Electronic Supplemental Material (ESM):
Journal: Oecologia
Title: Thermal and maternal environments shape the value of early hatching in a natural
population of a strongly cannibalistic freshwater fish
Authors: Thilo Pagel1,2, Dorte Bekkevold3, Stefan Pohlmeier1, Christian Wolter1 and Robert
Arlinghaus1,2
1
Department of Biology and Ecology of Fishes, Leibniz-Institute of Freshwater
Ecology and Inland Fisheries, Müggelseedamm 310, 12587 Berlin, Germany
2
Division of Integrative Fisheries Management, Albrecht-Daniel-Thaer Institute of
Crop and Agricultural Sciences, Faculty of Life Sciences, Humboldt-Universität zu
Berlin, Philippstraße 13, 10155 Berlin, Germany
3
National Institute of Aquatic Resources, Technical University of Denmark, Vejlsøvej
39, 8600 Silkeborg, Denmark
Thilo Pagel
Tel.:
+49(0)30 64181 724
Fax:
+49(0)30 64181 750
e-mail: pagel@igb-berlin.de
This supplement consists of five parts:
Online resource 1: Optimal air temperature averaging period
Online resource 2: Age validation
Online resource 3: Parentage assignment method
Online resource 4: Model results and parameter estimates
Online resource 5: Supplementary references
Online resource 1: Optimal air temperature averaging period
A linear regression model based on the method described by Matuszek and Shuter (1996) was
developed to calculate missing daily average water temperatures for 2008. The analysis was
based on daily air and water temperature measurements between 04 April and 19 June for all
three sampling years. Air temperature data were obtained from a weather station located 25
km from Kleiner Döllnsee. Daily air temperatures were calculated as the mean of daily
minimum and maximum temperatures. Water temperature in 2008, as mentioned in the main
text, was measured using YSI-Multi-Parameter-Sensor (YSI 6600, Yellow Springs, Ohio). In
the two subsequent years, water temperature was measured using 11 (2009) or 5 (2010)
temperature loggers (Hobo StowAway TidbiT v2). Independent variables used to predict
mean daily water temperature included mean air temperature (T) for 0, 5, 10, 15, 20, 25 and
30-day periods (each period extending back in time from the day the water temperature was
measured). In addition, day of the year (YDAY) and its transformations (square, cube and
logarithm) was included in the model (as a time function). The optimal air temperature
averaging period for predicting water temperature was then estimated based on maximum r2
(adjusted) and different measures of the goodness of fit (AICc and ΔAICc). The best model
was used to impute missing values.
Table 1a Model summary of linear regression models used to determine the optimal air
temperature averaging period for Kleiner Döllnsee in the three sampling years.
Model: Mean daily water temperature ̴
1. β0 + β1T10 + β2YDAY + β3YDAY2 + εi
2. β0 + β1T10 + β2YDAY + β3 logYDAY + εi
3. β0 + β1T10 + β2YDAY + β3 YDAY3 + εi
4. β0 + β1T10 + β2YDAY + εi
5. β0 + β1T5 + β2YDAY + εi
6. β0 + β1T15 + β2YDAY + εi
7. β0 + β1T20 + β2YDAY + εi
8. β0 + β1T25 + β2YDAY + εi
9. β0 + β1T30 + β2YDAY + εi
10. β0 + β1T0 + β2YDAY + εi
11. β0 + β1T0 + εi
adj r2
0.928
0.922
0.928
0.910
0.899
0.899
0.875
0.867
0.859
0.829
0.620
N
231
231
231
231
231
231
231
231
231
231
231
K
4
4
4
3
3
3
3
3
3
3
2
AICc
692.116
692.135
692.641
744.796
769.763
771.844
819.815
833.984
848.117
891.862
1075.847
ΔAICc
0
0.019
0.525
52.681
77.647
79.728
127.699
141.868
156.001
199.746
383.731
T = air temperature; YDAY = day of the year; β0 = intercept; εi = error term; N = total number of observations; K
= number of parameters; AICc = corrected Akaike`s information criterion; ΔAICc = delta AICc
Online resource 2: Parentage assignment method
DNA was extracted from caudal fin clips of all potential spawners and age-0 pike using the
E.Z.N.A.TM tissue DNA kit (Omega Bio-Tek, Inc.) following the manufacturer’s guidelines.
