Fluctuating asymmetry as an indicator of stress
and fitness in stickleback: a review of the
literature and examination of cranial structures
D.L. Lajus1, P.V. Golovin1, A.O. Yurtseva2, T.S. Ivanova1,
A.S. Dorgham1,3 and M.V. Ivanov1
1Saint-Petersburg State University, St. Petersburg, Russia,
2Zoological Institute of the Russian Academy of Sciences, St. Petersburg, Russia and
3Central Laboratory for Aquaculture Research, Abu-Hammad, Sharkia, Egypt
Hypothesis: Fluctuating asymmetry (FA) – random deviations from perfect symmetry that
are used as a measure of developmental stability – is an effective indicator of stress and fitness
in threespine stickleback.
Organisms: The threespine stickleback (Gasterosteus aculeatus) and two other species, the
brook stickleback (Culaea inconstans) and ninespine stickleback (Pungitius pungitius), were
the focus of a review of the literature. In addition, four populations of G. aculeatus – one
anadromous population from the Kamchatka River, two marine populations from the White
Sea, and one freshwater population from the White Sea basin – were studied in the field.
Methods: A review of the literature relating fluctuating asymmetry to different variables,
and a comparison of fluctuating asymmetry in four populations of stickleback, which differed
in geographical distribution and life history, using lateral plates and four cranial bones
(operculum, lachrymal, third suborbital, quadrate).
Results: An appraisal of the literature on fluctuating asymmetry suggests that decreasing
interest in FA studies has likely resulted from conflicting research results. To some extent, this
problem is likely caused by the morphological structures used in FA analysis, which are
generally limited to the lateral plates and pelvic fins. These structures can evolve quickly in
response to various environmental changes, thus their fluctuating asymmetry reflects not only
individual fitness and stress, but also multiple uncontrolled factors that may directly affect those
same structures. Using four cranial bones in our analysis showed lower fluctuating asymmetry
in anadromous stickleback from the Kamchatka Peninsula compared with marine and fresh-
water stickleback from the White Sea and its basin. This may be caused by more favourable
feeding conditions in the North Pacific than in the White Sea. The different environmental
conditions at these locations did not appear to have a significant effect on fluctuating asym-
metry, although the comparison of freshwater, anadromous, and marine populations showed
that the fluctuating asymmetry of the structures we used is responsive to these differences. Our
Correspondence: D.L. Lajus, Saint-Petersburg State University, 7–9 Universitetskaya nab., St. Petersburg 199034,
Russia. email: firstname.lastname@example.org
Consult the copyright statement on the inside front cover for non-commercial copying policies.
Evolutionary Ecology Research, 2019, 20: 83–106
© 2019 Dmitry L. Lajus
FA analysis of the selected bone structures reveals clear heterogeneity in stickleback with
different life histories. We suggest that these structures can be considered reliable for studies of
fluctuating asymmetry in stickleback fishes.
Keywords: cranial bones, fluctuating asymmetry, Gasterosteus aculeatus, literature review,
The stickleback is a model species for studies in ecology and evolution (Wootton, 1984; Bell and
Foster, 1994a; Östlund-Nilsson et al., 2007). Morphological analysis plays an important role in
many such studies, as it can be used to address various research questions (Bell and Foster, 1994a).
One promising morphological analysis is that of fluctuating asymmetry (FA), which
measures deviations from perfect morphological symmetry. In the late 1980s and early
1990s, fluctuating asymmetry was presented as a simple, inexpensive, and universal measure
of stress and fitness (Palmer and Strobeck, 1986; Zakharov, 1989; Parsons, 1990; Graham et al., 1993). Although
there are many definitions of stress, here we follow Graham and colleagues’ understanding
of the term as ‘. . . anything (physical, chemical, genetic, psychological, etc.) dissipating
energy away from growth and production’ (2010, p. 501). Such stress can be caused by sub-
optimal environmental conditions or by disruption of genetic co-adaptation (Graham et al.,
Fluctuating asymmetry is the most widely used means of measuring developmental
instability, which reflects an organism’s inability to develop the same phenotype under the
same environmental conditions (Waddington, 1957), i.e. the inability to follow a developmental
trajectory defined for a given genotype (Zakharov, 1989). Developmental instability represents
a third, stochastic component of phenotypic variance, as important as genotypic variation
and environmental heterogeneity (Lajus et al., 2003a). As stress reduces an organism’s available
energy for growth (Parsons, 1990, 2005; Hoffmann and Parsons, 1991; Graham et al., 2010) and mechanisms
directing growth also require energy (Koehn and Bayne, 1989; Sommer, 1996), stress may also reduce
energy allocated to developmental control. Therefore, increased developmental instability,
mediated by energy allocation in growing organisms, can be a direct consequence of stress
(Lajus et al., 2014).
Research has shown that fluctuating asymmetry can increase due to sub-optimal tem-
peratures, under both experimental (Leary et al., 1992; Campbell et al., 1998; Benderlioglu and Dow, 2017) and
natural conditions (Alados et al., 1993; Yurtseva et al., 2014; Kozlov and Zverev, 2018), and high population
densities (Wiener and Rago, 1987; Leary et al., 1991). The level of fluctuating asymmetry is inversely
associated with growth rate (Zakharov, 1989; Lajus, 2001), resistance to parasites (Leberg and Vrijenhoek,
1994; Escos et al., 1997; Reimchen, 1997; Pojas and Tabugo, 2015), and heterozygosity (Blanco et al., 1990; Leary et al.,
1992). Inbreeding can reduce developmental stability due to the expression of deleterious
recessive alleles (Leary et al., 1983; Carter et al., 2009; Baker and Hoelzel, 2013). A number of studies have
connected fluctuating asymmetry with the effects of pollutants (Valentine et al., 1973; Romanov and
Kovalev, 2004; Green and Lochman, 2006; Lajus et al., 2015a). Several other studies have associated higher
fluctuating asymmetry with elevated radiation levels (Zakharov and Krysanov, 1996; Lajus et al., 2014).
