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Sciurus niger niger (Southern Fox Squirrel) Density and the Diurnal Patterns, Occupancy, and Detection of Sympatric Southern Fox Squirrels and S. carolinensis (Eastern Gray Squirrel) on Spring Island, South Carolina
Katelyn Amspacher, Beau Bauer, Jayme Waldron, Ernie Wiggers, and Shane Welch

Southeastern Naturalist, Volume 18, Issue 2 (2019): 321–333

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Southeastern Naturalist 321 K. Amspacher, B. Bauer, J. Waldron, E. Wiggers, and S. Welch 22001199 SOUTHEASTERN NATURALIST 1V8o(2l.) :1382,1 N–3o3. 32 Sciurus niger niger (Southern Fox Squirrel) Density and the Diurnal Patterns, Occupancy, and Detection of Sympatric Southern Fox Squirrels and S. carolinensis (Eastern Gray Squirrel) on Spring Island, South Carolina Katelyn Amspacher1,*, Beau Bauer2, Jayme Waldron1, Ernie Wiggers2, and Shane Welch1 Abstract - Sciurus niger niger (Southern Fox Squirrel, hereafter, SFS) are habitat specialists within the Pinus palustris (Longleaf Pine) ecosystem of the southeastern US whose populations are declining due to habitat loss and fragmentation. Sciurus carolinensis (Eastern Gray Squirrel, hereafter, EGS) are generalists found throughout the eastern US that have historically avoided competition with SFSs through habitat-partitioning. Spring Island, SC, once reported the densest population of SFSs in the southeast, but residents of the island community recently reported decreasing SFS populations and increasing EGS populations. We used baited game-cameras at stratified random points to estimate SFS density, observe patterns of SFS and EGS diurnal activity, and examine the influence of habitat structure and management techniques (e.g., prescribed fire) on SFS and EGS occurrence on Spring Island. Our estimate of SFS density on Spring Island (0.28–0.97 SFSs/ha) was similar to the high density previously reported (0.758 SFSs/ha). SFSs and EGSs had similar diurnal patterns, but SFSs were most active around midday while EGSs were most active in early morning and evening hours. EGS occupancy was negatively associated with fire frequency. EGS detection probability was negatively associated with maximum daily temperature. These data suggest that habitat management via prescribed fire has allowed stable populations of both SFSs and EGSs to persist despite anthropogenic land changes. Introduction Species that are closely related and ecologically similar often have some niche dimension that differentiates them (Brown 1984). The niche breadth of specialist species is narrower than the niche breadth of generalist species, making specialists more vulnerable to habitat alterations (Brown 1984, Vázquez and Simberloff 2002, Wilson et al. 2008). Furthermore, generalist species typically adapt to humanaltered landscapes better than specialist species (Swihart et al. 2003). Sciurus niger niger L. (Southern Fox Squirrel, hereafter, SFS) and S. carolinensis Gmelin (Eastern Gray Squirrel, hereafter, EGS) are closely related, sympatric specialist and generalist species, respectively, that rely on habitat partitioning to limit competition (Edwards et al. 1998, Weigl et al. 1989). SFSs are habitat specialists associated with fire-maintained Pinus (pine) savannas and woodlands of the southeastern US that have experienced population 1Department of Biological Sciences, Marshall University, Huntington, WV 25755. 