DOI: 10.5061/dryad.cc2fqz6gk
title: Habitat associations of six-lined racerunners in longleaf pine managed with a short fire rotation for northern bobwhites

description: The longleaf pine (Pinus palustris) savanna ecosystem is an imperiled,
 fire-dominated community that supports exceptionally high levels of
 species richness and endemism. Area of this community has declined by more
 than 95% due to unsustainable logging, fire suppression, and changes in
 land-use practices. In recent decades, efforts to restore fire-dominated
 communities like longleaf pine savanna have gained popularity, especially
 in light of benefits to charismatic species like the northern bobwhite
 (Colinus virginianus). Although reptiles are important members of this
 ecological community, far less information exists as to how this group
 responds to longleaf pine management, especially when game bird
 conservation is a primary management focus. Although bobwhite management
 in these systems is mostly synonymous with longleaf pine restoration,
 additional conservation practices aimed at game birds (e.g., promoting
 fallow fields, supplemental feeding, meso-carnivore control, cross
 sectional mowing, etc.) might affect the extent to which squamates benefit
 from habitat management. To better understand how squamate reptiles may
 benefit from longleaf pine savanna managed for northern bobwhites, we
 surveyed for six-lined racerunners (Aspidoscelis sexlineata) across a
 large, contiguous tract of longleaf pine with varied land cover
 characteristics, managed to maximize the conservation of northern
 bobwhites. Racerunner detection probability on transect surveys was low
 ( = 0.23) however, occupancy probability, was relatively high
 ( = 0.60) across the property and driven by percent open ground
 (positive; 25m scale), percent grass cover (negative; 25m scale), and
 percent wetland (negative; 100m scale). Our findings support those of past
 studies about six-lined racerunners in longleaf pine savannas suggesting
 the species thrives in the context of a short fire rotation (e.g., 2-3
 years), even when game bird management is a primary objective of
 conservation efforts. Racerunners may also specialize on microhabitats
 (e.g., upland areas with relatively high bare ground cover) that occur
 most frequently in recently burned portions of bobwhite management units.
description: <em>Study area and sampling locations. </em>We
 studied six-lined racerunner ecology in Brunswick County, North Carolina.
 The study area falls into the southeastern Coastal Plain which is an area
 characterized by a subtropical climate, and nutrient-poor, well-drained
 soils (Peet and Allard 1993). Within Brunswick County, we surveyed a 1,850
 ha contiguous stand of longleaf pine savanna managed for hunted game
 species, especially northern bobwhites. Although the dominant land cover
 was mature longleaf pine savanna, there were other cover types naturally
 interspersed: wetlands, early successional longleaf pine, dirt
 roads/firebreaks, and open water (ponds, bays, etc.). There were also
 fallow fields throughout the property (usually 1-2 ha in size) that were
 sown with partridge-pea (<em>Chamaecrista fasciculata</em>)
 and/or ragweed (<em>Ambrosia artemisiifolia</em>) to support
 northern bobwhite brood-rearing. In addition to fallow field cultivation,
 the property implemented other conservation practices to support quail:
 supplemental feeding (~3 bushels/ac/year of wheat and sorghum), year-round
 mesocarnivore trapping, and prescribed fire with a rotation cycle of every
 2 years (half the property burned each year).
 <em>Six-lined racerunner surveys</em>. To establish
 sampling locations, we gridded the study area into 100 cells with a
 sampling point plotted in the center of each. This approach generated
 sampling locations that were each ~400m from its nearest neighbor. We used
 this systematic sampling approach to maximize coverage of the study area
 while maintaining the spatial independence of survey locations. Each of
 these 100 sampling locations became the centroid for a visual transect for
 six-lined racerunners. We conducted repeat surveys along 50m transects,
 oriented south to north, centered over the survey points, from 3 July
 through 22 August, 2023, between 0630 and 2300. We visited each of the 100
 points three times in random order (100 sites x 3 visits = 300 total
 surveys). We did not restrict surveys to particular weather conditions
 within this window because we were interested in understanding the factors
 that might explain variation in detection probability. Prior to each
 survey, we recorded survey covariates: cloud cover (estimated to the
 nearest 25%), precipitation (none, mist, light rain, heavy rain),
 temperature, Beaufort Wind Index, ordinal date, and time of day. We walked
 each transect for two minutes (<em>i.e</em>., 25 meters per
 minute) and recorded whether six-lined racerunners were detected during
 each survey (1/0). Because we visited each site three times, we generated
 a detection history of 1s and 0s that served as our response data for
 occupancy models (MacKenzie et al. 2002), described below.