At the same time, some FA analyses yielded conflicting results that generated a great deal of
scepticism as to the method’s usefulness as an indicator of stress and fitness (e.g. Bjorksten et al.,
2000a, 2000b; but see Møller, 2000; van Dongen and Lens, 2000).
Lajus et al.84
One reason for these disparate results may be the number and choice of characters used.
In studies of fish, including stickleback, only a few characters have generally been utilized,
such as the numbers of fin rays, gill rakers, and scales, as well as fin size and eye diameter
(Valentine and Soule, 1973; Valentine et al., 1973; Ames et al., 1979; Graham and Felley, 1985; Zakharov, 1989; Leary et al.,
1992; Kozhara, 1994; Prieto et al., 2005; Moodie et al., 2007; van Dongen et al., 2009) (see also evolutionary-
ecology.com/data/3165Appendix.pdf). Character choice can lead to problems such as
sampling error, measurement error, and departure from concordance in the fluctuating
asymmetry of different characters across samples (Lajus et al., 2014). One reason for the high
sampling error associated with FA studies is the low absolute value of fluctuating asym-
metry of some meristic characters. For instance, asymmetry in a number of fin rays may
occur in a small proportion of the population only; analysis would then require samples
sometimes exceeding a thousand individuals, as in one study relating fluctuating asymmetry
to pollution (Michaelsen et al., 2015).
Measurement error is of great concern in FA studies. As the absolute value of fluctuating
asymmetry of morphological structures usually approaches only a small fraction of its true
size [1–5% according to Merilä and Björklund (1995)], measurement error associated with FA
studies can be quite high, often exceeding 50% of variation in fluctuating asymmetry (Hubert
and Alexander, 1995; Merilä and Björklund, 1995; Lajus and Alekseev, 2000; Lajus, 2001; Lajus et al., 2015b). This means
that the real value of fluctuating asymmetry is usually significantly lower than the observed
value. High measurement error also increases sampling error, since much observed variation
in fluctuating asymmetry is associated with measurement noise and not biological signals.
It is also important to take account of other types of asymmetry, such as directional
asymmetry and anti-symmetry, when assessing fluctuating asymmetry (Graham et al., 1998).
Directional asymmetry occurs when a character on one side is consistently larger than on
the other side (i.e. both the presence and direction of asymmetry are predetermined). Anti-
symmetry occurs when a structure is more pronounced on one side than on the other, but
the side is not predetermined. These are adaptive types of symmetry. Structures with anti-
symmetry and directional symmetry also possess fluctuating asymmetry.
The relationship between fluctuating asymmetry and trait size can assume various guises
depending on the multiplicative and additive errors associated with structure growth (Graham
et al., 1998). This needs to be accounted for in FA studies, and different techniques can be
applied to address this type of error (Palmer and Strobeck, 2003).
Measuring developmental instability using fluctuating asymmetry is highly relevant to
research on stickleback, which are widely used to explore questions in various fields of
population biology and ecology (Wootton, 1984; Bell and Foster, 1994a; Östlund-Nilsson et al., 2007).
Fluctuating asymmetry drew the interest of stickleback researchers soon after the technique
appeared in other biological research (Moodie and Moodie, 1996; Reimchen, 1997). Yet today, fluctuat-
ing asymmetry is rarely used in stickleback research, despite its continued application in
studies of other fish (Allenbach, 2011).
Here, we review the literature on stickleback FA studies, and develop a new FA analytical
technique that employs the cranial bones as characters, using four populations of threespine
stickleback that differ in their life history and geographical location.
Fluctuating asymmetry as an indicator of stress and fitness in stickleback 85
METHODS AND MATERIALS
For articles indexed in the Web of Science – All Databases, we searched for combinations of
the keywords ‘stickleback’ and ‘fluctuating asymmetry’. In total, 41 articles were identified
in the database, but on closer examination we narrowed this down to 28 original research
studies describing the use of FA methods on stickleback (see Appendix). For each study
we: (1) recorded the traits used in FA analysis; (2) characterized the populations studied
by geographical location, life cycle (marine, anadromous, freshwater), and number of
populations involved; (3) identified the variables correlated with fluctuating asymmetry;
(4) described reported patterns of association between the studied variables and fluctuating
asymmetry; and (5) analysed how measurement error was addressed, if at all. This method
of data presentation allowed us to structure a variety of analytical procedures.
If more than one factor was analysed in a single paper, each factor was taken into
account separately. Where patterns of correlation varied for multiple traits, only the mean
value was used, i.e. if a positive correlation was found for one of three characters, with the
other two showing no correlation, the overall correlation was considered to be positive.
Field sampling and bone preparation
For the methodological study, we used threespine stickleback populations representing
different life cycles (resident freshwater, resident marine, and anadromous) and different
geographical regions (the White Sea and the Kamchatka Peninsula). Altogether, four
samples collected during the summer season of 2014 were used in the study (Table 1). Three
of the samples were collected from the White Sea basin – one in freshwater Lake Goreloe,
and two in geographically distant parts of the White Sea (Kandalaksha and Onega Bays).
We also sampled an anadromous population in the Kamchatka River (Kamchatka
Table 1. Characteristics of the threespine stickleback populations sampled
Life cycle Population Location Date No. males/females
Goreloe Lake Goreloe, Malyy Gorelyy
Island, Kandalaksha Bay, the
White Sea Basin (66⬚17⬘49″N,
28 June 2014 16/9
Anadromous Kamchatka Kamchatka River,
Kamchatka Peninsula, the
Bering Sea Basin
6–12 July 2014 16/8
Marine resident Seldianaya Seldianaya Inlet,
Kandalaksha Bay, the White
Sea (66⬚20⬘15″N, 33⬚37⬘26″E)
15 June 2014 15/15
Marine resident Onega Coastal waters of Onega
Peninsula, the White Sea
9–12 July 2014 8/19
Lajus et al.86
Peninsula, the Bering Sea). These samples reflect the variety of life cycles and geographical
distribution of the threespine stickleback.
The White Sea is a semi-enclosed sea connected to the Barents Sea to the north, with
reduced salinity (about 25 ppm at the surface) and a relatively continental climate.
