Electronic supplementary material
Indirect trophic interactions with an invasive species affect phenotypic divergence in a
top consumer
Hirsch P.E.* 1, Eklöv P. 2, Svanbäck R. 3
Department of Ecology and Genetics/Limnology
Evolutionary Biology Centre, Uppsala University
Norbyvägen 18 D
SE-75236 Uppsala
1
email: philipp.hirsch@unibas.ch
2
email: peter.eklov@ebc.uu.se
3
email: richard.svanback@ebc.uu.se
* current address corresponding author:
University of Basel
Program Man-Society-Environment
Vesalgasse 1
CH-4051 Basel
1
Detailed description of the lakes
We are aware of the fact that the studied lakes might potentially differ in other parameters
than the presence of zebra mussels. To approach these differences we provide detailed
background data on the lakes below. Overall, the lack of significant differences between the
lakes with and without zebra mussels lends strong support to our conclusion from the main
article, that morphological divergence between littoral and pelagic perch is an indirect effect
of the zebra mussels.
Limnological, geographical and geological properties of lakes
We chose lakes located at maximum 80 km apart from each other and with a high degree of
comparability in a large number of important abiotic factors (Tab. S3). All lakes lie on acid to
intermediate intrusive bedrock (i.e. granite, granodiorite) (SGU 2010). No major directional
differences in quantity or quality of aquatic vegetation exist and also the bottom structure and
hence the bottom substrate does not show any directional differences between lakes with and
without zebra mussels (Brunberg and Blomqvist 1998; personal observation).
The lakes show some differences in lake area and lakes with zebra mussels are on average
slightly, though not significantly, deeper and larger than lakes without zebra mussels. Lake
area is a good proxy for a large range of biotic and abiotic features of lakes ranging from
species richness to littoral production relative to whole-lake production (Post, et al. 2000;
Vadeboncoeur, et al. 2008). Also maximum depth or mean depth might be indirectly related
to the phenotypic divergence of perch. However, in a large lake survey whose data we used
here for demonstration (lakes not containing zebra mussels, P. Eklöv unpublished data) there
was no correlation or even trend found between lake area, mean or maximum depth, and
morphological divergence of perch (n=9; maximum depths of lakes ranging from 1.9m to
21m; r =-0.29, 0.26, 0.17, respectively; all P>0.52). Moreover, the divergence between littoral
and pelagic perch in all lakes without zebra mussels was in general substantially lower (Δ MI
2
= 2.0 ±0.4 SD) than in the lakes presented here with zebra mussels (Δ MI = 2.8 ±0.3SD). We
therefore conclude that differences in lake area or depth did not influence the morphological
divergence pattern we observed.
To further assess the relative amount of littoral to pelagic habitat we calculated the shoreline
development index (SDI) after Kent and Wong (1982). Based on detailed digitized lake maps
with similar scales provided in Brunberg and Blomqvist (1998) or by the local municipalities
we used the free software ImageJ to measure the lakes’ perimeters. Though only an indirect
measure, the SDI can be used to differentiate between reticulate and circular lake shapes and
serves as a proxy for the available littoral habitat relative to the pelagic habitat, in particular
for fish populations (Dolson et al. 2009). While there was great variation in SDI among the
lakes, we found no difference in SDI between lakes with and without zebra mussels (Table
S3).
Human influences
To our knowledge no commercial fisheries are conducted in any of the lakes. Accordingly, no
records of commercial fisheries exist. In all lakes the fishing rights belong to the local land
owners except for approx. 2% of the lake Valloxen’s area and approximately one third of the
lake Erken, which are owned by the local municipality. Hence, no detailed data on
recreational fisheries exist. Private fishing rights however, do not allow for public recreational
fishing. All lakes are situated in rural areas, which typically entail a rather low usage by
recreational anglers (Arlinghaus, et al. 2008). In addition, there is very limited motorized boat
access (no public boat launching sites which are accessible via ordinary roads). This led us to
the conclusion that commercial or recreational fisheries are negligible in the studied lakes.
Other human influences as water-level regulation and shoreline development can also be
dismissed due to their rural location reflecting a pristine nature of lakes which are subject to
3
only a minimum of human influences. Merely Lake Valloxen’s shoreline contains a small
strip of ca. 1 km rip-rap revetment.
