Do available products to mask human scent influence camera trap survey results?

Abstract

Camera traps (i.e. remotely, motion or heat triggered cameras) can be used to examine a variety of ecological factors ranging from the activity patterns (Oliveira-Santos et al. 31, Harmsen et al. 14) to habitat selection (Fedriani et al. 9, Kelly and Holub 19) of wildlife. Camera traps are also used to estimate abundance, density and the distribution of secretive or rare species (Karanth and Nichols 17, Trolle and Kéry 46, Larrucea et al. 23). Camera traps may be preferable to traditional mammal trapping techniques for various reasons, such as greater effectiveness for cryptic species (Sanderson and Trolle 39, De Bondi et al. 6) and high detection rates (Silveira et al. 42, Gompper et al. 11, Balme et al. 2). Despite their widespread use, there are still questions regarding appropriate protocols for the use of camera traps (Rowcliffe et al. 37, Hamel et al. 13; reviewed by Rovero et al. 35). For example, little effort has been placed on assessing how disturbance associated with researcher presence at cameras during maintenance (i.e. checking batteries and memory cards) influences capture rates. Past studies have noted that animals can learn to avoid camera locations or are generally wary of camera traps, which may be due to either camera flash or disturbance associated with researcher activity (Cutler and Swann 5, Sequin et al. 40, Lyra-Jorge et al. 24). Even minimal researcher disturbance may result in deposition of scent that may alarm wary species and result in avoidance of the area. Despite the need for further research on this topic, there has been no attempt to rigorously examine the role of human scent, or the masking of human scent, on camera trap effectiveness. Our goal was to ascertain if the number of wildlife detections differ when human scent is masked versus unmasked while researchers perform regular camera maintenance. We hypothesized that wildlife capture rates would differ based on the presence of researcher scent, and we predicted captures would be higher on cameras where human scent was masked. We also believed that if no treatment effect was observed, two possible conclusions could be made: 1) the target species in the study region does not alter behavior due to human scent at camera trap stations and/or 2) employing available scent-masking products does not improve capture rates during camera trap surveys. The results of this study have potentially broad implications for the utility of this increasingly common survey technique. Research was conducted at two sites in the Piedmont region of North Carolina, USA. We selected sites based on their similar habitat characteristics, remote location, and limited human traffic. The first site, referred to as the ‘Haw River’ site, is 16.2 ha of privately owned land in Alamance County, North Carolina. The property borders the Haw River and is predominantly alluvial forest habitat as described by Spira (44). This rural locality is mainly exurban with minor components of agriculture nearby. The second site, referred to as the ‘Rocky River’ site, is located in Chatham County, North Carolina. The 12.9-ha study site is bordered by the Rocky River, and is comprised of oak-hickory forest (Spira 44). This locality has been mostly uninhabited with only scattered agriculture close by. During the course of our study, human activity was documented once at the Haw River site (hiker) and twice at the Rocky River site (one hiker, one hunter). The Haw River site was surveyed outside of the recreational hunting season, and such activities are generally not permitted on this property. However, it is known that adjacent property owners hunt regularly. At the Rocky River site, hunting was also not permitted. On one occasion hunters were seen onsite, and on other occasions, hunters were heard in the surrounding area. To test our hypothesis, we deployed eight camera traps at each site in total. We randomly applied one of two treatments, ‘scent unmasked’ or ‘scent masked’, to each of the eight cameras at each site, which resulted in four replicates of each treatment per site. Camera traps were placed semi-randomly within each study site, using randomly generated initial locations from a geographic information system(GIS). We chose the nearest appropriate location within 15 m of the predetermined random locations, e.g. along a game trail, for camera deployment to ensure the highest chance of detection (Moen and Lindquist 25, Rowcliffe et al. 36, Brown and Gehrt 3, O'Connell et al. 29). We locked cameras in steel boxes and affixed them to trees at 23–27 cm above ground level to better capture both small and large animals (Kelly 18). Cameras were oriented north to avoid issues associated with receiving direct sunlight (Brown and Gehrt 3). We attempted to space cameras in order to reduce possible interaction between treatments. Given the limited size of our study sites, cameras were located at least 75–150 m from their nearest neighbor. Cutler and Swann (5) suggested that traditional white flash cameras may influence mammal activity. Although it is unclear if cameras with ‘no glow’ infrared flash offer significant benefits (reviewed by Rovero et al. 35), we used a ‘no glow’ camera to minimize potential confounding effects. Cameras were set to take five pictures when their passive infrared detectors were triggered (trigger speed: 0.3 s). Camera sensitivity was set to high with no recovery time between triggers. Once cameras were placed, they remained in the same location during the duration of the study. Prior to treatment application, cameras were deployed for one month without researcher disturbance. This was done to limit the impacts of scent deposition during camera installation. Pictures obtained in this baseline period were not directly comparable to data collected during treatment application period. Thus, pictures