Range-Wide Assessment of Livestock Grazing 

Across the Sagebrush Biome

Open-File Report 2011–1263

U.S. Department of the Interior

U.S. Geological Survey

Range-Wide Assessment of Livestock Grazing Across 

the Sagebrush Biome

By Kari E. Veblen1,2, David A. Pyke2, Cameron L. Aldridge3, Michael L. Casazza5, Timothy J. Assal4, and 

Melissa A. Farinha5

1

Wildland Resources Department, Utah State University.

2

U.S. Geological Survey Forest and Rangeland Ecosystem Science Center. 3

NREL, Department of Ecosystem Science and Sustainability, Colorado State University, in cooperation with U.S. 

Geological Survey Fort Collins Science Center.

4

U.S. Geological Survey Fort Collins Science Center. 5

U.S. Geological Survey, Western Ecological Research Center. 

Open-File Report 2011–1263

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

KEN SALAZAR, Secretary

U.S. Geological Survey

Marcia K. McNutt, Director

U.S. Geological Survey, Reston, Virginia: 2011

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Suggested citation:

Veblen, K.E., Pyke, D.A., Aldridge, C.L., Casazza, M.L., Assal, T.J., and Farinha, M.A., 2011, Rangewide assessment of livestock grazing across the sagebrush biome: U.S. Geological Survey Open-File 

Report 2011-1263, 72 p.

Any use of trade, product, or firm names is for descriptive purposes only and does not imply 

endorsement by the U.S. Government.

Although this report is in the public domain, permission must be secured from the individual 

copyright owners to reproduce any copyrighted material contained within this report.
iii

Contents

Executive Summary....................................................................................................................................... 1

Introduction.................................................................................................................................................... 3

Section I: Identification and Refinement of Existing Datasets........................................................................ 4

Section II: Monitoring of Livestock Grazing Effects and Expert Opinions ...................................................... 6

Section III: Exploring Relationships among Livestock Grazing, Land Health Standards, and Remotely 

Sensed Vegetation Characteristics.............................................................................................................. 20

Conclusions................................................................................................................................................. 29

Acknowledgments ....................................................................................................................................... 30

References Cited......................................................................................................................................... 30

Appendix 1. Spatial Allotment Data ............................................................................................................. 54

Appendix 2. Refinement of BLM Land Health Standards Dataset ............................................................... 63

Appendix 3. ESD Sites and Selection Protocol............................................................................................ 69

Appendix 4. Fundamentals of Rangeland Health Outlined in 43 CFR 4180.1 ............................................. 71

Figures

Figure 1. Sample sites in Harney County, Oregon where production was estimated with NRCS 

reconstruction methods. .............................................................................................................................. 34

Figure 2. Predicted ranges and field estimates of production ..................................................................... 35

Figure 3. Schematic of (1) the BLM allotment evaluation process which is based on quantitative 

monitoring data and (2) the Land Health Standards (LHS) Evaluation process which is based on 

rangeland health indicators.......................................................................................................................... 36

Figure 4. Box plot showing percentage of BLM files containing Actual Use, Utilization, Trend and 

Photo Point data .......................................................................................................................................... 37

Figure 5. Relationship between yearly AUMs billed (Billed Use) by the Bureau of Land Management 

and AUMs (Actual Use) reported by permittees on 171 BLM allotments ..................................................... 38

Figure 6. Spatial representation of whether BLM allotments have met Land Health Standards (LHS) 

(Upland, Riparian and Biodiversity) and whether livestock have contributed to unmet LHS........................ 39

Figure 7. Predicted probability (risk) of any given BLM allotment not meeting Land Health Standards 

(LHS) across Montana (Billings BLM Field Office, Panel A), Wyoming (State-wide, Panel B), and 

Colorado (Gunnison Basin, Panel C)........................................................................................................... 40

Figure 8. Predicted probability (risk) of not meeting standards, with livestock being the primary cause. .... 41

Tables

Table 1. Top table summarizes office file results from 310 allotments selected at random across 

13 Bureau of Land Management (BLM) field offices.................................................................................... 42

Table 2. Types of data (collected between 1997 and 2007) contained in a randomly selected sample 

of 310 allotment files from 13 Bureau of Land Management (BLM) offices across 6 States........................ 43

Table 3. Results of Land Health Standards (LHS) evaluations conducted by Bureau of Land 

Management (BLM) allotments between 1997 and 2007. ........................................................................... 44

Table 4. Results of informal conversations with federal and university rangeland science experts 

on how best to prioritize monitoring of rangeland condition and livestock impacts. ..................................... 48
iv

Table 5. Mean, standard deviation (Std) and t-test comparisons of sagebrush vegetation characteristics 

for allotments in Montana (Billings BLM Field Office), Wyoming (State-wide), and Colorado (Gunnison 

Basin) that have “Met” (798) versus “Not met” (333) Land Health Standards (LHS) assessments.............. 49

Table 6. Mean, standard deviation (Std) and t-test comparisons of sagebrush vegetation 

characteristics for allotments in Montana (Billings BLM Field Office), Wyoming (State-wide), 

and Colorado (Gunnison Basin) that did not meet Land Health Standards (LHS) assessments 

due to livestock (132) versus other causes (201) ........................................................................................ 49

Table 7. Candidate models for each metric [mean, median and standard deviation (std)] within 

each variable subgroup comparing sagebrush vegetation characteristics for allotments in Montana 

(Billings BLM Field Office), Wyoming (State-wide), and Colorado (Gunnison Basin) that have “Met” 

(798) versus “Not met” (333) Land Health Standards (LHS) assessments.................................................. 50

Table 8. Final candidate models comparing sagebrush vegetation characteristics for allotments in 

Montana (Billings BLM Field Office), Wyoming (State-wide), and Colorado (Gunnison Basin) that 

have “Met” (798) versus “Not met” (333) Land Health Standards (LHS) assessments................................ 51

Table 9. The coefficients (β) and standard errors (SE) of the variables in the top AICc-selected 

models comparing sagebrush vegetation characteristics for allotments in Montana (Billings BLM 

Field Office), Wyoming (State-wide), and Colorado (Gunnison Basin) that have “Met” (798) versus 

“Not met” (333) Land Health Standards (LHS) assessments....................................................................... 51

Table 10. Candidate models for each metric [mean, median and standard deviation (std)] within 

each variable subgroup comparing sagebrush vegetation characteristics for allotments in Montana 

(Billings BLM Field Office), Wyoming (State-wide), and Colorado (Gunnison Basin) that did not meet 

Land Health Standards (LHS) assessments due to livestock (132) versus other causes (201)................... 52

Table 11. Final candidate models comparing sagebrush vegetation characteristics for allotments 

in Montana (Billings BLM Field Office), Wyoming (State-wide), and Colorado (Gunnison Basin) that 

failed to meet Land Health Standards (LHS) due to livestock (132) versus other causes (201) .................. 53

Table 12. The coefficients (β) and standard errors (SE) of the variables in the top AICc –selected 

models comparing sagebrush vegetation characteristics for allotments in Montana (Billings BLM 

Field Office), Wyoming (State-wide), and Colorado (Gunnison Basin) that failed to meet Land 

Health Standards (LHS) assessments due to livestock (132) versus other causes (201)............................ 53

Conversion Factors

Multiply By To obtain

meter (m) 3.281 foot (ft)

square kilometer (km2) 0.3861 square mile (mi2)

kilometer (km) 0.6214 mile (mi)
1

Range-Wide Assessment of Livestock Grazing Across 

the Sagebrush Biome

By Kari E. Veblen1,2, David A. Pyke2, Cameron L. Aldridge3, Michael L. Casazza5, Timothy J. Assal4, and 

Melissa A. Farinha5

Executive Summary 

Domestic livestock grazing occurs in virtually all sagebrush habitats and is a prominent 

disturbance factor. By affecting habitat condition and trend, grazing influences the resources 

required by, and thus, the distribution and abundance of sagebrush-obligate wildlife species (for 

example, sage-grouse Centrocercus spp.). Yet, the risks that livestock grazing may pose to these 

species and their habitats are not always clear. Although livestock grazing intensity and 

associated habitat condition may be known in many places at the local level, we have not yet 

been able to answer questions about use, condition, and trend at the landscape scale or at the 

range-wide scale for wildlife species. A great deal of information about grazing use, 

management regimes, and ecological condition exists at the local level (for individual livestock 

management units) under the oversight of organizations such as the Bureau of Land Management 

(BLM). However, the extent, quality, and types of existing data are unknown, which hinders the

compilation, mapping, or analysis of these data. Once compiled, these data may be helpful for 

drawing conclusions about rangeland status, and we may be able to identify relationships 

between those data and wildlife habitat at the landscape scale.

The overall objective of our study was to perform a range-wide assessment of livestock 

grazing effects (and the relevant supporting data) in sagebrush ecosystems managed by the BLM. 

Our assessments and analyses focused primarily on local-level management and data collected at 

the scale of BLM grazing allotments (that is, individual livestock management units). Specific 

objectives included the following:

1. Identify and refine existing range-wide datasets to be used for analyses of livestock 

grazing effects on sagebrush ecosystems.

2. Assess the extent, quality, and types of livestock grazing-related natural resource data 

collected by BLM range-wide (i.e., across allotments, districts and regions).

 

1

Wildland Resources Department, Utah State University.

2

U.S. Geological Survey Forest and Rangeland Ecosystem Science Center.

3

NREL, Department of Ecosystem Science and Sustainability, Colorado State University, in cooperation with U.S. 

Geological Survey Fort Collins Science Center.

4

U.S. Geological Survey Fort Collins Science Center. 5

U.S. Geological Survey, Western Ecological Research Center. 
2

3. Compile and synthesize recommendations from federal and university rangeland science 

experts about how BLM might prioritize collection of different types of livestock 

grazing-related natural resource data.

4. Investigate whether range-wide datasets (Objective 1) could be used in conjunction with 

remotely sensed imagery to identify across broad scales (a) allotments potentially not 

meeting BLM Land Health Standards (LHS) and (b) allotments in which unmet standards 

might be attributable to livestock grazing.

Objective 1: We identified four datasets that potentially could be used for analyses of livestock 

grazing effects on sagebrush ecosystems. First, we obtained the most current spatial data 

(typically up to 2007, 2008, or 2009) for all BLM allotments and compiled data into a coarse, 

topologically enforced dataset that delineated grazing allotment boundaries. Second, we obtained 

LHS evaluation data (as of 2007) for all allotments across all districts and regions; these data 

included date of most recent evaluation, BLM determinations of whether region-specific 

standards were met, and whether BLM deemed livestock to have contributed to any unmet 

standards. Third, we examined grazing records of three types: Actual Use (permittee-reported), 

Billed Use (BLM-reported), and Permitted Use (legally authorized). Finally, we explored the 

possibility of using existing Natural Resources Conservation Service (NRCS) Ecological Site 

Description (ESD) data to make up-to-date estimates of production and forage availability on 

BLM allotments.

Objective 2: We investigated the availability of BLM livestock grazing-related monitoring data 

and the status of LHS across 310 randomly selected allotments in 13 BLM field offices. We 

found that, relative to other data types, the most commonly available monitoring data were 

Actual Use numbers (permittee-reported livestock numbers and season-of-use), followed by 

Photo Point, forage Utilization, and finally, Vegetation Trend measurement data. Data 

availability and frequency of data collection varied across allotments and field offices. Analysis 

of the BLM’s LHS data indicated 67 percent of allotments analyzed were meeting standards. For 

those not meeting standards, livestock were considered the causal factor in 45 percent of cases 

(about 15 percent of all allotments). 

Objective 3: We sought input from 42 university and federal rangeland science experts about 

how best to prioritize rangeland monitoring activities associated with ascertaining livestock 

impacts on vegetation resources. When we presented a hypothetical scenario to these scientists 

and asked them to prioritize monitoring activities, the most common response was to measure 

ground and vegetation cover, a variable that in many cases (10 of 13 field offices sampled) BLM 

had already identified as a monitoring priority. Experts identified several other traditional (for 

example, photo points) and emerging approaches (for example, high-resolution aerial 

photography) to monitoring.

