adaptive rangeland management beneﬁts grassland birds .... davis_2019_carm effects on...

Adaptive rangeland management benefits grassland birds utilizingopposing vegetation structure in the shortgrass steppe

KRISTIN P. DAVIS,1,6,7 DAVID J. AUGUSTINE,2 ADRIAN P. MONROE,3 JUSTIN D. DERNER,4 AND CAMERON L. ALDRIDGE5

1Graduate Degree Program in Ecology, Department of Ecosystem Science and Sustainability, Natural Resource Ecology Laboratory,Colorado State University, in cooperation with the U.S. Geological Survey, Fort Collins Science Center, Campus Delivery 1476,

Fort Collins, Colorado 80523-1476 USA2USDA-ARS Rangeland Resources and Systems Research Unit, 1701 Center Avenue, Fort Collins, Colorado 80526 USA

3Natural Resource Ecology Laboratory, Colorado State University, in cooperation with the U.S. Geological Survey,Fort Collins Science Center, 2150 Centre Avenue, Building C, Fort Collins, Colorado 80526 USA

4USDA-ARS Rangeland Resources and Systems Research Unit, 8408 Hildreth Road, Cheyenne, Wyoming 82009 USA5Department of Ecosystem Science and Sustainability, Natural Resource Ecology Laboratory, Colorado State University,in cooperation with the U.S. Geological Survey, Fort Collins Science Center, 2150 Centre Avenue, Building C, Fort Collins,

Colorado 80526 USA

Citation: Davis, K. P., D. J. Augustine, A. P. Monroe, J. D. Derner, and C. L. Aldridge. 2019. Adaptiverangeland management benefits grassland birds utilizing opposing vegetation structure in the shortgrasssteppe. Ecological Applications 00(00):e02020. 10.1002/eap.2020

Abstract. Rangelands are temporally and spatially complex socioecological systems onwhich the predominant land use is livestock production. In North America, rangelands also con-tain approximately 80% of remaining habitat for grassland birds, a guild of species that has expe-rienced precipitous declines since the 1970s. While livestock grazing management may benefitcertain grassland bird species by generating the vegetation structure and density they prefer,these outcomes are poorly understood for avian species breeding in the shortgrass steppe. Weevaluated how two grazing management systems, continuous, season-long grazing and adaptive,rest-rotational grazing, affected grassland bird abundance from 2013 to 2017 in Colorado’sshortgrass steppe. We examined grazing impacts in conjunction with ecological sites, which con-stitute unique soil and plant communities. When grazing management was evaluated in conjunc-tion with spatial variation in ecological sites, we found three of our five focal bird speciesresponded to grazing management. McCown’s Longspur abundance decreased in pasturesrested from grazing the previous year. The effect of grazing on Horned Lark and GrasshopperSparrow depended on ecological site: Horned Lark density was highest in pastures that wereintensively grazed and Grasshopper Sparrow density was highest in pastures that were rested theprevious year in the least productive ecological site. In addition, densities of all species variedacross ecological sites. Our results suggest consideration of soil and vegetation characteristicscan inform how adaptive management is applied on a landscape to benefit the full suite of breed-ing grassland birds, including species that have seemingly contrasting habitat needs. For example,a manager could target adaptive drought mitigation practices, such as resting pastures for 1 yr togenerate grassbanks, in less productive soils to benefit grassland birds that prefer taller/denservegetation structure, or could apply intensive, short-duration grazing on less productive soils tobenefit species preferring shorter/sparser vegetation. A single year of intensive, short-durationgrazing (i.e., one component of our rotational treatment) across the landscape, however, mightnot create sufficient habitat for species that prefer short/sparse vegetation in our system(e.g., McCown’s Longspur). Ultimately, our study indicates how cattle production on range-lands can congruently support grassland bird populations in the shortgrass steppe.

Key words: abundance; adaptive management; grassland birds; hierarchical count models; rotationalgrazing; shortgrass steppe.

INTRODUCTION

Rangelands comprise approximately 40% of theEarth’s terrestrial surface and today are managed

primarily in ways that allow native or naturalized vege-tation to support domestic livestock production (Hole-chek et al. 2011, Sayre et al. 2013). Domestic livestockproduction on rangelands currently supports the liveli-hoods of an estimated 1 billion people, and an estimated2 billion people rely on products from rangelands (Sayreet al. 2013, Briske et al. 2015). While rangelands ofAfrica and Asia have supported domestic livestock,often in conjunction with a diversity of native herbi-vores, for thousands of years, rangelands of North andSouth America have undergone a transformation over

Manuscript received 15 July 2019; accepted 30 August 2019.Corresponding Editor: Elisabeth Huber-Sannwald.

6 Present address: Graduate Degree Program in Ecology,Department of Fish, Wildlife, and Conservation Biology, Col-orado State University, Campus Delivery 1474, Fort Collins,Colorado 80523-1474 USA.

7 E-mail: [email protected]

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the past 200 yr from dominance by native grazers andbrowsers to dominance by domestic livestock. Despitethis focus on livestock production, these systems remainecologically diverse (Briske et al. 2015) and livestock canpotentially serve as critical ecological surrogates fornative grazers depending on how their abundance andspatiotemporal distribution are managed (Derner et al.2009, Allred et al. 2011). For example, livestock manage-ment practices can enhance native wildlife populations,such as ungulates in Africa (Odadi et al. 2017, Russellet al. 2018) and birds in Australia and North America(Commonwealth of Australia 2016, Golding and Dreitz2017, Lipsey and Naugle 2017). The degree to whichgrazing practices promote biodiversity and the potentialeconomic trade-offs for producers, however, rarely havebeen studied in an experimental framework. Further-more, evidence suggests current agricultural yield trends,including livestock production, are not sufficient to sup-port the world’s future population (Ray et al. 2013).With a projected global population increase of 2–4 bil-lion people by 2050 (Cohen 2003), understanding how tobalance livestock production needs with biodiversity con-servation in grazed systems is increasingly important.In North America, rangeland management histori-

cally emphasized practices that created a more uniformpattern of forage utilization across landscapes to mostefficiently consume forage without degrading plant com-munities (Fuhlendorf et al. 2017, Sayre 2017). However,rangelands are increasingly being recognized as spatially,temporally, and ecologically complex agroecosystems(Fuhlendorf et al. 2012, 2017) and, to the extent thatmanagement for uniform utilization leads to structuraland compositional homogenization of rangelands, suchpractices can also potentially decrease the capacity forrangelands to support native biological diversity. As aresult, land management agencies and conservationorganizations have recommended heterogeneous appli-cations of grazing, such as patch-burn grazing and rota-tional grazing systems that include both intense grazingand rest from grazing, to support native flora and fauna(Fuhlendorf et al. 2006, Toombs and Roberts 2009).Rotational grazing systems also are posited to generateheterogeneity by providing a sufficiently long releasefrom grazing pressure, thereby allowing vegetation torecover and attain structural characteristics that supportdifferent wildlife species compared to recently grazedcommunities (Briske et al. 2011).Although rotational grazing systems are currently

