Allegheny woodrats Neotoma magister are an imperiled small mammal species most associated with emergent rock habitats in the central Appalachian Mountains and the Ohio River Valley. The monitoring of populations and their spatiotemporal distributions typically has relied on labor-intensive livetrapping. The use of remote-detecting cameras holds promise for being an equally or more effective method to determine species presence, although trap-based captures permit the estimation of other parameters (e.g., survival, population size, site fidelity). In 2017, 2018, and 2020 we compared standard livetrapping with paired cameras for determining site occupancy of Allegheny woodrats in the central Appalachian Mountains of western Virginia. We further examined the influence of baited vs. unbaited cameras at several sites of confirmed occupancy in 2019. We observed that the detection probability using cameras was approximately 1.7 times that of live traps. Also, detection probability at baited camera traps was 1.3–2.0 times that of unbaited camera traps. Estimates of occupancy ranged from 0.44 to 0.49. Our findings suggest that the use of baited remote-detecting cameras provides a more effective method than livetrapping for detecting Allegheny woodrats. Our study provides a framework for the development of a large-scale, long-term monitoring protocol of Allegheny woodrats with the goals of identifying changes in the distribution of the species and quantifying local extinction and colonization rates at emergent rock outcrops and caves throughout the species' known distribution.

Effective management of rare or declining wildlife species depends on reliable and repeatable methods of accurately sampling such populations to estimate site occupancy. The effectiveness of monitoring methods varies by target species, location, and research interests (Smith et al. 1975; Garden et al. 2007; Thompson and Thompson 2007). Various sampling methods have been developed to monitor terrestrial small mammals (Garden et al. 2007; Thompson and Thompson 2007). Still, conventional small-mammal monitoring practices often involve invasive approaches such as livetrapping and handling (Smith et al. 1975; Williams and Braun 1983; McCay et al. 1998).

For small mammals, livetrapping methods can facilitate the estimation of population abundance and demographic vital rates (Nichols and Pollock 1983; Jones et al. 1996). However, livetrapping often is costly and labor-intensive, trap success varies by species and gear, and handling causes stress to the captured animals (Stanley and Royle 2005; Wiewel et al. 2007). Additionally, low detection probability or trap avoidance may lead to biased results (Diggins et al. 2016). Thus, if the primary research objective is to ascertain the probability of occurrence of a species in an area, survey methods such as track plates and remote-detection cameras (camera traps), which do not require physical capture and handling of animals, may provide effective alternatives or complements to livetrapping. The use of camera traps is common in large-mammal research and recently has gained popularity as an effective survey method for small mammals as camera technology and data storage have improved (De Bondi et al. 2010; Di Cerbo and Biancardi 2013; Diggins et al. 2016).

Allegheny woodrats Neotoma magister are emergent rock-obligate species distributed predominantly in the central Appalachian Mountains and Interior Low Plateau from New Jersey southward to Kentucky and Tennessee, with many populations concentrated in western Virginia and eastern West Virginia (Mengak 2002; Castleberry et al. 2006). Allegheny woodrats are listed as “Near Threatened” on the International Union for Conservation of Nature Red List (Linzey et al. 2008) and are likely extirpated from Connecticut and New York (Monty and Feldhamer 2002). This species is listed as “Endangered” in Maryland (MNHP 2021; NESCA 2021), New Jersey (NJESCA 1973; NJDFW 2012; Fowles 2018), and Ohio (BORCA 1978; ODNR 2020). Also, this species is considered “Threatened” in Pennsylvania (PWRCA 1986; PGC 2021), of “Special Concern” in North Carolina (NCGS 1987; NCWRC 2021), and “In Need of Management” in Tennessee (NETWSCA 1974; TWRA 2021). Although West Virginia currently has no formal state-level protected wildlife species legislation, Allegheny woodrats are described as “Vulnerable” in the West Virginia State Wildlife Action Plan (WVDNR 2015). In Virginia, the Allegheny woodrat is not provided state-level protection of “Threatened” or “Endangered” (VAC 2022); however, the species is designated as “Vulnerable” (S3; Campbell et al. 2010) and in need of moderate conservation (tier IV species) in Virginia's Wildlife Action Plan (VDGIF 2015). Alteration or loss of suitable habitat (e.g., undisturbed forests surrounding emergent rock outcrops and caves), isolation of populations from landscape-scale forest fragmentation, the near extinction of the American chestnut Castanea dentata to chestnut blight Cryphonectria parasitica, and exposure to raccoon roundworm Baylisascaris procyonis from increasing raccoon Procyon lotor populations likely have contributed to rangewide population declines (Balcom and Yahner 1996; LoGiudice 2003, 2006; Owens et al. 2004; Castleberry et al. 2006; Ford et al. 2006). Further, predation by hawks and owls (Fisher 1893; Powers et al. 2020a), fishers Pekania pennanti (McNeil et al. 2017), and timber rattlesnakes Crotalus horridus (G. Turner, Pennsylvania Game Commission, personal communication) may be components of population decline in even seemingly optimal habitat conditions. Habitat change and population declines across a large historic range have dictated the need for an effective and efficient monitoring approach for this species.