Objective 4: We used spatial allotment data (described in Objective 1) and remotely sensed 

vegetation data (sagebrush cover, herbaceous vegetation cover, litter and bare soil) to assess 

differences in allotment LHS status (“Not met” vs. “Met”; if “Not met” – livestock-caused vs. 

not). We then developed logistic regression models, using vegetation variables to predict LHS 

status of BLM allotments in sagebrush steppe habitats in Wyoming and portions of Montana and 

Colorado.
3

In general, we found that more consistent data collection at the local level might improve 

suitability of data for broad-scale analyses of livestock impacts. As is, data collection 

methodologies varied across field offices and States, and we did not find any local-level 

monitoring data (Actual Use, Utilization, Vegetation Trend) that had been collected consistently 

enough over time or space for range-wide analyses. Moreover, continued and improved emphasis 

on monitoring also may aid local management decisions, particularly with respect to effects of 

livestock grazing. Rangeland science experts identified ground cover as a high monitoring 

priority for assessing range condition and emphasized the importance of tracking livestock 

numbers and grazing dates. Ultimately, the most effective monitoring program may entail both 

increased data collection effort and the integration of alternative monitoring approaches (for 

example, remote sensing or monitoring teams). In the course of our study, we identified three 

additional datasets that could potentially be used for range-wide analyses: spatial allotment 

boundary data for all BLM allotments range-wide, LHS evaluations of BLM allotments, and 

livestock use data (livestock numbers and grazing dates). It may be possible to use these spatial 

datasets to help prioritize monitoring activities over the extensive land areas managed by BLM. 

We present an example of how we used spatial allotment boundary data and LHS data to test 

whether remotely sensed vegetation characteristics could be used to predict which allotments met 

or did not meet LHS. This approach may be further improved by the results of current efforts by 

BLM to test whether more intensive (higher resolution) LHS assessments more accurately 

describe land health status. Standardized data collection in more ecologically meaningful land 

units may improve our ability to use local-level data for broad-scale analyses.

Introduction

Domestic livestock grazing occurs in virtually all sagebrush habitats and is a prominent 

disturbance factor. By affecting habitat condition and vegetation trend, grazing may influence the 

resources required by, and thus, the distribution and abundance of sagebrush-obligate wildlife 

species. For example, livestock grazing is identified as a population risk to sage-grouse 

(Centrocercus spp.) in numerous local conservation plans (for example, Bi-State Local Planning 

Group, 2004; Northern Eagle/Southern Routt Work Group, 2004). Yet, the risks that livestock 

grazing may pose to these species and their habitats are not always clear, particularly at the 

landscape scale (Connelly and others, 2004; Crawford and others, 2004). An assessment of how 

livestock grazing management influences vegetation condition on rangelands is integral to our 

understanding of the long-term persistence of sagebrush-obligate wildlife species (Aldridge and 

others 2008). 

Although livestock grazing intensity and associated habitat condition may be known in 

many places at the local level (for example, individual livestock management units), we have not 

yet been able to answer questions about use, condition, and trend of habitat, at the landscape or 

range-wide scale for wildlife in sagebrush ecosystems (Crawford and others, 2004). Increasingly, 

our ability to successfully manage these ecosystems will call for examination of conditions and 

patterns at broad scales (Chambers and Wisdom, 2009). A great deal of local information about 

grazing use, management regimes, and ecological condition exists for individual management 

units under the oversight of Bureau of Land Management (BLM), U.S. Forest Service, National 

Resource Conservation Service (NRCS), State management agencies, Tribal lands, and private 

landowners. However, the extent, quality and types of existing data across sagebrush habitat are 

unknown. This hinders the compilation, mapping, or analysis of these data to draw conclusions. 
4

If quality data are compiled, we may be able to assess the status of existing rangelands across 

livestock management boundaries and evaluate habitat conditions for wildlife populations, and 

ultimately help identify priority areas for conservation and restoration.

The overall objective of our study was to perform a range-wide assessment of livestock 

grazing effects (and the relevant supporting data) in sagebrush ecosystems managed by the BLM. 

Specific objectives included the following:

1. Identify and refine existing range-wide datasets to be used for analyses of livestock 

grazing effects on sagebrush ecosystems. 

2. Assess the extent, quality, and types of livestock grazing-related natural resource data 

collected by BLM range-wide (that is, across districts and regions).

3. Compile and synthesize recommendations from federal and university rangeland science 

experts about how BLM might prioritize collection of different types of livestock 

grazing-related natural resource data. 

4. Investigate whether range-wide datasets (Objective 1) could be used in conjunction with 

remotely-sensed imagery to identify across broad scales (a) allotments potentially not 

meeting BLM Land Health Standards (LHS) and (b) allotments in which unmet standards 

were attributable to livestock grazing. 

Section I: Identification and Refinement of Existing Datasets

We identified, often with assistance from Bureau of Land Management (BLM) personnel, 

four datasets that potentially could be used for analyses of livestock grazing effects on sagebrush 

ecosystems and that could be applied to a spatial land area: (1) Allotment data, (2) Land Health 

Standards (LHS) data, (3) Actual, Billed and Permitted Use, and (4) Soil map units with their 

associated Ecological Site Descriptions (ESDs) and forage production data. Below we describe 

the origin and potential uses of each dataset and summarize how we refined data for analysis.

Spatial Allotment Data

We obtained spatial allotment boundary data as a first step in our analyses. Because most

monitoring and management occur at the allotment level, we anticipated that these data would 

form the basis of further spatial analyses. We first reviewed the BLM national grazing allotment 

spatial dataset available from the GeoCommunicator National Integrated Land System (NILS) 

website in 2007. We identified several limitations in those data and learned that some BLM 

States and/or field offices had updated their spatial data to rectify these limitations, but 

maintained the data outside of NILS. In some cases State Offices maintained an updated 

statewide dataset, although for other States the field office had the highest order of maintained 

data. We contacted appropriate BLM offices (State or field, 25 in all) to obtain the most recent 

data, assessed the data, established a development protocol, and compiled data into a coarse, 

topologically enforced dataset throughout the area of interest (that is, the pre-settlement 

distribution of Greater Sage-Grouse in the Western United States). Our goals were to develop a 

spatial product for mapping BLM allotments across the West, while limiting/eliminating

problems associated with gaps, slivers, edge matching, duplicate polygons, and inconsistent 

attribution. The product could then be linked with additional tabular data, such as billed and 

permitted use and allotment-specific LHS and sagebrush cover data. A detailed description of the 

methodology can be found in appendix 1.
5

Land Health Standards (LHS)

We identified a dataset that contained results of LHS evaluations for all BLM allotments 

across the region as of 2007. The BLM performs periodic evaluations of region-specific LHS 

(appendix 2, table 2-1) to determine rangeland health. Standards were developed by BLM State 

Offices in conjunction with regional resource advisory councils, and evaluations of these 

standards are intended to be completed at the time of grazing permit issuance and renewal (every 

10 years). In 2008, LHS data for all allotments in all regions were compiled by BLM in response 

to a Freedom of Information Act (FOIA) request made by a private organization. The BLM 

provided us with a copy of these data. The dataset provided three major types of information that 

were of interest to us: (1) date(s) (if any) of the most recent LHS evaluation for each allotment, 

(2) whether, when assessed, each region-specific standard (3–8, depending on region) had been 

met on a given allotment, and (3) whether livestock contributed to any of these standards not 

being met. A description of how we processed the original dataset to prepare data for analysis is 

detailed in appendix 2.

Actual, Billed, and Permitted Use

We examined actual, billed, and permitted use as potential metrics of grazing intensity on 

BLM allotments. Actual Use data are permittee-reported livestock numbers with turn-on and 

take-off dates for the livestock on the allotment. Completeness of this information relies on 

permittees completing those reports. Accuracy of the numbers and dates reported relies on both 

the honor system and oversight by BLM. As discussed in Section II, although Actual Use data 

were more available relative to other data types, overall the availability of Actual Use data across 

time and space was patchy and therefore generally lacking in sufficient coverage to be suitable 

for range-wide analyses. 

Complete Permitted and Billed Use records, however, are maintained for all allotments 

administered by the BLM and are potentially more useful for range-wide analyses. Permitted use 

dates and livestock numbers are the legal maximum grazing amounts for a given allotment, and 

legal adjustments to these numbers occur infrequently (although this does not preclude annual 

negotiations between BLM and permittees for adjustments based on climate, forage availability, 

etc.). Billed Use information more closely reflects actual year-to-year grazing dates and livestock 

numbers. These billing records are maintained in the Rangeland Administration System 

(http://www.blm.gov/ras/) and are used for calculations of permittees’ annual grazing bills. In 

Section II, we discuss relationships among these types of use records and their potential utility

for characterizing livestock grazing effects.

Ecological Site Descriptions (ESDs) and Forage Production

Given the logistical difficulties of administering the vast land area covered by BLM, 

permitted stocking rates on some allotments may not necessarily reflect the most current 

production estimates or other supporting data (for example, water point locations or topography, 

both of which influence the amount of production effectively available to livestock). We 

therefore investigated the possibility of using spatial (GIS-Geographic Information Systems) 

NRCS soil map unit data and their associated ESD data to make current estimates of forage 

availability for livestock grazing on BLM allotments. These data could then potentially be 
6

combined with water locations and topography data (for example, Digital Elevation Models) to 

provide production estimates and identify allotments that might benefit from closer inspection 

and potential adjustment of permitted and/or Actual Use.

As a first step, we field-validated estimates of potential vegetation production gleaned 

from soils data (NRCS Soil Survey Geographic Database - SSURGO) and the NRCS Ecological 

Site Information System (ESIS). We made on-the-ground estimates of production, using the 

NRCS Reconstruction Method (U.S. Department of Agriculture, Natural Resources Conservation 

Service, 2009b), at 42 randomly located sites in Harney County, Oregon (fig. 1). Our samples 

covered nine different ecological sites in Major Land Resource Area (MLRA) 010 (Central 

Rocky and Blue Mountain Foothills; see appendix 3 for full explanation of site selection). We 

then compared those on-the-ground herbaceous production estimates to estimates for those sites 

contained in the ESIS database. Based on our sample, we found that our estimates generally fell 

within the production range outlined in the corresponding ESDs (fig. 2).

Section II: Monitoring of Livestock Grazing Effects and Expert Opinions

Summary

Public land management agencies, such as the Bureau of Land Management (BLM), are

charged with managing land throughout the Western United States for multiple uses including 

livestock grazing and conservation of sensitive species and their habitats. Data on condition and 

trends of these rangelands—particularly with respect to livestock grazing—provide critical 

information for effective management of these multi-use landscapes. Accordingly, current 

grazing regulations require BLM to report rangeland condition on grazing allotments and use 

monitoring data to support stocking rate-related management decisions. Additionally, grazing 

permits are to be issued and renewed contingent on the meeting of State- or region-specific Land 

Health Standards (LHS). We therefore investigated the availability of BLM livestock grazingrelated monitoring data in sagebrush steppe and the status of LHS across the Western United 

States. We then sought input from university and federal rangeland science experts about how 

best to prioritize rangeland monitoring activities. We found that the most commonly available 

monitoring data (≥ 1 year of data between 1997 and 2007) were permittee-reported livestock 

numbers and season-of-use (71 percent of allotments) followed by Photo Point (58 percent), 

forage Utilization (52 percent), and finally, Vegetation Trend measurement data (37 percent). As 

of 2007, 57 percent of allotments had completed LHS. Of those, BLM indicated 67 percent of 

allotments were meeting standards. For the 33 percent not meeting standards, livestock were 

considered the causal factor in 45 percent of cases (about 15 percent of all allotments). Our data 

inspections, as well as conversations with BLM personnel, indicated a need for greater emphasis 

on collection of grazing-related monitoring data, particularly ground cover. We highlight 

commonalities between BLM monitoring approaches and expert-suggested priorities, present 

ideas for making the most of existing historical data, and finally discuss emerging ideas for 

rangeland monitoring.
7

Introduction

Livestock grazing is a dominant land use on BLM lands across the Western United 

States. The Secretary of the Interior is charged in the Federal Land Policy and Management Act 

(FLPMA) of 1976 to “manage the public lands under principles of multiple use and sustained 

yield...” and “shall, by regulation or otherwise, take any action necessary to prevent unnecessary 

or undue degradation of the lands” (Public Law 94-579, Sec. 302). In addition to managing these 

public lands for livestock grazing, BLM manages for conservation of endangered and threatened 

species and their habitat. To do so, BLM aims to achieve appropriate grazing practices that will 

prevent land degradation and facilitate the sustainability and compatibility of grazing and 

conservation.

