Management and Conservation

Spatially Explicit Modeling of LesserPrairie-Chicken Lek Density in Texas

JENNIFER M. TIMMER,1,2 Department of Natural Resources Management, Texas Tech University, P.O. Box 42125, Lubbock, TX 79409, USA

MATTHEW J. BUTLER, U.S. Fish and Wildlife Service, P.O. Box 1306, Albuquerque, NM 87103, USA

WARREN B. BALLARD,† Department of Natural Resources Management, Texas Tech University, P.O. Box 42125, Lubbock, TX 79409, USA

CLINT W. BOAL, U.S. Geological Survey, Texas Cooperative Fish and Wildlife Research Unit, Texas Tech University, Agricultural Sciences 218,Lubbock, TX 79409, USA

HEATHER A. WHITLAW,3 U.S. Fish and Wildlife Service, P.O. Box 42125, Lubbock, TX 79409, USA

ABSTRACT As with many other grassland birds, lesser prairie-chickens (Tympanuchus pallidicinctus) haveexperienced population declines in the Southern Great Plains. Currently they are proposed for federalprotection under the Endangered Species Act. In addition to a history of land-uses that have resulted inhabitat loss, lesser prairie-chickens now face a new potential disturbance from energy development. Weestimated lek density in the occupied lesser prairie-chicken range of Texas, USA, andmodeled anthropogenicand vegetative landscape features associated with lek density. We used an aerial line-transect survey methodto count lesser prairie-chicken leks in spring 2010 and 2011 and surveyed 208 randomly selected 51.84-km2

blocks. We divided each survey block into 12.96-km2 quadrats and summarized landscape variables withineach quadrat. We then used hierarchical distance-sampling models to examine the relationship between lekdensity and anthropogenic and vegetative landscape features and predict how lek density may change inresponse to changes on the landscape, such as an increase in energy development. Our best models indicatedlek density was related to percent grassland, region (i.e., the northeast or southwest region of the TexasPanhandle), total percentage of grassland and shrubland, paved road density, and active oil and gas welldensity. Predicted lek density peaked at 0.39 leks/12.96 km2 (SE¼ 0.09) and 2.05 leks/12.96 km2

(SE¼ 0.56) in the northeast and southwest region of the Texas Panhandle, respectively, which correspondsto approximately 88% and 44% grassland in the northeast and southwest region. Lek density increased withan increase in total percentage of grassland and shrubland and was greatest in areas with lower densities ofpaved roads and lower densities of active oil and gas wells. We used the 2 most competitive models to predictlek abundance and estimated 236 leks (CV¼ 0.138, 95% CI¼ 177–306 leks) for our sampling area. Ourresults suggest that managing landscapes to maintain a greater percentage of grassland and shrubland on thelandscape with a greater ratio of grasses to shrubs in the northeast Panhandle should promote greater lekdensity. Furthermore, increases in paved road and active oil and gas well densities may reduce lek density.This information will be useful for future conservation planning efforts for land protection, policy decisions,and decision analyses. � 2013 The Wildlife Society.

KEY WORDS aerial survey, energy development, grassland, hierarchical distance sampling, landscape characteristics,natural gas, oil, roads, shrubland, Tympanuchus pallidicinctus.

The occupied range of lesser prairie-chickens (Tympanuchuspallidicinctus) has been reduced by >90%, a decline attributedto direct habitat loss from conversion of native grassland tocropland, livestock overgrazing, and invasion of woody plants,and indirect habitat loss from disturbance by energy

development (Taylor and Guthery 1980, Applegate andRiley 1998, Hagen et al. 2004). As a result, the lesser prairie-chicken is currently proposed for protection as a threatenedspecies under the Endangered Species Act (U.S. Fish andWildlife Service [USFWS] 2012). Lesser prairie-chickenpopulations have faced steady declines during the past 100 yearsin Texas, USA (Jackson and DeArment 1963, Crawford andBolen 1976, Sullivan et al. 2000), andTexas Parks andWildlifeDepartment (TPWD) estimated a minimum of 6,000 birdsfrom mostly road-based surveys located in high quality lesserprairie-chicken habitat (Davis et al. 2008).Texas currently produces the most wind-generated

electricity in the United States (i.e., 22% of the nation’stotal; American Wind Energy Association 2012) and5 Competitive Renewable Energy Zones (CREZ) were

Received: 17 January 2013; Accepted: 1 October 2013Published: 16 December 2013

1E-mail: timmerj3@gmail.com2Present address: Colorado State University, 1499 Campus Delivery,Fort Collins, CO 80525, USA3Present address: United States Fish and Wildlife Service, 2609Anderson Avenue, Manhattan, KS 66502, USA†Deceased.

The Journal of Wildlife Management 78(1):142–152; 2014; DOI: 10.1002/jwmg.646

142 The Journal of Wildlife Management � 78(1)

designated in west Texas to encourage further wind energydevelopment (Electric Reliability Council of Texas[ERCOT] 2006). Transmission lines are being constructedto deliver wind-generated electricity from the CREZs tocustomers in urban centers (ERCOT 2006). The 2 CREZsin the Texas Panhandle overlap approximately 27% (3,288 km2)of the known occupied range of lesser prairie-chickens inTexas. Furthermore, the amount of active oil and gas wells inthe occupied range has increased >80% during the pastdecade (Railroad Commission of Texas 2012). However,little is known about the relationship of lesser prairie-chickenleks in Texas relative to existing landscape features such asroads, transmission lines, and oil and gas wells or how lesserprairie-chickens may respond to a change in these landscapefeatures.Several recent studies have examined impacts of energy

