4. No plant functional diversity effects on foliar fungal pathogens in experimental tree
5.4 Results
In total, three different parasitic foliar fungus species were observed. The most abundant fungus species on all silver birch clones was Discula betulina (J. Kickx f.) Arx with a mean and maximum pathogen load of 19.8 % and 53.5 %, respectively. Another frequent fungus
species was Venturia ditricha (Fr.) P. Karst. with a mean of 2.8 % and up to 20.5 % pathogen load. In contrast, Atopospora betulina (Fr.) Petr. was a very rare and less abundant fungus species, with a mean pathogen load below 1 %. Occurrence and load of these three fungus species was significantly different between the eight birch clones. Discula betulina was detected on every tree investigated and was most abundant on the clones White, Violet and Yellow (Figure 2A). Venturia ditricha occurred on every tree of the clone Orange and was also most abundant there, while it was almost absent on the clone Red (Figure 2B).
Atopospora betulina was absent on the clones Blue, Green and Yellow (Figure 2C). Among all birch clones, Red and Green exhibited the lowest values of mean pathogen species richness, while Red, Pink and Blue showed the lowest mean values of pathogen load (Table 1). In contrast, the clones Orange and Violet showed the highest mean values of pathogen richness and the clones White, Violet and Yellow the highest values of mean pathogen load (Table 1).
At the community level, the statistical analyses revealed a positive effect of plot tree clone richness on pathogen species richness, indicating an increase in the number of fungus species with increasing birch clone availability (Table 2; Figure 3). This pattern was mainly caused by the fact that at maximum two of the tree fungus species were encountered in any of the monoculture plots. In addition, the presence of the clone Red in a mixture decreased overall pathogen richness in the plot, whereas all other birch clones did not show any identity effects neither on pathogen richness nor on pathogen load (Table 2). In contrast, tree clone richness did not affect overall pathogen load (Table 2). This is reflected in predictions which explain the importance of birch clone identity to overall plot’s pathogen richness and load (Figure 4).
These predictions were based on particular mean response values assessed on all target
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Table 1 Silver birch clone properties and measured mean foliar fungal pathogen species richness and mean overall pathogen load (%) as well as of the three fungus species per individual for all eight birch clones.
Height (cm) was measured only in monoclonal plots in 2006.
Clone ID
Clone name
Height +/-SE (cm)
Mean number of pathogen species
Mean pathogen load (%)
Mean load (%) of Discula betulina
Mean load (%) of Venturia ditricha
Mean load (%) of Atopospora betulina
Blue K5834 406.5±17.7 1.98 15.80 13.66 2.14 -
Green JR ¼ 134.7±16.0 1.75 22.20 21.20 1.00 -
Orange 36 393.4±10.0 2.05 22.35 15.73 6.59 0.03
Pink K2674 317.8±13.8 1.84 14.77 13.91 0.81 0.02
Red O154 325.4±8.56 1.15 12.76 12.69 0.07 0.01
Violet V5818 450.4±22.1 2.02 27.88 24.29 3.55 0.04
White K1659 397.6±10.1 1.86 29.33 27.91 1.34 0.03
Yellow V5952 423.1±8.85 1.96 26.70 23.86 2.83 -
Figure 2 Mean pathogen load (%) of A) Discula betulina, B) Venturia ditricha, C) Atopospora betulina encountered on all target individuals for all eight clones. Small letters show significantly different differences after a Tukey post-hoc test.
individuals for a particular birch clone in its monocultures, which were averaged for all birch clones planted in a plot regarding the tree clone richness. The observed mean pathogen richness across all target individuals in a plot is well predicted from the mean pathogen richness on the different birch clones (Figure 4A). This was also the case for the observed pathogen load, which could closely be predicted from the birch clone-specific mean load values in the monocultures and the proportions of these clones in every plot (Figure 4B).
Figure 3 BEF relationship between tree clone richness and pathogen species richness of each plot at community level (n = 36).
