Northeastern Naturalist Vol. 26, No. 2 J.M. Haus, T.B. Eyler, and J.L. Bowman 2019 305 2019 NORTHEASTERN NATURALIST 26(2):305–324 A Spatially and Temporally Concurrent Comparison of Popular Abundance Estimators for White-tailed Deer Jacob M. Haus1,*, T. Brian Eyler2, and Jacob L. Bowman1 Abstract - The enumeration of Odocoileus virginianus (White-tailed Deer, hereafter, Deer) populations is an important objective for many managers, but no consensus regarding the most appropriate scientific methodology exists. Extensive research has been conducted involving comparisons of multiple methods to evaluate the effectiveness of estimating demographics of free-ranging populations; however, most fail to account for spatial or temporal differences in their comparisons. We estimated the density of an open population of Deer on a strict concurrent spatial and temporal scale during 3 separate 14-day periods (August 2012, February 2013, August 2013) using 4 methods: road-based distance sampling using spotlight surveys, FLIR surveys, and camera surveys using both the Jacobson analysis method or an N-mixture model abundance analysis. Spotlight surveys were affordable but required substantial effort to achieve the precision necessary for management decisions. FLIR surveys had greater detection probabilities relative to spotlight surveys and required less effort to achieve sufficient precision. Jacobson camera surveys appeared to overestimate Deer density and provided no measures of precision. The N-mixture model camera surveys provided sufficient precision and generated point estimate and detection probabilities similar to FLIR surveys. Camera surveys were costlier and more labor intensive relative to road-based surveys. We recommend road-based distance sampling using FLIR technology to estimate Deer density, but managers should understand the limitations and biases associated with any density estimate before incorporating the results into a management program. Introduction Odocoileus virginianus Zimmerman (White-tailed Deer, hereafter, Deer) is a species of importance to wildlife managers throughout North America (Waller and Alverson 1997). Deer are valued for consumptive and non-consumptive recreation alike (Conover et al. 1995), and the revenue generated through hunting-related expenditures is the largest source of funding for state wildlife agencies (Jacobson et al. 2010). An overabundance of deer, however, can be detrimental: degrading forest communities (Rossell et al. 2007, Russell et al. 2001, Tilghman 1989, Tymkiw et al. 2013), causing significant economic losses and damage to personal property (Bissonette et al. 2008, Conover et al. 1995, Romin and Bissonette 1996), and facilitating the spread of diseases such as Lyme disease (Rand et al. 2003) and Chronic Wasting Disease (Williams et al. 2002). A means for estimating Deer demographic information and monitoring population trends over time is a necessary tool for sound Deer management (Gibbs 2000, 1Department of Entomology and Wildlife Ecology, University of Delaware, 531 South College Avenue, Newark, DE 19716. 2Maryland Department of Natural Resources, 14038 Blairs Valley Road, Clear Spring, MD 21722. *Corresponding author - jakehaus@udel.edu. Manuscript Editor: James Cardoza Northeastern Naturalist 306 J.M. Haus, T.B. Eyler, and J.L. Bowman 2019 Vol. 26, No. 2 Jacobson et al. 1997). Such tools, however, are often limited by accuracy, reliability, and cost (Alves et al. 2013, Jenkins and Marchinton 1969). Pellet counts (Eberhardt and Van Etten 1956, Neff 1968), spotlight surveys (McCullough 1982, Progulske and Duerre 1964), aerial counts (Caughley 1977, Chrétien et al. 2016, Potvin et al. 2002), mark–recapture studies (McCullough and Hirth 1988), herd reconstruction from harvest data (Millspaugh et al. 2009, Roseberry and Wolf 1991), indirect distance sampling (Anderson et al. 2013, Marques et al. 2001, Urbanek et al. 2012), and motion-triggered camera surveys (Jacobson et al. 1997, Koerth and Kroll 2000) are methods commonly used to generate estimates, but vary in efficiency and cost. Furthermore, habitat type may limit the both the effectiveness and logistical feasibility of any given method (Acevedo et al. 2008, Anderson et al. 2013, Storm et al. 2011). Many studies have attempted to evaluate the utility of these methods, but accuracy is difficult to determine in wild populations where true abundance is unknown. Additionally, year-to-year stochasticity in population estimation can complicate the understanding of a method’s effectiveness through time. Researchers have attempted to overcome such issues by generating estimates from captive Deer herds of known abundance (McCoy et al. 2011, McKinley et al. 2006, Moore et al. 2014, Potvin and Breton 2005, Walock et al. 1997); however, DeYoung et al. (2006) advise caution when extending the results of captive studies to free-ranging Deer as animal behavior may be markedly different. Studies have approached the issue of estimating demographics of open populations by using multiple methods to generate estimates for what is assumed the same population (Anderson et al. 2013, Drake et al. 2005, Naugle et al. 1996, Smart et al. 2004, Urbanek et al. 2012). Due to logistical constraints, many of the latter studies performed comparisons of estimates obtained over varying spatial and temporal scales. Comparisons that ignore temporal effects are subject to immigration, emigration, births, and deaths that fundamentally alter the populations being compared (Burton et al. 2015). Additionally, a number of factors such as predation risk, temperature, snow depth, and food availability (Beier and McCullough 1990) can vary markedly over relatively small spatial and temporal scales, affecting movement patterns and habitat use. Failing to account for such variation can negatively affect estimate comparisons and likely biases results. Distance sampling via road spotlight counts remains the most commonly used survey design because of the method’s low cost and simplicity (Buckland et al. 2001, Fafarman and DeYoung 1986, LaRue et al. 2007, Whipple et al. 1994); however, variable detection probability, observer bias, and animal disturbance may limit accuracy (Belant and Seamans 2000; Collier et al. 2007, 2013; McCullough 1982). Forward-looking infrared (FLIR) equipment has been integrated into distancesampling methods in an attempt to increase detection and limit animal disturbance (Gill et al. 1997, Havens and Sharp 2015, La Morgia et al. 2015), but the initial cost of the unit may negate any benefit (Belant and Seamans 2000, Focardi et al. 2001, Wiggers and Beckerman 1993), and detection is complicated by ambient temperature and forest cover (DeYoung 2011). Additionally, convenience sampling of road transects has been a widely criticized study design for the potential bias associated Northeastern Naturalist Vol. 26, No. 2 J.M. Haus, T.B. Eyler, and J.L. Bowman 