Kandalaksha Bay is situated in the northwest of the White Sea. It features a steep, embayed
coastline with abundant inlets carpeted with macrophytes (brown algae and seagrass). In
contrast, the Onega Peninsula is situated in the southern part of the White Sea between two
bays: Onega and Dvina. Its coastline is smoother and the adjacent waters are shallower.
Stickleback density on the Kandalaksha Bay spawning grounds is much higher than in the
rest of the White Sea, including Onega Bay. The distribution of adult and juvenile stickle-
back in Kandalaksha Bay is primarily associated with the seagrass Zostera marina (Ivanova
et al., 2016; Rybkina et al., 2017). Goreloe, a small humified forest lake with a surface area of about
2 hectares and average depth of 2.8 m, is situated on a small island in Kandalaksha Bay
(Kuznetsova et al., 2007). At ∼5 m above sea level, Goreloe Lake is not connected to the sea. The
Kamchatka River is the largest river on the Kamchatka Peninsula. Stickleback spawn where
it flows into the Bering Sea, and juvenile fish spend their first several weeks there before
moving offshore (Bugaev et al., 2007).
Freshwater and marine stickleback were caught using a 7.5-m beach seine within 30 m of
the shore, photographed and preserved in 70% ethanol. Anadromous stickleback were
collected with a hoop-net in the Kamchatka River and preserved in 10% formaldehyde. The
sex of each fish was determined via dissection. Total length was measured from photos
of the fresh fish (White Sea samples) using ImageJ software (http://imagej.nih.gov/ij/
index.html), or measured with a ruler after capture (Kamchatka).
To obtain cranial bones, fish heads were kept in 2% NaOH solution at a temperature of
50⬚C for a few hours until the bones could be separated from the soft tissues. Then, the
bones were cleaned in water with a brush, dried at room temperature, and stored in
We analysed one meristic and 41 morphometric characters. The meristic character –
the number of lateral plates – was analysed under a stereo-microscope (MBS-10). For
morphometric analyses, we chose four relatively large, flat, paired cranial bones: the
operculum, lachrymal, third suborbital, and quadrate. They were scanned using an Epson
Perfection 4490 Photo scanner at a resolution of 1200 dpi. We used the coordinates of 20
distinguishing features, numbered as landmarks, on the digital images (Fig. 1) using ImageJ
software. For the morphometric characters, we used the distances between the landmarks
on each bone, calculated in Microsoft Excel (e.g. the distance between landmarks 3 and 5
was given the label ‘character 3-5’). To estimate measurement error, each bone was scanned
twice after a slight change of position. Thus, each character was measured four times,
since both left and right structures were measured twice. Lateral plates were also measured
twice on the left and right sides of the body. Measurements were not performed according
to sample order, but instead randomly and blindly, so that the operators were unclear
which images or samples they assessed. This minimized possible bias due to prior operator
experience when reading sample results, which has been observed in some studies (Lajus et al.,
2003b; Kozlov and Zvereva, 2015). Statistical analysis was performed using the Statistica v.10.0
Fluctuating asymmetry as an indicator of stress and fitness in stickleback 87
The fluctuating asymmetry of each of the 41 individual morphometric characters
was calculated using the index: FA =|R − L|/ 0.5(R + L), where R and L are the values
of the characters on the right and left side of the body, respectively (Palmer and Strobeck, 1986).
To normalize |R − L | distributions, we used the Box-Cox transformation (Box and Cox, 1964):
(|R − L| + λ1)λ2, with λ1=0.016 and λ2 varying from 0.2 to 0.6 for different characters
(λ1 and λ2 values minimize departure from the normal distribution). For lateral plates,
λ1=0.0159 and λ2=0.4.
The generalized FA index for an individual was based on the FA values of the 41
morphometric characters, according to Clarke and McKenzie (1992). We standardized all
individual FA values by variance to equalize the contribution of different characters in
the generalized index, then determined the mean FA value for all the characters of an
Fig. 1. Bones and landmarks used for analysis of fluctuating asymmetry in threespine stickleback.
Lajus et al.88
Before FA analysis, we performed a character selection procedure to avoid replicating
FA information from structurally related characters, such as characters 11-14 and 11-15.
After the first visual selection, we calculated the Pearson correlation coefficients between
the absolute FA values of all possible character pairs in a pooled sample, and removed pairs
with significant correlations (df =105; P<0.05). Therefore, only characters with non-
correlated FA values were used in the generalized index.
The significance of directional asymmetry (systematic differences between values on the
left and right sides) was estimated using mixed-model analysis of variance (ANOVA), with
‘body side’ (left or right) as a fixed factor and ‘individual’ as a random factor, with sub-
sequent Bonferroni correction (Palmer and Strobeck, 2003). Only characters without significant
directional asymmetry were used in the calculations.
The characters used in the final FA analysis were as follows: 1-2, 1-3, 1-4, 1-5, 2-3, 2-4,
2-5, 3-4, 3-5, 4-5, 6-7, 6-8, 6-10, 6-11, 7-8, 7-10, 7-11, 8-10, 8-11, 10-11, 12-14, 12-15, 12-16,
12-17, 12-18, 14-15, 14-16, 14-17, 14-18, 15-16, 15-17, 15-18, 16-17, 16-18, 17-18, and 19-20
(see Fig. 1 for numbered landmarks).
Measurement error was estimated using one-way ANOVA of the first and second repli-
cate FA measurements, with ‘individual’ as a factor (Palmer and Strobeck, 2003). This allowed us to
estimate the contribution of measurement error to the observed fluctuating asymmetry.
Publications on fluctuating asymmetry in sticklebacks
G.E.E. Moodie published the first paper examining fluctuating asymmetry in sticklebacks
in 1977, and the latest paper in our survey appeared in 2014 (Fig. 2; Appendix). During the
1990s and early 2000s, the number of publications on fluctuating asymmetry in sticklebacks
rose, as did publications featuring the keywords ‘fluctuating asymmetry’ and ‘stickleback’.
Subsequently, publications on fluctuating asymmetry in stickleback declined, whereas pub-
lications on fluctuating asymmetry in general remained stable, and stickleback publications
rapidly increased (Fig. 2).