At the individual level, both tree clone richness and tree clone diversity in the local neighbourhood did neither affect pathogen species richness nor pathogen load (Table 3).
However, increasing density of the clone Yellow in the local neighbourhood increased the pathogen richness of target individuals across all clones, whereas the density of the clone Red in the local neighbourhood decreased pathogen richness (Table 3). Similar density effects were encountered for pathogen load, as an increasing density of the clones Violet or Blue in the local neighbourhood increased or decreased overall pathogen load, respectively (Table 3).
Table 2 Linear model results at the community level for the effect of plot tree clone richness on plot foliar fungal pathogen richness and load (%) (n = 36). Pathogen richness refers to the number of fungus species encountered in a plot. In a subsequent model, remaining variance of the first model’s residuals was tested for birch clone identity effects by the presence of a particular birch clone in a particular plot. Significant results are indicated in bold fonts.
Pathogen richness Pathogen load [%]
Model Predictor Estimate P Estimate p
1 Intercept 1.937 <0.001 23.215 <0.001
Plot clone richness 0.124 0.006 -0.21 0.757
2 Blue 0.111 0.637 -5.016 0.128
Green -0.039 0.86 4.003 0.197
Orange 0.028 0.884 -4.683 0.081
Pink -0.012 0.961 -2.506 0.452
Red -0.472 0.039 -4.344 0.159
Violet 0.24 0.264 2.708 0.359
White 0.139 0.485 4.723 0.092
Yellow -0.022 0.912 4.22 0.124
Figure 4 A) Observed vs. predicted foliar fungal pathogen richness and B) observed vs. predicted foliar fungal pathogen load of all plots at community level (n = 36). Note that some dots for some plots are hidden behind others. The predictions are based on birch clone-specific mean richness and load values, respectively, of the monocultures and on the proportions of these birch clones in every plot. The lines are 1:1 lines and indicate a perfect prediction.
Table 3 Linear mixed effect model results at the individual level for effects of tree clone richness, tree clone diversity and tree clone density in the local neighbourhood on pathogen richness and pathogen load (%) encountered on all target individuals (n = 425). Significant results are indicated in bold fonts.
Pathogen richness Pathogen load [%]
Predictor Estimate p Estimate p
Intercept 1.825 <0.001 22.134 <0.001
Neighbourhood richness 0.013 0.559 0.221 0.724
Intercept 1.856 <0.001 22.282 <0.001
Neighbourhood diversity 0.002 0.976 0.569 0.756
Intercept 1.866 <0.001 23.608 <0.001
Blue density -0.092 0.517 -9.722 0.01
Intercept 1.864 <0.001 22.612 <0.001
Green density -0.071 0.607 0.474 0.9
Intercept 1.823 <0.001 22.264 <0.001
Orange density 0.204 0.068 2.311 0.473
Intercept 1.864 <0.001 23.035 <0.001
Pink density -0.09 0.554 -4.773 0.25
Intercept 1.944 <0.001 23.514 <0.001
Red density -0.769 <0.001 -7.513 0.034
Intercept 1.836 <0.001 21.518 <0.001
Violet density 0.173 0.196 9.208 0.012
Intercept 1.852 <0.001 22.343 <0.001
White density 0.034 0.775 2.178 0.511
Intercept 1.812 <0.001 22.18 <0.001
Yellow density 0.271 0.017 2.795 0.383
Separate analyses per birch clone at the tree clone level, revealed no effect of tree clone richness and tree clone diversity on pathogen species richness of any clone (Table 4).