widespread across North America’s rangelands, the tem-poral and spatial pattern of livestock movement varieswidely across individual ranches, with unknown implica-tions for sustaining biodiversity (Briske et al. 2008,Roche et al. 2015). Additionally, experimental evidencefor benefits of grazing for wildlife is lacking (Briskeet al. 2011). A review of grazing experiments indicatedrotational grazing is not superior to continuous, season-long grazing for a variety of plant and animal responses(Briske et al. 2008, 2011). This contradicts experiential

knowledge of some managers and producers (Teagueet al. 2013, Roche et al. 2015) as well as other evidencesuggesting the heterogeneous application of grazing cansupport biodiversity and wildlife populations on range-lands (Toombs et al. 2010, Ahlering and Merkord 2016,Golding and Dreitz 2017, Lipsey and Naugle 2017). Twoimportant limitations of grazing experiments conductedto date are that experiments occurred in artificially smallpastures that prevented livestock from expressing thetypes of foraging behavior that typically occur onranches (Briske et al. 2008, Teague and Barnes 2017),and the rest periods implemented in these studies weretoo short. With short rest periods (e.g., where pasturescould be grazed multiple times in a growing season),rotational grazing could potentially produce morehomogenous vegetation structure because vegetationwould not have sufficient time to regrow between restperiods (Fuhlendorf and Engle 2001). There is a clearneed to test the effects of grazing management on plantsand animals at greater spatiotemporal scales with stan-dardized stocking rates and longer rest periods (Briskeet al. 2008, 2011).The few grazing experiments that have examined man-

agement for heterogeneous vegetation structure andwildlife habitat occurred primarily in tallgrass andmixed-grass ecosystems of North America’s GreatPlains (Fuhlendorf et al. 2006, Derner et al. 2008,Briske et al. 2011, Golding and Dreitz 2017, Teague andBarnes 2017). Much less is known about the effects ofheterogeneity-based grazing management on biodiver-sity in semiarid ecosystems, such as the shortgrass steppeof North America. The shortgrass steppe occupies thedriest and warmest region of the Great Plains, and expe-riences more intra- and inter-annually variable precipita-tion than the Eastern Great Plains (Lauenroth et al.1999). This results in a unique, drought- and grazing-adapted vegetation community containing the shortestvegetation structure of North America’s grasslands(Lauenroth et al. 1999). The shortgrass steppe also con-tains some of the largest intact rangelands in the GreatPlains (Samson et al. 2004). This is important becausegrassland conversion to agriculture, rangeland manage-ment, and urban development in the Great Plains likelycontributed to notable sharp declines in wildlife popula-tions. For example, grassland birds breeding in the GreatPlains are one of the most threatened guilds of birds inNorth America (Brennan and Kuvlesky 2005, Saueret al. 2017). They also differentially utilize a gradient ofvegetation structure for breeding habitat, where somespecies prefer tall, dense structure while others prefershort, sparse structure (Knopf 1996). Research in thecooler and/or wetter mixed-grass and tallgrass prairiehas shown some grassland bird species benefit fromincreased heterogeneity in vegetation created by hetero-geneous grazing management (Fuhlendorf et al. 2006,Hovick et al. 2012, Ahlering and Merkord 2016). Thesestudies, however, have primarily investigated the interac-tive effect of fire and grazing (e.g., patch-burn grazing)

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on grassland birds rather than grazing alone. Prescribedfire is a much less common management tool on range-lands in the shortgrass steppe than in the rest of theGreat Plains, though patch-burn grazing in the short-grass steppe can create habitat for some declining grass-land bird species (e.g., mountain plover; Augustine andDerner 2015). It is currently unknown whether grazingalone (i.e., in the absence of fire) to promote vegetationheterogeneity can benefit grassland birds breeding in theshortgrass steppe.To examine how grazing management can sustain

wildlife populations, we implemented a grazing experi-ment on the Central Plains Experimental Range in Col-orado’s shortgrass steppe. This ongoing experiment isexamining the effects of traditional grazing management(i.e., continuous, season-long grazing) vs. collaborativeadaptive rotational grazing on multiple ecosystem ser-vices, including grassland bird abundance. One of theexplicit goals of this experiment is to maintain orincrease populations of five grassland birds breeding onthe Central Plains Experimental Range: GrasshopperSparrow (Ammodramus savannarum), Western Mead-owlark (Sturnella neglecta), Lark Bunting (Calamospizamelanocorys), Horned Lark (Eremophila alpestris), andMcCown’s Longspur (Rhynchophanes mccownii). Thesefive species experienced significant population declinesover the past 70 yr (Sauer et al. 2017), and a group oflocal stakeholders engaged in our project identified thesespecies as management priorities on the site. Addition-ally, McCown’s Longspur and Grasshopper Sparroware species of conservation concern in multiple stateswhere they breed (Oklahoma Department of WildlifeConservation 2005, Colorado Parks and Wildlife 2010,Guttery et al. 2017).Grassland birds differentially use a gradient of vegeta-

tion structural heterogeneity generated by disturbanceprocesses such as grazing: some species prefer short,sparse vegetation created by heavy grazing, whereasothers prefer tall, dense vegetation under lighter grazingregimes (Wiens 1973, Knopf 1996). We implemented ourthree grazing treatments in an attempt to create hetero-geneous habitat structure and determined if avian den-sity varied by treatment. We predicted our focal specieswould respond to grazing management in the mannerhypothesized by Knopf (1996), where species that prefertaller vegetation structure (e.g., Western Meadowlark,Grasshopper Sparrow; Vickery 1996, Davis and Lanyon2008) would be more abundant in recently rested pas-tures of the rotational grazing treatment, while speciesthat prefer shorter vegetation structure (e.g., HornedLark, McCown’s Longspur; Beason 1995, With 2010)would be more abundant in recently intensively grazedpastures of the rotational grazing treatment. Lark Bunt-ings prefer more moderate amounts of vegetation struc-ture (Shane 2000) and, because we expected vegetationstructure to be more homogenous in our traditionaltreatment, we predicted buntings would be most abun-dant in traditionally grazed pastures.