 <em>Vegetation surveys</em>. We sampled local
 vegetation (within 25m) of each transect (hereafter, “microhabitat”) once
 between 30 July and 12 August. We estimated percent cover of vegetation
 strata and vegetation within 25m of each site using the same transect as
 surveyed for racerunners, sampled with an ocular tube (James and Shugart
 1970) and a Nudds board (Nudds 1977), respectively. We estimated percent
 cover using an ocular tube at 10 “stops” per site, one at each of the
 following distances along the transect: 5m, 10m, 15m, 20m, and 25m, for
 north and south. At each “stop”, we recorded: bare ground, leaf litter,
 coarse woody debris, moss, grass, forb, shrub, fern, sapling, canopy. Bare
 ground was defined as un-vegetated soil. Leaf litter was defined as any
 organic debris smaller than 10cm. Coarse woody debris was any organic
 debris larger than 10cm in diameter. We considered moss to be any live,
 non-vascular plant. Grasses were any graminoid. Forbs were any
 broad-leafed dicotyledon. We defined a shrub as a woody plant with stems
 branching above ground. Ferns were seedless vascular plants. Saplings were
 any tree less than 10cm in diameter-at-breast-height (DBH); trees larger
 than 10cm in DBH were considered canopy trees (McNeil et al. 2018).
 Finally, we assessed vegetation density via a Nudds Board (2m tall with 20
 squares, each 20x20cm in size), placed at point center. We read the Nudds
 board from 10 meters north and south of the board from a height of 1m.
 Each reading consisted of a visual assessment of the number of squares
 (out of 20 possible) obscured at least halfway by vegetation. We averaged
 the two Nudds board readings at each site and the number of “hits” for
 each vegetation stratum (/10 stops) for percent cover of each microhabitat
 stratum. <em>Remote sensed data. </em>To
 assess habitat at a broader spatial extent, we extracted metrics from a
 land cover map (unpl. data) developed in qGIS (QGIS.org). Relevant land
 cover values included: 1. mature pine, 2. open grassland (including grassy
 areas with short, sapling pines), 3. wetland, 4. brood field, and 5. open
 water. We generated rasters that depicted ‘distance to nearest…’ for each
 of these covariates with the raster package in R (Hijmans 2023). We also
 generated distance to nearest road. In addition to these ‘distance to
 nearest…’ variables, we calculated the “percent cover within 100m” for
 each variable (except ‘road’ because it was a line vector file). Finally,
 we extracted the normalized difference vegetation index (NDVI) at each
 point using a raster developed for the property in 2019, obtained from the
 property manager.  <em>Analyses. </em>We
 created single-season occupancy models in the R package unmarked
 (MacKenzie et al. 2002, Fiske and Chandler 2011, R Core Team 2023) to
 assess factors influencing six-lined racerunner occupancy. We only
 specified univariate models in this study (no additive covariates) to
 avoid over-parameterizing our models. We began by fitting a ‘detection’
 model set where we held occupancy probability constant and specified
 models with each of the following survey covariates: temperature at the
 time of survey, ordinal date, Beaufort Wind Index, minutes since sunrise,
 cloud cover, and two habitat covariates that might explain observers’
 ability to see racerunners: NDVI and vegetation density. We also included
 quadratic terms for temperature and minutes since sunrise because these
 variables might exhibit a nonlinear trend. We also included a null
 (intercept only) detection model for comparison:
 <em>p</em>(.),<em>ᴪ</em>(.) in all model sets. We
 ranked all models in descending order of Akaike’s Information Criterion
 adjusted for small sample size (AIC<sub>c</sub>; Akaike 1973,
 Burnham and Anderson 2002). We also assessed the biological importance of
 covariates by examining whether <em>β</em> 95% confidence
 intervals overlapped zero, interpreting those that did so as having weak
 effects on detection/occupancy (Arnold 2010). After identifying a top
 detection model, we incorporated its detection terms into all of our
 following occupancy models. This second and final model set included
 univariate models for each of the microhabitat variables and each of the
 landscape variables. Finally, we also assessed model goodness-of-fit by
 calculating  via the MacKenzie and Bailey goodness-of-fit test
 (MacKenzie and Bailey 2004, Kéry and Royle 2015). If our top model was
 overdispersed ( &gt; 1.0), we adjusted our model ranking
 using Quasi AIC<sub>c</sub>
 (QAIC<sub>c</sub>). 
