10.3996Jfwm-20-020.S3/ by Guest on 26 September 2021

Surveying for the Federally Endangered Carolina Northern Flying Squirrel in Great Smoky Mountain National Park Using Ultrasonic Acoustics

Final Report to

Great Smoky Mountains Conservation Association Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/444194/pdf/10.3996jfwm-20-020.s3/ by guest on 26 September 2021

December 2018

Corinne Diggins. Ph.D. Department of Fish and Wildlife Conservation Virginia Tech Blacksburg, VA 24061

Introduction The federally endangered Carolina northern flying squirrel (CNFS; Glaucomys sabrinus coloratus) is a secretive, nocturnal mammal associated with disjunct, high-elevation sky islands of montane red spruce (Picea rubens)-Fraser fir (Abies fraseri) forests in the southern Appalachian Mountains (Payne et al. 1989, Weigl et al. 1992, Ford et al. 2014, 2015; Diggins et al. 2017). This subspecies represents the southernmost distribution of the northern flying squirrel in North America (Wells-Gosling and Heaney 1984, Arbogast et al. 2017). CNFS was listed under the Endangered Species Act in 1985 due to 1) the past and ongoing degradation and loss of spruce-fir forests and 2) direct parasite-mediated competition with the parapatric southern flying

squirrel (SFS; G. Volans; USFWS 1985, USFWS 1990). Exploitative industrial logging and Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/444194/pdf/10.3996jfwm-20-020.s3/ by guest on 26 September 2021 subsequent burning at the turn of the 20th century (Korstian 1937, Pyle and Schafale 1988, Hayes et al. 2006), followed by decades of atmospheric deposition (Eager and Adams 1992) and the introduced balsam woolly adelgid (BWA; Adelges piceae; Hay et al. 1978, Dull et al. 1988) contributed to a 35-57% loss of spruce-fir forests in the region (Boyce and Martin 1993). Due to the disjunct nature of this forest type, along with multiple factors contributing to reduced extent and decreased forest health, Noss et al. (1995) consider this forest-type the second most endangered forested ecosystem in the nation. Moreover, under most climate change scenarios, spruce-fir forests are predicted to be lost from most of the Southeast due to over the next century (Iverson et al. 2008). The Great Smoky Mountains National Park (GSMNP) is unique within the range of CNFS because it contains the largest old-growth spruce-fir forests in the Appalachian Mountains and contains the largest amount of contiguous CNFS habitat within the entire Southern Appalachians (Ford et al. 2015). With potential habitat loss, CNFS are predicted to be at high risk of extirpation in GSMNP (Burns et al. 2003). With current and past changes in habitat due to BWA and historic land use, along with the looming threat of climate change, mangers are increasingly concerned about short- and long-term conservation of CNFS (Weigl et al. 1992). Despite the relatively large amount of potential CNFS habitat in GSMNP, there has been limited survey effort to monitor this endangered species within the park. Previous surveys include exploratory live trapping surveys conducted by Dr. Pete Weigl in the late 1980’s (Weigl et al. 1992), limited nest box surveys by the North Carolina Wildlife Resources Commission during the 1990’s-2000’s, and one year of acoustic surveys at 3 sites in 2015 (C. Diggins, unpub. data). Additionally, minimal work has been conducted on this species on the Tennessee side of the park and were limited to nest box surveys or trapping surveys. Because no intensive or long- term effort to survey for CNFS since its federal listing in 1985 has occurred within the park, distribution and habitat occupancy rates of this endangered squirrel within GSMNP are currently unknown. The Great Smoky Mountains are considered an important Recovery Areas for CNFS under the ESA (USFWS 1990) and are a priority site for long-term monitoring by NCWRC (Chris Kelly, NCWRC, pers. comm.). The old-growth forests and large extent of contiguous habitat within GSMNP may provide more resilient habitat and/or be less susceptible to climate change than other smaller sky islands, providing insights on necessary habitat conditions and structure that can inform management across CNFS’s geographic range on Forest Service and state lands. CNFS are difficult to monitor using traditional techniques, such as live trapping or nest box surveys, as these methods produce low capture rates (< 6%) and are labor intensive (USFWS 1990, Weigl et al. 1992., Reynolds et al. 1999, Hughes 2006, Diggins et al. 2016). In multiple areas in the region, efforts to confirm CNFS presence via trapping or nest boxes often were

