Targeting environmental DNA (eDNA) for species monitoring and biodiversity assessment is a newly emerged technique. Surveys targeting eDNA involve the isolation of DNA shed into the environment by an organism to identify species utilizing a particular location. Despite uncertainties surrounding the technique, eDNA has begun to be used extensively for species assessments. Using the great crested newt (Triturus cristatus) as a model species, we (1) determined seasonal trends in eDNA with a view to optimising survey timing; (2) estimated the detection probabilities for eDNA and their covariates; and (3) explored how abundance estimates may be made from aquatic eDNA samples. We conclude that detection varies through the year, with most reliable detection coinciding with peak breeding. However, outside the breeding season detection is possible where larval numbers are high. Environmental and population factors may influence release of DNA from a target species or eDNA persistence in water and sediments. These include sediment type, number of both adults and larvae, changes in adult body condition, habitat variables and sampling location. As many external covariates were found to influence eDNA concentration, it would not be appropriate to use eDNA concentration as a predictor of abundance. However; we apply a modelling approach to generate estimates of abundance using genomic DNA, with a degree of accuracy deemed acceptable for ecological monitoring. The conclusions are directly relevant to refining survey design and analysis for the assessment of great crested newt populations. The results are also applicable more generally to the eDNA survey method, its development, survey design and interpretation, whether for single species analysis or community analysis.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:754840 |
| Date | January 2018 |
| Creators | Buxton, Andrew Stephen |
| Contributors | Griffiths, Richard ; Groombridge, Jim |
| Publisher | University of Kent |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | https://kar.kent.ac.uk/68449/ |
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