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Transcript of Intro: - Springer · Web view9.040 0.487 14. β 0 + β 1 Y + β 2 GDD + β 3 FA + β 4 FJG1 + β 5...
Intro:
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
1Department of Biology and Ecology of Fishes, Leibniz-Institute of Freshwater Ecology and Inland Fisheries, Mggelseedamm 310, 12587 Berlin, Germany
2Division of Integrative Fisheries Management, Albrecht-Daniel-Thaer Institute of Crop and Agricultural Sciences, Faculty of Life Sciences, Humboldt-Universitt zu Berlin, Philippstrae 13, 10155 Berlin, Germany
3National Institute of Aquatic Resources, Technical University of Denmark, Vejlsvej 39, 8600 Silkeborg, Denmark
Thilo Pagel
Tel.:+49(0)30 64181 724
Fax:+49(0)30 64181 750
e-mail: [email protected]
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 Dllnsee. 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 AICc). 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 Dllnsee in the three sampling years.
Model: Mean daily water temperature
adj r2
N
K
AICc
AICc
1. 0 + 1T10 + 2YDAY + 3YDAY2 + i
0.928
231
4
692.116
0
2. 0 + 1T10 + 2YDAY + 3 logYDAY + i
0.922
231
4
692.135
0.019
3. 0 + 1T10 + 2YDAY + 3 YDAY3 + i
0.928
231
4
692.641
0.525
4. 0 + 1T10 + 2YDAY + i
0.910
231
3
744.796
52.681
5. 0 + 1T5 + 2YDAY + i
0.899
231
3
769.763
77.647
6. 0 + 1T15 + 2YDAY + i
0.899
231
3
771.844
79.728
7. 0 + 1T20 + 2YDAY + i
0.875
231
3
819.815
127.699
8. 0 + 1T25 + 2YDAY + i
0.867
231
3
833.984
141.868
9. 0 + 1T30 + 2YDAY + i
0.859
231
3
848.117
156.001
10. 0 + 1T0 + 2YDAY + i
0.829
231
3
891.862
199.746
11. 0 + 1T0 + i
0.620
231
2
1075.847
383.731
T = air temperature; YDAY = day of the year; 0 = intercept; i = error term; N = total number of observations; K = number of parameters; AICc = corrected Akaike`s information criterion; AICc = 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 manufacturers 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.
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
NA
HE
HO
PIC
P
Source
B24
11
0.803
0.793
0.775
NS
Aguilar et al. 2005
B117
6
0.099
0.095
0.096
NS
Aguilar et al. 2005
B259
10
0.817
0.803
0.792
NS
Aguilar et al. 2005
B281
6
0.700
0.693
0.649
NS
Aguilar et al. 2005
B422
9
0.472
0.475
0.450
NS
Aguilar et al. 2005
B451
19
0.897
0.897
0.888
NS
Aguilar et al. 2005
B457
18
0.852
0.857
0.836
NS
Aguilar et al. 2005
Elu2
5
0.183
0.171
0.175
NS
Hansen et al. 1999
EluBe
10
0.538
0.548
0.457
P < 0.05
Launey et al. 2003
EluB38
7
0.326
0.312
0.335
P < 0.05
Launey et al. 2003
EluB108
9
0.328
0.304
0.311
NS
Launey et al. 2003
EluB118
5
0.675
0.660
0.614
NS
Launey et al. 2003
Elu51
4
0.276
0.272
0.238
NS
Miller and Kapuscinski 1996
Elu64
4
0.369
0.359
0.315
NS
Miller and Kapuscinski 1996
Elu37
17
0.732
0.695
0.708
P < 0.05
Miller and Kapuscinski 1997
Elu76
19
0.816
0.807
0.793
NS
Miller and Kapuscinski 1997
NS = non-significant locus specific test
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 cleithra-based 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 (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 Dllnsse (r = 0.994, P < 0.001, N = 198)
Fig. 3b Relation between cleithrum age (years) and scale age (years) of pike from Kleiner Dllnssee (r = 0.985, P < 0.001, N = 49)
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
Sum of Squares
df
F
P
Corrected model
94192.345a
6
205.549