Rangeland Monitoring

Monitoring the ecological status of rangelands is integral to successful grazing 

management and for insuring that proposed improvements are effective (Williams and others, 

2007). In an effort to improve unsatisfactory rangeland conditions and curtail any further 

degradation, the Public Rangelands Improvement Act of 1978 committed federal land 

management agencies to providing regular updates on the condition and trend of rangelands. 

Current grazing regulations also require that monitoring data and/or field observations be used to 

support any changes to allowable stocking rates on BLM allotments (43 CFR 4110.3).

Historically, monitoring of condition and trend on rangelands typically focused on plant 

community development (cover/biomass) in a successional framework, and vegetation recovery 

was assumed to occur following the lessening of grazing intensity (Dyksterhuis, 1949). 

However, in response to debate over the validity of the rangeland succession model (particularly 

the relationship between grazing intensity and vegetation recovery), Westoby and others (1989) 

proposed an alternative state-and-transition model. This model, in which thresholds govern 

management-driven transitions between different vegetation communities or states, has gained 

wide acceptance and recently been incorporated into the USDA Natural Resources Conservation 

Service (NRCS) Ecological Site Descriptions for rangelands (U.S. Department of Agriculture, 

Natural Resources Conservation Service, 2009a). A corollary of the state-and-transition 

framework is that monitoring of vegetation-grazing relationships should be expanded to include 

a greater complexity of ecological and physical processes or ecosystem attributes beyond 

vegetation (Herrick and others, 2005).

Despite progress in understanding what and how best to monitor, BLM monitoring 

efforts have been criticized over the last several decades as being hampered by 

funding/personnel issues and confusion and inconsistencies associated with monitoring 

approaches (West, 2003). It is not clear at regional, or range-wide scales which types of 

vegetation, soil, and livestock grazing-related monitoring data are being collected on BLM land, 

which methods are being used, or how consistently data are being collected, analyzed, and 

interpreted. Similarly, it is unclear whether these data are comprehensive and sufficiently 

consistent across time and space to make region-wide assessments of rangeland status or 

livestock grazing effects on rangeland status.
8

Land Health

Rangeland health indicators have long been used to determine rangeland status (West, 

2003) and typically are used to evaluate specific rangeland attributes or LHS. Over time, this 

method has expanded from using just a few select indicators to including a broader array. In 

particular, there have been efforts to include indicators not only relating directly to vegetation 

but to other ecological processes such as nutrient cycling, soil stability and hydrology (Tongway, 

1994; Pyke and others, 2002).

Changes in BLM rangeland policy have mirrored these changes in philosophy. In 1995, 

BLM identified fundamentals of rangeland health (43 CFR 4180.1; appendix 4) and created new 

grazing regulations that required each state, in consultation with relevant Resource Advisory 

Councils, to develop state or regional LHS and livestock management guidelines. These LHS 

and guidelines, subject to approval by the Secretary of the Interior, are required to address: (1) 

watershed function, (2) nutrient cycling and energy flow, (3) water quality, (4) habitat for 

endangered, threatened, proposed, candidate, and other special status species, and (5) habitat 

quality for native plant and animal populations and communities (43 CFR 4180.2). To assess 

whether standards are being met, BLM is required to perform on-the-ground evaluations of a 

suite of indicators associated with its State- or region-specific standards (appendix 2).

Since 1997, livestock grazing practices on BLM land have been linked to the status of 

LHS; if an allotment fails LHS due to livestock, appropriate corrective action must be taken and 

the terms and conditions of the grazing permit may be adjusted (43 CFR 4180.2). If an allotment 

fails one or more standards, ideally, monitoring data are used to identify causal factors (see fig. 

3). If grazing practices are identified as significant factors resulting in failure, management 

actions must be proposed to help achieve compliance (fig. 3; 43 CFR 4180.2). This emphasis on 

identification of causal factors underscores the importance of access to supporting monitoring 

data. These land health standard evaluations also represent a potentially comprehensive, broadscale dataset of land health status across western BLM land and may be useful for identifying 

relationships between rangeland health status and causal factors such as livestock grazing.

Objectives

The first major objective of our study was to address the availability and status of BLM 

rangeland health and livestock grazing-related monitoring data. Specifically, we (1) examined 

types, availability, and consistency of rangeland monitoring data from a sample of BLM offices 

that administer allotments in sagebrush steppe, (2) obtained and utilized existing BLM LHS data 

to ascertain rangeland health status across the Western United States, and (3) evaluated the 

degree to which data were available for identifying livestock grazing as a contributing factor in 

failure to meet LHS in sagebrush steppe. Our second major objective was to more closely 

examine current and potential future approaches to monitoring rangeland status and livestock 

grazing impacts on BLM land. In particular, we (1) compiled opinions of rangeland science 

experts about how best to prioritize rangeland monitoring activities, (2) compared and contrasted 

these opinions with current BLM practices, and (3) identified opportunities for new directions 

and making the most of existing BLM data.
9

Methods

Field Office Sampling

We visited BLM field offices to evaluate the availability of monitoring data commonly 

used for monitoring of rangeland status. These data types included: (1) Actual use – livestock 

numbers and grazing dates (self-reported by grazing allotment permittees), (2) Utilization –

percentage of current year’s vegetation production consumed by animals, and (3) Vegetation 

Trend – measures of community status over time, including both repeat photos and quantitative 

vegetation sampling. We also inspected files for presence of grazing plans or allotment 

management plans (AMPs), which can be written to help guide management of grazing 

allotments. These plans outline specific resource objectives relating to livestock grazing (for 

example, available AUMs, range improvements) and in the case of AMPs, also include 

objectives related to wildlife. Because plans must provide for monitoring to evaluate the 

effectiveness of management in achieving objectives (43 CFR 4120.2), we also investigated the 

presence of evaluations. We focused on data typically maintained by rangeland conservationists, 

and we did not inspect supporting data maintained by other specialists, including riparian (for 

example, PFC-Proper Functioning Condition), wildlife, or wild horse/burro data.

We inspected these data types for a total of 310 randomly selected allotments in 13 BLM 

offices (covering 15 BLM resource areas and 6 States) that fell within big sagebrush (Artemisia 

tridentata) steppe and potential Greater Sage-Grouse (Centrocercus urophasianus) range. In 

1982, BLM began classifying allotments as “Maintain” or “Improve,” with the intention of 

concentrating monitoring efforts on “Improve” allotments (BLM WO IM 82-292), so we 

stratified the 310 allotments to be one-third Maintain (n=109) and two-thirds Improve (n=201). 

We excluded custodial allotments from our sample because they typically are small, isolated 

pieces of federal land located within non-federal land areas. Our study focused on sagebrush 

steppe because it was included in a broader project focused on sage-grouse conservation.

Seven of the thirteen field offices we selected were among those already participating in a 

complementary BLM study aimed at exploring spatially explicit land health assessments. The 

remaining six offices were selected semi-randomly with preference given to offices with a

history of cooperation or collaboration on previous or related projects. Thus, our BLM office 

selection may be biased towards those with a greater willingness to participate and share 

monitoring data.

For each of the 310 allotments, we recorded presence or absence of each data type 

(Actual Use, Utilization, Vegetation Trend, Photo Point, grazing/allotment management plans, 

and evaluations) for every year between 1997 and 2007. We did not include earlier dates because 

data prior to 1997 were more likely to have been archived and difficult to access. Data were 

counted as present if any data were present in the given year; inconsistent naming of sample sites 

and variable sample site locations over time precluded our ability to distinguish when data were 

incomplete within allotments (that is, data were counted as present even if they were only present 

for a subset of pastures or key areas within that allotment).
10

We also determined which of the 310 allotments had not met LHS (see below), and we 

examined which types of monitoring information had been collected to potentially support 

determinations that livestock were contributing to unmet standards. We also accessed BLM 

billing information for the 310 allotments contained in the Rangeland Administration System 

(RAS; http://www.blm.gov/ras/) and examined the relationship between AUMs of actual use 

(permittee-reported livestock numbers) and BLM billing records. Billed Use data are more 

comprehensive than Actual Use data (because they are maintained for each allotment each year), 

but it is not clear how closely billing records reflect actual use. 

Land Health Standards Data

To determine LHS status range-wide, we used a dataset compiled by the national BLM 

office in 2008. Although specific standards varied across States in content and number (appendix 

table 2-1), we determined that several standards across all BLM regions fell into three broad 

categories relevant to livestock grazing: Upland, Riparian, and Biodiversity. The only exception 

was that the Mojave Region had no Riparian standards. Classifying standards into these three 

broad (and common) categories allowed us to examine patterns across the Western United States.

We first identified allotments with LHS evaluations completed between 1997 and 2007. Then for 

each of those allotments, we determined if BLM rated Upland, Riparian, and Biodiversity 

standards as being met. If a standard was not met, we determined if BLM deemed that livestock 

contributed to failure to meet the standard.

Expert Opinions

We assembled, through informal conversations, opinions of 20 federal rangeland 

scientists (representing USDA-ARS in six States, NRCS in four States, and USFS in one State) 

and 22 university rangeland scientists (representing 13 universities) on how best to monitor 

rangeland condition and livestock effects within the logistical and time constraints faced by the 

BLM. We selected rangeland experts based on his/her membership in the Society for Range 

Management, professional reputation, and record of peer-reviewed publication in rangeland 

science literature. Conversations took place at the 2009 Society for Range Management annual 

meeting in Albuquerque, New Mexico, or over the telephone. We presented scientists with a 

hypothetical monitoring scenario asking them to prioritize activities for monitoring of livestock 

grazing effects on rangeland resources: “Assuming a new piece of land has been acquired by the 

BLM or some other land management agency, how would you set up a monitoring program to 

(1) monitor rangeland condition, and (2) determine livestock impacts (that is, make explicit 

connection between livestock grazing and land condition)? First, what would be the single most 

important field measurement, and how would you interpret that data with respect to (1) and (2)? 

Second, if you could instate a full monitoring program for that piece of land, what would you 

do? Assume that one person can spend 1⁄2 day per year collecting this monitoring information. 

Also, assume that the number of livestock, dates of livestock grazing, and climate/rainfall 

information will be collected (outside of your 1⁄2 day monitoring program) and made available to 

you.”
11

Statistical Analyses

With our field office data, for each of the six data types (Actual use, Utilization, Trend,

Photo Points, AMP/Grazing plan, and Allotment evaluation) we used Pearson’s chi-square 

contingency tests to examine differences in the numbers of Maintain versus Improve allotments 

for which data were present. We then used contingency tests to compare data presence for 

Maintain and Improve allotments in the full sample of 310 allotments versus the subset failing 

due to livestock, for each of the four main data types (Actual use, Utilization, Trend, and Photo 

Point). Next, we used ANOVA to test for differences in percent data presence among those four

data types. Our model included a main effect of data type (n=4), a block effect of field office 

(n=13), and their interaction. The response variable was the arcsin-transformed percent presence 

of each data type.

For LHS data, we used a split-block ANOVA design to test for differences between 

allotment categories (Maintain/Improve) and among data types (Upland, Riparian, Biodiversity).

The model included state (for example, CO, UT, OR) as block, allotment category 

(Maintain/Improve) as subblock, data type as main treatment (Upland, Riparian, Biodiversity), 

and all 2-way interactions. The model was run twice, first with arcsin-transformed “percent of 

allotments meeting LHS” as the response variable, and second with arcsin-transformed “percent

of allotments with unmet LHS attributed to livestock” as the response variable. In all cases, we 

used Tukey post-hoc tests.

Results

Field Office Sampling

Overall, more data were present for the 201 “Improve” than the 109 “Maintain” 

allotments we sampled, although differences were not significant (table 1; p>0.05). We found 

that, between 1997 and 2007, allotment files contained significantly more Actual Use (permitteereported livestock number and season-of-use) data (59–77 percent) and repeat Photo Point data 

(53–61 percent) than quantitative Vegetation Trend data (34–38 percent), with forage Utilization

present an intermediate amount (51–52 percent) (fig. 4; Tukey p<0.05). We also found that field 

offices varied significantly with respect to data availability (F12,36=3.69, p=0.001).