METHODS

Study area

The Central Plains Experimental Range (CPER) is a6,270-ha experimental range site managed by the USDAand located outside of Nunn, Colorado (40°500 N,104°430 W) in the semiarid shortgrass steppe. Mean dailymaximum and minimum temperatures range from�12°C to 4°C in January and 15°C to 26°C in July (Ska-gen et al. 2018). Long-term mean annual precipitationon the CPER is 340 mm (Augustine and Derner 2015),greater than 80% of which occurs during the growingseason of April through September (Lauenroth andBurke 2008). Topography is flat to gently rolling; soilsrange from fine sandy loams on upland plains toalkaline salt flats bordering a large drainage runningnorth-south in the eastern portion of the site. Two C4

shortgrass species, blue grama (Bouteloua gracilis) andbuffalograss (Bouteloua dactyloides), comprise over 70%of aboveground net primary productivity at the CPER(Lauenroth and Sala 1992). C3 perennial grasses (Pas-copyrum smithii, Hesperostipa comata, and Elymus ely-moides), C4 bunchgrasses (Aristida longiseta, Sporoboluscryptandrus), plains pricklypear cactus (Opuntia polya-cantha), subshrubs (Gutierrezia sarothrae, Eriogonumeffusum, Artemisia frigida), and saltbush (Atriplex canes-cens) are less abundant but widespread and generate tal-ler structure on the landscape (Augustine and Derner2015).

Experimental design

The collaborative adaptive rangeland management(CARM) experiment began in 2013 to contrast theeffects of a collaborative form of rotational, multi-paddock grazing management with continuous, season-longgrazing management (traditional rangeland management[TRM]) on multiple ecosystem services, including grasslandbird diversity, abundance, and reproductive performance(Wilmer et al. 2018). For this experiment, 20 130-hapastures were paired into 10 blocks where each blockcontained two pastures similar in terms of soil and plantcharacteristics, topographic patterns as measured by atopographical wetness index (TWI; a remotely sensedindex of water flow on a landscape; Beven and Kirkby1979), and prior management history of season-longgrazing at moderate stocking rates. One pasture in eachpair was randomly assigned to the TRM treatment.Each TRM pasture was grazed throughout the growingseason (mid-May to early October) by a single herd ofyearling steers. The other pasture in each pair wasassigned to the CARM treatment (Fig. 1).While all TRM pastures were grazed (i.e., none were

rested) by 10 small herds of approximately equal size thatoccupied each pasture separately, some of the CARMpastures were grazed by a single large herd of yearlingsteers managed with an adaptive, rotational grazing

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system and some were rested each year. Details of the cat-tle management strategy applied to the CARM pastureswere decided by an 11-member stakeholder group thatdeveloped an initial grazing management plan in 2013,and subsequently met three times annually during 2014–2017 to review results from prior grazing seasons anddecide on the stocking rate and grazing sequence for thesubsequent grazing season. This stakeholder groupincluded ranchers, stewardship biologists from non-profitconservation organizations, and land managers from fed-eral and state agencies, who collectively made decisionsbased on consensus or supermajority (see Wilmer et al.2018 for details). The stakeholder group decided to man-age the CARM pastures using a single large herd of year-ling steers that would move among eight pastures eachyear, contingent on weather patterns, with the remainingtwo pastures planned for year-long rest. The same totalnumber of steers grazed in the CARM and TRM pas-tures each year, which was initially set at 214 yearlings in2014 based on the recommended moderate stocking rate

for the soil and plant communities present in the studyarea (equivalent to 0.61 animal unit months [AUM]/ha,where an animal unit month is the amount of forageneeded to support one month of grazing by the equiva-lent of a mature 1,000 pound cow (1 pound = 0.45 kg)with or without a suckling calf; USDA-NRCS 2007a, b,c, Society for Rangeland Management 2017). In subse-quent years, the stakeholder group adjusted the stockingrate in April of each year prior to the 15 May grazingstart date, depending on past vegetation conditions andweather forecasts for the upcoming grazing season. Dueto favorable weather conditions in 2014 and 2015, stake-holders increased stocking rate 5% each year to 0.64,0.67, and 0.70 AUM/ha in 2015, 2016 and 2017, respec-tively. The TRM stocking rate was also adjusted eachyear to match the CARM stocking rate, such that pas-tures in the two treatments differed only in the spatiotem-poral pattern of cattle grazing intensity. Pretreatmentdata were collected in 2013, when all 20 pastures receivedthe TRM treatment.

FIG. 1. Monitoring plots within each ecological site among 10 pasture pairs (blocks), where one pasture in each pair receivedthe collaborative adaptive rangeland management (CARM) treatment and one received a traditional rangeland management(TRM) treatment at the Central Plains Experimental Range (CPER) in northeastern Colorado. The light gray line represents theboundary of the CPER. (A) Grazing treatment applications in 2013 and 2014, in relation to the bird surveys; 2013 was a pretreat-ment year, so the entire site was traditionally grazed (TRM). Because birds were surveyed only a few weeks after grazing treatmentswere applied each year, we considered grazing treatment to have a lagged effect on abundance (Abundance models). Thus, we alsoconsidered 2014 as a pretreatment year. (B–D) Grazing treatment applications in 2015–2017, respectively.


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Management of the CARM pastures during 2014–2017 was designed to apply two contrasting grazingintensities to pastures, consisting of either (1) intense butshort-duration grazing by the large cattle herd (at 10times greater stocking density than TRM pastures,which we hereafter refer to as pulse grazing), or (2) year-long lack of grazing (which we hereafter refer to as rest).Which CARM pastures received the pulse grazing treat-ment and which were rested from grazing varied acrossyears and depended on the grazing sequence planned bythe stakeholders as well as on-the-ground, weather-dependent conditions (i.e., forage biomass and cattlebehavior) measured weekly during the grazing season.Based on weather and vegetation conditions experiencedduring our study, we applied year-long rest to 3, 6, 3,and 1 of the CARM pastures during 2014–2017, respec-tively, with the remaining 7, 4, 7, and 9 pastures receiv-ing pulse grazing (Fig. 1). The larger number of restedpastures in 2015 was a result of above-average forageproduction in both 2014 and 2015, which allowed theCARM cattle herd to meet its forage requirements bygrazing only four pastures over the growing season.To evaluate vegetation and avian responses to grazing

management, we sought to account for variation withinand among pastures in soil types and associated varia-tion in plant communities and habitat potential forbirds. To achieve this, we used maps of ecological sitesderived from the national soil survey (SSURGO), wherean ecological site represents a distinctive kind of landwith specific soil processes and properties that determinethe land’s ability to support certain kinds and amountsof vegetation (Duniway et al. 2010). Our study pasturesencompassed three types of ecological sites: loamyplains, sandy plains, and salt flats (USDA-NRCS 2007a,b, c). On the CPER, the loamy plains ecological site isdominated by C4 shortgrasses (B. gracilis, B. dacty-loides; USDA-NRCS 2007a) and is the most prevalentbut least productive ecological site; the sandy plains eco-logical site is characterized by increased codominance byC3 midgrasses (P. smithii, H. comata) and scatteredshrubs (A. canescens; USDA-NRCS 2007b) and is mod-erately prevalent and productive; and the salt flats eco-logical site is characterized by the dominance of C4

saltgrasses (Sporobolus airoides, Distichlis spicata;USDA-NRCS 2007c) and is the least prevalent but mostproductive ecological site.