Twenty-four of 28 publications focused on threespine stickleback, three on brook stickle-
back (Culaea inconstans) and one on ninespine stickleback (Pungitius pungitius). Three
studies (Reimchen, 1997; Reimchen and Nosil, 2001a, 2001b) did not separate fluctuating and directional
asymmetry, but did focus in the main on directional asymmetry (DA). Fluctuating asym-
metry creates additional noise in DA studies, which increases minor sampling error.
However, if fluctuating asymmetry is the main interest, the presence of directional asym-
metry will lead to magnitude-level changes, thus directional asymmetry must be removed
for accurate FA analysis (Graham et al., 1998; Palmer and Strobeck, 2003). Therefore, we removed the
three DA-focused studies from further analyses.
Two types of traits were used in stickleback FA studies – meristic or counted traits, and
morphometric or measured traits. Meristic traits included the number of fin rays, lateral
plates, gill rakers on the first arch, forks on the ascending branch of the pelvic girdle, and
neuromasts/neuromast pores in lateral line structures. The most commonly used traits were
the number of lateral plates, and of rays on the pectoral fin. Morphometric characters
included the length of pelvic spines and pectoral fins, the width of pectoral fins, the position
Fluctuating asymmetry as an indicator of stress and fitness in stickleback 89
and length of lateral plates, the diameter of the eye, the height and width of the ascending
process, the width at the base of the pectoral fins, the pelvic–dorsal distance, the length of
the anterior process, ascending branch, and pelvic spines, and the width and mass of gill
The number of traits used in analyses was quite limited in most cases: 11 publications
used just one trait, four studies used two traits, and another ten used 3–15 traits. Often,
variation in structures pertaining to external defence was analysed: 18 studies analysed
lateral plates (their number and position), while a further ten examined the length of pelvic
spines. Pectoral fins (their length and number of fin rays) were used in nine studies. In total,
about 35 different characters were used in the various studies (see Appendix).
Measurement error (ME) was addressed in 21 studies. In two studies (Moodie, 1977; Aguirre et al.,
2004), no ME analysis was performed, while in two further studies (Mazzi et al., 2003, 2004), the
authors looked at the association between female preference and male fluctuating asym-
metry using computer images of males, and hence ME analysis was not appropriate. In
six of the 21 studies that did address measurement error (Moodie and Moodie, 1996; Bergstrom and
Reimchen, 2005; Moodie et al., 2007; Reimchen et al., 2008; Reimchen and Bergstrom, 2009; Trokovic et al., 2012), the
authors assumed that measurement error was negligible based on previous studies. Lescak
et al. (2013) did not estimate the measurement error, but implicitly accounted for it by
scoring samples in random order to maximize measurement consistency and to minimize
the potential for shifting observer bias, thereby avoiding temporal trends in measurement
error. In all other studies, replicate measurements were undertaken, although one study did
not present the results of this procedure (Hermida et al., 2002). Two studies (Mazzi and Bakker, 2001;
Fig. 2. Number of publications in the bibliographical database ‘Web of Sciences – All Databases’ for
keyword searches ‘stickleback’, ‘fluctuating asymmetry’, and ‘fluctuating asymmetry’ AND ‘stickle-
back’ (note that these numbers were multiplied by 40 to plot them with the others) binned in five-year
periods from 1976 to 2015.
Lajus et al.90
Mazzi et al., 2002) undertook replicate measurements and used the mean value to halve the
variance caused by measurement error. Three studies examined whether measurement error
confounded fluctuating asymmetry (Hechter et al., 2000; Prieto et al., 2005; Kenney and von Hippel, 2014) but
found no such effect. Five studies provided data on the overall ratio of measurement error
in observed fluctuating asymmetry (Bergstrom and Reimchen, 2000; Bakker et al., 2006; van Dongen et al., 2009;
Loehr et al., 2012, 2013).
Directional asymmetry was often reported in the stickleback studies. Although we assess
only papers focusing on fluctuating asymmetry, we note that directional asymmetry does
inflate fluctuating asymmetry. Directional asymmetry was observed most often in pelvic
spines and lateral plates (left-biased directional asymmetry in both cases). In their detailed
study of the asymmetry patterns of lateral plates, Reimchen and Bergstrom (2009) found
that either type of asymmetry (directional or fluctuating asymmetry) may vary from plate
to plate and depend upon their position. At the same time, some studies reported no
directional asymmetry in plates or pelvic spines (Bergstrom and Reimchen, 2003; Bakker et al., 2006),
though directional asymmetry was observed in other structures (van Dongen et al., 2009; Lescak et al.,
2013; Kenney and von Hippel, 2014).
Concordance in fluctuating asymmetry among characters
Fourteen studies used more than one trait, so that the concordance of fluctuating asym-
metry across characters can be a potential concern. Of these studies, Trokovic et al. (2012)
used a composite FA index and did not address concordance. Eight other studies explicitly
analysed concordance, and three (Bergstrom and Reimchen, 2002; Robinson and Wardrop, 2002; Prieto et al.,
2005) reported significant concordance in the variation in fluctuating asymmetry across
characters. One of these three studies (Robinson and Wardrop, 2002) reported significant con-
cordance in fluctuating asymmetry among individuals within a population, and three others
found concordance among populations. Three studies reported a lack of concordance in the
fluctuating asymmetry of traits across samples, and four others reported similar results
across individuals within a population.
Relationship between fluctuating asymmetry and various environmental and fitness
The relationship between fluctuating asymmetry and abiotic variables was the focus of
several studies. Three reported geographical heterogeneity in fluctuating asymmetry, but did
not link fluctuating asymmetry to a particular environmental factor (Prieto et al., 2005; van Dongen
et al., 2009; Loehr et al., 2013). Fluctuating asymmetry was found to decrease with increased water
clarity (the authors argued that this association was mediated by higher pressure from
predators in clearer waters), increased altitude and pH (Bergstrom and Reimchen, 2002, 2003).
