Ultrasonic Acoustic Surveys of State Endangered Northern Flying Squirrels in the Pocono Mountains, Pennsylvania

Cryptic species are inherently difficult to survey, thereby hindering conservation and management actions that contribute to that species' persistence on the landscape. Advances in technology provide noninvasive techniques that improve detection rates and increase survey efficiency and effectiveness over traditional methods such as physical capture (O'Farrell et al. 1999; Diggins et al. 2016; Greene et al. 2016). These advances increase understanding of occupancy, distribution, and habitat use of rare species (McDonald et al. 2015; Diggins 2018a, 2018b). However, validation of new techniques ensures accurate species identification, especially between taxonomically similar species (Adams et al. 2010; Meek et al. 2013; Zsebők et al. 2015; Ancillotto et al. 2017; Potter et al. 2018). Correct species identification is important to preventing false positives or false negatives (Gu and Swihart 2004) that can directly influence management of target species, especially if it is a species of conservation concern (Britzke et al. 2002; Meek et al. 2015).

The northern flying squirrel Glaucomys sabrinus is a nocturnal, arboreal sciurid associated primarily with boreal forests in northern North America and montane coniferous forests southward through the central and southern Appalachians as well as the Rocky Mountains (Wells-Gosling and Heaney 1984; Weigl et al. 1992; Menzel et al. 2006; Diggins et al. 2017). Currently, the southernmost extent of the range of the northern flying squirrel subspecies Glaucomys sabrinus macrotis terminates in northern Pennsylvania, where it is listed as state endangered and a species of greatest conservation need (Pennsylvania Game Commission 2015). Once common in the 1950s and 1960s, recent monitoring of G. s. macrotis in Pennsylvania during the 1990s and 2000s suggested state wide population declines and a distribution reduced to highly isolated populations, and resulting in the rare status of this subspecies (Mahan et al. 1999; Steele et al. 2010). Generally, in the east, G. s. macrotis is primarily associated with eastern hemlock Tsuga canadensis, red spruce Picea rubens, and balsam fir Abies balsamea forests (Mahan et al. 1999, 2010; Steele et al. 2010). A drastic reduction in these forests occurred after European settlement, and they continue to be modified in extent and quality by hemlock woolly adelgid Adelges tsugae and balsam woolly adelgid Adelges balsamea infestations, acid precipitation, and deleterious herbivory by white-tailed deer Odocoileus virginianus (Edgar and Adams 1992; Eschtruth et al. 2006; Weigl 2007; Eschtruth and Battles 2008). Moreover, climate change exacerbates the historic and current stressors to these conifer forests, causing horizontal and vertical structural changes to the biotic and abiotic conditions within these forests (Dale et al. 2001; Elison et al. 2005). Further loss or alteration of these habitat types threatens G. s. macrotis locally and throughout the Appalachians (Weigl 2007).

Throughout its range in Pennsylvania, all known populations of G. s. macrotis are sympatric with those of the southern flying squirrel Glaucomys volans. Compared with G. s. macrotis, G. volans are more aggressive and are nest competitors with G. s. macrotis in areas of sympatry (Weigl 1978). Where both species co-occur, parasite-mediated competition of the nematode Strongyloides robustus from G. volans to G. s. macrotis often results in mortality in G. s. macrotis but has no fatal effects in G. volans (Wetzel and Weigl 1994; Krichbaum et al. 2010; Steele et al. 2010). Weigl et al. (1992) observed links between population declines of G. s. macrotis in areas where G. volans populations were high, hypothesizing that S. robustus infections in G. s. macrotis may have caused those declines. In the mid-20th century, G. s. macrotis and G. volans were not sympatric and the two species rarely occurred on the same sites. However, because of logging of old-growth hemlock and possible parasite-mediated competition, G. volans became more common at sites that historically had G. s. macrotis. There are several historical sites where G. volans displaced G. s. macrotis and has since extirpated G. s. macrotis from those areas (Mahan et al. 1999, 2010). In addition, potential hybridization between G. volans and G. s. macrotis is a concern for the long-term conservation of G. s. macrotis, especially because of amplified hybridization due to the northward G. volans range expansion caused by climate change (Bowman et al. 2005; Garroway et al. 2010, 2011). Moreover, there is documented hybridization between the two species in the northeastern portion of Pennsylvania (Garroway et al. 2010), highlighting the potential for hybridization throughout the rest of G. s. macrotis's range in the state. These stressors, coupled with reduction in suitable coniferous habitat, increase the potential extirpation of G. s. macrotis in this part of its range. Therefore, understanding changes in distribution, habitat occupancy, and co-occurrence with G. volans is important for long-term management and conservation objectives for G. s. macrotis within Pennsylvania.

