INTRODUCTION
Resource
polymorphism is a phenomenon whereby a single species exhibits two or
more morphs (morphotypes) showing
differences in morphology, behaviour, coloration, or life history
characteristics (e.g., growth characters) (Smith & Skulason, 1996).
These differences are considered to be an adaptation to habitat
heterogeneity through the differentiation of feeding biology and habitat
utilization. Resource polymorphism is not uncommon in freshwater fish
species; examples include Gasterosteus (McPhail, 1992),Coregonus (Ostbye et al., 2005; Ostbye et al., 2006)(Ostbye et
al. 2005; Ostbye et al. 2006), and Salvelinus species (Jonsson &
Jonsson, 2001; Savvaitova, 1995). For example, two trophic morphs ofGasterosteus aculeatus coexist in several postglacial lakes,
where one morph utilizes the littoral area and feeds on benthic
invertebrates while the other occupies the open-water area and feeds on
zooplankton (Schluter & Mcphail, 1992). The morphological adaptations
of such morphs appear to be similar among cases; that is, the
planktivorous morph often has longer and denser gill rakers, an
elongated head and a longer lower jaw, while the benthivorous morph is
characterized by shorter and sparser gill rakers, a blunt, round head
and a shorter lower jaw (Fraser, Adams, & Huntingford, 1998; Jonsson &
Jonsson, 2001; Smith & Skulason, 1996). In addition to the typical
benthivorous-planktivorous pairs, three or more morphs occur in some
lake systems. For instance, five and four sympatric morphs have been
found in Coregonus lavaretusin Fennoscandia and Salvelinus alpinus in Thingvallavatn,
respectively (Hudson, Vonlanthen, & Seehausen, 2010; Jonsson &
Jonsson, 2001; Praebel et al., 2013).
Phenotypic plasticity, a process by which individuals express alternate
phenotypes in response to different environments, is viewed as a
mechanism of resource polymorphism (Seehausen et al., 2014; Skulason et
al., 2019). Praebel et al. (2013) found that European whitefish
(C. lavaretus ) formed three sympatric morphs that differed in
gill raker counts due to rapid adaptive radiation into the littoral,
pelagic and profundal lacustrine environments in three northern
Fennoscandian lakes. The differences in size, diet, and jaw features
between lean and huronicus morphs of lake charr (Salvelinus
namaycush ) are typical examples of adaptation to shallow- and
deep-water environmental conditions (Chavarie et al., 2016). The morphs
of the African barb (Labeobarbus gananensis complex), which
differ in mouth morphology, gill rakers, diet and gut length, display a
typical example of adaptation involving distinct feeding strategies
(Levin et al., 2018). Muir et al. (2016) evaluated four morphs ofS. namaycush in many small post-glacial lakes throughout the
Holarctic. Although there were differences in the number of morphs among
lakes, the observed morphological variation resulted from adaptation to
the separation of food resources (piscivorous and invertivorous
feeding strategies), which was driven
by lacustrine environmental diversity.
Cyprinids, the largest family of vertebrates, also exhibit resource
polymorphism, with multiple morphs occurring in a single freshwater
system. In the Genale River (Ethiopian highlands, East Africa), a barb
(L. gananensis ) complex was found to be composed of six forms,
five of which were related to mouth morphology, which represents a
typical form of adaptive radiation in response to different resources
(Levin et al., 2018). Schizothoracins, the largest and most diverse
group of the Qinghai-Tibetan Plateau ichthyofauna (Chen & Cao, 2000),
also show morphological diversity within species or even sympatric
speciation within a single lake. For instance, four morphs ofSchizopygopsis stoliczkai were described in Lake Yashilkul, Pamir
(Savvaitova et al., 1987). Furthermore,
Zhao et al. (2009) demonstrated
sympatric speciation between Gymnocypris eckloni eckloni andG. e. scoliostomus in Lake
Sunmcuo, a small glacial lake on the Tibetan Plateau.
Two morphs were found in Schizopygopsis thermalis Herzenstein
1891 (Cyprinidae: Schizothoracinae) in Lake Amdo Tsonak Co during our
team field investigations in
2014-2018.
The two morphs correspond to a
resource axis in the lake: one form (planktivorous) predominately feeds
on plankton and inhabits pelagic lake habitats, and the
other form (benthivorous) mainly
feeds on benthos and dwells in the
benthic zone and a tributary (Nagchu River) of the lake. The former
morph possesses a normal lower jaw,
a terminal mouth, and moderately or highly dense gill rakers, while the
latter morph is characterized by a
shortened lower jaw, an inferior
mouth with a sharpened horny edge on the lower jaw and short and sparse
gill rakers.
However, the two morphs of S.
thermalis have not been verified via morphological analysis. In
addition, their biological characteristics (e.g., growth, feeding habit,
and reproductive traits) are still
unclear. To address these gaps in
knowledge and better understand the
ecological mechanisms of
polymorphism in S. thermalis , the specific objectives of this
study were to (1) characterize the
morphological variation of the two
significantly distinct morphs coexisting in Lake Amdo Tsonak Co and
quantitatively analyse their
morphological characters by a
combination of
morphometric
and traditional linear measures; (2) define the biological characters
(e.g., growth, feeding habit, and reproductive traits) of the two morphs
of S. thermalis ; and (3)
elucidate the potential ecological mechanism of resource polymorphism inS. thermali s.