development on prairie grouse species (Tympanachus andCentrocercus spp.) inhabiting rangelands with a high potentialfor wind, geothermal, and natural gas energy development(Hagen 2010, Jarnevich and Laubhan 2011, Naugleet al. 2011). Many of these studies demonstrate avoidanceof anthropogenic structures and human disturbance thatleads to habitat loss and fragmentation (Holloran 2005,Pitman et al. 2005, Walker et al. 2007, Doherty et al. 2008,Pruett et al. 2009). For example, Naugle et al. (2011)reviewed 7 studies to examine the impact of energydevelopment on greater sage-grouse (C. urophasianus).Each study reported negative responses by greater sage-grouse to energy development. Responses included adecrease in lek attendance within or near gas fields and anavoidance of development by nesting hens. Hagen (2010)conducted a meta-analysis of published and unpublishedreports pertaining to prairie grouse and the impacts of energydevelopment. He reported a general displacement of grouseby anthropogenic features and reduced demographic rates(e.g., nest success) from energy development. In contrast,Winder et al. (2013) documented an increase in adult survivalfor greater prairie-chicken hens after construction of a windenergy facility in Kansas and the authors attributed theincreased survival to reduced predation risk.Spatially explicit models allow researchers to associate

landscape features with animal occurrence, abundance, ordensity (Hedley and Buckland 2004, Royle et al. 2004).Identifying suitable habitat and predicting species occurrenceor density is especially useful when balancing energydevelopment and the needs of species of conservationconcern, such as lesser prairie-chickens (Jarnevich andLaubhan 2011). Models that incorporate presence-onlydata from opportunistic sampling have been used for prairiegrouse but are susceptible to problems associated withincomplete detectability of individuals and non-randomsamples, which can result in misleading relationshipsbetween species occurrence and environmental character-istics (Elith et al. 2011, Royle et al. 2012, Welsh et al. 2013).Hierarchical distance-sampling models provide a tractableway to account for incomplete detection and relate landscapefeatures to density (Royle et al. 2004, Sillett et al. 2012,Blank 2013).

Given the plan for energy development in west Texaswhere declining lesser prairie-chicken populations occur(ERCOT 2006), managers need a better understanding oflesser prairie-chicken distribution in relation to changescaused by energy development. This information will beuseful for future conservation planning efforts for landprotection and policy decisions and to guide energydevelopment decisions. In addition, McRoberts et al.(2011) identified a need for more effective monitoring oflesser prairie-chicken populations given their conservationstatus. Therefore, our objectives were to estimate lek densityin the lesser prairie-chicken occupied range of Texas andexamine the relationship of lek density with anthropogenicand vegetative landscape characteristics.

STUDY AREA

The occupied range of lesser prairie-chickens in Texas, asdelineated by Davis et al. (2008), lies mostly in the northeastand southwest regions of the Texas Panhandle, with a fewleks thought to be scattered throughout the central portion ofthe Panhandle. Lesser prairie-chickens occupied 34 countiesin Texas before 1980, but their range has declined to only 12counties in the Panhandle (Davis et al. 2008). Our samplingarea encompassed these 12 counties or 86.9% of the occupiedlesser prairie-chicken range in Texas (i.e., we excludedportions that were not lesser prairie-chicken habitat such asriparian woodlands and cotton fields). Our sampling areaintersected 2 of the CREZs in Texas.The northeast region of the study area was comprised of a

mixed-grass prairie dominated by sand sagebrush (Artemisiafilifolia) and little bluestem (Schizachyrium scoparium). Thesouthwest region of the study area was a short-grass prairiedominated by shinnery oak (Quercus havardii) and littlebluestem with some mesquite (Prosopis glandulosa). Cotton,winter wheat, and grain sorghum were the main crops grownin both regions (United States Department of Agriculture[USDA] 2008). The climate was mostly dry and the majorityof precipitation occurred during the fall and spring (PRISMClimate Group 2011). The southwest region of thePanhandle received an average of 40–51 cm of precipitationyearly and the northeast region received an average of50–61 cm of precipitation yearly (PRISM Climate Group2011).

METHODS

We used a stratified random sampling design to estimatelesser prairie-chicken lek density (Thompson et al. 1998).We defined 4 strata based on combinations of vegetationtypes believed to influence lesser prairie-chicken density(e.g., grassland, shrubland, agriculture, and a mosaic of the 3land cover types; Crawford and Bolen 1976, Taylor andGuthery 1980, Hagen et al. 2004) and areas with thepotential for energy development within lesser prairie-chicken-occupied habitat (Timmer et al. 2013). Wedelineated vegetation types based on the USDA Texascropland data layer (USDA 2008), which classified grasslandas patches comprised of>80% grass and shrubland as patchescomprised of shrubs <5m tall and �20% of the total

Timmer et al. � Spatial Models of Lek Density 143

vegetation. We divided the sampling area into 329, 7.2-km� 7.2-km survey blocks; at this size, we could complete 1survey block per morning. Of the 329 survey blocks coveringthe sampling area, we excluded 44 blocks as potential lesserprairie-chicken habitat because they were mostly urban, openwater, cotton fields, or woodland.We used ArcGIS 9.3 (Environmental Systems Research

