Biological Journal of the Linnean Soriety (1988), 35: 169- 184
Microgeographic variation in shell
characters of Littorina saxatilis
Olivi-a question mainly of size?
PER SUNDBERG
University o f Goteborg, Department of ,Ioology, P.O. Box 250 59, S-400 31 Goteborg,
Sweden
Received 6 December 1987, accepted for publication 6 April 1988
T h e question of whether there are shape differences between populations of Littorina s~xatilisliving
in different environmcnts is examined by multivariate analyses of 13 morphological characters.
Principal component analysis reveals that morphologic differences between populations from
habitats with contrasting degrees of wave exposure are mainly due to a general size factor,
including shell thickness. Utilizing the group structure among the snails, canonical variate analysis
discloses that the main character exrluding size that influences suhpopulation diffrrentiation is
pointedness.
KEY WORDS:
differentiation.
Lillorina saxatilis
-
shell morphology
-
intraspecific variation
population
-
C ONT E N TS
. . . . . . . . . . .
Introduction .
. . . . . . . . .
Material and methods .
T h e snails a n d their sampling sites .
. . . . .
Numerical analyses and characters measured .
. .
. . . . . . . . . . . . . .
Results
. . . . . . . . . . . .
Discussion.
. .
Genetic differentiation between subpopulations.
Correlation between morphs and environmental factors.
Size or shape?.
. . . . . . . . . .
Acknowledgements
. . . . . . . . . .
References.
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INTRODUCTION
I t is, more than 125 years after the publication of Darwin’s The Origin o f
Species, still a major challenge in contemporary biology to demonstrate selection
in natural populations. Consistent correlations between morphology and
environment are often taken as indirect evidence of selection; many gastropod
species provide us with such cases. Cooke (1895) for example noticed that there
were different forms of Nucillus lapillus living in different environments: “Forms
occurring in very exposed situations . . .are stunted, with a short spire and
relatively large mouth. . . . O n the other hand shells occurring in sheltered
169
0024-4066/88/100169+ 16 $03 OOjO
01988 T h e Linncaii
Society of London
170
P. SUNDBERG
situations. . . are comparatively of great size with. . . a mouth small”.
Morphology in several Litlorina species is correlated to environmental factors in
a similar manner. Within a species, snails from more exposed habitats are
reported to have a larger foot area and aperture size, relative to shell size, than
snails from more sheltered areas. This has been demonstrated for L. saxatilis
(Newkirk & Doyle, 1975; Heller, 1975; Raffaelli, 1979; Smith, 1981; Janson,
1982a); other examples from this genus are provided by Raffaelli (1982). I n
addition, shells of L. saxatilis from exposed sites usually have thinner shells than
those from sheltered and boulder shores (Rafaelli, 1978; Elner & Raffaelli, 1980;
Naylor & Begon, 1981; Hart & Begon, 1982; Janson, 1982a). Most authors
agree that the large aperture of Littorina occupying exposed habitats is a
consequence of increased foot size required for greater adhesion. O n sheltered
shores, on the other hand, adhesion is not the main selective force, and smaller
aperture size can instead be selectively favoured if it reduces water loss and/or
reduces the risk of fatal attacks from the shore crab Carcinus maenas (Heller, 1975;
Raffaelli, 1978; Johannesson, 1986). Raffaelli ( 1982) and Faller-Fritsch &
Emson (1985) review the evolutionary scenarios that have been proposed to
account for the observed differences in shell characters among Littorina. I n short,
the following physical causes of mortality can be distinguished: desiccation, heat
and dislodgement, extreme cold, crushing, and salinity. Biotic factors are
predation by mainly birds and crabs, parasitism, and inter- and intra-specific
competition.
It is mainly shape differences that have been considered when comparing
morphology in relation to habitat. A problem then is that the snails often differ
in size between the compared habitats. Primarily, two approaches have been
applied to yield size-free comparisons of snails: ratios and regressions, including
the related analysis of covariance (regression and analysis of covariance are used
in, for example, Janson, 1982a; Johannesson, 1986; Grahame & Mill, 1986).
The use of ratios in morphometry has long been disputed; as early as 1897 Karl
Pearson criticized using ratios as a way of removing size (Pearson, 1897).
Although its appropriateness has been amply debated (Atchley, Gaskins &
Anderson, 1976; Corruccini, 1977; Hills, 1978; Dodson, 1978; Albrecht, 1978;
Atcheley & Anderson, 1978; Atchley, 1978; Mosimann &James, 1979), I agree
with Atchley et al. (1976), Atchley & Anderson (1978), and others, that ratios
should be used with caution. The reasons are: ( 1 ) ratios can behave spuriously,
depending on the correlation of the numerator and denominator; (2) the ratio
depends upon the denominator (size) and is hence correlated with size, unless
the relationship between the numerator and denominator is linear and passes
through the origin (Gould, 1966; Thorpe, 1983).
