Research

Current projects –

Establishing an environmental DNA sampling program for the Rice Creek Watershed

Understanding the distribution and relative abundance of rare species is often difficult, yet essential to the development and implementation of effective conservation strategies. Environmental DNA (eDNA) is DNA that is released from plants and animals into the environment. eDNA methods are a new and highly effective approach for sampling rare species. Currently, there are critical uncertainties that must be studied to improve the field’s understanding of how eDNAs are transported through aquatic systems, how long eDNAs stay there, and where eDNAs are in high quantities – i.e., questions related to the ‘ecology’ of eDNA. Rice Creek Field Station is an ideal location to begin a long term eDNA monitoring program that could aid in our understanding of the factors that affect where and when rare species are detected.

Testing for genomic signatures of rapid adaptation in a Great Lakes invasive species

Invasive species have negatively affected ecosystems and economies globally. Given these deleterious effects, conservation agencies are studying a range of ecological and evolutionary questions associated with invasive species that may help to prevent the establishment of invasive species in non-native habitats and limit the spread of established populations.

From an evolutionary perspective, many invasive species have demonstrated the remarkable capacity to rapidly adapt to novel environments despite reduced genetic variation in the initial population. Such rapid adaptation is an intriguing, yet not well-understood aspect in evolutionary ecology. Currently, research suggests that rapid adaptation is possible if sufficient genetic variation is generated by several processes including multiple introductions – the mixing of genetic variation from multiple, independent populations.

Rainbow Smelt (Osmerus mordax) originally invaded the Great Lakes in 1923 through an accidental introduction into Lake Michigan from a stocked population in Crystal Lake, a small inland lake that drains into the Great Lakes. Initially, Rainbow Smelt were observed in Lake Michigan and Lake Superior in 1923, Lake Huron in 1925, and by 1935 they were observed in Lake Erie (Emery 1985). Interestingly, Rainbow Smelt were first observed in Lake Ontario in 1929, six years prior to that of Lake Erie (Emery 1985). Bergstedt (1983), a United States Geological Survey (USGS) fisheries scientist stationed in Oswego, proposed an alternative hypothesis that stated it was possible for Lake Ontario to have been established by an anadromous (fish that live in the ocean but spawn in rivers) strain from the Atlantic ocean. He proposed this because an anadromous strain was already being stocked in the Finger Lakes, which drain into Lake Ontario, at the time Rainbow Smelt were invading the upper Great Lakes. Bergstedt (1983) then suggested that Lake Ontario was subsequently colonized by Rainbow Smelt from the upper Great Lakes. The potential for multiple introductions into Lake Ontario may have provided additional genetic variation for local adaptation to occur, yet largely remains uninvestigated. This project seeks to genotype a genomic-scale set of single nucleotide polymorphisms to test the above competing hypotheses using population genetics theory. In addition, we will test for signatures of rapid adaption to the Great Lakes.

Testing the limits of genetic pedigree reconstruction methods in the genomics era

The use of genetic pedigree reconstruction methods to answer questions related to ecology and evolution has long and rich history. The basic approach is simple – based on Mendelian inheritance, related individuals can be identified with information associated with the alleles shared across a set of loci. Most computer programs associated with genetic parentage assignments, or more broadly genetic pedigree reconstruction, are based on sophisticated mathematical models that rely on maximum likelihood or Bayesian statistics. Most programs enable the identification of parent-offspring relationships or the identification of self using a couple dozen loci. Currently, researchers studying non-model organisms alike are able to genotype individuals of interest using thousands of loci, thereby increasing the power to detect more distant familial relationships – half-siblings, first-cousins, etc. However, much of the parameter space associated with the application of genomic-scale datasets in the context of commonly used programs like COLONY have yet to be explored. Students involved with this project will use a mixture of R and Linux coding on high performance computing clusters to study questions related to power, false-negative assignment rates, and violating program assumptions. Students will also be involved in writing, testing, and maintaining lab R packages on GitHub.

If you have your own research question, please don’t hesitate to share your ideas. There are ways to fund student-centered research questions.