Actual Use was reported in an average of 6–7of the 11 years sampled (1997 to 2007) 

(table 1). Actual Use data were present for 59 percent of the 109 Maintain and 77 percent of the 

201 Improve allotments (table 1), and availability of Actual Use data varied considerably across 

field offices. In addition, Actual Use data were not necessarily complete on an allotment in a 

given year, particularly on large multi-permittee allotments where all operators may not have 

reported numbers. (Similarly, Utilization, Photo Point, and vegetation cover data often were 

present only for a subset of pastures or key areas within a given allotment). In general, Billed 

Use information appeared to be a good predictor of Actual Use numbers (R2

=0.75, fig. 5).

Photo Points were the most commonly and frequently collected type of vegetation/soil 

monitoring data (fig. 4). Every Field Office we visited monitored with photo points, and 58

percent of allotments had photos taken at least once between 1997 and 2007 (table 1). 

Additionally, we observed that even those allotments with little or no photo data during this time 

period typically had early photo point dates in the 1960s, 1970s, and/or 1980s. 
12

Utilization data were collected in more than one-half (52 percent) of allotments. All 

offices had collected Utilization data during our sample period, and all but one office used the 

Key Species method of making ocular utilization estimates (table 2). Quantitative Vegetation 

Trend data had been collected in 34–38 percent of allotments and by 10 of 13 offices, although 

approaches to data collection varied across offices (tables 1 and 2). Cover data were collected by 

10 of 13 offices, with five different methods, and frequency data were collected by six offices, 

using three different methods (table 2).

We found that 26 percent of Improve allotments and 17 percent of Maintain allotments 

contained either grazing or allotment management plans that had been updated since 1997 

(although an additional 35 and 29 percent, respectively, contained plans that had last been 

updated prior to 1997) (table 1). Few allotment evaluations of the objectives contained in those 

plans had been conducted in the previous 10 years for either Improve or Maintain allotments (15

and 8 percent, respectively, table 1).

Land Health Standards Data

The percentage of allotments with LHS evaluations completed between 1997 and 2007 

was 57 percent, ranging from 22 to 95 percent, depending on state (table 3). Of the allotments 

with completed LHS evaluations (fig. 6), the BLM found 67 percent to be meeting all LHS, with 

“Maintain” allotments more commonly meeting standards than “Improve” allotments (table 3, 

fig. 6). Of all 5,991 allotments evaluated, 15 percent failed at least one standard due to livestock. 

Riparian standard failures were attributed to livestock significantly more (63 percent of cases) 

than were Upland or Biodiversity failures (52 and 46 percent, respectively) (table 3, Tukey 

p<0.05); this effect appears to have been driven largely by the failure of Riparian Improve 

allotments (significant standards * allotment status interaction, table 3). We found that the use of 

systematic rating systems of key indicators of rangeland health (for example, Pellant and others, 

2005) varied across offices. Three offices did not use systematic indicator ratings, while nine did 

(and one is unknown).

Land Health Standards and Monitoring Data

We wanted to know which types of information could be used to support determinations 

that livestock were contributing to rangeland health issues. In our sampling of 310 allotments, we 

found that, when livestock were identified as the reason for not meeting a land health standard 

(n=62), Actual Use data (quantitative data on livestock number and season-of-use) were present 

for 47 percent of Maintain and 84 percent of Improve allotments (table 1). Forage utilization 

measurements had been made in one-half (52 percent) of allotments that did not meet standards 

due to livestock (table 1). Quantitative vegetation data were present for 35 percent of allotments

failing due to livestock, although additional vegetation data could potentially be gleaned from 

photos at permanent photo plots, which were present for 69 percent of allotments (table 1). A full 

complement of monitoring data (four data types listed above and in table 1) was present for 27

percent of allotments (table 1). Overall, the amount of data associated with the 62 Maintain and 

Improve allotments failing standards due to livestock did not differ significantly from the full 

dataset of 310 allotments (Actual Use χ2

= 2.3, p=0.1, Utilization χ2=0.53, p=0.5, Trend χ2=0.25, 

p=0.6, Photo Points χ2

=0.68, p=0.4). 
13

Expert Opinions

Overall, federal and university scientists expressed relatively similar opinions on our 

discussion topics (table 4). For data presentation, we separate our results for these two groups, 

but given our small sample sizes we did not attempt to analyze group differences statistically.

Ground cover (including vegetation, litter, rocks, biotic crusts and bare soil) was the 

variable most consistently identified by federal and university rangeland scientists (55 and 70

percent, respectively) as an important field measure for monitoring rangeland condition and 

livestock effects (table 4). Although measures of bare ground are implicit in some approaches to 

cover measurement/estimation, 45 percent of federal and 21 percent of university scientists who 

mentioned cover also specifically mentioned bare ground measurements, as did one other federal 

scientist (who had not specifically mentioned cover). Additionally, 5 percent of federal and 

university scientists mentioned gap measurements (which quantify the proportion of ground 

occupied by inter-plant gaps and provide information about potential for erosion). Overall, only 

25 percent of federal and 10 percent of university scientists specifically mentioned soil 

measurements such as aggregate stability or compaction (but not including bare ground). 

Utilization measures were suggested by 35 percent of federal and 25 percent of university 

scientists as a highest monitoring priority (with an additional 15 percent of university scientists 

mentioning utilization as a secondary measure). Methodological approaches included utilization 

cages (3 federal/2 university scientists), stubble height or residual biomass (4 federal/5 

university), use pattern mapping (2 university), and height/weight calculations (1 university). 

Thirty percent of federal and 40 percent of university scientists stressed the importance of 

having a reference for comparison when monitoring (table 4). These bases for comparison 

included ungrazed reference areas (cattle excluded) (4 federal/3 university), moderately grazed 

reference areas (3 university), and NRCS ecological site descriptions (3 federal/4 university).

Thirty percent of federal and 15 percent of university scientists recommended using 

repeat photo points as a primary approach to vegetation and soil monitoring (with an additional 

15 percent of university mentioning it secondarily) (table 4). Approaches included traditional 

methods of returning regularly to fixed locations to take landscape and ground plot photos, as 

well as photo sampling along transects. 

The use of remote sensing was suggested by 30 percent of federal and 35 percent of 

university scientists (table 4). Approaches included high resolution aerial photography (from 

airplane or lower flying remotely controlled device) and satellite imagery. In many of these 

cases, remote sensing was suggested as a tool for identifying risk and/or prioritizing monitoring 

activities. Overall, 25 percent of federal and 20 percent of university scientists mentioned the 

importance of using some type of tool or indicator (for example, remote sensing or other groundbased assessment) to prioritize monitoring.

Discussion

We found that BLM LHS evaluations, particularly with respect to determination of 

livestock grazing effects, would benefit from increased availability of monitoring data. This 

monitoring information, especially if collected with more consistent methodology, also would 

facilitate reporting of condition and trend of BLM rangelands and provide data-supported 

justification for management decisions. Rangeland experts emphasized the importance of 
14

continued efforts to monitor metrics of livestock use and vegetation trend, as well as collect

climate data. Experts also suggested that monitoring programs could be refined to better 

prioritize monitoring locations and activities, capitalize on livestock operator involvement,

and/or form specialized regional monitoring teams.

Land Health Standards 

Since 1997, regulations have linked grazing practices on BLM land to the status of LHS; 

if an allotment fails LHS due to livestock, appropriate corrective action must be taken and the 

terms and conditions of the grazing permit may be adjusted (43 CFR 4180.2). In practice, this 

has meant that BLM has sought to complete LHS evaluations for allotments prior to permit 

renewal. Range-wide, however, both meeting of standards and the purported role of livestock

varied considerably. Although these contrasting results likely reflect some true differences in 

grazing management across regions and/or field offices, they also likely reflect different 

approaches to evaluating and interpreting LHS (for example, use of systematic indicator ratings 

by only some of the offices). A more uniform and systematic approach to LHS data collection 

would likely maintain usefulness of the evaluation to individual field offices, but would greatly 

improve the reliability of the LHS dataset for making range-wide assessments of land health.

When a Land Health Standard is not met on a given allotment, the BLM must use 

additional information (“all available data”) to determine whether livestock grazing is the cause 

(fig. 3). That is, the key indicators used to determine whether a standard is met do not provide 

information about causality. Rather, monitoring information such as livestock numbers, 

Utilization, Vegetation Trend, and Photo Point data should be used to help managers determine 

livestock causality. We found that collection of these types of data could be improved. Of the 62 

allotments that failed LHS due to livestock, less than one-third (27 percent) possessed a full 

complement (all four data types) of monitoring data that could be used to quantitatively support 

the conclusion that livestock grazing contributed to poor land health. For 20 percent of the 62 

allotments, none of the four data types existed. In cases when supporting data do not exist, 

although expert opinion (of BLM range staff) may provide accurate assessments of the effects of 

livestock grazing on an allotment, lack of quantitative long-term data makes grazing 

management decisions difficult to defend. 

Actual Use and Utilization

Grazing intensity—including stocking rate, duration and frequency—has consistently 

been identified as having impacts on ecosystem and rangeland health (Vallentine, 1990; Briske 

and others, 2008). Similarly, the timing of grazing, particularly relative to plant phenology, can 

influence the sustainability of grazing (Briske and Richards, 1995). We found that grazing 

intensity and timing information (that is, Actual Use data) were present for 71 percent of the 

allotments and for an average of 6–7 of the 11 years between 1997 and 2007. The BLM typically 

sends Actual Use Forms to livestock operators (permittees) who must self-report livestock 

numbers and grazing dates. This Actual Use information greatly improves the ability of BLM to 

retrospectively examine the appropriateness of stocking rates and make official adjustments to 

allowable AUMs on a given allotment. Although annual adjustments can be negotiated between 

the assigned BLM staff and permittee, permanent adjustments to legal grazing amounts incur 

administrative costs and therefore may occur infrequently. The latter types of grazing 

adjustments may be increasingly necessary in the future. Although the potential effects of 

climate change on rangeland ecosystems are not clear, permitted grazing amounts may need to 
15

be re-evaluated to cope with altered climate patterns. Improved and continued efforts to send out 

forms and solicit responses, as well as ensure accuracy of these Actual Use reports would 

improve the quality of this critical grazing information.

Our comparison of Actual and Billed Use suggests that both under- and over-reporting 

occurs (fig. 5). In addition to a reluctance to report over-grazing, incorrect interpretation of 

grazing regulations may lead to a perceived disincentive to accurately report livestock numbers 

less than the legal maximum for fear that grazing privileges may be taken away and given to 

someone who will utilize them fully. In such cases, it may be prudent to positively reinforce 

behavior (for example, destocking) that recognizes benefits associated with rest and lowered 

stocking numbers. Nonetheless, in general, we found BLM billing records to be a relatively good 

predictor of Actual Use. Because billing records are kept for all allotments bureau-wide, billing 

information may be a useful tool for performing broad-scale analyses or comprehensively 

depicting approximate grazing intensity across the Western United States. Similarly, Permitted 

Use data (see Section I), which also exist for all allotments, theoretically represent maximum 

grazing, and a ratio of billed:permitted may serve as an index (actual:maximum) of grazing 

intensity. 

Aside from examination of livestock numbers, measures of utilization (herbivory by 

animals) immediately following grazing periods can help determine if livestock are contributing 

to rangeland resource problems. We asked our rangeland experts to assume that Actual Use 

information would be collected in their range monitoring scenarios, but more than a one-third of 

the experts also recommended collection of Utilization data to make the causative link between 

rangeland condition and livestock effects. In our BLM office visits, we found that Utilization 

data were collected in more than one-half of the allotments we sampled, and 12 of 13 offices 

used ocular estimates of key species. Although this ocular estimate approach is a relatively quick 

source of information and appears to have been widely applied across the Bureau, the major 

disadvantage is that the key forage method typically provides information only for the most 

common forage species in a given area. If an area has been previously degraded, the most 

common species currently at that site may not necessarily be the preferred forage species or the 

dominant species expected under reference conditions described in the ecological site 

description. Thus, while utilization of more common, less-preferred key species may be 

monitored, use of less-common (but more preferred and appropriate to the site) species may 

exceed appropriate levels, eventually leading to declines. Rangeland experts suggested a number 

of other approaches to utilization, although in all cases highlighted significant drawbacks to the 

method. In general, measuring utilization can be problematic (Jasmer and Holechek, 1984). 