2019 307 with non-random transects (Anderson 2001, Anderson et al. 2013, Beaver et al. 2014, Ellingson and Lukacs 2003). Motion- and heat-triggered trail cameras (hereafter, cameras) have steadily gained in popularity as a survey tool with the advancement of digital technology and the commercial availability of cameras (Cutler and Swann 1999, Jacobson et al. 1997, Koerth and Kroll 2000). Camera surveys can be less labor intensive, less invasive (Cutler and Swann 1999, Rowcliffe et al. 2008), and less limited by thick vegetation, weather, or observer bias (Larrucea et al. 2007, Rowcliffe et al. 2008) compared to other survey methods. Jacobson et al. (1997) developed a survey method (hereafter, Jacobson method) to estimate Deer population size by enumerating the photographic rate of adult males using uniquely identifiable antler characteristics and extrapolating that ratio to the remaining population. The Jacobson method has been criticized for failure to generate measures of precision (Curtis et al. 2009) and the assumption, which may not be met, of equal detectability among age and sex classes (McCoy et al. 2011, Moore et al. 2014). Camera surveys maybe limited to specific seasons (McKinley et al. 2006), are subject to variable detection rates based on landscape features (Kolowski and Forrester 2017, Mann et al. 2015), and are highly dependent on the use of bait (Koerth and Kroll 2000), which may not be legal in some areas, may alter Deer behavior, and may facilitate disease spread. Recent modifications to the camera survey methodology address such limitations (Cusack et al. 2015, Gulsby et al. 2015, Weckel et al. 2011); however, such methods are more analytically intensive and not often utilized by managers. Recently, Royle’s (2004) N-mixture model has been evaluated as an effective alternative to the Jacobson camera method to estimate Deer abundance (Keever et al. 2017). N-mixture models use replicated count data to generate abundance estimates that incorporate detection probability. The model requires assumptions that all individuals within the sampling unit have the same probability of being detected, individuals are not counted at >1 point, and the study population is demographically closed (Royle 2004). While N-mixture models using cameras share many of the limitations associated with the Jacobson method, benefits include both an estimate of detection probability, a measure of precision, no need for unique identification of individuals, and do not necessitate the use of bait. The drawbacks and limitations of both road-based distance sampling and camera surveys to enumerate Deer populations have been outlined extensively in the literature. Until a more affordable and easily applied method is available, managers will continue to utilize current techniques despite these limitations. In order to understand the impact on management decisions, each method should be compared with alternative estimators in order to evaluate effectiveness (Collier et al. 2007). Point estimates alone, however, are insufficient when evaluating the utility of any given method to estimate demographics of an open Deer population. Our objective was to generate demographic and density estimates using spotlight and FLIR roadbased distance sampling, as well as camera surveys using the Jacobson method and N-mixture models for the same population of Deer over multiple survey periods. We evaluated method efficacy using accuracy relative to other methods, precision, Northeastern Naturalist 308 J.M. Haus, T.B. Eyler, and J.L. Bowman 2019 Vol. 26, No. 2 and cost. In addition, we estimated density using strict spatial and temporal homogeneity to meet assumptions of population closure that may have influenced previous research. Field-Site Description We conducted our research on a 2000-ha section of the Green Ridge State Forest (hereafter Green Ridge) located in eastern Allegany County, MD (Fig. 1). The study area was located near the Stafford Ridge in the center of Green Ridge (39°36'49"N, 78°28'24"W). Green Ridge was open to public Deer hunting from 7 September through 31 January during the study period. Late successional forests comprised >90% of the landscape in Green Ridge. Dominant overstory species included Quercus prinus L. (Chestnut Oak), Quercus rubra L. (Northern Red Oak), Carya glabra (Sweet) (Pignut Hickory), Carya ovalis Sarg. (Red Hickory), Acer rubrum L. (Red Maple), Acer saccharum Marsh. (Sugar Maple), and Quercus bicolor Willd. (Swamp White Oak), interspersed with occasional Pinus virginiana Mill. (Virginia Pine) and Pinus strobus L. (White Pine) (MDNR 2012). Common midstory and understory species include Vaccinium corymbosum L. (Northern Highbush Blueberry), Cornus florida L. (Flowering Dogwood), Smilax spp. (greenbriar), Cercis canadensis L. (Eastern Redbud), and Sassafras albidum (Nees) (Sassafras). Green Ridge varied in elevation from 152 m along the Potomac River to 620 m at the Figure 1. Map of the White-tailed Deer population estimation study area in eastern Allegany County, MD showing the network of twenty 100-ha grid cells (blue) used for camera placement. We arranged grid cells to avoid private lands on the western portion of the study area. Northeastern Naturalist Vol. 26, No. 2 J.M. Haus, T.B. Eyler, and J.L. Bowman 2019 309 highest ridge (Evans and Gates 1997). Average temperature (1981‒2010) varied from a winter low of -5.33 °C in January to a summer high of 31.06 °C in July (National Oceanic and Atmospheric Administration 2013). Allegany County received an average (1981‒2010) of 95 cm of precipitation each year (NOAA 2013). Weather conditions during the study period did not diverge from the 30-year (1981‒2010) monthly averages (August and February; NOAA 2013). Methods We conducted all survey methods during 3 separate 14-day periods (7‒20 August 2012, 1‒14 February 2013, and 7‒20 August 2013. We drove road transects for distance sampling 12 of 14 nights. We performed spotlight and FLIR surveys on alternating nights for a total of 6 replicates per period for each method, with 2 nights allotted for inclement weather. We maintained cameras and bait sites through the duration of the 14-day survey periods. Surveys began 1 hour after sunset, with start and stop locations constant across survey nights and survey periods. The survey route followed continuous roads throughout the study area for a total of 45 km (Fig. 2). We surveyed all drivable roads within the study area, and all roads were low-use, minimum-maintenance forested roads with no median and little surrounding development. A 2-person Figure 2. Map of the Whitetailed Deer population estimation study area in eastern Allegany County, MD showing the 45 km of continuous road used for distance sampling (black) in relation to the network of camera grid cells (blue). Northeastern Naturalist 310 J.M. Haus, T.B. Eyler, and J.L. Bowman 2019 Vol. 26, No. 2 team (driver and observer) traveled