However, both tree clone richness and tree clone diversity had a positive effect on pathogen load of the Blue clone (Table 4). In contrast, tree clone richness significantly affected pathogen load of the clone Green and marginally that of the clone Violet, with decreasing pathogen load with increasing genotype richness (Table 4). There was no effect of the density of a particular birch clone in the local neighbourhood of a target tree individual on the pathogen richness and pathogen load of the same target birch clone (Table 4). However, neighbourhood analyses of particular birch clones revealed positive density effects of the clones Blue and Violet and negative effects of the clones Green and Orange on pathogen richness of particular clones (Table 4). For instance, pathogen richness of the clone Orange increased with growing densities of Blue and Violet in the local neighbourhood. In addition, pathogen richness of the clone Red increased with the density of the clone Violet. In contrast, pathogen richness of the clone Violet was diminished with increasing density of the clones Green and Orange, and pathogen richness of the clone Yellow which decreased with increasing density of the clone Green in the local neighbourhood. Similar patterns were encountered for pathogen load of particular clones, which in many cases was positively or negatively influenced by the density of particular birch clones in the local neighbourhood (Table 4). For instance, pathogen load of the clones Blue and Red increased with the density of the clone Yellow. In contrast, pathogen load of the clone Violet was reduced by increasing density of the clones Blue and Orange in the local neighbourhood, whereas the clone Yellow was negatively affected by the density of the clone Green.
At the fungus species level, we could not detect any effects of tree clone richness and tree clone diversity of the local neighbourhood on the three fungus species (Table 5). As for overall pathogen load, pathogen load of the most abundant fungus D. betulina either increased or decreased with growing density of the clones Violet or Blue in the local neighbourhood, respectively (Table 5). In addition, pathogen load of V. ditricha became larger with the density of the clone Orange in the local neighbourhood, but decreased with the presence of the clone Red. Pathogen load of A. betulina was unaffected by the density of any particular birch clone.
Table 4 Linear mixed effect model results at the tree clone level for effects of tree clone richness, tree clone diversity and tree clone density in the local neighbourhood on pathogen richness and pathogen load (%) encountered on all target individuals, separately for all eight birch clones. Significant results are indicated in bold fonts. D = Density, E = Estimate, Nh = Neighbourhood.
Blue (n = 44) Green (n = 28) Orange (n = 80) Pink (n = 31) Red (n = 46) Violet (n = 59) White (n = 58) Yellow (n = 79)
Variable Predictor E p E p E p E p E p E p E p E p