Environmental data

Pastures were stratified by ecological site and monitor-ing plots were placed randomly within strata. Each plotcontained a systematic grid of four 25-m transects ori-ented north-south and spaced 106 m apart where wemeasured various vegetation metrics. Because vegetationstructure is known to influence grassland bird popula-tions (Knopf 1996, Fisher and Davis 2010, Lipsey andNaugle 2017), we used visual obstruction readings(VOR) to quantify vegetation height density. We

measured VOR by placing a Robel pole (Robel et al.1970; modified with 1-cm increments) at eight locationsspaced every 3 m along the transects in each monitoringplot and recorded the highest band on the pole that wasat least partially obscured by vegetation. We measuredVOR annually in June and calculated the mean VOR foreach avian point count location per year. Data are pro-vided in Data S1.

Avian data

We collected avian abundance data using a standard6-minute point count (Hanni et al. 2013; Fig. 1). Avianpoint count locations were placed in the center of eachplot, such that vegetation surrounding the point countlocation was quantified along with avian abundance.Point count locations were surveyed between sunrise and~10:30 twice during the breeding season between 26May and 17 June. One survey of all points was con-ducted by the same observer each year, and a second sur-vey of all points was conducted by an observer thatchanged each year. Surveys were separated by a range of1–9 d. Observers used a rangefinder to record the dis-tance to all individual birds detected (i.e., the pointcount had no fixed radius) and recorded the sex (if deter-minable) and method of detection (e.g., aural, visual) foreach individual. Data are provided in Data S1.

Statistical approach

We fit hierarchical distance sampling models (K�eryand Royle 2016) in a Bayesian framework to examinethe effect of grazing management on grassland birdabundance. These models use a joint likelihood to simul-taneously estimate parameters associated with the detec-tion process and the biological process (in this case,abundance).

Model structure

We assumed observed counts y at point-year k (withcounts summed over the two surveys) were the outcomeof a binomial distribution conditioned on abundance Nk

and detection probability pk:

yk � binomialðNk; pkÞ.

We estimated the detection process using a distancesampling model where pk was a function of detectiondistances from each observation (Detection model). Wethen modeled Nk for each species as a Poisson randomvariable with mean abundance kk:

Nk �PoissonðkkÞ.We specified covariates to kk on the log-scale, and we

included an offset for the number surveys at each point-year (log(2)). We therefore estimated the number of indi-viduals present and available per survey (Nichols et al.


2009), and we assumed probabilities of presence andavailability were constant or did not vary by treatmentor ecological site (Monroe et al. 2019). We then calcu-lated density based on abundance and the maximumdetection radius for each species (K�ery and Royle 2016).We fit models to all detections of both sexes for Wes-

tern Meadowlark, Horned Lark, and Grasshopper Spar-row because sex could rarely be distinguished in the fieldwith certainty. Lark Bunting and McCown’s Longspurmales, however, have unique flight displays (skylarking)during the breeding season to attract mates (Shane 2000,With 2010). Skylarking males of these species are sub-stantially more detectable than females (which do notskylark), and over 90% of our detections for these spe-cies were males. Due to this skew in detection, we fitmodels for Lark Bunting and McCown’s Longspur tomale detections only. In addition, there was only onedetection of a McCown’s Longspur on a non-loamyplains ecological site over the 5 yr of our surveys.Therefore, we fit models for McCown’s Longspur todetections of males on the loamy plains ecological siteonly.

Detection model

To account for potential variation in detection proba-bility, we first truncated the furthest 10% of our distancesampling data as they can be difficult to model (sensuBuckland et al. 2001). We then used distance samplingwith a hazard rate detection function to model the shapeof the detection function (Buckland et al. 2001). Weused the hazard rate instead of the half-normal detectionfunction because preliminary analyses of our data in theprogram Distance (version 6.3; Thomas et al. 2010) sug-gested the hazard rate function fit best for our data. Weused a log-link function to model covariates on the scaleparameter of the detection function (r; Royle et al.2004). These covariates included VOR and a randomterm for year. We specified a vague normal prior withmean 0 and variance 100 for the coefficient for VOR.The random term for year was normally distributed withmean 0. Due to convergence issues in preliminary analy-ses, we specified a weakly informative half-Cauchy priorfor the standard deviation of the random term for year(Gelman 2006).

Abundance models

For each species, we first fit a model on abundance thatincluded effects of year, grazing management, and ecolog-ical site (hereafter, our base treatment model). Weincluded an effect of year in these models (with the inter-cept representing the reference year, 2013) because grass-land bird populations vary annually (Green et al. 2019)and preliminary investigations of our raw data revealedthat yearly changes in abundance were not linear. We cat-egorized grazing management as three treatments: thetwo components of the CARM treatment (pulse grazed

and rested) and the traditional, season-long grazing treat-ment (TRM, which was the reference treatment). We trea-ted grazing management as a lagged effect on abundancebecause birds were surveyed each year at the beginning ofthe growing season, when grazing treatments had onlybeen implemented for a few weeks. We did not examinethe contrast between TRM and CARM where CARMwas a single treatment (i.e., combining pulse grazed andrested pastures into one adaptive rotational treatment)because we hypothesized that pulse grazing and restwould have substantially different and species-specificeffects on bird abundance. Our ecological sites includedthe three dominant ecological sites on the CPER (Experi-mental design): loamy plains (which was the referencesite), sandy plains, and salt flats.To account for the experimental design of the CARM

experiment, we modeled an effect of experimental block(i.e., pasture pairs, see Experimental design; n = 10) onthe intercept of abundance for all models and speciesexcept McCown’s Longspur. For McCown’s Longspur,we modeled a pasture-specific intercept because theparameters associated with block were unidentifiable.This resulted in the following base treatment model forspecies-specific abundance:

logðkkÞ¼b0;blockk or pasturekþb12014kþb22015kþb32016k

þb42017kþb5pulse grazedkþb6restedkþb7salt flatskþb8sandyplainskþ logð2Þ:

In addition to the base treatment model for abun-dance, we fit an abundance model with covariates fromthe base treatment model and an interaction betweengrazing management and ecological site. Our base treat-ment model represented our interest in how grazingmanagement affected grassland bird abundance whileaccounting for known sources of variation across yearsand ecological sites on our study site. We consideredinteractions between treatment and ecological sitebecause the ecological sites on the CPER differ in pro-ductivity (USDA-NRCS 2007a, b, c) and previousresearch found soil productivity explained grazingeffects on grassland birds (Lipsey and Naugle 2017).Because we were interested in specific covariate relation-ships rather than overall model predictive ability, we didnot formally compare models with model selection.For all abundance models, we specified vague normal

priors with mean 0 and variance 100 for all coefficientsand the mean of the block or pasture random terms. Wespecified a uniform prior from 0 to 10 for the standarddeviation of the block or pasture random terms.

Computing

We fit all models using JAGS (version 4.2.0, Plummer2003) and the package jagsUI (version 1.5.0; Kellner2018) in R (version 3.4.3; R Development Core Team2017) with 200,000–900,000 iterations from three


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parallel MCMC chains each and discarded 100,000–850,000 iterations per chain as burn-in. To assess param-eter convergence, we visually evaluated traceplots andused the Gelman-Rubin statistic (Gelman et al. 2013).We considered parameters with Gelman-Rubin statistics≤1.10 as converged (Gelman et al. 2013). To assessmodel fit, we used posterior predictive checks (BayesianP values) based on chi-square discrepancy test statistics(K�ery and Royle 2016). We considered Bayesian P values≤0.1 or ≥0.9 to indicate a lack of fit. We also calculatedmeans and 95% credible intervals (CRI) for all modelparameters. We considered a covariate to have strongsupport for affecting a focal process (abundance ordetection) if the CRI of the covariate did not includezero.

RESULTS

We surveyed 92 point count locations twice per yearfor 5 yr, resulting in 460 counts by point-year. LarkBunting males were detected the most (2,660 detections)and McCown’s Longspur males were detected theleast (287 detections). For both sexes combined ofWestern Meadowlark, Horned Lark, and GrasshopperSparrow, we recorded 1,367, 1,327, and 715 detections,respectively.We first fit the base treatment model for all species

(Table 1). We then fit an interaction model that includedthe same covariates as the base treatment model plusinteraction terms for grazing treatments and ecologicalsites (see Abundance models; Table 2). Interactionsbetween grazing treatment and ecological site were sup-ported only for Horned Lark and Grasshopper Sparrow(Table 2). We therefore interpret estimates from theinteraction models for these species. For all other spe-cies, we interpret estimates from the base treatmentmodels (Table 1). All models we interpret had adequatefit.Vegetation height–density (i.e., VOR) affected detec-

tion probability of most of our focal species (Tables 1, 2).The probability of detecting Western Meadowlarkdecreased with increasing VOR (Table 1). The probabilityof detecting Grasshopper Sparrow increased while theprobability of detecting Horned Lark decreased withincreasing VOR (Table 2).We found three of our five focal species responded to

adaptive grazing management. For two species, HornedLark and Grasshopper Sparrow, these responsesdepended on ecological site. Our interaction modelsrevealed that intensive grazing in the loamy plains eco-logical site benefited Horned Larks in the subsequentyear, whereas grazing had more neutral effects on den-sity in the sandy and salt flats ecological sites (Table 2,Fig. 2). In the loamy plains ecological site, Horned Larkdensity increased by an average of 0.213 or 0.202 birds/hain pulse-grazed pastures compared to rested (i.e., nograzing throughout the previous growing season) or tra-ditionally grazed pastures, respectively. In the sandy

plains and salt flats ecological sites, however, HornedLark density ranged from decreasing by 0.202 birds/hato increasing by 0.086 birds/ha on average in pulsegrazed pastures compared to rested or traditionallygrazed pastures. This pattern was similar but the oppo-site for Grasshopper Sparrow—resting pastures in theloamy plains ecological site benefited Sparrows in thesubsequent year, whereas grazing had more neutraleffects in the sandy and salt flats ecological sites(Table 2, Fig. 3). In the loamy plains ecological site,Grasshopper Sparrow density increased by an average of0.415 or 0.361 birds/ha in rested pastures compared topulse or traditionally grazed pastures, respectively. Inthe sandy plains and salt flats ecological sites, however,Grasshopper Sparrow density ranged from decreasingby 0.022 birds/ha to increasing by 0.164 birds/ha onaverage, in rested pastures compared to pulse- or tradi-tionally grazed pastures, respectively.From our base treatment models, we found resting

pastures negatively affected McCown’s Longspur popu-lations in the subsequent year (Table 1, Fig. 4). In theloamy plains ecological site, male McCown’s Longspurdensity increased by an average of 0.024 or 0.018 birds/ha in traditionally and pulse grazed pastures, respec-tively, compared to rested pastures. Grazing treatmentdid not strongly influence densities of Lark Buntingmales or Western Meadowlark (Table 1, Figs. 4, 5).For all focal species, year and ecological site influenced

bird density more than grazing management (i.e., thestandardized coefficient estimates for years and ecologi-cal sites were greater than those of grazing; Tables 1, 2).Density for each species differed between 2013 (i.e., thepretreatment year) and 2016, and differed in at least oneyear compared to 2013 (e.g., male Lark Bunting densitywas lower in 2016 compared to 2013; Table 1).McCown’s Longspur was detected almost exclusively inthe loamy plains (i.e., the least productive) ecological site,and Horned Larks were more abundant on the loamyplains ecological site compared to the sandy plains andsalt flats ecological sites (i.e., the moderately and mostproductive ecological sites, respectively; Fig. 2). LarkBunting males were more abundant in the loamy andsandy plains ecological sites than in the salt flats ecologi-cal site (Table 1). In contrast, Western Meadowlarks andGrasshopper Sparrows were more abundant in the saltflats and sandy plains ecological sites (Tables 1, 2).