Within the Appalachians, G. s. macrotis can be difficult to detect and study because of the rarity of these squirrels on the landscape and their nocturnal and arboreal habits, leading to low detection rates when using traditional methods such as live trapping or nest boxes (Stihler et al. 1995; Reynolds et al. 1999; Ford et al. 2010; Diggins et al. 2016) and difficulty in collecting adequate data to determine management and conservation objectives for this species (Weigl 2007). Mahan et al. (1999) conducted monitoring of G. s. macrotis in Pennsylvania by using nest boxes and live trapping, although both methods produce extremely low capture rates (Ford et al. 2010; Diggins et al. 2016). Where sympatric, identification of G. s. macrotis and G. volans in-hand is relatively straightforward because these species exhibit diagnostic physical characteristics that allow for easy identification, particularly via the coloration of the ventral hairs (Dolan and Carter 1977; Wells-Gosling and Heaney 1984). Camera trapping is a promising new passive technique for surveying flying squirrels and has high detection rates compared with traditional methods (Bolerice and VanFleet 2016; Diggins et al. 2016). However, camera traps can be an unreliable survey technique because identification of morphologically similar species without distinct coloration or markings is difficult (Meek et al. 2013). Photos obtained from camera traps typically capture flying squirrels positioned on tree trunks, thereby only giving dorsal or lateral views of an individual and hiding the hairs on the venter, the most diagnostic characteristic for species identification. Therefore, camera traps are currently not an applicable technique in areas where North American flying squirrels Glaucomys spp. are sympatric (Diggins et al. 2016).

All species of North American flying squirrels produce sonic and ultrasonic vocalizations between 5 and 40 kHz (Gilley 2013; Diggins 2018a; Gilley et al. 2019). Certain common call-types, such as trills, tonal chirps, and upsweeps, are species specific (Gilley et al. 2019). Thus, ultrasonic acoustic surveys have potential as a monitoring tool to determine G. s. macrotis presence and show differences in occupancy relative to habitat type and condition (Diggins et al. 2016, 2020; Diggins 2018b). The recent discovery and characterization of ultrasonic vocalizations of North American flying squirrels (Gilley 2013; Gilley et al. 2019) provide the necessary proof of concept for use of acoustics to survey flying squirrels in the wild (Diggins et al. 2016, 2020; Diggins 2018a). However, this technique has only been used in the Carolina northern flying squirrel Glaucomys sabrinus coloratus populations (Diggins et al. 2016, 2020). A federally endangered subspecies occurring in western North Carolina, G. s. coloratus, has had generally stable populations in recent decades and the majority of its habitat is protected on federal and state lands. G. s. coloratus is typically parapatric with G. volans, only co-occurring with this species in low-quality habitat, not in high-quality habitat (Diggins et al. 2016, 2017, 2020). Although sky islands of G. s. coloratus habitat are highly disjunct, the patches typically connect and are contiguous within the sky islands (Ford et al. 2015). Within Pennsylvania, G. s. macrotis are sympatric with G. volans throughout their range because their habitat exists in small, highly fragmented patches on private and state lands. G. s. macrotis has shown population declines in the past several decades, including extirpation in areas in the western proportion of its range. Therefore, this novel modeling technique needs its applicability confirmed in regions where G. s. macrotis populations are declining and G. volans populations co-occur in suitable habitat. Using the call libraries from captive and wild G. sabrinus and G. volans, our objective was to deploy ultrasonic acoustics to survey at long-term nest box monitoring sites in the Pocono Mountains, a region where the majority of extant populations of G. s. macrotis occur. We measured detection probability and latency to detection to determine the feasibility of using this technique to monitor for endangered populations of G. s. macrotis in Pennsylvania.

