DOI: 10.5061/dryad.7m0cfxq1r
title: Data from: Nine-banded Armadillo (Dasypus novemcinctus) occupancy and density across an urban to rural gradient

description: The nine-banded Armadillo (Dasypus novemcinctus) is the only species of
 Armadillo in the United States and alters ecosystems by excavating
 extensive burrows used by many other wildlife species. Relatively little
 is known about its habitat use or population densities, particularly in
 developed areas, which may be key to facilitating its range expansion. We
 evaluated Armadillo occupancy and density in relation to anthropogenic and
 landcover variables in the Ozark Mountains of Arkansas along an urban to
 rural gradient. Armadillo detection probability was best predicted by
 temperature (positively) and precipitation (negatively). Contrary to
 expectations, occupancy probability of Armadillos was best predicted by
 slope (negatively) and elevation (positively) rather than any landcover or
 anthropogenic variables. Armadillo density varied considerably between
 sites (ranging from a mean of 4.88 – 46.20 Armadillos per km2) but was not
 associated with any environmental or anthropogenic variables.
description: Site Selection Our study took place in
 Northwest Arkansas, USA, in the greater Fayetteville metropolitan area. We
 deployed trail cameras (Spypoint Force Dark (Spypoint Inc, Victoriaville,
 Quebec, Canada) and Browning Strikeforce XD cameras (Browning, Morgan,
 Utah, USA) over the course of two winter seasons, December 2020-March
 2021, and November 2021-March 2022. We sampled 10 study sites in year one,
 and 12 study sites in year two. All study sites were located in the Ozark
 Mountains ecoregion in Northwest Arkansas. Sites were all Oak Hickory
 dominated hardwood forests at similar elevation (213.6 – 541 m). Devils
 Eyebrow and ONSC are public natural areas managed by the Arkansas Natural
 heritage Commission (ANHC). Devil’s Den and Hobbs are managed by the
 Arkansas state park system. Markham Woods (Markham), Ninestone Land Trust
 (Ninestone) and Forbes, are all privately owned, though Markham has a
 publicly accessible trail system throughout the property. Lake Sequoyah,
 Mt. Sequoyah Woods, Kessler Mountain, Lake Fayetteville, and Millsaps
 Mountain are all city parks and managed by the city of Fayetteville.
 Lastly, both Weddington and White Rock are natural areas within Ozark
 National Forest and managed by the U.S. Forest Service.  We sampled 5
 sites in both years of the study including Devils Eyebrow, Markham Hill,
 Sequoyah Woods, Ozark Natural Science Center (ONSC), and Kessler Mountain.
 We chose our study sites to represent a gradient of human development,
 based primarily on Anthropogenic noise values (Buxton et al. 2017, Mennitt
 and Fristrup 2016). We chose open spaces that were large enough to
 accommodate camera trap research, as well as representing an array of
 anthropogenic noise values. Since anthropogenic noise is able to permeate
 into natural areas within the urban interface, introducing human
 disturbance that may not be detected by other layers such as impervious
 surface and housing unit density (Buxton et al. 2017), we used dB values
 for each site as an indicator of the level of urbanization.
 Camera Placement We sampled ten study sites in
 the first winter of the study. At each of the 10 study sites, we deployed
 anywhere between 5 and 15 cameras. Larger study areas received more
 cameras than smaller sites because all cameras were deployed a minimum of
 150m between one another. We avoided placing cameras on roads, trails, and
 water sources to artificially bias wildlife detections. We also avoided
 placing cameras within 15m of trails to avoid detecting humans.
 At each of the 12 study areas we surveyed in the second winter
 season, we deployed 12 to 30 cameras. At each study site, we used ArcGIS
 Pro (Esri Inc, Redlands, CA) to delineate the trail systems and then
 created a 150m buffer on each side of the trail. We then created random
 points within these buffered areas to decide where to deploy cameras. Each
 random point had to occur within the buffered areas and be a minimum of
 150m from the next nearest camera point, thus the number of cameras at
 each site varied based upon site size. We placed all cameras within 50m of
 the random points to ensure that cameras were deployed on safe topography
 and with a clear field of view, though cameras were not set in locations
 that would have increased animal detections (game trails, water sources,
 burrows etc.). Cameras were rotated between sites after 5 or 10 week
 intervals to allow us to maximize camera locations with a limited number
 of trail cameras available to us. Sites with more than 25 cameras were
 active for 5 consecutive weeks while sites with fewer than 25 cameras were
 active for 10 consecutive weeks. We placed all cameras on trees or tripods
 50cm above ground and at least 15m from trails and roads. We set cameras
 to take a burst of three photos when triggered. We used Timelapse 2.0
 software (Greenberg et al. 2019) to extract metadata (date and time)
 associated with all animal detections. We manually identified all species
 occurring in photographs and counted the number of individuals present.