description: # Habitat associations of six-lined racerunners in longleaf pine managed
 with a short fire rotation for northern bobwhites
 [https://doi.org/10.5061/dryad.cc2fqz6gk](https://doi.org/10.5061/dryad.cc2fqz6gk) ## Description of the data and file structure This "Samek_etal_2024_racerunners_readme.txt" file was generated on 11 November 2024 by Darin J. McNeil Jr. GENERAL INFORMATION 1\. Title of Dataset: Habitat associations of six-lined racerunners in longleaf pine managed with a short fire rotation for northern bobwhites 2\. Author Information ``` A. Corresponding Author Contact Information Name: Darin James McNeil Jr. Institution: University of Kentucky Address: 104 T.P. Cooper Building Lexington, KY 40546-0073 Email: darin.j.mcneil@uky.edu ``` 3\. Date of data collection (single date, range, approximate date): July 2023 through August 2023 4\. Geographic location of data collection: Brunswick County, North Carolina, United States of America 5\. Information about funding sources that supported the collection of the data: This project was supported by McIntire-Stennis Capacity Grant #KY009043 SHARING/ACCESS INFORMATION 1\. Licenses/restrictions placed on the data: None to report 2\. Links to publications that cite or use the data: Samek, I. W., S. J. Price, T. M. Terhune II, and D. J. McNeil. 2024. Habitat associations of six-lined racerunners in longleaf pine managed with a short fire rotation for northern bobwhites. Ecosphere. 3\. Links to other publicly accessible locations of the data: None to report 4\. Links/relationships to ancillary data sets: None to report 5\. Was data derived from another source? yes/no ``` A. If yes, list source(s): ``` No 6\. Recommended citation for this dataset: Samek, Isaiah et al. (2024), Habitat associations of six-lined racerunners in longleaf pine managed with a short fire rotation for northern bobwhites, Dryad, Dataset, [DOI] DATA & FILE OVERVIEW 1\. File List: File: SLRA_analysis_data_fordryad.csv Description: This file contains all data needed to replicate analyses presented in Samek et al. 2024 2\. Relationship between files, if important: Not applicable 3\. Additional related data collected that was not included in the current data package: None 4\. Are there multiple versions of the dataset? No ``` A. If yes, name of file(s) that was updated: i. Why was the file updated? Not applicable ii. When was the file updated? Not applicable ``` METHODOLOGICAL INFORMATION 1\. Description of methods used for collection/generation of data: See the following sections from Samek et al. (2024), Methods: \&gt; Study area and sampling locations \&gt; Six-lined racerunner surveys \&gt; Vegetation surveys \&gt; Remote sensed data 2\. Methods for processing the data: See the following sections from Samek et al. (2024), Methods: \&gt; Analyses 3\. Instrument- or software-specific information needed to interpret the data: See the following sections from Samek et al. (2024), Methods: \&gt; Study area and sampling locations \&gt; Six-lined racerunner surveys \&gt; Vegetation surveys \&gt; Remote sensed data \&gt; Analyses 4\. Standards and calibration information, if appropriate: None to repoort 5\. Environmental/experimental conditions: See Samek et al. (2024), Methods. 6\. Describe any quality-assurance procedures performed on the data: See Samek et al. (2024), Methods. 