at a speed not exceeding 20 km/hr and surveyed only the right side of the road. To avoid bias, we used the same observer for the duration of the study. We used a 12-volt spotlight (Cyclops Solutions LLC, Grand Prairie, TX) for spotlight surveys and a Thermal-Eye 250D Digital FLIR device (L-3 Communications Infrared Products, Dallas, TX) for FLIR surveys to continuously search for Deer. When we detected a Deer or cluster of Deer, we used either the thermal signature of the Deer (Fig. 3) or a spotlight to determine the sex and age (adult/fawn) of each individual. We defined clusters of Deer in accordance with protocols established by Lovely et al. (2013). We recorded distance to the original position if we observed Deer moving in response to the approaching vehicle. We measured the perpendicular distances to the individual Deer or Deer cluster using a laser rangefinder (± 1 m accuracy; Leica Camera AG, Solms, Germany). We used Program DISTANCE 6.0, version 2 (Thomas et al. 2010) to estimate density. We analyzed distance-sampling data with both Figure 3. White-tailed Deer thermal signatures observed through a forward-looking infrared imager in Green Ridge State Forest, MD. During the summer, (A) vascularized antler tissue identifies adult males, and (B) body size can be used to identif y adult females with fawns. Northeastern Naturalist Vol. 26, No. 2 J.M. Haus, T.B. Eyler, and J.L. Bowman 2019 311 the conventional distance sampling model (CDS) and the multiple covariate distance sampling model (MCDS). We used a Kestrel pocket weather meter (Nielsen-Kellerman Co., Chester, PA) to obtain and record weather data for each night, including temperature, wind speed, relative humidity, precipitation, and barometric pressure. We used weather measurements as covariates in the MCDS model, but underlying model structure was similar to CDS, and allowed for model comparison. We performed model selection using Akaike information criterion corrected for small sample size (AICc; Hurvich and Tsai 1989). The models with the lowest AICc value were considered to have more support and be more parsimonious than models(i) with ΔiAICc ≥ 2 (ΔiAICc = AICci - AICcmin; Burnham and Anderson 2002, Posada and Buckley 2004). We established camera sites in accordance to Jacobson et al. (1997). We divided the study area into square 100-ha grid cells (Fig. 1) and placed a Reconyx HC600 infrared camera (n = 20; Reconyx Inc., Holmen, WI) at the center; however, we adjusted exact placement to provide ease of access and increase likelihood of visitation by Deer (Jacobson et al. 1997). Grid size was based on the average annual home-range size of adult Deer (113 [SE = 18] ha) in a comparable habitat within the Appalachian Mountain range (Randolph County, WV; Campbell et al. 2004). We used a handheld GPS unit (Garmin Ltd., Olathe, KS) to mark locations of camera sites. All cameras were oriented north and had vegetation and debris cleared from the area to prevent sun glare and false triggers. We baited camera sites with 11 kg of shelled corn placed ~5 m from the camera. During a pilot-study conducted in July 2012, we found that photographic rates did not increase after the third day, so we performed a 3-day pre-baiting period with cameras present prior to all survey periods. Cameras were active for 14 days on 24-hour capture mode with a 3-minute delay, taking 3 photographs per event over a period of 20 seconds. The pilot study found that Deer averaged 4 to 6 minutes feeding at a bait site. A 3-minute delay conserved battery life and reduced the amount of repeat pictures of a single individual, while ensuring that Deer visiting the bait site without triggering the camera was unlikely. The use of 3 photographs per event provided multiple images of antlered males that aided in identification. We checked camera sites every third day to change memory cards and ensure bait was constantly available throughout the survey period. We compiled and analyzed all photographs by camera site at the end of each 14-day survey period. We followed methods previously described by Jacobson et al. (1997) for photograph analysis. We used distinguishable antler characteristics to uniquely identify individual males. We calculated total counts for males, females, and fawn images, including known repeats of individuals. We divided the number of unique males by the number of male images to get a population multiplier (unique-male-to-total male images). We applied this multiplier to the total images of females and fawns to get an estimated number of unique females and fawns for the survey area. We added those estimates to the known number of individual males for an overall abundance estimate. We applied the abundance estimate to the known area of the camera grid to generate a density estimate. Northeastern Naturalist 312 J.M. Haus, T.B. Eyler, and J.L. Bowman 2019 Vol. 26, No. 2 N-mixture models require spatially and temporally replicated counts. We used the same photograph data as the Jacobson method, but subset the photos to only include images from a 2-hour period (1800–2000 Eastern Standard Time) each night, for a total of 14 replicates at each camera location. We subset the data to minimize the possibility of double counting individuals with repeated visits while still capturing peak activity. We classified animals as adult male, adult female, or fawn and constructed encounter histories for all 3 demographics. When a group of Deer consisted of multiple individuals of the same demographic, we restricted the count to the maximum number observed in a single photograph. This conservative approach was necessary to reduce the probability of double counts of an individual animal. We generated abundance estimates using package ‘unmarked’ in Program R version 3.1.3 (Fiske and Chandler 2011). While both camera survey methods estimate abundance of Deer, we applied the abundance estimates for both methods to the known area of the camera grid to generate density estimates, which is a typical practice among managers despite the subjectivity of defining a unit of area for a free-ranging population (Foster and Harmsen 2012). Our study used estimates from a population of unknown Deer density, meaning we could not evaluate estimates based on accuracy alone. We compared estimates within survey periods using point-density estimates (number of Deer/km2), 95% confidence interval (CI) overlap, percent coefficient of variation (CV), detection probability, and cost to evaluate the effectiveness of each method at the management-unit scale. We concluded that methods generated different estimates if there was no CI overlap. An estimate was considered sufficient for rough management decisions if the CV was <25%, and sufficient for precise management if <12.8% (Skalski et al. 2005). Results We did not observe a uniquely identifiable Deer at >1 camera site during any of the 3 survey periods. CDS models were the top