Given, that the growth traits described above varied with time, it is worth considering whether those patterns were held when accounting changes in plant dry mass. The growth parameters that have been previously plotted against time were re-plotted against plant dry mass to assess whether the observed trends were held when accounting for ontogenetic drift. If low N Table 3.3 Results of a three-way analysis of variance (ANOVA) for growth parameters related to nitrogen economy considering time (T), genotype (G) and N treatment (N) as factors with the three-way interaction term is shown as T x G x N. Growth parameters belong to first and second harvests were not considered in this analysis due to pooling of samples when determining N concentration by Kjeldahl analysis. Degrees of freedom (df), F - values and significance are presented. *p < 0.05, **p < 0.01, ***p < 0.001. n=320.
Effect df PNC LNR SNR RNR T 3 22.03*** 14.27*** 5.59*** 5.49*** G 9 3.13*** 3.16*** 6.58*** 2.11* N 1 2091.84*** 377.24*** 5.00* 623.34*** T x G 27 0.68 0.47 0.50 0.95 T x N 3 31.02*** 23.08*** 11.93*** 7.81*** G x N 9 1.04 2.29* 1.17 1.05 T x G x N 27 0.51 0.76 1.03 1.09 Error 238
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supply had a direct effect on each growth parameter, then trait values should differ between 0.06 and 2 mM N grown plants when compared at a common plant size. Moreover, it may be that in cases where there was no apparent effect of N supply on traits when not accounting for plant size, differences may emerge at common plant sizes. An example of the latter is SLA, where the results of a three-way ANOVA (Table 3.1) suggested no main effect of N supply, even though SLA values appeared to be lower in 0.06 mM grown plants than in their 2 mM counterparts, when compared at common plant sizes (Fig. 3.5B). Thus, when accounting for plant size, N supply resulted in lower SLA values across the 10 genotypes. For other traits where there was an apparent N dependence when assessed against time, the effect of N treatment was either maintained or strengthened when accounting for plant size (e.g. LAR, NAR, RMR and NP). The overall conclusion from inspection of Figures 3.4-3.6, therefore, is that patterns observed on a time basis are largely held or strengthened when ontogeny is accounted for.
To further assess the role of plant size, nutrient supply and genotype in influencing trait values, hierarchical multiple regressions were conducted (Table 3.4). The independent variables were entered in four steps - in model 1, ln plant dry mass (ln DM) was entered and held constant. In model 2, time was entered. In model 3, N treatment was entered. Similarly, in model 4, genotype was entered. Therefore, predictors in model 1, 2, 3 and 4 were plant mass; plant mass and time; plant mass, time and N treatment; and, plant mass, time, N treatment and genotype respectively. Addition of plant mass alone (in model 1) predicted variations in LAR; however, the explanatory power of the model was relatively low. By contrast, addition of time (in model 2) and N treatment (in model 3) resulted statistically significant increase in model fits, the unique variance (r2)of 0.41 and 0.20, respectively, confirming that both time and N treatment had strong influences on LAR.
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Table 3.4 Results of Hierarchical multiple regression analysis to assess time, N supply and genotypic dependent changes in growth parameters while controlling for plant size
Trait Variable Model 1 Model 2 Model 3 Model 4
B β B β B β B β
LAR Constant 12.68*** 19.41*** 22.83*** 22.55***
ln plant dry mass -0.22** -0.10** 0.80*** 0.36*** -1.18*** -0.53*** -1.14*** -0.51*** Time -1.64*** -0.79*** -0.57*** -0.28*** -0.59*** -0.29*** N treatment -6.03*** -0.85*** -5.95*** -0.84*** Genotype 0.05 0.04 r2 0.010 0.426 0.627 0.629 F 6.95** 258.12*** 390.24*** 294.10*** ∆r2 0.010 0.416 0.202 0.001 ∆F 6.95** 504.28*** 376.18*** 2.75 SLA Constant 33.23*** 47.15*** 49.91*** 49.52***
ln plant dry mass -1.65*** -0.34*** 0.46** 0.09** -1.17*** -0.24*** -1.12*** -0.23*** Time -3.40*** -0.75*** -2.51*** -0.56*** -2.53*** -0.56*** N treatment -4.96*** -0.32*** -4.85*** -0.31*** Genotype 0.07 0.03 r2 0.115 0.497 0.526 0.527 F 88.42*** 337.37*** 251.94*** 189.13*** ∆r2 0.115 0.383 0.029 0.001 ∆F 88.42*** 519.23*** 41.25*** 0.86 LMR Constant 0.38*** 0.42*** 0.48*** 0.47***
ln plant dry mass 0.01*** 0.38*** 0.02*** 0.57*** -0.02*** -0.44*** -0.01*** -0.42*** Time -0.01*** -0.33*** 0.01*** 0.26*** 0.01*** 0.25*** N treatment -0.10*** -0.97*** -0.10*** -0.96*** Genotype 0.001 0.05 r2 0.144 0.215 0.482 0.484 F 117.53*** 95.83*** 216.31*** 163.38*** ∆r2 0.144 0.071 0.267 0.002 ∆F 117.53*** 63.21*** 359.08*** 2.86 SMR Constant 0.35*** 0.36*** 0.37*** 0.354***
ln plant dry mass 0.02*** 0.45*** 0.02*** 0.48*** 0.01*** 0.34*** 0.01*** 0.38***