DISCUSSION

Heterogeneity-based grazing management is positedto maintain avian biodiversity by generating the entirespectrum of vegetation structure utilized by grasslandbirds on their breeding and non-breeding grounds(Knopf 1996, Fuhlendorf and Engle 2001, Derner et al.2009, Toombs et al. 2010, Hovick et al. 2014). In ourstudy, heterogeneous grazing management affected thedensity of three grassland bird species that prefer theopposite extremes of grassland vegetation structure in


TABLE1.

Coefficient

means

and95

%credibleintervals(C

RI)from

theba

setreatm

entmod

elsforeffectsmod

eled

onfocalp

rocesses

affectinggrasslan

dbird

abun

danceby

survey

pointat

theCentral

PlainsExp

erim

entalR

ange.

Factor

McC

own’sLon

gspu

rHornedLark

LarkBun

ting

Western

Meado

wlark

Grassho

pper

Sparrow

Mean

CRI

Mean

CRI

Mean

CRI

Mean

CRI

Mean

CRI

Abu

ndan

ceIntercept

�1.781

�3.822,�

0.26

20.92

20.66

9,1.16

41.35

51.16

4,1.54

3�0

.275

�0.553,0

.006

�0.768

�1.357,�

0.16

420

14�0

.125

�0.569,0

.371

0.38

10.13

1,0.63

11.20

51.03

9,1.37

30.59

70.27

2,0.92

00.27

7�0

.350,0

.886

2015

0.26

4�0

.145,0

.697

0.64

50.41

8,0.86

80.89

70.72

2,1.07

21.16

10.88

6,1.43

41.32

50.73

6,1.89

720

16�0

.666

�1.232,�

0.10

80.73

90.50

4,0.97

2�0

.369

�0.608,�

0.13

22.08

41.82

0,2.35

11.55

30.93

0,2.18

020

17�0

.207

�0.661,0

.268

0.32

30.09

1,0.55

3�0

.203

�0.416,0

.009

1.30

41.02

1,1.58

50.08

4�0

.534,0

.699

Pulse

grazed

�0.214

�0.729,0

.291

0.09

9�0

.034

,0.233

0.11

0�0

.009,0

.228

�0.048

�0.190,0

.093

�0.023

�0.218,0

.170

Rested

�1.278

�2.314,�

0.38

5�0

.038

�0.200

,0.120

0.05

0�0

.103,0

.200

0.02

1�0

.141,0

.179

0.37

80.14

3,0.60

7Sa

ndyplains

�0.557

�0.744

, �0.37

20.03

2�0

.089,0

.155

0.21

50.03

7,0.38

90.94

10.67

3,1.20

5Sa

ltflats

�1.240

�1.523

,�0.96

7�0

.412

�0.580,�

0.24

50.52

10.31

6,0.72

51.30

81.01

1,1.60

3Detection

Intercept

0.86

90.65

7,1.17

80.77

00.64

9,0.93

81.19

41.00

3,1.48

51.47

31.09

3,2.02

90.56

70.29

7,0.99

6VOR

�0.152

�0.332,0

.001

�0.074

�0.114

,�0.03

50.01

5�0

.008,0

.037

�0.036

�0.064,�

0.00

80.07

40.02

7,0.12

6SD

year

0.13

80.00

4,0.56

30.16

20.05

3,0.46

10.17

00.06

1,0.48

40.28

90.11

3,0.77

00.52

90.20

3,1.39

5

Notes:Effectson

abun

danceinclud

edyear

(201

3–20

17),grazingtreatm

ent(tradition

al,p

ulse

grazed,rested),a

ndecolog

ical

site

(loa

myplains,san

dyplains,saltflats;i.e.,theba

setreat-

mentmod

elon

abun

dance).Allmod

elswerefitwitharand

omterm

forblock(orpa

sture,

inthecase

ofMcC

own’sLon

gspu

r)mod

eled

ontheinterceptforabun

dance;

theintercept

repo

rted

here

represents

thetrad

itiona

lgrazing

treatm

entin

theloam

yplains

ecolog

ical

site

in20

13,a

veragedacross

blocks

(orpa

stures,inthecase

ofMcC

own’sLon

gspu

r).E

ffects

ondetectionprob

ability

includ

edvisual

obstructionread

ing(VOR)an

darand

omterm

foryear

(SD

year

representing

thestan

dard

deviationfortherand

omterm

).Valuesin

boldface

type

deno

tethosewhere

thecredible

interval

ofthecoefficientdo

esno

tinclud

e0.

Abu

ndan

ceforLarkBun

ting

was

mod

eled

usingmaledetections

only.A

bund

ance

forMcC

own’sLon

gspu

rwas

mod

eled

usingmaledetections

ontheloam

yplains

ecolog

ical

site

only.


Vol. 0, No. 0

TABLE2.

Coefficient

means

and95%

credibleintervals(C

RI)from

thegrazing-ecolog

ical

site

interactionmod

elsforeffectsmod

eled

onfocalp

rocesses

affectinggrasslan

dbird

abun

dance

bysurvey

pointat

theCentral

PlainsExp

erim

entalR

ange.