model in all 3 survey periods for both spotlight (Table 1) and FLIR surveys (Table 2), so method comparisons were performed using only the CDS models. We did not perform model averaging, as no model containing weather parameters was competitive. FLIR surveys resulted in greater counts of Deer clusters and greater detection probabilities (0.57, 0.45, 0.36) relative to spotlight surveys (0.51, 0.32, 0.35) in all 3 survey periods. CV estimates for FLIR surveys were also consistently lower than those for spotlight surveys; however, N-mixture models resulted in the lowest CV estimates and had the narrowest 95% CI for all 3 survey periods (Table 3). We observed overlap of all the 95% CI for FLIR, spotlight, and N-mixture density estimates in the August 2012 and February 2013 periods; however, during the August 2013 period there was no CI overlap between spotlight and N-mixture, although both methods overlapped with the CI for the FLIR survey (Fig. 4). Point estimates from the Jacobson camera survey were well outside the upper CI for all other methods during the August 2012 and February 2013 periods, but within the CI estimates for the FLIR and N-mixture Northeastern Naturalist Vol. 26, No. 2 J.M. Haus, T.B. Eyler, and J.L. Bowman 2019 313 Table 1. The top 6 models for spotlight surveys in each period, including conventional distance sampling (CDS) and 5 multiple covariate distance sampling (MCDS) models, to estimate White-tailed Deer density in the Green Ridge State Forest, MD. AICc = Akaike’s information criterion adjusted for small n, ω = Akaike weight, and Bp = barometric pressure. Model Parameters AICc ΔAICc ω August 2012 CDS 114.15 0.00 0.97 MCDS Precipitation 121.85 7.70 0.01 Temperature 124.23 10.08 0.01 BP 124.98 10.83 0.01 Precip + temp 142.12 27.97 0.00 Bp + precip 146.52 32.38 0.00 February 2013 CDS 116.14 0.00 0.40 MCDS Temperature 119.77 3.63 0.21 Precipitation 120.11 3.97 0.20 Bp 121.00 4.86 0.12 Wind speed 121.71 5.57 0.07 Precip + temp 168.23 52.09 0.00 August 2013 CDS 101.70 0.00 0.93 MCDS Bp 110.13 8.42 0.04 Precipitation 112.39 10.69 0.01 Temperature 112.65 10.95 0.01 Bp + precip 138.09 36.39 0.00 Bp + temp 139.49 37.79 0.00 Figure 4. White-tailed Deer density estimates (Deer/km2) for each method by survey period for Green Ridge State Forest, MD. Northeastern Naturalist 314 J.M. Haus, T.B. Eyler, and J.L. Bowman 2019 Vol. 26, No. 2 Table 2. The top 6 models for FLIR surveys in each period, including conventional distance sampling (CDS) and 5 multiple covariate distance sampling models (MCDS), to estimate White-tailed Deer density in the Green Ridge State Forest, MD. AICc = Akaike’s information criterion adjusted for small n, ω = Akaike weight, and Bp = barometric pressure. Model Parameters AICc ΔAICc ω August 2012 CDS 106.96 0.00 0.94 MCDS Bp 114.64 7.68 0.06 Temperature 128.44 21.48 0.00 Bp + temp 129.61 22.64 0.00 Precipitation 139.99 30.03 0.00 Bp + precip + temp 158.21 51.25 0.00 February 2013 CDS 88.21 0.00 0.86 MCDS Precipitation 91.07 2.85 0.13 Bp 95.81 7.60 0.01 Temperature 99.08 10.86 0.00 Bp + precip 116.23 28.02 0.00 Bp + temp 125.80 37.59 0.00 August 2013 CDS 110.60 0.00 0.92 MCDS Bp 114.78 4.18 0.03 Precipitation 114.82 4.22 0.03 Temperature 115.81 5.21 0.02 Bp + precip 136.99 26.39 0.00 Bp + temp 158.21 47.61 0.00 Table 3. White-tailed Deer density estimates and measures of precision obtained via 4 survey methods during August 2012, February 2013, and August 2013 in Green Ridge State Forest, MD. Survey period Method Deer/km2 (SE) CV (%) 95% CI August 2012 FLIR 6.38 (1.11) 17.4 4.29‒9.49 Spotlight 6.00 (1.57) 26.1 3.53‒10.21 N-mixture 7.16 (1.05) 14.6 5.10‒9.05 Jacobson 12.42 NA NA February 2013 FLIR 3.33 (0.94) 28.3 1.87‒5.90 Spotlight 3.16 (1.00) 31.7 1.66‒6.00 N-mixture 4.68 (0.80) 17.1 3.22‒6.19 Jacobson 12.03 NA NA August 2013 FLIR 8.10 (1.20) 14.8 5.98‒11.35 Spotlight 3.93 (0.86) 22.1 2.47‒6.27 N-mixture 8.16 (1.00) 12.3 6.54‒10.72 Jacobson 8.65 NA NA Northeastern Naturalist Vol. 26, No. 2 J.M. Haus, T.B. Eyler, and J.L. Bowman 2019 315 model during the August 2013 period (Table 3). Density point estimates were consistently lowest among spotlight surveys, whereas FLIR surveys and N-mixture models produced similar estimates in all 3 periods (Table 3). Demographic estimates were highly variable (Table 4), and we did not detect any pattern in the estimates of adult sex ratio between methods or survey period. Estimates for fawn recruitment were greater for the road-based surveys than for the camera-survey methods in the August 2012 and August 2013 periods. We were unable to estimate demographic information for FLIR and spotlight methodologies during the February 2013 survey period because of difficulty discerning between sex and age due to seasonal morphological changes. The recorded harvest within the study area during the 2012–2013 hunting season was 2.13 Deer/km2 (MDNR 2013). Between the August 2012 and February 2013 survey periods, FLIR, spotlight, and N-mixture model estimates showed a reduction in density of 3.05, 2.84, and 2.48 Deer/km2, respectively, while the Jacobson method only estimated an overall reduction of 0.39 Deer/km2. However, the recorded antlered harvest in the study area was 1.30 antlered Deer/km2 (MDNR 2013), which was identical to the observed reduction in identifiable males based on demographic estimates generated by the Jacobson method. Discussion The limitations of the 4 methods presented are well established and documented in the literature. An important caveat to our analysis is that we did not attempt to Table 4. Estimates of adult sex ratio (females:male) and fawn recruitment (fawns/adult female) for White-tailed Deer obtained via 4 survey methods during August 2012, February 2013, and August 2013 in Green Ridge State Forest, MD. Survey period Method Adult sex ratio Fawns / female August 2012 FLIR 2.7:1 0.46 Spotlight 2.0:1 0.38 N-mixture 2.6:1 0.25 Jacobson 2.5:1 0.28 February 2013 FLIRA NA NA SpotlightA NA NA N-mixture 2.8:1 0.44 Jacobson 3.1:1 0.63 August 2013 FLIR 2.3:1 0.46 Spotlight 2.3:1 0.74 N-mixture 2.7:1 0.27 Jacobson 2.4:1 0.36 AThe use of road survey equipment proved inadequate to reliably estimate demographic parameters due to difficulty differentiating adult females, large fawns, and shed antlered males. Northeastern Naturalist 316 J.M. Haus, T.B. Eyler, and J.L. Bowman 2019 Vol. 26, No. 2 correct or control for known limitations, but rather perform surveys in a manner consistent with methods typically used by Deer managers. A conclusion regarding which method generated the most accurate estimates is not possible given that we surveyed an open Deer population of unknown abundance and demographics. Although detection probability and the number of observed Deer clusters varied, the number of clusters exceeded the recommended minimum of 40 clusters (Buckland et al. 2001, Burnham et al. 1980) in all survey periods for both methods. Spotlight surveys