Our study occurred in the Pocono Mountains, a subsection of the glaciated Appalachian Plateau subphysiographic region of the Appalachian Mountains in eastern Pennsylvania (41°6′50.4″N, 75°32′48.6″E). We selected study sites in Luzerne County (State Game Land 149), Carbon County (State Game Land 129), Monroe County (Davy Run and Pumphouse at Pocono Lake Preserve; State Game Land 127), and Wayne County (State Game Land 312; Figure 1). These sites had known G. s. macrotis populations, with monitoring occurring via nest boxes for the past 2 decades. Study sites ranged from 360 to 625 m in elevation. The dominant vegetation in our study sites was eastern hemlock and northern hardwood (e.g., sugar maple Acer saccharum, American beech Fagus grandifolia, yellow birch Betula alleghaniensis) forests. The G. s. macrotis habitat in this region was more highly disjunct and fragmented than to the south in the Allegheny Mountains of West Virginia or to the north in the Catskill Mountains of New York. Heavy logging, with repeated cutting and subsequent wildfires, in the region drastically reduced the extent of conifer forests and resulted in localized stands of eastern hemlock, red spruce, and balsam fir (Powell and Considine 1982). Most habitats where G. s. macrotis were found in Pennsylvania are poor-quality mixed stands that lack other conifer species that G. s. macrotis are associated with in other parts of their range in the Appalachian Mountains (Menzel et al. 2006; Diggins and Ford 2017; Diggins et al. 2017). However, the Pocono Mountains were the only major area, with exception of two smaller locations in western Pennsylvania, where recent detections of G. s. macrotis occurred within the past few decades and populations were still known to persist (Mahan et al. 2010).

Using data from the Pennsylvania Game Commission long-term nest box program, we chose six long-term monitoring areas currently known to be occupied in the Pocono Mountains. The Pennsylvania Game Commission monitored nest boxes for G. s. macrotis within all of our study sites since 2001. Detection rates obtained by using nest boxes were typically very low (i.e., 1–6%; Stihler et al. 1995; Reynolds et al. 1999), although occupancy estimates from previous monitoring along with forest overstory composition allowed managers to identify which sites may provide higher quality habitat. We considered habitat within each study site as high quality (hereafter “high”) if 1) G. s. macrotis occupancy across multiple years was >30% and 2) the overstory of the stand was conifer dominant (primarily eastern hemlock). By contrast, we classified sites as low quality (hereafter “low”) if 1) G. s. macrotis occupancy was ≤30% and 2) the overstory of the stand contained mixed montane conifers and northern hardwoods (Mahan et al. 2010). At each survey site, we randomly placed two Petterrson D500x ultrasonic detectors (Pettersson Eleckronik AB, Uppsala, Sweden; N = 12 detectors) separated by >50 m (138 ± 2 m; range: 61–225 m) to ensure independence (Diggins et al. 2016). We considered each detector station as our sampling unit. We surveyed detector stations during June 2017 for 9 nights each. We secured detectors 1.5 m high on the bole of a tree with bungee cords and faced detectors toward a mid- or overstory canopy gap (Weller and Zabel 2002; Diggins et al. 2016). We placed detectors in modified ammunition boxes (30.5 × 15.6 × 19 cm; model MA21, BLACKHAWK!, Overland Park, KS) fitted with a 35° elbow polyvinyl chloride pipe that served as weatherproofing to reduce damage to the detector from weather or wildlife (Britzke et al. 2010). We set detectors to run between sunset and sunrise and automatically shut down during the day to reduce the number of noise files and conserve battery power. We did not bait detector stations. We set detectors to start recording 30 min after sunset and stop recording 30 min before sunrise to reduce recordings of diurnal species.

