One of the most difficult challenges for wildlife managers is reliably estimating wildlife populations. Camera traps combined with spatial capture-recapture (SCR) models are a popular tool for population estimation. They have limitations, however, including long data processing times. Drones with thermal imagery are an emerging tool for estimating wildlife populations, but how they compare to other methods remains poorly studied. We compared the use of camera traps and SCR models to drone surveys for estimating population densities of Sitka black-tailed deer (Odocoileus hemionus sitkensis) on Afognak Island, Alaska. We deployed 26 camera traps from 1 September until 6 October 2022 and individually identified males using antler characteristics, for the SCR model. At the same site, we conducted three drone surveys between October and December 2022, identified sex composition, and obtained deer counts. The estimated density from the SCR model was 3.7 males ± 0.8 (SE) /km², and 14.1 ± 3.1 adults/km² of clear-cut forest. Results from the drone survey produced similar estimates with 2.1 ± 0.9 males/km² and 13.4 ± 1.6 adults / km². The similarity in estimates suggests that both methods converged on an accurate representation of the population in this habitat, but these methods diverge in levels of sampling effort, duration, and financial cost. Camera traps offer further insights on behavior and home-range size but require longer data processing times, can be subject to malfunctions, and are difficult to deploy and maintain in remote areas. Drones are subject to legal restrictions, have difficulty in closed canopy habitats, and can be initially costly, but they provide results faster and require less data analysis. Camera traps and drones are useful for determining population dynamics but are subject to their limitations. Wildlife managers should make survey decisions based on their specific goals, habitat type, focal species ecology, and financial limitations.

We placed 26 camera traps (Browning Strike Force HD- 20MP) at un-baited sites, spaced on average 400 m apart along inactive logging roads. Roads here are heavily used by deer as the adjacent clear-cut areas are comprised heavily of slash piles, making movement more difficult (Finnegan et al. unpublished data). Roads and trails have been used for camera locations in previous studies focused on deer densities in Central America (Soria-Díaz et al. 2015). Cameras were fixed to poles or naturally occurring stumps at an average height of approximately 100 cm off the ground. We removed any vegetation in front of the camera view shed to reduce false triggers. Cameras were active from 1 September until 6 October 2022 and were set to operate 24 hours a day with a three-photo capture series upon trigger, and a three-second delay between trigger events.

SCR models

Within the SCR modelling framework, focal animal detections should be independent of each other, therefore we used a six-minute cutoff between deer capture events at a given camera based on prior information on deer activity at camera sites (Macaulay et al. 2020). For each encounter, we identified the sex of individuals when possible (bucks, does, does with fawns) and recorded group size, date, time, and camera location. One observer individually identified bucks manually based on antler characteristics (e.g., shape, length, number of points, brow point morphology) and other natural markings such as body scars (Hinojo et al. 2022; Fig.2). These identifications were then confirmed by a second independent observer to reduce potential observer bias. We attempted to identify does where possible with natural markings (Macaulay et al. 2020). For both the camera trapping and drone surveys we determined the sex ratio (buck:doe) as the number of independent encounters of bucks divided by the sum of independent encounters of does and bucks (Hinojo et al. 2022).

We analysed camera trap data using spatially explicit photographic capture-recapture models (SCR) following the methodology described to estimate roe deer densities in Hinojo et al. (2022). SCR is a set of methods for modelling animal capture-recapture data collected with an array of detectors (Efford 2020), in our case camera traps. Animal density is directly estimated using information on capture histories in combination with spatial locations of captures (Efford 2020). We used the “secr” package (Efford 2020) in Program R (R Core Team 2019) and the input data of individual male Sitka black-tailed deer encounters (i.e., site number, date, time of the encounter, male individual identification), the location of the camera sites, and camera deployment details including dates when cameras were active. We defined a sampling occasion as a time frame of 24 hours starting at noon, which results in 32 sampling occasions.