Pathogen Intercept 2.007 <0.001 1.726 <0.001 2.033 <0.001 2.033 <0.001 1.109 <0.001 2.142 <0.001 1.702 <0.001 1.982 <0.001 richness Nh richness -0.01 0.519 0.005 0.937 0.006 0.739 -0.065 0.209 0.014 0.736 -0.043 0.09 0.053 0.17 -0.007 0.645
Intercept 2.005 <0.001 1.821 <0.001 2.021 <0.001 1.992 <0.001 1.115 <0.001 2.111 <0.001 1.749 <0.001 1.997 <0.001 Nh diversity -0.032 0.457 -0.086 0.607 0.034 0.557 -0.18 0.177 0.038 0.737 -0.104 0.161 0.127 0.267 -0.039 0.38 Intercept 1.953 <0.001 1.8 <0.001 2.013 <0.001 1.891 <0.001 1.154 <0.001 2.04 <0.001 1.837 <0.001 1.958 <0.001 Blue D 0.058 0.42 -0.617 0.315 0.636 <0.001 -0.566 0.246 -0.023 0.936 -0.685 0.052 0.376 0.497 0.181 0.572 Intercept 1.99 <0.001 1.582 <0.001 2.072 <0.001 1.849 <0.001 1.137 <0.001 2.029 <0.001 1.894 <0.001 1.985 <0.001 Green D -0.11 0.432 0.365 0.159 -0.245 0.206 -0.118 0.849 0.182 0.706 -1.491 0.049 -0.561 0.367 -0.559 0.017 Intercept 1.969 <0.001 1.754 <0.001 2.079 <0.001 1.851 <0.001 1.147 <0.001 2.12 <0.001 1.829 <0.001 1.955 <0.001 Orange D 0.115 0.575 -0.122 0.867 -0.07 0.489 -0.27 0.683 0.147 0.883 -1.513 <0.001 0.166 0.566 0.032 0.705 Intercept 2.002 <0.001 1.815 <0.001 2.067 <0.001 1.659 <0.001 1.23 <0.001 2.021 <0.001 1.858 <0.001 1.957 <0.001 Pink D -0.428 0.057 -0.446 0.471 -0.32 0.281 0.388 0.066 -0.637 0.093 -0.049 0.834 0.213 0.835 0.074 0.705 Intercept 1.992 <0.001 1.754 <0.001 2.048 <0.001 1.949 <0.001 1.217 <0.001 2.033 <0.001 1.848 <0.001 1.972 <0.001 Red D -0.071 0.498 -0.096 0.869 0.21 0.673 -1.379 0.056 -0.164 0.375 -0.226 0.379 0.947 0.483 -0.186 0.303 Intercept 1.974 <0.001 1.718 <0.001 2.008 <0.001 1.884 <0.001 1.052 <0.001 2.018 <0.001 1.877 <0.001 1.954 <0.001 Violet D 0.11 0.733 1.333 0.541 0.816 0.002 -0.539 0.367 1.18 0.011 >-0.001 0.998 -0.098 0.741 0.056 0.604 Intercept 1.97 <0.001 1.808 <0.001 2.072 <0.001 1.839 <0.001 1.159 <0.001 1.977 <0.001 1.928 <0.001 1.97 <0.001 White D 0.092 0.607 -1.617 0.145 -0.13 0.358 -0.015 0.987 -0.294 0.719 0.239 0.07 -0.16 0.421 -0.133 0.414 Intercept 1.974 <0.001 1.763 <0.001 2.062 <0.001 1.872 <0.001 1.096 <0.001 1.982 <0.001 1.825 <0.001 1.942 <0.001 Yellow D 0.131 0.741 -0.381 0.679 -0.048 0.686 -0.43 0.509 0.754 0.121 0.162 0.321 0.519 0.258 0.051 0.524
Blue (n = 44) Green (n = 28) Orange (n = 80) Pink (n = 31) Red (n = 46) Violet (n = 59) White (n = 58) Yellow (n = 79)
Variable Predictor E p E p E p E p E p E p E p E p
Pathogen Intercept 11.204 <0.001 29.36 <0.001 22.603 <0.001 12.051 <0.001 13.152 <0.001 32.713 <0.001 28.993 <0.001 27.187 <0.001 load [%] Nh richness 1.65 0.047 -2.357 0.048 -0.163 0.834 0.904 0.324 -0.123 0.88 -1.707 0.055 0.1 0.91 -0.275 0.803
Intercept 11.522 <0.001 27.187 <0.001 21.824 <0.001 12.179 <0.001 12.507 <0.001 31.192 <0.001 28.64 <0.001 28.389 <0.001 Nh diversity 5.109 0.025 -5.939 0.102 0.333 0.888 2.982 0.187 0.295 0.897 -3.855 0.117 0.729 0.782 -2.209 0.506 Intercept 18.472 <0.001 22.169 <0.001 22.275 <0.001 15.796 <0.001 13.632 <0.001 28.831 <0.001 29.342 <0.001 26.628 <0.001 Blue D -5.458 0.163 -6.549 0.567 -2.547 0.766 -8.148 0.356 -4.541 0.413 -28.734 0.05 -0.657 0.956 -10.716 