2 unsuccessful, even if habitat quality and quantity was similar to occupied areas (Weigl et al. 1992; Chris Kelly, NCWRC, pers. comm.; Rick Reynolds, VDGIF, pers. comm.). Techniques with higher rates of detection probability (POD; probability of detecting a species at a site given that is present) and lower latency to detection (LTD; the number of survey nights at a sites prior to an initial detection) are indicative of a more effective and efficient survey technique (Gommper et al. 2006). The recent discovery and characterization of ultrasonic vocalizations of North American flying squirrels (Glaucomys spp., Gilley 2013; Gilley et al. In Review) has increased the potential for use of acoustics to survey flying squirrels. With developed captive squirrel call libraries for southern flying squirrels (SFS; G. volans) and CNFS, acoustics can be

used to determine habitat occupancy of each species, even at sites where they are sympatric Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/444194/pdf/10.3996jfwm-20-020.s3/ by guest on 26 September 2021 (Diggins and Ford 2017). This is due to the common species-specific calls that each species produces (Gilley 2013, Gilley et al. In Review). Diggins et al. (2016) compared ultrasonic acoustics to live trapping and camera trapping. Both acoustics and camera traps had much higher POD (0.37±0.06 and 0.30±0.06, respectively) compared to live traps (0.01±0.005), although acoustics had significantly lower LTD than camera traps (1.5 nights and 3.25 nights, respectively; P = 0.0017). Additionally, acoustics can be used to differentiate species, whereas this is not currently possible with camera traps (Diggins et al. 2016). Preliminary acoustic data I collected during 2015 CNFS surveys in GSMNP showed an average total detection probability of 0.18 across sites. Since ultrasonic acoustics are an effective and efficient method to survey for CNFS, I will conduct acoustic surveys across historical and unsurveys areas of GSMNP in areas with varying habitat type (montane conifer vs. northern hardwood), BWA infestation, and past logging disturbance (second-growth vs. old growth forests). The goal of my study is to determine varying landscape scale factors that might influence occupancy and how detection and LTD vary between different habitat types and disturbance regimes, while simultaneously conducting surveys to further understand this endangered species’ distribution within the park

Objectives 1) Expand the preliminary acoustic work I conducted within GSMNP in 2015 and survey historic and unconfirmed sites to determine the distribution and occupancy of CNFS and SFS in high-elevation forests within GSMNP. 2) Establish baseline data for NCWRC and Tennessee Wildlife Resources Agency (TWRA) to incorporate into a long-term acoustic monitoring program for CNFS, including guiding long-term survey site selection. 3) Gather data in old-growth spruce-fir forests to compare CNFS habitat occupancy rates between old-growth and second-growth forests in the southern Appalachians. These data will be used to improve Ford et al. 2015’s range-wide habitat occupancy map by incorporating acoustic data with nest box and telemetry data, allowing researchers to more accurately assess the probability of CNFS occurrence and to predict the effects of climate change habitat modification on CNFS persistence in southern Appalachians in the future.

Methods Survey Sites My study sites occurred in high-elevation spruce-fir and spruce-northern hardwood forests in Great Smoky Mountains National Park in Swain and Haywood County, North Carolina and Sevier County, Tennessee. I selected sites using historic capture records and the predictive

3 habitat model from Ford et al. (2015). I stratified sites across elevation and forest stand condition to include a range of forest habitats, including old-growth/second growth, stands impacted by BWA, and historically logged areas. I determined forest habitat type from aerial images and geospatial data. I determined old-growth vs. second growth forests using a geospatial dataset derived from Pyle (1985). I determined BWA intensity from aerial maps constructed by Dull et al. (1988). Sites occurred along a gradient of elevations ranging from 1,510 – 1,965 meters. During the 5 week study period, average temperature was 60.1°F (Range: 50.5-70°F) and average precipitation was 11.9 inches. I obtained weather data from weather stations located at Newfound Gap and Mount Le Conte.

