Sample Grain Influences the Functional Relationship Between Canopy Cover and Gopher Tortoise (Gopherus polyphemus) Burrow Abandonment

Chelonian Conservation and Biology, 2014, 13(2): 166 172 g 2014 Chelonian Research Foundation Sample Grain Influences the Functional Relationship Between Canopy Cover and Gopher Tortoise (Gopherus polyphemus) Burrow Abandonment CHRISTOPHER P. CATANO 1,3, *,JAMES J. ANGELO 1,2, AND I. JACK STOUT 1 1 Department of Biology, University of Central Florida, Orlando, Florida 32816 USA [jstout@ucf.edu]; 2 Present address: Department of Mathematics, University of Central Florida, Orlando, Florida 32816 USA [james.angelo@knights.ucf.edu]; 3 Present address: Department of Biology, Washington University in St. Louis, St. Louis, Missouri 63130 USA [chcatano@gmail.com] *Corresponding author ABSTRACT. Change in vegetation structure alters habitat suitability for the threatened gopher tortoise (Gopherus polyphemus). An understanding of this dynamic is crucial to inform habitat and tortoise management strategies. However, it is not known how the choice of the sample grain (i.e., cell size) at which vegetation structure is measured impacts estimates of tortoise habitat relationships. We used lidar remote sensing to estimate canopy cover around 1573 gopher tortoise burrows at incrementally larger sample grains (1 707 m 2 ) in 450 ha of longleaf pine (Pinus palustris) savanna. Using an information theoretic approach, we demonstrate that the choice of grain size profoundly influences modeled relationships between canopy cover and burrow abandonment. At the most supported grain size (314 m 2 ), the probability of burrow abandonment increased by 1.7% with each percent increase in canopy cover. Ultimately, detecting the appropriate sample grain can lead to more effective development of functional relationships and improve predictive models to manage gopher tortoise habitats. KEY WORDS. Akaike Information Criterion; lidar remote sensing; logistic regression; longleaf pine savanna; sandhill habitat; spatial scale; species-habitat management The gopher tortoise (Gopherus polyphemus) has suffered population declines of approximately 80% during the past century, primarily from habitat loss and degradation (Auffenberg and Franz 1982; McCoy et al. 2006). The critical state of tortoise populations resulted in federal protection as a threatened species west of the Mobile and Tombigbee rivers in 1987. Recently, the gopher tortoise was added as a candidate species for listing in the eastern portion of its range (US Fish and Wildlife Service 2011). The gopher tortoise is an ecosystem engineer (Kaczor and Hartnett 1990; Kinlaw and Grasmueck 2012) and is of direct management concern in the southeastern United States because burrows created by this semifossorial reptile are critical for the persistence of various other species, many of which are also imperiled (Jackson and Milstrey 1989; Lips 1991). The current status of the gopher tortoise brings increasing attention to the proactive measures that could be used by land managers to maintain or improve habitat conditions. Justification for this concern stems from a restudy of populations in protected habitats that suggests downward trends in gopher tortoise abundance on public lands (McCoy et al. 2006). Therefore, legal protection and habitat sequestration alone are not sufficient to ensure favorable population trends into the future because management may not be meeting the gopher tortoise s habitat needs. Development of improved tortoise-habitat models is essential to quantify responses to changes in habitat (Guisan and Zimmermann 2000) and develop predictive frameworks to guide management strategies (Schmolke et al. 2010). The primary habitat requirements of gopher tortoises include loose, well-drained soils and adequate sunlight penetration for nest incubation and to permit groundcover growth of herbaceous foods (Auffenberg and Franz 1982). Nearly all tortoise habitats are dependent on periodic fire to maintain suitable vegetation structure and plant species composition. Fire suppression and improper management have led to remaining tortoise habitats that are often degraded and lack critical habitat features (e.g., native ground cover; Diemer 1986). Increasing canopy cover and density of hardwoods are valid indicators of declining habitat quality for the gopher tortoise (Aresco and Guyer 1999; Boglioli et al. 2000; McCoy