of wildlife from the baseline period were excluded from analyses, although patterns detected between pre- and post-treatment were used to make suggestions for future experimental design. Once treatment application began, we visited cameras for maintenance every two weeks (hereafter referred to as sampling period). Camera maintenance occurred over two subsequent days. All cameras in the scent unmasked treatment were visited on day one, while all cameras in the scent masked treatment were visited on day two. We used a GPS to map routes to each camera so that a maximum possible distance (100–150 m) was maintained from adjacent cameras in the opposite treatment. For cameras in the unmasked treatment, normal fieldwork clothes were worn to mimic how a typical researcher might visit a camera location. For cameras in the scent masked treatment, we used a suite of commercially available scent-masking products shown to minimize or eliminate human-scent output (e.g. Pickering v. A.L.S. Enterprises 33). We chose common commercial brands because we were interested in investigating the effectiveness of readily available items that researchers may employ. Scent-masking clothes include carbon fabric layers that reportedly bind to odor molecules, adsorbing them and preventing their release. Clothing consisted of scent-controlling boots, pants, socks, shirts, jacket, head-wear and gloves. In between site visits, these items were stored in a scent-obscuring bag that contained leaves and twigs from the respective study sites to further help mask unusual odors that may persist on clothing. The outfit was washed every six weeks with an odor-eliminating detergent, following manufacturer's guidelines. Additionally, prior to maintenance of scent-masked cameras, researchers bathed with shampoo, conditioner, and soap meant to obscure scent, and they applied scent-masking deodorant. A set of non-specialized clothing was washed in detergent to act as a ‘transition outfit’ during travel to the research site. Once on-site, the specialized outfit was carried to the edge of the study area and then adorned. Before entering the study area, the researcher lightly misted the outfit with a spray to further ensure that any incidental scent accidentally transferred to the outfit was eliminated. Before handling cameras, scent blocking spray was re-applied to the outside of the gloves to remove any incidental scent from accidental contact with researcher skin or hair. After each camera was handled, a fine mist of scent-blocking spray was applied to the camera's security enclosure. Upon leaving the site, the researcher changed back into the transition outfit and the scent-obscuring clothes were appropriately stored. The products include proprietary formulas, so no information is available on their active ingredients. From the company website, these products rely on converting odor molecules, oxidation, bonding of molecules, and neutralization of odor (<www.hunterspec.com/products/all/all/Scent-A-Way> ; accessed 3/25/2014). While the efficacy of such scent-masking products is hotly debated, product testing has shown scent-masking clothes can adsorb up to 99% of produced odors (Pickering v. A.L.S. Enterprises 33). Many complications can arise when analyzing camera trap images, such as multiple individuals captured in a single image, a long image series taken of a single individual, or false trigger events (Royle et al. 38). The variable of interest during this study was the number of wildlife detections during each sampling period (capture rate). Therefore, if multiple animals were captured in a single image, they were each counted as separate detection events. In addition, if an animal spent extended amounts of time in front of the camera (based on time stamp), dozens of images would only count as one detection event. Likewise, if an animal left the field of view and returned from the same side it originally departed within two minutes, we did not consider it a new detection event. Therefore, each sampling period generated a count of animal detections (hereafter referred to as ‘captures’), which we statistically analyzed. We compared captures between treatments with a general estimating equation (GEE) analysis (α = 0.05). GEEs are a ‘semi-parametric’ extension of the generalized linear model (GLM) and allow for analysis of repeated measures in a similar fashion to a repeated-measures analysis of variance (ANOVA). However, GEEs are particularly robust to data that break assumptions of normality and independence (Nelder and Wedderburn 27, Ballinger 1). Furthermore, GEEs are highly appropriate for analysis of count data due to their quasi-likelihood method of estimation (Zeger et al. 48, Ballinger 1). Because wildlife are not uniformly distributed across a landscape, captures were highly associated with camera location, so we selected an exchangeable correlation structure to account for this. Standard error was calculated with a model-based estimator which performs better for data with few subjects and many repeated measures (Hardin and Hilbe 15). The model selected for analysis was Poisson-loglinear (Gardner et al. 10, O'Connell et al. 29). Considering many mammal species exhibit seasonal variation in activity, we included survey period as another explanatory variable. For all species analyzed we assessed the likelihood of our models (treatment only, survey period only, both treatment and survey period) with quasi-Aikake's information criterion (QIC; Pan 32). All statistical analyses were conducted with SPSS 20. Camera traps were active at the Haw River site from early February to mid-July 2011, with each camera simultaneously in operation for 158 trap-nights. In July 2011, three cameras were stolen from this property, and research at the site immediately ceased. The study resumed at the Rocky River site and ran from September 2011 to the beginning of March 2012, with each camera