Acoustic Surveys Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/444194/pdf/10.3996jfwm-20-020.s3/ by guest on 26 September 2021 I surveyed 23 sites for 8-16 days each during mid-May to early June 2018 (Appendix 1). I deployed 1 Pettersson D500x ultrasonic acoustic detector (Pettersson Eleckrontik AB, Uppsala, Sweden) at each site. Due to the usually high amount of rainfall that occurred during the study, I extended surveys at several sites and had to reduce the number of sites from 25 to 23 in order to fit all of the surveys within the study period. The D500x detectors were full spectrum, recorded in real time, and stored call files on compact flash cards allowing for multiday passive surveying. I set detectors to run between sunset and sunrise and programmed detectors to shut down during the daytime so as to reduce noise files and conserve battery power. I placed each detector in a steel ammunition box (30.5 x 15.6 x 19 cm; model MA21; BLACKHAWK!, Overland Park, KS) modified with a 35° polyvinyl chloride (PVC) elbow to provide weatherproofing for the detector (Britzke et al. 2010), while simultaneously protecting the detector from wildlife (e.g., black bears, Ursus americanus). I attached detectors approximately 1.5 m on the bole of a tree with bungee cords. I pointed the detection in the direction with the least clutter, typically towards a midstory gap, to increase recording quality. I camouflaged weatherproofing and bungee cords to prevent park visitors from noticing the detectors. I set all detectors a minimum of 15 m from trails and roads to reduce notice or disturbance by park visitors. Acoustic Data Analysis I scrubbed all acoustic data through SonoBat Batch Scrubber 5.4 (DND Design, Arcata, CA, USA) to remove noise files (i.e., wind, rain, or static files). I assessed remaining files visually in SonoBat 2.9.8 (DND Design) to determine if flying squirrel calls were recorded (Gilley 2013). I confirmed CNFS and SFS calls using a captive call library (Gilley 2013). I estimated nightly CNFS detection at each site. Occupancy Modeling To determine habitat occupancy and detection probability for CNFS, I used an occupancy modeling framework. For each point, I quantified topographic, vegetation, and disturbance characteristics (i.e., site covariates). I calculated elevation, slope, aspect, and landform index (LFI) from 10m digital elevation maps (DEMs) in ArcGIS 10.2 (Environmental Systems Research Institute, Inc., Redlands, CA). I derived elevation from DEMs and created new raster files in Spatial Analysis Surface for slope and aspect. To determine how sheltered a site was, I calculated LFI using Spatial Analysis using 100 m radius in Focal Statistics. More sheltered landforms, such as drainages, are represented by negative values, whereas more exposed landforms, such as ridgelines, are represented by positive values (McNab et al. 1999). To evaluate vegetative characteristics, I defined two habitat types: montane conifer dominant forests and mixed conifer-northern hardwood forests. I acquired land cover data from the Southeastern Gap Analysis Program (SEGap; www.basic.ncsu.edu/segap/). Land cover data for montane conifer forests was taken from SEGap and validated with aerial photography. Land cover data

4 for mixed-conifer northern hardwood forests was taken from modeling by Evans et al. (2014). I determined the percentage of montane conifer canopy cover within a 100 m radius of each acoustic point using aerial photography in ArcGIS. Flying squirrels are known to be sensitive to overstory disturbance, such as logging and insect outbreaks (Carey et al. 2000, Weigl 2007, Holloway and Smith 2011). Within the southern Appalachian spruce-fir forests, past logging, acid precipitation, and balsam woolly adelgid outbreaks altered habitat structure, with the latter two disturbances causing increased overstory mortality of montane conifer trees in the last half a century (Dull et al. 1988, Eager and Adams 1992). I assessed past logging using geo-referenced historical data gathered from Pyle (1985). I determined BWA overstory canopy kill severity by

geo-referencing maps from Dull et al. (1988), which were originally drawn during low flights Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/444194/pdf/10.3996jfwm-20-020.s3/ by guest on 26 September 2021 over GSMNP. I ranked areas as high (>70%), medium (30-70%), low (<30%), or none (0%) based off of categories outlined in Dull et al. (1988).