We analyzed acoustic data by initially filtering all data through SonoBat Batch Scrubber 5.4 (DND Design, Arcata, CA) to remove files that did not contain potential animal calls (i.e., noise files from wind or rain). Nonscrubbed call files included calls from flying squirrels, bats, mice, insects, and other animals. We sorted the nonscrubbed files visually to identify flying squirrel calls following methods described by Gilley (2013). We confirmed flying squirrel calls by using a captive and wild call library and a double observer method with two experienced observers that independently confirmed calls (C.A.D. and L.M.G.; Gilley et al. 2019). To determine the effectiveness of ultrasonic acoustics as a survey technique for monitoring G. s. macrotis, we determined probability of detection (POD) and latency of detection (LTD; i.e., the number of survey nights until the initial detection of a species at that site; Gompper et al. 2006, Diggins et al. 2016, 2020) between high- and low-occupancy sites. To determine POD, we used an occupancy-modeling framework that uses a maximum likelihood approach and estimates the probability of detecting a species given that it is present at a site (MacKenzie et al. 2005). To determine variation in detection probability between high and low sites, we conducted a single-season model in program PRESENCE 12.24 (Gompper et al. 2006; Diggins et al. 2016; Hines 2018). To determine whether POD or LTD was significantly different for G. s. macrotis and G. volans between high and low sites, we used a nonparametric Wilcoxon rank-sum test in program R 3.1 (R Development Core Team 2018), with P values ≤0.05 considered significant.

We surveyed a total of 108 detector survey nights during June 2017. We obtained 67.4 GB (23,654 sound files) of data over the course of the study. After we filtered files, we identified 10,306 call files (29.3 GB) as potential animal calls. After we screened these calls to remove all nonsquirrel calls, we tallied a total of 478 flying squirrel calls: 384 G. s. macrotis calls, 57 G. volans, and 37 flying squirrel calls not identified to species (Data S1, Supplemental Material). We recorded the following call-types: trill (N = 423), yelp/bark (N = 21), chirp (N = 20), crow (N = 9), and downsweep (N = 5; Data S1). We differentiated between species for trills and chirps. Because of a small number of call library recordings of certain call-types, we were unable to reliably differentiate yelp/bark, crow, and downsweep calls to species. We recorded G. s. macrotis calls at four study sites (three high sites and one low site) and G. volans calls at all six study sites.

We estimated average detection probability between high and low sites for both species. For G. s. macrotis, POD was significantly different between the high (0.28 ± 0.06) and low (0.09 ± 0.4; W = 30, P = 0.05) sites. LTD for G. s. macrotis was also significantly different (W = 5.5, P = 0.047), with 2.7 ± 0.8 nights at high sites and 7.83 ± 1.5 nights at low sites. For G. volans, POD was similar between high (0.13 ± 0.06) and low (0.17 ± 0.06; W = 14.5, P = 0.62) sites. LTD was also similar between high (5 ± 1.6 nights) and low (3.8 ± 1.5 nights; W = 22, P = 0.56) sites for G. volans.