Still another conceptual problem with the use of ratios as a way of removing
size is the question of whether size really can be represented by a single distance
measure, like shell height. Humphries et al. (1981) hold that size has to be
viewed as a factor that leaves the smallest residuals when used to predict all the
distance measures within a population, rather than as a single measure. Size is
thus viewed as a linear combination of all traits that accounts for as much as
possible of the associations among other distance characters. This view
abandons the use of a ratio, since no single character then can represent ‘size’ in
the denominator. I t also singles out the second common approach, to use
univariate regression analysis since regression only partials out the effect of the
INTRASPECIFIC VARIATION IN LITTORINA SAXATILIS
171
independent variable (size) from the dependent. If size is designated as one
single variable, then only that character is singled out. Hence, no univariate
approach can account for size reduction in a conceptually acceptable way.
Instead, some multivariate approach has to be considered when studying shape
differences free of size.
Multivariate morphometric analysis is a n alternate approach to the problem
of size and shape, but a way that has not yet been commonly utilized for
studying shape differences related to environmental factors. Another point in
favour of such a n approach is that selection acts on the whole phenotype and
thus it is essential to analyse many characters simultaneously and to consider the
covariation between these characters, and not, which has been the case so far, to
view only a small number of characters in isolation, like size of shell aperture,
pointedness, and shell thickness. This study will apply multivariate techniques
to describe the inter-population differences in size and shape between four
populations of the rough periwinkle Littorina saxatilis from a small area of the
Swedish west coast. Littorina saxatilis has earlier been considered a subspecies
within the ‘L. saxatilis-complex’, and James (1968) for example described a
number of subspecies within this complex. The consensus at present, however, is
that four good species can be distinguished: L. saxatilis, L. arcana, L. nigrolineata,
and L. neglecta (Ward & Warwick, 1980; Raffaelli, 1982; Smith, 1982; Janson &
Ward, 1985; Ward & Janson, 1985). Littorina saxatilis is encountered in Sweden
in two distinct types of habitats: sheltered boulder bays and exposed rocky
shores, but also at sites intermediate in degree of exposure. The aim of this study
is to show that snails vary in their shell characters in a way concordant with
degree of exposure, and to clarify if this variation is due to size or shape
differences by two kinds of multivariate analyses. The results show that there is
a correlation between size, where size includes a measure of thickness, and
degree of exposure, but that there are no clear differences in shape between
localities.
MATERIAL AND METHODS
The snails and their sampling sites
Samples were collected from four sites at the island of Salto, close to Tjarno
Marine Biological Laboratory, Swedish west coast. The sites comprise an
environmental cline with respect to degree of wave exposure with a general
decrease in exposure from sampling site 4 to site 1. T h e exposed locality in the
sampled area is typically a naked cliff with a swell battering more or less
continuously, and the supralittoral fauna consists chiefly of L. saxatilis. T h e
main mortality factors for a snail in this habitat are probably dislodgement and
bird predation. The sheltered habitat is a completely different milieu. It is
typically a boulder bay with seaweed like Fucus spp and Ascophyllum nodosum,
both indicating a low degree of physical stress from waves. T h e snails are
exposed to predation from the common shore crab, C. maenas, and furthermore
here is a risk of shell injuries from loose pebbles, stones, and boulders.
Shells were also sampled from two additional sites, intermediate in degree of
exposure between the typical exposed and sheltered localities, localities 2 and 3.
The sampling area is described in more detail in Janson & Ward (1984)) and
Janson (1982a).
172
P. SUNDBERG
Figurr 1. Schrmatir reprrsentation ol' a snail showing the 12 slirll measurements used in the
analysis. In addition to thrsr, a thirtethnth charactcr, thc cmpty shcll weight, was included in the
analvsis.
Numerical anahses and characters measured
Twelve characters (Fig. 1 ) were measured on 30 shells from each locality after
the soft body parts had been removed. Each empty shell was also weighed, thus
yielding a total of 13 characters. Shell weight was transformed by taking the
cube-root of the original values to make these the same dimension as length
measurements. The character can be viewed as a rough measure of shell
thickness. In order to standardize the variances, the characters were logtransformed prior to the multivariate analyses (Reyment, Blackith & Campbell,
1984).
Differences between the sampling sites in the measured characters were tested
both by univariate analysis of variance (ANOVA) and multivariate analysis of
variance (MANOVA). The Student-Newman-Keul procedure (Sokal & Rohlf,
1969) was used for an a posteriori test of differences between individual sites when
there was a significant difference between them. The same procedure was
applied to the principal component scores.