Institute, Inc., Redlands, CA) to create a set of grid cells(7.2 km� 7.2 km) over the extent of the occupied lesserprairie-chicken range in Texas. We re-classified the Texascropland data layer (USDA 2008) into 8 categories (i.e.,cotton, grains, other crops, grassland or idle pasture,shrubland, woodland, open water, and barren or developedareas) and calculated the area of grassland, shrubland, andgrain in each survey block. We combined vegetation area foreach grid cell with a CREZ designation (i.e., cell was eitherfully or partially in a CREZ or not at all). We assigned surveyblocks to 1 of 4 strata and randomly selected blocks from eachstratum (strata defined below).We divided our sampling areainto 2 regions for the 2 field seasons. During spring 2010, wesurveyed blocks in the northeast and central regions of thePanhandle (hereafter, northeast region) and during spring2011, we surveyed blocks in the southwest and west-centralregions (hereafter, southwest region).The first stratum was composed of survey blocks that were

within a CREZ and �50% grassland (i.e., native grassland,Conservation Reserve Program land [CRP], or idlecropland). The second stratum was composed of surveyblocks that were also �50% grassland, but not within aCREZ. The third stratum was composed of survey blockswith >50% shrubland. The fourth stratum was composed ofsurvey blocks with a �75% combination of grassland,shrubland, and grain field (this mosaic was comprised of30–50% grassland, �50% shrubland, and >0% grain field).The specifications for this stratum were meant to includepotential lesser prairie-chicken habitat while excluding non-habitat, such as urban areas, water bodies, cotton fields, andwoodland regions (e.g., riparian cottonwood [Populusdeltoides] galleries). None of the blocks with vegetativecharacteristics of the third stratum were within a CREZ andonly 1 block in the fourth stratum was located within aCREZ.We allocated samples to each stratum using the following

formula

ui ¼ U � gi

where ui is the number of survey blocks allocated to eachstratum i, U is the total number of survey blocks (n¼ 180)allocated for the 2-year study and gi is the weighting factorfor each stratum i. We calculated the weighting factor as

gi ¼riPri

where riwas the rank for each stratum i. We ranked the stratafrom 1 to 4 with 4 representing the highest priority stratum(i.e., survey blocks within a CREZ and �50% grassland).Because our research was part of a larger project designed toexamine lek density in areas subject to wind energy

development, we prioritized the strata based on the potentialfor wind energy development to impact lek distribution.Based on the weighting factors, we randomly selected 72

survey blocks in the first stratum, 54 in the second stratum,36 in the third stratum, and 18 in the fourth stratum. Wewere able to survey more blocks than originally planned inthe second year, but there were no additional blocks instratum 1 in the southwest region. Therefore, we randomlyselected additional samples in stratum 2.With the additionalblocks, we surveyed 76 survey blocks in the first stratum, 73in the second stratum, 39 in the third stratum, and 20 in thefourth stratum (Timmer et al. 2013).We used ArcGIS 9.3 to generate a flight path for each

survey block and measure the nearest distance from each lekdetection to a transect (Hiby and Krishna 2001). Weoriented transects north-south with 400-m spacing betweenthem following the survey protocol used by McRoberts et al.(2011). The observer’s global positioning system (GPS) unitrecorded a track log of each flight path to provide the actualtransect lengths that were surveyed. We set the track logs torecord points at least every 2 seconds.We conducted our surveys from an R-22 helicopter

(Robinson Helicopter Co., Torrance, CA), which seated anobserver and the pilot, who also served as an observer. Totrain technicians, we also conducted flights early in each fieldseason from an R-44 helicopter (Robinson Helicopter Co.).We conducted flights between early March and lateMay 2010–2011 and surveyed from sunrise until approxi-mately 2.5 hours post-sunrise. We surveyed at a targetaltitude of 15m above ground level and a target speed of60 km/hr (McRoberts et al. 2011). We did not includeportions of transects that were surveyed outside the set surveyprotocol (e.g., when the pilot increased the helicopter’saltitude to avoid houses or feedlots) in the final transectlengths. When we detected lesser prairie-chickens, the pilotdeviated from transect and flew over the center of the groupof birds or the center of the location from where birdsflushed. We used a GPS unit to record the exact location ofdetected lesser prairie-chickens (Marques et al. 2006). Weclassified a detection as a lek if �1 displaying male wasdetected.

Data AnalysisWe selected 10 vegetative and anthropogenic variables thatcould influence lek density based on previous literature andour research objectives (Crawford and Bolen 1976, TaylorandGuthery 1980, Fuhlendorf et al. 2002, Hagen et al. 2004,Pitman et al. 2005; Table 1). We divided each survey blockinto 4, 12.96-km2 quadrats and summarized landscapevariables for each quadrat. We developed 3 a priori modelsets (Table 2). Our vegetation model set included percentgrassland (i.e., native grassland, CRP, or idle cropland),percent shrubland (i.e., shrubs<5m tall), total percentage ofgrassland and shrubland, percent grain field (e.g., corn,winter wheat, or grain sorghum), and edge density of allpatches (km/km2; USDA 2008).We included a quadratic termfor percent grassland and percent shrubland because previousliterature has suggested that optimum lesser prairie-chicken

144 The Journal of Wildlife Management � 78(1)

habitat consists of native grassland interspersed withshrubland (Copelin 1963, Taylor and Guthery 1980,Applegate and Riley 1998). We also included an interactionbetween percent grassland and region and between percentshrubland and region. The northeast and southwest regionsare composed of different vegetation types (i.e., shinneryoak-dominated type in the southwest region and sand

sagebrush with bunchgrasses in the northeast region;Lyons et al. 2009) and the distinct lesser prairie-chickenpopulations (Corman 2011) may respond differently to thelandscape in these 2 regions. Our road model set includedpaved road density (km/km2), unpaved road density (km/km2),and all road density (km/km2; U.S. Environmental ProtectionAgency 1998, Texas Department of Transportation 2011). Our

Table 1. Descriptions of the landscape variables we included in hierarchical distance-sampling models of lesser prairie-chicken lek density in Texas, USA,2010–2011.

Variablea Description Mean SD Min. Max.