 Because density estimation requires the calculation of detection rates
 (number of Armadillo detections divided by the total sampling period), we
 wanted to reduce double counting individuals. Therefore, we grouped
 photographs of Armadillos into “episodes” of 5 minutes in length to reduce
 double counting individuals that repeatedly triggered cameras (DeGregorio
 et al. 2021, Meek et al. 2014). A 5 min threshold is relatively
 conservative with evidence that even 1-minute episodes adequately reduces
 double counting (Meek et al. 2014). Landcover
 Covariates To evaluate occupancy and density of
 Armadillos based on environmental and anthropogenic variables, we used
 ArcGIS Pro to extract variables from 500m buffers placed around each
 camera (Table 2). This spatial scale has been shown to hold biological
 meaning for Armadillos and similarly sized species (DeGregorio et al.
 2021, Fidino et al. 2016, Gallo et al. 2017, Magle et al. 2016). At each
 camera, we extracted elevation, slope, and aspect from the base ArcGIS Pro
 map. We extracted maximum housing unit density (HUD) using the SILVIS
 housing layer (Radeloff et al. 2018, Table 2). We extracted anthropogenic
 noise from the layer created by Mennitt and Fristrup (2016, Buxton et al.
 2017, Table 2) and used the “L50” anthropogenic sound level estimate,
 which was calculated by taking the difference between predicted
 environmental noise and the calculated noise level. Therefore, we assume
 that higher levels of L50 sound corresponded to higher human presence and
 activity (i.e. voices, vehicles, and other sources of anthropogenic noise;
 Mennitt and Fristrup 2016). We derived the area of developed open
 landcover, forest area, and distance to forest edge from the 2019 National
 Land Cover Database (NLDC, Dewitz 2021, Table 2). Developed open landcover
 refers to open spaces with less than 20% impervious surface such as
 residential lawns, cemeteries, golf courses, and parks and has been shown
 to be important for medium-sized mammals (Gallo et al. 2017, Poessel et
 al. 2012). Forest area was calculated by combing all forest types within
 the NLCD layer (deciduous forest, mixed forest, coniferous forest), and
 summarizing the total area (km2) within the 500m buffer. Distance to
 forest edge was derived by creating a 30m buffer on each side of all
 forest boundaries and calculating the distance from each camera to the
 nearest forest edge. We calculated distance to water by combining the
 waterbody and flowline features in the National Hydrogeography Dataset
 (U.S. Geological Survey) for the state of Arkansas to capture both
 permanent and ephemeral water sources that may be important to wildlife.
 We measured the distance to water and distance to forest edge using the
 geoprocessing tool “near” in ArcGIS Pro which calculates the Euclidean
 distance between a point and the nearest feature. We extracted Average
 Daily Traffic (ADT) from the Arkansas Department of Transportation
 database (Arkansas GIS Office). The maximum value for ADT was calculated
 using the Summarize Within tool in ArcGIS Pro. We
 tested for correlation between all covariates using a Spearman correlation
 matrix and removed any variable with correlation greater than 0.6.
 Pairwise comparisons between distance to roads and HUD and between
 distance to forest edge and forest area were both correlated above 0.6;
 therefore, we dropped distance to roads and distance to forest edge from
 analyses as we predicted that HUD and forest area would have larger
 biological impacts on our focal species (Kretser et al. 2008).
 Occupancy Analysis In order to better
 understand habitat associations while accounting for imperfect detection
 of Armadillos, we used occupancy modeling (Mackenzie et al. 2002). We used
 a single-species, single-season occupancy model (Mackenzie et al. 2002)
 even though we had two years of survey data at 5 of the study sites. We
 chose to do this rather than using a multi-season dynamic occupancy model
 because most sites were not sampled during both years of the study. Even
 for sites that were sampled in both years, cameras were not placed in the
 same locations each year. We therefore combined all sampling into one
 single-season model and created unique site by year combinations as our
 sampling locations and we used year as a covariate for analysis to explore
 changes in occupancy associated with the year of study.