2Nemours Wildlife Foundation, Yemassee, SC 29945. *Corresponding author - katelyn.amspacher@siu.edu. Manuscript Editor: Michael Cove Southeastern Naturalist K. Amspacher, B. Bauer, J. Waldron, E. Wiggers, and S. Welch 2019 Vol. 18, No. 2 322 declines throughout their range due to habitat loss and fragmentation (Perkins and Conner 2004, Weigl et al. 1989). SFS life history might contribute to the species’ inability to rebound from population declines. SFSs are one of the largest and most ground dwelling of all North American tree squirrels (Weigl et al. 1989). They are relatively long-lived (7–10 y lifespan) and typically have just 1 litter per year with an average 2.5 pups, which suggests they are a k-selected species (Tappe and Guynn 1998, Weigl et al. 1989). SFSs are normally found in low densities (less than 0.35 SFS/ha; Weigl et al. 1989). Eastern Gray Squirrels are habitat generalists found in dense forests with woody groundcover throughout the eastern US (Edwards et al. 1998). They typically live 8–9 y and frequently have 2 litters per year, averaging 2.6 pups per litter (Barkalow et al. 1970). In addition, EGSs are often very abundant and reach high densities of up to 15 squirrels/ha (Gurnell 1983). Spring Island, SC, reported the densest SFS population in the southeast during the 1990s (Lee 1999), but residents reported decreased SFS populations and increased EGS populations in the 2010s (C. Marsh and T. Mills, Spring Island Trust, pers. comm.). In this study, we sought to evaluate whether changes in SFS and EGS population changes occurred on Spring Island. We employed game cameras to estimate SFS density, and occupancy models to examine the influence of habitat structure and management technique (e.g., prescribed fire frequency) on SFS and EGS occurrence. We hypothesized that decreased SFS density would be associated with the shift in land use from open pine savanna—sustained by frequent prescribed burning—to nature preserve/residential community in the early 1990s (Lee et al. 2008). We expected that this shift in land use led to a subsequent shift in habitat management, limiting prescribed fire prevalence, altering habitat structure (e.g., increased canopy cover and denser forest), and allowing EGSs to encroach on SFS habitat. We further hypothesized that habitat alterations limited the ability of SFSs and EGSs to partition habitat, leading to temporal partitioning instead. We expected EGS site occupancy to be greater than SFS site occupancy because EGSs are generalists, and SFS occupancy to be positively associated with fire frequency and negatively associated with canopy cover because of their association with the Pinus palustris Mill. (Longleaf Pine) ecosystem. We predicted that the detection probability of SFSs and EGSs would be similar because they are closely related species, and to be negatively associated with maximum daily temperature and daily precipitation because of reduced squirrel activity in heat and heavy rain (Weigl et al. 1989). Field-Site Description Spring Island is a 1200-ha sea island in South Carolina (Fig. 1). The island was managed as a quail plantation from the mid-1900s until 1990 when it was sold for residential development. Managers of quail plantations typically use frequent, lowintensity prescribed fire to maintain large tracts of low basal-area pine/hardwoods with an open, diverse understory (Moser et al. 2002, Stoddard 1931, Van Lear et al. 2005). Management with frequent, low-intensity fire also provided habitat for other species of interest such as Picoides borealis Vieillot (Red-cockaded Woodpecker) Southeastern Naturalist 323 K. Amspacher, B. Bauer, J. Waldron, E. Wiggers, and S. Welch 2019 Vol. 18, No. 2 and SFSs (Moser et al. 2002). Development on Spring Island led to increased human activity and road construction, which fragmented the landscape and introduced a new source of mortality for SFSs (Lee et al. 2008). Spring Island is now considered a lowdensity housing development hidden behind dense vegetation along the perimeter of the island to maintain privacy for 400 families. Land use includes a golf course, clay range, sports complex (e.g., tennis courts and pool), and farm for residents’ use. The Spring Island Trust, a nonprofit organization, was established to protect the environmental and cultural