Depending on the trait, the fluctuating asymmetry was either higher or lower in stream
than in lake dwellers (Moodie, 1977). Two studies found no association between fluctuating
asymmetry and acidification or chemical water pollution (Mazzi and Bakker, 2001; Kenney and von
Hippel, 2014). Fluctuating asymmetry was found to be lower in marine than in freshwater
habitats, a difference probably due to local adaptation rather than to direct responses to
different environments (Trokovic et al., 2012).
Of the biotic factors, studies focused on parasites and predators. A positive association
between fluctuating asymmetry and parasite infection would confirm expectations that
Fluctuating asymmetry as an indicator of stress and fitness in stickleback 91
individuals with lower fitness have lower developmental stability and less resistance to
parasites. Such an association was found in two cases (Bergstrom and Reimchen, 2002; Reimchen and
Bergstrom, 2005), but only for population-level comparisons (Bergstrom and Reimchen, 2002), and for
only one of three studied parasites, Eustrongylides sp. (Reimchen and Bergstrom, 2005). Prieto and
colleagues (2005) reported a negative association between fluctuating asymmetry and parasite
loading by Eustrongylides sp. in one of four studied populations and for one of six studied
characters (no data on correction for multiple comparisons was provided).
Some research looked at whether stickleback with lower fitness, and therefore higher
fluctuating asymmetry, are more vulnerable to predators. Although it is difficult to test this
hypothesis directly, three studies used it to interpret empirical results (Moodie, 1977; Bergstrom
and Reimchen, 2003, 2005), two of which concluded that fluctuating asymmetry is lower in the
presence of predators.
The correlation of fluctuating asymmetry with various fitness proxies is expected to be
negative. Contrary to expectations, however, Moodie and Moodie (1996) found a positive
correlation between fluctuating asymmetry and characteristics of male reproductive success
(number of fry and eggs in nests, and proportion of empty nests). In another study with a
similar design, Bakker et al. (2006) found this correlation to be negative. Turning to females,
Hechter et al. (2000) reported that fluctuating asymmetry is negatively correlated with
characteristics of female reproduction success (fecundity and weight of ovaries). Mazzi and
colleagues (2002) found a negative correlation between fluctuating asymmetry (one of three
characters) and levels of heterozygosity. Robinson and Wardrop (2002) observed fluctuating
asymmetry to increase in fish at higher water temperatures and with richer diets, resulting in
faster growth. Although this contradicts theory, the authors explained the incongruity via
confounding environmental effects. Also contrary to theory, Moodie et al. (2007) noted a
positive correlation between fluctuating asymmetry and growth rate in large males, but not
in small males or females. Mazzi et al. (2003, 2004) showed that inbred females preferred more
symmetrical males, which confirmed theory, but outbred females did not. Van Dongen et al.
(2009) tested hypotheses of whether directional selection and evolutionary changes increase
average levels of fluctuating asymmetry, and whether they also increase the strength of
association between fluctuating asymmetry and population-level genetic variation. Their
results supported the second hypothesis, but not the first. Lescak et al. (2013) reported an
increase in fluctuating asymmetry with increased pelvic expression but did not interpret
these results in terms of fluctuating asymmetry–fitness relationships. Analysis of FA
heritability showed that it was significant in one case (Loehr et al., 2012) but non-significant in
two others (Hermida et al., 2002; Aguirre et al., 2004).
In summary, 11 studies confirmed expected correlations (i.e. positive with stress indices
and negative with fitness proxies), three contradicted expectations, and in nine cases no
correlation was observed.
Field analysis of fluctuating asymmetry in lateral plates and cranial bones
Stickleback mean length was 44.5 ±0.89, 64.4 ±0.61, 64.6 ±1.06, and 83 ±0.79 mm for
the freshwater, two marine (Onega Bay and Seldianaya Inlet in Kandalaksha Bay), and
anadromous populations, respectively. Paired comparisons showed significant differences
between all pairs of samples (t-test, P<0.05), except between the two marine populations.
Mean measurement error in fluctuating asymmetry for all characters in all samples was
0.327, with the lowest measurement error for character 3-5 (0.150) and the highest for
Lajus et al.92
character 19-20 (0.668). We tested for directional asymmetry using the paired Student’s
t-test. One character (2-3) showed statistically significant directional asymmetry (df =104,
P<0.05), which became non-significant after sequential Bonferroni correction (Rice, 1989).
For cranial bones, fluctuating asymmetry was at its the highest in the resident freshwater
and two marine populations; for lateral plates, it was highest in the freshwater population.
The anadromous population displayed relatively low fluctuating asymmetry for both cranial
bones and lateral plates (Fig. 3). Significant differences (t-test, P<0.05) in the fluctuating
asymmetry of cranial characters were found in all comparisons of anadromous fish with the
other populations, as well in all comparisons of freshwater fish with the other populations,
whereas the two populations of marine stickleback did not differ from one another. No
differences were observed in fluctuating asymmetry for cranial bones or lateral plates
between males and females in all samples.
Fig. 3. Generalized index of the fluctuating asymmetry (mean ±S.D.) of (A) cranial bones based on
41 morphometric characters (see Fig. 1 and explanations in text) and (B) the number of lateral plates
in four samples of threespine stickleback described in Table 1.
Fluctuating asymmetry as an indicator of stress and fitness in stickleback 93
Number of lateral plates divided the populations into two groups: (1) anadromous
(32.94 ±0.166 plates) and marine (Seldianaya) (32.88 ±0.124) stickleback with a relatively
high number of plates, and (2) freshwater (Goreloe) (30.76 ±0.499) and marine (Onega)
(31.05 ±0.203) stickleback with significantly fewer plates. Pairwise comparisons in all
cases revealed significant (t-test, P<0.05) differences between groups (1) and (2), but no
differences between populations from the same group. In the freshwater population, almost
half of the fish we examined (11 out of 25) had missing plates on the middle parts of their
bodies, something that was not observed in the other populations.
