title: Elk Abundance Estimation and Road Ecology in Whatcom and Skagit Counties, Washington
Creators: name: Rice, Nathan C.
Publisher: name: Western Washington University
Year: 2017

description: <p>Chapter 1 – Elk abundance estimation using genetic mark-recapture in the South Fork Nooksack Valley, Whatcom County Washington. Non-invasive genetic mark-recapture is an increasingly useful method for estimating the abundance of elusive wildlife. This method was used to estimate the size of an elk population (Cervus canadensis) in the South Fork Nooksack River valley in northwestern Washington where dense forest cover can hamper aerial surveys. We genotyped 250 elk fecal DNA samples that were collected in a single sampling session. Only 103 samples amplified sufficiently after one PCR for genotype matching, which resulted in 49 unique genotypes. Program Capwire estimated a population size of 91 elk (95% CI = 83 - 130), possibly an underestimate of actual abundance. Unfortunately, funding limitations precluded necessary lab work to determine consensus genotypes so genotyping errors could not be corrected. For this reason, these results must be considered with caution. While genetic mark-recapture has many advantages over traditional mark-recapture methods, the potential for genotyping error can inflate laboratory expenses and should be carefully considered. Chapter 2 – Elk road ecology on state Highway 20 in Skagit Valley, Skagit County, Washington. Wildlife-vehicle collisions pose a significant hazard to humans and wildlife. In Skagit Valley, Washington,158 elk (Cervus canadensis) roadkills were documented between 2002 and 2014 on 34.8 kilometers of state highway 20 between the towns of Sedro-Woolley and Concrete. In the current study, I documented road crossing activity between July and December 2013 between the towns of Sedro-Woolley and Concrete using string traps and remote cameras on game trails (n = 722 trail detections). Roadkill data were compiled from agency reports over comparable time periods for spatial analysis (July to December 2013 (n = 22)) and modeling (January 2012 to January 2014 (n =103)). Roadkill locations were weakly correlated with road crossing locations across the study area (Kendall’s tau = 0.23, P < 0.001). Statistically significant hotspots were found for roadkills (n = 4) and road crossing activity (n = 5) (P < 0.05). One roadkill hotspot coincided with one road crossing hotspot. Presence / absence of road crossing activity and roadkills in 216 0.16-km road segments were each modeled against 10 habitat variables and 4 road variables using logistic regression. The best road crossing model indicated that road crossing activity was negatively associated with distance to forest, distance to streams, distance to crops, percent developed area, and guardrail length. Road crossing predictors with the highest relative importance values in the best model were Distance to forest (RI = 1.00), Distance to crops (RI = 1.00), and Distance to streams (RI = 1.00); however, Distance to streams had 95% confidence intervals containing zero. The best roadkill model indicated that roadkills were negatively associated with distance to pasture/hay, percent developed area, and roadside slope, and positively associated with percent forest cover. Roadkill predictors with the highest relative importance values were Distance to pasture/hay (RI = 01.00) and Percent forest cover (RI = 1.00). Understanding the spatial distribution of road crossing activity and roadkills, combined with the habitat and road factors associated with them, can inform management of wildlife and vehicles in rural areas.</p>