Vegetation Trend 

Ground cover was identified by 63 percent of rangeland experts as being one of the most 

important field measures for monitoring rangelands and livestock impacts. Cover measurements 

made by species, life-form, or functional group can provide important information about the 

health and functioning of the plant community and ecosystem properties (Herrick and others, 

2005). Furthermore, many cover measurements include measurements of bare ground and total 

cover, with higher-than-normal bare ground typically reflecting increased potential for soil 

degradation. Basal gap measurements (mentioned by one federal and one university scientist) 

may be useful supplemental indicators of longer-term change (Herrick and others, 2005). 

Minimizing soil degradation is essential to maintaining rangeland health (Task Group on Unity 

in Concepts and Terminology Committee Members, 1995), and one-third of experts emphasized 
16

this point (that is, cited the importance of bare ground measurements). Any type of bare ground 

measurements, however, should be interpreted in the context of species cover data because bare 

ground can be negatively correlated with cover of undesirable invasive species.

Considering the importance placed upon cover measurements by our rangeland experts, 

increasing the frequency of cover measurements over time and across BLM allotments appears

critical for monitoring rangeland condition. We found that cover data collection had occurred for 

only 37 percent of the allotments we examined and in those allotments had only been collected 

an average of one time between 1997 and 2007. Temporal cover information (collected over 

multiple years) coupled with annual growing season precipitation data provides information that 

can be used to evaluate trends in condition. Although 10 of the 13 BLM Field Offices we visited 

had collected cover data, methods of cover data collection varied across offices, making any 

potential comparisons or merging of datasets across regions difficult. Moving towards more 

consistent cover methodology—even within BLM districts—may aid landscape-scale 

management.

Another approach to assessing Vegetation Trend, collection of frequency data, was used 

by 6 of 13 BLM Offices. Although frequency data may be easier and faster to collect, it 

generally serves as a poor early warning indicator due to an inability to detect small changes in 

plant communities unless high levels of initial frequency have been previously recorded (Smith 

and others, 1986) and was mentioned by only one rangeland expert. Whereas cover methods can 

be used to indirectly detect declines in plant biomass, frequency methods are more likely to 

detect changes associated with plant mortality (Elzinga and others, 2001b); once significant 

mortality has occurred recovery of smaller populations of less vigorous individuals is more 

problematic. Frequency methods can be used to complement cover methods and for example 

may be especially helpful for monitoring spread of undesirable species (Elzinga and others, 

2001b), although this approach would require implementing and repeating two techniques. 

For specific plant species or functional groups (for example, rare plants, invasive species, 

woody species), additional methods may be necessary to best assess their status and make 

predictions about future distributions. Accordingly, BLM Field Offices typically measure these 

in separate studies. Monitoring of these plant groups may be especially important as they can be 

important correlates of other variables (for example, invasive species and native plant cover) 

(Anderson and Inouye, 2001). Additionally, general vegetation monitoring programs may benefit 

from the addition of assessments of spatial cover distributions to capture patchiness and spatial 

processes in plant communities and make predictions about future cover.

Repeat Photos

Thirty percent of experts recommended photo sampling. At a given photo point, pictures 

can be taken of an overhead view of a small (for example, 1 ×1 m) permanent plot as well as of a 

landscape view, and this procedure can be repeated over time to detect large changes in 

vegetation (Elzinga and others, 2001b; Herrick and others, 2001). Overhead photos can be used 

to track bare ground and cover by functional groups, and in the cases of larger plants, individual 

species. Landscape photos provide insight into not only the general appearance of the landscape, 

but changes in woody species. Experts also recommended modified photo methods for more 

intensive sampling. For example multiple overhead photos can be taken along transects, or high 

resolution panoramic images could be incorporated into sampling (for example, Nichols and 

others, 2009). 
17

An appeal to a photo sampling approach is that it is a quick inexpensive field method that 

requires little training, and qualitative or quantitative analyses of the photos can be delayed and 

performed in the office at a later time. One expert also suggested that photo points are the most 

compelling evidence in court cases for illustrating vegetation trends to people who lack 

rangeland expertise or are unfamiliar with data interpretation.

According to our BLM data survey, photo points were the most common and frequently 

collected (in 58 percent of allotments) vegetation monitoring data. Even those allotments with 

little or no photo data during this time period typically had early photo point dates in the 1960s, 

1970s, and/or 1980s. Overall, BLM photo point data appear to represent the most complete 

historic vegetation information, spanning the longest time period. Continued and increased 

efforts to repeat photos would be inexpensive, promote time spent in the field, and provide one 

type of continuous long-term information about vegetation change on BLM allotments. 

Reference Areas and Climate

We did not systematically assess use of reference areas by BLM, but 35 percent of 

experts expressed support for use of reference areas when assessing livestock impacts. Ideas 

included completely ungrazed areas (for example, exclosures, highway rights-of-way, or far from 

water points), grazing gradients (for example, different distances from water points), and use of 

reference state community descriptions in NRCS Ecological Site Descriptions (ESD). Some 

approaches, such as building and maintaining exclosures can be expensive, whereas other 

approaches, such as using existing NRCS ESD data would be more cost-efficient and practical. 

Accordingly, the Interpreting Indicators of Rangeland Health technique changed from using 

reference areas to using ESD data because of difficulties in finding appropriate reference areas 

(Pyke and others, 2002). When feasible, however, pairing a particular site with a similar nearby 

reference site would be useful.

When using reference communities, considerations for interpretation of results include: 

choice of reference community (for example, historical vegetation versus more recent, grazed 

versus ungrazed), impacts of wild ungulates, and how long-term herbivore exclusion might alter 

vegetation-soil dynamics. Use of reference areas may be especially important when considering 

the dramatic yearly variations in climate and weather and the effects of those variations on 

plants, soils and their relationships with grazing. 

Climate and weather data, particularly rainfall patterns that can exhibit dramatic interand intra-annual variation, provide necessary context for interpreting vegetation and livestock 

monitoring information. For instance, yearly rainfall amounts have direct bearing on the impacts 

of a given grazing intensity (Thurow and Taylor, 1999), and the timing of grazing relative to 

rainfall (and phenology) also determines overall grazing effects (Briske and Richards, 1995). 

Likewise, any long-term trends in vegetation cover would be strongly affected by lengthy 

drought periods, both with and without grazing. Although we did not specifically sample for the 

availability of climate information at BLM offices, climate and weather station data are supposed 

to be included within their monitoring programs. Inclusion of their own climate data in grazing 

files, as well as data regularly retrieved from other sources (for example, NOAA), would aid 

interpretation of monitoring data. For example, plotting long-term actual use numbers relative to 

growing season precipitation values could help guide stocking rate decisions. Similarly, 

assessments of these types of long-term relationships could provide insights into how rangelands 

might respond to pending climate change.
18

Prioritization of Monitoring Efforts

Given the vast land area administered by the BLM and time constraints associated with 

monitoring activities, prioritization of where and when to monitor is essential. Although we 

found that allotments BLM classified as “Improve” were more likely to have failed standards due 

to livestock, we did not find significantly more monitoring data for Improve allotments. This 

suggests a need for further efforts at prioritization. 

Currently, monitoring efforts focus largely on key areas. These areas were established as 

representative of larger areas (for example, pastures or allotments) and contained dominant 

forage plants for livestock grazing. Although current monitoring also includes critical areas, 

BLM may benefit from further emphasis on critical or at-risk areas rather than key areas. 

Twenty-three percent of the experts we interviewed specifically mentioned identification of 

high-risk areas for concentration of monitoring efforts. For example, identifying areas that 

appear to be at or near thresholds of change (in a state-and-transition framework) may be a cost 

effective approach to identifying areas where management actions are sufficient to sustain or 

improve range condition (Bestelmeyer, 2006). Approaches include use of on-the-ground 

indicators (for example, bareground, vegetation gaps, and biotic crusts which are sensitive to 

grazing), Geographic Information Systems (for example, combine known stocking rates with 

information on ecological sites that may be more vulnerable or less resilient to grazing, see 

Section I) and remote sensing (see Section III and Homer and others, in press). A major benefit 

of the latter is that it can be used at multiple scales. For instance, satellite imagery can be used at 

the broadest scales as a primary indicator (for example, production and rainfall/drought effects 

across a region). Satellite imagery and/or high resolution aerial photographs also can be used at 

the landscape scale to assess ecosystem properties that have implications for wildlife and land 

health, such as bare ground or woody plant cover and structure (Booth and Cox, 2008; Rango 

and others, 2009; Homer and others, in press). At this scale, it may be possible to identify 

indicators of thresholds where more intensive monitoring efforts should be concentrated (see 

Xian and others, in press; Homer and others, in press; Section III, this report), and even use 

remote sensing to monitor changes in rangeland health conditions (see Xian and others, in press; 

Section III, this report).

Increased Involvement of Livestock Operators

In addition to helping maintain genial BLM-permittee relationships, increased 

involvement of livestock operators in the monitoring process could provide useful 

complementary monitoring data. First, as discussed above, permittee reporting of livestock 

numbers and grazing dates could be refined to be more complete and accurate and therefore 

provide a better picture of grazing intensity on BLM land. Second, as two experts recommended, 

livestock in-out weights or end-of-season body condition scores could provide insight into forage 

conditions. This type of information is of clear use to the permittee, and for BLM, would provide 

information to complement Utilization or Vegetation Trend numbers. Third, permittees may be 

interested in maintaining livestock exclosures as reference areas. Fourth, permittees could be 

more involved in collecting all types of BLM monitoring, from easier tasks such as reporting rain 

gauge readings on their allotments to more difficult tasks such as helping collect Vegetation 

Trend data. Although motivation and interest in participation are likely to vary considerably 

among permittees, local knowledge supplied by permittees has the potential to play a critical role 

in rangeland monitoring (for example, Bestelmeyer and others, 2009).
19

Monitoring Teams

A potential impediment to success of most of the above monitoring approaches is a lack 

of calibration and practice with the methods (for example, visual cover, production or utilization 

estimates, species identification). Similarly, monitoring may be hampered by an inability to visit 

field sites, particularly at the same time each year. One possibility for alleviating these types of 

problems is to designate field monitoring teams that cover wide geographical areas. One example 

suggested by a university rangeland expert was to create state- or regional-level teams that 

monitor long-term variables less frequently (for example, every 5 years). Workloads could be 

staggered so that multiple monitoring techniques could be applied to a given land area over time, 

but each technique would not necessarily be applied in a given year, nor all land areas monitored. 

Potential advantages include increased expertise and practice with monitoring techniques, 

insurance that either the method or decision-making process for deciding among a suite of 

methods is consistent across sites and time. A potential model for the monitoring team approach 

is the Utah Division of Wildlife Resources Range Trend Project, which hires seasonal 

technicians to collect yearly trend data at designated key areas throughout the State 

(http://wildlife.utah.gov/range/).

Employing a State- or regional-level team need not release range conservationists from 

the responsibility of visiting the field or collecting short-term data (for example, Utilization, 

Actual Use). Whereas the numbers collected by the monitoring team could provide a solid 

scientific justification for management decisions, range conservationists could spend time in the 

field making their own qualitative observations and short-term data measurements. Regardless of 

whether special monitoring teams are used in the future, prioritization of monitoring visits by the 

assigned range conservationist would still be beneficial. Several range scientists emphasized a 

need to maintain the ‘art’ of range management and the freedom to apply adaptive management.

Conclusions

In monitoring programs it is important to maintain continuity, use consistent 

methodology over time, and take into account all historical data to examine long-term trends. 