 For each sampling location, we created a detection history with 7
 day sampling periods, allowing presence/absence data to be recorded at
 each site for each week of the study. This allowed for 16 survey periods
 between 01 December 2020, and 11 March 2021 and 22 survey periods between
 01 November 2021 and 24 March 2022. We treated each camera as a unique
 survey site, resulting in a total of 352 sites. Because not all cameras
 were deployed at the same time and for the same length of time, we used a
 staggered entry approach. We used a multi-stage fitting
 approach in which we used Akaike’s Information Criterion (AIC) to select
 for the best detection covariate. We modeled the survey period (to allow
 detection to vary over time), year (to evaluate detection across the two
 years of the study), weekly mean precipitation (to evaluate if
 precipitation influenced Armadillo activity and thus detection), and
 weekly mean temperature (to evaluate if temperature influenced Armadillo
 activity and thus detection) as covariates for detection against null
 occupancy parameters and selected the top covariate model with lowest AIC
 score. The top-ranked detection covariate(s) was then used in all
 subsequent analyses of occupancy. We acquired temperature and
 precipitation data from the NOAA weather station closest to each site for
 each detection date. For occupancy covariates, we used
 distance to the nearest water source, distance to the nearest road,
 elevation, slope, aspect, maximum ADT, maximum anthropogenic noise,
 developed open space, area of forest, and maximum housing unit density. We
 then evaluated all single variable models using an AIC approach with an
 apriori cutoff of 2 ∆AIC (Burnham and Anderson 2002).
 Density Estimation To calculate Armadillo
 density at each of the study sites, we used the Random Encounter Model
 (REM). The REM was developed to estimate density of unmarked animals
 through camera trap data (Rowcliffe et al. 2008). The three assumptions of
 the REM are: 1) that animals move randomly throughout their environment
 and thus cameras are not set on any features that might increase their
 detection probability (e.g., trails, roads, bait etc.), 2) detection
 episodes are of individual animals, and 3) that the study population is
 closed (Rowcliffe et al. 2008). We used the REM equation to calculate
 Armadillo density at each camera using Microsoft Excel (Microsoft
 corporation).  In the REM, the y represents the total
 detections of Armadillo at each camera. Total trap nights in hours (the
 measure of trapping effort) is represented by t. V refers to the day range
 (how far an individual travels in a 24-hour period) of the Armadillo. We
 derived a mean day range for Armadillo from day ranges reported in the
 literature (Table 3). Detection radius (r) and detection angle (θ) were
 measured at each camera in the field through walk tests. The walk tests
 involved walking directly towards each camera to calculate the detection
 radius and from each side at 5m from the camera to calculate the detection
 angle in degrees. Detection was determined by whether or not the detection
 light on the camera was triggered during the walk test. We then converted
 the detection angle to radians for density calculations (Caravaggi et al.
 2015, Rowcliffe et al. 2008). We were not able to calculate the detection
 angle and radius at 14 of the cameras due to camera malfunction (no
 detection light during walk test), and so we used the average detection
 angle for the given camera model (Schaus et al. 2020).
 We evaluated if Armadillo density correlated to anthropogenic or
 landcover variables. We used linear models in R, using the packages “lme4”
 and “AIC modavg”. We only included data from the second year of sampling
 in our density calculations as detection radius and angle were not
 collected during the first year of the study. We evaluated Armadillo
 density against HUD, anthropogenic noise, distance to water, forest area,
 development, and ADT. We modeled all single- and two-way combinations of
 these variables. However, we did not include ADT and HUD in the same
 models due to high correlation between these covariates. Thus, we
 evaluated 22 candidate models including the null and global models. In
 each model, we included site as a random effect. We considered models
 within 2 ∆AIC to be competitive (Burnham and Anderson 2002).