Region Indicator variable for the 2 regions of the lesser prairie-chicken range inTexas where northeast region¼ 0 and southwest region¼ 1.

Grass Percent of the quadrat composed of grassland patches (native grassland,Conservation Reserve Program, or idle cropland comprising >80% of the total vegetation)including a quadratic term.

0.61 0.299 0.00 1.00

Shrub Percent of the quadrat composed of shrubland patches(shrubs <5m tall comprising �20% of the total vegetation) including a quadratic term.

0.25 0.291 0.00 1.00

Grass-shrub Total percentage of grassland and shrubland. 0.86 0.173 0.21 1.00Grain Percent of the quadrat composed of grain field patches (e.g., winter wheat, corn,

or grain sorghum).0.09 0.136 0.00 0.73

Edge Edge density for all landcover patches (km/km2). 11.30 6.086 0.10 27.65Paved Paved road density (km/km2). 0.14 0.183 0.00 0.84Unpaved Unpaved road density (km/km2). 0.29 0.278 0.00 1.37Roads Paved and unpaved road density (km/km2). 0.44 0.336 0.00 1.68Trans lines Transmission line (>69 kv) density (km/km2). 0.07 0.164 0.00 1.74Well Active oil and gas well density (wells/km2). 1.25 2.834 0.00 23.69

a Each variable was calculated for a 12.96-km2 quadrat based on the United States Department of Agriculture (USDA) Texas cropland data layer(USDA 2008).

Table 2. Three sets of hierarchical distance-sampling models predicting lesser prairie-chicken lek density in Texas, USA, 2010–2011. For each candidatemodel, we give �2� log-likelihood (�2LL), number of parameters (K), Akaike’s Information Criterion (AIC), difference in AIC compared to lowest AICof the model set (Di), AIC weight (wi), predicted lek abundance (N), and coefficient of variation for abundance (CV).

Modela �2LL K AIC Di wi N CV

Vegetation model setGrassþ regionþ grass� regionþ grass2þ grass-shrub 876.115 7 890.115 0.000 0.662 237.6 0.138Grassþ regionþ grass� regionþ grass2þ grass-shrubþ edge 875.889 8 891.889 1.773 0.273 237.6 0.135Shrubþ regionþ shrub� regionþ shrub2þ grass-shrub 881.376 7 895.376 5.260 0.048 237.3 0.139Shrubþ regionþ shrub� regionþ shrub2þ grass-shrubþ edge 881.342 8 897.342 7.227 0.018 237.3 0.142Grassþ regionþ grass� regionþ grass2þ grain 900.130 7 914.130 24.014 <0.001 240.5 0.139Grassþ regionþ grass� regionþ grass2þ grainþ edge 898.898 8 914.898 24.782 <0.001 240.5 0.136Shrubþ regionþ shrub� regionþ shrub2þ grain 909.872 7 923.872 33.756 <0.001 237.8 0.137Grass-shrubþ edge 917.543 4 925.543 35.427 <0.001 254.7 0.134Grass-shrub 919.611 3 925.611 35.496 <0.001 253.9 0.136Shrubþ regionþ shrub� regionþ shrub2þ grainþ edge 909.802 8 925.802 35.686 <0.001 237.6 0.140Grassþ regionþ grass� regionþ grass2 919.550 6 931.550 41.434 <0.001 242.7 0.138Shrubþ regionþ shrub� regionþ shrub2þ edge 917.790 7 931.790 41.675 <0.001 235.1 0.134Shrubþ regionþ shrub� regionþ shrub2 920.863 6 932.863 42.748 <0.001 240.4 0.134Grassþ regionþ grass� regionþ grass2þ edge 918.935 7 932.935 42.820 <0.001 242.8 0.141Grain 929.760 3 935.760 45.645 <0.001 252.2 0.140Grainþ edge 929.320 4 937.320 47.204 <0.001 252.5 0.132Edge 940.916 3 946.916 56.801 <0.001 249.7 0.137

Road model setPaved 930.447 3 936.447 0.000 0.640 245.0 0.139Pavedþ unpaved 929.922 4 937.922 1.475 0.306 245.8 0.137Roads 935.543 3 941.543 5.096 0.050 249.4 0.140Unpaved 940.399 3 946.399 9.953 0.004 250.2 0.138

Energy model setWell 928.397 3 934.397 0.000 0.544 250.4 0.138Wellþ trans lines 926.770 4 934.770 0.372 0.452 248.8 0.138Trans lines 938.344 3 944.344 9.946 0.004 248.5 0.132

aGrass, shrub, grass-shrub, and grain represent the percentage of the quadrat composed of grassland, shrubland, grassland and shrubland combined, and grainfields, respectively. Region indicates northeast or southwest Texas. Edge, paved, unpaved, roads, well, and trans lines represent the densities of edges, pavedroads, unpaved roads, all roads, active oil and gas wells, and transmission lines, respectively.