Historically, the presence or absence of Allegheny woodrats was ascertained using livetrapping methods (Mengak 2002; Ford et al. 2006). However, the central Appalachian Mountains include a large expanse of potentially suitable habitat, much of which is difficult to access, particularly with live traps. Therefore, the logistics of daily trapline visits often limit the size and scope of surveys (Mengak et al. 2008). From 2017 through 2020, we primarily trapped “legacy” woodrat sites (those with woodrats documented in the last century) in western Virginia to compare detection probability predicted using remote-detecting cameras (herein cameras) with that of the traditional method of livetrapping. During 2017, 2018, and 2020, we sought to determine whether cameras alone were sufficient to confirm site occupancy for future monitoring. Additionally, we used a subset of these sites in 2019 with confirmed occupancy to compare the efficacy of bait at cameras to increase the detection probability of Allegheny woodrats. We hypothesized that the detection probability of Allegheny woodrats would differ between trapping methods and predicted that cameras would produce a detection probability greater than that of live traps. Also, we predicted that detection probability would be greater when using baited vs. unbaited cameras. Further, we predicted that detection probability would increase with an increasing number of trap-nights for all trapping methods.

We surveyed for Allegheny woodrats from May through October 2017 (38 sites), 2018 (29 sites), 2019 (24 sites), and 2020 (11 sites) in the Blue Ridge and Ridge and Valley physiographic provinces in western Virginia. In 2017, 2018, and 2020, the number of sites varied by year because of site accessibility, funding, and personnel limitations. In 2020, fewer sites were surveyed than in 2017 or 2018 because of travel and personnel limitations related to the Covid-19 pandemic. Because we paired baited and unbaited cameras at sites in 2019, equipment limitations further reduced the number of sites we were able to survey in that year. Further, to focus solely on detection probability in 2019, we limited study sites to those in which Allegheny woodrats had been detected in 2017 and 2018. Our study sites included open-canopied or close-canopied erosion-resistant sandstone emergent rock outcrops, boulder fields, and limestone-solution caves (inside and at or around entrances; Figure 1) throughout the George Washington and Jefferson national forests, Virginia state landholdings, and numerous private lands currently or historically occupied by Allegheny woodrats. Distance between nearest neighboring study sites ranged from 180.7 to 21,231.0 m (mean = 5,189.0, SE = 115.6 m), which greatly exceeds the average nightly foraging distance and home range extent for Allegheny woodrats in the central Appalachians (Castleberry et al. 2001). Thus, we assumed within-season closure of study sites. Elevations ranged from approximately 200 to 1,200 m among study sites.

Overall, the Valley and Ridge and Blue Ridge provinces are generally characterized by long mountain ridges with moderate-to-steep side slopes. Above cleared valleys, our study areas were heavily forested (Homer et al. 2015). Dominant forest types were Appalachian oak (e.g., Quercus alba, Q. prinus, Q. rubra, Q. velutina) and mixed pine (e.g., Pinus pungens, P. rigida.) with smaller patches of other hardwood (e.g., Acer saccharum, Liriodendron tulipifera, Fagus grandifolia) and eastern hemlock Tsuga canadensis depending on aspect and elevation. Dense shrub layers of mountain laurel Kalmia latifolia and rosebay rhododendron Rhododendron maximum occurred throughout the study area (Kniowski and Ford 2018).

Detection surveys

We paired 3–18 baited Tomahawk live traps model #201 (40.6 × 12.7 × 12.7 cm) and #202 (48.3 × 15.2 × 15.2 cm; Tomahawk Live-Trap Co., Tomahawk, WI) with an equal number of infrared remote-detecting cameras (Stealth Cam, model STC-G45NGX, Grand Prairie, TX; Bushnell Trophy Cam HD, model 119739, Overland Park, KS; or Reconyx HypeFire, model HC500, Holeman, WI) at discrete emergent rock formation or cave entrances separated from other emergent rock by > 100 m. Because topography and site accessibility varied at each site, the number of livetrap/camera pairs deployed was based on suitable, accessible habitat, with an upper limit per site (18) determined by the number of personnel assisting. Allegheny woodrats are strongly associated with emergent rock and movements typically follow nonrandom paths between ridgeline and side-slope topographic features such as drainages where colluvium occurs (Wood 2008). Thus, within study sites, we chose to place paired live traps and cameras (trapping sites) strategically to increase our probability of detecting Allegheny woodrats. Trapping sites were selected on the basis of two criteria: 1) the location of the trapping site must be characterized as having a cave entrance, a rocky crevice, or a rocky overhang present to place a live trap in or under, and 2) the trapping site must contain a tree or large rock to affix a camera to within 1–3 m of where the live trap would be placed. Cameras were fixed to a tree at ground level or set against a large rock facing directly at the trap. We separated the trapping sites by approximately 10 m within each study site (Figure 1). We programmed the cameras to take a rapid set of three photos at a minimum of 1-min intervals. In 2017, 2018, and 2020 our live traps were baited with half an apple following the protocol used by state agencies (Mengak et. al. 2008) and the methods of Castleberry et al. (2014). During these years, we deployed live traps and cameras for three consecutive nights at 31, 26, and 11 total sites, respectively. We revisited the live traps each morning to check for captured woodrats and replace missing bait but left cameras undisturbed until the conclusion of the survey period. Because of personnel limitations in 2020, only 11 sites were included in the survey. Additionally, live traps and cameras were only used for two consecutive nights when woodrats were detected during the first night. After live capture, we weighed, aged (adult, subadult, juvenile; Mengak 2002), sexed, and ear-tagged (National Band and Tag Co., Newport, KY; style 1005) the captured individuals using methods described by Castleberry et al. (2002; Figure 2). All camera and livetrapping methods were approved by the Radford University Institutional Animal Care and Use Committee (protocol no. FY17-07 and FY20-08) and Virginia Polytechnic Institute and State University Institutional Animal Care and Use Committee (protocol no. 17-070).

