Results and Discussion

Results and Discussion

Number of species and genus per site by observer

This presence absence data shows in general there are several more species identified using rbcL and matK then with common conventional methodology (Fig.9). There is a potential that even when we removed the singletons, there is still too much noise in the sequencing data, overpopulating the community data with diverse taxa. When it is resolved to genus, there is still more genus identified that are collected. This indicates that there may be issues surrounding the cleanup of sequencing data and I will work with a bioinformatician in the future to clarify the signal to noise in the sequencing data. 

Figure 9: A comparison of the number of species  and genera by the observer at the different sites. 

Different issues at different sites

The NMDS ordinations in Figure 10 and 11 and breaking up the data by ecoregion and then coloured by site and shaped by the survey method. We see clear overlap between the surveyor one and two for both ecoregions where as our genetics methods are telling a different story. In the grasslands (Fig 10), we see Genetics 1 clustered together and Genetics 2 clustered separately. Each of these primer sets are capturing different parts of the community. In the forest ecoregion (Fig 11), we see that the Surveyors one and two have quite a bit of overlap in these three sites, capturing 3 different communities. The Genetics methods are a bit less clustered, however they are distinct from the conventional surveys. 

Conclusion

In conclusion, these analyses underline the complex, variable community compositions within a habitat and how monitoring methods vary in their biodiversity estimates. By comparing the different methodologies to tease apart the diversity of a site we can see differences in species identification using genetics methods. By grouping the composition by the different species assemblages, we can begin to understand the patterns by ecosite and sites themselves. By clustering OTUs in metabarcoding data, there is a potential that there can be a misassignment to species level ID. Instead, by setting the division of sequences into ASVs (amplicon sequence variants) and filtering lower quality reads, has the potential to solve this issue. By visualizing these communities we can explore the optimization of species identification using molecular methods. 

As biodiversity trends continue to decline, species identification and assessment will become increasingly important. By comparing the different monitoring tools we can understand the most efficient way forward. The ability to detect consistent patterns in plant assemblages across different methodologies suggests that biodiversity monitoring could benefit from integrating multiple methods, combining both conventional morphological methods and molecular approaches to get a fuller picture of community. Monitoring biological diversity is important for understanding ecological processes such as succession and disturbance and testing different ways to estimate biodiversity, which is key for their management and protection.