consistently estimated the lowest density during each survey period; however, CI overlap suggested no difference in estimates produced by spotlight and FLIR surveys in all 3 survey periods and no difference between spotlight and N-mixture model estimates in August 2012 and February 2013. Density estimates from Jacobson camera surveys differed from both distance sampling techniques and N-mixture model estimates in the August 2012 and February 2013 survey periods and from spotlight surveys in August 2013. During the February survey period, FLIR surveys were similar to both camera and spotlight surveys. FLIR surveys and N-mixture model estimates were similar throughout all 3 survey periods and appeared to provide conservative density estimates with adequate precision compared to spotlight and Jacobson camera surveys. Focardi et al. (2001) found no difference in the performance of spotlight and FLIR techniques in Dama dama L. (Fallow Deer); however, we observed a reduced detection probability during spotlight surveys at a rate similar to that reported by Collier et al. (2007), who determined that spotlight surveys detected 30–78% of the Deer observed during FLIR surveys. Furthermore, habitat features (i.e., elevation, rivers, cover type) influenced the location of transects (roads) and the distribution and movement of the Deer population (Anderson et al. 2013, Long et al. 2010, Peterson et al. 2017), which may have resulted in a violation of experimental design and introduced potential bias to detection and thus density estimates (McShea et al. 2011) in both distance-sampling methods. Montague et al. (2017), however, found road-based distance sampling using FLIR to be a viable technique in a mountainous region of Virginia, a habitat comparable to our study area. Precision was not sufficient for rough management decisions (Skalski et al. 2005) for spotlight estimates in 2 survey periods (August 2012 and February 2013) and 1 survey period (February 2013) for FLIR surveys. The seasonal difference for both methods between August 2012 and February 2013 is comparable to the recorded harvest for the area, which supports past research advocating distance sampling as a means to monitor population trends (DeYoung 2011, Fafarman and DeYoung 1986, Whipple et al. 1994). We detected no discernible advantages of either distance-sampling technique when estimating demographic parameters other than density. Due to the variability both among methods and among survey periods for estimates of adult sex ratio and fawn recruitment, we cannot make a conclusion regarding which method may be preferable for Deer managers. With the Jacobson camera method, the assumption of equal detectability is essential to density estimates and is based on detection probability of individually identifiable males. Jacobson et al. (1997) stated that bias by gender would bias Northeastern Naturalist Vol. 26, No. 2 J.M. Haus, T.B. Eyler, and J.L. Bowman 2019 317 estimates of Deer populations. Past research has documented such gender-specific bias of camera surveys (Cutler and Swann 1999, Larrucea et al. 2007, Moore et al. 2014). Behavioral responses to baiting may also violate the assumption of equal detectability (Campbell et al. 2006, Cutler and Swann 1999, McCoy et al. 2011, Roberts et al. 2006). Furthermore, detection rates from cameras may not accurately reflect Deer densities on the landscape (Parsons et al. 2017). Our results are in agreement with Roberts et al. (2006) who concluded that camera surveys result in significantly higher estimates of Deer density than road-based sampling methods (16.5 Deer/km2 via road surveys and 36.0 Deer/km2 via cameras). The lack of estimated sampling variances diminish the reliability of camera survey density estimates (Dougherty and Bowman 2012, White et al. 1982) and complicate method comparison. The lowest density estimate generated by cameras (8.65 Deer/km2; August 2013) was similar to the FLIR survey and N-mixture estimates for that period, suggesting the Jacobson method may be prone to overestimating Deer density at large scales. Moore et al. (2014), however, found that camera surveys generated lower density estimates relative to an aerial survey in Texas. Both camera survey methods were capable of providing an estimate of demographic parameters both pre- and post-harvest, although accuracy of those estimates was difficult to discern. The failure of the Jacobson method to detect a reduction in the post-harvest density estimate (February 2013) relative to the pre-harvest estimate (August 2012) may suggest that the Jacobson method is not as sensitive to seasonal population changes as the alternative methods. The Jacobson method accurately estimated the reduction in antlered males due to hunting; however, we observed an increase in the antlerless segment of the population post-harvest that negated the reduction in antlered Deer. We detected the greatest fawn recruitment in the February 2013 survey period, suggesting an underestimation of fawn recruitment during pre-harvest camera surveys. We attribute the discrepancies to weaning fawns having less interest in bait sites or not yet being mobile enough to move with the doe, resulting in a reduced fawn detection probability during the August surveys (Chitwood et al. 2017, DeYoung and Miller 2011, McKinley et al. 2006). Table 5. Cost breakdown per survey method for FLIR, spotlight, and both camera surveys during August 2012, February 2013, and August 2013 in Green Ridge State Forest, MD. Total cost sums all expenses, while cost per survey period estimates cost assuming a 5-year life of reusable field equipment (e.g., spotlights, remote cameras, laser range finder). Expense FLIR Spotlight Cameras Equipment $8769.96 $869.97 $13,399.00 Seasonal suppliesA $0.00 $0.00 $616.40 Field hours $432.00 $432.00 $864.00 Analysis hours $48.00 $48.00 $240.00 Mileage $92.00 $92.00 $192.50 Total cost $9341.96 $1441.97 $15,311.90 Cost/survey $2325.99 $745.99 $4592.70 ASeasonal supplies were not reusable (e.g., corn and camera batteries) and are differentiated from reusable equipment costs. Northeastern Naturalist 318 J.M. Haus, T.B. Eyler, and J.L. Bowman 2019 Vol. 26, No. 2 Given the inability to estimate sampling variance that would allow for an evaluation of accuracy, the lack of sensitivity to seasonal changes, and the underestimation of pre-harvest fawn recruitment, Jacobson camera surveys do not seem to warrant the high cost of equipment and labor (Table 5). The CI overlap between spotlight and FLIR surveys is partially due to the relatively high variance resulting from reduced detection probability in spotlight surveys. FLIR surveys clearly outperformed spotlight surveys but the similarity in the methods and high cost of the FLIR unit may dissuade Deer managers. The increased labor and mileage costs necessary to provide sufficient precision for spotlight surveys is an important consideration, but the