Management and Conservation Article Efficient, Noninvasive Genetic Sampling for Monitoring Reintroduced Wolves JENNIFER L. STENGLEIN, University of Idaho, Department of Fish and Wildlife Resources, Moscow, ID 83844, USA LISETTE P. WAITS, 1 University of Idaho, Department of Fish and Wildlife Resources, P.O. Box 441136, Moscow, ID 83844, USA DAVID E. AUSBAND, Montana Cooperative Wildlife Research Unit, 205 Natural Sciences Building, University of Montana, Missoula, MT 59812, USA PETER ZAGER, Idaho Department of Fish and Game, 3316 16th Street, Lewiston, ID 83501, USA CURT M. MACK, Nez Perce Tribe, Gray Wolf Recovery Project, P.O. Box 1922, McCall, ID 83638, USA ABSTRACT Traditional methods of monitoring gray wolves (Canis lupus) are expensive and invasive and require extensive efforts to capture individual animals. Noninvasive genetic sampling (NGS) is an alternative method that can provide data to answer management questions and complement already-existing methods. In a 2-year study, we tested this approach for Idaho gray wolves in areas of known high and low wolf density. To focus sampling efforts across a large study area and increase our chances of detecting reproductive packs, we visited 964 areas with landscape characteristics similar to known wolf rendezvous sites. We collected scat or hair samples from 20% of sites and identified 122 wolves, using 8–9 microsatellite loci. We used the minimum count of wolves to accurately detect known differences in wolf density. Maximum likelihood and Bayesian single-session population estimators performed similarly and accurately estimated the population size, compared with a radiotelemetry population estimate, in both years, and an average of 1.7 captures per individual were necessary for achieving accurate population estimates. Subsampling scenarios revealed that both scat and hair samples were important for achieving accurate population estimates, but visiting 75% and 50% of the sites still gave reasonable estimates and reduced costs. Our research provides managers with an efficient and accurate method for monitoring high-density and low-density wolf populations in remote areas. KEY WORDS Canis lupus, noninvasive genetic monitoring, population density, population estimation, probability of capture, wolf. Since their reintroduction into the Northern Rocky Moun- tain (NRM) ecosystem, gray wolves (Canis lupus) have been carefully monitored, primarily using radiotelemetry. Radio- telemetry is a preferred method for monitoring small populations of wolves because it is reliable for obtaining territory size, calculating density, determining dispersal distance, and documenting pack size and breeding status (Fritts 1983, Fuller and Snow 1988, Ballard et al. 1998, Mitchell et al. 2008). Although telemetry has many advantages, it is labor intensive and requires trapping and handling of animals, and it can typically be maintained only in a small subset of the population or at a smaller spatial scale (Kunkel et al. 2005). Also, radiotelemetry largely depends on visual detection from the air for pack counts (Fuller and Snow 1988), so it is potentially less effective in areas with dense tree cover. As the NRM wolf population grows and stabilizes, it will be difficult to maintain radiocollars in a high percentage of the packs, but the requirements for monitoring will persist. For long-term management of NRM wolves, it is imperative to develop a cost-effective and efficient method for monitoring the population. Noninvasive monitoring techniques, such as howling surveys, winter tracking, hair collection, camera trapping, and scat surveys have been useful for monitoring carnivores (Harrington and Mech 1982a, Paquet 1991, Lucchini et al. 2002, Clevenger and Waltho 2005, Long et al. 2008). Even though they exist in low densities, wolves are well-suited to noninvasive monitoring because they are territorial and often leave sign in prominent places along roads, trails, or junctions (Barja et al. 2004, MacKay et al. 2008). Of these methods, scat surveys are an established wildlife manage- ment technique that has been shown to be useful in detecting the presence of a target species, but it has limited utility in monitoring population trends because individuals cannot be identified and field identification of sign is difficult (Reynolds and Aebischer 1991, Davison et al. 2002). With genetic analysis, however, individuals can be identified from scat samples, providing a method for monitoring wolf populations (Waits and Paetkau 2005). Long-term wildlife monitoring programs require methods that can efficiently produce reliable data annually, and genetic monitoring has been proven a useful tool (Schwartz et al. 2006). A monitoring method using genetic data collected noninvasively can produce minimum counts and home ranges, pack counts, accurate population estimates, and territory sizes and can document breeding status of individual packs (Taberlet et al. 1997, Lucchini et al. 2002, Lukacs and Burnham 2005, Adams 2006, Ausband et al. 2010). Over time, a genetic monitoring program could assess connectivity between recovery areas, track population trends, detect hybridization events, and assess genetic impacts from harvest (Adams et al. 2003, Prugh et al. 2005, Williams et al. 2009). To assess whether noninvasive genetic sampling (NGS) could effectively and efficiently contribute to long-term gray wolf monitoring, we conducted a 2-year study in Idaho, USA. We surveyed areas with a high probability of having reproductive wolf packs. Our objectives were to 1) estimate the minimum number of 1 E-mail: lwaits@uidaho.edu Journal of Wildlife Management 74(5):1050–1058; 2010; DOI: 10.2193/2009-305 1050 The Journal of Wildlife Management N 74(5)