In 2019, we surveyed 24 sites known to be occupied in 2017 or 2018 using cameras only (Figure 2). We paired 3–12 unbaited cameras with cameras baited with 1 cup of commercially available livestock sweet feed (Multitext 12.5% multipurpose livestock feed, Augusta Cooperative Farm Bureau, Staunton, VA) over four consecutive nights (sample occasions). In 2019 we used sweet feed as bait instead of apples to reduce the likelihood of the entire bait source being removed during the night because the bait was not secured inside a Tomahawk trap as it was in 2017, 2018, and 2020. We did not replenish bait daily; however, we were able to discern that the bait was not completely diminished by the fourth night by examining photos taken on subsequent nights for presence of bait. For all years, we defined “sample occasion” as 24-h intervals starting from the day of camera deployment (i.e., first night, second night, third night, and fourth night). A “livetrap detection” occurred with the successful capture of a woodrat in at least one live trap at a site within a sample occasion and a “camera detection” occurred when at least one photo of a woodrat was captured at a site within a sample occasion.

Modeling detection and occupancy probabilities

We created sitewide detection histories of Allegheny woodrats for each trapping method each year (i.e., live trap vs. camera in 2017, 2018, and 2020; baited vs. unbaited in 2019). We estimated the probability of detection (p) and probability of site occupancy (ψ) using single-season models (MacKenzie et al. 2002) with package unmarked (Fiske and Chandler 2011) in Program R (R Core Team 2021; Text S1, Supplementary Material). We examined the effect of trapping method (live traps vs. camera), sample occasion (first, second, or third night), and cumulative trapping effort (cumulative number of trap-nights) on the detection probability of Allegheny woodrats. Because we selected survey sites with known or historic occurrences of Allegheny woodrats that were characterized by similar habitat features (emergent rock, rock overhangs, karst habitat), we held occupancy constant (Ψ(.)) in all models under the assumption that habitat features did not have an appreciable effect on site occupancy per se. We constructed 10 a priori models to examine additive and interactive combinations of the detection covariates (Table 1). We assessed model performance using Akaike's information criterion corrected for small sample sizes (AICc) and model weight (wi; Burnham and Anderson 2002). We identified models with a difference in ΔAICc ≤ 2.0 and the highest wi as the best-performing models (Burnham and Anderson 2002). Data from 2017, 2018, and 2020 were pooled to increase the sample size. To assess the effect of the use of bait on the detection probability of Allegheny woodrats, we created a set of eight a priori additive and interaction detection models (Table 2). Detection covariates included bait method (baited vs. unbaited), sample occasion (first, second, third, or fourth night), and cumulative trapping effort. Occupancy probability was held constant. We assessed the model fit of the best-performing models for both the trap- and bait-method analyses using the MacKenzie and Bailey (2004) goodness-of-fit test using 5,000 permutations on the best-performing trap- and bait-method models.

Our number of trap-nights varied among years with 595 camera trap-nights and 604 Tomahawk trap-nights in 2017, 633 camera trap-nights and 585 Tomahawk trap-nights in 2018, and 279 camera trap-nights and 279 Tomahawk trap-nights in 2020. In 2019, we sampled 1,204 baited camera trap-nights and 1,236 unbaited camera trap-nights. Overall, the number of detections of Allegheny woodrats varied among years (Figure 3). In 2017, we detected Allegheny woodrats at 16 of 31 sites including 11 sites where they were captured in traps and 16 where they were detected by cameras. In 2018, we detected woodrats at 13 sites including three sites where woodrats were captured in traps and 13 where they were detected by a camera. In 2019, we detected woodrats at 13 of 24 sites; woodrats were detected by baited cameras at 12 sites and unbaited cameras at 13 sites. In 2020, a total of 6 of the 11 sites had woodrat detections by both cameras and live traps at each of the six sites.

For the trap-method assessment, Ψ(.)p(detection method) was the best-performing model (Table 1; Data S1, Supplementary Material) with no lack of fit indicated (χ2 = 11.46, df = 155, P = 0.18). The probability of detection (p) using cameras was 0.78 (SE = 0.04), whereas p using live traps was 0.51 (SE = 0.09). Occupancy probability was 0.42 (SE = 0.05). For the bait-method assessment, the Ψ(.)p(bait method + trap night) model outperformed all other models (Table 2; Data S1, Supplementary Material) with no lack of fit indicated (χ2 = 11.95, df = 153, P = 0.74). Detection probability of Allegheny woodrats was greater using bait than without bait. For both bait methods, detection probability increased from the first night to the third night but did not increase after the third night (Figure 4). Detection probability using bait was 0.63 (SE = 0.06) on night 1, 0.78 (SE = 0.06) on night 2, 0.91 (SE = 0.03) on night 3, and 0.78 (SE = 0.06) on night 4. Without bait, p = 0.27 (SE = 0.07) on night 1, p = 0.43 (SE = 0.09) on night 2, p = 0.69 (SE = 0.09) on night 3, and p = 0.43 (SE = 0.09) on night 4. Occupancy probability was 0.38 (SE = 0.04).

