The rapid improvement of camera traps in recent decades has revolutionized biodiversity monitoring. Despite clear applications in conservation science, camera traps have seldom been used to model the abundance of unmarked animal populations. We sought to summarize the challenges facing abundance estimation of unmarked animals, compile an overview of existing analytical frameworks, and provide guidance for practitioners seeking a suitable method. When a camera records multiple detections of an unmarked animal, one cannot determine whether the images represent multiple mobile individuals or a single individual repeatedly entering the camera viewshed. Furthermore, animal movement obfuscates a clear definition of the sampling area and, as a result, the area to which an abundance estimate corresponds. Recognizing these challenges, we identified 6 analytical approaches and reviewed 927 camera‐trap studies published from 2014 to 2019 to assess the use and prevalence of each method. Only about 5% of the studies used any of the abundance‐estimation methods we identified. Most of these studies estimated local abundance or covariate relationships rather than predicting abundance or density over broader areas. Next, for each analytical approach, we compiled the data requirements, assumptions, advantages, and disadvantages to help practitioners navigate the landscape of abundance estimation methods. When seeking an appropriate method, practitioners should evaluate the life history of the focal taxa, carefully define the area of the sampling frame, and consider what types of data collection are possible. The challenge of estimating abundance of unmarked animal populations persists; although multiple methods exist, no one method is optimal for camera‐trap data under all circumstances. As analytical frameworks continue to evolve and abundance estimation of unmarked animals becomes increasingly common, camera traps will become even more important for informing conservation decision‐making.

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.

Rapid change in wildlife populations can challenge managers to promote species conservation while maintaining public support for wildlife. Wolf management during recolonization in Wisconsin, United States demonstrates the complexities of inconsistent management authority, public attitudes, and illegal killing of wolves. State management authority to control depredating wolves oscillated during a period of intense sociopolitical conflict over wolf status under the federal Endangered Species Act. We demonstrate that swings in wolf status led to inconsistent management authority, declining local public support for wolves, and possibly the unintended backlash of more illegal kills and a legislatively mandated public wolf hunt. A new Wildlife Management Matrix illustrates an idealized relationship between lethal control options and perceptions of wildlife. Moderating the sociopolitical drivers of swings in policy over short periods is essential to allow wildlife managers greater flexibility in achieving species-specific goals. To our knowledge, this research provides the first demonstrated link between illegal wildlife killing and management authority under the Endangered Species Act, and suggests that illegal behavior may be moderated with responsible and effective wildlife management programs. We recommend states avoid prescriptive harvest legislation, and we suggest a more incremental shift from federal to state management authority.

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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.