Several methods used by BLM were among those suggested by our conversations with rangeland 

experts (for example, photo points, point-sampling of vegetation). This suggests that those 

methods with the greatest support could be emphasized and potentially expanded on for the 

future. When we presented a hypothetical scenario to the university and federal rangeland 

science experts and asked them prioritize monitoring activities, the most common response was 

to measure ground/vegetation cover, a variable that in many cases (10 of 13 offices sampled) 

BLM had already identified as a monitoring priority. Although monitoring data were scant over 

our sample period, existing cover data nonetheless could serve as a basis for designing future 

monitoring efforts and be used for examination of long-term trends. Moreover, those areas where 

Vegetation Trend data are lacking altogether may present an opportunity to revise and produce 

protocols for more standardized field measures for future broader-scale analyses.

Because monitoring approaches vary so greatly across time and space, it may be 

necessary to introduce alternative approaches to effectively monitor at scales broader than 

individual management units. Effective management may not necessarily require that methods be 

uniform across allotments or regions. However, consistency of monitoring approaches across 

allotments or regions, along with collection of local-level data that are amenable to broader-scale 

analyses, are critical for issues such as conservation and maintenance of ecosystem services that 

transcend field office and political boundaries. Because travel time to and from management 
20

units is one of the more prohibitive aspects of monitoring, it may be reasonable to both continue 

previous methods (particularly important when consistent and abundant historic data exist) and 

add new methods to achieve more uniformity across management units. Several handbooks, 

guides and research programs already exist to guide monitoring efforts (for example, Bureau of 

Land Management, 1999; Elzinga and others, 2001a; Herrick and others, 2009; U.S. Department 

of Agriculture, Natural Resources Conservation Service, 2009c). Collaborations between 

research and management could produce quantitative models for making transitions from former 

to newer techniques, allowing for continuity of Vegetation Trend data and eventually leading to 

elimination of methodological overlaps. However, to achieve consistency, either across 

allotments or within allotments over time, will require decisions and greater guidance from levels 

above individual BLM field offices. 

BLM faces significant obstacles for maintaining monitoring programs, particularly lack 

of time, labor, and prioritization (West, 2003). In addition to the time necessary to perform 

monitoring tasks, many monitoring methods must be performed at the same time every year, 

which can be problematic when a single range conservationist is assigned to multiple allotments. 

The most efficient and realistic monitoring approaches may include some more comprehensive 

and labor-intensive methods to track and evaluate long-term trends (for example, gaps or species 

composition). These methods could be carried out less frequently, be completed by special 

monitoring teams, or use photo or remote-sensing approaches that allow data processing at other 

times of year. Results could then be viewed in the context of potentially less field-intensive 

yearly monitoring (for example, Utilization, Actual Use) that is done more frequently, potentially 

with some assistance from permittees. In the absence of further funding or added personnel, 

strictly prioritizing field monitoring activities for a given time period each year is the most 

critical step towards achieving the most effective monitoring.

Section III: Exploring Relationships among Livestock Grazing, Land Health 

Standards, and Remotely Sensed Vegetation Characteristics

Introduction

The impacts of livestock grazing in sagebrush steppe, one of the largest biomes in North 

America, are difficult to measure, but are critical to understanding and determining sound land 

management practices across the West. Although appropriate grazing practices may be 

sustainable and compatible with conservation, inappropriate livestock grazing can alter species 

composition of communities, disrupt ecosystem function, and alter ecosystem structure 

(Dyksterhuis, 1949). Quantitative measures are needed to adequately assess grazing impacts and 

offer insight into improved land management, especially in the face of global change. 

Understanding the impacts of current grazing practices as well as identifying where habitats may 

be at risk is crucial to the persistence of sagebrush habitats and the species which rely on them 

(Aldridge and others, 2008).

Greater Sage-Grouse (Centocercus urophasianus) are a landscape species occurring 

across a broad range of sagebrush habitats throughout the West (Schroeder and others, 2004). 

Recently, the U.S. Fish and Wildlife Service ruled that the species was warranted for listing 

under the Endangered Species Act (Federal Register, 2010) but precluded by higher priority 

listing actions. Understanding risks associated with grazing practices across the range of this 

species will allow for conservation measures which ensure the persistence of this and other 

sagebrush obligates. Long-term monitoring programs which provide an accurate assessment of 
21

the impacts of grazing on the sagebrush ecosystem will enable land managers to assess how 

grazing effects landscape change. Current monitoring data regarding these impacts are not 

available in a form useful for relating to sage-grouse populations (Miller and others, 2011).

In the Western United States, approximately one-half of remaining sagebrush steppe is 

public grazing land. One of the major public land management agencies, the Bureau of Land 

Management (BLM), manages its grazing program using an allotment-based approach. A 

grazing allotment is defined as an area of land designated and managed for livestock grazing 

(Bureau of Land Management, 2001) and consists of an area of land with boundaries based on a 

variety of factors such as land ownership, topography, and State boundaries (see fig. 6). Most 

data for monitoring livestock grazing and its effects, such as assessments of Land Health 

Standards (LHS), are collected by BLM and recorded for individual allotments (Bureau of Land 

Management, 2001). Ideally, LHS address rangeland health, defined as the degree to which the 

integrity of the soil and ecological processes are functioning properly to maintain the structure, 

organization and activity of the system over time (Bureau of Land Management, 2001). Land 

Health Standards are assessed by BLM on an approximate 10-year cycle by allotment, and 

allotments can fail to meet LHS for various reasons such as impacts from wildfire, invasive 

species, drought, or off-highway vehicle (OHV) use. In cases where current grazing management 

is identified as a significant causal factor contributing to unmet LHS, changes to the grazing 

regime are required to be implemented by the subsequent grazing season.

The results of LHS assessments across multiple allotments could be used to depict 

rangeland health across a broader landscape. Up to this point, however, this has been difficult, as 

a comprehensive geographic dataset has not been previously available. Additionally, combining 

high resolution vegetation maps derived from remotely-sensed data (Homer and others, 2008; 

Homer and others, in press) with LHS data collected at the allotment level may yield important 

application to on-the-ground management and provide the BLM with tools to identify “at risk” 

habitats. Similar tools are already being used to monitor habitat changes over time (Xian and 

others, in press). Although these remotely sensed products offer great opportunities to enhance 

much-needed long-term monitoring (see Xian and others, in press), understanding potential 

mechanisms related to these changes also could provide managers with more insights into 

management of sagebrush ecosystems. Assessing the relationship between vegetation 

characteristics and rangeland health assessments across large spatial extents would help aid land 

managers with understanding consequences of management actions.

Our overall goal was to show how these allotment data can be used in an applied 

management context, directly assisting BLM with management activities. More specifically, we 

were interested in testing if high resolution vegetation maps derived from remotely sensed 

imagery (Homer and others, 2008; Homer and others, in press),could be used as a tool to help 

identify allotments where rangeland health has been degraded, and if differences in 

characteristics could be discerned in allotments where current livestock management practices 

are indicated as the cause. If so, these products may be useful as landscape-level rangeland 

monitoring and management tools. We predict that allotments where LHS are “Not met”will 

have more bare ground, less shrub cover, and less vegetation (herbaceous and litter) cover. 

Similarly, we predict that where livestock are responsible for the failure to meet standards, these 

allotments will have more bare ground and less herbaceous and litter cover based on knowledge 

that cattle and sheep have grazing preferences for herbaceous forage and tend to treat shrubs 

(browse) as undesirable (Stoddart and others, 1995) or poisonous as in the case of big sagebrush 

(Johnson and others, 1976).
22

Methods

Datasets and Spatial Compilation

We used spatial allotment data compiled as part of this larger project (see Section I of this 

report) and joined it with tabular data on LHS synthesized previously and described in Section I. 

Our LHS synthesis classified each allotment as having “Met” (if it met Upland, Riparian, and 

Biodiversity standards) or “Not met” (if it failed to meet at least one standard). Appendixes 1 and 

2 detail the procedure used in the identification and refinement of the spatial allotment data and 

LHS, respectively.

Recently, products mapping estimated percent cover of sagebrush rangeland vegetation 

characteristics were developed, using fractional vegetation predictions to calculate cover 

percentage values within individual 30 m pixels (see Homer and others, 2008; Homer and others, 

in press). These map products have been completed for sagebrush habitats across Wyoming 

(sampled in 2006–07), within the Gunnison Basin of Colorado (sampled in 2007), and for the 

area covered by the Billings, Montana BLM Field Office (sampled in 2008). Model application 

varied slightly across sites, but all followed the same field sampling protocols and modeling 

processes using similar remotely sensed imagery to model eight main rangeland vegetation 

components; see Homer and others (2008) and Homer and others (in press) for details. Here, we 

utilize four products to make comparison across allotments: percent cover of sagebrush (all 

species combined), herbaceous vegetation, litter, and bare soil. For each individual allotment, we 

summarized per-pixel cover estimates across all pixels within that allotment. To do this, we 

calculated the mean and median estimated percent cover across all pixels, assessing overall 

vegetation or bare ground cover within an allotment. However, activities that affect the 

rangeland health assessment of an allotment, also may affect the heterogeneity of vegetation 

cover within an allotment (for example, Adler and others, 2001).Thus, we also calculated the 

standard deviation of per-pixel cover values across all pixels within each allotment, providing an 

estimate of heterogeneity for each variable of interest. All summarized allotment values (means, 

medians, or standard deviation) were calculated using a zonal statistic in ArcGIS 9.3.1 (ESRI, 

Redlands, CA, USA). These mean, median, and standard deviation values for each allotment 

were used to compare vegetation characteristics across LHS classes. Any allotment that was not 

within the extent of the sagebrush map data was omitted from the analysis. 

Statistical Analyses

Initially, we conducted simple comparisons using a two-tailed t-test with unequal 

variances to compare mean differences in rangeland variables for each comparison of interest. 

These included first contrasting “Not met” (1) versus “Met” (0) allotments, and then contrasting 

when “Not met” were deemed to have been caused by livestock (1) versus ‘other’ causes (0). 

BLM is only required to specify causal factors of failure when livestock are deemed responsible. 

To further assess our ability to predict the probability of a given allotment failing a LHS 

Assessment, we developed logistic regression models (Hosmer and Lemeshow, 2000) using 

different combinations of sagebrush rangeland vegetation characteristics within a given allotment 

to predict LHS failure.
23

Model Development

We included ‘state’ as a fixed effect with the most prevalent State (Wyoming) as the 

reference category to account for any inherent state-level difference in LHS assessments. 

Clearly, many of our metrics (mean, median, or standard deviation) within each variable class 

(cover of sagebrush, herbaceous, litter, bare) could be highly correlated. Thus, for both analyses, 

we initially assessed each individual metric and combinations (mean or median each with 

standard deviation) for each variable subgroup in a separate analysis to identify the most 

predictive form of each variable. This resulted in four subgroup analyses, each consisting of five 

different models. The most predictive subgroup model based on Akaike’s Information Criterion 

corrected for small sample sizes (AICc; Burnham and Anderson, 2002) was carried forward for 

the development of candidate models considering all four top subgroup model forms for model 

building. Correlated variables (Pearson’s r ≥ |0.7|) were prevented from occurring in the same 

model. Combined candidate models were again assessed and ranked using AICc.

Model Evaluation

Ideally, we would challenge our models with an independent set of data to evaluate the 

ability of our model(s) to predict LHS failure. Given the limited data available at this time on 

LHS assessments, we chose not to fold our data into training and testing datasets, as is common 

practice. Thus, for both models, we only present within sample assessment of model predictions. 

We used a Hosmer and Lemeshow χ 2 goodness of fit test (Hosmer and Lemeshow, 2000) to 

assess model fit to the data and receiver operating characteristic (ROC) curves to estimate 

predictive accuracy (Fielding and Bell, 1997). ROC values greater than 0.9 have high model 

accuracy, 0.7–0.9 good model accuracy, and < 0.7 low model accuracy (Swets, 1988; Manel and 

others, 2001). We identified the optimal probability classification point for each final model by 

minimizing the absolute value of the difference between sensitivity and specificity curves (Liu 

and others, 2005). We estimated the model’s overall predictive classification accuracy at the 

identified classification point using percentage correctly classified (PCC), and considered scores 

of ≥ 70 percent to have reasonable prediction and ≥ 80 percent excellent prediction (Nielsen and 

others, 2004; Aldridge and Boyce, 2007).