et al. 2006; Yager et al. 2007). When local conditions become unsuitable, gopher tortoises abandon their burrows. Although we currently lack a complete understanding of the social and environmental factors that cause tortoises to abandon or reoccupy burrows (Guyer et al. 2012), vegetation structural change is the most well-documented driver of this response in forested ecosystems. Prior studies have quantified tortoise responses to overstory vegetation conditions; however, inferences were based on measurements made at single spatial scales. Furthermore, the choice of sample grain varied widely among studies (e.g., 0.008 ha [Aresco and Guyer 1999]; area directly above burrows [measured by spherical densiometer; Boglioli et al. 2000]; mean at level of burn unit [0.8 7.3 ha; Ashton et al. 2008]). Because species responses are generally scale-dependent (Karl et al. 2000; Guisan and Thuiller 2005), it is not clear how

CATANO ET AL. Gopher Tortoise Burrow Abandonment 167 the choice of sample grain affects the development of models of tortoise habitat relationships. Therefore, studies are needed that explicitly address the spatial scale at which tortoises respond to vegetation structural change, particularly at larger extents and in relatively optimal habitats that can serve as reference conditions for management. However, field methods are time- and resource-intensive, producing an inherent trade-off in resolution (spatial extent of the sample area and spatial grain at which measurements are made; Wiens 1989). At larger extents, sample grain is generally limited to coarser metrics of structure for logistical reasons, often obscuring the mechanisms driving patterns or failing to detect them altogether. Alternatively, sampling at a finer grain often requires a reduction in extent and can limit generality. Light detection and ranging (lidar) remote sensing is a powerful technology for mapping the three-dimensional structure of vegetation at multiple spatial grains (fine to very course) and over broad spatial extents (Lefsky et al. 2002). To date, lidar applications have proven effective for modeling species habitat relationships for avian species (Goetz et al. 2010), invertebrates (Vierling et al. 2011), and mammals (Zhao et al. 2012). The multiscale inferences possible with lidar data have been demonstrated to be particularly promising for wildlife management (Vierling et al. 2008; Seavy et al. 2009). We present a novel use of lidar in a managed landscape to better understand the relationship between vegetation structure and habitat use by a threatened, burrowing reptile. Our objective was to demonstrate how the choice of sample grain can influence estimates of the relationship between canopy cover and gopher tortoise burrow abandonment. Then, we used measurements at the optimal spatial grain to develop a functional relationship between canopy cover and burrow abandonment. Our analysis of these data corroborated prior studies and provided a more rigorous quantitative interpretation of the process of burrow abandonment, ultimately demonstrating how such models can inform management strategies for gopher tortoises. METHODS Study Site. We conducted our study at Wekiwa Springs State Park (WSSP) in central Florida, USA (28u449500N, 91u299440W), because it contains a large area of managed longleaf pine (Pinus palustris) savanna (also called sandhill ) supporting gopher tortoise populations. Our study area was subdivided into contiguous burn units that have been maintained on a 3 5-yr prescribed-fire rotation for. 3 decades. Sandhill, the primary habitat of the gopher tortoise across its range (Diemer 1986; McCoy et al. 2006), is a subclimax system requiring frequent, low-severity fires to maintain a savanna-like state dominated by wiregrass (Aristida spp.) ground cover and a sparse canopy of longleaf pine (Myers 1990). WSSP is generally considered a reference site for well-managed sandhill habitat (S. Carr, pers. comm., September 2013). Therefore, we were able to focus on the responses of tortoises across this landscape to canopy cover, where burrow abandonment was less likely to be influenced by small patch size or other problems related to poor habitat quality (McCoy et al. 2013). Burrow Location and Classification. Assessment of tortoise burrow features is necessary to determine use and occupancy of habitat at large extents because gopher tortoises spend approximately 