In this study, we found that detection of Allegheny woodrats was influenced by detection method (live traps vs. camera traps) and whether bait was used with cameras. When using camera traps, the detection probability of Allegheny woodrats was almost twice that when using live traps, which is consistent with our predictions. By aiming the cameras directly at the live traps rather than placing traps near cameras (but not in the frame), we were able to observe that Allegheny woodrats often examined baited live traps but did not enter the traps. We suspect two potential explanations for these observations. First, “trap avoidance” can be attributed to many variables such as trap size, the appearance of the trap to the animal, residual scent, trap placement, previous exposure to capture and handling, and individual variation (Sealander and James 1958; Jolly and Dickson 1983). This species has been generally described as “trap happy,” whereby exposure to a bait source increases an individual's likelihood of being captured (Castleberry et al. 2002). Additionally, traps baited with apples have been successfully used in numerous studies of Allegheny woodrat with a capture–recapture focus (e.g., Johnson 2002; Chamblin et al. 2004; Mengak et al. 2008; Manjerovic et al. 2009; Castleberry et al. 2014). Nonetheless, at multiple sites in this study, we detected woodrats on camera during all three nights but did not capture woodrats in live traps that had remained open. This suggests that studies using only livetrapping methods underestimate the occurrence of Allegheny woodrats by recording false absences. A second potential explanation may be the premature or errant trigger of traps (e.g., disturbance of trap by a nontarget species or other cause), which prevents the capture and therefore detection of a target species investigating a closed trap. In contrast, camera traps may be less perceptible or intimidating to an animal investigating bait and remain “open” regardless of the number of animals that approach the bait, affording greater opportunity to detect the target species.

Our results also suggest that the use of baited cameras is more effective at detecting Allegheny woodrats than the use of unbaited cameras, thereby supporting our second prediction. These results are consistent with recent research in which the use of bait was found to increase the detectability of cryptic species including rodents (Paull et al. 2010; Rendall et al. 2021) and mesocarnivores (Eng and Jachowski 2019; Ferriera-Rodríquez and Pombal 2019; Mills et al. 2019) at camera traps. Although passive camera sampling is often used successfully for large species, Allegheny woodrats are a small, cryptic species and hence have less “apparency” in the environment (Castleberry et al. 2006). Because population densities of Allegheny woodrat are low, nonrandomly distributed, and movements are restricted to isolated and disjunct emergent rock habitats (Castleberry et al. 2001), the use of bait could increase the chance of detecting this species at an occupied site, thereby reducing the chance of a false negative (type II error; Stewart et al. 2019).

When using baited cameras, we found that the detection probability of Allegheny woodrats was greater on the first night of sampling than when using unbaited cameras. Detection probability increased through the third night for both bait methods; however, it did not increase on the fourth night. This suggests that three nights of survey effort are adequate to detect Allegheny woodrats at occupied sites. In contrast, the lack of additional gain in detection probability on the fourth night also may be explained by a dispersal or depredation event that may be falsely perceived as avoidance. Although no direct observations of dispersal or trap mortality were made during this study itself, we did observe one event of predation by a barred owl (Strix varia) during this time in other research efforts (Powers et al. 2020a). Additionally, the removal of bait by target or nontarget species may also reduce the likelihood of animals visiting the camera site. Thus, a longer-lasting bait source such as a solid bait block (Gooley and Schauber 2018) may reduce the risk of the complete consumption of bait and improve detection over longer survey periods.

Our results concur with similar studies of small-mammal detection methodology in montane habitats (Castleberry et al. 2014; Diggins et al. 2016; Smith and Weston 2017) and contribute to mounting research underlining the importance of imperfect detection of wildlife in occupancy studies (e.g., Kéry and Schmidt 2008; Otto and Roloff 2011; Brubaker et al. 2014). Baited camera surveys may provide a detection method that maximizes detection probability on the basis of the behavior and ecology of the target species. Further, the importance of addressing imperfect detection and maximizing the detection probability of Allegheny woodrats is emphasized by the low estimates of occupancy probability in our study. Despite sampling sites with known recent or historic detections of Allegheny woodrats, our number of captures was low and our best-performing trap-method model produced an occupancy estimate of approximately 0.42. Moreover, our bait-method analysis found occupancy probability to be approximately 0.38, though we sampled only sites where Allegheny woodrats were detected in 2017 and 2018. Both estimates were lower than that found by Mengak et al. (2008).

Our study provides a framework (remote camera surveys) for the development of a large-scale, long-term monitoring protocol of Allegheny woodrats, specifically with the goals of identifying changes in the distribution of the species and quantifying local extinction and colonization rates in a manner that does not require physical capture and handling of animals. Long-term monitoring and large databases are necessary for effective predictive modeling in small areas or across a large geographic or temporal scale (Busch and Trexler 2003). Thus, we suggest that the rangewide monitoring protocol of Allegheny woodrat optimally be set for 3 nights with baited camera traps at legacy and potentially occupied sites to monitor the persistence of contemporary populations. In addition to higher detectability, the use of cameras is more cost-effective than conventional livetrapping methods for small mammals (Welbourne and MacGregor 2015; Thomas et al. 2020). Further, cameras can generate other data or insights into Allegheny woodrat behavior (e.g., foraging times) and inter- and intraspecies interactions including competition and predation (Bridges and Noss 2011; Lazenby et al. 2015; Powers et al. 2020a).

