Abundance and density estimates are central to the field of ecology and are an important component of wildlife management. While many methods exist to estimate abundance from individually identifiable animals, it is much more difficult to estimate abundance of unmarked animals. One step toward noninvasive abundance estimation is the use of passive detectors such as remote cameras or acoustic recording devices. However, existing methods for estimating abundance from cameras for unmarked animals are limited by variable detection probability and have not taken full advantage of the information in camera trapping rate. We developed a time to event (TTE) model to estimate abundance from trapping rate. This estimate requires independent estimates of animal movement, so we collapsed the sampling occasions to create a space to event (STE) model that is not sensitive to movement rate. We further simplified the STE model into an instantaneous sampling (IS) estimator that applies fixed‐area counts to cameras. The STE and IS models utilize time‐lapse photographs to eliminate the variability in detection probability that comes with motion‐sensor photographs. We evaluated the three methods with simulations and performed a case study to estimate elk (Cervus canadensis) abundance from remote camera trap data in Idaho. Simulations demonstrated that the TTE model is sensitive to movement rate, but the STE and IS methods are unbiased regardless of movement. In our case study, elk abundance estimates were comparable to those from a recent aerial survey in the area, demonstrating that these new methods allow biologists to estimate abundance from unmarked populations without tracking individuals over time.

A suite of recently developed statistical methods to estimate the abundance and density of unmarked animals from camera traps require accurate estimates of the area sampled by each camera. Although viewshed area is fundamental to achieving accurate abundance estimates, there are no established guidelines for collecting this information in the field. Furthermore, while the complexities of the detection process from motion sensor photography are generally acknowledged, viewable area (the common factor between motion sensor and time lapse photography) on its own has been underemphasized. We establish a common set of terminology to identify the component parts of viewshed area, contrast the photographic capture process and area measurements for time lapse and motion sensor photography, and review methods for estimating viewable area in the field. We use a case study to demonstrate the importance of accurate estimates of viewable area on abundance estimates. Time lapse photography combined with accurate measurements of viewable area allow researchers to assume that capture probability equals 1. Motion sensor photography requires measuring distances to each animal and fitting a distance sampling curve to account for capture probability of <1.

Monitoring the abundance of rare carnivores is a daunting task for wildlife biologists. Many carnivore populations persist at relatively low densities, public interest is high, and the need for population estimates is great. Recent advances in trail camera technology provide an unprecedented opportunity for biologists to monitor rare species economically. Few studies, however, have conducted rigorous analyses of our ability to estimate abundance of low‐density carnivores with cameras. We used motion‐triggered trail cameras and a space‐to‐event model to estimate gray wolf (Canis lupus) abundance across three study areas in Idaho, USA, 2016–2018. We compared abundance estimates between cameras and noninvasive genetic sampling that had been extensively tested in our study areas. Estimates of mean wolf abundance from camera and genetic surveys were within 22% of one another and 95% CIs overlapped in 2 of the 3 years. A single camera with many detections appeared to bias camera estimates high in 2018. A subsequent bootstrapping procedure produced a population estimate from cameras equal to that derived from genetic sampling, however. Camera surveys were less than half the cost of genetic surveys once initial camera purchases were made. Our results suggest that cameras can be a viable method for estimating wolf abundance across broad landscapes (>10,000 km²).

The space to event (STE), time to event (TTE), and instantaneous sampling (IS) methods were developed to estimate abundance of unmarked animals from camera trap images (Moeller et al. in Ecosphere 9(8):e02331, 2018). The space and time to event models use camera data in a different way than other abundance estimation methods do. Instead of using counts of animals over independent events, STE uses a measure of sampled space before the first detection of the target species, and TTE uses the time until the first detection. We introduce , a free and open-source R package designed to assist in the implementation of the STE and TTE models, along with the IS estimator. This package takes the user through the steps of transforming data, defining sampling effort, selecting sampling occasions, building encounter histories, and estimating abundance from camera data using these three methods. The package is designed for users with a baseline level of knowledge of R and statistics, without requiring expertise in either.

Population monitoring can take many different forms, and monitoring elusive and endangered species frequently involves a variety of sparse data from different sources. Small populations are often hard to sample precisely and without bias, so when estimates of vital rates like survival or recruitment point to conflicting population trends, it can be hard to determine which is more correct. Furthermore, data can be extremely hard to collect on small populations and it can be helpful to find a way to use all available hard‐won data. To address these issues, we developed an integrated population model (IPM) using all available data to estimate vital rates and abundance for a case study of an endangered woodland caribou (Rangifer tarandus caribou) population. This IPM allowed us to incorporate data from juvenile recruitment surveys, telemetry‐based survival, aerial population counts and mark–resight data, and non‐invasive capture–recapture DNA data to better understand population status and trend. We estimated survival, abundance, and recruitment of four age classes of male and female caribou: young, juveniles, subadults, and adults. The four‐age class structure of the IPM allowed us to estimate recruitment from reproductive‐aged female caribou alone, even though it can be difficult to distinguish age classes—and even sexes—in the field. As part of our IPM, we developed a novel mixture model to break apart data from different age classes when age is unobservable, as it typically is from non‐invasive DNA samples. This helped us decrease bias in juvenile and adult survival estimates from scat data, which was important to our understanding of the population dynamics. Overall, our integrated model provided more precise estimates of population trends than any one method (e.g., telemetry or non‐invasive DNA) alone. This IPM provides a useful, flexible tool for biologists to monitor populations and provides a valuable example of the benefits of integrated population modeling approaches for endangered species management and recovery.

Quantifying the demography of wildlife is vital to population monitoring; however, studies using physical capture methods can prove challenging. Camera traps have gained popularity as a density estimator tool in recent decades due to noninvasive data collection, reduced labor, cost efficiency, and large‐scale monitoring capabilities. Many wildlife populations are comprised of individuals with no unique natural markers for individual identification, resulting in need for unmarked abundance models. The recently developed Space‐to‐Event (STE) model offers a method for density estimation of unmarked populations using timelapse photography. STE relates detections of animals to camera sampling area (i.e., viewshed), resulting in density estimates that can be extrapolated to abundance over large areas. Consequently, this makes STE sensitive to estimates of viewshed area as small changes in viewshed could significantly affect density estimation. Using STE, we estimated density and recruitment of white‐tailed deer (Odocoileus virginianus) in a densely forested landscape using measurements of viewshed per camera. We compared estimates of abundance derived from uniquely measured viewshed to estimates of abundance derived from an assumed viewshed area held constant across all cameras. When using a constant viewshed across all cameras, our point estimates of abundance shifted away from uniquely measured viewshed estimates in predictable ways, depending upon how much area was sampled. Additionally, we demonstrated the need for further exploration of animal availability at fine temporal scales by comparing estimates of density derived from sampling the full diel period to estimates derived from periods of peak activity (i.e., crepuscular periods). Finally, we extended the usefulness of the STE model by using densities of fawns and adult females to derive estimates of fawn recruitment.