Zebra mussel presence, potential competitor biomass and predation threat
Three of the lakes contain lake-wide occurrence of zebra mussels. Estimates of zebra mussel
density were only available in two of the three zebra mussel lakes and were 323 and 342
individuals m-2, respectively. We assume zebra mussel density in the lake were we do not
have density data to be similar. In one lake zebra mussels were first observed in the late 1970s
(Naddafi et al. 2007). No first record exists for the other two lakes. However, since zebra
mussels invaded the catchment area where the lakes are situated as early as 1923 (Grandin et
al. 2006) we assume that all lakes contain zebra mussels since several decades. All lakes host
a similar fish fauna which is dominated (> 95% of total biomass) by perch (Perca fluviatilis)
and roach (Rutilus rutilus). We also assessed the abundance of non-piscivorous perch
(intraspecific competitors) and roach (interspecific competitors) through CPUE (catch per unit
effort). Since roach can efficiently feed on zooplankton through their ontogeny (Svanbäck et
al. 2008) we assumed that all size-classes of roach could have an impact on zooplankton and
hence constitute possible competitors of perch. In northern lakes as the ones studied here,
perch switch from foraging on invertebrates or zooplankton to become more piscivorous from
a total length of 120mm on (Persson 1988). We accordingly classified an individual’s biomass
into non-piscivorous and hence a possible competitor. The data are presented in Table S3.
Another potentially important factor to consider in the comparison of the study lakes is
predation pressure. Previous studies on perch morphology that involved predation threat as an
experimental factor found that, even in the face of predation threat, resource acquisition is the
factor ultimately determining perch morphology (Eklöv and Svanbäck 2006). Moreover, the
perch we sampled in our lakes were on average around 120 mm in total length (with no
directional difference in size between mussel and non-mussel lakes see table S4). Previous
4
research suggests that predation threat is an important environmental factor for juvenile perch
smaller than 100mm total length. With larger sizes however, the predator-induced mortality in
perch decreases steeply (Svanbäck and Eklöv 2011). We are thus confident that, while in
general an important factor to consider, predation threat in our study did not cofound the
differences we observed in our study.
Caveats and chances of current and future statistical testing of this study’s hypotheses.
Clearly, despite the statistical models show significant effects there is some overlap in the
means of some parameters between lakes with and without mussels. This is no surprise given
the large number of parameters we considered on several trophic levels and the complexity of
different lakes as replicate units. Yet, in our study we consider a wide range of parameters and
possibly confounding factors that, in sum, allowed us to present the effect of zebra mussels as
the one most plausible explanation for differences in perch divergence. A future experimental
test for the effect of zebra mussels should ideally include artificial lakes or mesocosms that
are large enough to allow for lake-like food webs to develop but small enough to be
experimentally manipulated (i.e. addition or removal of species).
5
Table S1. Multiple analysis of covariance (MANCOVA) of body-shape of perch (Perca
fluviatilis). Body-shape as response variable comprises all partial and uniform scores from the
geometric morphometric analysis. Centroid size was used as a covariate accounting for the
size of individuals. The significant interaction term of the two categorical predictor variables
lake and form of perch (littoral or pelagic) suggests that differences in body-shape between
forms varies across lakes.
Wilks‘ λ
F
lake
form
(littoral/pelagic)
centroid size
0.33
5.37
7
252
<0.000
0.92
2.9
1
36
<0.000
0.83
6.57
1
36
<0.000
lake*form
0.6
2.37
7
252
<0.000
df df Effect
P
6
Table S2. Statistics of the lake-wise discriminant function analyses (DFA) and the DFA that
combined all lakes in one analysis. As the a priori discriminant factor we used lake habitat
(littoral vs. pelagic) and the uniform and partial warp scores from the multivariate geometric
shape analysis were used as input data (see Materials and Methods section in the main article
for more details). Discriminant success for each DFA is presented as the percentage of the
individuals that the DFA correctly assigned into the habitat in which they were caught.
zebra mussel
lake
Wilks' λ
F
P
with
with
with
without
without
without
without
without
Erken
Lang
Funbo
Vallox
Fälaren
St. Hall
Hall
Strand
0.27
0.23
0.27
0.41
0.34
0.55
0.33
0.55
15.35
10.46
6.23
3.99
2.42
2.50
2.72
3.98
<0.0000
<0.0000
<0.0000
<0.0000
0.0029
<0.0001
<0.0006
<0.0000
0.79
8.71
<0.0000
All lakes combined
Dicriminant success (% correct)
littoral
pelagic
total
95.65
99.00
98.38
97.22
90.91
95.39
97.92
95.83
96.67
83.58
89.19
86.52
80.77
98.15
92.50
70.59
90.72
83.78
92.59
87.10
90.59
79.27
87.40
84.21
43.54
87.51
73.01
7
Table S3. Background data on the studied lakes with (grey shaded) and without zebra mussels. Intraspecific (intraspec.) and interspecific (interspec.)