Two multivariate techniques were employed: principal component analysis,
and canonical variate analysis; see for example Reyment et al. (1984) and Neff
& Marcus (1980) for accounts of these techniques. If the principal component
analysis is based on log-transformed observation values and the correlation
matrix, the first eigenvector will closely approximate an isometric size vector
(Reyment et al., 1984) if such a vector exists within the sampled range of sizes.
Jolicoeur (1963) has shown that, on theoretical grounds, the elements in the first
eigenvector should equal (A')-I/', where N is the number of characters, to be a n
isometric size vector. T h e first principal component is assumed to represent size
I N T R ASPECIFIC VAR I AT1O N I N LZTTORZNA SAXA TZLZS
173
if all elements are of about equal size, positive, and correlated to the original
variables, but this view is disputed; Mosimann (1970), Sprent (1972), and
Mosimann & James (1979) have pointed out that the interpretation of
component one as size, and the remaining components as shape is rather
arbitrary. There have been suggestions to improve the interpretability of the
components (e.g. Humphries el al., 1981; Somers, 1986). Still, the general
opinion seems to be that the first principal component can be interpreted as size
in many studies. However, it is important to understand that the first
component only attempts to summarize as much of the covariation displayed
among the input variables as it can. The correlation structure of the original
characters will completely determine the orientation of component one and the
interpretation of it follows the analysis, and not the other way around.
Principal component analysis does not presume any group structure in the
material. When there is information about group structures in the sample, as in
this case from previous studies like e.g. Janson & Sundberg (1983), it is
recommended (Reyment et al., 1984) that this information should be used by
applying canonical variate analysis. Despite this, I will start with a principal
component analysis based on the pooled within-group covariances, even though
the univariate tests will confirm the group structure. T h e factors of canonical
variate analysis are not, however, as easy to interpret as the principal
components, and Humphries et al. (1981) have argued that canonical variates
confound size and shape. The analysis loses information on patterns of character
covariation when maximizing the differences between among-group and withingroup covariations.
The canonical variate analysis is based on the standardized within-groups
sums of squares and cross products matrix W and the between-groups sums of
squares and products matrix B. These were standardized into their correlation
forms (W* and B*) by pre- and post-multiplication by the inverse of the
diagonal matrix S = (diag W)'I2 for reasons of statistical stability (Campbell &
Reyment, 1978), such that
W"
B*
=
s-1
ws-'
= S-' BS-'.
The eigenvalues and eigenvectors were extracted from these matrices and the
canonical variates computed (Campbell & Reyment, 1978). T h e degree of
correlation between, and the variances of, the original character values
determine the degree and direction of maximum between- to within-group
variation, Characters with high positive within-groups correlation, and negative
between-groups correlation, and vice versa, will provide maximum
discrimination (Lubischew, 1962). The lower the absolute value of within-group
correlation, the poorer is the discrimination. The distance between the group
centroids in the multivariate space is measured with Mahalanobis D 2 , a
measure which takes into account the correlations between variables (Manly,
1986).
Various approaches have been proposed to determine which characters
contribute most to the group separation. Probably the most widely used one is
based on the relative magnitudes of the standardized canonical variate
coefficients (Reyment el al., 1984). These coefficients are obtained by
multiplying the original coefficients with the pooled within-groups standard
P. SUNDBERG
174
deviations. Variables with large absolute values are taken to be more important
in the discrimination of the groups. The principal component analysis can thus
be used to, first, discover multiple groups in the sample, and second, to
determine the shape and size components respectively. T h e canonical variate
analysis can, as a second step, be used as a n aid in finding the characters that
cause most of the groupings between specimens.
A number of SAS (SAS Institute Inc., 1985) procedures were used for the
analysis and graphical assessment of the observation values, and for the
principal component analysis (ANOVA, PLOT, PRINCOMP) . A program,
CANVAR, written by R. Reyment (University of Uppsala, Sweden) was used
for the canonical variate analysis, and for calculating Mahalanobis' distance
between group centroids.
RESULTS
There is general increase in variation in the shell characters with increasing
degree of exposure (Table 1). It is not clear from this study if this is due to
increasing measurement error in the smaller shells from the exposed habitat, or
if it reflects less stabilizing selection in this habitat. T h e shells differ significantly
in the measured characters between the sampling sites (ANOVA, P < 0.001).
The a posteriori test reveals that it is shells from the most exposed habitat
(sampling site 4) that differ from shells from all the other localities in general,
although there are differences between the others as well in certain characters
(Table 2).
