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.

The abundance of low-density species like carnivores is logistically difficult to directly estimate at a meaningful scale. Predictive distribution models are often used as a surrogate for density estimation. But because density can continue to increase as occupancy asymptotes at 1, occupancy may have little value as an index, and home range expansion in marginal habitat may further confound the association. We sought to estimate bobcat population size at a landscape scale (14,286 km 2 ) in central Wisconsin, which provided an opportunity to relate predicted occurrence to individual space use and population density. We sampled bobcats using motion-sensitive trail cameras at 9 arrays across central Wisconsin. We estimated bobcat sitespecific occupancy, and regressed these estimates as linear or asymptotic functions of site-specific density to determine the strength and shape of their association. We subsequently modeled both parameters relative to habitat covariates and repeated the regression process. A linear functional relationship between density and occupancy was most supported when detection parameters were held constant (w i ¼ 0.97, R 2 ¼ 0.72) and when detection, occurrence, and density were modeled as a function of habitat covariates (w i ¼ 0.99, R 2 ¼ 0.95). This suggests that repeated presence-absence data alone may be an efficient and reliable method for inferring spatial patterns in bobcat density or identifying habitat types with greater density potential in the northern parts of its range. Bobcat occupancy and density were both positively associated with surrounding woody cover and wetland edge density. Our most supported spatially explicit capture-recapture model estimated bobcat abundance as 362 adult individuals (95% CI 272-490) across the study area.

It is common to use multiple field sampling methods when implementing wildlife surveys to compare method efficacy or cost efficiency, integrate distinct pieces of information provided by separate methods, or evaluate method-specific biases and misclassification error. Existing models that combine information from multiple field methods or sampling devices permit rigorous comparison of method-specific detection parameters, enable estimation of additional parameters such as false-positive detection probability, and improve occurrence or abundance estimates, but with the assumption that the separate sampling methods produce detections independently of one another. This assumption is tenuous if methods are paired or deployed in close proximity simultaneously, a common practice that reduces the additional effort required to implement multiple methods and reduces the risk that differences between method-specific detection parameters are confounded by other environmental factors. We develop occupancy and spatial capture-recapture models that permit covariance between the detections produced by different methods, use simulation to compare estimator performance of the new models to models assuming independence, and provide an empirical application based on American marten (Martes americana) surveys using paired remote cameras, hair catches, and snow tracking. Simulation results indicate existing models that assume that methods independently detect organisms produce biased parameter estimates and substantially understate estimate uncertainty when this assumption is violated, while our reformulated models are robust to either methodological independence or covariance. Empirical results suggested that remote cameras and snow tracking had comparable probability of detecting present martens, but that snow tracking also produced false-positive marten detections that could potentially substantially bias distribution estimates if not corrected for. Remote cameras detected marten individuals more readily than passive hair catches. Inability to photographically distinguish individual sex did not appear to induce negative bias in camera density estimates; instead, hair catches appeared to produce detection competition between individuals that may have been a source of negative bias. Our model reformulations broaden the range of circumstances in which analyses incorporating multiple sources of information can be robustly used, and our empirical results demonstrate that using multiple field-methods can enhance inferences regarding ecological parameters of interest and improve understanding of how reliably survey methods sample these parameters.

Measurement or observation error is common in ecological data: as citizen scientists and automated algorithms play larger roles processing growing volumes of data to address problems at large scales, concerns about data quality and strategies for improving it have received greater focus. However, practical guidance pertaining to fundamental data quality questions for data users or managers—how accurate do data need to be and what is the best or most efficient way to improve it?—remains limited. We present a generalizable framework for evaluating data quality and identifying remediation practices, and demonstrate the framework using trail camera images classified using crowdsourcing to determine acceptable rates of misclassification and identify optimal remediation strategies for analysis using occupancy models. We used expert validation to estimate baseline classification accuracy and simulation to determine the sensitivity of two occupancy estimators (standard and false‐positive extensions) to different empirical misclassification rates. We used regression techniques to identify important predictors of misclassification and prioritize remediation strategies. More than 93% of images were accurately classified, but simulation results suggested that most species were not identified accurately enough to permit distribution estimation at our predefined threshold for accuracy (<5% absolute bias). A model developed to screen incorrect classifications predicted misclassified images with >97% accuracy: enough to meet our accuracy threshold. Occupancy models that accounted for false‐positive error provided even more accurate inference even at high rates of misclassification (30%). As simulation suggested occupancy models were less sensitive to additional false‐negative error, screening models or fitting occupancy models accounting for false‐positive error emerged as efficient data remediation solutions. Combining simulation‐based sensitivity analysis with empirical estimation of baseline error and its variability allows users and managers of potentially error‐prone data to identify and fix problematic data more efficiently. It may be particularly helpful for “big data” efforts dependent upon citizen scientists or automated classification algorithms with many downstream users, but given the ubiquity of observation or measurement error, even conventional studies may benefit from focusing more attention upon data quality.

Determining cost-effective field methods for detecting carnivores is critical for effective survey and monitoring studies. As the bobcat (Lynx rufus) undergoes range expansion in the northern and eastern United States, field methods may be useful for informing revisions in population management. We paired 2 scat detection-dog teams and 16 remote cameras at 4 survey sites within central Wisconsin, during summer 2011, and compared detection totals, detection probabilities, and costs between methods. Laboratory expenditures are an additional cost for scat collection, and we modeled the probability that a collected scat was genetically confirmed as bobcat as a function of dog, handler, site, and the strength of the dog's behavior. We estimated that detection-dog surveys required only 2 days to achieve a 90% probability of detecting a bobcat in a 4-km 2 area, while a single camera station would require 7-8 weeks. But a month of detection-dog surveys cost 33% more than a 4-month camera survey, with projected cost differences increasing annually. There were dog-specific differences in collection rate, and the probability that a collected scat was genetically confirmed as bobcat was best predicted by the individual dog associated with collection and the survey area, rather than the handler or the dog's observed response. We recommend cameras as a generally more cost-efficient bobcat survey method, and we advise against relying on the strength of an individual dog's response as a means of screening samples for genetic analysis. However, the most appropriate survey method is likely to be goal-dependent, and we recommend that detection-dog contractors both advertise and match the strengths and weaknesses of specific dogs with the needs of clientele. Ó 2014 The Wildlife Society.