Polymerase chain reaction (PCR) was used to amplify 16 microsatellite loci, which were
visualized and size fractioned using a BaseStation and an ABI 3139 Genetic Analyser
(Applied Biosystems, Forster City, USA). Maternity was determined using the approach
implemented in CERVUS 3.0 (Kalinowsky et al. 2007). CERVUS was first used to estimate
the statistical power for assigning maternity to offspring. A large number of offspring
(10,000) were simulated based on allele frequency estimates for 16 microsatellite loci in all
parental candidates collected across all three years (N = 1,130). Then, the statistical power to
correctly assign age-0 pike to a sampled female was estimated based on assigning the
simulated offspring, assuming that 85% of all spawning females in the lake had been sampled.
This estimate was based on the average proportion of sampled mature females in relation to
the estimated total mature female population size (Pagel 2009). Based on the assignments of
simulated offspring, the critical delta associated with 95% correct assignment was estimated,
following Kalinowski et al. (2007). The power to identify the correct mother was compared
with the power to simultaneously identify both mother and father, where all sampled mature
males and pike of unknown sex, were used as paternal candidates. Numbers of sampled
maternal and paternal candidates varied over the three years (2008 to 2010) at respectively
338, 439 and 520 candidate mothers and 392, 473 and 584 candidate fathers. Using that
approach, some fraction of offspring could in theory have been erroneously assigned paternity
to a mother who could not be sexed on collection. However this was not expected to lead to
bias in the current analysis, where only offspring that could be assigned to a specific maternal
candidate were used on subsequent analyses. The probability of identity, defined as the
probability of two randomly sampled individuals from our data set having the same genotype,
was also estimated with CERVUS.
3
Sixteen microsatellite loci were typed in a total of 1,130 parental candidates and in 66,
104 and 134 age-0 pike from the respective collection years 2008, 2009 and 2010. Loci
exhibited from 4 to 19 alleles, scoring success was high at 99.95% across loci and individuals,
and none of the sixteen loci exhibited statistically significant deviation from Hardy-Weinberg
proportions (Table 1a). The Pid was estimated at 0.016. Simulation analyses showed that
applying critical delta for the three analysis years of respectively 3.67, 3.47 and 3.53 would
lead to 95% of all assignments being to correct mothers. In comparison, critical delta for
correct assignment of fathers were somewhat higher (3.65, 4.03, 4.15), due to the assumed
lower sampling efficiency on mature males.
Table 2a Summary data for microsatellite marker types in all candidate parent individuals
collected across the three years. Listed for each locus is the observed number of alleles (NA),
the expected (HE) and observed (HO) heterozygosity, the polymorphic information content
(PIC) together with tests for deviation from Hardy-Weinberg expectations (P) and the original
source. No locus retained significance following correction for multiple testing
Locus
B24
B117
B259
B281
B422
B451
B457
Elu2
EluBe
EluB38
EluB108
EluB118
Elu51
Elu64
Elu37
Elu76
NA
11
6
10
6
9
19
18
5
10
7
9
5
4
4
17
19
HE
0.803
0.099
0.817
0.700
0.472
0.897
0.852
0.183
0.538
0.326
0.328
0.675
0.276
0.369
0.732
0.816
HO
0.793
0.095
0.803
0.693
0.475
0.897
0.857
0.171
0.548
0.312
0.304
0.660
0.272
0.359
0.695
0.807
PIC
0.775
0.096
0.792
0.649
0.450
0.888
0.836
0.175
0.457
0.335
0.311
0.614
0.238
0.315
0.708
0.793
P⃰
NS
NS
NS
NS
NS
NS
NS
NS
P < 0.05
P < 0.05
NS
NS
NS
NS
P < 0.05
NS
Source
Aguilar et al. 2005
Aguilar et al. 2005
Aguilar et al. 2005
Aguilar et al. 2005
Aguilar et al. 2005
Aguilar et al. 2005
Aguilar et al. 2005
Hansen et al. 1999
Launey et al. 2003
Launey et al. 2003
Launey et al. 2003
Launey et al. 2003
Miller and Kapuscinski 1996
Miller and Kapuscinski 1996
Miller and Kapuscinski 1997
Miller and Kapuscinski 1997
⃰ NS = non-significant locus specific test
4
Online resource 3: Age validation
Age data from scales notoriously underestimate fish age and thus need to be calibrated before
it can be accepted as valid and reliable method to age a given fish species (Campana 2001).