90% of their time underground (McCoy et al. 2006; Castellón et al. 2012; Guyer et al. 2012). The validity and repeatability of gopher tortoise population studies rests on the way burrows are classified (Smith et al. 2005 and references therein). In June 2011, we conducted total area surveys via systematic transects (with 10-m spacing) over approximately 450 ha of sandhill habitat and mapped tortoise burrow locations with 1-m accuracy using a handheld Global Positioning System receiver (Garmin GPSMAPH 60Cx). We followed the burrow classification of Hermann et al. (2002) and recorded burrows as either active or abandoned. Active burrows are elliptical at the entrance, approximating the shape of a tortoise s carapace; and they exhibit tortoise tracks, digging, or plastron scrapes (Fig. 1a). Abandoned burrows show no physical evidence of recent entry or exit by a tortoise, and may be partially or completely collapsed, or occluded by plant material (Fig. 1b; Smith et al. 2005). Arguably, the classification of burrows as either abandoned or active is less subjective than including a third intermediate category inactive as variously defined (Aresco and Guyer 1999; Castellón et al. 2012). Furthermore, the number of active burrows has been shown to correlate strongly (r 5 0.9) with the number of tortoises (Ashton et al. 2008). Generally, all occupied burrows will show conspicuous signs of activity by early April when tortoises regularly emerge from their burrows for basking or foraging (McRae et al. 1981). Because we censused burrows in June, during the active season but when burrow status is most static, we could conclude with a reasonable degree of certainty that burrows we classified as abandoned were unoccupied by resident adult tortoises (but see Mushinsky and Esman 1994). Lidar Data Acquisition and Processing. The National Center for Airborne Laser Mapping acquired the lidar data over WSSP 25 June 2011 specifically for this study. The data were collected using an Optec Gemini Airborne Laser Terrain Mapper (with 5 35-cm elevation accuracy, laser-pulse repetition frequency of 70 khz, and an average density of approximately 7 points m 22 ). The full validation report and data set are available at http:// opentopo.sdsc.edu/gridsphere/gridsphere (doi:10.5069/ G94M92GW). We created a 1-m-resolution digital elevation model from the lidar points classified as ground returns using FUSION Version 2.90. We used FUSION to calculate the number of nonground lidar returns occurring above and below 1 m in height in each 1-m 2 cell of the study area.

168 CHELONIAN CONSERVATION AND BIOLOGY, Volume 13, Number 2 2014 Figure 1. Photographs of burrow openings exemplify the 2 status categories used in the study: (a) active : sand at burrow opening compacted from tortoise plastron; (b) abandoned : burrow opening severely weathered and occluded with debris. Photos taken by C. Catano at Wekiwa Springs State Park (WSSP). Because anthropogenic structures were absent, all of the nonground lidar returns were reflected off of vegetation. All vegetation. 1 m in height restricts light and functionally serves as canopy to gopher tortoises. We calculated the percent canopy cover in each cell as (number of returns. 1 m in height/total number of returns) 3 100% (Fig. 2a). We used a relative measure of canopy cover to compensate for the spatial variability in the density of lidar returns intrinsic to the airborne acquisition process. Comparative studies have shown that lidar and field-based vegetation structure estimates produce unique, but comparable results (Sexton et al. 2009; Zellwegger et al. 2014). Statistical Analyses. We classified the status of each burrow as a binary response variable (1 5 abandoned, 0 5 active) and fit a series of logistic regression models of burrow status versus canopy cover with the glm function of R (R Version 2.11.1). Because glm uses maximum likelihood (ML) estimation and our sample size was large (n. 1500), our parameter estimates possessed the large-sample properties of ML estimators, including asymptotic normality and optimality (i.e., minimum variance). Also, to ensure that the burrows represented independent observations, we performed a spatial autocorrelation analysis using SAM Version 4.0. We used the block statistics spatial analyst tool in ESRI ArcGIS Version 10.0 (Redlands, CA) to calculate the mean percent canopy cover from the lidar data in circles of varying