquire a more precise picture of the similarity between correlations, we calculated the absolute difference between all comparable matrix elements.

Fig. 1. UPGMA trees generated from (a) genetic and (b) phenotypic correlation matrices. Both trees are well supported (correlations between original and cophenetic matrices 0.87 and 0.81). Distances between nodes indicate the strength by which different clusters are supported (both trees in same scale). Trait abbreviations as in Table 1.


However, none of the three matrix comparisons revealed significant differences (Wilcoxon sign-rank, z = - 1.33, p > 0.15; z = - 1.29, p > 0.15; z = - 1.19, p > 0.20; for rg -r,,, raPr, and rP- r,, comparisons respectively). The similarity between genetic and phenotypic correlation matrices was also borne out from eigenvector correlations (see Lofsvold, 1986). Three first eigenvectors were closely correlated (rv:s 1.0-0.94) but the similarity between the last significant eigenvectors was low (rv = 0.38; Table 7). Thus, although these tests indicate some similarity between genetic and phenotypic correlation structures, there are, nevertheless, certain differences which are not obvious from previous comparisons.

UPGMA trees generated from these two matrices show that the correlation structure of these two matrices is dissimilar in certain respects (Fig. 1). The tree for genetic matrix places three bill dimensions in their own cluster and splits the first primary from other feather measures, placing it in the same branch as tarsus length. The phenotypic tree, on the other hand, places three feather measures together and puts beak length on the same branch as tarsus length. While beak dimensions are more closely related in genotype as compared to feather measures, the opposite is indicated at the level of phenotype.

Genetic and phenot.vpipic comriunces

Although the proportionality among genetic and phenotypic covariances was high (r = 0.87, r2 = 0.77, p = 0.001; Tab. 6) phenotypic covariances were, on the average, higher than their genetic counterparts (Wilcoxon sign-rank, z = - 3.98, p < O.OOOl). Nevertheless, eigenvector correlations indicated significant similarity between covariance structures (Tab. 7).

Table 6. Genetic and phenotypic variance-covariance matrices for scvcn morphometric traits in the

collared flycatcher. Genetic covariances below diagonal and phenotypic covariances above. Additive

genetic (VA = 2 * covop) and phenotypic (V,,) variances based on offspring-midparent regressions at the

bottom. All values are calculated from In-transformed data and multiplied by IO’.

Trait BW BD BL TS WI TL PR

BW

BD

BL

TS WI

TL

PR

0.5346

0.4623

0.1532

-0.0147

0.0301

0.1 I81 0.0273

0.2763

0.2538

0. IS25

~ 0.0000

0.0337

0.0356

-0.1048

0.201 I

0.1054

0.1935

0.1218 0.0638

0.0629 0.0229

0.1138

0.0176

0.1935

0. I390

0.1172

0.0598

0.1696

0.1889

0.2149

VA 0.8577 0.6123 0.3846 0.3882 0.2680

VP I.080 0.7802 0.8302 0.3623 0.2607

0.1367

0.1069

0.0946

0.0872

0.2730

0.2161

0.3642

0.4018

0.2419

0.0426

0.1910

0.2633

0.3336

0.2756

0.9508

0.8861

388 Me& and Gustafsson

Table 7. Eigenvector correlations (ry) between four first eigenvectors extracted from phenotypic and genetic correlation and variance-covariance matrices.

Matrices

Correlation

Covariance

I

1.00

0.98

Eigenvector number

2 3

0.93 0.90

0.92 0.72

4

0.38

0.77

Discussion

Herituhilities

Midparent-midoffspring regressions show that offspring resemble their parents for the three feather and four skeletal traits chosen for study. This resemblance is caused by shared genes and not by shared environments among parents and offspring, as confirmed by cross-fostering experiments (Gustafsson and Merila, subm.). Past episodes of selection have thus not eliminated genetic variability from the considered traits, which still have the potential to respond genetically to current selection.