I t is obvious that shells from site 4 are much smaller than the rest, but the
question is whether there are also shape differences. All measured characters are
highly correlated to each other (Table 3), and it can be expected that a
principal component analysis will be successful in reducing the original
characters into a small number of transformed variables. T h e first eigenvalue
does also account for 94.1% of the sample variance. T h e elements of the
TABLE1. Summary statistics, mean and coefficient of variation (CV), for untransformed
measurements (in millimetres) of 30 snails from each of the four localities where site 1 is thc most
sheltered, and 4 the most exposed. The characters (except weight, in mg) are explained in Fig. 1
Sampling site
2
1
3
4
Char.irter
Mean
cv
Mran
cv
Mean
cv
Mean
CV
A
5.04
8.14
12.01
10.91
37.00
3.98
1.37
1.68
0.75
5.63
1.71
1.45
0.44
17.2
12.6
15.5
16.5
47.6
16.7
16.9
20.7
37.9
20.3
11.7
29.1
55.3
4.48
7.05
10.35
9.46
23.37
3.34
1.18
1.41
0.63
4.92
1.56
I .22
0.49
24.2
22.8
25.0
23.9
73.7
23.3
28.3
32.4
32.8
25.6
35.5
31.4
30.6
4.07
6.50
9.37
8.67
14.00
3.00
1.08
1.28
0.55
4.53
I .49
1.09
0.44
15.6
17.2
17.9
16.9
74.3
19.9
18.9
24.4
28.5
18.6
23.5
25.8
31.8
1.94
2.87
3.91
3.91
1.10
1.35
0.28
0.51
0.20
1.95
0.63
0.36
0.11
26. I
26.6
31.9
25.1
105.4
24.8
30.4
32.9
35.3
32.6
35.4
45.1
60.7
B
C
D
Weight
R
R1
R2
R3
Y
Y1
Y2
Y3
IN'TRASPECIFIC VARIATION IN LITTORINA SAXATIIJS
175
TABLE
2. Analysis of variance (ANOVA) of the differences in
mean character values between shells from the four sampling sites,
and the a posteriori test of the differences. Significant (P< 0.001) F
values are italicized
Significant (P< 0.001)
differenres between sites
Character
A
B
C
D
Weight
R
R1
R2
R3
Y
Y1
Y2
Y3
1,2
1,2
1,2
1,2
1,2
1,2
1,3
1,3
1,3
1,3
1,3
1,3
1,3
1,3
1,3
1,3
1,3
1,3
1,4
1,4
1,4
1,4
1,4
1,4
1,4
1,4
1,4
1,4
1,4
1,4
1,4
2,4
2,4
2,4
2,4
2,4
2,4
2,4
2,4
2,4
2,4
2,4
2,4
2,4
F value
3,4
3,4
3,4
3,4
3,4
3,4
3,4
3,4
3,4
3,4
3,4
3,4
3,4
83.9
114.1
101.5
95.9
105.1
99.3
163.9
66.4
44.2
77.4
135.9
61.9
34.6
corresponding eigenvector are all positive and most of them close to the
predicted value of
= 0.28 (Jolicoeur, 1963) for an isometric size vector
(Table 4). The elements of the first eigenvector are all also significantly
( P < 0.001) correlated to the original character values. Taken together with the
eigenvector elements, this component can be viewed as an index of the size of
the snails, and it can thus be concluded that about 94% of the variation in the
measured characters is in fact related to size. The second eigenvalue accounts
for 1.97% of the variation; its corresponding eigenvector (Table 4) is not trivial
to interpret in terms of shape. It is mainly a contrast of characters from the
aperture together with the main shell characters including weight, and
pointedness of the shell. The remaining 11 eigenvectors are equally difficult to
interpret in any obvious shape manner. T h e eigenvalues for them account for
3.9% of the sample variance. The first nine components are reported in
Table 4, the remaining four explain only 0.2% of the variance.