Analysis of publications
Although the first article on fluctuating asymmetry in stickleback appeared in 1977, real
interest only began to be shown by stickleback researchers some 20 years later, after FA
studies of other organisms had become common (Fig. 2). However, interest waned in the
mid-2000s, despite continued research on fluctuating asymmetry in other species and an
exponential growth in the number of publications on stickleback (Fig. 2). Evidently, FA
research results did not meet stickleback researchers’ expectations, so we analysed their
publications to determine why.
In many cases, the results of FA analyses did not show the expected associations with
stress or fitness. And many more negative results likely remained unpublished because
of the difficulties associated with publishing negative results in high ranking journals
(Kicinski, 2013; Duyx et al., 2017). This probably reduced the incentive for continuing research on
fluctuating asymmetry in stickleback. Scepticism about fluctuating asymmetry as an indica-
tor of stress and fitness became widespread in the early 2000s – not only in stickleback
fishes, but in other organisms as well (see references in Introduction). However, publications
since that time have remained steady overall (Fig. 2), which suggests that interest in the
approach has persisted, but that standards for publishing FA results have become more
stringent, requiring tests for measurement error, other types of asymmetry, size dependence,
and so on. Conducting these tests reduces noise and yields FA data that can support
detailed interpretation (Palmer and Strobeck, 1986, 2003), but this is rarely done in stickleback FA
studies. Below we consider different issues affecting FA analyses.
Although the importance of measurement error has been recognized for a long time in FA
research (Palmer and Strobeck, 1986), it still receives relatively little attention. According to
Allenbach (2011), measurement error was assessed in only 30 out of 81 studies of naturally
occurring fluctuating asymmetry in fish caused by exogenous stress. This is important
because high measurement error can influence the results of sample comparisons. As meas-
urement error can differ in different samples, when measured by different operators, or even
by the same operators at different times (see, for instance, Lajus et al., 2003b), it may confound real
differences between samples or suggest spurious differences. There are two ways to address
this issue. The first is to analyse specimens from different samples in random order, which
equalizes high measurement error across samples and does not affect the results of sample
comparison. This is effective when the magnitude of measurement error is affected not only
by heterogeneous analytical conditions, but also by the specific patterns of morphological
Lajus et al.94
structures. It is always important to estimate the contribution of measurement error to the
observed fluctuating asymmetry. The second way to address this issue is to assess measure-
ment error in each sample separately, expressed as a proportion of fluctuating asymmetry,
and to use true fluctuating asymmetry (i.e. fluctuating asymmetry minus measurement
error) when comparing samples. This is the only rigorous way to compare samples that
differ intrinsically in morphological structures, such as the muscle prints on the internal
surface of mollusc shells (Lajus et al., 2015b). Not accounting for measurement error in stickle-
back FA studies likely caused noise in the results, and introduced ambiguity when interpret-
Concordance in fluctuating asymmetry among characters
Often when comparing samples in FA studies, concordance in fluctuating asymmetry of
different characters is implicitly assumed as a precondition for considering developmental
instability as a genome-wide effect (Dufour and Weatherhead, 1996; Clarke, 1998; Zakharov, 2003; but see Leung
and Forbes, 1996 and Bjorksten et al., 2000a, 2000b for a contrary opinion). If this is not the case, developmental
instability is merely a property of individual traits. There are conflicting empirical results for
concordance in fluctuating asymmetry across populations, however. Møller and Swaddle
(1997) reported finding concordance in 12 of 17 papers. Clarke (1998) found concordance in
four out of his 11 data sets. Quantitative estimates of concordance, as measured by the
Kendall coefficient of concordance, are usually low (Leamy, 1992; Leary et al., 1992; Kozhara, 1994;
Auff ray et al., 1999). This is because different characters may respond differently to stress (Hoffmann
and Woods, 2003; Freeman et al., 2005). In particular, functionally important characters display lower
fluctuating asymmetry and may be less suitable for FA studies (Palmer and Strobeck, 1986; Leung and
Forbes, 1996). Different patterns of fluctuating asymmetry may be explained by the different
development of structures. For instance, meristic characters such as number of fin rays are
determined early in ontogenesis and do not change later, while morphometric characters
change throughout the life of a fish. Departure from concordance also results from different
patterns of association between the size of characters and the magnitude of fluctuating
asymmetry in different populations (Lajus, 2001). Thus, it is intuitively clear that departure
from concordance can confound correlations between fluctuating asymmetry and both
stress and fitness; however, we lack a simple means of accounting for it in practical research.
In eight of the 24 studies that addressed the concordance in fluctuating asymmetry of
different traits when comparing samples, four reported concordance while the other four
reported departure from concordance. Such ambiguity can exacerbate the heterogeneity of
results, thus making their interpretation more difficult. Concordance can also be a problem
in studies where only one trait, or a composite FA index based on several traits, is used
(almost half of the total number). Although addressed explicitly, concordance can still
contribute to widely disparate findings.
Trait selection predetermines the success of FA studies because it materially affects the
chances of distinguishing differences among several samples. Thus, trait selection should be
optimized before testing the association of fluctuating asymmetry with stress or fitness (Lajus
et al., 2015a). Unfortunately, such optimization rarely occurs. An ideal trait for FA analysis
must be easily measured or counted without unnecessary preparation and manifest ideal
fluctuating asymmetry, i.e. where normally distributed R − L values approach zero, are
not correlated with size, display negligible measurement error, and are not subject to
Fluctuating asymmetry as an indicator of stress and fitness in stickleback 95
unaccounted environmental or genetic effects that complicate interpretation of the results.
The majority of stickleback studies have used lateral plates and pelvic fins specific to the
species, thus not making comparisons with other fish possible. These features can evolve
quickly in populations responding to various selection regimes (van Dongen et al., 2009). For this
reason, fluctuating asymmetry of these characters can change not only due to one of the
traits being considered, such as environmental stress or fitness characteristics, but also as a
result of selection pressure on that particular trait.
