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Transcript of Intro: - Springer · Web view9.040 0.487 14. β 0 + β 1 Y + β 2 GDD + β 3...

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: 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 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 approa