MATERIAL AND METHODS
2.1
Study area
Lake Amdo Tsonak Co (31.55-32.08°N, 91.25-91.33°E) is
a resource-poor and freshwater
headwater lake of the Salween River system on the Tibetan Plateau,
with a surface area of 182 km2, an elevation of
4,587 m above sea level, and a maximum depth of over 20 m. The annual
mean air temperature is -3-0 °C, the annual total rainfall is 350-420
mm, the mean conductivity is 581.5
μs/cm (534.2-563.6 μs/cm), the
mean salinity is 0.29‰, the average pH is 8.61 (range: 8.5-8.7), the
average oxygen concentration is 6.53 mg/L (range: 5.63-7.21 mg/L), the
mean phosphorus concentration is 0.028 mg/L, and the mean nitrogen
concentration is 0.59 mg/L. The
water
chemistry values were measured and assessed by our colleagues.
2.2 Field sampling
Fishes
were collected from Lake Amdo Tsonak Co and its tributary (Nagchu
River) (Figure 1) in May and September 2017 and April and July 2018
with gill nets (mesh size: 30 mm), cast nets and hand nets. We
labelled and photographed the left lateral side of each captured fish
in the field. The standard length (SL, 0.1 mm), total length (TL, 0.1
mm), total weight (W ), sex, and stage of maturity of each
specimen were recorded in the field. Only adult individuals were
considered in the subsequent analyses due to the less obvious
characteristics of the juveniles. We also
measured gill raker number and
length and pharyngeal tooth row
number and recorded the presence
or absence of parasites and the degree of mucus cavity development. In
addition, we also measured the width of the horny edge of the lower
jaw of some samples with the method shown in Figure 2 and described
their lower jaw traits.
2.3 Morphologicalanalysis
With
respect to the analysis of both morphometric and linear traits, 154
specimens were analysed in this study. The left lateral side of the
specimens was photographed using a Canon PowerShot G12 camera under
natural light conditions with a ruler for scale. To obtain meaningful
landmarks (homologous points), specimens were placed with the fins
straightened and mouth closed in an orthogonal position. Specifically,
the camera was fixed on a tripod with the lens parallel to the surface
of the samples. Data on body shape and linear measurements for each
fish were collected using digital landmarks on the photographs. A
total of 24 landmarks were marked on each photograph (Figure 3) with
tpsUtil and tpsDig2 (Rohlf, 2010). Body shapes were estimated by
extracting the individual centroid size in MorphoJ 1.06 (Klingenberg,
2008). Simultaneously, new coordinates (
xy ) for each fish were
extracted using generalized Procrustes superimposition for subsequent
analysis (Markevich, Esin, & Anisimova, 2018). To increase the
accuracy of body shape estimates, some linear head traits (e.g., snout
length, upper jaw length, lower jaw length, head length, head height
and distance between the anterior termini of the upper and lower jaws)
were measured.
Allometry
is common in fish, indicating that morphology and body size are
typically related (Zelditch, Swiderski, & Sheets, 2004). Thus, a
multivariate regression of body shape (Procrustes coordinates) on
centroid size was used to correct for allometric effects, and
regression residuals were used in
geometric morphometric analyses
(Elmer et al., 2014). Principal
component analysis (PCA) was conducted via
MorphoJ 1.06 to assess body shape
variation (a geometric
morphometric trait) among individuals without
a priori grouping
and to capture the maximum amount of variation with the smallest
number of variables. The abbreviations and standardization of the
linear head traits were as
follows: HeadL = head length,
SnoutL = snout length, Upper2 = upper jaw length, LowerL = lower jaw
length and JawD = the distance between the anterior termini of the
upper and lower jaws, which were normalized by
SL , and HeadH =
head height and UpperL1 = upper jaw length, which were standardized by
head length and lower jaw length, respectively.
Morphs were initially identified with unweighted pair-group method
with arithmetic mean (UPGMA)
cluster analysis using Past 3.2.6
(
http://folk.uio.no/ohammer/past/) based on 7 linear traits and scores
from the first two principal components (PCs) of body shape from
geometric morphometric analysis (Chavarie, Howland, & Tonn, 2013).
To quantify the importance of
each variable for the ordination axes and thus to summarize the
variation in the morphs identified with the cluster analysis, PCA of
body shape PCs and linear traits
was then conducted. This procedure was performed with the ggbiplot
package in R (version 0.55; Vu, 2011). Discriminant function analysis
(DFA) and posterior jackknife cross-validation were performed with
SPSS 22.0 on morphs defined by cluster analysis to determine whether
the morphs were significantly different. The
efficacy of the DFA was evaluated
with Wilks’ λ, which varies between 0 and 1, with zero indicating
perfect identification. Finally, we performed multivariate analysis of
variance (MANOVA) with Tukey’s honestly significant difference (HSD)
post hoc comparisons (SPSS 22.0) on body shape PCs and linear traits
of samples between morphs to test the validity of the discriminant
analysis results.