Age estimates of pike were validated by three different approaches. Firstly, we compared the
scale-read age of fish with the true age obtained from tag-recapture data. In total, 208 pike
were tagged and recaptured in the period between 2007 and 2011. Ideally, first tagging takes
place very early in life where age estimates are pretty certain (e.g., age-1). Accordingly, we
only used pike of age-1 to age-3 at first capture for tagging, assuming that the initial aging
error was negligible for these young fish. Using this approach, a high correspondence
between true age (y) and scale-read age (x) was found (linear regression without intercept: y =
1.007x, r = 0.990, P < 0.001, N = 133). Age estimates at first tagging for all pike age-4 to
age-6 were corrected using the parameters of this model. This allowed us to include more and
also older individuals in the final analysis (all pike age-1 to age-6 at first tagging). As shown
in Figure 2a, a high correspondence was observed between true age and scale-read age (linear
regression without intercept: y = 1.014x, r = 0.994, P < 0.001, N = 198), indicating that our
age estimates were reasonable and reliable. Secondly, for some pike caught in the study lake
on 13 April in 2005, age estimates by one reader were cross-checked with those obtained by
the same reader from cleithra. According to Laine et al. (1991), cleithra yield more accurate
age estimates for pike especially for old individuals. Therefore, it was assumed that cleithrabased estimates reflect the true age of pike (Babaluk and Craig 1990; Casselman 1996). Total
length of pike investigated ranged between 14.7 and 74.5 cm, and age estimates varied
between 0 to 7 years. A high agreement between age estimates by both scales und cleithra
(linear regression without intercept: y = 1.016x, r = 0.985, P < 0.001, N = 49) was obtained as
shown in Figure 2b. However, age estimates using scales tended to underestimate the true
(cleithrum) age slightly. Finally, regression analysis was used to compare age estimates by
scales from two different readers using the same pike. Again, high agreement was observed
5
(linear regression without intercept: y = 1.011x, r = 0.960, P < 0.001, N = 48; not shown).
Based on these three lines of evidence, it was assumed that the age estimates in our study and
back-calculated data such as juvenile growth by mature females reflected the true values well,
acknowledging a tendency for underaging old fish.
Fig. 3a Relation between true age (years) and scale-read age (years) of pike from Kleiner
Döllnsse (r = 0.994, P < 0.001, N = 198)
Fig. 3b Relation between cleithrum age (years) and scale age (years) of pike from Kleiner
Döllnssee (r = 0.985, P < 0.001, N = 49)
6
Online resource 4: Model results and parameter estimates
Table 4a General linear model (GLM) with total length of age-0 pike in early summer as
dependent variable, year as a fixed factor, hatch date and age as covariate
Source
Corrected model
Intercept
Year
Hatch date
Age
Year × hatch date
a
Sum of Squares
94192.345a
258.010
608.224
341.584
4232.645
620.250
df
6
1
2
1
1
2
F
205.549
3.378
3.982
4.472
55.419
4.061
P
<0.001
0.067
0.020
0.035
<0.001
0.018
S.E.
