DOI: 10.5061/dryad.g1jwstqqx
title: Testing the precision and sensitivity of density estimates obtained with a camera-trap method revealed limitations and opportunities

description: The use of camera traps in ecology helps affordably address questions
 about the distribution and density of cryptic and mobile species. The
 Random encounter model (REM) is a camera-trap method that has been
 developed to estimate population densities using unmarked individuals.
 However, few studies have evaluated its reliability in the field,
 especially considering that this method relies on parameters obtained from
 collared animals (i.e. average speed, in km/h), which can be difficult to
 acquire at low cost and effort. Our objectives were to (1) assess the
 reliability of this camera-trap method and (2) evaluate
 the influence of parameters coming from different populations on density
 estimates. We estimated a reference density of black bears (Ursus
 americanus) in Forillon National Park (Québec, Canada) using a spatial
 capture-recapture estimator based on hair-snag stations. We calculated
 average speed using telemetry data acquired from four different bear
 populations located outside our study area and estimated densities using
 the REM. The reference density, determined with a Bayesian spatial
 capture-recapture model, was 2.87 individuals/10km2 [95% CI: 2.41–3.45],
 which was slightly lower (although not significatively different) than the
 different densities estimated using REM (ranging from 4.06–5.38
 bears/10km2 depending on the average speed value used). Average speed
 values obtained from different populations had minor impacts on REM
 estimates when the difference in average speed between populations was
 low. Bias in speed values for slow-moving species had more influence on
 REM density estimates than for fast-moving species. We pointed out that a
 potential overestimation of density occurs when average speed is
 underestimated, i.e. using GPS telemetry locations with large fix-rate
 intervals. Our study suggests that REM could be an affordable alternative
 to conventional spatial capture-recapture, but highlights the need for
 further research to control for potential bias associated with speed
 values determined using GPS telemetry data.
description: We estimated a “reference density” using a hair-snag genotyping
 survey and a DNA-based capture-recapture statistical method. We divided
 the study area into 37 irregular cells of ~7.5 km<sup>2</sup>
 and placed a hair-snag station as close as possible to the center of 33 of
 these cells; the four remaining cells were discarded due to their
 inaccessibility and steep slopes. Hair-snag stations were composed of two
 barbwires fixed on tree trunks 35 cm and 65 cm aboveground to delineate an
 enclosure of 5 x 5 m. Scent lures were used to attract bears in the
 hair-snag stations in order to increase the probability of “capture”.
 Three types of scent lures were used: a mustelid lure in a pierced plastic
 bottle was hung in the air at the center of the station, while 100 mL of
 seal oil was applied to a wood pile in the middle of the station (on the
 ground), and a 1:1 mix of vegetable oil and anise oil was sprayed on tree
 trunks found within the barbwire section of the station. Lures were
 refreshed every week. Stations were sampled for 5 weeks, from 6 July to 18
 August 2015, as black bears usually increase their movement rate at this
 time, increasing the probability of recapture and consequently yielding
 more precise density estimates. Stations were visited weekly and each hair
 sample was collected individually (i.e. multiple hairs tied on a single
 barb were considered one sample) and identified to record its location on
 the barbwire (lower vs. upper barbwire, and barb ID-#). After each visit,
 the barbwires were burned using a blowtorch to prevent DNA contamination
 between sessions. Each hair sample was stored in a paper envelope with
 silica desiccant and freeze-dried for 24 hours. All samples were sent to
 Wildlife Genetic International (hereafter WGI) for genotyping. Hair
 samples were subselected based on the “Mowat 1-in-3” method, developed by
 WGI ; this method offers a good compromise between genotyping many samples
 that belong to the same individual and missing genetic material from other
 individuals. DNA was extracted from the samples using QIAGEN DNeasy tissue
 kits, and the genotyping followed a standard three-phase approach (i.e.
 first pass, cleanup and error-check), using seven microsatellite markers
 (G10L, G10H, UarMU23, UarMU50, MSUT-2 and G10X) plus ZFX/ZFY for
 gender. We used automated camera to estimate bear
 density with the Random Encounter Model. The encounter rate was assessed
 using 47 remote cameras (Spypoint model I-6: n = 13 and model Tiny: n =
 22; Reconyx model RM45: n = 4; Moultrie model A-7i: n = 8). All cameras
 used passive infrared and movement sensors, and the trigger sensitivity
 was set to its maximum. The cameras were programmed to take three
 consecutive pictures when triggered, and were randomly distributed within
 the 7.5-km<sup>2</sup> sampling cells by generating 10 random
 points in each cell; one to five of these points were then selected based
 on their accessibility (on foot or with an ATV). The radius (r) of the
 camera detection zone was assessed through several trials during which a
 person crossed the camera detection zone perpendicularly. The detection
 arc (θ) of the camera detection zone was assumed to be the value found in
 the specifications of each camera model. At micro-site scale, the cameras
 were installed in a direction that provides a relatively clear detection
 zone, without being faced deliberately towards a trail used by wildlife.
 We removed branches and stems in front of the cameras to maintain
 consistency across cameras regarding the detection zone area and to reduce
 the number of empty pictures triggered by branch movements that trigger
 the sensors. At each selected point, a camera was fixed on a tree 75 cm
 aboveground for a minimum of 21 days, leading to the placement of 110
 cameras from 1 July to 9 September 2015. The average speed (i.e. movement
 rate, measured as the Euclidian distance between two successive
 relocations divided by the time elapsed between them) was not available
 for the study population (Forillon), so we used GPS telemetry data
 gathered from four different black bear populations in the province of
 Québec during the last 19 years (see Table 1 for more details). For each
 dataset, we calculated average speed (v) and its standard error using only
 the relocations collected from July to September (i.e. the same period
 during which of our camera-trap and hair-snag data were collected) with a
 minimum fix-rate of 8 locations/day (i.e. 1 location every 3 hours) to
 reduce bias in the estimation of average speed.