T h e samples differ only significantly in their component scores along the first
and second axis (ANOVA, P < 0.001; Table 5); sampling site 4 differs
(m-'''
TABLE
3. Pearson product-moment correlation coefficient between the 12 characters explained in
Fig. 1, together with shell weight (W)
A
B
C
D
w
R
R1
R2
K3
Y
YI
Y2
Y3
A
B
C
D
W
R
R1
R2
R3
Y
Y1
Y2
Y3
1.00
0.99
0.97
0.99
0.98
0.98
0.91
0.96
0.92
0.97
0.95
0.91
0.85
1.00
0.98
0.99
0.99
0.99
0.92
0.97
0.93
0.98
0.96
0.92
0.85
1.00
0.98
0.98
0.98
0.90
0.97
0.92
0.97
0.95
0.93
0.85
1.00
0.99
0.99
0.91
0.97
0.93
0.98
0.96
0.92
0.86
1.00
0.99
0.90
0.98
0.94
0.98
0.95
0.93
0.85
1.00
0.92
0.97
0.94
0.98
0.96
0.93
0.85
1.00
0.87
0.87
0.91
0.94
0.86
0.82
1.00
0.93
0.98
0.95
0.94
0.86
1.00
0.95
0.91
0.91
0.86
1.00
0.98
0.96
0.89
1.00
0.95
0.89
1.00
0.88
1.00
P. SUNDBERG
176
'I'ABLE4. Result of the principal component analysis based on the correlation matrix between the
13 characters (Fig. 1). T h e first cigcnvcctor is significantly ( P < 0.001) corrclated with the
original character values. Only the first nine eigenvectors with corresponding eigcnvalues are
reported; the remaining four eigenvalues account for less than 0.2% of the sample variance
Component
1
12.2
Eigenvalue
Cumulative
94.1
'>u
Rigenvectors
Character
A
0.281
0.283
0.281
0.283
0.282
0.283
0.266
0.280
0.273
0.284
0.280
0.273
0.256
R
c
D
Wright
K
KI
R2
K3
Y
YI
Y2
Y3
-0.75
Correlation*
2
3
4
5
0.26
0.18
0.10
0.09
96.1
97.5
98.2
-0.198
-0.202
-0.171
-0.169
-0.211
-0.182
-0.079
-0.055
0.104
0.037
0.107
0.287
0.818
0.028
0.021
-0.087
-0.046
-0.148
-0.022
0.813
-0.308
-0.222
-0.098
0.243
-0.237
0.085
0.18
0.20
6
0.03
98.9
99.2
0.058
0.304
0.158 -0.033
0.088 -0.159
0.218 -0.065
0.110 -0,019
0.047
0.049
0.160
-0.198
-0.043 -0.127
0.890
-0,160
-0.077 -0.073
-0.270 -0.232
-0.658 -0.262
0.484 -0.044
-0.04
7
8
9
0.02
0.01
0.01
99.5
-0.462 -0.071
-0.214
0.027
0.404
0.337
-0.191
0.030
-0.081
0.114
0.066
0.181
0.205
0.208
0.616 -0.218
-0.041 -0.085
0.057 -0.385
-0,107 -0.633
-0.297
0.417
0.055
0.107
0.01
0.08
0.07
99.7
-0.245
0.078
-0.729
0.099
0.326
0.334
0.129
0.274
-0.173
0.036
-0.218
0.033
0.056
-0.34
99.8
0.516
-0.009
-0.194
-0.014
-0.188
-0.481
0.196
0.518
0.018
-0.217
-0.232
0.137
-0.032
0.15
*Spearman correlation coefficient between principal components and sampling sites, i.e. degree of exposure.
Significant valurs (P < 0.01) are italicized.
significantly from the remaining three sites along the first axis, and so does 1
from 2. T h e only sites that differ along the second axis are 1 from 3, and 1 from
2. Only the first and eighth component are correlated to the group structure, i.e.
to degree of exposure (Table 4). The representation of the individual shells
along the first and second components in combination (Fig. 2) clearly shows
that almost all differences between localities, and hence degree of exposure, can
be explained by size differences. T h e same graphical pattern is apparent from
all plots of component one versus the remaining (these are not shown in Fig. 2).
The interpretation of the first axis as size is reinforced by the position in the plot
TABLE
5. Analysis of variance (ANOVA) of the differences in
principal component scores between shells from the four sampling
sites. Significant ( P < 0.001) F values are italicized
Significant (P < 0.001)
differences hetween sites
Component
1
2
3
1,2
1,3
1,3
Not signifirant
7
8
9
1,4
2,4
F value
3,4
172.0
8.5
2.7
1.1
0.2
0.2
I .o
2.3
1.4
IN'I'R ASPECIFI C VARIATION IN LI TTORlNA SA X A TILIS
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I
-1 5>
-8
-6
-4
-2
0
2
4
F i r s t principal component axis
Figure 2. Plot of the 120 shells against values for the first two principal components, the first
explaining 94.1%) and the second 2.0;; of the variation in the sample. T h e numbers on the
specimens refer to habitat, where 4 is the most exposed and 1 the most sheltered habitat. Five
observations are hidden in the plot since they overlap with othcr observations within their own
group. The aberrant, large, specimen from habitat 4 discusscd in the text is marked with a n
asterisk.
of one particularly large specimen from the exposed locality which does in fact
cluster together with the larger specimens from the other three localities. This
large specimen could have been an immigrant from a sheltered locality, but its
thin shell typical of the exposed habitat contradicts this.
T h e conclusions from the principal component analysis are:
(1) There is a group differentiation among the shells from the four localities,
representing a cline in degree of exposure, due to size differences including shell
weight (shell thickness) and which is correlated to the environmental factor
exposure, either direct, or through some, unmeasured and unobserved factor.