radii (0, 5, 10, 15 m) centered at each burrow entrance (Fig. 2b). We then used Akaike s Information Criterion (AIC) to select the logistic regression model with the best fit to the observed data. More parsimonious models will have lower AIC scores and can be ordered based on the difference in AIC between a given model and the model with the lowest AIC (Di 5 AICi 2 AICmin; Burnham and Anderson 2002). The best model was selected based on the model weight vi ~ e {1=2Di PR r{1 e {1=2Dr ; which represents the probability that the ith model is the best in the set (Anderson 2008). We also fitted models with time-since-fire (TSF) as a covariate because some studies have documented a relationship between fire and its effects on vegetation structure, and the probability of burrow abandonment by gopher tortoises (Aresco and Guyer 1999; Ashton et al. 2008). Finally, we performed a HosmerLemeshow goodness-of-fit test to assess the overall fit of our final logistic regression model. RESULTS We located and categorized 1573 burrows (48.15% active; 757 active, 816 abandoned). This burrow density (, 3.5 ha21) is comparable to highly suitable sites such as the Wade Tract in southern Georgia (Guyer et al. 2012). Moran s I-values were less than ± 0.05 at all distance classes, 4 km and oscillated randomly around zero, indicating the absence of autocorrelation in the spatial distribution of the status of burrows (Fortin and Dale 2005). Particularly, lack of larger positive Moran s I-values (i.e., approaching + 1), indicate that spatial clustering of active or abandoned burrows was minimal. Among the different sample grains (Fig. 3), mean canopy cover within a 10-m radius of the burrow entrance was the best predictor of burrow abandonment (Table 1). Including TSF as a covariate resulted in models with poorer fits (i.e., higher AIC scores than those without this predictor) in all cases. For example, the AIC for the TSF model with a 10-m radius was 2157.7 (compared with 2152.4 for the same model that excluded the TSF covariate). Our best model (10-m radius) was 6.9 AIC points lower than the next best (15-m radius), indicating substantial improve-

CATANO ET AL. Gopher Tortoise Burrow Abandonment 169 Figure 2. Map of a subset of the Wekiwa Springs State Park study area exemplifying (a) burrow distributions and activity status with percent canopy cover, and (b) percent canopy cover within varying radii surrounding a burrow. Each pixel is 1 m2. ment in model fit. The AIC model weight for this model (v1 5 0.968) indicates an approximately 97% probability that this model is the best in the set (31 times more likely than the next best at the 15-m radius; v2 5 0.031). Based on these results, we used mean percent canopy cover within a 10-m radius (, 314 m2) of each burrow entrance, without the TSF covariate, to predict the mean probability of a burrow being classified as abandoned. The probability of abandonment increased in a positive, nearly linear manner from a mean of 0.389 (95% CI: 0.336 0.441) with 0% relative canopy cover to a mean of 0.775 (95% CI: 0.698 0.853) with 100% relative canopy cover (Fig. 4). The Hosmer-Lemeshow goodness-of-fit test failed to reject the null hypothesis that this final logistic regression model was a good fit to the observed data (x218 5 20.117, p 5 0.3263). DISCUSSION Our study builds upon previous studies of gopher tortoise response to vegetation structure by demonstrating the scale-dependent nature of this relationship. We found that the relationship between canopy cover and the probability that a burrow was abandoned varied considerably with the size of the sample grain. Abandonment was most associated with canopy cover within a 10-m radius (314-m2) of individual burrows. Each percent increase in canopy cover at this grain increased the probability that a burrow was abandoned by 1.7%. At the 5-m (79-m2) radius, the effect size was reduced by approximately 55%; this was a nontrivial difference. Estimates of canopy cover at the smallest grain (1 m2 directly above the burrow) were too noisy to detect an effect on abandonment altogether (Fig. 3), even with our large sample size. Although the predictions derived from the 314- and 707-m2 grains were qualitatively similar, there is little justification for the greater than twofold difference in sampling effort that would be required to produce a less effective model at the larger grain. Among studies that quantified the effect of canopy cover on burrow abandonment, explicit consideration of sample grain prior to testing such effects has not been described. Furthermore, the sample grains ranged from 1 m2 to. 1 ha, with unknown implications for the estimation of