The ability to differentiate species using ultrasonic acoustic surveys is important to ensure that species identification via vocalizations is correct (Adams et al. 2010; Zsebők et al. 2015; Ancillotto et al. 2017). Our study showed that ultrasonic acoustic surveys can be used to survey for flying squirrels in the wild where species are sympatric. This capability is extremely important in the eastern United States, where in addition to G. s. macrotis in Pennsylvania, the federally endangered G. s. coloratus (USFWS 1990) and the recently delisted Virginia northern flying squirrel Glaucomys sabrinus fuscus (USFWS 2013) are also sympatric with G. volans in parts of their regional distribution. The ability to positively identify G. s. macrotis from G. volans by using acoustics, especially in areas where G. s. macrotis have experienced population declines, may be critical to aiding management and conservation decisions to support G. s. macrotis (e.g., Rentch et al. 2007, 2016). This ability is especially important because other methods produce low detection rates and may be slower at capturing G. s. macrotis extirpation events in Pennsylvania. Several historic G. s. macrotis sites in the Allegheny Mountains in western Pennsylvania revealed no detections of this species with traditional methods in recent decades (Mahan et al. 2010). Within North Carolina, the discovery of four new populations of G. s. coloratus was made by using ultrasonic acoustics in areas where previous efforts with traditional methods were unsuccessful (C. Kelly, North Carolina Wildlife Resources Commission, personal communication). We did not survey at historic G. s. macrotis areas during this study because our objective was to confirm the validity of using this technique in areas with current populations. However, ultrasonic acoustic surveys could help determine persistence or extirpation of G. s. macrotis populations in historic areas in Pennsylvania.

Our acoustic surveys detected both G. s. macrotis and G. volans, and we observed that at all sites where G. s. macrotis occurred, G. volans was also present. This detection was similar to previous trapping and nest box surveys that occurred at these sites, indicating the G. s. macrotis and G. volans are sympatric year-round (G.G.T., personal observation). We only surveyed for 9 nights, a timeline that falls within the recommended minimum survey effort for high- and medium-quality habitat in the southern Appalachian Mountains (Diggins et al. 2020). Because G. s. macrotis was confirmed at all six of our survey sites before this study, we assumed that this survey period would be long enough to detect G. s. macrotis at these sites. However, the habitat quality in Pennsylvania is much poorer (due to declining stands of pure conifer) than that of northern flying squirrel habitat in the southern Appalachian Mountains (Ford et al. 2015; Diggins et al. 2017). Therefore, it is possible that we needed longer survey times at the low sites to determine probable absence, although absence is extremely difficult to determine from one season of surveys. One low study site (State Game Land 312) had low captures of G. s. macrotis via traditional methods and the overstory had higher amounts of conifer than that of the other two low sites; however, we did not detect G. s. macrotis at this site. There is the possibility of G. s. macrotis extirpation at this study site, but our study was only robust enough to determine nondetection. Because we only surveyed sites for one season, multiyear surveys would help clarify whether G. s. macrotis populations persist at low sites where we did not detect this species.

During our monitoring, we recorded five call-types. However, we were only able to differentiate between G. s. macrotis and G. volans for two call-types: trills and chirps. Both trills and chirps are commonly produced by flying squirrels in captivity and in the wild (Diggins et al. 2016, 2020; Gilley et al. 2019). Trills are the easiest call-type to differentiate between the species because the bandwidth, the frequency, and the number of calls produced per 500 ms are significantly different between species (Figure 2; Gilley et al. 2019). Novice but trained observers successfully identify these calls, although this call-type is more prevalent in the spring than in other seasons (Diggins et al. 2020). Chirps are also species specific (Gilley et al. 2019), although observers need more advanced training to identify this call-type to species. Recordings of captive colonies show that both species produce barks/yelps and downsweeps; however, we only recorded crows in the G. volans colony (Gilley et al. 2019). Because the call library for G. s. macrotis invariably is far from complete, we did not assume that crows recorded at a sympatric site were produced by G. volans. In addition, barks/yelps and downsweeps have not been successfully differentiated between the species due to small sample sizes and lack of independent samples from the captive colonies (Gilley et al. 2019). Therefore, a wild call library wherein these specific call-types are obtained from G. s. macrotis–only or G. volans–only sites will be important in differentiating these call-types between species. Until such wild call libraries are further developed, surveys should conservatively use common call-types to identify species.