For research goals in which demographic information such as abundance and vital rates are desired, we suggest that camera traps be used complementary to livetrap methods to ensure species presence before livetrapping and to improve livetrapping success. Additionally, the complementary use of cameras and live traps may lessen the amount of capture and handling of individuals and thus lower the chance of subsequent stress and capture myopathy. Whereas capture–recapture methods require repeat trapping and handling of individuals, mark–resight methods for estimating population density involve initial capture and marking of individuals and subsequent use of cameras for resighting marked individuals (Arnason et al. 1991; White and Shenk 2001; McClintock et al. 2009). Mark–resight methods also provide an alternative for demographic studies that is less invasive than mark–recapture, which requires repeated capture and handling (McClintock et al. 2009, Rich et al. 2014). Additionally, mark–resight methods may be a viable option when similarly less-invasive methods such as passive integrated transponder units (tags) may be cost-prohibitive. Because individual Allegheny woodrats do not have unique markings, artificial markings such as patterns dyed or shaved into the fur are required for individual identification. Accordingly, the initial trapping of Allegheny woodrats can be used to mark individuals and collect ancillary data (e.g., morphometrics, parasite presence). Subsequent camera observations can be used to estimate population density (Powers et al. 2020b).

Please note: The Journal of Fish and Wildlife Management is not responsible for the content or functionality of any supplemental material. Queries should be directed to the corresponding author for the article.

Text S1. Program R code used to conduct the trapping method and bait method single-season occupancy analyses for Allegheny woodrats Neotoma magister from May through October 2017, 2018, and 2020 in the George Washington and Jefferson national forests and surrounding public and private lands in Virginia.

Available: https://doi.org/10.3996/JFWM-21-037.S1 (49 KB RTF)

Data S1. Detection history and detection covariates for Allegheny woodrats Neotoma magister throughout the George Washington and Jefferson national forests and additional state and private lands in Virginia from May through October 2017, 2018, and 2020 and from April through June 2019.

Available: https://doi.org/10.3996/JFWM-21-037.S2 (44 KB XLSX)

Reference S1. Monty AM, Feldhamer GA. 2002. Conservation assessment for the Eastern woodrat (Neotoma floridana) and the Allegheny woodrat (Neotoma magister). Milwaukee, Wisconsin: U.S. Forest Service, Eastern Region. Available: https://doi.org/10.3996/JFWM-21-037.S3 (479 KB PDF) and https://www.fs.usda.gov/Internet/FSE_DOCUMENTS/fsm91_054316.pdf

Funding and equipment provided by the Virginia Department of Wildlife Resources through a Wildlife Restoration Grant from the U.S. Fish and Wildlife Service and Radford University. We thank many private landowners for providing access survey sites. We are grateful to J. Bentley, J. Brown, C. Bryan, J. Crotts, H. Custer, M. Dimas, E. Gladin, A. Leon, N. McDonald, B. Mullen, K. Nelson-Anderson, L. Platt, L. Van Meter, and C. Wozniak for field assistance. We also thank three anonymous reviewers and the Associate Editor who provided comments that improved an earlier version of this manuscript.

Any use of trade, product, website, or firm names in this publication is for descriptive purposes only and does not imply endorsement by the U.S. Government.