(2) A separation of the shells from the sampling sites is intimated along the
second axis, especially between snails from sites 1, 2 and 3 . The second axis is
difficult to interpret in any well defined shape expression, but is in general a
measure of pointedness.
The group structure was utilized in the canonical variate analysis (Table 6,
and Fig. 3 ) , where the main aim was to find the characters most influential in
this differentiation. This analysis sets out to maximize the difference between the
groups in the multivariate space and to describe it in a simplified manner. The
groups differ significantly when all characters are analysed simultaneously
(MANOVA, P < 0.0001, Wilks' lambda = 0.06), and the Mahalanobis D2 is
significant between all four sampling sites. The plot of the specimens along the
first and second canonical variates shows a profound cline along the first axis
(Fig. 3 ) . Only shells from sites 1, 2 and 3 differ along the second axis, while the
variation of the most exposed shells covers the other three sites. A similar picture
emerges in the combination of the first and third variates (not shown in the
figure), while the plot of the second and third variates does not show any
178
1’. SUNDBERG
30
0 D e n o t e s group meon
1
11 1
4
4
15
g
-e
o
8
-15
$pb
4
L
1
4
4
Li
1
4
4
-4-
>-4-4?
4
4
4
TI
0
4
VI
3
3
-3 0
-50
2
25
0
-20
First
conanicol v o r i o t e
axis
Figurr 3. Plot of the 120 shells along the first (83.4‘:/, of the variation) and second ( 12.2c)’o)canonical
variatc axes together with a minimum spanning tree between group centroids. Specimen numbers
rcfer to habitat (4 is the most exposed and 1 the most sheltered habitat). Group centroids are
marked by encircled numbers. Eight observations overlap with others and are missing in the plot.
These spccimens do not, however, deviate from their respective group. T h e aberrant, large,
spcrimen from habitat 4 discussed in the text is marked with an asterisk.
TABLE
6. Result of the canonical variate analysis based on log-transformed character
values, together with Mahalanobis’ (D2) generalized distance betwren group
centroids. The structure coefficient expresses the correlation betwecn the original
character values and their corresponding canonical variate coefficients.
Standardized canonical variate
coefficient for canoniral
variant:
Character
A
B
c
D
Wriaht
K
R1
K2
K3
Y
Y1
Y2
Y3
1
2
3
1
2
3
- 13.9
1.6
-15.8
-2.3
-23.5
17.4
0.3
- 9.4
-8.6
-2.1
-1.3
13.4
-3.9
-3.1
-9.6
19.9
-3.9
18.9
-9.3
-0.2
3.5
3.4
-1.8
-5.2
14.4
-1.2
-5.0
0.64
0.75
0.69
0.71
0.76
--0.21
0.66
0.62
0.54
0.65
0.54
0.59
0.50
0.03
0.04
0.04
0.05
0.19
0.18
0.02
0.06
0.01
0.00
0.04
-0.01
-0.33
0.27
0.40
0.33
0.37
0.31
- 0.04
0.41
0.38
0.16
0.32
0.4 1
0.26
-0.15
6.7
5.9
5.3
4.1
0.1
5.0
- 1.6
0.5
- 1.5
- 17.4
6.7
2.4
Mahalanobis’ D Ybetween group centroids:
Sampling site:
1
1
0
2
3
4
*Significant at
Structure coefficient for
canonical variant
P = 0.001 ( F test).
2
3.6*
0
3
9.7*
3.5*
0
4
51.8*
44.3*
35.0*
0
INTRASPECIFIC VARIATION IN LI?TOR~.IVX SAXA TILIS
I79
obvious grouping. The standardized canonical coefficients (Table 6) point in
the direction that it is height of the shell (character Y1 in Fig. 1) followed by
aperture width (character A) which influences the group separation along the
first axis. These two characters are contrasted mainly to the characters aperture
length (character B), shell width and height (characters D and C), weight and
pointedness (Y2). The position of the specimens along the second axis is a
contrast between aperture length and shell width (characters B and D), and
weight and pointedness ( Y l ) (Table 6). It is interesting to notice also in this
analysis the position of the unusually large specimen from the exposed locality.
It is placed among the specimens from the other localities along the first axis,
while slightly outside the range along the second axis thus indicating some shape
differences in pointedness and relative aperture size.
When the canonical variate analysis is combined with the results from the
principal component analysis, the overall conclusions from the multivariate
analyses are:
( 1 ) Shells from localities differing in degree of wave exposure vary in a
consistent way with this environmental factor (the generality of this statement
will be supported in the Discussion).
(2) Most of the variation in the characters among subpopulations is caused
by size differences and the differentiation between sites is thus mainly a question
of a general size factor, including thickness.
(3) No clear shape differences in characters like relative aperture size and
form of shell have been possible to establish, although differences in pointedness
between sites are intimated.
