Abstract
Biodiversity, the variability of all living organisms, is declining at a rapid rate. To effectively target efforts for preventing biodiversity loss, monitoring declining diversity is necessary. Surveying efforts include non-invasive sampling, such as the use of environmental genetics. Environmental DNA (eDNA) methods have increasingly been used in this context. While eDNA techniques have previously been used for observing community and single-species biodiversity, our understanding of how to effectively use eDNA methods for answering population genetic questions remains limited. This body of work explores eDNA techniques for monitoring biological communities and focuses on developing eDNA methods for population-level genetic variation.
I start by asking how metazoan community composition varies across a gradient of four different water masses along the Munida oceanic transect. Using eDNA metabarcoding methods, I found differences in OTUs and genus-level biodiversity between water masses. These biological differences were mainly driven by planktonic biodiversity, not free-swimming fish. In addition, I found dissimilarities in biological composition between eDNA samples taken on the surface and at depth from the westernmost oceanic station in sub-Antarctic waters. These findings provide a snapshot of local biodiversity, adding to eDNA-observed metazoan biodiversity along the Munida transect.
Next, I move from genus-level taxonomy to population-level diversity and test how mitochondrial (mtDNA) haplotypic ratios vary with concentration. This experiment explores how PCR, sequencing, and bioinformatic pipelines affect population genetic eDNA outcomes. I find that the concentration of starting template plays a large role in observed haplotypic ratio, with low concentration haplotypic ratios being affected by stochastic PCR and sequencing processes. This study reinforces the importance of replication in eDNA population genetic study designs that want to identify rare genetic variation.
I apply this knowledge to then investigate how comparable eDNA-obtained mtDNA haplotypes are to tissue-obtained mtDNA haplotypes in a field setting. Using the taonga (cultural keystone) species blackfoot pāua (Haliotis iris) as a model organism, I recover common haplotypic variation with eDNA methods, but find that allelic drop-out occurs for rare variation. Furthermore, I find that sampling multiple sites may aid the bioinformatic detection of rare genetic variation. These findings improve our understanding of how eDNA studies may be designed to decrease allelic dropout. This study highlights the need for more extensive eDNA-tissue comparative testing.
Finally, I attempted to capture and compare whole mitogenomes from eDNA samples of two species with different life histories—pāua (H. iris) and New Zealand fur seals (Arctocephalus forsteri). I successfully captured fur seal mitogenomes with > 99% coverage from high-concentration samples, however, I was unsuccessful in the capture of pāua mitogenome eDNA. Potentially, I did not get enough pāua-specific eDNA in our samples to effectively capture mitogenome DNA. For fur seals, I showed that more than one individual’s genetic diversity could be captured in a single eDNA sample. Furthermore, some eDNA genetic variation matches previously sampled tissue-obtained mtDNA haplotypes, indicating our eDNA genetic variation likely reflected real genetic diversity. With this chapter, I demonstrated that eDNA methods have promise for population genetic monitoring, laying some of the groundwork for noninvasively monitoring the mtDNA diversity of a species with conservation interest.
Overall, this thesis shows eDNA techniques can be used as a broad-scale tool for monitoring biodiversity. I show this method can be used to monitor metazoan biodiversity across marine gradients and can be used to observe common mtDNA haplotypic variation for pāua and fur seals. While many challenges remain, such as allelic drop-out, individual organism identification, and bioinformatic and biostatistical hurdles, this thesis adds to the growing body of evidence demonstrating that eDNA shows promise as a non-invasive population genetic monitoring method.