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

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

Oceanological and Hydrobiological Studies
Volume 48 (2019): Issue 3 (September 2019)
By: Tomislav Vladić,  Torbjörn Järvi and  Erik Petersson  
Open Access
|Sep 2019

Figures & Tables

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 traitPC 1PC 2
SEA AGE0.98−0.07
SMOLT SIZE−0.45−0.78
GROWTH RATE−0.120.96
logSoma0.880.23
Eigenvalue2.121.37
% of variance54.734.2

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)

SourcedfMean squareFBeta (tactic x soma)SE Betap
Model SST30.071109.756.4850.8480.000
Model EI35.8443.759−2.4282.7560.026
Age x soma40.99917.62(0.133; 0.318)1.061; 3.448)0.000
Error SST220.0001
Error EI221.555
DOI: https://doi.org/10.2478/ohs-2019-0025 | Journal eISSN: 1897-3191 | Journal ISSN: 1730-413X
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Language: English
Page range: 279 - 289
Submitted on: Nov 6, 2018
Accepted on: Mar 12, 2019
Published on: Sep 22, 2019
Published by: University of Gdańsk
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
Atlantic salmon,
age at maturity,
ejaculate investment,
resource acquisition,
resource allocation
Related subjects:
Chemistry,
Chemistry, other,
Geosciences,
Geosciences, other,
Life sciences,
Life sciences, other

© 2019 Tomislav Vladić, Torbjörn Järvi, Erik Petersson, published by University of Gdańsk
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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