Figure 1. Carolina northern flying squirrel (Glaucomys sabrinus coloratus) ultrasonic acoustic survey sites in Great Smoky Mountains National Park during May and June 2018.

I also determined factors that might influence flying squirrel detection at each acoustic site (i.e., survey covariates). Unusually high amounts of precipitation fell during the study period. Rain can interfere with an acoustic detector’s ability to pick-up an animal vocalization by producing noise that falsely triggers the detector. Therefore, I estimated average rainfall between

5 the Mount LeConte and Newfound Gap weather stations. Additionally, survey length can influence the ability to detect a squirrel using ultrasonic acoustic surveys (Diggins et al. In Review). Due to a large number of rainy nights early in the season, I extended surveys at several sites. Additionally, another site had to be relocated due to black bear disturbance. Therefore, survey effort may have also influenced the ability to detect flying squirrels at the study sites. All continuous site and survey covariates were standardized. I determined if any of my site or survey covariates were correlated using a Pearson correlation matrix, omitting covariates that were correlated >0.7. I did not find any covariates that met this criteria. Using the site variables, I developed 17 a priori models to assess CNFS occupancy from

past studies and the natural history of the species in the Southern Appalachians: 1) habitat type Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/444194/pdf/10.3996jfwm-20-020.s3/ by guest on 26 September 2021 (conifer vs. mixed conifer-northern hardwood; Ford et al 2014, Diggins et al. 2017), 2) topographical features (Odom and McNab 2000, McGrath and Patch 2003), 3) structural disturbance and montane conifer canopy cover (BWA and logging; Holloway and Smith 2011), 4) habitat type and topographic features (Ford et al. 2015), and 5) habitat type, structural disturbance, and elevation (Dull et al. 1988, Hayes et al. 2006). I modelled data in a single-season occupancy framework using Program PRESENCE 2.12 (Hines 2018). Occupancy (ψ) is the probability that a species is occupying an area and detection probability (p) is the probability of detecting a species at a site if the species is present (McKenzie et al. 2006). Within program PRESENCE, all models are ranked using Akaike’s Information Criterion (AIC). I used a two-step process, first estimating survey covariates that would influence p to reduce the risk of overparameterizing my models. After I screened each survey covariate alone, I selected the survey covariates ≤2 ΔAIC units from the best preforming model. After I determined the survey covariates, I ran my 17 a priori models for site variables using the selected survey covariate. Since there were multiple competing models within 2 ΔAIC from the top model, I used model averaging to estimate occupancy and detection probability for all models with ΔAIC <2 (Burnham and Anderson 2003), which accounted for >70% of the total weight (wi). Additionally, I estimated LTD (Gommper et al. 2006) across all sites for CNFS. I determined if there was variation in p and LTD between habitat type, logging history, and BWA infestation using a non-parametric Kruskal-Wallis (K-W) test and Wilcoxon Rank-Sum (WR-S) in Program R 3.1 (R Development Core Team 2015). For p, I used model averaged estimates for each site. I considered p-values ≤0.05 as significant.

Results I surveyed 23 sites over 239 survey nights, where average survey length per site was 10.4 days (range 8-16 days). I extended past 10 days at 9 sites due to extremely rainy conditions during the beginning of the field season. During the study, there were two detector failures at the Goshen Ridge and Balsam High Top. Of the 21 successfully surveyed sites, 7 were historical sites and 14 were new sites where CNFS had not been documented in the past by any method. I recorded a total of 344 GB (120,720 sound files) of data during the study. After scrubbing data, I analyzed 7.8 GB (2,798 sound files) of data that contained potential animal calls. Calls analyzed visually included bat, Peromyscus, and flying squirrel calls, as well as a series of unknown animal sounds and vocalizations. I obtained a total of 113 squirrel calls files at 12 sites, including 4 of the historical sites (Figure 1). Of the 113 call files, 110 were CNFS and 3 were SFS. I recorded CNFS at 57.1% sites, including 4 historical sites. I detected SFS at two sites: Beech Gap and Mount Sterling Ridge. Both of these sites had positive CNFS detections and were