history of the island and maintain 445 ha in nature preserves. Methods Data collection We used historic landcover classifications (Lee 1999) and current satellite imagery to establish 20 random points stratified by habitat class on Spring Island, SC Figure 1. Twenty stratified random points established on Spring Island, SC, as sampling sites for Southern Fox Squirrels and Eastern Gray Squirrels. Southeastern Naturalist K. Amspacher, B. Bauer, J. Waldron, E. Wiggers, and S. Welch 2019 Vol. 18, No. 2 324 (Fig. 1). We set 250 m as the minimum distance between random points (Tye et al. 2015), but in practice, the minimum distance between sampled points was 285 m and all other points were >300 m apart. We used a convex spherical crown densiometer (Forestry Suppliers, Inc., Jackson, MS) to measure canopy cover at each point in early June 2016, when sites were established. We digitized and georeferenced 25 y of historical prescribed burn maps from 1992 to 2017 using ArcGIS (Esri, Redlands, CA) and calculated the average number of times each point was burned during a 5-y period to reflect historical fire-return intervals (Stambaugh et al. 2011). We placed a game camera (Bushnell 14 MP Trophy Cam HD Aggressor, Bushnell Outdoor Products, Overland Park, KS) 70 cm above ground and 1.5 m from a pecan/ corn bait pile (~19 L) at each point and set motion-activated cameras to take 3 pictures every 10 sec on normal sensitivity settings (Tye et al. 2015). We visited cameras every other day to change SD cards and replenish bait. We conducted camera surveys from 24 to 30 June 2016 and from 20 to 26 April 2017. Statistical analysis Density and diurnal patterns. We calculated the area sampled using a 142.5-m (half the minimum distance between points) radius around each point. A circle with a diameter of 285 m around the random point covers 6.4 ha, which is similar to the previously reported 6.5-ha average annual home range of SFSs on Spring Island (Lee et al. 2008). We identified individual SFSs from photographs using unique pelage characteristics (Tye et al. 2015). We divided the number of individuals identified by the area sampled to estimate SFS density. We used package camtrapR (Niedballa et al. 2017) in Program R (R Core Team 2018) to catalogue photographs and create a record table with entries for both EGSs and SFSs. We formatted the record table to only include records of the same species at the same station with at least 30 min between photographs to maintain independence between records. We used this record table and package camtrapR in Program R to calculate kernel density diel activity overlap between EGSs and SFSs using the overlap coefficient Dhat1 (Ridout and Linkie 2009). We assumed an overlap coefficient ≥0.5 indicated similar species activity patterns and no avoidance behavior. Occupancy. We used single-species multi-season occupancy models in program PRESENCE (MacKenzie et al. 2002) to identify patterns of occupancy and detection for both SFSs and EGSs. We assumed constant local extinction (ε) and colonization (γ) for all models. We formatted encounter history with 2 seasons (2016 and 2017) and 7 replicates per season (14 total replicates) for each random point. The SFS multi-season model had poor fit, so we adjusted SFS occupancy analysis to 2 separate single-species, single-season occupancy models. The 2017 single-season SFS models were of poor fit and we excluded them from analysis. We retrieved maximum daily temperature and daily precipitation data from the National Oceanic and Atmospheric Administration (NOAA) station at the Marine Corps Air Station Beaufort (38.6 km from Spring Island), z-standardized them, and incorporated the data in models as detection covariates (Table 1). We used canopy cover and burn frequency as occupancy covariates (Table 1). We used Spearman rho correlation to Southeastern Naturalist 