Timmer et al. � Spatial Models of Lek Density 145

energy model set included density of transmission lines�69 kv (km/km2; Platts 2011) and active oil and gas welldensity (wells/km2; Railroad Commission of Texas 2011). Weperformed a correlation analysis in program R (RDevelopmentCore Team 2011) for the landscape variables (Timmer2012). We did not include variable(s) in the same modelthat had a pair-wise correlation �0.50 to avoid potentialproblems with multicollinearity (Zar 1999, Ribic and Sample2001, Graham 2003).We analyzed our data using the distsamp function of

package unmarked (Fiske and Chandler 2011) in program R,which implements the multinomial-Poisson mixture model(hierarchical distance sampling; Royle et al. 2004). Webinned our distance data into 7 intervals (i.e., 0–35m,35–50m, 50–70m, 70–90m, 90–120m, 120–150m, 150–179m) according to recommendations by Buckland et al.(2001) and used the half-normal model to describe thedetection function (Royle et al. 2004, Sillett et al. 2012,Timmer et al. 2013). We standardized all predictor variables(Fiske and Chandler 2011) and used the 3 a priori model sets(vegetative variables, road variables, and energy infrastruc-ture variables) to model the lek density relationships(Table 2). For all model sets, we included models for eachindividual variable and the variables combined (Table 2).However, for the vegetation model set, we did not allowpercent grassland or percent shrubland to appear together inthe same model to reduce the complexity and avoidmulticollinearity among the variables. For the modelincluding all road density, we did not include either pavedor unpaved road density to avoid multicollinearity.We determined competitive models as a model withDAIC� 2 and excluded models with uninformative param-eters (Arnold 2010). We considered the best model(s) fromeach model set and combined those models (i.e., combinedall covariates from the best models) in a final model set alongwith a null model (Table 3). We selected competitive modelsfrom this final model set and evaluated goodness-of-fit of thebest models using a Freeman–Tukey chi-squared procedurewith 1,000 bootstrap replicates (parboot; Fiske andChandler 2011). We model averaged among our mostcompetitive models to account for model selection uncer-tainty (Burnham and Anderson 2002). We model averagedpredicted lek density for each 12.96-km2 quadrat covering

the lesser prairie-chicken range in Texas and created a map.We summed the model-averaged predicted lek densitieswithin each quadrat to estimate the total number of leks forour sampling area and used the parametric bootstrapprocedure with 1,000 bootstrap replicates to estimateuncertainty for estimates of total lek abundance and averagedensity (parboot; Fiske and Chandler 2011).

RESULTS

During spring 2010 and 2011, we inventoried 208, 51.84-km2

survey blocks across the lesser prairie-chicken range in Texas(as delineated by Davis et al. 2008). We surveyed 88.6% ofthe sampling area (10,782.7 of 12,167.1 km2) and detected96 leks during our surveys. We estimated 109 leks(CV¼ 0.191, 95% CI¼ 72–151 leks) in the northeastregion and 127 leks (CV¼ 0.157, 95% CI¼ 92–170 leks)in the southwest region for a total of 236 leks (CV¼ 0.138,95% CI¼ 177–306 leks) in our sampling area. Estimated lekdensity ranged from <0.001 leks/12.96 km2 (SE< 0.001) to0.38 leks/12.96 km2 (SE¼ 0.09) in the northeast region(mean¼ 0.16 leks/12.96 km2, CV¼ 0.156) and from<0.001leks/12.96 km2 (SE< 0.001) to 1.77 leks/12.96 km2 (SE¼

0.47) in the southwest region (mean¼ 0.27 leks/12.96 km2,CV¼ 0.160). Mean lek density was 0.21 leks/12.96 km2

(CV¼ 0.126, 95% CI¼ 0.16–0.27 leks/12.96 km2) in Texas.For our vegetation model set, we did not include percentage

of grassland and shrubland in the same model or totalpercentage of grassland and shrubland and percent grain fieldin the same model because these variables were correlated(r¼�0.828, P¼< 0.001 and r¼�0.854, P¼< 0.001,respectively). We found 2 competitive models from ourvegetation model set: percent grasslandþ regionþ percentgrassland� regionþ percent grassland2þ total percentageof grassland and shrubland (AIC weight [wi]¼ 0.662) anda similar model that also included edge density (DAIC¼

1.773, wi¼ 0.273; Table 2). The model containing edgedensity was�2DAIC units of the top-ranked model and theparameter estimate for edge density did not differ from 0(b¼�0.093, SE¼ 0.194, P¼ 0.633); therefore, it wasprobably an uninformative parameter (Arnold 2010). Lekdensity increased with an increase in the total percentage ofgrassland and shrubland (b¼ 1.172, SE¼ 0.214, P� 0.001).

Table 3. Best overall hierarchical distance-sampling models predicting lesser prairie-chicken lek density in Texas, USA, 2010–2011. For each candidatemodel, we give �2� log-likelihood (�2LL), number of parameters (K), Akaike’s Information Criterion (AIC), difference in AIC compared to lowest AICof the model set (Di), AIC weight (wi), predicted lek abundance (N), and coefficient of variation for abundance (CV).

Modela �2LL K AIC Di wi N CV

Grassþ regionþ grass� regionþ grass2þ grass-shrubþwellþ paved 853.749 9 871.749 0.000 0.559 235.5 0.136Grassþ regionþ grass� regionþ grass2þ grass-shrubþwell 856.223 8 872.223 0.474 0.441 237.0 0.140Grassþ regionþ grass� regionþ grass2þ grass-shrubþ paved 870.176 8 886.176 14.427 <0.001 235.4 0.141Grassþ regionþ grass� regionþ grass2þ grass-shrub 876.115 7 890.115 18.367 <0.001 237.6 0.138Wellþ paved 920.697 4 928.697 56.948 <0.001 245.6 0.135Well 928.397 3 934.397 62.649 <0.001 250.4 0.138Paved 930.447 3 936.447 64.698 <0.001 245.0 0.139Null 940.926 2 944.926 73.177 <0.001 249.7 0.143

aGrass represents the percentage of the quadrat composed of grassland. Region indicates northeast or southwest Texas. Grass-shrub represents the percentageof the quadrat composed of grassland and shrubland combined. Well and paved represent the densities of active oil and gas wells and paved roads,respectively.