7\. People involved with sample collection, processing, analysis and/or submission: IWS, SJP, TMT, and DJM conceived the ideas and designed the study; IWS collected the data; DJM analyzed the data; DJM and TMT secured funding for the study; All authors wrote and edited the paper and gave final approval for publication. DATA-SPECIFIC INFORMATION FOR: SLRA_analysis_data_fordryad.csv ### Files and variables #### File: SLRA\_analysis\_data\_fordryad.csv **Description:**  ##### Variables * name: transect; description: category; transect identity (i.e., site) * name: obs1; description: binary; detection of racerunners during occasion 1 (1 = detected, 0 = not detected) * name: obs2; description: binary; detection of racerunners during occasion 2 (1 = detected, 0 = not detected) * name: obs3; description: binary; detection of racerunners during occasion 3 (1 = detected, 0 = not detected) * name: wind1; description: category; Beaufort wind index on occasion 1 * name: wind2; description: category; Beaufort wind index on occasion 2 * name: wind3; description: category; Beaufort wind index on occasion 3 * name: cloud1; description: continuous; percent cloud cover on occasion 1 * name: cloud2; description: continuous; percent cloud cover on occasion 2 * name: cloud3; description: continuous; percent cloud cover on occasion 3 * name: ordinal1; description: continuous; ordinal date on occasion 1 * name: ordinal2; description: continuous; ordinal date on occasion 2 * name: ordinal3; description: continuous; ordinal date on occasion 3 * name: mssr1; description: continuous; time, expressed in "minutes since sunrise" on occasion 1 * name: mssr2; description: continuous; time, expressed in "minutes since sunrise" on occasion 2 * name: mssr3; description: continuous; time, expressed in "minutes since sunrise" on occasion 3 * name: temp1; description: continuous; temperature (degrees F) on occasion 1 * name: temp2; description: continuous; temperature (degrees F) on occasion 2 * name: temp3; description: continuous; temperature (degrees F) on occasion 3 * name: ot_bare; description: ocular tube reading at point averaged for all bare ground * name: ot_wat; description: ocular tube reading at point averaged for all water * name: ot_lit; description: ocular tube reading at point averaged for all leaf litter * name: ot_cwd; description: ocular tube reading at point averaged for all coarse woody debris * name: ot_moss; description: ocular tube reading at point averaged for all moss * name: ot_grass; description: ocular tube reading at point averaged for all grass * name: ot_forb; description: ocular tube reading at point averaged for all forbs * name: ot_fern; description: ocular tube reading at point averaged for all ferns * name: ot_shrub; description: ocular tube reading at point averaged for all shrubs * name: ot_sap; description: ocular tube reading at point averaged for all saplings * name: ot_can; description: ocular tube reading at point averaged for all canopy * name: nudds; description: average Nudd's board reading at point location * name: ndvi; description: normalized difference vegetation index at survey point * name: perc_mpine; description: percent cover, within 100m, of mature pine * name: perc_grassy; description: percent cover, within 100m, of grassy * name: perc_decid; description: percent cover, within 100m, of deciduous cover * name: perc_bf; description: percent cover, within 100m, of brood fields (i.e., fallow fields) * name: perc_water; description: percent cover, within 100m, of open water * name: DTN_road; description: distance-to-nearest road (m) * name: DTN_mpine; description: distance-to-nearest mature pine cover (m) * name: DTN_grassy; description: distance-to-nearest grassy cover (m) * name: DTN_decid; description: distance-to-nearest deciduous cover (m) * name: DTN_bf; description: distance-to-nearest brood/fallow field cover (m) * name: DTN_water; description: distance-to-nearest open water (m) * name: perc_burn; description: percent cover, within 100m, of land burned within the past 1 year * name: burn_stat; description: binary; whether (1) or not (0) a location had been burned within the past 1 year ## Code/software No code available ## Access information Other publicly accessible locations of the data: * None Data was derived from the following sources: * See Methods