325 K. Amspacher, B. Bauer, J. Waldron, E. Wiggers, and S. Welch 2019 Vol. 18, No. 2 examine collinearity among covariates, retaining covariates with coefficients less than r = 0.7. We constructed 1 constant model, 2 models examining single-detection covariates, 2 models testing single-occupancy covariates, and 11 additive models for each species (16 EGS models and 16 SFS models; Tables 2, 3). We ranked models with Akaike’s information criterion (AIC) and adjusted SFS models to QAIC due to overdispersion (ĉ2016 = 2.64; Akaike 1973, Burnham and Anderson 2002). Results We captured 12,584 photographs of SFSs on 9 cameras and 13,742 photographs of EGSs on 9 cameras in 2016. We captured 37,645 photographs of SFSs on 17 cameras and 25,895 photographs of EGSs on 11 cameras in 2017. We identified 36 individual SFSs during the summer 2016 sampling season and 124 individual SFSs during the spring 2017 sampling season. We estimated 0.28 SFSs/ha in 2016, and 0.97 SFSs/ha in 2017. After adjusting the record table to maintain independence between records, we incorporated 774 photographs of SFSs and 563 photographs of EGSs in the diurnal pattern analysis. Activity overlap indicated that SFSs and EGSs had similar diel activity patterns, but EGSs were most active in early morning and late evening hours while SFSs were most active around midday (Dhat1 = 0.62; Fig. 2). Occupancy and detection covariates were not correlated (r = -0.47 and 0.19, respectively). Only one EGS occupancy model, ψ(PF+CC), γ(.), ε(.), p(Temp), Table 1. Explanation of occupancy and detection covariates. Covariate Description Occupancy Burn frequency (PF) Average number of burns in a 5-y period from 1992 to 2017 Canopy cover (CC) Percent canopy cover Detection Temperature (Temp) Maximum daily temperature in °C, z-standardized Precipitation (Precip) Daily precipitation in cm, z-standardized Figure 2. Kernel density estimate of Eastern Gray Squirrel (EGS) and Southern Fox Squirrel (SFS) activity throughout the day (0:00– 24:00). Shaded area indicates activity overlap between species (Dhat1 = 0.62). Southeastern Naturalist K. Amspacher, B. Bauer, J. Waldron, E. Wiggers, and S. Welch 2019 Vol. 18, No. 2 326 Table 3. Southern Fox Squirrel occupancy models ranked according to QAIC model selection, 2016. Supported models (ΔQAIC < 2.0) are indicated by an asterisk (*). PF = prescribed fire frequency, CC = canopy cover, Temp = maximum daily temperature, and Precip = daily precipitation. Model QAIC ΔQAIC ωi Likelihood k Deviance ψ(.) , p(.)* 40.67 0.00 0.2523 1.0000 2 96.68 ψ(.), p(Temp)* 42.26 1.59 0.1139 0.4516 3 95.59 ψ(PF), p(.)* 42.48 1.81 0.1021 0.4045 3 96.18 ψ(.), p(Precip)* 42.55 1.88 0.0985 0.3906 3 96.34 ψ(CC), p(.)* 42.58 1.91 0.0971 0.3848 3 96.43 ψ(PF), p(Temp) 44.07 3.40 0.0461 0.1827 4 95.09 ψ(.), p(Temp+Precip) 44.12 3.45 0.0449 0.1782 4 95.23 ψ(CC), p(Temp) 44.17 3.50 0.0438 0.1738 4 95.34 ψ(PF+CC), p(.) 44.19 3.52 0.0434 0.1720 4 95.41 ψ(PF), p(Precip) 44.36 3.69 0.0399 0.1580 4 95.84 ψ(CC), p(Precip) 44.45 3.78 0.0381 0.1511 4 96.09 ψ(PF+CC), p(Temp) 45.78 5.11 0.0196 0.0777 5 94.32 ψ(PF), p(Temp+Precip) 45.93 5.26 0.0182 0.0721 5 94.72 ψ(CC), p(Temp+Precip) 46.03 5.36 0.0173 0.0686 5 94.98 ψ(PF+CC), p(Precip) 46.06 5.39 0.0170 0.0675 5 95.06 ψ(PF+CC), p(Temp+Precip) 47.64 6.97 0.0077 0.0307 6 93.95 Table 2. Eastern Gray Squirrel occupancy models ranked according to AIC model selection. Supported models (ΔAIC < 2.0) are indicated by and asterisk (*). PF = prescribed fire frequency, CC = canopy cover, Temp = maximum daily temperature, and Precip = daily precipitation. Model QAIC ΔQAIC ωi Likelihood k Deviance ψ(PF+CC), γ(.), ε(.), p(Temp)* 203.34 0.00 0.9594 1.0000 7 189.34 ψ(PF), γ(.), ε(.), p(Temp) 210.92 7.58 0.0217 0.0226 6 198.92 ψ(PF), γ(.), ε(.), p(Temp+Precip) 212.85 9.51 0.0083 0.0086 7 198.85 ψ(PF+CC), γ(.), ε(.), p(Temp+Precip) 213.98 10.64 0.0047 0.0049 8 197.98 ψ(CC), γ(.), ε(.), p(Temp) 216.42 13.08 0.0014 0.0014 6 204.42 ψ(PF), γ(.), ε(.), p(.) 