Arnason
AN,
Schwarz
CJ,
Gerrard
JM.
1991
.
Estimating closed population size and number of marked animals from sighting data
.
Journal of Wildlife Management
55
:
716
730
.
Balcom
BJ,
Yahner
RH.
1996
.
Microhabitat and landscape characteristics associated with the threatened Allegheny woodrat
.
Conservation Biology
10
:
515
523
.
[BORCA] Baldwin's Ohio Revised Code Annotated. Title XV. Conservation of Natural Resources. Chapter 1518. Endangered Species. R.C. § 1518.01–1518.99; 1531.25, 1531.99 (August 1978).
Bridges
AS,
Noss
AJ.
2011
.
Behavior and activity patterns
.
Pages
57
69
in
O'Connell
AF,
JD,
Nichols
Karanth
KU,
editors.
Camera traps in animal ecology
.
Tokyo: Springer. Available: doi: 10.1007/978-4-431-99495-4_5 (June 2022)
Brubaker
DR,
Kovach
AI,
Ducey
MJ,
Jakubas
WJ,
O'Brien
KM.
2014
.
Factors influencing detection in occupancy surveys of a threatened lagamorph
.
Wildlife Society Bulletin
38
:
515
523
.
Burnham
KP,
Anderson
DR.
2002
.
Model selection and multimodel inference: a practical information-theoretic approach. 2nd edition
.
New York
:
Springer
.
Busch
ED,
Trexler
JC.
2003
.
The importance of monitoring in regional ecosystem initiatives
.
Pages
1
26
in
Busch
ED,
Trexler
JC,
editors.
Monitoring ecosystems: interdisciplinary approaches for evaluating ecoregional initiatives
.
Washington, D.C.
:
Island Press
.
Campbell
JW,
Mengak
MT,
Castleberry
SB,
Mejia
JD.
2010
.
Distribution of status of uncommon mammals in the southern Appalachian Mountains
.
Southeastern Naturalist
9
:
275
302
.
Castleberry
NL,
Castleberry
SB,
Ford
WM,
Wood
PB,
Mengak
MT.
2002
.
Allegheny woodrat (Neotoma magister) food habits in the central Appalachians
.
American Midland Naturalist
147
:
80
92
.
Castleberry
SB,
Ford
WM,
Wood
PB,
Castleberry
NL,
Mengak
MT.
2001
.
Effects of forest management on movements, home range, and habitat use of Allegheny woodrats
.
Journal of Wildlife Management
65
:
148
156
.
Castleberry
SB,
Mengak
MT,
Ford
WM.
2006
.
Neotoma magister
.
Mammalian Species
789
:
1
5
.
Castleberry
SB,
Mengak
MT,
Menken
TE.
2014
.
Comparison of trapping and camera survey methods for determining presence of Allegheny woodrats
.
Wildlife Society Bulletin
38
:
414
418
.
Chamblin
HD,
Wood
PB,
Edwards
JW.
2004
.
Allegheny woodrat (Neotoma magister) use of rock drainage channels on reclaimed mines in southern West Virginia
.
American Midland Naturalist
151
:
346
354
.
De Bondi
N,
White
JG,
Stevens
M,
Cooke
R.
2010
.
A comparison of the effectiveness of camera trapping and live trapping for sampling terrestrial and small mammal communities
.
Wildlife Research
37
:
456
465
.
Di Cerbo
AR,
Biancardi
CM.
2013
.
Monitoring small and arboreal mammals by camera traps: effectiveness and applications
.
Acta Theriologica
58
:
279
283
.
Diggins
CA,
Filley
LM,
Kelly
CA,
Ford
WM.
2016
.
Comparison of survey techniques on detection of northern flying squirrels
.
Wildlife Society Bulletin
40
:
654
662
.
Eng
YY,
Jachowski
DS.
2019
.
Evaluating detection and occupancy probabilities of eastern spotted skunks
.
Journal of Wildlife Management
85
:
1244
1253
.
Ferriera-Rodríquez
N,
Pombal
MA.
2019
.
Bait effectiveness in camera trap studies in the Iberian Peninsula
.
Mammal Research
55
:
155
164
.
Fisher
AK.
1893
.
The hawks and owls of the United States in their relation to agriculture (No. 3)
.
Washington, D.C
.:
U.S. Department of Agriculture, Division of Ornithology and Mammalogy
.
Fiske
I,
Chandler
R.
2011
.
Unmarked: an R package for fitting hierarchical models of wildlife occurrence and abundance
.
Journal of Statistical Software
43
:
1
23
.
Ford
WM,
Castleberry
SB,
Mengak
MT,
Rodrigue
JL,
Feller
DJ,
Russell
KR.
2006
.
Persistence of Allegheny woodrats (Neotoma magister) across the Appalachian Highlands landscape of the mid-Atlantic, USA
.
Ecography
29
:
745
754
.
Fowles
G.
2018
.
Species of greatest conservation need mammal research and management final report
.
Trenton
:
NJ Department of Environmental Protection Division of Fish and Wildlife Endangered and Nongame Species Program Report Number W-71-R-2
.
Garden
JG,
McAlpine
CA,
Possingham
HP,
Jones
DN.
2007
.
Using multiple survey methods to detect terrestrial reptiles and mammals: what are the most successful and cost-efficient combinations?
Wildlife Research
34
:
218
227
.
Gooley
AC,
Schauber
EM.
2018
.
Pursuing packrats: an evaluation of non-invasive detection methods for Neotoma
.
Wildlife Society Bulletin
42
:
701
705
.
Homer
C,
Dewitz
JA,
Yang
L,
Jin
S,
Danielson
P,
Xian
G,
Coulston
J,
Herold
ND,
Wickham
JD,
Megown
K.
2015
.
Completion of the 2011 National Land Cover database for the conterminous United States—representing a decade of land cover change information
.
Photogrammetric Engineering and Remote-sensing
81
:
345
354
.
Johnson
SA.
2002
.
Reassessment of the Allegheny woodrat (Neotoma magister) in Indiana
.
Proceedings of the Indiana Academy of Science
1
:
56
66
.
Jolly
GM,
Dickson
JM.
1983
.
The problem of unequal catchability in mark–recapture estimation of small mammal populations
.