Time -0.002 -0.06 0.001 0.024 0.000 -0.003 N treatment -0.02* -0.14* -0.01 -0.11 Genotype 0.002*** 0.12*** r2 0.200 0.202 0.208 0.221 F 177.52*** 90.02*** 62.02*** 50.21*** ∆r2 0.200 0.002 0.006 0.013 ∆F 177.52*** 2.21 5.02* 11.92*** RMR Constant 0.27*** 0.22*** 0.15*** 0.17***
ln plant dry mass -0.03*** -0.66*** -0.04*** -0.84*** 0.004* 0.10* 0.002 0.05 Time 0.01*** 0.30*** -0.01*** -0.25*** -0.009*** -0.22*** N treatment 0.12*** 0.90*** 0.12*** 0.87*** Genotype -0.003*** -0.11*** r2 0.441 0.499 0.725 0.736 F 557.40*** 352.00*** 618.65*** 491.29*** ∆r2 0.441 0.058 0.225 0.012 ∆F 557.40*** 82.41*** 577.30*** 30.79*** PNC Constant 27.82*** 36.86*** 60.94*** 61.19***
ln plant dry mass 4.25*** 0.69*** 5.10*** 0.82*** -2.43*** -0.39*** -2.46*** -0.40***
Time -3.49*** -0.40*** 0.18 0.02 0.19 0.02 N treatment -24.26*** -1.25*** -24.33*** -1.25*** Genotype -0.03 -0.01 r2 0.471 0.610 0.852 0.852 F 280.98*** 246.70*** 602.14*** 450.51*** ∆r2 0.471 0.140 0.242 0.000 ∆F 280.98*** 112.92*** 512.24*** 0.205
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This confirms that the trends previously observed were direct effects of time and N supply rather ontogeny (Fig. 3.4B). Addition of genotype (in model 4) did not significantly improve the r2 (Table 3.4). The differences observed among genotypes for LAR (Table 3.1) were due to differences in plant sizes. For SLA, including plant mass alone resulted in a model with an r2 of 0.115 suggesting that SLA changed with plant size. Addition of time significantly improved the model fit by accounting for a further 38.3% of the variance. The effect of N when correcting for ontogeny was not large, yet statistically significant contributing a further 2.9% to the variance. Thus, N supply had an effect on SLA when controlling for plant mass, albeit with the effect being relatively minor (Fig. 3.5B, Table 3.4); importantly, accounting for genotype had no effect on model fits for SLA (Table 3.4).
Did time, N supply and genotypic differences alter trends in biomass allocation among organs when normalized to a common mass? N contributed strongly (26.7%) to the unique variance in LMR as shown by Figure 3.5D while, plant mass, time and genotypic differences accounted 14.4, 7.1 and 0.2% respectively (Table 3.4). The fact that the model significantly improved following inclusion of plant mass alone indicates LMR changed with plant size. Time also influenced LMR to some extent, while effects due to differences in genotypes were small. Hence, the genotypic variation observed for LMR (Table 3.1) could be due to discrepancies in plant sizes at each harvest. For SMR, the contribution by plant mass, time, N and genotypes for the unique variance were 20, 0.2, 0.6 and 1.3% respectively. Similar to LMR, SMR being predicted alone by
Note: The independent variables were entered in four steps - in model 1, ln plant dry mass
(ln DM) was entered and held constant. In model 2, time was entered. In model 3, N treatment was entered. In model 4, genotype was entered. Therefore, predictors in model 1, 2, 3 and 4 are ln DM, ln DM and time, ln DM, time and N treatment, ln DM, time, N treatment and genotype respectively. The change in r2 in model 2 indicates the amount of unique variance accounted for by the independent variables in the second step. The change in r2 in model 3 indicates the amount of unique variance accounted for by the independent variables in the third step. Similarly, the change in r2 in model 4 indicates the amount of unique variance accounted for by the independent variables in the fourth step. B indicates the unstandardized coefficient and β indicates the standardized coefficient. *p < 0.05, **p < 0.01, ***p < 0.001. n=720 for growth traits except PNC, where n=320 for PNC.
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plant mass suggests that SMR varied with plant size. However, neither N nor time significantly contributed to the unique variance of SMR. Hence, the time and N effects on SMR proposed by the three-way ANOVA (Table 3.1) could be due to differences in plant sizes at each harvest. Inspection of Figure 3.5F supports the conclusions that variations in SMR are largely independent of time and N supply. Unlike LMR there was a small, yet significant genotypic effects on this trait. Plant mass alone contributed to 44.1% of unique variance in RMR; however the effect of plant mass on RMR was not significant when considered along with other parameters. Time, N supply and genotype significantly changed the unique variance for RMR by 5.8, 22.5, and 1.2% respectively. The effect of N supply on RMR was relatively large as shown in Figure 3.5H. Taken together, the above results point to LMR and RMR being actively influenced by time and N supply, with SMR and RMR also being influenced by genotype, when accounting for ontogeny.
From N economy perspective, plant mass, time and N treatment accounted for 47.1, 14 and 24.2% of PNC variance. Overall, N had a strong influence on PNC, which changed with plant mass. Genotype had no influence on PNC in accord with Fig 3.6D.
3.4.3 To what extent do the factors underlying variation in RGR