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.

We used rendezvous site locations of wolf (Canis lupus) packs recorded during 1996–2006 to build a predictive model of gray wolf rendezvous site habitat in Idaho, USA. Variables in our best model included green leaf biomass (Normalized Difference Vegetation Index), surface roughness, and profile curvature, indicating that wolves consistently used wet meadow complexes for rendezvous sites. We then used this predictive model to stratify habitat and guide survey efforts designed to document wolf pack distribution and fecundity in 4 study areas in Idaho. We detected all 15 wolf packs (32 wolf pack‐yr) and 20 out of 27 (74%) litters of pups by surveying <11% of the total study area. In addition, we were able to obtain detailed observations on wolf packs (e.g., hair and scat samples) once we located their rendezvous sites. Given an expected decrease in the ability of managers to maintain radiocollar contact with all of the wolf packs in the northern Rocky Mountains, rendezvous sites predicted by our model can be the starting point and foundation for targeted sampling and future wolf population monitoring surveys.

We investigated the influence of sampling location within a faeces on DNA quality by sampling from both the outside and inside of 25 brown bear (Ursus arctos) scats and the side and the tip of 30 grey wolf (Canis lupus) scats. The outside of the bear scat and side of the wolf scat had significantly lower nuclear DNA microsatellite allelic dropout error rates (U. arctos: P = 0.017; C. lupus: P = 0.025) and significantly higher finalized genotyping success rates (U. arctos: P = 0.017; C. lupus: P = 0.012) than the tip and inside of the scat. A review of the faecal DNA literature indicated that <45% of studies report the sampling location within a faeces indicating that this methodological consideration is currently underappreciated. Based on our results, we recommend sampling from the side of canid scats and the outside portion of ursid scats to obtain higher quality DNA samples. The sampling location within a faeces should be carefully considered and reported as it can directly influence laboratory costs and efficiency, as well as the ability to obtain reliable genotypes.

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Various monitoring methods have been developed for large carnivores, but not all are practical or sufficiently accurate for long-term monitoring over large spatial scales. From 2009 to 2010, we used a predictive habitat model to locate gray wolf rendezvous sites in 4 study areas in Idaho, USA and conducted noninvasive genetic sampling (NGS) of scat and hair found at the sites. We evaluated species and individual identification PCR success rates across the study areas, and estimated population size with a single-session population estimator using 2 different recapture-coding methods. We then compared NGS population estimates to estimates generated concurrently from telemetry data. We collected 1,937 scat and 166 hair samples and identified 193 unique individuals over 2 years. For fecal DNA samples, species identification success rates were consistently high (>92%) across areas. Individual identification success rates ranged from 78% to 80% in the drier study areas and dropped to 50% in the wettest study area. The degree of agreement between NGS-and telemetry-derived population estimates varied by recapture-coding method with considerable variability in 95% confidence intervals. Population estimates derived from NGS methods were most influenced by the average number of detections per individual. We demonstrate how changes in field effort and recapture-coding method can affect population estimates in a widely used single-session population estimation model. Our study highlights the need to further develop reliable population estimation tools for single-session NGS data, especially those with large differences in capture frequencies among individuals stemming from severe capture heterogeneity (i.e., overdispersion). Ó