6 spruce-northern hardwood forests below 1,625 m. I did not estimate p, or LTD for SFS because detection rates were extremely low. Averaged occupancy models suggested that 72.1% ± 5.6 of sites surveyed were occupied by CNFS with an average detection rate of 22.2% ± 0.03 (Table 1). The 6 of the 7 top models included habitat type, where model average β was 19.1±5.0, indicating higher occupancy of montane conifer forests over montane conifer-northern hardwood forests. BWA (β = -4.5 ± 0.04) was included in 2 of the 7 top models and the negative direction value highlights that higher levels of overstory canopy morality caused by increased BWA severity has a negative effect on occupancy. Disturbance was only included in 1 of the top models and had a slightly positive

direction (β = 0.98, no SE available), specifying somewhat higher occupancy in logged stands Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/444194/pdf/10.3996jfwm-20-020.s3/ by guest on 26 September 2021 vs. old growth stands. Both LFI and elevation were in 2 of 7 top models. LFI (β = 10.6 ± 8.1) displayed a positive direction indicating higher occupancy on exposed landforms vs. sheltered landforms, whereas elevation (β = -1.1 ± 0.75) displayed a negative direction with higher occupancy at lower elevation sites. Average LTD across the study sites was 6.9 ± 1.2 survey nights. LTD did not vary between montane conifer and mixed conifer-northern hardwood forests (WR-S test: W = 52, p- value = 0.859), varying levels of BWA severity (K-W test: X2 = 0.748, df = 3, p-value = 0.861), or second-growth vs. old growth forests (WR-S test: W = 32.5, p-value = 0.119). For detection probability, there was no difference in p between habitat types (WR-S test: W = 77, p-value = 0.099) or BWA severity (K-W test: X2 = 2.042, df = 3, p-value = 0.563). However, there was a difference in p between second-growth and old-growth forests, with higher detection rates in old growth forests (WR-S test: W = 85.5, p-value = 0.021).

Table 1. Single-season occupancy model results from ultrasonic acoustic survey data for the Carolina northern flying squirrel (Glaucomys sabrinus coloratus) in Great Smoky Mountains National Park in May and June 2018. The models below represent the competing models with ΔAIC < 2 units of the top model. Variables present in the top models include Habitat (i.e., habitat type), balsam wooly adelgid infestation level (BWA), landform index (LFI), Elevation, and Disturbance (i.e., logging history). Data presented includes ΔAIC, model weight (wi), number of model parameters (K), occupancy estimate (Ψ) and detection probability (p). Additionally, the 7 top models accounted for 70% of wi and were averaged across models to estimate model averaged Ψ and p. The remaining 11 models that were not within <2 ΔAIC from the best-approximating model are not shown and were not included in model averaged estimates.

Model ΔAIC wi K Ψ (± SE) p(± SE) Ψ (Habitat + BWA) p (Effort) 0.00 0.169 5 0.662 (0.068) 0.228 (0.006) Ψ (Habitat) p (Effort) 0.06 0.165 4 0.736 (0.061) 0.224 (0.007) Ψ (LFI) p (Effort) 1.33 0.087 4 0.780 (0.066) 0.204 (0.006) Ψ (Habitat + Elevation) p (Effort) 1.60 0.076 5 0.733 (0.065) 0.223 (0.006) Ψ (Habitat + Disturbance) p (Effort) 1.62 0.075 5 0.732 (0.065) 0.224 (0.007) Ψ (Habitat + LFI) p (Effort) 1.77 0.070 5 0.740 (0.063) 0.223 (0.007) Ψ (Habitat + BWA + Elevation) p (Effort) 1.99 0.063 5 0.661 (0.069) 0.228 (0.006) Model averaged 0.721 (0.056) 0.222 (0.003)