Canadian Journal of Zoology
61
:
922
927
.
Jones
C,
McShea
WJ,
Conroy
MJ,
Kunz
TH.
1996
.
Capturing mammals
.
Pages
115
155
in
Wilson
DE,
FR,
Cole
JD,
Nichols
Rudran
R,
Foster
MS,
editors.
Measuring and monitoring biological diversity: standard methods for mammals
.
Washington, D.C
.:
Smithsonian Institute Press
.
Kéry
M,
Schmidt
B.
2008
.
Imperfect detection and its consequences for monitoring for conservation
.
Community Ecology
9
:
207
216
.
Kniowski
AB,
Ford
WM.
2018
.
Spatial patterns of white-tailed deer herbivory in the central Appalachian Mountains
.
Environmental Monitoring and Assessment
190
:
248
.
Lazenby
BT,
Mooney
NJ,
Dickman
CR.
2015
.
Detecting species interactions using remote cameras: effects on small mammals of predators, conspecifics, and climate
.
Ecosphere
6
:
1
18
.
Linzey
AV,
NatureServe
(
Hammerson
G,
JC,
Whittaker
Norris
SJ
).
2008
.
Neotoma magister
.
The IUCN Red List of Threatened Species 2008
:
e.T14581A4446084
.
LoGiudice
K.
2003
.
Trophically transmitted parasites and the conservation of small populations: raccoon roundworm and the imperiled Allegheny woodrat
.
Conservation Biology
17
:
258
266
.
LoGiudice
K.
2006
.
Toward a synthetic view of extinction: a history lesson from a northern rodent
.
BioScience
56
:
687
93
.
MacKenzie
DI,
Bailey
LL.
2004
.
Assessing the fit of site-occupancy models
.
Journal of Agricultural, Biological, and Environmental Statistics
9
:
300
318
.
MacKenzie
DI,
Nichols
JD,
Lachman
GB,
Droege
S,
Royle
JA,
Langtimm
CA.
2002
.
Estimating site occupancy rates when detection probabilities are less than one
.
Ecology
83
:
2248
2255
.
Manjerovic
MB,
Wood
PB,
Edwards
JW.
2009
.
Mast and weather influences on populations trends of a species of concern: the Allegheny woodrat
.
American Midland Naturalist
162
:
52
61
.
[MNHP] Maryland Natural Heritage Program.
2021
.
List or rare, threatened, and endangered animals of Maryland. Maryland Department of Natural Resources, 50 Tayler Avenue, Annapolis, MD
.
DNR 03-111921-291. Available: https://dnr.maryland.gov/wildlife/Documents/rte_Animal_List.pdf (June 2022)
McCay
TS,
Laerm
J,
Menzel
MA,
Ford
WM.
1998
.
Comparison of methods used to sample shrews and the importance of habitat structure
.
Brimleyana
25
:
110
119
.
McClintock
BT,
White
GC,
Antolin
MF,
Tripp
DW.
2009
.
Estimating abundance using mark–resight when sampling is with replacement or the number of marked individuals is unknown
.
Biometrics
65
:
237
246
.
McNeil
DJ
Jr,
Nicks
CA,
Wester
JC,
Larkin
JL.
2017
.
Diets of fishers (Pekania pennanti) and evidence of intraspecific consumption in Pennsylvania
.
American Midland Naturalist
177
:
200
210
.
Mengak
MT.
2002
.
Analysis and summary of eleven years of Allegheny woodrat trapping data in southwest Virginia, 1990–2000
.
Richmond
:
Research report to the Virginia Department of Game and Inland Fisheries, VDGIF contract no. 2002–04796
.
Mengak
MT,
Butchkoski
CM,
Feller
DJ,
Johnson
SA.
2008
.
Lessons from long-term monitoring of woodrat populations
.
Pages
109
132
in
Peles
JD,
Wright
J,
editors.
The Allegheny woodrat
.
New York
:
Springer
.
Mills
D,
Fattebert
J,
Hunter
L,
Slotow
R.
2019
.
Maximizing camera trap data: using attractants to improve detection of elusive species in multi-species surveys
.
PLoS ONE 14:e0216447. Available: https://doi.org/10.1371/journal.pone.0216447
Monty
AM,
Feldhamer
GA.
2002
.
Conservation assessment for the Eastern woodrat (Neotoma floridana) and the Allegheny woodrat (Neotoma magister)
.
Milwaukee, Wisconsin
:
U.S. Forest Service, Eastern Region (see Supplemental Material, Reference S1)
.
[NJDFW] New Jersey Division of Fish and Wildlife.
2012
.
New Jersey's endangered and threatened wildlife
.
Trenton
:
New Jersey Division of Fish and Wildlife
.
[NJESCA] New Jersey Endangered Species Conservation Act of 1973.
Trenton, New Jersey: Public law 1973, c. 309, §1 (December 14, 1973).
Nichols
JD,
Pollock
KH.
1983
.
Estimation methodology in contemporary small mammal capture–recapture studies
.
Journal of Mammalogy
64
:
253
260
.
[NESCA] Nongame and Endangered Species Conservation Act, as amended, published. Annapolis: Maryland Natural Resources Code §10-2A-01
(2021)
.
[NETWSCA] Nongame and Endangered or Threatened Wildlife Species Act of 1974.
Nashville, Tennessee: T. C. A. §70-8-101 to 112.
[NCGS] North Carolina General Statutes, Chapter 113, Article 25. Raleigh, North Carolina: Endangered and threatened wildlife and wildlife species of special concern
(1987)
.
[NCWRC] North Carolina Wildlife Resources Commission.
2021
.
Protected wildlife species of North Carolina
.
[ODRN] Ohio Division of Natural Resources.
2020
.
Columbus: Ohio's listed species
.
Otto
CRV,
Rolloff
GJ.
2011
.
Using multiple methods to assess detection probabilities of forest-floor wildlife
.
Journal of Wildlife Management
75
:
423
431
.
Owens
SF,
Edwards
JW,
Ford
WM,
Crum
JM,
Wood
PB.
2004
.
Raccoon roundworm in raccoons in central West Virginia
.
Northeastern Naturalist
11
:
137
142
.
Paull
DJ,
Claridge
AW,
Barry
SC.
2010
.