Spatial Application and Multi-Model Inference

For both modeling approaches, we considered all models from the candidate set within 2 

AICc points of the top model to have strong support, and used model averaging over this set to 

produce more robust spatial predictions and strengthen inference (Burnham and Anderson, 

2002). When necessary, prior to applying the adjusted weights to the spatial prediction for each 

model across all allotments, we rescaled model weights to sum to 1, only considering models 

within the 2 AICc model set. Weighted predictions were then added together to produce a final 

probability of LHS failure surface. We first applied the probability of failure (“Not met” versus 

“Met”) model to all allotments across our three study areas where we had spatial data, regardless 

of whether LHS had previously been assessed. We then used the optimal classification point (see 

above) to classify all allotments into “Met” or “Not met” status. The second model predicting 

the risk of failure due to livestock compared to ‘other’ failures was subsequently only applied to 

allotments having previously been predicted to have “Not met” (first analysis).
24

Results 

Comparison Across Allotment Status

Our final dataset with LHS information and sagebrush rangeland vegetation 

characteristics consisted of 798 allotments that met and 333 allotments that did not meet LHS. 

Allotments that met LHS had different sagebrush rangeland vegetation characteristics than those 

that did not meet LHS (table 5). Generally, allotments that met LHS had significantly greater 

cover of sagebrush (9.8versus 8.2 percent), herbaceous vegetation (24.3 versus 18.2 percent), and 

litter (21.0 versus 18.2 percent), but significantly lower cover of exposed bare soil (42.7 versus 

52.4 percent; table 5). Heterogeneity in cover for all four variables (standard deviation of pixels 

within allotments) decreased for those that did not meet LHS, but was not significant for bare 

soil (table 5).

Livestock were identified as the reason for unmet standards for 132 of the 333 allotments 

that did not met standards. Compared to other allotments, allotments where unmet standards 

were attributed to livestock had more cover of sagebrush (9.0 versus 7.6 percent), herbaceous 

vegetation (20.1 versus 17.0 percent) and litter (19.5 versus 17.4 percent), but less exposed bare 

soil (48.6 versus 54.8 percent; table 6). Variability in cover (standard deviation) was greater 

where livestock were deemed the cause across all four cover variables, though not significant for 

herbaceous (table 6).

“Met-“Not Met” Models

State-level differences were inherent in the logistic regression analyses comparing “Met” 

versus “Not met” allotments. Thus, all further models included ‘state’ as a fixed effect. Top 

metrics for each variable subgroup included mean cover of sagebrush (sb_mean), mean litter 

cover (lt_mean), herbaceous cover and variability (hb_mean + hb_std), and bare soil and 

variability (ba_mean + ba_std; table 7). These top four subgroup models were carried forward 

for development of candidate models using all combinations of models. Bare soil (mean) was 

inversely correlated with mean herbaceous cover and mean litter cover, resulting in a total seven 

combined candidate models (table 8). The top AICc-selected failure model predicted that LHSfailed pastures have less sagebrush cover (βsb-mean = -0.118), less cover of herbaceous vegetation 

(βhb-mean = -0.065), but more variability in herbaceous cover (βhb-std = 0.044, table 9). Relative to 

Wyoming, Colorado (βstate-1 = 3.468) had higher rates of failing to meet standards, whereas rates

of allotments having “Not met” standards were slightly lower in Billings, MT (βstate-2 = -0.146, 

table 9). 

This top “Met”-”Not met” model had strong support with a high weight of evidence 

given our final candidate set of models (wi = 1.0, table 8), and was used for spatial modeling. 

This model had reasonable fit to the data (H-L χ8

2 = 13.35, P = 0.10) and good model accuracy 

(ROC = 0.722). Although variance explained for this relatively simply model was reasonable 

(pseudo-R2 =13.24 percent), overall prediction based on the optimal classification point (0.271) 

was low (PCC = 65.61 percent).
25

“Livestock” Versus “Other” Models

State was again used as a fixed effect for logistic regression analyses comparing 

allotments where unmet standards were attributed to “livestock” versus “other” reasons. Top 

metrics for each variable subgroup included cover of sagebrush (sb_mean), variability of litter 

cover alone (lt_std), herbaceous cover (hb_mean), and cover and variability of bare soil 

(ba_mean + ba_std; table 10). All these four top subgroup models were used for combined 

candidate modeling of all combinations; mean bare soil cover was inversely correlated with 

herbaceous cover, and these two variables were not considered together, resulting in 10 

candidate models (Ttble 11). Two models had reasonable support within the candidate set (table 

11). The top AICc-selected model (wi = 0.416, table 11) predicted that “livestock” allotments 

had more sagebrush cover than “other” allotments (βsb-mean = 0.084), with a weaker effect of less

bare soil (βba-mean = -0.005) but stronger effect of increased variability in bare soil cover (βba-std = 

0.096, table 12). Both Colorado (βstate-1 = 1.154) and Montana (βstate-2 = 0.952 table 12) sites had 

higher rates of unmet standards due to livestock relative to Wyoming. The second model had 

moderate support (wi = 0.255, table 11), and differed only in the addition of mean litter cover to 

the model, predicting less litter in “livestock” allotments (βlt-mean = -0.109, table 12), although the 

effect was very weak (SE = 0.100, table 12). Magnitude and direction of responses to other 

metrics in this second model were similar to that of the top model (see table 12).

The top “livestock” model had good fit to the data (H-L χ8

2 = 5.52, P = 0.700), but low 

model accuracy (ROC = 0.688). The second ranked model had similar fit (H-L χ8

2 = 5.06, P = 

0.751) and accuracy (ROC = 0.693). Variance explained for the top and second models was 

reasonable (pseudo-R2 = 7.81 and 8.08 percent, respectively). Combining these two top models 

to generate an overall model averaged prediction resulted in similar model accuracy (ROC = 

0.690) and prediction (PCC = 62.67 percent) based on the optimal classification point (0.398).

Spatial Application

Despite the moderate to low model accuracy and prediction success for these two 

relatively simply models, we felt it was useful to develop spatial applications of these models, to 

(1) illustrate the application of these datasets for aiding in management, and (2) directly help 

with identification of areas that could be considered for more local rangeland management 

assessments and management priorities. When applied spatially to all 3,564 allotments where 

we had sagebrush rangeland vegetation characteristics, our model predicts that 1,510 ( about 42

percent) are at risk of not meeting LHS (fig. 7). The majority of these occur in northwestern 

Wyoming into the south-central portions of the Billings MT BLM Field Office, southwestern 

Wyoming, and across all allotments in the Gunnison Basin, Colorado (fig. 7). Conversely, when 

the reason for not meeting standards was considered, risk of failing to meet standards due to 

livestock was the greatest for allotments in central Wyoming, central southwestern Wyoming, 

most of the allotments not meeting standards in Billing, Montana, and all allotments not meeting 

standards in Gunnison, Colorado (ig. 8). Although we show risk of failure to meet standards due 

to livestock as a continuous probability surface (fig. 8), using the optimal classification point 446 

of the 1,510 failed allotments (about 30 percent) are predicted to have not met standards, with 

livestock as the primary cause.
26

Discussion

We conducted a relatively simple comparison of vegetation characteristics across grazing 

allotments, contrasting allotments that have “Met” versus “Not met” LHS. We had a unique 

opportunity to use recently developed remotely sensed vegetation classifications (Homer and 

others, in press) and the spatial grazing assessment information compiled for this project. 

Allotments that did not meet LHS had more bare ground, but less sagebrush, litter, and 

herbaceous vegetation, as predicted (table 5). The top AICc-identified spatial model (tables 8 and

9) indicated that “Not met” allotments were best differentiated from “Met” allotments based on 

lower amounts of sagebrush cover, less herbaceous cover, and a higher variability of herbaceous 

cover. Bare ground also was greater in allotments that had not met LHS, although it had a strong 

inverse correlation with herbaceous cover and was not included in the top candidate model. 

Following are potential explanations for these patterns. 

First, loss of sagebrush cover can constitute a loss or reduction in dominance of a major 

structural plant group and thus contribute to departures from expected LHS (Pyke and others, 

2002). Similarly, because wildlife can be strongly influenced by changes in vegetation structure 

decreased sagebrush cover may contribute to failure to meet wildlife-related standards. In our

sampling area, loss of sagebrush cover commonly occurs following wildfire. Second, it is not 

surprising that allotments with decreased herbaceous cover were more likely to fail LHS because 

ground cover is a key predictor of plant community and ecosystem health and functioning 

(Herrick and others, 2005). Likewise, minimizing soil degradation (associated with bare ground) 

is at the basis of maintaining ecosystem health (Task group on unity in concepts and terminology 

committee members, 1995). Third, although overall herbaceous cover might be higher in some 

disturbed areas due to cheatgrass, high variability in herbaceous cover also is likely associated 

with disturbances and land uses that would cause an allotment to fail LHS. For example, mining 

activity or heavy grazing may occur in concentrated areas within an allotment (for example, 

mine site, or near water points), decreasing herbaceous cover relative to the rest of the landscape. 

Allotments receiving light use may appear more homogeneous.

We predicted that when livestock are responsible for unmet standards, those allotments 

will have more bare ground and less litter and herbaceous cover. Contrary to our predictions, on 

average, allotments where standards were unmet due to livestock had less bare ground (about 6

percent), more herbaceous cover (about 3 percent), and more litter (about 2 percent) than other 

allotments (table 6). Our top two AICc-selected models both suggested that “livestock” 

allotments had more sagebrush and slightly less bare ground, with more variability in bare 

ground cover than “other” allotments (table 12). Although the second model suggested there 

was less litter in “livestock” allotments, this was a very weak effect (see table 12). These 

patterns may be partially explained by other factors (aside from livestock grazing) that contribute 

to failure to meet LHS. For example drought, OHV use or recent fires likely create more intense 

disturbances than livestock; these more intense disturbances would be associated with increased 

bare ground and decreased herbaceous and shrub cover, and in some cases, decreased litter. 

When more intense land uses are present, the relative impacts of livestock grazing may be 

minimal, or the area may pass a threshold of degradation, such that it is no longer suitable for 

livestock grazing (and unmet standards are not attributable to livestock). Other factors BLM 

cited as contributing to unmet standards include but are not limited to: energy development, 

timber harvest, historical mining, road development, woody species encroachment, fire 

suppression, past effects of livestock grazing, weeds, non-native species invasions, recreation, 

wild horses, and wildlife (K.Veblen, personal observation).
27

Although the strength of our models could be improved, there also were several 

limitations to this initial analysis that ultimately reduced our ability to detect patterns and trends. 

BLM grazing data were incomplete in many cases (see Section II), and BLM record numbers for 

spatial and tabular data did not always match, limiting our sample sizes. Additionally, LHS 

assessments were conducted over an 11-year period (1997–2007), and vegetation models and 

remotely sensed imagery were static (over 1 or 2 years, depending on area). Our results probably 

also were weakened by the fact that vegetation characteristics could not be summarized for the 

specific portion of the allotment that resulted in the ‘failing’ assessment, and instead were 

summarized across entire allotments, some portions of which were probably ‘healthy.’ Current 

efforts are underway by BLM to investigate whether conducting LHS assessments for individual 

parcels within allotments will provide a more accurate picture of overall land health status (S. 

Karl, written commun., Bureau of Land Management, 2010). Despite the limitations to our 

approach, patterns in sagebrush vegetation components were still evident across “Not met” 

versus “Met” allotments, and to a lesser extent, between allotments where standards were unmet 

due to livestock versus other reasons.

We see potential in using the spatial predictions from our analyses as a management tool. 

