Date of Award
2017
Publication Type
Master Thesis
Degree Name
M.Sc.
Department
Earth and Environmental Sciences
Supervisor
MacIsaac, Hugh
Rights
info:eu-repo/semantics/openAccess
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Abstract
Non-indigenous species (NIS) newly introduced to a novel environment usually experience a lag time before the population grows to a detectable level. Management of the NIS during the lag phase provides a better opportunity for eradication than at later stages when the population is larger and established. However, low population density limits detection by conventional methods. Here I tested the effect of intensive sampling on a population of a newly introduced NIS, zebra mussels (Dreissena polymorpha), in Lake Winnipeg. Zebra mussel presence can be determined by the presence of their larvae (veligers). I hypothesized that veligers will be detected in the south basin where they were previously reported, but not in the north basin where they were never reported. I also compared detection success as well as the cost and time of three methods of analysis of plankton samples: cross-polarized light microscopy (CPLM), flow cytometry and microscopy (FlowCAM), and environmental DNA (eDNA). I detected veligers throughout Lake Winnipeg, even in the north basin, with varying abundances. As expected, veliger abundance was highest in the south, and very low in the north. Abundance and prevalence were significantly lower with FlowCAM and eDNA analysis, indicating lower success when compared to CPLM. FlowCAM is the most expensive method used, while eDNA is the least expensive. eDNA represents the cheapest and fastest method, and combined with intensive sampling, is the best candidate for wide scale zebra mussel monitoring programs for rapid response.
Recommended Citation
Lavigne, Sharon, "Intensive sampling and comparison of methods in detection of non-indigenous species" (2017). Electronic Theses and Dissertations. 7372.
https://scholar.uwindsor.ca/etd/7372