competitor density is given in catch per unit effort (CPUE). SDI stands for shoreline development index after Kent and Wong (1982). For orientation
purposes the data for the phenotypic divergence are also given as Δ MI. Δ MI refers to the difference in means of all morphological indices (MI) from
all individuals in the littoral and pelagic zone in each lake. We also provide the P-values of T-tests testing for differences between lakes with and
without zebra mussels with the different background parameters as dependent variable.
Lake
Funbo
Lang
Erken
Vallox
Falar
Hall
St.Hall
Strand
T-tests
lakes with
vs lakes
without
zebra
mussels
Secchi
depth
(m)
Total P
(µg
mL-1)
Chl a
content
(µ L-1)
SDI
mean
max surface retention
depth depth area (ha) time (days)
(m)
(m)
2.6
5.6
5.4
1.1
2.5
3.5
1.6
1.5
66.25
20.34
35.31
70.44
22.22
29.98
16.14
46.96
6.5
4.89
5.78
81.83
5.88
13.35
21.29
12.86
15.70
10.75
23.76
16.17
13.58
12.25
13.96
16.52
0.055
0.845
0.057
0.617 0.296
1.7
6.3
9.0
3.8
1.5
5.0
1.5
1.7
Intraspec.
Intraspec.
Intraspec.
Interspec.
Interspec.
Interspec.
competitor
competitor
competitor
competitor
competitor
competitor
(perch)
(perch) CPUE
(perch)
(roach)
(roach)
(roach)
CPUE(g net-1)
(g net-1)
CPUE(g net-1) CPUE(g net-1) CPUE(g net-1) CPUE(g net-1)
pelagic
littoral
total
pelagial
littoral
total
5.5
12.5
21.0
9.0
2.6
10.0
4.4
4.0
207
250
2370
270
205
20
19
13
114
1059
2409
620
249
730
300
180
2105.0
509.1
1834.7
301.8
1801.8
4695.6
1082.4
6091.7
4543.0
443.1
139.5
3189.6
452.4
1674.4
669.5
3159.9
6648.1
952.2
1974.2
3491.4
2254.2
6370.0
1751.9
9251.6
651.6
1040.0
1180.4
219.4
111.2
3933.4
883.9
713.1
1649.7
131.7
613.0
2981.8
161.9
1289.1
472.7
1700.3
2301.3
1171.7
1793.4
3201.3
273.1
5222.4
1356.6
2413.5
0.254
0.362
0.363
0.326
0.943
0.555
0.780
0.466
0.451
8
Table S4. Habitat-specific means of all analyzed variables for all the lakes. The bold P-value indicates significant differences after correcting the
alpha-level by applying the FDR (False Discovery Rate)-add-in “QVALUE” in R (Storey 2004)
zebra
mussel
lake
with
with
with
without
without
without
without
without
with
with
with
without
without
without
without
without
Erken
Funbo
Lang
Hall
St.Hall
Strand
Vallox
Falar
Erken
Funbo
Lang
Hall
St.Hall
Strand
Vallox
Falar
infaunal
habitat taxa as
mean % of
diet
littoral
littoral
littoral
littoral
littoral
littoral
littoral
littoral
pelagic
pelagic
pelagic
pelagic
pelagic
pelagic
pelagic
pelagic
epifaunal
cladocera as
taxa as
mean % of
mean % of diet
diet
copepods and fish as
other
mean %
zooplanton as of diet
mean % of
diet
density large
cladocera
[>0.46mm]
-1
(mg L )
density small density
cladocera
cyclopoid
[<0.46mm] (mg copepods
-1
-1
L )
(mg L )
density
calanoid
copepods
-1
(mg L )
indiv zoopl epi -faunal infaunal
biomass
taxa (mg taxa (mg
-2
-2
(mm)
m )
m )
indiv
benthic
biomass
(mg)
total
length
(mm)
age
(years)
growth 1.
