* Reliable estimates of animal density and abundance are essential for effective wildlife conservation and management. Camera trapping has proven efficient for sampling multiple species, but statistical estimators of density from camera trapping data for species that cannot be individually identified are still in development. * We extend point-transect methods for estimating animal density to accommodate data from camera traps, allowing researchers to exploit existing distance sampling theory and software for designing studies and analysing data. We tested it by simulation, and used it to estimate densities of Maxwell's duikers (Philantomba maxwellii) in Taï National Park, Côte d'Ivoire. * Densities estimated from simulated data were unbiased when we assumed animals were not available for detection during long periods of rest. Estimated duiker densities were higher than recent estimates from line transect surveys, which are believed to underestimate densities of forest ungulates. * We expect these methods to provide an effective means to estimate animal density from camera trapping data and to be applicable in a variety of settings

Summary1. Population density is a critical ecological parameter informing effective wildlife management and conservation decisions. Density is often estimated by dividing capture-recapture (C-R) estimates of abundance (N) by size of the study area, but this relies on the assumption of geographic closure -a situation rarely achieved in studies of large carnivores. For geographically open populationsN is overestimated relative to the size of the study area because animals with only part of their home range on the study area are available for capture. This bias ('edge effect') is more severe when animals such as large carnivores range widely. To compensate for edge effect, a boundary strip around the trap array is commonly included when estimating the effective trap area (Â). Various methods for estimating the width of the boundary strip are proposed, butN ⁄ estimates of large carnivore density are generally mistrusted unless concurrent telemetry data are available to defineÂ. Remote sampling by cameras or hair snags may reduce study costs and duration, yet without telemetry data inflated density estimates remain problematic. 2. We evaluated recently developed spatially explicit capture-recapture (SECR) models using data from a common large carnivore, the American black bear Ursus americanus, obtained by remote sampling of 11 geographically open populations. These models permit direct estimation of population density from C-R data without assuming geographic closure. We compared estimates derived using this approach to those derived using conventional approaches that estimate density asN ⁄Â. 3. Spatially explicit C-R estimates were 20-200% lower than densities estimated asN ⁄Â. AIC c supported individual heterogeneity in capture probabilities and home range sizes. Variable home range size could not be accounted for when estimating density asN ⁄Â. 4. Synthesis and applications. We conclude that the higher densities estimated asN ⁄ compared to estimates from SECR models are consistent with positive bias due to edge effects in the former. Inflated density estimates could lead to management decisions placing threatened or endangered large carnivores at greater risk. Such decisions could be avoided by estimating density by SECR when bias due to geographic closure violation cannot be minimized by study design.

The extension of distance sampling methods to accommodate observations from camera traps has recently enhanced the potential to remotely monitor multiple species without the need of additional data collection (sign production and decay rates) or individual identification. However, the method requires that the proportion of time is quantifiable when animals can be detected by the cameras. This can be problematic, for instance, when animals spend time above the ground, which is the case for most primates. In this study, we aimed to validate camera trap distance sampling (CTDS) for the semiarboreal western chimpanzee (Pan troglodytes verus) in Taï National Park, Côte d'Ivoire by estimating abundance of a population of known size and comparing estimates to those from other commonly applied methods. We estimated chimpanzee abundance using CTDS and accounted for limited availability for detection (semiarboreal). We evaluated bias and precision of estimates, as well as costs and efforts required to obtain them, and compared them to those from spatially explicit capture‐recapture (SECR) and line transect nest surveys. Abundance estimates obtained by CTDS and SECR produced a similar negligible bias, but CTDS yielded a larger coefficient of variation (CV = 39.70% for CTDS vs. 1%/19% for SECR). Line transects generated the most biased abundance estimates but yielded a better coefficient of variation (27.40–27.85%) than CTDS. Camera trap surveys were twice more costly than line transects because of the initial cost of cameras, while line transects surveys required more than twice as much time in the field. This study demonstrates the potential to obtain unbiased estimates of the abundance of semiarboreal species like chimpanzees by CTDS.HIGHLIGHTS Camera trap distance sampling produced accurate density estimates for semiarboreal chimpanzees. Availability for detection must be accounted for and can be derived from the activity pattern.

Spatially explicit capture-recapture (SECR) models are gaining popularity for estimating densities of mammalian carnivores. They use spatially explicit encounter histories of individual animals to estimate a detection probability function described by two parameters: magnitude (g 0 ), and spatial scale (r). Carnivores exhibit heterogeneous detection probabilities and home range sizes, and exist at low densities, so g 0 and r likely vary, but field surveys often yield inadequate data to detect and model the variation. We sampled American black bears (Ursus americanus) on 43 study areas in ON, Canada, 2006Canada, -2009. We detected 713 animals 1810 times; however, study area-specific samples were sometimes small (6-34 individuals detected 13-93 times). We compared AIC c values from SECR models fit to the complete data set to evaluate support for various forms of variation in g 0 and r, and to identify a parsimonious model for aggregating data among study areas to estimate detection parameters more precisely. Models that aggregated data within broad habitat classes and years were supported over those with study area-specific g 0 and r (DAIC c C 30), and precision was enhanced. Several other forms of variation in g 0 and r, including individual heterogeneity, were also supported and affected density estimates. If study design cannot eliminate detection heterogeneity, it should ensure that samples are sufficient to detect and model it. Where this is not feasible, combing sparse data across multiple surveys could allow for improved inference.