added cost may be justifiable when considered the investment needed for a long-term monitoring program. N-mixture models achieved the greatest precision in all 3 survey periods. Similar to the Jacobson method, however, N-mixture estimates require the high cost of cameras, bait, and labor. Management actions should always be based on the best available science. Road-transect surveys appeared to provide estimates of Deer density informative to rough management practices despite violations of study design due to nonrandom transects (LaRue et al. 2007, Morelle et al. 2012). Spotlight surveys are affordable but require substantial effort to achieve precision. N-mixture models of abundance appeared to be effective at estimating Deer density and provided high levels of precision, but are computationally intensive and costly. Keever et al. (2017) found that N-mixture models generated adequate density estimates in as little as 5 survey nights for an enclosed population. Reductions in replicates would substantially lower cost, but would likely reduce precision in unfenced populations with more variable detection probabilities. Given the failure to generate estimates of sampling variance and the disparate estimates of Deer density however, we do not recommend managers and biologists utilize traditional camera surveys to estimate overall density if an alternative method is practical. Camera surveys may be a useful option in areas where roads are not abundant or do not accurately represent the habitat of the study area, or when managers are interested in the antlered male segment of the population only. If camera surveys are the only practicable method, we recommend using N-mixture abundance models to analyze the photographic data instead of the Jacobson method. FLIR surveys provided similar density estimates relative to other methods, provided sufficient precision for rough management, required less effort than spotlight surveys to achieve adequate precision, and were more cost effective than camera-based survey methods. We recommend incorporating infrared technology into road surveys to estimate Deer population parameters. Acknowledgments We would like to thank the Maryland Department of Natural Resources and Wildlife Heritage Service and the Federal Aid in Wildlife Restoration Act for funding this project, grant number W-61-R-23. This work was also supported by the USDA National Institute of Food and Agriculture, Hatch project DEL00712 and McIntire Stennis DEL00672. We acknowledge J. Cardoza and 2 anonymous reviewers who provided comments that greatly improved this manuscript. Northeastern Naturalist Vol. 26, No. 2 J.M. Haus, T.B. Eyler, and J.L. Bowman 2019 319 Literature Cited Acevedo, P., F. Ruiz-Fons, J. Vicente, A.R. Reyes-García, V. Alzaga, and C. Gortáza. 2008. Estimating Red Deer abundance in a wide range of management situations in Mediterranean habitats. Journal of Zoology 276:37‒47. Alves, J., A.A. da Silva, A.M.V.M. Soares, C. Fonseca. 2013. Pellet-group count methods to estimate Red Deer densities: Precision, potential accuracy, and efficiency. Mammalian Biology 78:134‒141. Anderson, C.W., C.K. Nielsen, C.M. Hester, R.M. Hubbard, J.K. Stroud, and E.C. Schauber. 2013. Comparison of indirect and direct methods of distance sampling for estimating density of White-tailed Deer. Wildlife Society Bulletin 37:146‒154. Anderson, D.R. 2001. The need to get the basics right in wildlife field studies. Wildlife Society Bulletin 29:1294‒1297. Beaver, J.T., C.A. Harper, R.E. Kissell Jr., L.I. Muller, P.S. Basinger, M.J. Goode, F.T. Van Manen, W. Winton, and M.L. Kennedy. 2014. Aerial vertical-looking infrared imagery to evaluate bias of distance-sampling techniques for White-tailed Deer. Wildlife Society Bulletin 38:419‒427. Beier, P., and D.R. McCullough. 1990. Factors influencing deer activity and habitat use. Wildlife Monographs 109:3‒51. Belant, J.L., and T.W. Seamans. 2000. Comparison of three devices for monitoring Whitetailed Deer at night. Wildlife Society Bulletin 28:154‒158. Bissonette, J.A., C.A. Kassar, and L.J. Cook. 2008. Assessment of costs associated with deer–vehicle collisions: Human death and injury, vehicle damage, and deer loss. Human– Wildlife Conflicts 2:17‒27. Buckland, S.T., D.R. Anderson, K.P. Burnham, J.L. Laake, D.L. Borchers, and L. Thomas. 2001. Introduction to Distance Sampling: Estimating Abundance of Biological Populations. Oxford University Press, UK. 448 pp. Burnham, K.P., and D.R. Anderson. 2002. Model Selection and Multimodel Inference. Second Edition. Springer˗Verlag, New York, NY. 488 pp. Burnham, K.P., D.R. Anderson, and J.L. Laake. 1980. Estimation of density from line transect sampling of biological populations. Wildlife Monographs 72:3‒202. Burton, A.C., E. Neilson, D. Moreira, A. Ladle, R. Steenweg, J.T. Fisher, E. Bayne, and S. Boutin. 2015. Wildlife camera trapping: A review and recommendations for linking surveys to ecological processes. Journal of Applied Ecology 52:675‒685. Campbell, T.A., B.R. Laseter, W.M. Ford, and K.V. Miller. 2004. Movements of female White-tailed Deer (Odocoileus virginianus) in relation to timber harvests in the central Appalachians. Forest Ecology and Management 199:371‒378. Campbell, T.A., C.A. Langdon, B.R. Laseter, W.M. Ford, J.W. Edwards, and K.V. Miller. 2006. Movements of female White-tailed Deer to bait sites in West Virginia, USA. Wildlife Research 33:1–4. Caughley, G. 1977. Sampling in aerial survey. Journal of Wildlife Management 41:605‒615. Chitwood, M.C., M.A. Lashley, J.C. Kilgo, M.J. Cherry, L.M. Connor, M. Vukovich, H.S. Ray, C. Ruth, R.J. Warren, C.S. DePerno, and C.E. Moorman. 2017. Are camera surveys useful for assessing recruitment in White-tailed Deer? Wildlife Biology:wlb.00178. DOI:10.2981/wlb.00178. Chrétien, L., J. Théau, and P. Ménard. 2016. Visible and thermal infrared remote sensing for the detection of White-tailed Deer using an unmanned aerial system. Wildlife Society Bulletin 40:181–191. Northeastern Naturalist 320 J.M. Haus, T.B. Eyler, and J.L. Bowman 2019 Vol. 26, No. 2 Collier, B.A., S.S. Ditchkoff, J.B. Raglin, and J.M. Smith. 2007. Detection probability and sources of variation in White-tailed Deer spotlight surveys. Journal of Wildlife Management 71:277‒281. Collier, B.A., S.S. Ditchkoff, C.R. Ruth Jr., and J.B. Raglin. 2013. Spotlight surveys for White-tailed Deer: Monitoring panacea or exercise in futility? Journal of Wildlife Management 77:165‒171. Conover, M.R., W.C. Pitt, K.K. Kessler, T.J. DuBow, and W.A. Sanborn. 1995. Review of human injuries, illnesses, and economic losses caused by wildlife in the United States. Wildlife Society Bulletin 23:407‒414. Curtis, P.D., B. Boldgiv, P.M. Mattison, and J.R. Boulanger. 2009. Estimating deer abundance in suburban areas with infrared-triggered cameras. Human-Wildlife Conflicts 3:116‒128. Cusack, J.J., A. Swanson, T. Coulson, C. Packer, C. Carbone, A.J. Dickman, M. Kosmala, C. Lintott, and J.M. Rowcliff. 