170 CHELONIAN CONSERVATION AND BIOLOGY, Volume 13, Number 2 2014 Figure 3. Percent canopy cover estimated from lidar data at increasing larger sample grains surrounding abandoned (AB) and active (A) burrows. The sample radius (top margin) is the radius from each tortoise burrow to the perimeter of the sample area. The horizontal bar is the median, the box encompasses the upper and lower quartiles, dashed lines represent the nominal range of associated canopy cover, and circles are canopy estimates outside this range. these effects. The 314-m 2 sample grain is biologically reasonable because daily activities (e.g., foraging and basking) are generally within the immediate vicinity of the burrow entrance and rarely much greater than 10 m (McRae et al. 1981); but see discussion by Ashton and Ashton (2008). Dense canopy cover within this area reduces sunlight reaching the forest floor, ultimately restricting energy inputs required for thermoregulation, clutch development, and growth of food resources (Landers and Speake 1980; McRae et al. 1981; Boglioli et al. 2000). In large contiguous, well-managed habitats, gopher tortoises will abandon their burrows when local habitat features become unsuitable, leaving the area to search for more suitable conditions. A subset of abandoned burrows may be reused by tortoises at some future date; however, in isolated or fire-suppressed habitats, prolonged or permanent abandonment could result (Boglioli et al. 2000; McCoy et al. 2006). In addition to choosing an appropriate sample grain, the development of a functional relationship between canopy cover and burrow abandonment allows us to interpret the Figure 4. Probability that the gopher tortoise burrow observed was Abandoned as a function of canopy cover within a 10-m radius of the burrow entrance. Solid line is mean probability; dashed lines represent 95% confidence intervals. Note that Abandoned represents the static condition of the burrow when it was censused, but not an observed change in the burrow during the study. nature of this effect. The relatively linear relationship demonstrates the lack of a threshold in abandonment (i.e., large nonlinear change with a small increase in canopy cover). Therefore, restoration to more suitable canopycover conditions may have high potential to recover tortoise populations in landscapes with adequate connectivity and recruitment. Although this multigrain, lidar-based methodology yielded important insights into modeling tortoise burrow abandonment in response to vegetation structure, the specific results have limitations. We currently lack a complete understanding of the myriad of social and physical factors that lead to burrow abandonment by the gopher tortoise (Guyer et al. 2012). The number of tortoise burrows always exceeds the number of resident tortoises; hence, a portion of the tortoise burrows will be abandoned as a normal outcome of tortoise behavior. Reasonable interpretations have been offered to explain patterns of abandonment for breeding-age females, but are lacking for breeding-age males and nonbreeding-age males and females. Eubanks et al. (2003) showed that in 1 yr, almost half of the females that apparently nested on or near the aprons of their burrows abandoned them but returned in the autumn to the same burrows for Table 1. Logistic regression models of gopher tortoise burrow abandonment status versus relative percentage of canopy cover at varying radii from burrow entrances. Akaike Information Criterion (AIC) is used to evaluate the models, where model weight (v i ) represents the probability of the model being best in the set. Note: The null is an intercept-only model. n/a 5 not applicable. Radius from burrow entrance (m) Parameter estimate SE p-value Model AIC score v i Null n/a n/a n/a 2179.0 0.000 0 0.0014 0.0014 0.321 2180.0 0.000 5 0.0096 0.0025, 0.001 2166.1 0.001 10 0.0169 0.0032, 0.001 2152.4 0.968 15 0.0161 0.0035, 0.001 2159.3 0.031

