Robert Lonsinger, Ph.D.
Phone: (715) 346-2755
Office: TNR 344
Robert Lonsinger is a Pennsylvania native, though he has spent his career working across the intermountain west from southern New Mexico up to northern Idaho. Rob's past research has focused on carnivore ecology and conservation and often utilizes molecular ecology techniques. Rob has extensive experience investigating, managing and reintroducing imperiled species across the west. Prior to joining the University of Wisconsin-Stevens Point, Rob worked with non-profit organizations, state and federal agencies, and tribal nations. Rob remains active in The Wildlife Society (including the Molecular Ecology and Military Lands working groups) and the American Society of Mammalogists.
- B.S. Biology, Gannon University
- M.S. Wildlife Science, New Mexico State University
- Ph.D. Natural Resources, University of Idaho
Current Research Projects
Rob is an applied ecologist. His professional interests include conservation genetics, landscape ecology, and the roles of predation and competition in driving space-use by species. Rob finds carnivores to be particularly interesting. He also finds desert systems and the behavioral and physiological adaptions required for species to persist in these harsh environments intriguing. Rob's research tends to utilize carnivore species as model organisms to (1) advance our understanding of ecological processes and (2) improve and inform wildlife monitoring and management strategies. Additional information on previous and ongoing research can be found at http://www.roblonsinger.com/research.html.
For a list of publications please visit http://www.roblonsinger.com/publications.html.
ConGenR is an R based conservation genetics script that facilitates rapid determination of consensus genotypes from replicated samples, determines amplification success rates, and quantifies genotyping error rates. ConGenR is intended for use with codominant, multilocus microsatellite data generated primarily through noninvasive genetic sampling and processed with a multi-tubes approach. Amplification success and genotyping error rates can be evaluated by sample class and by locus, offering insights into processes driving amplification success and genotyping error rates, and expediting the identification of problematic loci, respectively. ConGenR also supports matching of multilocus consensus genotypes, identifying samples with fully or partially matching multilocus genotypes. An option to consider or ignore loci with uncertainty provides a flexible framework for identifying matches even when uncertainty exists. ConGenR allows the incorporation of sample location data, which can be numeric (i.e., XY data, such as UTMs) or categorical (e.g., region, county, study area). When numeric locations are provided, the results will include a distance between each focal sample (the sample to which other samples are being compared) and each sample determined to be a match. Alternatively, if locations are categorical, the location of each sample will be added to the result file, facilitating comparisons between the focal sample and matching samples. More information is available at http://www.roblonsinger.com/congenr.html.