216.64 13.30 0.0012 0.0013 5 206.64 ψ(.), γ(.), ε(.), p(Temp) 217.44 14.10 0.0008 0.0009 5 207.44 ψ(PF+CC), γ(.), ε(.), p(.) 217.77 14.43 0.0007 0.0007 6 205.77 ψ(CC), γ(.), ε(.), p(Temp+Precip) 218.36 15.02 0.0005 0.0005 7 204.36 ψ(PF), γ(.), ε(.), p(Precip) 218.41 15.07 0.0005 0.0005 6 206.41 ψ(.), γ(.), ε(.), p(Temp+Precip) 219.38 16.04 0.0003 0.0003 6 207.38 ψ(PF+CC), γ(.), ε(.), p(Precip) 219.54 16.20 0.0003 0.0003 7 205.54 ψ(CC), γ(.), ε(.), p(.) 222.14 18.80 0.0001 0.0001 5 212.14 ψ(.), γ(.), ε(.), p(.) 223.19 19.85 0.0000 0.0000 4 215.19 ψ(CC), γ(.), ε(.), p(Precip) 223.92 20.58 0.0000 0.0000 6 211.92 ψ(.), γ(.), ε(.), p(Precip) 224.96 21.62 0.0000 0.0000 5 214.96 Southeastern Naturalist 327 K. Amspacher, B. Bauer, J. Waldron, E. Wiggers, and S. Welch 2019 Vol. 18, No. 2 was supported (ΔAIC < 2.0; Table 2). According to the supported model, average EGS occupancy was 0.45 ± 0.14 (95% CI = 0.21–0.69) and average daily detection probability was 0.72 ± 0.04 (95% CI = 0.64–0.80). The model failed to detect a significant association between canopy cover and occupancy (β = 2.98, SE = 3.29; Table 4), but occupancy was negatively associated with burn frequency (β = -1.751, SE = 0.864; Fig. 3). Maximum daily temperature had a negative effect on Figure 3. Relationship between burn frequency and Eastern Gray Squirrel occupancy (ψ), based on output from ψ(PF), γ(.), ε(.), p(Temp). Line = estimate, gray area = 95% CL. Table 4. Beta output from supported Eastern Gray Squirrel occupancy model, ψ(PF+CC), γ(.), ε(.), p(Temp). SE = standard error, LCL = lower 95% confidence limit, and UCL = upper 95% confidence limit. Significance is denoted by an asterisk (*). PF = prescribed fire frequency, CC = canopy cover, Temp = maximum daily temperature, and Precip = daily precipitation. Parameter β SE LCL UCL ψ -3.02 2.93 -8.76 2.72 ψ.PF* -1.75 0.86 -3.45 -0.06 ψ.CC 2.98 3.29 -3.48 9.43 γ -0.56 0.63 -1.80 0.67 ε -1.26 0.80 -2.83 0.32 p* 9.72 1.24 7.29 12.15 p.Temp* -0.28 0.04 -0.35 -0.20 Southeastern Naturalist K. Amspacher, B. Bauer, J. Waldron, E. Wiggers, and S. Welch 2019 Vol. 18, No. 2 328 detection probability (β = -0.278, SE = 0.039; Fig. 4). Burn frequency and canopy cover were important EGS occupancy covariates, accounting for 99.7% and 96.7% of cumulative model weights, respectively. Maximum daily temperature was the most important detection covariate, accounting for 99.7% of model weight. Daily precipitation was a poor predictor of detection probability and only accounted for 1.5% of cumulative model weight. Five SFS occupancy models were supported (ΔAIC < 2.0; Table 3), but the constant model was most supported. Model-averaged SFS occupancy from the 5 supported models was 0.30 ± 0.08 (95% CI = 0.16–0.45) in 2016. However, naïve SFS occupancy in 2017 was 85%. Model-averaged detection probability was 0.51 ± 0.04 (95% CI = 0.42 – 0.57) in 2016. Three of the 5 supported models included constant occupancy (Table 3). Burn frequency was included in 1 supported model, but we failed to detect a significant association between burn frequency and SFS occupancy (β2016 = -0.31, SE2016 = 0.72; Table 5). Likewise, canopy cover was included as an occupancy covariate in 1 supported model, but we failed to detect a significant association between canopy cover and SFS occupancy (β2016 = -0.95, SE2016 = 3.11; Table 5). Three of the 5 supported models included constant detection probability (Table 3). Maximum daily temperature was included as a detection covariate in one supported model, but we failed to detect a significant association between Figure 4. Relationship between Eastern Gray Squirrel detection probability (P) and maximum daily temperature, based on output from ψ(PF), γ(.), ε(.), p(Temp). Line = estimate, gray area = 95% CL. Southeastern Naturalist 329 K. Amspacher, B. Bauer, J. Waldron, E. Wiggers, and S. Welch 2019 Vol. 18, No. 2 maximum daily temperature and SFS detection probability (β2016 = -0.15, SE2016 = 0.13; Table 5). Daily precipitation was included in one supported model, but we failed to detect a significant association between precipitation and SFS detection probability (β2016 = 0.49, SE2016 = 1.37; Table 5). Burn frequency and canopy cover were moderate predictors of SFS occupancy based on parameter weights (29.4% and 28.4%, respectively, in 2016). Maximum daily temperature and daily precipitation were moderate predictors of SFS detection probability based on parameter weights (31.2% and 28.2%, respectively, in 2016). Discussion Lee et al. (2008) reported 0.758 SFSs/ha on Spring Island in 1999. At that time, Spring Island SFS density was much higher than density estimates of other SFS populations, which varied from 0.084 to 0.38 SFSs/ha (Conner et al. 1999, Edwards et al. 1989, Hilliard 1979, Kantola and Humphrey 1990, Weigl et al. 1989). We expected that Spring Island residents accurately perceived a decline in SFS populations due to anthropogenic development and the general trend of declining SFS populations (Wood and Davis 1981). We recorded 0.28 SFSs/ha and 0.97 SFSs/ha in June 2016 and April 2017, respectively. Lee (1999) estimated density in May, and his estimate fell about halfway between our density estimates from April and June. The difference in our density estimates is likely associated with decreased Table 5. Beta output from supported SFS occupancy models, 2016. SE = standard error, LCL = lower 95% confidence limit, UCL = upper 95% confidence limit, significance is denoted by an asterisk (*). PF = prescribed fire frequency, CC = canopy cover, Temp = maximum daily temperature, and Precip = daily precipitation. Model/Parameter β SE LCL UCL ψ(.), p(.) ψ -0.20 0.73 -1.63 1.23 p* 1.16 0.48 0.22 2.11 ψ(.), p(Temp) ψ -0.20 0.73 -1.63 1.23 p 6.34 4.57 -2.60 15.29 p.Temp -0.15 0.13 -0.42 0.11 ψ(PF), p(.) ψ 0.12 1.03 -1.91 2.14 ψ.PF -0.31 0.72 -1.71 1.10 p* 1.16 0.48 0.22 2.11 ψ(.), p(Precip) ψ -0.20 0.73 -1.63 1.23 p 1.01 0.64 -0.24 2.25 p.Precip 0.49 1.67 -2.19 3.16 ψ(CC), p(.) ψ 0.55 2.58 -4.51 5.62 ψ.CC -0.95 3.11 -7.03 5.14 p * 1.16 0.48 0.22 2.11 Southeastern Naturalist K. Amspacher, B. Bauer, J. Waldron, E. Wiggers, and S. Welch 2019 Vol. 18, No. 2 330 activity of SFSs during summer months due to increased temperature and decreased food availability (Weigl et al. 1989), consequently leading to decreased ability to detect SFSs during summer months. Our SFS occupancy models did not indicate a significant effect of temperature on detection probability, likely because the variation in temperatures within sampling seasons was far less than variation between sampling seasons. Thus, the spring 2017 density estimate (0.97 SFSs/ha) is likely representative of the current Spring Island SFS population. Our hypothesis that Spring Island SFS density had declined since the 1990s was not supported, as SFS density appears to be similar to what Lee reported in 1999. Though our estimate of SFS density (0.97 SFSs/ha) is high for the species, it is still much lower than densities achieved by many EGS populations (up to 15 EGSs/ha; Gurnell 1983). Habitat cues such as canopy cover and illumination influence prey species’ behavior due to perceived predation risk (Thorson et al. 1992). Anthropogenic activity altered the habitat structure on Spring Island and may have affected the behavior of both SFSs and EGSs and the population metrics perceived by residents. For instance, as habitat changed from open pine savannas and woodlands managed with fire to denser mixed pine–hardwood forests, EGSs likely began foraging in these newly suitable areas, giving the perception of many EGSs where there had once been SFSs. In