Factor

HornedLark

LarkBun

ting

Western

Meado

wlark

Grassho

pper

Sparrow

Mean

CRI

Mean

CRI

Mean

CRI

Mean

CRI

Abu

ndan

ceIntercept

0.89

90.64

1,1.14

11.34

31.15

0,1.53

2�0

.298

�0.584

,�0.00

9�0

.908

�1.499,�

0.31

320

140.38

80.13

9,0.64

11.20

71.04

0,1.37

40.59

50.26

9,0.92

30.33

2�0

.253,0

.917

2015

0.65

20.42

7,0.87

90.89

20.71

6,1.06

91.15

50.87

8,1.43

41.34

60.78

4,1.89

920

160.73

70.50

7,0.97

2�0

.346

�0.584

,�0.10

82.08

61.81

8,2.35

41.64

41.04

9,2.26

020

170.32

30.09

3,0.55

4�0

.201

�0.414

,0.010

1.30

31.02

1,1.58

80.14

5�0

.438,0

.734

Pulse

grazed

0.18

30.03

1,0.33

40.12

4�0

.031

,0.276

0.00

6�0

.205

,0.212

�0.201

�0.656,0

.227

Rested

�0.012

�0.186,0

.157

0.11

4�0

.049

,0.276

0.09

0�0

.117

,0.293

0.76

40.43

3,1.09

5Sa

ndyplains

�0.445

�0.649,�

0.24

30.05

7�0

.075

,0.190

0.26

00.05

5,0.45

91.01

10.69

8,1.32

7Sa

ltflats

�1.222

�1.558,�

0.90

0�0

.367

�0.553

,�0.18

40.55

80.32

1,0.79

21.46

91.12

8,1.80

9Pulse

grazed

9Sa

ndyplains

�0.351

�0.668,�

0.04

3�0

.015

�0.230

,0.197

�0.137

�0.435

,0.159

0.33

4�0

.157,0

.845

Rested9

Sand

yplains

�0.342

�0.833,0

.117

�0.329

�0.741

,0.057

�0.103

�0.464

,0.250

�0.624

�1.131,�

0.12

2Pulse

grazed

9Sa

ltflats

�0.337

�0.985,0

.267

�0.106

�0.459

,0.236

�0.026

�0.376

,0.319

0.04

9�0

.475,0

.591

Rested9

Saltflats

0.41

9�0

.299,1

.077

�0.591

�1.465

,0.152

�0.250

�0.676

,0.161

�0.797

�1.350,�

0.26

4Detection

Intercept

0.76

50.64

7,0.92

01.18

41.01

0,1.41

41.51

01.12

0,2.40

80.59

00.35

3,1.04

7VOR

�0.076

�0.117,�

0.03

80.01

6�0

.007

,0.038

�0.035

�0.064

,�0.00

70.07

30.02

6,0.12

3SD

year

0.16

10.05

2,0.45

40.16

30.06

2,0.42

70.30

10.11

3,0.89

50.52

10.19

8,1.47

2

Notes:Effects

onabun

danceinclud

edyear

(201

3–20

17),grazingtreatm

ent(tradition

al,pu

lsegrazed,rested),ecolog

ical

site

(loa

myplains,sand

yplains,saltflats),an

dinteractions

ofgrazingtreatm

entan

decolog

ical

site

(i.e.,theinteractionmod

elon

abun

dance).Allmod

elswerefitwitharand

omterm

forblockmod

eled

ontheinterceptforabun

dance;

theintercept

repo

rted

here

represents

thetrad

itiona

lgrazingtreatm

entin

theloam

yplains

ecolog

ical

site

in20

13,averaged

across

blocks.Effects

ondetectionprob

ability

includ

edvisual

obstruction

read

ing(VOR)an

darand

omterm

foryear

(SD

year

representing

thestan

dard

deviationfortherand

omterm

).Valuesin

boldface

type

deno

tethosewhere

thecredible

interval

ofthe

coefficientdo

esno

tinclud

e0.

Abu

ndan

ceforLarkBun

ting

was

mod

eled

usingmaledetections

only.Wecouldno

tfitagrazing-ecolog

ical

site

interactionmod

elforabun

dancefor

McC

own'sLon

gspu

rbecausethisspeciesoccurred

almostexclusivelyon

theloam

yplains

ecolog

ical

site

ontheCPER.


the shortgrass steppe (Knopf 1996, Augustine and Der-ner 2015). For two of these three species, effects of graz-ing depended on ecological site: intensive grazing andrest from grazing, respectively, positively affected densityof Horned Lark and Grasshopper Sparrow on the leastproductive ecological site. Ecological site and year irre-spective of grazing were also important drivers of den-sity for all focal species. This suggests grassland birdresponses to grazing are nuanced and depend on sourcesof landscape variation beyond just vegetation structure.As we predicted, a lack of grazing for an entire grow-

ing season negatively affected male McCown’s Longspurdensity in the subsequent year. This matches with thisspecies’ breeding preference for short, sparse vegetation(With 2010). We also predicted Horned Larks would

respond to intensive, short-duration grazing andGrasshopper Sparrows would respond to rest (Beason1995), so our results for these species were expected. Itwas interesting, however, that these species’ responseswere associated only with the least productive ecologicalsite. For Grasshopper Sparrows, this suggests restingpastures in the least productive ecological site may gen-erate structure tall enough for them to utilize the follow-ing breeding season. For Horned Larks, this supportstheir preference for short, sparse vegetation, as intensive

FIG. 2. Predicted posterior distributions of Horned Larkdensity (birds/ha) by grazing treatment and ecological site in2017 on the Central Plains Experimental Range. Densities werecalculated from the model with an interaction between grazingtreatment and ecological site on abundance.

FIG. 3. Predicted posterior distributions of GrasshopperSparrow density (birds/ha) by grazing treatment and ecologicalsite in 2017 on the Central Plains Experimental Range. Densi-ties were calculated from the model with an interaction betweengrazing treatment and ecological site on abundance.

FIG. 4. Predicted posterior distributions of male density(males/ha) of McCown’s Longspur and Lark Bunting in thetraditional, pulse grazed, and rested treatments in 2017 on theloamy plains ecological site on the Central Plains ExperimentalRange. Density of male Lark Buntings was calculated from thebase treatment model with year, ecological site, and grazingtreatment effects on abundance. Density of male McCown’sLongspurs was calculated from the model with year and grazingtreatment effects on abundance. Note that the x- and y-axesvary by species.

FIG. 5. Predicted posterior distributions of Western Mead-owlark density (birds/ha) in the traditional, pulse grazed, andrested treatments in 2017 on the loamy plains ecological site onthe Central Plains Experimental Range. Densities were calcu-lated from the base treatment model with year, ecologicalsite and grazing treatment effects on abundance.


Vol. 0, No. 0

grazing in the least productive ecological site would gen-erate the shortest, most sparse vegetation on the site. Wewere surprised to find that intensive grazing in the moreproductive ecological sites (i.e., the sandy plains or saltflats ecological sites) had more neutral effects on HornedLark density. We would expect pulse grazing on thesandy plains ecological site, for example, to benefitHorned Larks by generating the shorter structure theyprefer (Beason 1995) in a more productive ecologicalsite. However, preliminary analyses of vegetation datacollected over the course of our study on the CPER sug-gest average vegetation structure (i.e., VOR) in the pulsegrazed treatment was taller than in the traditional treat-ment across all ecological sites (D. Augustine, unpub-lished data). This may reflect the complexity ofimplementing adaptive management to achieve desiredoutcomes across space and time (Aldridge et al. 2004).Our pulse grazing treatment was designed to create shortvegetation through grazing at high stock density, buteach grazed pasture in the CARM treatment was onlypulse grazed once during a growing season. If the pas-ture was pulse grazed early in the growing season, vege-tation could regrow such that the pulse-grazed pasturecould potentially support tall, dense vegetation by theend of a growing season, and this structure would carryover to the following breeding season. In contrast, vege-tation in pastures pulse grazed at the end of the growingseason could remain relatively short until the next grow-ing season (i.e., when we conducted our surveys). Thetiming of grazing could be even more important depend-ing on whether it was a wet or dry year (Ahlering andMerkord 2016, Lipsey and Naugle 2017), where a pas-ture pulse grazed early in the season would be morelikely to support tall, dense vegetation by the end of thegrowing season in a wet year. This illustrates the inherentand significant complexity in rangeland systems, theneed to explicitly consider and manage for this complex-ity, and the importance of examining adaptive manage-ment in an experimental framework. Explicitly testingthe role of timing of adaptive rotational grazing on vege-tation and avian responses would be a useful futurestudy.We found no effect of grazing management on Wes-