simultaneously in operation for 210 trap-nights. We obtained 2085 and 3358 mammal captures at the Haw River and Rocky River site respectively. Between both sites, 11 mammal species were observed (Table 1). The most frequently captured species at both sites were white-tailed deer Odocoileus virginianus, eastern gray squirrel Sciurus carolinensis and raccoon Procyon lotor (Table 1). Several species were omitted from statistical analyses due to low capture rates (Table 1), including the eastern cottontail rabbit Sylvilagus floridanus at the Rocky River site. Our analyses revealed that treatment is a likely factor explaining differences in white-tailed deer captures at the Haw River site only (p = 0.013; QIC = 0.376.8; mean scent masked captures/survey period = 5.23 ± 0.63 SE; 95% CI = 4.1–6.6; mean unmasked captures/survey period = 3.38 ± 0.49 SE; 95% CI = 2.5-4.5; Fig. 1). Although not formally compared, captures of white-tailed deer at the Haw River differed between baseline and treatment period. For example, we obtained fewer deer captures by cameras that would receive the scent-masked treatment (4.75 ± 2.1 SE) than captures by cameras that would receive the unmasked treatment (16.75 ± 15.5 SE) during the baseline period (Fig. 1). Similar ‘switch’ patterns were seen for raccoon and eastern cottontail at the Haw site, and opossum at the Rocky site. For the other species at both sites, survey period (seasonality) was a stronger predictor of captures (Table 2, 3). However, when examining the treatment effects for species at both sites, there are general trends that imply scent (or scent masking products) cannot be ruled out as affecting mammal activity at camera locations (Table 4). Even though the use of scent-masking products did not significantly affect model selection for other species, we include the confidence intervals of the scent effect sizes to give a sense of the strength of our results, as recommended by Steidl et al. (45) and Johnson (16). Our hypothesis and prediction (i.e. that capture rates would differ and be higher at scent masked cameras) were only statistically supported for white-tailed deer at the Haw River site. Other species did not exhibit significant treatment responses, yet the treatment related effect sizes for most species indicate that scent-masking could have a more subtle effect than our study was able to detect (Table 4). Many had an average GEE slope parameter (Beta) that indicated higher captures at cameras where scent was masked. Thus, the impacts of human scent and scent-masking products on wildlife activity and survey effectiveness appear to be complex. Our focal species are generally wary of human activity, with the exception of habituated individuals in more urban or suburban areas. Our inability to detect a difference based on treatment may seem unexpected for several of the species that we captured, which are often considered scentmotivated (e.g. raccoons). Yet, it is likely that scent motivation in these species relates to food rather than aversion to humans. It is not surprising that white-tailed deer showed a response based on treatment type. This species is hunted recreationally in North Carolina. Hunting pressures are known to cause changes in home range size, movement and activity patterns of white-tailed deer (Kilpatrick and Lima 20). This may be the result of an anthropogenic ‘landscape of fear’ created by hunters. As reviewed by Laundr é et al. (22), it is beneficial for prey species to maintain a baseline level of fear of predation. Without such fear, prey species may undertake behavior that could to the fear that due to the of by human hunters may fear in various game species in the of our study sites, such as white-tailed deer et al. et al. 4). As a game species may human scent with of in our study and activity from camera locations where scent was We to this by the capture rates of white-tailed deer during the baseline period to the treatment period. For example, during the baseline period at the Haw site we had more white-tailed deer detections at cameras that would be scent unmasked versus scent masked Fig. 1). Once treatment application began, human scent may have activity at unmasked camera For species other than white-tailed scent effects may be due to hunting pressures or to an response to human The ‘landscape of fear’ that animals human activity as a hunting may reduce the in a response to human deer captures survey at the Haw River site County, cameras in the unmasked treatment, and white cameras in the scent-masked treatment. February is baseline data and was excluded from While white-tailed deer showed a treatment effect at the Haw River site, this was not detected at the Rocky River site. was more documented human activity at the Rocky River site, and the presence of hunters on several occasions, it is possible that could have treatments in survey Additionally, it is possible that potential differences in the density of white-tailed deer per site, subtle variation in habitat at each site, differences in the surrounding or the time of during which each survey occurred any treatment effect at the Rocky River site. This factor may the The of human scent deposition and the duration of scent in the area surrounding camera traps is likely to be higher in the of the and in North Carolina. The and in which at the Rocky River site would be when researcher odor due to would be at a Additionally, during the of study at the Haw River site, a were higher during research at the Rocky River site. is to during and a large role in scent detection and and as seen in studies Thus, the likelihood that researcher scent wildlife from a area may be higher in Several camera trap studies have on the need for research to the influence of human activity and scent on camera trap effectiveness (Cutler and Swann 5, Lyra-Jorge et al. Rowcliffe et al. 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