97db79a2-fe69-42a1-9652-e9ecca73df3b
eDNA metabarcoding (marker: COI) of reef and mangrove habitats in Honduras and Kenya
These raw data come from a two-year eDNA metabarcoding study in reef and mangrove habitats in Tela and Utila, Honduras (L1-L3 files) and from one sampling session in Watamu, Kenya (L4 files).   Primer pair used: mlCOIintF/jgHCO2198  Metadata for analyses and tag sequences for demultiplexing can be found as restricted access files in Zenodo:  https://doi.org/10.5281/zenodo.5825652   Note that sequences are in mixed orientations (both fwd and rev primers present in R1 and R2), as final sequence adapters were ligated on the amplicons using a PCR free ligation method.  Abstract lightly modified from thesis summary: The ability of metabarcoding to reflect currently accepted biodiversity patterns was assessed. I used standardised methods across two vastly different yet characteristically similar regions, namely the Caribbean Sea and the Western Indian Ocean, to be in a position to directly compare how metabarcoding performs in different environments. Despite quite a range of habitats being studied with metabarcoding to date, to my knowledge no one has done a comparison of this scale to evaluate metabarcoding performance. The results of this study show clearly how an understanding of underlying biodiversity levels is crucial for effective experimental and monitoring design, as well as bioinformatics processing when using metabarcoding approaches.   The second part of analysis took a closer look at the temporal data, by analysing the data from the reef and mangrove habitats in Tela, Honduras, that were sampled four times over two years. Metabarcoding efforts often suffer from a lack of resolution as a result of gaps in reference databases, and therefore this chapter examined the differences in spatio-temporal patterns that could be found whether using taxonomically higher resolution data or data that purely passed through bioinformatics processes and was thus assumed to be of good quality. The taxonomically unassignable data was found to be more informative in terms of finding temporal changes, yet it is clear that taxonomic information of some level is required to draw conclusions and make predictions from the observed changes.
These raw data come from a two-year eDNA metabarcoding study in reef and mangrove habitats in Tela and Utila, Honduras (L1-L3 files) and from one sampling session in Watamu, Kenya (L4 files).   Primer pair used: mlCOIintF/jgHCO2198  Metadata for analyses and tag sequences for demultiplexing can be found as restricted access files in Zenodo:  https://doi.org/10.5281/zenodo.5825652   Note that sequences are in mixed orientations (both fwd and rev primers present in R1 and R2), as final sequence adapters were ligated on the amplicons using a PCR free ligation method.  Abstract lightly modified from thesis summary: The ability of metabarcoding to reflect currently accepted biodiversity patterns was assessed. I used standardised methods across two vastly different yet characteristically similar regions, namely the Caribbean Sea and the Western Indian Ocean, to be in a position to directly compare how metabarcoding performs in different environments. Despite quite a range of habitats being studied with metabarcoding to date, to my knowledge no one has done a comparison of this scale to evaluate metabarcoding performance. The results of this study show clearly how an understanding of underlying biodiversity levels is crucial for effective experimental and monitoring design, as well as bioinformatics processing when using metabarcoding approaches.   The second part of analysis took a closer look at the temporal data, by analysing the data from the reef and mangrove habitats in Tela, Honduras, that were sampled four times over two years. Metabarcoding efforts often suffer from a lack of resolution as a result of gaps in reference databases, and therefore this chapter examined the differences in spatio-temporal patterns that could be found whether using taxonomically higher resolution data or data that purely passed through bioinformatics processes and was thus assumed to be of good quality. The taxonomically unassignable data was found to be more informative in terms of finding temporal changes, yet it is clear that taxonomic information of some level is required to draw conclusions and make predictions from the observed changes.