tern Meadowlark or male Lark Bunting density. Previ-ous research found Western Meadowlark abundancewas both higher and lower in season-long vs. rotation-ally grazed pastures in mixed-grass prairie depending onthe year (Ranellucci et al. 2012), suggesting ambiguityin how grazing affects meadowlark populations. LarkBuntings prefer moderate structure (Shane 2000) andwere more abundant in continuously grazed pastures ina sagebrush system (Golding and Dreitz 2017), but evi-dence suggests that the distribution and abundance ofthis species is driven more by current and trends inannual and regional precipitation patterns than localhabitat conditions (Skagen and Adams 2012, Wilsonet al. 2018). We observed this pattern anecdotally on oursite; in the above-average precipitation years during our

study on the CPER (2014 and 2015), the Lark Buntingpopulation appeared to be substantially higher acrossthe entire site, while their population was noticeablylower in the average precipitation years (2013, 2016 and2017). Our findings for Lark Bunting support theseobservations, as male bunting density varied more byyear than by treatment in our models (Table 1). Overall,our results and these previous studies suggest that (1)both grazing management systems can generate suffi-cient habitat for male Lark Buntings and Western Mead-owlarks in the shortgrass steppe, (2) that these speciesare responding to a resource on the landscape that is notaffected by grazing management (e.g., insect abundance;Newbold et al. 2014), and/or (3) that abundance is dri-ven by processes occurring at scales larger than our site(Skagen and Adams 2012, Wilson et al. 2018, Greenet al. 2019).Our study supports the emerging paradigm of manag-

ing for heterogeneous vegetation structure in rangelandsto sustain native biodiversity (Derner et al. 2009,Toombs et al. 2010, Fuhlendorf et al. 2017), but addsadditional critical insight. We found heterogeneous graz-ing treatments were not always sufficient to achievedesired outcomes. Rather, applying grazing in the rightplace at the right time was necessary to benefit focal spe-cies. For example, McCown’s Longspur remainedrestricted to the loamy plains ecological site even thoughother portions of the CPERwere pulse grazed each yearto generate the short vegetation structure that longspursprefer (With 2010). Similarly, while Grasshopper Spar-rows benefitted from rest, rest affected density only whenit was applied in the least productive ecological site, andthis species was always most abundant in the salt flatsand sandy plains ecological sites regardless of grazingmanagement. This leads to an important conclusion: bytaking into account specific soil and vegetation charac-teristics, grazing can be implemented to simultaneouslymanage for species that have seemingly conflicting habi-tat needs in the shortgrass steppe. In other words, ignor-ing small-scale ecological context would suggest anintractable conflict between managing for species thatprefer short and tall vegetation structure. Our study sug-gests that managing ecological sites independently canpromote both short- and tall-grass species concurrently.Given these results, adaptive changes to the CARMgrazing rotation were made in 2017 and 2018 to testwhether targeting more frequent pulse grazing on theloamy plains ecological site would benefit McCown’sLongspur and targeting more frequent rest on sandyplains and salt flats would benefit Grasshopper Sparrow.The effects of these changes will be evaluated insubsequent years.We believe our findings are directly applicable to man-

agement for grassland birds across the Great Plains andin other semiarid rangelands. Recent studies in NorthAmerica’s grasslands have found similar context depen-dency of rangeland management for benefiting grasslandbirds. For example, in tallgrass prairie, Ahlering and


Merkord (2016) found precipitation conditions couldinfluence avian community composition regardless ofgrazing management. In Montana’s northern steppe,Lipsey and Naugle (2017) suggested grazing manage-ment that did not consider edaphic or precipitation con-ditions could be insufficient to generate grassland birdhabitat. Our results add to this growing body of evidencethat grazing management for grassland birds acrossNorth America’s grasslands should consider moresources of annual and landscape variation than just veg-etation structure (Knopf 1996, Ahlering and Merkord2016, Lipsey and Naugle 2017). We also believe ourresults are relevant to wildlife management in othersemiarid rangelands, where vegetation productivity issensitive to variable precipitation and disturbance pro-cesses (i.e., grazing and/or drought) are integral parts ofevolutionary history. Our findings may not be as rele-vant to areas without regular disturbance or with lessvariable climates, as resources utilized by wildlife inthese systems may be more predictable or reliable.Ultimately, we found grazing management on range-

lands can enhance habitat for a diverse grassland birdcommunity. Achieving this outcome may require focusedand flexible management where grazing is applied toareas with appropriate topoedaphic conditions to sup-port the habitat needs of focal species. The applicationof collaborative approaches to adaptive managementcan build trust among stakeholders, and therebyenhance understanding of such context dependency(Wilmer et al. 2018). Our study addressed shortcomingsof prior rotational grazing research by employing realis-tic spatial scales and controlling for stocking rate effects(Briske et al. 2011, Teague and Barnes 2017), and repre-sents one of the first experiments to document effects oflivestock movement patterns on wildlife habitat (see alsoGolding and Dreitz 2017). Our work suggests the effectsof grazing management on grassland birds are morecontext specific than suggested by previous grazing/vegetation heterogeneity models (e.g, Knopf 1996) andsupport the view of rangelands as spatially and tempo-rally complex ecological systems (Fuhlendorf et al.2017). Applying adaptive management in the context ofthis complexity may allow rangelands to serve as impor-tant repositories of biodiversity under future climateand population change, while continuing to support live-stock and human livelihoods.

ACKNOWLEDGMENTS

We gratefully acknowledge our funding source, USDA-NIFAAward #2015-67019-23009, and support for equipment andfield transportation provided by USGS. We thank the techni-cians who collected point count data and vegetation data, andthe stakeholder group and CPER employees who managed theCARM and TRM herds. We thank Courtney Amundson forproviding valuable feedback on analyses for this manuscript,and Jessie Golding for generously sharing R code to visualizesome model results. We are grateful for insightful com-ments from two anonymous reviewers which improved thismanuscript. Any use of trade, firm, or product names is for

descriptive purposes only and does not imply endorsement bythe U.S. Government.

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