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Abstract Camera traps (CT) have been used to study a wide diversity of wildlife around the world. However, despite their widespread use, standardized protocols are lacking, potentially leading to reduced efficiency and inhibiting study comparisons, generalizability, and repeatability. While there are general guidelines and considerations researchers should be aware of when designing a CT survey, studies have shown the vital importance of selecting sampling schemes and camera settings tailored to specific characteristics of the wildlife system of interest. For many species and regions, optimal sampling protocols have not been thoroughly evaluated, especially in vast open landscapes. We used CT data on barren‐ground caribou (Rangifer tarandus) in the open landscape of arctic Alaska as a case study to evaluate and quantify the influence of camera trigger type (i.e., motion detection vs. time‐lapse) and time‐lapse interval on data generation to inform sampling protocols for future CT research in this system or others like it. Comparing camera trigger types, we found 5 min interval time‐lapse generated seven‐times more images containing caribou compared to motion detection. However, the detection rate of motion detection was over 11‐times greater than time‐lapse resulting in more efficient data collection with respect to camera battery life, data storage, and data processing time. Exploring the effect of time‐lapse interval length, we found detections were highly sensitive to interval length with a 30 min interval producing 33.7% fewer images containing caribou and identifying 22.2% fewer trap days containing caribou compared to a 5 min interval. Our results provide insight into effective CT sampling protocols for open landscapes and highlight the importance of critically evaluating and selecting camera settings that account for characteristics of the study system to ensure adequate data is generated efficiently to address study objectives. 

Abstract As camera trapping has become a standard practice in wildlife ecology, developing techniques to extract additional information from images will increase the utility of generated data. Despite rapid advancements in camera trapping practices, methods for estimating animal size or distance from the camera using captured images have not been standardized. Deriving animal sizes directly from images creates opportunities to collect wildlife metrics such as growth rates or changes in body condition. Distances to animals may be used to quantify important aspects of sampling design such as the effective area sampled or distribution of animals in the camera's field‐of‐view.We present a method of using pixel measurements in an image to estimate animal size or distance from the camera using a conceptual model in photogrammetry known as the ‘pinhole camera model’. We evaluated the performance of this approach both using stationary three‐dimensional animal targets and in a field setting using live captive reindeerRangifer tarandusranging in size and distance from the camera.We found total mean relative error of estimated animal sizes or distances from the cameras in our simulation was −3.0% and 3.3% and in our field setting was −8.6% and 10.5%, respectively. In our simulation, mean relative error of size or distance estimates were not statistically different between image settings within camera models, between camera models or between the measured dimension used in calculations.We provide recommendations for applying the pinhole camera model in a wildlife camera trapping context. Our approach of using the pinhole camera model to estimate animal size or distance from the camera produced robust estimates using a single image while remaining easy to implement and generalizable to different camera trap models and installations, thus enhancing its utility for a variety of camera trap applications and expanding opportunities to use camera trap images in novel ways. 

Abstract For wildlife inhabiting snowy environments, snow properties such as onset date, depth, strength, and distribution can influence many aspects of ecology, including movement, community dynamics, energy expenditure, and forage accessibility. As a result, snow plays a considerable role in individual fitness and ultimately population dynamics, and its evaluation is, therefore, important for comprehensive understanding of ecosystem processes in regions experiencing snow. Such understanding, and particularly study of how wildlife–snow relationships may be changing, grows more urgent as winter processes become less predictable and often more extreme under global climate change. However, studying and monitoring wildlife–snow relationships continue to be challenging because characterizing snow, an inherently complex and constantly changing environmental feature, and identifying, accessing, and applying relevant snow information at appropriate spatial and temporal scales, often require a detailed understanding of physical snow science and technologies that typically lie outside the expertise of wildlife researchers and managers. We argue that thoroughly assessing the role of snow in wildlife ecology requires substantive collaboration between researchers with expertise in each of these two fields, leveraging the discipline‐specific knowledge brought by both wildlife and snow professionals. To facilitate this collaboration and encourage more effective exploration of wildlife–snow questions, we provide a five‐step protocol: (1) identify relevant snow property information; (2) specify spatial, temporal, and informational requirements; (3) build the necessary datasets; (4) implement quality control procedures; and (5) incorporate snow information into wildlife analyses. Additionally, we explore the types of snow information that can be used within this collaborative framework. We illustrate, in the context of two examples, field observations, remote‐sensing datasets, and four example modeling tools that simulate spatiotemporal snow property distributions and, in some cases, evolutions. For each type of snow data, we highlight the collaborative opportunities for wildlife and snow professionals when designing snow data collection efforts, processing snow remote sensing products, producing tailored snow datasets, and applying the resulting snow information in wildlife analyses. We seek to provide a clear path for wildlife professionals to address wildlife–snow questions and improve ecological inference by integrating the best available snow science through collaboration with snow professionals.