description: # Nine-banded Armadillo (Dasypus novemcinctus) occupancy and density
 across an urban to rural gradient Data Description McTigue and DeGregorio
 Armadillo Occupancy Data 2023: This dataset contains wildlife detections
 from game cameras at 17 study sites in Northwest Arkansas, ranging from
 urban green space sites within downtown Fayetteville to more natural sites
 within the Ozark Mountain Ecoregion. Each row represents a wildlife
 detection on motion-triggered game camera. * Camera ID: Cameras at each
 site were given a unique ID. The ID is a combination of the site name and
 camera number. The number was assigned to each camera before deployment
 based on camera inventory. * Date of Detection: The date the camera
 detected this particular wildlife occurrence. * Study Year: Value of 1 or
 2 indicating which year of the study the data was recorded. Study year 1
 was conducted between December 2020 and March 2021. Study Year two was
 conducted between November 2021 and March 2022. * Species: The species
 that was present in the photo. * Number: the number of individuals of a
 given species detected in a 5 minute episode of photos taken by remote
 motion triggered camera traps. * Start Date: The date of deployment for
 the specific camera (the day the camera began collecting data). * End
 Date: The date of collection for the specific camera (the date the camera
 was no longer collecting data). * Week: Week of the year the detection
 occurred. * Survey: For each survey year, detections were grouped in one
 week survey periods. Year one had 16 survey periods, and year two had 22
 survey periods. The Survey column describes which of the survey periods
 the detection occurred in. * WID : Week Identity. Combines the survey
 period indicated in the Survey Column with the Study Year. This provides
 us with what year and survey the detection occurred in. McTigue and
 DeGregorio Armadillo Density 2023: This dataset contains nine-banded
 armadillo density estimates from game cameras at 12 study sites in
 Northwest Arkansas, ranging from urban green space sites within downtown
 Fayetteville to more natural sites within the Ozark Mountain Ecoregion.
 Each row represents a camera location from the study for which density was
 estimated with the Random Encounter Model. * Site: Site describes the
 study area where the camera was placed. * Camera ID: Cameras at each site
 were given a unique ID. The ID is a combination of the site name and
 camera number. The number was assigned to each camera before deployment
 based on camera inventory. * Species: The species that was present in the
 photo. * Density: Density estimated by the Random Encounter Model
 (Rowcliffe et al. 2008). Density is written in individuals/km2. * Distance
 to Road (km): We measured the distance from each camera to the nearest
 road using the Near tool in ArcGIS Pro. Road data was derived from the
 Arkansas DOT GIS Layer. Distance was measured in Kilometers. * Distance to
 Road (m): In our analysis, we used distance to road in meters. This column
 was calculated from the kilometer value from Distance to Road (km*1000). *
 Elevation (m): in ArcGIS, we extracted the elevation of each point using
 the “Summarize Elevation” geoprocessing tool. When doing this, we selected
 the option to include slope and elevation. * Slope (degrees): The slope at
 each camera location derived using the “Summarize Elevation” geoprocessing
 tool when calculating elevation. * Aspect (degrees): The aspect at each
 camera location derived using the “Summarize Elevation” geoprocessing tool
 when calculating elevation. * Distance to Water (km): In ArcGIS, we
 combined the waterbody and flowline layers from the National Hydrography
 Database. We measured the distance from each camera to the nearest water
 source using the Near tool in ArcGIS Pro. * Distance to Water (m): In our
 analysis, we used Distance to Water in meters. This column was calculated
 from the kilometer value from Distance to Water (km*1000). * Annual
 Density of Traffic: Maximum value describing the number of vehicles
 traveling on each road in Arkansas per year. Data derived from the
 Arkansas Department of Transportation. * Anthropogenic Noise (dB):
 Anthropogenic Noise level in decibels (dB). Estimates were derived from
 the layer created by Mennitt and Fristrup (2016). Anthropogenic noise
 levels were based on the difference between predicted natural sound levels
 and recorded sound levels. The maximum value was calculated within the
 500m buffer around each camera. * Forest Area (km2): We combined forest
 types (Deciduous, Mixed, Coniferous) from the 2019 National Land Cover
 Database (NLCD). The total area of forest was measured within each 500m
 buffer around each camera. * Housing Unit Density (units/km2): Using the
 SILVIS Housing Data Layer (Hammer et al. 2004) we calculated the maximum
 number of housing units within the 500m buffer around each camera. *
 Developed Open Space (km2): Developed open space was derived from the 2019
 NLCD layer. The total area of this landcover class was measured within
 each 500m buffer around each camera. Citations: Hammer, R. B., Stewart, S.
 I., Winkler, R. L., Radeloff, V. C., & Voss, P. R. (2004).
 Characterizing dynamic spatial and temporal residential density patterns
 from 1940–1990 across the North Central United States. Landscape and Urban
 Planning, 69(2-3), 183–199.
 Mennitt, D. J.,
 Fristrup, K. M. (2016). Influence factors and spatiotemporal patterns of
 environmental sound levels in the contiguous United States. Noise Control
 Engineering Journal. 64: 342–353. Rowcliffe, J. M., Field, J., Turvey, S.
 T., Carbone, C. 2008. Estimating animal density using camera traps without
 the need for individual recognition. Journal of Applied Ecology. 45(4),
 1228–1236.