However, common environmental effects, probably due to shared nest environment, were indicated by substantially closer resemblance among full-sibs than among parents and offspring. Inflated full-sib estimates of heritability could also be obtained if dominance variance was present, because twice the component of variance among full-sib families estimates the additive genetic variance plus half of the dominance variance (Falconer, 1989). However, even assuming complete dominance, the overestimate will be less than 25%, and with partial dominance obvi- ously even less (Wright, 1952). Full-sib correlation was especially high for tarsus length, which reaches its full size before the young leave the nest. The other traits which reach their full size after fledging seem to be less affected by a common environment, as judged from the smaller differences between estimates from full-sib and regression analyses (Tab. 5).

Heritability estimates were rather low (all below 0.60) as compared to those obtained in other quantitative genetic studies of birds, which often report heritability values of 60-70% (see Boag and van Noordwijk, 1987, for a review). The mean heritability of traits studied here (0.46) is the same as the mean of 570 heritabilities (morphometric) reviewed by Willis et al. (1991). Moderate to low heritabilities are not very surprising considering the moderate measurement error for most of the traits (Table 1). Since the measurement error may lower our heritability estimates, these should be viewed as conservative estimates of parametric heritabilities which may be somewhat higher than observed here (Boag, 1983).

Offspring-father resemblance was lower than offspring-mother resemblance lead- ing to some ambiguity in the exact magnitude of parametric heritabilities. If the


primary cause of low offspring-father resemblance is extra-pair copulations (EPC), observed heritabilities will underestimate parametric heritabilities (Alatalo et al., 1984, 1989). On the other hand, if the cause is maternal effects, parametric heritabilities may be lower than observed. We can think of three different explanations for this difference in paternal and matermal heritability estimates, namely EPC, maternal effects and sex-linked variance. Alatalo et al. ( 1984, 1989) proposed that low offspring-father resemblance in tarsus length could be ascribed to EPC, known to occur in this species (Gelter et al., 1992). However, when offspring-parent relationships were partitioned to father-son, father-daughter, mother-son and mother-daughter comparisons, it became obvious that the direct cause of low father-offspring heritabilities was low father-son heritabilities, while the father- daughter heritabilities were of the same magnitude as offspring-mother heritabilities (unpublished). This finding suggests that EPC are far from the only cause of low father-offspring resemblance, and indicates that sex-linked variance could explain differences in offspring-father and offspring-mother regressions (Mather and Jinks, 1971; Cha IO). This is because EPC should affect both father-daughter and -son regressions in a similar manner. It is also difficult to imagine some maternal effects that would contribute to differential resemblance among parents and male and female offspring, because the rearing environment and time are similar for both sexes. The question of possible sex-linked variance will be discussed elsewhere (in

prep.).

Genetic correlations

Genetic correlations among traits were low to moderate as were their phenotypic and environmental counterparts. In general, the strongest genetic correlations occurred between three feather measures (0.37-0.61) as well as between three beak dimensions (0.27-0.64). Moderate correlations among these traits could be a result of selection for the integration of characters that make an important joint contribution to individual fitness (Olson and Miller, 1968). For example, Kurten (1953) observed that functionally related teeth in certain mammals tend to be more highly correlated with each other than with vestigial teeth. However, an alternative explanation for high correlations among beak and feather dimensions could be their developmental proximity. As pointed out by Atchley (1987) traits may become genetically correlated irrespective of whether they are functionally related. For example, Cowley and Atchley (1990) observed that in Drosophila melanogaster traits that share the same developmental origin and undergo simultaneous development were tied together by stronger genetic correlations as compared to developmentally unrelated traits. We have no way of deciding between these two hypotheses, but the strongest correlations in this study were found among traits which are, at least functionally, and probably also developmentally coupled.