This can confound the interpretation of stickleback FA studies and introduce inconsis-
tency in the analysis of different traits. For instance, Bergstrom and Reimchen (2005) reported
that the relationship between the fluctuating asymmetry of the number of lateral plates
of threespine stickleback and their parasite load is stronger in habitats where the plates’
functional importance and selection against their asymmetry was reduced. The authors
suggested that the inconsistent connection between fluctuating asymmetry and parasitism
may be due to ‘undetected biomechanical selection against asymmetry’. In general, stickle-
back researchers have usually studied the fluctuating asymmetry of traits that are also
widely used to study phenotypic plasticity – and may not produce the best results. Since
criticism of fluctuating asymmetry in the early 2000s, research on other species became
more discriminating in selecting structures for analysis; however, stickleback offer a limited
choice of structures and this may have stifled research. In addition, lateral plates and pelvic
spines may manifest directional asymmetry, which is of interest in a number of stickleback
studies, but may inflate fluctuating asymmetry if not addressed properly. While some studies
have considered directional asymmetry as an indicator of environmental stress, most
presented directional asymmetry as part of normal development and inappropriate for
indicating environmental stress (Graham et al., 1993, 1998; Marques et al., 2005).
Although the above issues should be taken into account in FA research – and failure to do
so has likely obscured the expected correlations between fluctuating asymmetry and stress
and fitness (for reviews papers, see Polak, 2003) – there is no simple way to address them. Neverthe-
less, we suggest that using bony structures, such as cranial bones, may increase considerably
the power and reliability of FA analysis. Few analyses of the fluctuating asymmetry of
internal fish bones have been conducted (Romanov, 1983; Kozhara, 1989; Masson et al., 2011). Bone shape
depends on developmental conditions (Ørnsrud et al., 2004; Georgakopoulou et al., 2007; Yurtseva et al., 2010;
Berg et al., 2012) or on genetics (Sutterlin et al., 1987; Benfey, 2001; Sadler et al., 2001). While they require
some preparation before analysis (dissolving of soft tissues, cleaning), bones provide a large
number of characters that are suitable for digital analysis. In our studies of several fish
species, we have used 24–48 characters per species (Lajus, 1991, 2001; Lajus et al., 2003c, 2014, 2015a;
Yurtseva et al., 2010, 2014). Measurement error was variable, but was usually below 20% in morpho-
metric characters (Lajus, 2001; Lajus et al., 2015a). An important advantage of using cranial bones is
the large number of convenient characters, which reduces sampling error as well as the number
of fish required to obtain statistically significant results. Nevertheless, all of our FA studies
using multiple bone traits displayed statistically significant differences among samples (Lajus,
2001; Lajus et al., 2003c, 2014, 2015a; Yurtseva et al., 2010, 2014), some of which could not be associated with
genetic or environmental variables and, therefore, severely limited their interpretation.
Recently, tomographic techniques have become more widely used in morphological
studies. These techniques do not require labour-intensive preparation of individual bones
and allow for collocation of different bones in relation to one another (Lauridsen et al., 2011;
Lajus et al.96
Hilton et al., 2015). Tomography, therefore, may have great potential in studies of fluctuating
Field studies of fluctuating asymmetry in stickleback
We obtained conflicting results when comparing different characters in our four popula-
tions of stickleback. For cranial bones, fluctuating asymmetry was most marked in the
resident freshwater and the two marine populations, whereas for lateral plates it was highest
in the freshwater population. The anadromous population showed relatively low fluctuating
asymmetry in both cranial bones and lateral plates.
How should we interpret the observed differences? The number of lateral plates in three-
spine stickleback depends on their life history. Marine and anadromous populations usually
have more than 30 lateral plates, whereas freshwater populations usually have up to 14
(Hagen, 1967; Ziuganov, 1983; Wootton, 1984; Bell and Foster, 1994b; Reimchen, 1994). In our case, plates in the
freshwater population were similar in number to marine forms, despite the fact that this
population has probably inhabited Goreloe Lake for a thousand years, given that it lies
5 m above sea level and the rate of post-glacial rebound in this area is about 5 mm per year
(Kolka et al., 2005; Romanenko and Shilova, 2012). This suggests that the Goreloe stickleback population
has maintained its full number of lateral plates, as has been observed in other freshwater
populations (Mori, 1990). However, we observed fewer lateral plates on some of the lake fish,
whereas the marine and anadromous populations presented full sets of plates. This may
mark the beginning of the process of lateral plate reduction, which may increase fluctuating
asymmetry in the number of lateral plates, and thus requires careful interpretation when
relating differences in fluctuating asymmetry to population fitness.
Interpreting fluctuating asymmetry in cranial bones does not present such problems. As a
large number of characters are involved in FA analysis, the somewhat high sampling error
that can occur in FA studies is avoided (Fuller and Houle, 2003; van Dongen, 2007). Another advantage
of using these structures is that they lend themselves to analysis via computerized tech-
niques, which are more precise than manual methods and less influenced by factors that
increase measurement error (Muñoz-Muñoz and Perpiñán, 2010; Lajus et al., 2015b). Moreover, bones
growing throughout an animal’s life should reflect the totality of its environmental condi-
tions, whereas the number of lateral plates is fixed early in ontogenesis. For this reason, we
advocate the use of cranial structures when interpreting the results of FA analyses, which
likely indicate a great deal more about the overall developmental stability of a population.
Why did an anadromous stickleback population from the Pacific Ocean have higher fitness
than a freshwater population from a lake near the White Sea, or marine stickleback in the
White Sea itself? First, fluctuating asymmetry is inversely correlated with the size of adult
fish – the smaller the fish, the more asymmetrical they are. Considering growth as another
fitness surrogate (Arnold, 1983; Palmer, 1983) confirms the differences we observed among the four
Anadromous stickleback on the Kamchatka Peninsula spend the first weeks of their lives
in a river, before migrating to the sea (Bugaev, 1992). Their growth – and hence their bone
formation – takes place mostly at sea (Bugaev, 1992). The habitats and migration patterns of
North Pacific stickleback are not well known, but the White Sea is covered with ice for 8–10
months in the north and is completely ice-free in the south, and likely offers a wide variety of
environmental conditions for migrating fish. The North Pacific Ocean supports the largest
fisheries worldwide (Garcia and Newton, 1994) due to its high biological productivity compared
Fluctuating asymmetry as an indicator of stress and fitness in stickleback 97
with other regions of the world’s oceans (Grebmeier et al., 2006). This suggests that there are
favourable conditions for fish there in different seasons. Another factor in organism fitness
is quality of food items. According to data obtained in spring at Avacha Bay on the
Kamchatka Peninsula, planktonic crustaceans form the basis of the marine zooplankton
community, particularly copepods (Batishcheva, 2008), which have high nutrition value com-
pared with freshwater invertebrates (Watanabe et al., 1981, 1983; cited by Novoselova, 2012). They likely
provide a rich food source for stickleback.
