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Life histories of anadromous salmon males reveal a trade-off between primary and secondary sexual traits

Tomislav Vladić

Tomislav Vladić

Karolinska Universitetssjukhuset, Department of Female and Children’s HealthStockholm, Sweden
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Vladić, Tomislav
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Torbjörn Järvi

Torbjörn Järvi

Swedish University of Agricultural Sciences, Department of Aquatic Resources, Freshwater LaboratoryDrottningholm, Sweden
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Järvi, Torbjörn
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Erik Petersson

Erik Petersson

Swedish University of Agricultural Sciences, Department of Aquatic Resources, Freshwater LaboratoryDrottningholm, Sweden
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Petersson, Erik
  
Sep 22, 2019
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Article Category: Original research paper
Published Online: Sep 22, 2019
Page range: 279 - 289
Received: Nov 06, 2018
Accepted: Mar 12, 2019
DOI: https://doi.org/10.2478/ohs-2019-0025
Keywords
© 2019 Faculty of Oceanography and Geography, University of Gdańsk, Poland
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1

Polynomial form of the trade-off between primary and secondary sexual traits ANCOVA, log Ejaculate investment in depending on log10 SST index with strategy (grilse vs multi-sea-winter) as a covariate. Grilse least square mean EI = 1.97, multi sea-winter least square mean EI = 1.31; Model: r2 = 0.36, F = 6.345, p = 0.006
Polynomial form of the trade-off between primary and secondary sexual traits ANCOVA, log Ejaculate investment in depending on log10 SST index with strategy (grilse vs multi-sea-winter) as a covariate. Grilse least square mean EI = 1.97, multi sea-winter least square mean EI = 1.31; Model: r2 = 0.36, F = 6.345, p = 0.006

Figure 2

PC results for the correlation matrix of the four life history variables (sea age, smolt size, growth rate and log soma)
PC results for the correlation matrix of the four life history variables (sea age, smolt size, growth rate and log soma)

Figure 3

a. Polynomial function slopes depicting the effect of somatic condition/growth rate (ALLOCATION trade-off ) on ejaculate investment in grilse and anadromous adult males (see Table 2 for the components in the column PC1). ANCOVA reduced model, r2 = 0.301, F2,23 = 4.965, p = 0.016; within-cell regressions, strategy, F1,23 = 1.012, p> 0.05; SMOLT condition by fish age, F1,23 = 0.668, p > 0.05; b. Polynomial function slopes depicting the relationship between smolt size/growth rate on log10 SST investment in grilse and anadromous adult males (see Table 2 for the components in the column PC1). ANCOVA reduced model, r2 = 0.747, F2,23 = 33.90, p<0.001; within-cell regressions, strategy, F1,23 = 0.175, p> 0.05; SMOLT condition by fish age, F1,23 = 17.720, p < 0.01
a. Polynomial function slopes depicting the effect of somatic condition/growth rate (ALLOCATION trade-off ) on ejaculate investment in grilse and anadromous adult males (see Table 2 for the components in the column PC1). ANCOVA reduced model, r2 = 0.301, F2,23 = 4.965, p = 0.016; within-cell regressions, strategy, F1,23 = 1.012, p> 0.05; SMOLT condition by fish age, F1,23 = 0.668, p > 0.05; b. Polynomial function slopes depicting the relationship between smolt size/growth rate on log10 SST investment in grilse and anadromous adult males (see Table 2 for the components in the column PC1). ANCOVA reduced model, r2 = 0.747, F2,23 = 33.90, p<0.001; within-cell regressions, strategy, F1,23 = 0.175, p> 0.05; SMOLT condition by fish age, F1,23 = 17.720, p < 0.01

Figure 4

a. Model 1) The Model 1(M1) of the form of equation: y = a+b1×ALL+b2×AQS+c1×ALL2+c2×AQS2+d1×(ALL×AQS) assessed the amount of variation in SST (y) in relation to the allocation (ALL) and acquisition (AQS) trade-offs. Proportion of variance accounted for: 81%, R = 0.90 vs 0.57 for p=0.001 (Rolf and Sokal 2012; Table R); b. Model 2) The Model 2 (M2) of the form of equation: y = a+b1×ALL+b2×AQS+c1×ALL2+c2×AQS2+d1×(ALL×AQS) assessed the amount of variation in EI (y) in relation to the allocation (ALL) and acquisition (AQS) trade-offs. Proportion of variance accounted for: 51%, R = 0.71 vs 0.57 for p = 0.001 (Rolf & Sokal 2012; Table R)
a. Model 1) The Model 1(M1) of the form of equation: y = a+b1×ALL+b2×AQS+c1×ALL2+c2×AQS2+d1×(ALL×AQS) assessed the amount of variation in SST (y) in relation to the allocation (ALL) and acquisition (AQS) trade-offs. Proportion of variance accounted for: 81%, R = 0.90 vs 0.57 for p=0.001 (Rolf and Sokal 2012; Table R); b. Model 2) The Model 2 (M2) of the form of equation: y = a+b1×ALL+b2×AQS+c1×ALL2+c2×AQS2+d1×(ALL×AQS) assessed the amount of variation in EI (y) in relation to the allocation (ALL) and acquisition (AQS) trade-offs. Proportion of variance accounted for: 51%, R = 0.71 vs 0.57 for p = 0.001 (Rolf & Sokal 2012; Table R)

PC of the life history traits used to determine the allocation and acquisition of energy for ejaculate quality and secondary sexual traits

Life history trait PC 1 PC 2
SEA AGE 0.98 −0.07
SMOLT SIZE −0.45 −0.78
GROWTH RATE −0.12 0.96
logSoma 0.88 0.23
Eigenvalue 2.12 1.37
% of variance 54.7 34.2

Analysis of covariance (separate slopes model) of log10 SST and log ejaculate investment in the male age tactic with the covariate log soma mass and after the removal of the interaction term (reduced model)

Source df Mean square F Beta (tactic x soma) SE Beta p
Model SST 3 0.071 109.75 6.485 0.848 0.000
Model EI 3 5.844 3.759 −2.428 2.756 0.026
Age x soma 4 0.999 17.62 (0.133; 0.318) 1.061; 3.448) 0.000
Error SST 22 0.0001
Error EI 22 1.555
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