Morphological variation between the sexes was estimated by
implementing t-tests of body shape PCs and linear traits. To visualize
body shape differences between morphs, we
reconstructed
body shapes using landmark coordinates based on DFA of only geometric
morphometric data (Procrustes coordinates) (Elmer et al., 2014;
Jakubavičiūtė, De Blick, Dainys, Ložys, & Olsson, 2018). For
countable variables (e.g., gill
raker number and pharyngeal tooth row number) and the measurable
variable (e.g., gill raker length, which was standardized by SL), the
Kolmogorov-Smirnov test and Levene’s test were performed. Analysis of
variance (ANOVA) was conducted for variables that were normally
distributed with variance homogeneity, and
nonparametric tests were
implemented for variables (after logx transformation) that showed a
non-normal distribution or variance heterogeneity. To test whether the
presence or absence of parasites affected body shape, analysis of
covariance (ANCOVA) was performed using parasites as a covariate,
morph as a factor and linear traits and PCs as dependent variables.
2.4 Diet analysis
To evaluate the feeding habits of
the fish, we inspected the gut
contents (since cyprinid fish do not have an obvious stomach, we chose
the anterior intestine: the first bend from the pharynx to the
intestine, where the food had not yet been digested) of 25 individuals
of one morph (planktivorous) and 30 individuals of the other morph
(benthivorous) and dissected them under a dissecting stereomicroscope
before other gelatinous substances were excluded, such as gastric
juices. Prey items were identified at the phylum or genus level. Then,
we divided all prey items into six categories: zooplankton, small fishes
(including fishes and their remains), hydrophilic insects, periphytic
algae, zoobenthos and others (hydrophyte debris, organic debris and
small grains of sand). First, the diet compositions were estimated by
occurrence rate and wet weight
percentage.
F % = (Ni /Ntotal )
× 100%
F % is the percentage of
occurrence of prey i ,Ni is the frequency of occurrence of preyi, andNtotalrepresents the total number of gut samples with food.
W % = (Wc /Wtotal )
×100%
W % is the wet weight percentage of prey category c (one
of the six food categories), Wc represents the
wet weight of prey category c , and Wtotalrepresents the total weight of all prey items in each sample. Schoener’s
index (Dxy ) (Schoener, 1970) of proportional diet
overlap was calculated and used to evaluate the difference in food
composition between the two morphs.
Cxy =1﹣0.5{∑|Pxc ﹣Pyc |}
Cxy represents the diet overlap index between the
two forms (x and y ). Pxc andPyc represent the shared food category cof form x and y (W % ), respectively. Values range
between 0 (no diet overlap) and 1 (complete diet overlap), and values
greater than 0.6 generally indicate biologically significant overlap
(Wallace, 1981). Finally, the nonparametric Kruskal-Wallis test (Zar,
1999) was implemented to estimate the food composition (W % )
difference between the two morphs.
2.5 Growth analysis
In total, 140 specimens (lapillus
otoliths) were used to study the growth characteristics of the two
morphs (planktivorous, N = 66; benthivorous, N = 74).
Preparation of otolith sections and age determination were performed by
an experienced worker. Each otolith was interpreted three times, and
otoliths without at least 2 identical interpretations were excluded from
the analysis. Photographs of all otolith sections were captured using
MicroPublisher (5.0 RTV) under a light microscope (BH2; Olympus Optical,
Tokyo, Japan). Otolith radius and
ring diameter were measured with Autook (Image Analyser 2.0).
The
relationship of SL with otolith
radius was described by Frase-Lee
regression, and the back-calculated SL of all ages was obtained
using the modified Frase-Lee function (Johnson & Noltie, 1997):
logeLi = a +
(logeLc -a )(logeOi /logeOc )
where Lc is the SL of the specimens,a is the intercept of the SL -otolith radius regression,Oc is the radius of the otolith at capture,Oi is the radius of the otolith at age iand Li is the back-calculated SL at agei .
Back-calculated SL was used to fit
a
von Bertalanffy growth function (VBGF) (von Bertanlanffy, 1938) and
obtain the growth parameters of each morph:
Lt = L∞ (1 -
exp(-k ×(t-t0 )))
where Lt is the back-calculated SL at aget , L∞ is the asymptotic SL ,k is the growth coefficient, t is age, andt0 is the theoretical age at zero length.
To compare growth parameters, the
growth performance index (φ ) was calculated according to the
equation of Munro & Pauly (1983):
φ =
log10K +2log10L∞
where K is the growth coefficient and L∞is the asymptotic SL .
Because of the nonlinear formulation of the
VBGFs, a general linear model could
not be used for ANCOVA. Instead, an analysis of residuals of the sum of
squares (ARSS) was performed to compare
the VBGFs between sexes and morphs
(Chen, Jackson, & Harvey, 1992), and the degree of fit was denoted by
the correlation coefficient and coefficient of determination
(R2 ).