Discussion Understanding species distribution and occupancy is important in determining management and conservation actions for an endangered species. In order to properly manage and conserve CNFS, especially with the looming threat of climate change, managers need to know 1) where they occur, 2) have better refined occupancy relationships with habitat conditions, especially in old growth stands to improve occupancy and species distribution models, and 3) determine methodology for a robust monitoring project that can determine long- term occupancy trends for this species in conjunction with long-term nest box surveys, such as acoustics. My study helped fill distribution gaps for CNFS in GSMNP, including 14 new sites

for the species within the park. Occupancy rates were high across my study sites and indicated Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/444194/pdf/10.3996jfwm-20-020.s3/ by guest on 26 September 2021 that montane conifer forests have higher occupancy rates than mixed montane conifer-northern hardwood forests, which aligns with habitat selection studies using radio-collared CNFS (Ford et al. 2014, Diggins et al. 2017). Work in second growth forests in the southern Appalachians showed on multiple spatial scales CNFS preferentially select montane conifer forests more than expected given their availability on the landscape (Diggins et al. 2017). Even though montane conifer-northern hardwood forests and northern hardwood forests were more available on the landscape, CNFS were found not to select for either of these habitat types. Forest disturbance and specific topographic features influenced habitat occupancy, although habitat type was the main driver. At higher elevations in GSMNP, especially above 1,500 m, spruce-fir forests become the dominant forest type. Although there was somewhat of a relationship indicating that CNFS occupancy increased at lower elevation sites, there are two potential explanations for this. First, no survey sites occurred in pure northern hardwood stands and all survey sites were within suitable habitat, so lower elevation sites within this study are still above 1,525 m, which is >300 m higher in elevation than the lowest elevational range for this species. Secondly, elevation is directly related to both historical logging disturbance and BWA infestation. Logging disturbance was prevalent at lower elevations and areas with gentler slopes (Pyle 1985, Pyle and Schafale 1988, Hayes et al. 2006) and did not disturb spruce-fir forests at some of the highest elevations or topographically steep sites. However, these high elevation areas are also some of the hardest hit by BWA, especially in fir-dominant stands (Dull et al. 1988). Therefore, habitat type, elevation, and disturbance regimes may be closely linked and could influence occupancy and detection rates. Historical disturbance data for GSMNP is rudimentary, however, and the type, intensity, and frequency of logging and BWA disturbance may have influenced stand trajectories and structure. Therefore, future work should consider further evaluating these types of stand conditions on habitat occupancy coupled with with-in stand forest metrics at each acoustic site. Additionally, integrating information about canopy height from LiDAR (light intensity and data ranging) may help advance understanding of habitat and disturbance regimes on CNFS occupancy, especially because CNFS are known to select for stands with larger trees and higher canopies (Ford et al. 2014). Despite rainy weather, detection rates for CNFS during this study were similar to other surveys conducted on northern flying squirrels in the Appalachian Mountains during this time of year. My work in western North Carolina and the Pocono Mountains in Pennsylvania obtained detection rates >20% during May/June. Large amounts of rain during May/June may have decreased detection rates as flying squirrels are less active during periods of precipitation and probably vocalize less due to increased ambient noise and lessened activity. Moreover, rain falsely triggers the ultrasonic detectors and the noise from the rain may mask flying squirrel calls. Detection rates did not varying between habitat types or BWA severity, but did in old-