It is interesting that beak dimensions appeared to be more closely related at the genotype than at the phenotype, while the opposite was true for feather dimensions (Fig. 1). This difference in genetic and phenotypic integration between these two

390 MeriG and Gustafsson

trait sets can be understood in terms of environmental correlations. Environmental correlations between feather traits are higher than those among beak traits. This means that in the presence of environmentally induced variation, part of the phenotypic correlations among feather traits are made up of environmental correlations, while environmental variation in beak traits acts as a random deviate. Phenotypic correlations among beak traits will therefore be lower than those among feather traits.

Given the relatively large samples used to calculate these estimates, our results suggest that genetic correlations are not likely to impose any profound constraints on the evolution of the traits considered here. That is, the weak correlations suggest that there is ample variation in proportionality among traits (“shape”) in this population. This should permit the population to evolve in many directions, even under complex multivariate selection. Possible exceptions to this general pattern might be the correlations between beak width and height, as well as between wing and tail length (rG > 0.6).

An eigenanalysis of the additive genetic correlation matrix gave a genetic size function (PC]) as the largest component of genetic covariation (Appendix 2). This conclusion was supported by the finding that PC1 of the phenotypic correlation matrix can be interpreted as a heritable composite size function. These findings suggest that as the underlying genetic expression in one trait changes, there is a corresponding change in almost all other traits, i.e. Wright’s (1968) universal pleiotropy. Morphological shape appears to be under additive genetic control, as shown by the large heritabilities of composite shape functions, PC224 (Tab. 3). Similar findings for morphometric traits have been reported by Atchley (1983, and refs there in), Boag ( 1983) and Grant ( 1983).

Sirnilurity between genetic, phenotypic and endronmental correlations

The interest in comparing genetic and phenotypic correlations has been encour- aged by the difficulty in estimating genetic correlations, which are required in evolutionary models to predict responses to natural selection or genetic drift (Lande, 1979). If the phenotypic correlations were generally similar to their genetic counterparts, this might encourage their use as a substitute when estimation of genetic correlations is not possible (Cheverud, 1988). Although the comparison of these two types of correlations (the same applies to comparison of phenotypic and environmental correlations) is not strictly acceptable from the statistical point of view (see below), such comparisons have been frequently reported, and similarity is often found (e.g. Cheverud, 1982, 1988; Grant, 1983; Schluter and Smith, 1986; but see Willis et al., 1991). A close resemblance between these two correlations can arise if heritabilities are uniformly high, since the correlations between environmental effects will then exert little influence on the phenotype. Since the observed heritabilities were relatively low and of varying magnitude, it is surprising that genetic and phenotypic correlations were found to be quite similar (r = O.SS), as were the corresponding covariances (r = 0.87). Another situation where genetic and pheno-


typic correlations are expected to be similar is when genetic and environmental correlations are similar, since phenotypic correlations are the weighted sums of genetic and environmental correlations (Cheverud, 1984, 1988). This occurs regard- less of the level of heritability (ibid). Such parallel genetic and environmental associations commonly occur in morphological studies (Atchley, 1983 and refs. therein). In this study, however, similarity between genetic and environmental correlations was weak (u = 0.1 I), indicating that environmental variation did not follow the pattern of genetic correlations. Since the environmental correlations express the degree to which two traits respond to variations in environmental conditions, this result clearly suggests that genetical and environmental factors do not reinforce each other during the ontogeny of traits. Cheverud (1982) and Grant ( 1983) found that the genetic and environmental correlations in rhesus macaque and a Darwin’s finch were negatively correlated. Nevertheless, given moderate to low heritabilities and a poor fit between environmental and genetic correlations we find it hard to understand how the observed similarity between phenotypic and environmental correlations can be accounted for. One possible explanation is that environmental correlations, which were very low, exerted only a weak influence on phenotype as compared to the genetic correlations. Or alternatively, because all the estimates of correlations (especially genetic) were made with some degree of error (Falconer, 1989) the sampling error in both phenotypic and genetic correlations could account for the discrepancy.