146 The Journal of Wildlife Management � 78(1)

We found 2 competitive models from the road model set:paved road density (wi¼ 0.640) and paved road densityþunpaved road density (DAIC¼ 1.475, wi¼ 0.306; Table 2).Unpaved road density was an uninformative parameterbecause the model containing it was �2DAIC units of thetop-ranked model but the parameter estimate did not differfrom 0 (b¼�0.075, SE¼ 0.105, P¼ 0.474). Paved roaddensity was inversely related to lek density (b¼� 0.387,SE¼ 0.129, P¼ 0.003).We found 2 competitive models from the energy model set:

active oil and gas well density (wi¼ 0.544) and active oil andgas well densityþ transmission line density (DAIC¼ 0.372,wi¼ 0.452; Table 2). Although the model that includedtransmission line density was �2DAIC units of the top-ranked model, the parameter estimate for transmission linedensity did not differ from 0 (b¼�0.156, SE¼ 0.132,P¼ 0.238), indicating that the model was likely spurious(Arnold 2010). The best model indicated an inverserelationship between lek density and active oil and gaswell density (b¼�0.718, SE¼ 0.279, P¼ 0.010).We included the covariates from the top competitive

models from the 3 sets of models to fit the final model set(Table 3). Our best model included percent grassland�regionþ percent grassland2þ total percentage of grasslandand shrublandþ active oil and gas well densityþ paved roaddensity (wi¼ 0.559; Table 3). The second best model wassimilar but did not include paved road density (DAIC¼

0.474, wi¼ 0.441; Table 3). The goodness-of-fit testindicated good model fit for both models (P¼ 0.512 andP¼ 0.498, respectively).We model averaged estimates from these models to

produce a map of predicted lek density across the study area(Fig. 1). The quadratic relationship between lek densityand percent grassland indicated lek density peaked at0.39 leks/12.96 km2 (SE¼ 0.09) and 2.05 leks/12.96 km2

(SE¼ 0.56) in the northeast and southwest region,respectively when approximately 88% and 44% of a quadratwas composed of grassland patches in the northeast andsouthwest region, respectively (i.e., paved road density andactive oil and gas well density held constant at 0 and totalproportion of grassland and shrubland held constant at100%; Fig. 2). The actual range of landscape composed ofgrassland was 7.1–99.5% and 0–99.5% in the northeast andsouthwest region, respectively. The predictor variables usedin the final models exhibited relationships with lek densitysimilar to the ones described above (Table 4). A positiverelationship between lek density and total proportion ofgrassland and shrubland was indicated (Fig. 3). Both pavedroad density (Fig. 4) and active oil and gas well density(Fig. 5) exhibited inverse relationships with lek density.

DISCUSSION

Though both grassland and shrubland patches (i.e.,contiguous areas of native grassland and low-growingshrubs, respectively) are recognized as integral componentsof lesser prairie-chicken habitat, the proportion of each typeneeded on the landscape is an important consideration.Researchers have debated the optimal proportion of

grassland and shrubland patches in lesser prairie-chickenhabitat because of sparse documentation of their historicalhabitat and seasonal variation in habitat use (Taylor andGuthery 1980, Silvy 2006). For example, one habitatguideline recommends landscapes (i.e., >100 km2) ofapproximately 80% native grassland and 20% shrubland tosupport lesser prairie-chicken populations (Bidwell 2003).Declining lesser prairie-chicken populations in NewMexico,Oklahoma, and Texas were associated with landscapescontaining less shrubland cover compared to populations thatwere not declining (Woodward et al. 2001), whereas percentgrassland or percent grassland and CRP were importantpredictors of lesser prairie-chicken lek occurrence in Kansas(Jarnevich and Laubhan 2011). Our models indicate theoptimal mix of grassland and shrubland patches for lesserprairie-chicken lek density varies by region within Texas.However, habitat structure and composition at smaller scalesthan our landscape metrics are likely important as well (i.e.,vegetation structure and composition within a patch; Hagenet al. 2004).Given that the northeast and southwest regions of the

Texas Panhandle represent 2 distinct populations of lesserprairie-chickens (Corman 2011) and the vegetation compo-sition and structure differ between the regions (Lyonset al. 2009), it is not surprising that lek density variesaccording to the landscape in these 2 regions (Fig. 2). Lekdensity is 1 index of prairie grouse population trends(Cannon and Knopf 1981), but other studies have reached asimilar conclusion for different lesser prairie-chickenpopulation parameters. Lyons et al. (2009) documentedgreater survival rates for lesser prairie-chickens in thenortheast region of the Texas Panhandle compared to thesouthwest region and attributed the difference in survival rateto vegetation differences for nesting and brood-rearing.Lyons et al. (2009) concluded that the shinnery oakmonoculture in the southwest region may not contain theinsect density and residual cover of the sand sagebrushgrassland in the northeast region. In contrast, the survivalrates reported by Grisham (2012) for the southwest regionare among the highest in the literature for hens during thebreeding season. Grisham (2012) partly credited the highsurvival rate to shinnery oak-dominated areas, which providean important winter food source (i.e., acorns), as well asescape and nesting cover. Patten et al. (2005) examined adultsurvival for lesser prairie-chickens in southeastern NewMexico and northwestern Oklahoma, which are similar invegetation composition to the southwest and northeastregion of the Texas Panhandle, respectively. Adult survivor-ship did not differ between their 2 study sites.Our models predicted higher lek densities in the southwest

region compared to the northeast region. In the northeastregion, lek density peaked when approximately 88% of thelandscape was composed of grassland patches and lek densitypeaked at approximately 44% grassland in the southwestregion (Fig. 2). Further, because lek density increased withan increase in the total percentage of grassland and shrubland(Fig. 3), our models suggested that lek density would bemaximized if the remaining proportion of the landscape was

Timmer et al. � Spatial Models of Lek Density 147

Figure 1. Predicted lesser prairie-chicken (LEPC) lek density for 12.96-km2 quadrats covering the occupied lesser prairie-chicken range in Texas, 2010–2011,based on the 2most competitive hierarchical distance-samplingmodels.We classified white areas inside the occupied range as non-lesser prairie-chicken habitatand did not include them in the sampling area.