DISCUSSION
Genetic dzferentiation between subpopulations
Before assessing the morphological differences between shells from the
contrasting habitats, it is important to establish that they really are conspecific,
and that we are not dealing with different species. Janson & Ward (1984) found
a substantial degree of genetic variation by electrophoretic screening of a variety
of allozymes both within and among subpopulations of L. saxatilis from the same
sampling area used in this study. However, they could not relate enzyme
variation with degree of exposure. They concluded that the subpopulations are
conspecific, but that gene flow between them is constrained.
Janson ( 1982b) performed transfer experiments with snails from typical
exposed and sheltered habitats and measured their growth rates in the natural
as well as the ‘opposite’ site. By these experiments, she convincingly
demonstrated that differences in growth rates between snails from sheltered and
exposed habitats were partly genetic. The growth rate was slower for snails from
exposed habitats, and this has also been established for L. saxatilis (Roberts &
Hughes, 1980). Although Janson’s experiment could not tell if these differences
are consistent with habitat type they still indicate genetic differences between
localities, and that growth (and size?) differences between habitats are not only
due to environmental effects. However, it is not evident from Janson (1982b)
how much, if any, of the genetic difference in growth rate is caused by additive
genetic variation, and it is thus not possible at this stage to say if selection on
growth rate can have any evolutionary consequences (Endler, 1986).
180
P. SUNDBERG
Correlation between morphs and environmental factors
In this study, there is a correlation between shell morphology and degree of
exposure. This correlation is consistent within the sampled area (Jansen,
personal communication; Janson, 1982a; Janson & Sundberg, 1983; Janson &
Ward, 1984). Atkinson & Newbury (1984) have demonstrated consistent shell
adaptations in relation to risk of dislodgement as a function of waves and wind
in L. saxatilis. The effects of exposure and predation on shell size and shell shape
of the two winkles L. nigrolineata and L. saxatilis were studied by Heller (1975),
who concluded that smaller and more globose shells are favoured on exposed
shores, together with shells with relatively wider mouth. Smith (1981) regarded
the area of the aperture the best measure of shell shape and observed that this
area was directly correlated to wave exposure in 1,.saxatilis.
Such correlations have been taken as an indication of natural selection, and
the general view (Raffaelli, 1982) is that exposure to waves selects for thin shells
with large foot area, allowing for a greater adhesive power and lower physical
drag. O n sheltered shores, on the other hand, the main mortality factor is
supposedly crab predation, selecting for larger and thicker shells with small
apertures. This interpretation is, however, based on circumstantial evidence,
and the correlations found have been to degree of wave exposure and not with
the assumed selective agents. Before drawing any evolutionary conclusions from
possible difference in the selective regimes, it is important that we make sure
that either (i) the traits are independent of environmental effects, or (ii) that the
traits’ heritabilities are known so that environmental effects can be statistically
removed (Endler, 1986).
Another possible problem with most of the studies on functional significance
of shell characters and selection in Littorina is that they are concerned with just
one or two traits, and do not consider selective interactions among the
characters. Natural selection affects the whole organism and many of an
animal’s morphological characters will contribute to its survival and ability to
mate. Studies considering only a few, or a single character may therefore be
misleading. There have been some attempts to incorporate several aspects of the
morphology in one single factor. One approach (Newkirk & Doyle, 1975)
originates in Raup’s (1966) seminal paper on how the entire repertoire of
gastropod shell forms essentially could be derived from elementary
mathematical functions. Newkirk & Doyle (1975) demonstrated how three
compound measurements of shell characteristics varied with degree of exposure,
concluding that shells are more angular, aperture less circular in shape, and the
ratio R l / R 2 (see Fig. 1) becoming smaller, with decreasing degree of exposure.
They further argued that the correlation between morphology and habitat can
be either an effect either of direct environmental influences, or of genetic
differentiation of the populations caused by selection. They were, however, able
to establish from mother-offspring and sib analysis that there was in fact genetic
differentiation among their subpopulations in the analysed shell traits. Ekaratne
& Crisp (1983) derived a shell conversion factor that incorporates the three
parameters of Raup’s (1966) expression. Their factor will remain constant for
shells growing isometrically, whereas changes in shell shape with size will
influence this factor. They found a general increase in their factor with
increasing degree of exposure when reanalysing Newkirk & Doyles’s figures.
INTRASPECIFIC VARL4TION I N LITTOHINA SAXA‘TILIS
181
However, it is difficult to interpret their factor in shell characters, and it is not
obvious in what way shape and exposure are correlated. None of these attempts
consider inter-character correlations as opposed to many forms of multivariate
analysis. Even though the how and why of selection has to be experimentally
studied, a multivariate analysis of character variation is a step forward to the
understanding of the underlying causes, and a way of generating hypotheses
that can later be tested in a more rigorous way.