8 growth vs. second growth habitat. Although I did not take site-level vegetation measurements, such as tree density, I anecdotally observed that old-growth sites had more open understories and higher canopies, which may have increased detectability at those sites due to reduce clutter. Although CNFS were only detected at 57% of the sites, habitat at all of the sites looked suitable for the species. It is important to note that lack of detection during this study does not conclusively mean absence from the study site and these sites should be resurveyed to confirm if CNFS are truly absent. Additionally, LTD was higher than other studies conducted in the southern Appalachians (Diggins et al. 2016, Diggins et al. In Review). However, the higher LTD may have been attributed to survey sites where I extended the length of the survey due to high

and frequent rain events that occurred during the beginning of the study. Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/444194/pdf/10.3996jfwm-20-020.s3/ by guest on 26 September 2021 I detected both CNFS and SFS at two sites, both lower elevation sites in this study: Beech Gap at 1,550 m and Mount Sterling Ridge at 1,620 m. The site at Beech Gap was a small stand of spruce surrounded by northern hardwood forest, whereas Mount Sterling Ridge was spruce- northern hardwood forest. SFS are known to occupy higher elevation sites at the spruce-northern hardwood ecotone or montane conifer stands adjacent to northern hardwood forests (Urban 1988; C. Diggins, unpub. data). Whether SFS persist at these sites year-round is unknown or are subject periodic local colonization and extinction based on annual winter severity and hard mast availability as occurs in the central Appalachians (W. Ford, unpub. data) is unknown. However, understanding potential elevational shifts in occupancy for SFS due to climate change may be important to understanding long-term occupancy persistence of CNFS. Current latitudinal shifts of SFS indicated that this species will respond positively to climate change. Recent climate- induced latitudinal shifts of this species has occurred in Canada, New England, and the Great Lake states (Bowman et al. 2005, Myers et al. 2009, Wood et al. 2016), but it is unknown if elevational shifts are occurring in mountainous areas at lower latitudes where the two species are sympatric. SFS are known to hybridize with northern flying squirrels and they carry a parasitic nematode that is lethal to northern flying squirrels (Weigl et al. 1992, Wetzel and Weigl 1994, Krichbaum et al. 2010). Therefore, expansion of SFS into higher elevation sites may be detrimental for the long-term persistence of CNFS in the Southern Appalachians. Potential expansion of SFS into higher elevation sites could be monitored using ultrasonic acoustics, since common calls between CNFS and SFS are species specific. Focusing on long-term monitoring along an elevation gradient might be beneficial to understanding if overlap zones of these parapatric species are shifting due to a warming climate. Although I documented 14 new sites for CNFS in GSMNP during this study, there are large areas of the park I was not able to survey. There is over 23,000 hectares of suitable habitat for CNFS in the park (Ford et al. 2015), however the backcountry is so vast that my study was only able to sample a small proportion of it during this study. Future survey areas should include Monte Le Conte, Mount Guyot, Mount Chapman, eastern Balsam Mountain, Dashoga Ridge, and northern Balsam Mountain, including more sites on the Tennessee side of the park and at lower elevations (1350 - 1524 m). These lower elevation sites may be zones of parapatry between CNFS and SFS, so including them may be improve in determining potential range expansion of SFS or range contraction of CNFS due to climate change. Occupancy models for CNFS from Ford et al. (2015) could help guide additional areas of GSMNP to target for future survey work. Therefore, even though this study added essential information about CNFS in GSMNP, further work needs to be done to obtain data in other unsurveyed regions of the park. Overall, my study has provided important data for both GSMNP and North Carolina Wildlife Resources Commission on the distribution and occupancy of CNFS in the park. The

9 data obtained in this study will be incorporated in a range-wide dataset to model current CNFS distribution and distribution under climate change. National Park Service lands, especially those located at higher elevations, are experiencing a disproportionally higher magnitude of climate change versus other federal and private lands (Gonzales et al. 2018). CNFS is predicted to become extirpated from GSMNP with increasing temperatures from anthropogenic climate change (Burns et al. 2003). As climate change becomes an increasing threat to high-elevation systems in the region (Koo et al. 2015, Walter et al. 2017), monitoring of CNFS should continue to determine occupancy trends across the park, allowing managers to monitor the vital signs of this federally endangered species. Future acoustic surveys, including expanded surveys to

isolated areas of the park that have yet to be surveyed, should continue as a part of a robust Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/444194/pdf/10.3996jfwm-20-020.s3/ by guest on 26 September 2021 range-wide survey effort for CNFS.

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