Date of Award

January 2013

Document Type


Degree Name

Doctor of Philosophy (PhD)



First Advisor

Jefferson A. Vaughan


Host-seeking mosquitoes are taxing for people and wildlife alike in the Red River Valley (RRV). During the summer massive numbers of mosquitoes swarm the RRV, yet little is known about the ecology and biology of the mosquito species that inhabit this area. This research will help to fill some of those knowledge gaps by studying the ecology of host seeking mosquitoes in the RRV.

Host-seeking mosquitoes were collected using CO2-baited MMXTM traps. Trapping was conducted in two very different rural settings within the RRV. One site, a 40-acre hardwood forest with closed canopy, the other a farmstead consisting of open agricultural fields interspersed with forested wind-rows. Trapping was conducted 2-3 times weekly throughout the mosquito season (May through August). Each night's catch was sorted, counted and identified to species. During sorting, all engorged and partially engorged mosquitoes were removed, identified to species and stored at -80°C.

DNA was extracted from individual mosquito blood meals and analyzed via polymerase chain reaction (PCR) assays multiple times to determine the host feeding preferences and parasitic infection status of the host. The first round of PCR assays determined the host species from which the blood originated (e.g., deer, dog, human, etc.). Analyzing the host composition of many mosquito blood meals produced information on the preference of host species that were most commonly fed upon by the various mosquito species within their natural environment. The following rounds of

PCR assays examined mosquito blood meals for the presence of blood-borne pathogens (e.g., filarial nematodes, avian malaria, etc.). This process, known as xenomonitoring, uses mosquitoes as a sampling tool to acquire blood samples from wildlife without having direct contact with the vertebrate host. Thus, xenomonitoring is an indirect way of estimating the prevalence of infection among vertebrate populations.

Mosquito counts from the forest and farm sites along with Grand Forks “Skeeter Meter” counts from the years of 2002-2010 were used to construct predictive models to understand the effects of climate on mosquito population dynamics and abundance throughout the summer. Generalized linear models are used to determine how climate variables play roles on everyday mosquito activity, while cross-correlation maps were used to determine correlation values of preceding weather variables to trap counts. This allowed for the determination of which climate variables can be used to predict how mosquito populations will fluctuate in the future.

This research provides a critical foundation by describing the species composition of mosquitoes that inhabit two unique rural study sites within the RRV. Species composition is crucial to the initial component of mosquito-borne vector transmission of diseases, presence of mosquito vectors. Building from the composition, this study provides information describing the population trends of multiple mosquito populations throughout the summers of 2009-2011 at these two rural sites. Because mosquito population trends differed between sites, several meteorological variables were identified as affectors of mosquito abundance and activity. By understanding how these meteorological factors affect mosquito populations, vital data is provided for the future design of predictive models that will allow for focused mosquito control, but also lend information in potential disease risk-assessment map production.

To further build on the potential for zoonotic and enzootic pathogen transmission, it is important to understand the feeding habits of local mosquito species. These feeding preferences determine which hosts are more commonly fed upon by given mosquito species and offer a background to determine which vector transmitted diseases are currently present in the RRV as well as potential diseases, that upon introduction to the region, which could be transmitted within the valley.