10.884
4.643
5.261
0.284
0.122
0.332
0.317
t
2.149
-1.722
-2.791
2.955
7.444
-2.849
-1.931
P
0.032
0.086
0.006
0.003
<0.001
0.005
0.054
Corrected model: r2 = 0.806 (adjusted r2 = 0.802)
Parameter estimates
Intercept
Year (2009)
Year (2010)
Hatch date
Age
Year (2009) × hatch date
Year (2010) × hatch date
β
23.392
-7.996
-14.682
0.839
0.908
-0.945
-0.612
Table 4b General linear model (GLM) with total length of age-0 pike in early summer as
dependent variable, year as fixed factor and growing degree-day (GDD) from individual hatch
date to catch date as a covariate
Source
Corrected model
Intercept
Year
GDD
Year × GDD
a
Sum of Squares
94977.950a
1586.708
1755.773
4401.025
1440.660
df
5
1
2
1
2
F
258.506
21.593
11.947
59.892
9.803
P
<0.001
<0.001
<0.001
<0.001
<0.001
S.E.
15.789
19.839
16.926
0.015
0.022
0.018
t
5.556
-4.767
-4.264
0.839
4.134
3.496
P
<0.001
<0.001
<0.001
0.402
<0.001
0.001
Corrected model: r2 = 0.813 (adjusted r2 = 0.809)
Parameter estimates
Intercept
Year (2009)
Year (2010)
GDD
Year (2009) × GDD
Year (2010) × GDD
β
87.729
-94.569
-72.173
0.012
0.092
0.065
7
Table 4c General linear model (GLM) with mean daily growth rate (DGR) of age-0 pike as
dependent variable, year as a fixed factor and hatch date as a covariate
Source
Corrected model
Intercept
Year
Hatch date
Year × hatch date
a
Sum of Squares
6.259a
31.172
0.420
0.030
0.548
df
5
1
2
1
2
F
66.878
1665.463
11.214
1.615
14.631
P
<0.001
<0.001
<0.001
0.205
<0.001
S.E.
0.049
0.065
0.058
0.004
0.005
0.005
t
21.504
-0.194
-3.761
3.607
-5.129
-2.079
P
<0.001
0.847
<0.001
<0.001
<0.001
0.038
Corrected model: r2 = 0.529 (adjusted r2 = 0.521)
β
1.053
-0.013
-0.217
0.015
-0.027
-0.010
Parameter estimates
Intercept
Year (2009)
Year (2010)
Hatch date
Year (2009) × hatch date
Year (2010) × hatch date
Table 4d General linear model (GLM) with mean daily growth rate (DGR) of age-0 pike as
dependent variable, year as a fixed factor and growing degree-day (GDD) from individual
hatch date to catch date as a covariate
Source
Corrected model
Intercept
Year
GDD
Year × GDD
a
Sum of Squares
6.934a
1.432
1.391
0.083
1.177
df
5
1
2
1
2
F
84.294
87.032
42.286
5.054
35.787
P
<0.001
<0.001
<0.001
0.025
<0.001
S.E.
0.236
0.297
0.253
<0.001
<0.001
<0.001
t
10.503
-9.179
-7.195
-5.356
8.286
5.746
P
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
Corrected model: r2 = 0.586 (adjusted r2 = 0.579)
Parameter estimates
Intercept
Year (2009)
Year (2010)
GDD
Year (2009) × GDD
Year (2010) × GDD
β
2.481
-2.725
-1.822
-0.001
0.003
0.002
8
Table 4e Linear mixed-effect model (LME) with total length in early summer of age-0 pike
as dependent variable, year as fixed factor, maternal identity as a random factor, growing
degree-day (GDD) and female juvenile growth in the second year of life (FJG2) as a covariate
Parameter estimates
Intercept
Year (2009)
Year (2010)
FJG2
GDD
Year (2009) × GDD
Year (2010) × GDD
β
83.249
-67.494
-75.920
0.033
0.012
0.055
0.073
S.E.
