Date of Award

Winter 2008

Abstract

Abundance estimates for black bears (Ursus americanus) are an important tool for effective management. Recent advancements in DNA technology have enabled genetic tagging mark-recapture population estimates using DNA from hair samples. I conducted a population estimate using genetic tagging in 2 study sites presumed to have different bear densities in northern New Hampshire (Pittsburg and Milan). To test repeatability, I conducted the genetic tagging estimates in 2 consecutive years. I also compared these estimates to those derived from traditional methods used by the New Hampshire Fish and Game Department (NHFG) using hunter harvest and mortality data. I found that the density estimates produced from the genetic tagging methods were consistent in the 2 years, and were similar to those derived from traditional methods. In 2006, the estimated number of bears in Pittsburg (315 km2) was 70, corresponding to a density of 0.16-0.28 (95% CI) bears/km2 . In 2007, the Pittsburg (400 km2) estimate was similar: 78 bears with a density of 0.15-0.24 bears/km2. In Milan (440 km2) during 2006, the estimated number of bears was 106 corresponding to a density of 0.13-0.35 bears/km2. The 2007 Milan estimate (371 km2) was similar with 99 bears and a density of 0.19-0.34 bears/km2. While the traditional methods may be appropriate and more cost effective for density estimation at a regional scale, I found that the genetic tagging methods were able to detect demographic variation at a local scale. In addition to generating population estimates, I used the genetic information to identify population and spatial genetic structure and to determine if landscape features such as roads and rivers caused resistance to gene flow. I tested for population distinction using the program STRUCTURE, FST values, and a mean relatedness function. I used a Mantel test of isolation by distance and spatial autocorrelation for the spatial analyses. To assess landscape resistance, I used an analysis of mean relatedness between subpopulations divided by landscape features. Through consensus, I found that the 2 study sites were genetically distinct (F ST = 0.024, P = 0.05). I also found a positive relationship between genetic and geographic distance (R = 0.13, P>0.0001), and that females showed spatial autocorrelation through 5 km. Regarding landscape resistance to gene flow, I found that the presence of Route 3 in Pittsburg did not cause genetic differentiation between subpopulations on either side of the road, while the Route 16-Androscoggin River corridor in Milan influenced the genetic population structure of females.

First Advisor

Peter Pekins

Department or Program

Natural Resources and the Environment: Wildlife Ecology

Degree Name

Master of Science

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