Habitat quality can influence detection rates depending on the survey method. Flying squirrels will den in dreys, subterranean nests, and cavities (Carey et al. 1997; Diggins et al. 2015, 2017), with the latter den type representing the best thermoregulatory option that simultaneously reduces access by ground predators. Arboreal nest boxes provide supplemental dens that are similar to cavities in their thermoregulatory capabilities. As a result, nest boxes possibly had increased capture rates in low-quality habitat due to lack of denning resources in young mixed forests or in forests with declining coniferous overstories (Ford et al. 2010). However, in high-quality habitat, reduced captures were due to increased decadence (i.e., snags, coarse woody debris) that resulted in more denning resources (Ford et al. 2015), indicating habitat quality may inversely influence detection rates for nest boxes. Habitat quality can also influence acoustic detection rates, but higher habitat quality produces higher detection rates than lower habitat quality (Diggins et al. 2020). Because next box capture rates are extremely low by using nest boxes, only presence–absence data, not population abundance data, can be determined using the nest box survey method. Therefore, acoustics might be better suited to determining squirrel occupancy in high- and low-quality habitats without factors that would create misleading biases via resource supplementation.

Because flying squirrels are active and vocalize year-round (Gilley 2013; Diggins et al. 2020), further work should determine whether G. s. macrotis and G. volans are sympatric at our study sites year-round or seasonally. At more southern sites in the Appalachian Mountains, G. volans are known to occur in montane-conifer–dominant habitat during the summer if the montane-conifer patch is located close to or adjacent to large stands of northern hardwood forests (Urban 1988; Diggins 2016). The seasonality of potential interactions between the species may influence rates of hybridization if both species are sympatric during the breeding season (Garroway et al. 2010). In addition, transmission of the nematode S. robusuts from G. volans to G. s. macrotis may increase during milder times of the year because the nematode is cold intolerant (Wetzel and Weigl 1994). Because G. s. macrotis and G. volans are sympatric year-round in Pennsylvania (G.G.T., personal observation), there may be an increased probability S. robustus transmission between these species or potential hybridization of these species (Wetzel and Weigl 1994; Krichbaum et al. 2010). Although it is easier to survey in warmer seasons, thereby avoiding the detrimental effects of cold weather on equipment and site inaccessibility in snowy conditions (Diggins et al. 2020), methods could be developed to increase equipment function in cold weather.

Climate change is expected to alter geographic distributions of many species (Gaston 2003; Sexton et al. 2009), with predicted species range shifts upward in elevation or poleward in latitude (Walther et al. 2002). These shifts will make the species in mountainous regions at more southern latitudes or at the southern extent of their range more vulnerable to range contractions (Guralnick 2006). These trends are already materializing in flying squirrels, where G. s. macrotis is experiencing range contractions and G. volans is experiencing range expansions (Myers et al. 2009; Garroway et al. 2011; Wood et al. 2016). The rates of expansion with climate change may accelerate with an increasingly warming climate (Chen et al. 2011), and a method that can rapidly assess shifts in species distribution is needed. Therefore, because ultrasonic acoustics can successfully differentiate between G. volans and G. s. macrotis and has low LTD (Diggins et al. 2016, 2020; Gilley et al. 2019), this technique may be a more effective and efficient way to monitor these trends for North American flying squirrels over time.