Size or shape?
When applying multivariate analyses based on the correlations between
characters, a different pattern from previous studies on functional significance of
shell characters emerges. I t is clear that most of the variation among the
sampled L.saxatilis shells between localities is due to size differentiation. T h e
main correlation between environmental factors and the phenotype is not with
one single character, but can be best described by a factor accounting for ‘size’.
This factor includes weight as well as different linear distance measures from the
shell. By tradition, size is often neglected in morphometric studies of variation.
Jolicoeur & Mosimann (1960) established the model for most of this type of
studies by statements like “size of most organisms is more affected than their
shape by fluctuations of the external environment”, and “shape tends to provide
more reliable indications than size on the internal constitution of organisms”.
This has been accepted more or less as a truism, by many biologists, and much
effort has gone into attempts to partition the morphological covariation into size
and shape. Attempts to expel size as irrelevant in favour of size-free differences
in shape have been assumed to be more biologically informative and interesting.
However, whether one can say that shape is more important than size in
describing underlying components of biological variation is questionable
(Atchley, 1983). Body size has been shown to be a moderately to highly
heritable characteristic within many species (Atchley, 1983) and it is plausible
to assume that size differences between populations are also inherited.
Size is thus a body character equally important to study and consider as
shape. As concluded from this multivariate morphometric analysis of interpopulation differences, size is the main morphological difference between
populations of L. saxatilis confined to different habitats. Size can be directly
selected, or there can be selection on growth rate. Atkinson & Newbury (1984)
reported on size differences between populations of L. rudis ( = L. saxatilis) from
sheltered and exposed habitats. They argue that exposed shells are subject to
more severe mortality throughout life than shells from boulder bays, which
would select for increased reproduction at the expense of growth, and hence
smaller snails. Janson (1982b) showed in her experiment that size differences
between exposed and sheltered populations are determined by differences in
growth rate and that this rate is more genetically than environmentally
determined. Thus, growth rate seems to be under selection, although the
underlying causes are still unknown.
Janson has also, in one of the few experiments aiming at increasing our
understanding of what causes morphological differentiation, shown that size in
itself is strongly selected (Janson, 1983). I n that study, transfer experiments and
estimates of survival rates in natural populations clearly demonstrate mortality
182
P. SUNDBERG
TABLF.
7. Survival rates (o/o) of large (shell height, A in Fig. 1 ,
>G.5 mm), and small (height <G.5 mm) L. saxatilis morphs
from sheltered and exposed habitats (from Janson, 1983)
Sheltered (S)
Exposed (E)
Intermediate
Large S
Small S
28.9
32.2
2.9
14.1
22.1
21.0
Large E
Small E;
4.0
17.9
20.7
40.0
15.4
19.0
Shell morphs
Italicized values are significantly different
--x2
test,
P < 0.001
selection on large exposed forms, and that the exposed habitat selects for small
snails. The relevant part of her results are reproduced in Table 7.
The results in this study support the view that shells from exposed habitats
are in general thinner (Janson, personal communication; Johannesson, 1986).
However, the conclusion that most of the variation between populations of
L. saxatilis from sheltered and exposed habitats is due to size differences
contradicts, to some degree, other studies which have established
environmentally consistent shape differences between populations (e.g. Janson,
1982a; Janson & Sundberg, 1983; Grahame & Mill, 1986; Johannesson, 1986).
These studies, except Janson & Sundberg ( 1983), have used regression
techniques considering only one single distance measure as size. Janson and
Sundberg do not explicitly discuss the morphological differences they found
between morphs from exposed, intermediate and sheltered localities in terms of'
shape but just conclude that there were morphological differences. Nevertheless,
it is clear from their plot of specimens in a plane along first and second principal
components that the main separation is along the first axis which can be
interpreted as size according to the eigenvector elements.
Janson (1983) has provided us with strong evidence that size is selected on in
I,. saxahlis, and she has also established that growth rate differences between
populations from the two extreme types of habitats are partly genetic. Until it is
established that the genetic component contains additive genetic variation we
will not be able to hypothesize about possible evolutionary consequences of the
phenotypic selection on size and growth rate. Size, and not only shape, has to be
duly considered in future work when assessing the variation in selective regimes
and its evolutionary role.
ACKNOWLEDGEMENTS
This study has gained substantially from my many discussions with K. Janson
and B. Johannesson, and their valuable comments on an earlier draft, although
not implying that they share my views or conclusions. I also thank R . Reyment
for his helpful comments on this manuscript and K. Janson for obtaining the
specimens. This study was supported by the Swedish Natural Science Research
Council (B-BU 872 1- 102).
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