CATANO ET AL. Gopher Tortoise Burrow Abandonment 171 overwintering. This behavior pattern could be associated with reduced egg loss to predators at burrows showing no recent tortoise activity. It is not known whether this pattern is ubiquitous or how it varies with context; however, if present, it would not affect the shape of the functional relationship we documented but may decrease its magnitude. Also, Ashton and Ashton (2008) proposed that depletion of preferred food plants near burrows may also lead to abandonment. Ground cover less than 1 m in height falls within the error of the lidar measurements, therefore precluding quantification; yet, we observed that canopy cover and herbaceous ground cover were inversely proportional (r. 0.7; data not shown). In other longleaf pine habitats, the effects of suitable ground cover on burrow abandonment have been mixed. A telemetry study by Castellón et al. (2012) documented strong burrow fidelity by female tortoises even with suboptimum food availability. In low-canopy or nonforested systems (such as dry prairies or ruderal habitat), abandonment may be more linked to food availability or disturbance (Waddle et al. 2006). Also, we modeled this relationship over a small range of spatial grains (1 707 m 2 ), thereby isolating local effects. Future efforts would greatly benefit by incorporating a much broader range of spatial scales to understand both local-scale (e.g., canopy closure) and landscape-scale (e.g., hardwood stand configuration and structural heterogeneity) drivers of abandonment. Last, the factors influencing burrow abandonment and the nature of this relationship likely vary among habitat types (e.g., scrub, flatwoods, and sandhill) and should be further explored. Despite these limitations, we show how the relationship between burrow abandonment and canopy closure is significantly affected by the researcher s choice of sample grain size. In the sandhill habitat in this study, this relationship was positive and remained linear over the range of conditions observed. These results provide an independent, robust confirmation of the accumulating evidence that in forested systems, which comprise the majority of gopher tortoise habitat, the primary driver of abandonment is linked to vegetation structural changes (Aresco and Guyer 1999; Yager et al. 2007; Guyer et al. 2012). Management Considerations. The selection of an appropriate spatial grain and the development of functional relationships can substantially improve predictive tortoise-habitat models. Such models would allow managers to quantitatively evaluate how fire prescriptions or physical manipulations (e.g., timber sales) that alter vegetation structure could impact resident populations. Also, identification of canopy cover as a powerful driver of habitat use by gopher tortoises offers management options, even if land managers do not have the benefit of lidar imagery and detailed spatial data on tortoise burrows. Whether remotely sensed or field-based, regular reconnaissance with attention to midcanopy and treecanopy development should signal conditions changing from favorable to nonfavorable for tortoise populations. Actions in response to closing canopies and increasing vegetation densities will likely be idiosyncratic among lands and agencies, but could include selective cutting of trees (Noel et al. 1998) and regular application of growing-season prescribed fires (Yager et al. 2007; Ashton et al. 2008; Rickey et al. 2013). Restoration to old-growth like conditions with open canopies would be beneficial to gopher tortoise populations and other biotic components of pine savannas across the southeastern United States (Noel et al. 1998). ACKNOWLEDGMENTS We thank the Gopher Tortoise Council for providing funds and the National Center for Airborne Laser Mapping for collecting the lidar data. We also thank D. Jenkins for review comments, L.H. Bhatti and J. Abelson for assistance with fieldwork, and staff at Wekiwa Springs State Park, including A. Bard and P. Lammardo. We are grateful for the very helpful comments by the editors and 2 anonymous reviewers. The welfare of all animals was ensured in accordance with the University of Central Florida Institutional Animal Care and Use Committee (approval no. 10-39W), Florida Department of Environmental Protection (permit no. 04071113), and Florida Fish and Wildlife Conservation Commission (permit no. LSSC-11-00033). LITERATURE CITED ANDERSON, D.R. 2008. Model Based Inference in the Life Sciences: A Primer on Evidence. First edition. New York: Springer, 148 pp. ARESCO, M.J. AND GUYER, C. 1999. 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