19.938
27.621
22.267
0.025
0.018
0.032
0.026
t
4.175
-2.444
-3.410
1.320
0.635
1.706
2.795
P
<0.001
0.016
0.001
0.190
0.527
0.090
0.006
Table 4f Linear mixed-effect model (LME) with mean daily growth rate (DGR) of age-0 pike
as dependent variable, year as a fixed factor, maternal identity as a random factor, growing
degree-day (GDD) and female total length (FT) as a covariate
Parameter estimates
Intercept
Year (2009)
Year (2010)
FT
GDD
Year (2009) × GDD
Year (2010) × GDD
β
2.414
-2.368
-1.830
<0.001
-0.001
0.002
0.002
S.E.
0.292
0.399
0.324
<0.001
<0.001
<0.001
<0.001
t
8.276
-5.933
-5.650
0.539
-4.255
4.911
4.349
P
<0.001
<0.001
<0.001
0.591
<0.001
<0.001
<0.001
Table 4g Linear mixed-effect model (LME) with hatch date as dependent variable, year as
fixed factor, maternal identity as a random factor and female juvenile growth in the second
year of life (FJG2) as a covariate
Parameter estimates
Intercept
Year (2009)
Year (2010)
FJG2
β
13.647
1.320
-1.394
-0.017
S.E.
1.505
0.806
0.795
0.011
t
9.068
1.638
-1.753
-1.553
P
<0.001
0.103
0.082
0.124
9
Table 4h Model summary of linear mixed-effect models explaining variation in mean daily growth rate (DGR, mm day-1)
Model: Mean daily growth rate ̴
N
K
AICc
ΔAICc
wi
1. β0 + β1Y + β2GDD + β3Y×GDD + ai + εi
179
8
-187.876
0
44.697
2. β0 + β1Y + β2GDD + β3FT + β4Y×GDD + ai + εi
179
9
-185.943
1.933
17.003
3. β0 + β1Y + β2GDD + β3FT + β4FT² + β5Y×GDD + ai + εi
179
10
-184.505
3.370
8.287
4. β0 + β1Y + β2GDD + β3FT + β4Y×GDD + β5FT²×GDD + ai + εi
179
10
-184.504
3.371
8.283
5. β0 + β1Y + β2GDD + β3FT + β4Y×GDD + β5FT×GDD + ai + εi
179
10
-183.763
4.112
5.718
6. β0 + β1Y + β2GDD + β3FA + β4Y×GDD + ai + εi
177
9
-182.404
5.472
2.898
7. β0 + β1Y + β2GDD + β3FJG1 + β4Y×GDD + ai + εi
177
9
-182.241
5.635
2.671
8. β0 + β1Y + β2GDD + β3FA + β4FA² + β5Y×GDD + ai + εi
177
10
-182.033
5.843
2.407
9. β0 + β1Y + β2GDD + β3FA + β4Y×GDD + β5FA²×GDD + ai + εi
177
10
-182.033
5.843
2.407
10. β0 + β1Y + β2GDD + β3FA + β4Y×GDD + β5FA×GDD + ai + εi
177
10
-181.445
6.431
1.794
11. β0 + β1Y + β2GDD + β3FJG1 + β4Y×GDD + β5FJG1×GDD + ai + εi
177
10
-180.340
7.536
1.032
12. β0 + β1Y + β2GDD + β3FJG2 + β4Y×GDD + ai + εi
175
9
-179.894
7.982
0.826
13. β0 + β1Y + β2GDD + β3FT + β4FJG1 + β5Y×GDD + β5FT×FJG1 + ai + εi
177
11
-178.836
9.040
0.487
14. β0 + β1Y + β2GDD + β3FA + β4FJG1 + β5Y×GDD + β5FA×FJG1 + ai + εi
177
11
-178.622
9.254
0.437
15. β0 + β1Y + β2GDD + β3FJG1 + β4FJG2 + β5Y×GDD + ai + εi
175
10
-178.328
9.548
0.377
16. β0 + β1Y + β2GDD + β3FJG2 + β4Y×GDD + β5FJG2×GDD + ai + εi
175
10
-178.114
9.762
0.339
17. β0 + β1Y + β2GDD + β3FT + β4FJG2 + β5Y×GDD + β5FT×FJG2 + ai + εi
175
11
-176.933
10.943
0.188
18. β0 + β1Y + β2GDD + β3FA + β4FJG2 + β5Y×GDD + β5FA×FJG2 + ai + εi
175
11
-175.715
12.161
0.102