2015. Applying a random encounter model to estimate Lion density from camera traps in Serengeti National Park, Tanzania. Journal of Wildlife Management 79:1014–1021. Cutler, T.L., and D.E. Swann. 1999. Using remote photography in wildlife ecology: A review. Wildlife Society Bulletin 27:571‒581. DeYoung, C.A. 2011. Population dynamics. Pp.147–180, In D.G. Hewitt (Ed.). Biology and Management of White-tailed Deer. CRC Press, Boca Raton, FL. 674 pp. DeYoung, R.W., and K.V. Miller. 2011. White-tailed Deer behavior. Pp. 147–180, In D.G. Hewitt (Ed.). Biology and Management of White-tailed Deer. CRC Press, Boca Raton, FL. 674 pp. DeYoung, R.W., S. Demarais, R.L. Honeycutt, K.L. Gee, and R.A. Gonzales. 2006. Social dominance and male breeding success in captive White-tailed Deer. Wildlife Society Bulletin 34:131‒136. Dougherty, S.Q., and J.L. Bowman. 2012. Estimating Sika Deer abundance using camera surveys. Population Ecology 54:357‒365. Drake, D., C. Aquila., and G. Huntington. 2005. Counting a suburban deer population using forward-looking infrared radar and road counts. Wildlife Society Bulletin 33:656‒661. Eberhardt, L., and R.C. Van Etten. 1956. Evaluation of the pellet group count as a deer census method. Journal of Wildlife management 20:70‒74. Ellingson, A.R., and P.M. Lukacs. 2003. Improving methods for regional land bird monitoring: A reply to Hutto and Young. Wildlife Society Bulletin 31:896‒902. Evans, D.R., and J.E. Gates. 1997. Cowbird selection of breeding areas: The role of habitat and bird species abundance. The Wilson Bulletin 109:470‒480. Fafarman, K.R., and C.A. DeYoung. 1986. Evaluation of spotlight counts of deer in south Texas. Wildlife Society Bulletin 14:180–185. Fiske, I., and R. Chandler. 2011. Unmarked: An R package for fitting hierarchical models of wildlife occurrence and abundance. Journal of Statistical So ftware 43:1–23. Focardi, S., A.M. De Marinis, M. Rizzotto, and A. Pucci. 2001. Comparative evaluation of thermal infrared imaging and spotlighting to survey wildlife. Wildlife Society Bulletin 29:133–139. Foster, R.J., and B.J. Harmsen. 2012. A critique of density estimation from camera-trap data. Journal of Wildlife Management 76:224–236. Gibbs, J.P. 2000. Monitoring populations. Pp 213‒252, In L. Boitani and T.K. Fuller (Eds.). Research Techniques in Animal Ecology. Columbia University Press, New York, NY. 464 pp. Northeastern Naturalist Vol. 26, No. 2 J.M. Haus, T.B. Eyler, and J.L. Bowman 2019 321 Gill, R.M.A., M.L. Thomas, and D. Stocker. 1997. The use of portable thermal imaging for estimating deer population density in forest habitats. Journal of Applied Ecology 34:1273–1286. Gulsby, W.D., C.H. Killmaster, J.W. Bowers, J.D. Kelly, B.N. Sacks, M.J. Statham, and K.V. Miller. 2015. White-tailed Deer fawn recruitment before and after experimental Coyote removals in central Georgia. Wildlife Society Bulletin 39:258–255. Havens, K.J., and E. Sharp. 2015. Thermal-imaging Techniques to Survey and Monitor Animals in the Wild. Academic Press, Cambridge, MA. 354 pp. Hurvich, C.M., and C.L. Tsai. 1989. Regression and time-series model selection in small samples. Biometrika 76:297–307. Jacobson, C.A., J.F. Organ, D.J. Decker, G.R. Batcheller, and L. Carpenter. 2010. A conservation institution for the 21st century: Implications for state wildlife agencies. Journal of Wildlife Management 74:203–209. Jacobson, H.A., J.C. Kroll, R.W. Browning, B.H. Koerth, and M.H. Conway. 1997. Infrared-triggered cameras for censusing White-tailed Deer. Wildlife Society Bulletin 25:547‒556. Jenkins, J.H., and R.K. Marchinton. 1969. Problems in censusing the White-tailed Deer. Pp 115–118, In L.K. Halls (Ed.). White-tailed Deer in the Southern Forest Habitat: Proceedings of a Symposium. US Forest Service, Southern Forest Experimental Station, New Orleans, LA. 130 pp. Keever, A.C., C.P. McGowan, S.S. Ditchkoff, P.K. Acker, J.B. Grand, and C.H. Newbolt. 2017. Efficacy of N-mixture models for surveying and monitoring White-tailed Deer populations. Mammal Research 62:413‒422. Koerth, B.H., and J.C. Kroll. 2000. Bait type and timing for deer counts using cameras triggered by infrared monitors. Wildlife Society Bulletin 28:630‒635. Kolowski J.M., and T.D. Forrester. 2017. Camera-trap placement and the potential for bias due to trails and other features. PLoS ONE 12:e0186679. La Morgia, V., R. Calmanti, A. Calabrese, and S. Focardi. 2015. Cost-effective nocturnal distance sampling for landscape monitoring of ungulate populations. European Journal of Wildlife Resources 61:285‒298. Larrucea, E.S., P.F. Brussard, M.M. Jaeger, and R.H. Barrett. 2007. Cameras, Coyotes, and the assumption of equal detectability. Journal of Wildlife Management 71:1682‒1689. LaRue, M.A., C.K. Nielsen, and M.D. Grund. 2007. Using distance sampling to estimate densities of White-tailed Deer in south-central Minnesota. The Prairie Naturalist 39:57‒68. Long, E.S., D.R. Diefenbach, B.D. Wallingford, and C.S. Rosenberry. 2010. Influence of roads, rivers, and mountains on natal dispersal of White-tailed Deer. Journal of Wildlife Management 74:1242‒1249. Lovely, K.R., W.J. McShea, N.W. Lafon, and D.E. Carr. 2013. Land parcelization and deer population densities in a rural county of Virginia. Wildlife Society Bulletin 37:360–367. Mann, G.K.H., M.J. O’Riain, and D.M. Parker. 2015. The road less traveled: Assessing variation in mammal detection probabilities with camera traps in a semi-arid biodiversity hotspot. Biodiversity and Conservation 24:531–545. Maryland Department of Natural Resources (MDNR). 2012. Green Ridge State Forest annual works plan. Forest Service, Annapolis MD. 21 pp. MDNR. 2013. Maryland annual deer report. Wildlife Heritage Service, Annapolis, MD. 28 pp. Marques, F.F.C., S.T. Buckland, D. Goffin, C.E. Dixon, D.L. Borchers, B.A. Mayle, and A.J. Peace. 2001. Estimating deer abundance from line transect surveys of dung: Sika Deer in southern Scotland. Journal of Applied Ecology 38:349–363. Northeastern Naturalist 322 J.M. Haus, T.B. Eyler, and J.L. Bowman 2019 Vol. 26, No. 2 McCoy, J.C., S.S. Ditchkoff, and T.D. Steury. 2011. Bias associated with baited camera sites for assessing population characteristics of deer. Journal of Wildlife Management 75:472‒477. McCullough, D.R. 1982. Evaluation of night spotlighting as a deer study technique. Journal of Wildlife Management 49:963‒973. McCullough, D.R., and D.H. Hirth. 1988. Evaluation of the Peterson–Lincoln estimator for a White-tailed Deer population. Journal of Wildlife Management 52:534‒544. McKinley, W.T., S. Demaris, K.L. Gee, and H.A. Jacobson. 2006. Accuracy of the camera technique for estimating White-tailed Deer population characteristics. Proceedings from the Annual Conference of the Southeastern Association of Fish and Wildlife Agencies 60:83‒88. McShea, W.J., C.M. Stewart, L. Kearns, and S. Bates. 