In inshore areas of the White Sea, the diet of juvenile threespine stickleback consists to a
large extent of copepods, mostly Temora longicornis and Microsetella norvegica (Demchuk et al.,
2015; Rybkina et al., 2016). In the deeper marine waters, the copepods Calanus glacialis, Metridia
longa, and Pseudocalanus minutus predominate (Kosobokova and Pertsova, 2005), and these high-
quality food items are likely consumed by stickleback. At the same time, subarctic winter
conditions on the inland White Sea – the Kandalaksha Gulf is covered with ice for six
months – are probably more severe than in the Bering Sea. Because the White Sea is located
near the northern border of the threespine stickleback distribution, habitat conditions may
be suboptimal and stickleback populations will be sensitive to environmental changes.
Significant fluctuations in stickleback abundance in the White Sea bolster this supposition
(Lajus et al., 2013). We know little about any differences between the two White Sea marine
populations in this study. It has been found, however, that the gonads of Onega Bay
stickleback have higher levels of essential polyunsaturated (docosahexaenoic and
eicosapentaenoic) and monounsaturated (palmitoleic) fatty acids, which may reflect their
more favourable trophic status (Murzina et al., 2018). These differences, however, are not reflected
in fluctuating asymmetry.
Environmental conditions are likely even more severe in Goreloe Lake. Food organisms
available for stickleback in the lake are quite different from those in the White Sea. The
rotifers Bipalpus hudsoni and Asplanchna priodonta predominate in number, and the
cladoceran Polyphemus pediculus in biomass (Kuznetsova et al., 2007). Stickleback can also feed
on benthic organisms. Comparing food organisms available to fish in different environments
suggests more favourable feeding conditions for marine and anadromous stickleback than
for the landlocked freshwater population. Additional stress in the lake can be caused by the
tapeworm Schistocephalus solidus, which uses stickleback as an intermediate host and is
common in lakes and reservoirs with low water flow (Barber and Scharsack, 2010). The larvae of
this parasite can be very efficient energy consumers, requiring four to five times more energy
to grow compared with their host fish (Walkey and Meakins, 1970). In previous studies of the
threespine stickleback (Reimchen and Nosil, 2001a, 2001b; Bergstrom and Reimchen, 2002), as well as other
species such as Atlantic salmon, Salmo salar (Yurtseva et al., 2010), higher fluctuating asymmetry
and lower developmental stability were found in populations infected with parasites. Studies
of parasites in marine stickleback (Shulman and Shulman-Albova, 1953; Rybkina et al., 2016) did not reveal
species as debilitating as S. solidus. Like the White Sea, Goreloe Lake is completely covered
with ice in winter, and conditions there can be even more severe than in the White Sea.
Although mortality among stickleback males is higher than among females in the White
Sea, which is likely associated with a female-biased sex ratio (Golovin et al., 2019), no differences
in fluctuating asymmetry between the sexes was observed. This suggests that the higher
male mortality is due to differences in the life history of the sexes, not their fitness.
In summary, the results of our analysis of fluctuating asymmetry in four populations of
stickleback that differ in life cycle and habitat, can be meaningfully interpreted in terms
of species biology.
Lajus et al.98
Interest in fluctuating asymmetry in stickleback, expressed as the number of publications
with the keywords ‘fluctuating asymmetry’ and ‘stickleback’, showed growth in the late
1990s, a plateau in the early 2000s, and a falling away of interest thereafter. At the same
time, the number of publications with the keywords ‘fluctuating asymmetry’ also grew
quickly but remained constant as the 2000s progressed, while publications with the keyword
‘stickleback’ grew rapidly throughout. Critical analysis of these publications suggests that
the decline in interest in stickleback fluctuating asymmetry was due to highly disparate
research results and difficulties interpreting the relationship between fluctuating asymmetry
and stress and fitness. To some extent, these difficulties may have been caused by the limited
number of morphological structures used in FA analysis in stickleback, particularly lateral
plates and pelvic fins which can quickly evolve due to various environmental changes. Thus,
changes in fluctuating asymmetry in these structures can change not only due to stress and
fitness, but also due to multiple uncontrolled factors that may specifically affect these very
In this study, we used cranial bones, which had not been used for FA analysis in stickle-
back before, but have shown promise in other fish species. Our analyses showed less
fluctuating asymmetry in cranial bones in anadromous stickleback (Kamchatka Peninsula)
than marine and freshwater stickleback from the White Sea. This may be because of more
favourable feeding conditions in the North Pacific than in the White Sea locations. At the
same time, we found similarities in fluctuating asymmetry among the White Sea popula-
tions. The different environmental conditions in the lake and at sea were not reflected in
marked differences in fluctuating asymmetry, although comparison with the anadromous
fish from the North Pacific shows that the fluctuating asymmetry of the structures we used
is sensitive to such conditions. Therefore, our results show that FA analysis using cranial
structures reveals patterns in stickleback heterogeneity that can be interpreted in terms of
different life histories. Thus, cranial bones can be considered suitable structures for FA
studies of stickleback.
We are grateful to A. Kucheryavyy for providing material from the Kamchatka River, V. Spiridonov
for organizing an expedition to the ‘Onezhskoe Pomor’e’ National Park, Arkhangelsk region, and
the administration of the ‘Belomorskaya’ Marine Biological Station of Saint-Petersburg State
University (SPbSU). Karen Alexander helped improve our English. Financial support was
provided by the Ministry of Science and Higher Education of the Russian Federation, project
AAAA-A19-119020790033-9, to A. Yurtseva, and SPbSU grant 1.40.529.2017 to the authors from
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