There's no accounting for taste: bait attractants and infrared digital cameras for detecting small to medium ground-dwelling mammals
.
Wildlife Research
38
:
188
195
.
[PGC] Pennsylvania Game Commission.
2021
.
Allegheny woodrat
.
[PWRCA] Pennsylvania Wild Resources Conservation Act.
1986
.
Harrisburg: Pa.C.S.A. §2167 (July 8, 1986)
.
Powers
KE,
Dimas
MS,
Leon
AI,
Vanmeter
LM.
2020
a.
Observed predation of Neotoma magister (Allegheny woodrat) by Strix varia (barred owl) in Virginia
.
Northeastern Naturalist
27
:
N1
N5
.
Powers
KE,
Mengak
MT,
Sheehy
RR,
Ford
WM,
Reynolds
RJ.
2020
b.
Bot fly parasitism of Allegheny woodrats (Neotoma magister) in Virginia
.
American Midland Naturalist
184
:
62
72
.
R Core Team.
2021
.
R: a language and environment for statistical computing
.
Vienna
:
R Foundation for Statistical Computing
.
Available: https://www.R-project.org/ (June 2022)
Rendall
AR,
White
JG,
Cooke
R,
Whisson
DA,
Schneider
T,
Beilharz
L,
Poelsma
E,
Ryeland
E,
Weston
MA.
2021
.
Taking the bait: the influence of attractants and microhabitat on detections of fauna by remote-sensing cameras
.
Ecological Management and Restoration
22
:
72
79
.
Rich
LM,
Kelly
MJ,
Sollmann
R,
Noss
AJ,
Moffei
L,
Arispe
RL,
Paviolo
A,
De Angelo
CD,
Di Blanco
YE,
Di Bitetti
MS.
2014
.
Comparing capture–recapture, mark–resight, and spatial mark–resight models for estimating puma densities via camera traps
.
Journal of Mammalogy
95
:
382
391
.
Sealander
JA,
James
D.
1958
.
Relative efficacy of different small mammal traps
.
Journal of Mammalogy
39
:
215
223
.
Smith
DHV,
Weston
KA.
2017
.
Capturing the cryptic: a comparison of detection methods for stoats (Mustela erminea) in alpine habitats
.
Wildlife Research
44
:
418
426
.
Smith
MH,
Gardner
RH,
Gentry
JB,
Kaufman
DW,
O'Farrell
MH.
1975
.
Density estimation of small mammal populations
.
Pages
25
453
in
Golley
FB,
Petrusewicz
K,
Ryszkowski
L,
editors.
Small mammals: their productivity and population dynamics
.
Cambridge, UK
:
Cambridge University Press
.
Stanley
TR,
Royle
JA.
2005
.
Estimating site occupancy and abundance using indirect detection indices
.
Journal of Wildlife Management
69
:
874
883
.
Stewart
FE,
Volpe
JP,
Fisher
JT.
2019
.
The debate about bait: a red herring in wildlife research
.
Journal of Wildlife Management
83
:
985
992
.
[TWRA] Tennessee Wildlife Resources Agency.
2021
.
Nashville: Rules of the Tennessee Wildlife Resources Agency Biodiversity—Chapter 1660-01-32 Rules and Regulations for in need of management, threatened, and endangered species
.
Thomas
ML,
Baker
L,
Beattie
JR,
Baker
AM.
2020
.
Determining the efficacy of camera traps, live capture traps, and detection dogs for locating cryptic small mammal species
.
Ecology and Evolution
10
:
1054
1068
.
Thompson
GG,
Thompson
SA.
2007
.
Usefulness of funnel traps in catching small reptiles and mammals, with comments on the effectiveness of the alternatives
.
Wildlife Research
34
:
491
497
.
[VAC] Virginia Administrative Code 4VAC15-20-130. Endangered and threatened species; adoption of federal list; additional species enumerated. Richmond: published article §29.1-563 (February2022).
[VDGIF] Virginia Department of Game and Inland Fisheries.
2015
.
Virginia's 2015 wildlife action plan
.
Welbourne
DJ,
MacGregor
C.
2015
.
The effectiveness and cost of camera traps for surveying small reptiles and critical weight range mammals: a comparison with labour-intensive complementary methods
.
Wildlife Research
42
:
414
425
.
[WVDNR] West Virginia Division of Natural Resources.
2015
Charleston: West Virginia state wildlife action plan
.
White
GC,
Shenk
MT.
2001
.
Population estimation with radio-marked individuals
.
Pages
329
350
in
Millspaugh
J,
Marzluff
JM,
editors.
Radio tracking and animal populations
.
San Diego, California
:
Academic Press
.
Wiewel
AS,
Clark
WR,
Sovada
MA.
2007
.
Assessing small mammal abundance with track-tube indices and mark–recapture population estimates
.
Journal of Mammalogy
88
:
250
260
.
Williams
DF,
Braun
SE.
1983
.
Comparison of pitfall and conventional traps for sampling small mammal populations
.
Journal of Wildlife Management
47
:
841
845
.
Wood
PB.
2008
.
Woodrat population dynamics and movement patterns
.
Pages
45
62
in
Peles
JD,
Wright
J,
editors.
The Allegheny woodrat: ecology, conservation and management of a declining species
.
New York
:
Springer
.
Wright
J.
2010
.
History and current status of the Allegheny woodrat
.
Pages
3
22
in
Peles
JD,
Wright
J,
editors.
The Allegheny woodrat: ecology, conservation and management of a declining species
.
New York
:
Springer
.

The findings and conclusions in this article are those of the author(s) and do not necessarily represent the views of the U.S. Fish and Wildlife Service.

Author notes

Citation: Thorne ED, Powers KE, Reynolds RJ, Beckner ME, Ellis KA, Ford WM. 2022. Comparison of two detection methods for a declining rodent, the Allegheny woodrat, in Virginia. Journal of Fish and Wildlife Management 13(2):396–406; e1944-687X. https://doi.org/10.3996/JFWM-21-037

Supplemental Material