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Differences in the composition of fungal communities could be explained mainly by the origin from above or below ground tissues for the HTS dataset, while the isol- ation dataset added more resolution separating above-ground communities clearly between stem and leaf associated fungi. Furthermore the composition of fungal communities was different in three sampling regions in New Zealand according to both datasets.

Whether a fungal community detected by HTS was from the above or below ground part of the cabbage plant explained 17.6% of the differences between com- munities, while the region where the host cabbage was grown explained 7.5% and the interaction between these two factors explained an additional 4.4% of the vari- ability at p = 0.001 (Figure 3.9).

Similarly fungal communities detected by isolation through culture were different in the various tissues from which they were isolated as well as region where the cabbage plant was grown (tissue effect was 24.8% at p = 0.001 and the region and the interaction both were 7.3% at p <0.01). However, some of the cabbage tissue samples had no fungi detected (2 stems and 1 root sample from Nelson and 1 root sample from Pukekohe) (Figure 3.9).

Communities in respect to site

Fourteen species were isolated from all three regions (Figure 3.10). Although more species were expected to be shared between the regions, those shared were some of the most abundant and included Alternaria alternata, Boeremia exigua, Cla- dosporium exasperatum, Fusarium circinatum, Fusarium equiseti, Fusarium sporo- trichoides,Monographella cucumerina,Plectosphaerella oratosquillae,Pyrenochaeta lycopersici and Stemphylium herbarum. Although isolated from all three regions, most of the Alternaria alternata strains originated from Nelson, while Fusarium equiseti was recovered from Nelson more than from Pukekohe. Distribution of spe- cies in the sampled regions is summarised on a heat map in Figure 3.11.

Similarly, 88 out of 314 species were detected in all three sampled regions by the sequence based approach (Figure 3.12). These were again some of the most abundant species across the whole dataset and include Acremonium alternatum, Alternaria eichhorniae, Boeremia strasseri, Cladosporium exasperatum, Chalasto- spora ellipsoidea, Didymosphaeria variabile,Fusarium circinatum,Fusarium equis- eti,Juncaceicola alpina,Monographella cucumerina, Plectosphaerella alismatis Py- renochaeta lycopersici, Stemphylium herbarum and Tetracladium setigerum. Again some of these species were unevenly distributed, as was the case with Didymo- sphaeria variabile andMonographella cucumerina detected mainly in samples from Pukekohe,Alternaria eichhorniae in Nelson orChalastospora ellipsoidea Ilyonectria sp. in Canterbury (Figures 3.13 – 3.16).

The trends inαdiversity were not consistent between HTS and isolation, as the lowest average number of species per plant was detected in Pukekohe by HTS (p

< 0.01) but this was not the same for the isolation dataset. However β diversity indices showed similar patterns for both methods where plants from Nelson had a significantly differentβ diversity than those in Canterbury (p <0.04). (Table 3.2).

Species (total) α diversity β diversity

Region HTS Isolation HTS Isolation HTS Isolation

Pukekohe 163 51 37.06* 12.75 0.3626 0.447

Nelson 202 53 47.88 13.63 0.3414 0.376

Canterbury 204 40 44.88 10 0.3809 0.4943

Table 3.2: Diversity indices for sampled regions and detection methods.

Communities in respect to plant part

The highest average number of species per plant was isolated from stems (p = 0.043), followed by leaves and roots. In contrast there was no significant difference between the number of species detected by HTS above and below ground. There were no differences in β diversity of plant tissues as detected by both methods (Table 3.3).

Species (total) α diversity β diversity

Plant part HTS Isolation HTS Isolation HTS Isolation

Leaves/Stems 235 46/54 26.73 4.41/6.33* 0.425 0.5209/0.506

Roots 230 41 26.44 4.13 0.4335 0.5172

Table 3.3: Diversity indices for sampled plant parts and detection methods. Nine species were isolated from all three plant parts, which was lower than ex- pected by chance (Figure 3.17). Fungi, such as Monographella cucumerina and Plectosphaerella oratosquillae, were recovered evenly from various plant tissues. Other taxa, although isolated from all three tissues, were predominnantly recovered from a certain tissue. Stemphylium herbarum and Cladosporium exasperatum were

entirely from stems, except there was one isolate of each species originating from roots. Pyrenochaeta lycopersici and Fusarium circinatum were isolated predomin- antly from roots (Figure 3.18).