148 The Journal of Wildlife Management � 78(1)

composed of shrubland patches (i.e., approximately 12% and56% shrubland in the northeast and southwest region,respectively). Therefore, any land use activity that alters orreduces the total percent of grassland and shrubland on thelandscape (e.g., cultivation) will likely reduce lek density.Our models indicated anthropogenic disturbances (i.e.,

paved roads and oil and gas wells) negatively affected lekdensity (Figs. 4 and 5). Anthropogenic features, such asroads, can fragment contiguous rangeland and result inhabitat loss due to avoidance of these features by lesserprairie-chickens (Pruett et al. 2009, Hagen et al. 2011).Other studies have observed a similar relationship betweenpaved roads and prairie grouse. Lesser prairie-chicken nestsin Kansas were located farther than expected from paved andhigh-traffic graveled roads even though otherwise-suitablehabitat surrounded those features (Pitman et al. 2005).Pruett et al. (2009) concluded that highways do not appear toimpede lesser prairie-chicken movement, but noise andtraffic associated with highways may render surroundinghabitat unsuitable. Niche modeling of lesser prairie-chickenleks in Kansas showed an increase in lek habitat suitability

with increasing distance from highways (Jarnevich andLaubhan 2011) and a separate study in Kansas documentedan avoidance of paved roads by radio-marked hens (Hagenet al. 2011). An avoidance of high road densities at a 5-kmscale was a significant predictor of greater prairie-chicken(T. cupido) lek locations in Kansas (Gregory et al. 2011), andHagen (2010) found that prairie grouse displacement byanthropogenic features in several studies was greatest fortransmission lines and roads.In contrast, we did not identify a significant relationship

between lek density and transmission line density, which iscontrary to recent data. For instance, Hagen et al. (2011)documented avoidance of transmission lines by lesser prairie-chickens in Kansas and lesser prairie-chicken lek suitabilityincreased with increasing distance from transmission lines(Jarnevich and Laubhan 2011). However, in 77.9% of oursurvey blocks, transmission line density was zero (<1.7 km/km2

in the others) but is expected to increase with wind energydevelopment.Lesser prairie-chickens will use oil or gas pads as lek sites if

the associated activity or traffic is minimal (Jamisonet al. 2002), but they also exhibit avoidance of oil and gasactivity. In a southwestern Kansas study, lesser prairie-chicken nests were located farther than expected from oil andgas wellheads, although this distance was only significant in 1study area, possibly because of differences in topography andsize or noise levels of pump jacks (Pitman et al. 2005).Distance from oil or gas wells was the most influentialanthropogenic feature affecting lek occurrence (for leklocations recorded after 1995) in Kansas and oil or gas welldensity was the most influential feature affecting lekoccurrence at the largest scale (i.e., 3,000m; Jarnevich andLaubhan 2011).Research has not documented direct effects on lesser

prairie-chicken productivity from oil and gas activity (Hagenet al. 2011), but evidence suggests that productivity of greatersage-grouse, a closely related species, may be directly andindirectly affected (e.g., Lyon and Anderson 2003,Holloran 2005, Walker et al. 2007). In a natural gas fielddevelopment region in western Wyoming, greater sage-grouse hens nested farther from leks within 3 km of a natural

0.00

0.15

0.30

0.45

0.60

0.75

0.90

1.05

1.20

1.35

1.50

1.65

1.80

1.95

2.10

2.25

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Nu

mber

of

lek

s/q

uad

rat

(12

.96

km

2)

Percent of the landscape as grassland

Northeast Region Southwest Region

Figure 2. Predicted lesser prairie-chicken lek density for the northeast andsouthwest regions of the Texas Panhandle in response to the percent of thelandscape composed of grassland patches, 2010–2011, based on the 2 mostcompetitive hierarchical distance-sampling models. We held paved roaddensity constant at 0, oil and gas well density constant at 0, and totalpercentage of grassland and shrubland at 100%.

Table 4. Parameter estimates (b), standard errors (SE), and P-values for the 2 most competitive hierarchical distance-sampling models predicting lesserprairie-chicken lek density in Texas, USA, 2010–2011.

Variablea

Model 1b Model 2c

b SE P b SE P

Intercept �5.553 0.378 <0.001 �5.548 0.378 <0.001Grass 1.169 0.419 0.005 1.140 0.419 0.006Region 2.012 0.407 <0.001 2.015 0.406 <0.001Grass2 �0.629 0.137 <0.001 �0.619 0.138 <0.001Grass-shrub 1.189 0.209 <0.001 1.250 0.209 <0.001Well �0.934 0.321 0.004 �0.981 0.319 0.002Paved �0.196 0.130 0.131Grass� region �1.852 0.489 <0.001 �1.823 0.490 <0.001

aGrass represents the percentage of the quadrat composed of grassland. Region indicates northeast or southwest Texas. Grass-shrub represents the percentageof the quadrat composed of grassland and shrubland combined. Well and paved represent the densities of active oil and gas wells and paved roads,respectively. All variable values were standardized (i.e., [observation–mean]/SD).

b Our most competitive model of lek density was grassþ regionþ grass� regionþ grass2þ grass-shrubþwellþ paved.c Our second most competitive model of lek density was grassþ regionþ grass� regionþ grass2þ grass-shrubþwell.