2011. Road bias for deer density estimates at 2 national parks in Maryland. Wildlife Society Bulletin 35:177–184. Millspaugh, J.J., J.R. Skalski, R.L. Townsend, D.R. Diefenbach, M.S. Boyce, L.P. Hansen, and K. Kammermeyer. 2009. An evaluation of sex-age-kill (SAK) model performance. Journal of Wildlife Management 73:442‒451. Montague, D.M., R.D. Montague, M.L. Fies, and M.J. Kelly. 2017. Using distance-sampling to estimate density of White-tailed Deer in forested, mountainous landscapes in Virginia. Northeastern Naturalist 24:505‒519. Moore, M.T., A.M. Foley, C.A. DeYoung, D.G. Hewitt, T.E. Fulbright, and D.A. Draeger. 2014. Evaluation of population estimates of White-tailed Deer from camera survey. Journal of the Southeastern Association of Fish and Wildlife Agencies 1:127‒132. Morelle, K., P. Bouché, F. Lehaire, V. Leeman, and P. Lejeune. 2012. Game species monitoring using road-based distance sampling in association with thermal imagers: A covariate analysis. Animal Biodiversity and Conservation 35:253–265. National Oceanic and Atmospheric Administration (NOAA). 2013. Climatography of the United States No. 21 1981‒2010. Available online at http://cdo.ncdc.noaa.gov/cgibin/ climatenormals/climatenormals.pl. Accessed 8 September 2013. Naugle, D.E., J.A. Jenks, and B.J. Kernohan. 1996. Use of thermal infrared sensing to estimate density of White-tailed Deer. Wildlife Society Bulletin 24:37‒43. Neff, D.J. 1968. The pellet-group count technique for big game trend, census, and distribution: A review. Journal of Wildlife Management 32:597‒614. Parsons, A.W., T. Forrester, W.J. McShea, M.C. Baker-Whatton, J.J. Millspaugh, and R. Kays. 2017. Do occupancy or detection rates from camera traps reflect deer density? Journal of Mammalogy 98:1547‒1557. Peterson, B.E., D.J. Storm, A.S. Norton, and T.R. Van Deelen. 2017. Landscape influence on dispersal of yearling male White-tailed Deer. Journal of Wildlife Management 81:1449–1456. Posada, D., and T.R. Buckley. 2004. Model selection and model averaging in phylogenetics: Advantages of Akaike information criterion and Bayesian approaches over likelihoodratio tests. Systematic Biology 53:793–808. Potvin, F., and L. Breton. 2005. Testing 2 aerial survey techniques on deer in fenced enclosures: Visual double counts and thermal infrared sensing. Wildlife Society Bulletin 33:317‒325. Potvin, F., L. Breton, and L.P. Rivest. 2002. Testing a double-count aerial survey technique for White-tailed Deer, Odocoileus virginianus, in Quebec. Canadian Field-Naturalist 116:564‒572. Progulske, D.R., and D.C. Duerre. 1964. Factors influencing spotlight counts for deer. Journal of Wildlife Management 28:27‒34. Northeastern Naturalist Vol. 26, No. 2 J.M. Haus, T.B. Eyler, and J.L. Bowman 2019 323 Rand, P.W., C. Lubelczyk, G.R. Lavigne, S. Elias, M.S. Holman, E.H. Lacombe, and R.P. Smith Jr. 2003. Deer density and the abundance of Ixodes scapularis (Acari: Ixodidae). Journal of Medical Entomology 40:179‒184. Roberts, C.W., B.L. Pierce, A.W. Braden, R.R. Lopez, N.J. Silvy, P.A. Frank, and D. Ransom Jr. 2006. Comparison of camera- and road-survey estimates for White-tailed Deer. Journal of Wildlife Management 70:263‒267. Romin, L.A., and J.A. Bissonette. 1996. Deer–vehicle collisions: Status of state monitoring activities and mitigation efforts. Wildlife Society Bulletin 24:276‒283. Roseberry, J.L., and A. Wolf. 1991. A comparative evaluation of techniques for analyzing White-tailed Deer harvest data. Wildlife Monographs 117:3‒59. Rossell, C.R., S. Patch, and S. Salmons. 2007. Effects of deer browsing on native and non‒ native vegetation in a mixed oak‒beech forest on the Atlantic coastal plain. Northeastern Naturalist 14:61‒72. Rowcliffe, J.M., J. Field, S.T. Turvey, and C. Carbone. 2008. Estimating animal density using camera traps without the need for individual recognition. Journal of Applied Ecology 45:1228‒1236. Royle, J.A. 2004. N-mixture models for estimating population size from spatially replicated counts. Biometrics 60:108‒115. Russell, F.L., D.B. Zippin, and N.L. Fowler. 2001. Effects of White-tailed Deer (Odocoileus virginianus) on plants, plant populations, and communities: A review. American Midland Naturalist 146:1‒26. Skalski, J.R., K.E. Ryding, and J.J. Millspaugh. 2005. Wildlife Demography: Analysis of Sex, Age, and Count Data. Elsevier Academic Press, San Diego, CA. 656 pp. Smart, J.C, A.I. Ward, and P.C.L. White. 2004. Monitoring woodland deer populations in the UK: An imprecise science. Mammal Review 34:99‒114. Storm, D.J., M.D. Samuel, T.R. Van Deelen, K.D. Malcolm, R.E. Rolley, N.A. Frost, D.P. Bates, and B.J. Richards. 2011. Comparisons of visual-based helicopter and fixed-wing forward-looking infrared surveys for counting White-tailed Deer, Odocoileus virginianus. Wildlife Biology 17:431‒440. Thomas, L., S.T. Buckland, E.A. Rexstad, J.L. Laake, S. Strindberg, S.L. Hedley, J.R.B. Bishop, T.A. Marques, and K.P. Burnham. 2010. Distance software: Eesign and analysis of distance-sampling surveys for estimating population size. Journal of Applied Ecology 47:5‒14. Tilghman, N.G. 1989. Impacts of White‒tailed Deer on forests regeneration in northwestern Pennsylvania. Journal of Wildlife Management 53:524‒532. Tymkiw, E.L., J.L. Bowman, and W.G. Shriver. 2013. The effect of White-tailed Deer density on breeding songbirds in Delaware. Wildlife Society Bulletin 37:714‒724. Urbanek, R.E., C.K. Nielsen, T.S. Preuss, and G.A. Glowacki. 2012. Comparison of aerial survey and pellet-based distance sampling methods for estimating deer density. Wildlife Society Bulletin 36:100‒106. Waller, D.M., and W.S. Alverson. 1997. The White-tailed Deer: A keystone herbivore. Wildlife Society Bulletin 25:217–226. Walock, S.C., H.A. Jacobson, J.L. Bowman, and D.S. Coggin. 1997. Comparison of the camera estimate to program CAPTURE to estimate antlered White-tailed Deer populations. Proceedings of the Annual Conference of Southeastern Association of Fish and Wildlife Agencies 51:217–224. Weckel, M., R.F. Rockwell, and F. Secret. 2011. A modification of Jacobson et al.’s (1997) individual branch-antlered male method for censusing White-tailed Deer. Wildlife Society Bulletin 35:445–451. Northeastern Naturalist 324 J.M. Haus, T.B. Eyler, and J.L. Bowman 2019 Vol. 26, No. 2 Whipple, J.D., D. Rollins, and W.H. Schacht. 1994. A field simulation for assessing accuracy of spotlight deer surveys. Wildlife Society Bulletin 22:667–673. White, G.C., D.R. Anderson, K.P. Burnham, and D.L. Otis. 1982. Capture–recapture and removal methods for sampling closed populations. Special Technical Report. Los Alamos National Laboratory, Los Alamos, NM. 13 pp. Wiggers, E.P., and S.F. Beckerman. 1993. Use of thermal infrared sensing to survey Whitetailed Deer populations. Wildlife Society Bulletin 21:263‒268. Williams, E.S., M.W. Miller, T.J. Kreeger, R.H. Kahn, and E.T. Thorne. 2002. Chronic wasting disease of deer and elk: A review with recommendations for management. Journal of Wildlife Management 66:551–563.