Although the use of ultrasonic acoustics is successful at surveying sites where G. s. macrotis and G. volans overlap, further work is needed to refine this technique. For example, location of detectors could influence species detection. Often, G. s. macrotis spends more time on the ground than G. volans (Wells-Gosling and Heaney 1984), indicating that altering detector height could increase detection of one species over another species. In addition, circadian patterns of when G. volans and G. s. macrotis produce calls could vary, possibly allowing for easier identification due to potential variations in nightly peaks in vocalization. Within Pennsylvania, G. s. macrotis and G. volans are known to hybridize, and hybridization may increase with climate change or reduced extent of boreal habitat conditions (Garroway et al. 2010). We do not currently know the vocal repertoire of a hybridized individual or whether it would be identifiable from calls of genetically pure G. s. macrotis and G. volans. If the vocal repertoire of hybridized individuals significantly varies between pure G. s. macrotis and G. volans, acoustics may have potential to indicate regional hybridization in areas of sympatry. However, a call library of hybridized individuals would need to be developed to validate this potential usefulness. We believe our study has demonstrated the ability of ultrasonic acoustics to be used to monitor for declining populations of G. s. macrotis in Pennsylvania and an acoustic monitoring program could be modeled after the G. s. coloratus monitoring program in North Carolina.

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.

Data S1. Flying squirrel Glaucomys spp. call-type files per site as collected on State Gamelands in Pocono Mountains, Pennsylvania, in 2017.

Found at DOI: https://doi.org/10.3996/JFWM-20-020.S1 (508 KB PDF).

Reference S1.Diggins CA. 2018a. Acoustic surveys of San Bernardino flying squirrels (Glaucomys sabrinus californicus) in the San Bernardino and San Jacinto mountains. Report to the San Diego Museum of Natural History, San Diego, California.

Found at DOI: https://doi.org/10.3996/JFWM-20-020.S2 (459 KB PDF).

Reference S2.Diggins CA. 2018b. Surveying for the federally endangered Carolina northern flying squirrel in Great Smoky Mountains National Park using ultrasonic acoustics. Report to the Great Smoky Mountains Conservation Association, Knoxville, Tennessee.

Found at DOI: https://doi.org/10.3996/JFWM-20-020.S3 (390 KB PDF).

Reference S3.Ford WM, Moseley KR, Stihler CW, Edwards JW. 2010. Area occupancy and detection probabilities of the Virginia northern flying squirrel (Glaucomys sabrinus fuscus) using nest-box surveys. Pages 37–47 in Rentch JS, Schuler TM, editors. Proceedings from the conference on the ecology and management of high elevation forests in the central and southern Appalachian Mountains. General Technical Report NRS-P-64, U.S. Department of Agriculture, Forest Service, Northern Research Station, Newton Square, Pennsylvania.

Found at DOI: https://doi.org/10.3996/JFWM-20-020.S4 (117 KB PDF).

Reference S4.Powell, DS, Considine TJ. 1982. An analysis of Pennsylvania's forest resources. Resource Bulletin NE-69, U.S. Department of Agriculture, Forest Service, Northeastern Forest Experimental Station, Broomall, Pennsylvania.

Found at DOI: https://doi.org/10.3996/JFWM-20-020.S5 (9.2 MB PDF).

Reference S5.[USFWS] U.S. Fish and Wildlife Service. 1990. Appalachian northern flying squirrel (Glaucomys sabrinus fuscus and Glaucomys sabrinus coloratus) recovery plans. U.S. Fish and Wildlife Service, Annapolis Field Office, Annapolis, MD.

Found at DOI: https://doi.org/10.3996/JFWM-20-020.S6 (3.54 MB PDF).

Reference S6.[USFWS] U.S. Fish and Wildlife Service. 2013. Endangered and threatened wildlife and plants: Reinstatement of removal of the Virginia northern flying squirrel from the list of endangered and threatened wildlife. Federal Register 78:14022.

Found at DOI: https://doi.org/10.3996/JFWM-20-020.S7 (227 KB PDF).

This project was funded by Pennsylvania Game Commission Grant Agreement #PEV5R5XH to Virginia Polytechnic Institute and State University. J.A. White, the Associate Editor, and three reviewers provided comments that helped improve this manuscript. The authors of this manuscript have no known conflicts of interest in relation to this work.

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