Timmer et al. � Spatial Models of Lek Density 149

gas well pad or road than more distant leks (Lyon andAnderson 2003). The authors attributed this behavior to anavoidance of vehicular traffic associated with the gas wells. Ina separate study in northwestern Wyoming, Holloran (2005)found that nesting sage-grouse hens avoided areas with ahigh density of active natural gas wells, the number of malesdisplaying at leks decreased with increasing gas field-relateddisturbances around leks, and leks surrounded by gas fielddevelopment had low juvenile male recruitment and highdisplacement of adult males. Walker et al. (2007) observed agreater decline in male sage-grouse lek attendance and adecline in number of active leks in areas near coal-bed naturalgas development. Doherty et al. (2008) found that sage-grouse hens avoided coal-bed natural gas developmentsurrounded by otherwise-suitable winter habitat.

The hierarchical modeling technique we used is differentthan the techniques used in previous studies examining lekoccurrence and landscape features. We established a formalsampling design that provided spatial coverage of oursampling area and used probabilistic sampling for theoccupied lesser prairie-chicken range in Texas. Weaccounted for incomplete detection of leks by modeling adetection function and were thus, able to extrapolaterelationships between lek density and our predictive variablesto the entire lesser prairie-chicken range in Texas (Royleet al. 2004). In contrast, previous modeling efforts to predictgreater and lesser prairie-chicken lek occurrence and describerelationships with landscape characteristics have not beenbased on formal statistical designs, which can introducepotential biases (e.g., Gregory et al. 2011, Jarnevich andLaubhan 2011). However, conclusions from our study arelimited to lek density within the lesser prairie-chicken rangein Texas. A similar modeling effort across the lesser prairie-chicken range could inform regional habitat-priority maps,which are currently lacking (Hagen 2010). This wouldaccount for inherent variability throughout the lesser prairie-chicken range due to variation in anthropogenic activity,grazing intensity, fire frequency, soil types, local weather, anda suite of other factors and thus, improve local managementefforts within a regional framework. Further, modelingdemographic parameters such as nest success with change inhabitat composition or anthropogenic features over timecould improve our ability to adaptively manage lesser prairie-chicken populations in a dynamic landscape.

MANAGEMENT IMPLICATIONS

Balancing grassland and shrubland on the landscape willpromote greater lesser prairie-chicken lek densities in Texas.Our results suggested that in the northeast region, lek densitywill be maximized on landscapes composed of 88% grasslandand 12% shrubland, whereas in the southwest region,greatest lek density will likely occur on landscapes composedof 44% grassland and 56% shrubland. This can be achieved

Figure 3. Predicted lesser prairie-chicken lek density for the northeast andsouthwest regions of the Texas Panhandle in response to total percentage ofgrassland and shrubland, 2010–2011, based on the 2 most competitivehierarchical distance-sampling models.We held paved road density constantat 0, active oil and gas well density constant at 0, and percentage grasslandconstant at 50%.

Figure 4. Predicted lesser prairie-chicken lek density for the northeast andsouthwest regions of the Texas Panhandle in response to paved road density,2010–2011, based on the 2 most competitive hierarchical distance-samplingmodels. We held active oil and gas well density constant at 0, percentage ofgrassland constant at 66%, and total percentage of grassland and shrubland at100%.

Figure 5. Predicted lesser prairie-chicken lek density for the northeast andsouthwest regions of the Texas Panhandle in response to active oil and gaswell density, 2010–2011, based on the 2 most competitive hierarchicaldistance-sampling models. We held paved road density constant at 0,percentage of grassland constant at 66%, and total percentage of grasslandand shrubland at 100%.

150 The Journal of Wildlife Management � 78(1)

through habitat management techniques, such as prescribedburns or herbicide treatments to control trees or shrubs andpromote native grass recruitment (Applegate and Riley 1998,Patten et al. 2005). We observed a negative relationshipbetween lek density and oil and gas well density in the TexasPanhandle, which is consistent with observed negativeimpacts of anthropogenic development and activities onlesser prairie-chickens and other prairie grouse. Therefore,increasing oil and gas development in the occupied rangecould be a significant factor in the long-term conservation oflesser prairie-chicken populations in Texas. Given that mostof our lek detections and greatest predicted lek densityestimates occurred in Gray, Hemphill, and Lipscombcounties in the northeast Panhandle, and in Bailey, Cochran,and Yoakum counties in the southwest Panhandle (Fig. 1),careful consideration of oil and gas well placement and theconstruction of additional paved roads in these areas iswarranted to reduce potential negative impacts on lesserprairie-chickens.

ACKNOWLEDGMENTS

We thank J. Bonner and R. Martin from TPWD fororganizing and leading TPWD’s contribution to the aerialsurveys in 2010 and 2011, respectively. We also thankTPWD for sharing the transmission line and oil and gasdata for our modeling efforts. We thank J. Ashling, J. Leal,J. R. Leal, and K. Pyle for assisting with the aerial surveys.We also could not have completed our aerial surveyssuccessfully or safely without the skill and guidance of ourpilots, A. Wheatly, D. Wooten, A. Lange, M. Huggins, K.Lange, R. Norris, and T. Webb. This project was funded bya U.S. Department of Energy grant and additional financialcontribution from TPWD and Texas Tech University. Thefindings and conclusions in this article are those of theauthors and do not necessarily represent the views of the U.S.Fish and Wildlife Service. The use of trade, firm, or productnames is for descriptive purposes only and does not implyendorsement by the U.S. Government. This is Texas TechUniversity, College of Agricultural Science and NaturalResources technical publication T-9-1232.

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