Camera trapping is an effective non-invasive method for collecting data on wildlife species to address questions of ecological and conservation interest. We reviewed 2,167 camera trap (CT) articles from 1994 to 2020. Through the lens of technological diffusion, we assessed trends in: (1) CT adoption measured by published research output, (2) topic, taxonomic, and geographic diversification and composition of CT applications, and (3) sampling effort, spatial extent, and temporal duration of CT studies. Annual publications of CT articles have grown 81-fold since 1994, increasing at a rate of 1.26 (SE = 0.068) per year since 2005, but with decelerating growth since 2017. Topic, taxonomic, and geographic richness of CT studies increased to encompass 100% of topics, 59.4% of ecoregions, and 6.4% of terrestrial vertebrates. However, declines in per article rates of accretion and plateaus in Shannon's H for topics and major taxa studied suggest upper limits to further diversification of CT research as currently practiced. Notable compositional changes of topics included a decrease in capture-recapture, recent decrease in spatial-capture-recapture, and increases in occupancy, interspecific interactions, and automated image classification. Mammals were the dominant taxon studied; within mammalian orders carnivores exhibited a unimodal peak whereas primates, rodents and lagomorphs steadily increased. Among biogeographic realms we observed decreases in Oceania and Nearctic, increases in Afrotropic and Palearctic, and unimodal peaks for Indomalayan and Neotropic. Camera days, temporal extent, and area sampled increased, with much greater rates for the 0.90 quantile of CT studies compared to the median. Next-generation CT studies are poised to expand knowledge valuable to wildlife ecology and conservation by posing previously infeasible questions at unprecedented spatiotemporal scales, on a greater array of species, and in a wider variety of environments. Converting potential into broad-based application will require transferable models of automated image classification, and data sharing among users across multiple platforms in a coordinated manner. Further taxonomic diversification likely will require technological modifications that permit more efficient sampling of smaller species and adoption of recent improvements in modeling of unmarked populations. Environmental diversification can benefit from engineering solutions that expand ease of CT sampling in traditionally challenging sites.

Effective wildlife management often relies on estimates of animal density, and cue counting is a viable estimation strategy.A key component of density estimation from dung, a form of cue counting, is estimation of the persistence time, t ^, of dung piles. However, differences between observers on what constitutes a dung pile may alter subsequent density estimates.Additionally, many researchers studying white-tailed deer (Odocoileus virginianus) have substituted for t ^the number of days between the date in which 98% of deciduous trees shed leaves in autumn and field sampling. To address these 2 concerns, we compared 3 methods for estimating t ^of whitetailed deer pellet groups: (1) a common modelling approach based on observations from a single observer (single-observer method), (2) a method that accommodates interobserver variation on the status of dung during field surveys (interobserver method), and (3) the days elapsed since 98% of deciduous trees shed autumn leaves (leaf-off method). We then applied these 3 t ^estimates to distance-sampling data on pellet groups from white-tailed deer that we collected along transects during 3 sampling seasons from 2019-2021 in westcentral Indiana. We estimated habitat-and year-specific deer densities. Persistence probability of pellet groups varied across habitats and years, positively with age and number of pellets, and negatively with precipitation and temperature. In several instances, we found strong or marginal differences between densities estimated using the leaf-off method and the other

1. Spatially explicit densities of wildlife are important for understanding environmental drivers of populations, and density surfaces of intraspecific classes allow exploration of links between demographic ratios and environmental conditions.Although spatially explicit densities and class densities are valuable, conventional design-based estimators remain prevalent when using camera-trapping methods for unmarked populations.2. We developed a density surface model that utilized camera trap distance sampling data within a hierarchical generalized additive modelling framework. We estimated density surfaces of intraspecific classes of a common ungulate, whitetailed deer Odocoileus virginianus, across three large management regions in Indiana, United States. We then extended simple statistical theory to test for differences in two ratios of density.3. Deer density was influenced by landscape fragmentation, wetlands and anthropogenic development. We documented class-specific responses of density to availability of concealment cover, and found strong evidence that increased recruitment of young was tied to increased resource availability from anthropogenic agricultural land use. The coefficients of variation of the total density estimates within the three regions we surveyed were 0.11, 0.10 and 0.06. Synthesis and applications.Our strategy extends camera trap distance sampling and enables managers to use camera traps to better understand spatial predictors of density. Our density estimates were more precise than previous estimates from camera trap distance sampling. Population managers can use our methods to detect finer spatiotemporal changes in density or ratios of intraspecific-class densities. Such changes in density can be linked to land use, or to management regimes on habitat and harvest limits of game species.

Aerial vehicles equipped with infrared thermal sensors facilitate quick density estimates of wildlife, but detection error can arise from the thermal sensor and viewer of the infrared video. We reviewed published research to determine how commonly these sources of error have been assessed in studies using infrared video from aerial platforms to sample wildlife. The number of annual articles pertaining to aerial sampling using infrared thermography has increased drastically since 2018, but past studies inconsistently assessed sources of imperfect detection. We illustrate the importance of accounting for some of these types of error in a case study on white-tailed deer Odocoileus virginianus in Indiana, USA, using a simple double-observer approach. In our case study, we found evidence of false negatives associated with the viewer of infrared video. Additionally, we found that concordance between the detections of two viewers increased when using a red-green-blue camera paired with the infrared thermal sensor, when altitude decreased and when more stringent criteria were used to classify thermal signatures as deer. We encourage future managers and ecologists recording infrared video from aerial platforms to use double-observer methods to account for viewer-induced false negatives when video is manually viewed by humans. We also recommend combining infrared video with redgreen-blue video to reduce false positives, applying stringent verification standards to detections in infrared and red-green-blue video and collecting data at lower altitudes over snow when needed.

Density estimates for animal populations often inform conservation and management decisions. Many methods to estimate animal density exist but deciding between competing alternatives traditionally has depended upon assessing multiple factors (e.g., precision, total cost, area sampled) independently and often in an ad hoc manner. Cost‐effectiveness analysis is a tool that economists use to decide objectively between competing alternatives. We extend cost‐effectiveness analysis to simultaneously integrate precision and per‐area cost of sampling when selecting between competing techniques used to estimate animal density both after a single application of a method and across several applications of capital equipment. Our extension allows for weighting of factors that may vary with the objectives and constraints of decision makers. We apply our extension of cost‐effectiveness analysis to a case study in which population density of white‐tailed deer (Odocoileus virginianus) was estimated in 3 large management units in Indiana, USA, using 3 competing distance‐sampling methods: fecal‐pellet, camera‐trap, and aerial sampling. The unweighted cost effectiveness of aerial sampling with color and infrared sensors was usually superior after a single application of each method and was always superior across several applications in differing landscapes. Pellet sampling was the most cost effective after a single application of each method in an agriculturally‐dominated management unit. Although camera sampling has increased in popularity, the cost effectiveness of camera sampling was poorer than the other 2 methods, even when allowing for potential future innovations to streamline data processing. Cost‐effectiveness analysis can be useful when selecting among competing methods for monitoring animal populations of conservation and management importance. The same principles used in our cost‐effectiveness analysis can be used to decide between competing alternatives related to any ecological monitoring in addition to density estimation.