year (mm)
8.54
1.32
45.79
42.50
15.03
12.82
11.63
30.39
1.24
8.17
7.21
9.03
5.50
6.70
13.02
5.29
18.98
12.97
17.73
21.08
5.72
29.67
8.68
17.16
2.38
18.56
4.76
2.94
2.29
6.53
7.88
0.34
59.18
66.98
27.94
1.79
37.23
30.80
6.53
26.22
65.29
48.80
80.12
24.12
9.78
29.54
22.34
36.03
13.30
16.04
5.21
30.08
42.02
20.95
7.20
21.34
29.31
20.16
3.14
60.96
64.49
12.96
11.98
58.34
0.00
2.69
3.33
4.55
0.00
4.26
65.96
4.88
1.82
4.31
4.76
2.94
17.94
44.26
44.78
0.00
79.31
70.23
199.12
79.45
2.95
12.44
6.49
16.86
223.82
70.46
130.80
68.74
17.92
24.80
14.12
23.46
2.38
5.42
6.79
67.38
0.05
5.96
1.08
1.95
1.21
0.00
1.70
0.74
0.04
2.17
12.24
0.76
41.15
1.68
13.76
17.95
1.50
6.05
6.78
10.88
51.62
37.53
58.53
99.39
0.93
10.52
50.75
99.89
97.83
2.89
0.17
0.09
0.00
1.18
0.78
0.00
150.48
77.72
12.88
104.60
0.43
41.57
42.28
0.00
0.63
0.51
0.55
0.45
0.50
0.60
0.46
0.52
0.87
0.71
0.80
0.57
0.54
0.63
0.47
0.58
6.15
9.58
4.26
1.87
0.79
0.64
0.24
1.71
0.21
2.02
0.00
0.00
0.06
0.50
4.03
0.00
1.95
0.60
0.16
6.30
1.08
1.40
0.59
3.29
6.60
1.62
0.03
0.02
0.10
0.88
1.93
1.02
0.34
0.46
0.85
0.04
0.24
0.61
0.22
0.24
2.52
1.31
0.10
0.23
0.54
0.14
0.11
0.30
120.77
84.71
74.48
100.16
90.21
99.21
170.71
115.59
117.20
103.10
136.58
80.18
106.70
144.90
161.71
131.55
1.82
1.33
0.55
2.22
1.60
2.03
4.90
3.13
1.67
2.03
2.97
0.94
2.69
3.62
4.76
3.90
87.97
58.54
69.59
59.32
61.22
60.02
51.60
72.49
92.42
61.24
71.84
61.83
65.46
63.18
49.92
73.69
T-tests lakes with
vs. lakes without
zebra mussels only littoral
0.812
0.984
0.101
0.102
0.330
0.144
0.471
0.475
0.410
0.289
0.064
0.219
0.203
0.324
0.067
0.325
T-tests lakes with
vs. lakes without
zebra mussels only pelagial
0.417
0.464
0.028
0.140
0.131
0.123
0.392
0.895
0.407
0.017
0.867
0.428
0.271
0.739
0.253
0.310
T-tests difference
littoral-pelagic with
vs. difference
littoral-pelagic
without zebra
mussels
0.923
0.896
0.702
0.293
0.695
0.788
0.588
0.556
0.736
0.000
0.007
0.123
0.460
0.526
0.553
0.347
9
Figure S1: Box-plots of individuals’ morphological scores in the littoral and the pelagic habitats of lakes with and without zebra mussels. (Boxes :2575% quartiles, medians:heavy horizontal line in center, range:vertical lines, outliers: asterisks). Morphological scores are taken from a combined DFA
analysis comprising all lakes. To improve clarity the body morphology visualizations depict a three-fold exaggeration of both the littoral and pelagic
ends of the morphology spectrum. Visualizations showing the observed littoral and pelagic ends in each lake are given in figure S2.
Littoral end of the morphology space
Morphological index (MI)
4
+5
3
2
1
0
-1
-2
-3
-5
-4
Funbo
Lang
Vallox
Falar
St.Hall
Hall
Pelagial
Littoral
Pelagial
Littoral
Pelagial
Littoral
Pelagial
Littoral
Pelagial
Littoral
Pelagial
Littoral
Erken
Pelagial
Lake
Littoral
Habitat
Littoral
Pelagial
Pelagic end of the morphology space
Strand
10
Figure S2. Visualizations of morphological divergence between littoral and pelagic in all
studied lakes. Outlines depict the body shape as the (connected) landmarks of the most
extreme littoral and pelagic individual in each of the eight studied lakes.
11
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