The sequence-based approach detected 151 species shared in above and below ground samples, which was slighlty less than expected by chance (Figure 3.19). Con- sistent with the dataset obtained from isolation, some of these fungi were evenly dis- tributed (Monographella cucumerina,Plectosphaerella alismatis) or predominantly detected in samples from above ground tissues (Acremonium alternatum,Alternaria eichhorniae, Ascochyta phacae, Chalastospora ellipsoidea, Cladosporium exasper- atum,Entyloma cosmi,Erysiphe hypophylla,Gibellulopsis piscis,Stemphylium herb- arum and Ulocladium chartarum) or below ground tissues (Fusarium circinatum, Fusarium oxysporum,Ilyonectria sp. Juncaceicola alpina andPyrenochaeta lycoper- sici) (Figure 3.20 – 3.23).

Figure 3.2: Species ranked by the abundance (sum of frequencies) in samples (squareroot scale) as detected by high-throughput sequencing (HTS). Species detec-

Figure 3.3: (Continued from previous page) Species ranked by the abundance (sum of frequencies) in samples (squareroot-scale) as detected by high-throughput se- quencing (HTS). Species detected only by HTS are represented by red bars while species that were also isolated (Figure 3.5) are in turquoise (Continued on next

Figure 3.4: (Continued from previous page) Species ranked by the abundance (sum of frequencies) in samples (squareroot-scale) as detected by high-throughput se-

Figure 3.5: Species ranked by the total number of isolates. Those detected only by isolation are represented by red bars while species also detected by HTS (Figures

(a) HTS matched to isolation (b) isolation matched to HTS

Figure 3.6: Length and similarity of a BLAST match of sequences from either HTS or isolation dataset with the closest match from the other dataset. Sequences that were assigned species names found in both datasets are blue, while red circles repres- ent sequences that were assigned taxons unique for respective method of detection.

Figure 3.7: Phylum composition (rectangle area represents fraction of sequences from HTS or strains from isolation) of datasets as obtained by HTS (left) and

Figure 3.8: Species-accumulation curves plotted as obtained (solid line) and based on randomisation of samples (dashed line) with standard deviation (shaded area) are shown for high-throughput sequencing (blue) and isolation through culturing (red).

(a) Multidimensional scaling of fungal communities as detected by HTS

(b) Multidimensional scaling of fungal communities as detected by isolation

Figure 3.9: Multidimensional scaling of fungal communities as recovered from leaves (green), stems (olivegreen) or above ground tissues for HTS (green) and roots (brown) from 3 regions in New Zealand (Canterbury – circle, Nelson – triangle

(a) Observed

(b) Expected

Figure 3.10: Venn diagram showing observed(a) and expected(b) distribution of species in regions as detected by isolation. Expected numbers were based on random sampling of 9999 iterations with 95% confidence intervals shown (lower confidence interval - mean - upper confidence interval).

(a) Observed

(b) Expected

Figure 3.12: Venn diagram showing observed(a) and expected(b) distribution of species in sampled regions as detected by high-throughput sequencing. Expected numbers were based on random sampling of 9999 iterations with 95% confidence intervals shown (lower confidence interval - mean - upper confidence interval).

Figure 3.14: (Continued from previous page) Heat map showing distribution of species in sampled regions as detected by high-throughput sequencing (Continued on next page).

Figure 3.15: (Continued from previous page) Heat map showing distribution of species in sampled regions as detected by high-throughput sequencing (Continued

Figure 3.16: (Continued from previous page) Heat map showing distribution of species in sampled regions as detected by high-throughput sequencing.

(a) Observed

(b) Expected

Figure 3.17: Venn diagram showing observed(a) and expected(b) distribution of species in sampled plant parts as detected by isolation. Expected numbers were based on random sampling of 9999 iterations with 95% confidence intervals shown (lower confidence interval - mean - upper confidence interval).

Figure 3.18: Heat map showing distribution of species in sampled plant parts as detected by isolation.

(a) Observed

(b) Expected

Figure 3.19: Venn diagram showing observed(a) and expected(b) distribution of species in sampled plant parts as detected by high-throughput sequencing. Expec- ted numbers were based on random sampling of 9999 iterations with 95% confidence intervals shown (lower confidence interval - mean - upper confidence interval).

Figure 3.20: Heat map showing distribution of species in sampled plant parts as detected by high-throughput sequencing (Continued on next page).

Figure 3.21: (Continued from previous page) Heat map showing distribution of spe- cies in sampled plant parts as detected by high-throughput sequencing(Continued

Figure 3.22: (Continued from previous page) Heat map showing distribution of spe- cies in sampled plant parts as detected by high-throughput sequencing (Continued on next page).

3.4

Discussion