In conclusion, the findings of this study show that the external morphology of the collared flycatcher is largely under additive genetic control, and that genetic correlations among traits are low to moderate. This indicates that genetic variances and covariances are not likely to exert any profound constraints on the further evolution of morphology in this species.

Acknowledgements

We thank Mats Bjiirklund, Kjell Larsson, Dolph Schluter, and Staffan Ulfstrand for helpful and critical remarks on this manuscript. Special thanks are given to MB for his generous help with matrix statistics. Many people helped with field work, in particular Giiran Carlen, Ryszard Corona, Tomas Part and Dag Nordling. Study was supported by grants from the C. F. and Maria von Wahlberg’s

Foundation (JM), the Swedish Natural Science Research Council (LG), and Magnus Bergwall Founda- tion (LG).

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Recctved 7 September 1992;

accepted 10 November 1992. Corresponding Editor: G. Miiller

Appendix I. Means, standard deviations and coefficients of variation for seven morphomctric traits for

offspring and parents in the offspring-parent regressions presented in Table 4. All trait values are age-corrected and male values are adjusted to female equivalents (see methods).

Skeletal measures Feather measures

Group Mean SD N CV Mean SD N CV

Midoflspring

Midparents

Fathers

Mothers

Midoffspring

Midparents

Fathers

Mothers

Midoffspring

Midparcnts

Fathers

Mothers

Mrdoffspring

Midparents

I-athers

Mothers

Beok width (mm) JVin,q Iqqh (mm)

4.10 0.17 300 4.25 81.34 1.48 681 1.81

4.14 0.14 300 3.27 81.45 1.31 681 1.61

4.13 0.19 305 4.69 81.44 1.67 692 2.05

4.15 0.18 336 4.36 81.43 1.79 748 2.19

Beak depth (mm) Tail length (mm)

3.22 0.1 I 29X 3.61 51.29 1.36 611 2.67

3.26 0.09 298 3.67 51.42 1.03 611 2.00

3.27 0.14 305 4.22 51.47 1.42 628 2.75

3.26 0.12 335 3.67 51.39 1.48 617 2.74

Bruk kqqih (mm) 1.~1 primq lengfh (mm)

13.47 0.36 681 2.68 37.19 1.46 585 3.93

13.46 0.29 681 1.61 36.94 1.09 585 2.91

13.47 0.41 695 3.08 37.05 1.52 616 4.09

13.45 0.39 748 2.91 36.84 1.48 655 4.01

Tarsus l~wgth (mm)

19.47 0.48 691 2.45

19.54 0.37 691 1.90

19.54 0.54 710 2.67

19.54 0.48 755 2.48


Appendix 2. Eigenvectors (VEC) for Collared Flycatcher phenotypic and genotypic correlation matrices. EIG are the associated elgenvalucs, % is the proportion of the total variance accounted for by each elgenvaluc.

VEC. EIG. % BW BD BL TS WI TL PR

PCI PC2 PC3 PC4

PC1 PC2 PC3 PC4

2.13 30.5 1.30 18.9 0.95 13.5 0.79 Il.2

2.38 34.0 0.27 0.22 I .75 25.0 0.56 0.62 0.99 14.2 -0.17 -0.05 0.73 10.4 -0.39 -0.15

0.31 0.20 0.47 0.61

-0.15 -0.37 -0.62 0.1 I

Phcnotypic correlrrion nru,rix 0.35 0.39 0.50 0.33 0.07 m-o.35

0.34 0.55 -0.15

0.62 -0.03 0.04

Genefic~ corrrlation mutria

0.33 0.39 0.50 0.28 - 0.20 -0.21

0.53 0.61 -0.23

0.54 -0.25 0.27

0.40 0.42 -0.28 -0.30

-0.58 0.23 0.27 -0.37

0.47 0.39 -0.1 I -0.37

-0.51 -0.00 0.30 -0.55

inheritance of size and shape in a natural population of collared flycatchers, ficedula albicollis

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