0.621 Intercept 16.419 <0.001 18.053 <0.001 22.148 <0.001 13.687 <0.001 12.647 <0.001 27.811 <0.001 30.017 <0.001 27.625 <0.001 Green D 1.387 0.839 8.467 0.159 -0.259 0.972 11.412 0.269 1.846 0.843 8.343 0.794 -12.236 0.337 -28.694 0.047 Intercept 16.556 <0.001 23.198 <0.001 22.414 <0.001 14.783 <0.001 13.092 <0.001 29.512 <0.001 29.723 <0.001 25.259 <0.001 Orange D 0.783 0.942 -17.438 0.206 -0.752 0.854 2.135 0.845 -18.491 0.325 -26.531 0.008 -2.146 0.729 5.486 0.408 Intercept 17.956 <0.001 22.696 <0.001 21.692 <0.001 16.71 <0.001 12.595 <0.001 27.414 <0.001 29.057 <0.001 25.26 <0.001 Pink D -19.238 0.129 -6.738 0.565 9.76 0.397 -4.211 0.23 1.414 0.838 7.07 0.452 9.969 0.651 16.422 0.182 Intercept 15.946 <0.001 20.494 <0.001 22.007 <0.001 13.349 <0.001 13.859 <0.001 28.321 <0.001 29.168 <0.001 26.367 <0.001
Red D 2.912 0.615 9.049 0.384 7.6 0.7 18.241 0.113 -2.647 0.467 -6.57 0.528 8.388 0.763 -0.105 0.993
Intercept 15.989 <0.001 22.452 <0.001 22.235 <0.001 13.994 <0.001 10.98 <0.001 25.712 <0.001 27.785 <0.001 25.401 <0.001 Violet D 19.535 0.22 -44.651 0.253 -2.088 0.832 9.783 0.299 21.647 0.012 5.609 0.125 9.749 0.147 6.274 0.444 Intercept 16.837 <0.001 22.003 <0.001 22.634 <0.001 14.89 <0.001 13.285 <0.001 27.125 <0.001 31.297 <0.001 26.486 <0.001 White D -2.535 0.772 -10.948 0.622 -3.306 0.538 2.106 0.882 -16.13 0.305 4.518 0.415 -4.896 0.281 -1.876 0.87 Intercept 15.445 <0.001 22.202 <0.001 21.539 <0.001 14.864 <0.001 11.322 <0.001 29.454 <0.001 28.395 <0.001 28.871 <0.001 Yellow D 46.433 0.011 -11.906 0.464 2.864 0.546 0.99 0.926 20.362 0.014 -7.496 0.211 12.632 0.198 -6.493 0.294
Table 5 Linear mixed effect model results at the fungus species level for effects of tree clone richness, tree clone diversity and tree clone density in the local neighbourhood on pathogen load (%) encountered on all target individuals, separately for all fungus species (n = 425). Significant results are indicated in bold fonts.
Discula betulina Venturia ditricha Atopospora betulina
Predictor Estimate P Estimate p Estimate p
Intercept 19.143 <0.001 2.975 <0.001 0.011 0.325
Neighbourhood richness 0.31 0.609 -0.087 0.666 0.002 0.587
Intercept 19.415 <0.001 2.853 <0.001 0.01 0.316
Neighbourhood diversity 0.702 0.691 -0.128 0.826 0.007 0.438
Intercept 20.677 <0.001 2.902 <0.001 0.016 0.006
Blue density -8.203 0.025 -1.375 0.286 0.004 0.883
Intercept 19.663 <0.001 2.94 <0.001 0.018 0.002
Green density 2.187 0.549 -1.754 0.146 -0.017 0.542
Intercept 19.85 <0.001 2.068 <0.001 0.016 0.013
Orange density 0.181 0.954 4.19 <0.001 0.003 0.902
Intercept 20.212 <0.001 2.815 <0.001 0.019 0.001
Pink density -4.217 0.295 -0.598 0.659 -0.028 0.318
Intercept 20.456 <0.001 3.027 <0.001 0.019 0.001
Red density -5.049 0.151 -2.268 0.048 -0.027 0.29
Intercept 18.834 <0.001 2.665 <0.001 0.017 0.006
Violet density 8.423 0.018 0.825 0.51 -0.002 0.94
Intercept 19.538 <0.001 2.785 <0.001 0.013 0.032
White density 2.35 0.461 -0.126 0.908 0.026 0.26
Intercept 19.615 <0.001 2.578 <0.001 0.013 0.049
Yellow density 1.543 0.62 1.108 0.289 0.022 0.339