addition, citizen scientists and opportunistic data are often biased towards detections in frequently travelled areas, especially when searching for low-density populations such as SFSs (Crall et al. 2010, Fitzpatrick et al. 2009). Observations near homes with dense vegetation likely affected EGS and SFS population perceptions on Spring Island. We expected that the shift in land use from managed quail plantation to nature preserve/residential community in the early 1990s altered the degree to which prescribed fire was used to manage wildlife habitat. We predicted this change allowed EGSs to disperse as habitat grew increasingly denser, increasing interactions between SFSs and EGSs. We observed both SFSs and EGSs at the same camera at half the sites sampled. Spring Island SFSs and EGSs had similar activity patterns, indicating that they were not avoiding each other temporally (Fig. 2), but EGSs did appear to be most active in early morning and late evening hours; SFSs were most active around midday. Lashley et al. (2018) observed that SFSs in North Carolina were also most active in the middle of the day. Occupancy analysis indicated that EGSs were negatively associated with prescribed fire (Fig. 3). Fire maintains an open understory and prevents woody stem encroachment within the Longleaf Pine ecosystem of the Southeast (Frost 1993), but EGSs prefer dense forests with woody undergrowth (Edwards et al. 1998). SFSs prefer open, grassy understories (Edwards et al. 1998), but our SFS occupancy analysis for this high-density population failed to detect significant effects of burn frequency or canopy cover on occupancy. Previous SFS analyses similar to ours have varied results. Prince et al. (2016) found no direct effect of fire on SFSs and indicated the importance of hardwoods as a source of food and refugia within pine savannas and woodlands. However, Boone et al. (2017) indicated a negative association between occupancy by Fox Squirrels and fire interval. Greene and McCleery Southeastern Naturalist 331 K. Amspacher, B. Bauer, J. Waldron, E. Wiggers, and S. Welch 2019 Vol. 18, No. 2 (2017) observed that open habitat structure was important for S. n. shermani Moore (Sherman’s Fox Squirrel) even when they occurred in altered/human-impacted landscapes, linking the subspecies to the fire-maintained Longleaf Pine ecosystem and indicating the importance of management to maintain open canopies and understories. Occupancy analysis indicated that EGS detection probability was negatively associated with maximum daily temperature (Fig. 4). SFS density analysis suggested a negative relationship between maximum daily temperature and SFS detection, reflecting reduced activity during hot summer months when food is generally scarce (Weigl et al. 1989). Spring Island, SC, still supports a high-density population of SFSs. Exurbanization influences habitat structure, particularly near heavily trafficked areas. The perceived decline in SFS population on Spring Island is likely due to the dense vegetation surrounding and hiding houses and observations biased towards areas with high human activity. SFSs are typically found in comparatively low densities in more open areas, often managed with fire. In contrast, EGSs are found in higher densities in thicker vegetation that is not managed with fire. Residential communities within the Longleaf Pine ecosystem that strive to maintain natural diversity should preserve and manage habitat for specialists with management tools such as prescribed fire or mechanical thinning. Acknowledgments We thank Chris Marsh, Tony Mills, and Tom Murphy for their assistance in establishing this project and David Barron for his assistance in the field. Funding for this project was provided by the Spring Island Trust, Nemours Wildlife Foundation, and Marshall University. 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