DOI: 10.5061/dryad.brv15dvk2
title: Sitka Black-tailed Deer Camera trap data for density estimation from Afognak Island, Alaska

description: 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.
description: 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 unpublished data).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.
description: # Sitka Black-tailed Deer Camera trap data for density estimation from
 Afognak Island, Alaska
 [https://doi.org/10.5061/dryad.brv15dvk2](https://doi.org/10.5061/dryad.brv15dvk2) ## Description of the data and file structure ### Bucks\_capture\_history Site = The camera name Time = The date and time of the capture event Deer_Name = The individual Male deer ID ### Bucks\_does\_fawns Site = The camera name Date_time = The date and time of the capture event Sex_age = The sex of the cohort which is male, female, or female with young present Group Size = The number of animals recorded in the image Deer_ID = The deer individual ID when known (UF= Unidentified female, UM= Unidentified Male) ### Camera locations Site = The camera name x = the easting coordinate of the camera location y = the Northing coordinate of the camera location ### Trap\_nights Site = The camera name Night1, Night2, Night3, etc. = Each individual night the camera was deployed with 1 (camera worked) or 0 (camera was not working) ## Access information NA