19. β0 + β1Y + β2H + β3Y×H + ai + εi
179
8
-174.098
13.778
0.046
20. β0 + β1Y+ ai + εi
179
5
-165.889
21.987
0.001
-1
Y = year; H = hatch date (days); A = age of age-0 pike (days); GDD = growing degree-days (°C day ); FA = female age (years); FA² = female age squared (years²); FT =
female size (mm); FT² = female size squared (mm²); FJG1 = female juvenile growth in the first year (mm yr -1); FJG2 = female juvenile growth in the second year (mm yr -1);
β0 = intercept; ai = random intercept (maternal identity); εi = error term; N = total number of observations; K = number of parameters; AICc = corrected Akaike`s information
criterion; ΔAICc = delta AICc; wi = Akaike weight
10
Online resource 5: Supplementary references
Aguilar A, Banks JD, Levine KF, Wayne RK (2005) Population genetics of northern pike
(Esox lucius) introduced into Lake Davis, California. Can J Fish Aquat Sci 62:15891599
Babaluk JA, Craig JF (1990) Tetracycline marking studies with pike, Esox lucius L. Aqua
Fish Manage 21:307-315
Campana SE (2001) Accuracy, precision and quality control in age determination, including a
review of the use and abuse of age validation methods. J Fish Biol 59:197-242
Casselman JM (1996) Age, growth and environmental requirements of pike. In: Craig JF (ed)
Pike: biology and exploitation. Chapman and Hall, London, pp 69-101
Hansen MM, Taggart JB, Meldrup D (1999) Development of new VNTR markers for pike
and assessment of variability at di- and tetranucleotide repeat microsatellite loci. J Fish
Biol 55:183-188
Kalinowski ST, Taper ML, Marshall TC (2007) Revising how the computer program
CERVUS accommodates genotyping error increases success in paternity assignment.
Mol Ecol 16:1099-1106
Laine AO, Momot WT, Ryan PA (1991) Accuracy of using scales and cleithra for aging
northern pike from an oligotrophic Ontario Lake. N Am J Fish Manag 11:220-225
Launey S, Krieg F, Morin J, Laroche J (2003) Five new microsatellite markers for northern
pike (Esox lucius). Mol Ecol Notes 3:366-368
Matuszek JE, Shuter BJ (1996) An empirical method for the prediction of daily water
temperatures in the littoral zone of temperate lakes. T Am Fish Soc 125:622-627
Miller LM, Kapuscinsky AR (1996) Microsatellite DNA markers reveal new levels of genetic
variation in northern pike. T Am Fish Soc 125: 971-977
Miller LM, Kapuscinsky AR (1997) Historical analysis of genetic variation reveals low
effective population size in a northern pike (Esox lucius) population. Genetics
147:1249-1258
Pagel T (2009) Determinants of individual reproductive success in a natural pike (Esox lucius
L.) population: a DNA-based parentage assignment approach. Master thesis,
Humboldt-Universität
zu
Berlin,
Germany.
Available
at:
http://www.adaptfish.rem.sfu.ca/Theses/Thesis_MSc_Pagel.pdf
11