In Section II of this report we found that 20 percent of federal and 25 percent of university 

rangeland science experts identified a need for monitoring prioritization tools, and 30 and 35

percent, respectively, specifically cited the utility of using remote-sensing approaches. Agencies 

such as the BLM, could spatially prioritize areas/allotments based on our predicted risk of failing 

to meet standards (figs. 7 and 8), identifying allotments most at risk of degraded rangeland 

conditions, especially given that only 1,131 of 3,564 (about 31 percent) of allotments in these 

three regions have been assessed between 1997 and 2007. Our model predicts that 1,510 of these 

allotments are at risk of not meeting LHS. Of the 2,433 allotments which had not yet had LHS 

assessments conducted as of 2007, our model predicts that 1,018 of these (about 42 percent) are 

at risk of not meeting LHS. This model and map (fig. 7) may provide a decision support tool to 

aid in the allocation of limited resources, prioritizing future assessments and management actions 

in areas of greatest concern (that is, highest risk of failure). For instance, our models predict a 

large number of unmet standards occurring in the Worland Basin in northeast Wyoming, and 

south of Kemmerer in southwest Wyoming, as well as across all available allotments in the 

Gunnison Basin (fig. 7). Although most of these allotments with available data in the Gunnison 

Basin, Colorado, were assessed and subsequently identified as allotments that did not meet 

standards (29 of 31), management efforts could focus on understanding potential causes of unmet 

standards in these areas in Wyoming and Colorado. Obviously, this does not alleviate the need 

to confirm the predictive capacity of these models, evaluating some allotments where our model 

predicts both met and unmet LHS. Ultimately, our model should be challenged with independent 

data, possibly by using ongoing LHS assessments by BLM (post-2007) or by using standardized 

LHS assessments conducted by an independent team of range ecologists in the field.

We believe that this initial application of allotment data illustrates the utility of both the 

grazing allotment data compiled in this report and the new remotely sensed vegetation 

classifications (Homer and others, 2008; Homer and others, in press), allowing for applied 

questions to be asked related to the monitoring and management of healthy sagebrush 

ecosystems. Our models could be improved by more complete (post-2007) coverage of LHS 

data. Field offices typically prioritize monitoring efforts in problem allotments (see Section II); 

thus, in districts or offices where a limited number of LHS assessments had been conducted, the 

sample may have been biased towards those less likely to meet LHS. For example, it is not clear 
28

whether Colorado showed lower rates of meeting standards relative to other States due to real 

differences in livestock management, more conservative LHS assessments, or a sample biased 

towards only conducting assessments in problem allotments. Our models also could be improved 

with the addition of other ancillary data, such as soils or ecological site information, as well as 

simple topographic information, such as digital elevation models or terrain indices. For example, 

addition of ecological site data might help explain why certain allotments were identified as 

higher risk than others in our maps and distinguish ecological from managerial causes. 

Additionally, the identification of specific locations within allotments where LHS are not being 

met, would allow for more robust models to be developed based on site-specific information, 

rather than averaging conditions across an entire allotment, improving model prediction. Finally, 

both the robustness of range-wide analyses and utility of this tool for managers would benefit 

from better standardization of LHS protocols. It would also be helpful to have finer resolution of 

which areas within allotments are failing to meet standards because an individual allotment 

typically is comprised of a multitude of ecological sites that vary in the types and amounts of 

vegetation independently of land use. As discussed in Section II of this report, LHS assessment 

protocols were not necessarily standardized across State or Office boundaries.

Our models and ‘risk’ maps could currently be used as initial grazing management tools, 

even though limitations exist. This application illustrates potential (if these limitations were 

addressed) to use these grazing datasets to ask future questions about how grazing activities 

might alter sagebrush ecosystem components across large landscapes, the potential consequences 

for associated species of concern, such as Greater Sage-Grouse, and ultimately, provide 

understanding of these system interactions to allow for improved management, where needed. In 

the future, information contained in these spatial datasets on LHS or changes in livestock use 

over time could be used to assess potential consequences of changes for wildlife populations; for 

example, temporal sage-grouse lek dynamics (Fedy and Aldridge, in press), or impacts to longterm changes in vegetation characteristics (Xian and others, in press), using time-series analyses 

of vegetation and/or temporal shifts in climate within these systems.
29

Conclusions

This project represents a first attempt to compile local-level, livestock grazing-related 

monitoring data from the BLM for the purpose of spatially analyzing broad-scale patterns in 

vegetation characteristics in sagebrush steppe habitat. This type of landscape level assessment 

would help inform future adaptive management of landscape level species such as Greater SageGrouse, especially in the context of multiple use management and climate change. Prior to our 

work, although local-level monitoring data existed, the data had not yet been critically evaluated 

for suitability in range-wide analyses, nor had there been any attempts to use it for such analyses. 

In general, we found that more consistent data collection methodologies across local-level (field) 

offices might improve the suitability of data for broad-scale analyses. We also did not find any 

local-level (on-the-ground) monitoring data (Actual Use, Utilization, Vegetation Trend) that had 

been collected consistently enough over time and space for range-wide, or even state-wide, 

analyses.

Continued and improved emphasis on monitoring also may aid local management 

decisions, particularly with respect to the effects of livestock grazing. Rangeland science experts 

identified ground cover as a high monitoring priority for assessing range condition and 

emphasized the importance of tracking livestock numbers and grazing dates. Ground cover is one 

of a handful of key variables currently emphasized in BLM monitoring programs, and individual 

offices are required to collect and report livestock information (numbers, dates). However, the 

frequency and regularity with which monitoring and livestock data were collected varied 

considerably across allotments and field offices. The most effective monitoring program may 

entail both increased data collection effort and the integration of alternative monitoring 

approaches (for example, remote sensing or monitoring teams).

We also identified three (non-monitoring) datasets that could potentially be used for 

range-wide analyses. First, BLM maintains spatial (GIS) allotment boundary data. We compiled 

and corrected the most up-to-date data for use in our analyses. Future efforts would be more 

streamlined if updated spatial data were maintained in a central location (whereas during our 

study, we had to obtain data from individual State and Field Offices). Second, at the time of our 

study, BLM had conducted LHS assessments for 57 percent of all allotments across the west (or 

for which we had data). After the data had been compiled we mapped land health status across 

our study region. Third, the BLM maintains allotment-level records of billed and permitted use. 

We found Billed Use to be a satisfactory indicator of actual timing and intensity of livestock 

grazing, while permitted use describes the legal maximums. Overall, the BLM may be able to 

build upon and make refinements to these datasets (for example, rectify cases where an allotment 

appeared in the spatial boundary dataset, but not the Rangeland Administration System 

(http://www.blm.gov/ras/), or vice versa; in such cases, we excluded the allotment from our 

analyses). For all three datasets, the use of allotment boundaries as a basis for geospatial analysis 

is not without limitations. The allotment boundary is often inclusive of areas not subject to 

grazing, multiple permittees can operate within an allotment, and often an entire allotment may 

fail to meet LHS due to factors occurring over a small portion of the allotment.
30

It nonetheless may be possible to use these spatial datasets to help prioritize monitoring 

activities over the extensive land areas managed by BLM. For example, we used spatial 

allotment boundary data and LHS data to test whether remotely sensed vegetation characteristics 

(see Homer and others, 2008) could be used to predict which allotments met or did not meet 

LHS. Preliminary results (pending further model validation) suggest that we may be able to use 

this approach to create risk maps to help BLM prioritize monitoring efforts.

Acknowledgments

Thank you to Sherm Karl, Phil Cooley, Bill Andersen, Craig MacKinnon, Bob Hopper, 

John Sadowski, Tim Carrigan, John Reitsma, Mike Pellant, Pat Clark, Steve Smith, Steve Knick, 

Kenneth Visser, and Zack Bowen for their insights and advice, and to additional State and 

District BLM staff who provided their cooperation and assistance. Thank you to Chris Campton 

for assistance with field office data collection. We thank Collin Homer, Deb Meyer, and Mike 

O’Donnell from the USGS for assistance with derived sagebrush map products. We also thank 

the university and federal range scientists whose insights helped us develop and refine many of 

the ideas we have presented here; throughout the paper, we have tried to identify concepts that 

we attribute largely to their input. This research was funded as a part of USGS Sagebrush 

Ecosystems Coordinated Research (SECR).

References Cited

Adler, P.B., Raff, D.A., and Lauenroth, W.K., 2001, The effect of grazing on the spatial 

heterogeneity of vegetation: Oecologia, v. 128, no. 4, p. 465-479.

Aldridge, C.L., and Boyce, M.S., 2007, Linking occurrence and fitness to persistence: Habitatbased approach for endangered Greater Sage-Grouse: Ecological Applications, v. 17, no. 2, p. 

508-526.

Aldridge, C.L., Nielsen, S.E., Beyer, H.L., Boyce, M.S., Connelly, J.W., Knick, S.T., and 

Schroeder, M.A., 2008, Range-wide patterns of greater sage-grouse persistence: Diversity and 

Distributions, v. 14, no. 6, p. 983-994.

Anderson, J.E., and Inouye, R.S., 2001, Landscape-scale changes in plant species abundance and 

biodiversity of a sagebrush steppe over 45 years: Ecological Monographs, v. 71, no. 4, p. 531-

556.

Bestelmeyer, B.T., 2006, Threshold concepts and their use in rangeland management and 

restoration: The good, the bad, and the insidious: Restoration Ecology, v. 14, no. 3, p. 325-329.

Bestelmeyer, B.T., Tugel, A.J., Peacock, G.L., Robinett, D.G., Sbaver, P.L., Brown, J.R., 

Herrick, J.E., Sanchez, H., and Havstad, K.M., 2009, State-and-Transition Models for 

Heterogeneous Landscapes: A Strategy for Development and Application: Rangeland Ecology 

& Management, v. 62, no. 1, p. 1-15.

Bi-State Local Planning Group. 2004, Greater Sage-Grouse conservation plant for the bi-state 

plan area of Nevada and eastern California, First Edition.

Booth, D.T., and Cox, S.E., 2008, Image-based monitoring to measure ecological change in 

rangeland: Frontiers in Ecology and the Environment, v. 6, no. 4, p. 185-190.
31

Briske, D.D., Derner, J.D., Brown, J.R., Fuhlendorf, S.D., Teague, W.R., Havstad, K.M., Gillen, 

R.L., Ash, A.J., and Willms, W.D., 2008, Rotational grazing on rangelands: Reconciliation of 

perception and experimental evidence: Rangeland Ecology & Management, v. 61, no. 1, 

p. 3-17.

Briske, D.D., and Richards, J.H., 1995, Plant responses to defoliation: a physiological, 

morphological, and demographic evaluation, in Bedunah, D.J., and Sosebee, R.E., eds., 

Wildland plants: physiological ecology and developmental morphology: Denver, CO, Society 

for Range Management, p. 635-710.

Bureau of Land Management, 1999, Interagency Technical Reference 1734-4, Bureau of Land 

Management National Applied Resources Sciences Center.

Bureau of Land Management, 2001, Rangeland Health Standards Handbook H-4180-1. BLM 

Manual Rel. 4-107.

Burnham, K.P., and Anderson, D.R., 2002, Model selection and multimodel inference: a 

practical information-theoretic approach. Second edition.: New York, USA, Springer-Verlag, 

Inc.

Chambers, J.C., and Wisdom, M.J., 2009, Priority Research and Management Issues for the 

Imperiled Great Basin of the Western United States: Restoration Ecology, v. 17, no. 5, p. 707-

714.

Connelly, J.W., Knick, S.T., Schroeder, M.A., and Stiver, S.J., 2004, Conservation assessment of 

Greater Sage-grouse and sagebrush habitats: Western Association of Fish and Wildlife 

Agencies. Unpublished report. Cheyenne, WY.

Crawford, J.A., Olson, R.A., West, N.E., Mosley, J.C., Schroeder, M.A., Whitson, T.D., Miller, 

R.F., Gregg, M.A., and Boyd, C.S., 2004, Ecology and management of sage-grouse and sagegrouse habitat: Journal of Range Management, v. 57, no. 1, p. 2-19.

Dyksterhuis, E.J., 1949, Condition and management of range land based on quantitative ecology: 

Journal of Range Management v. 2, p. 104-115.

Elzinga, C.L., Salzer, D.W., and Willoughby, J.W., 2001a, Measuring and monitoring plant 

populations, BLM Technical Reference 1730-1.

Elzinga, C.L., Salzer, D.W., Willoughby, J.W., and Gibbs, J.P., 2001b, Monitoring plant and 

animal populations: Malden, Massachusetts, Blackwell Science, Inc.

Federal Register 2010, Department of the Interior, Fish and Wildlife Service 50CFR Part17, 

Endangered and threatened wildlife and plants; 12-month findings for petitions to list the 

greater sage-grouse (Centocercus urophasianus) as threatened or endangered, v. 75, no. 55, p. 

13910-14014.

Fedy, B.C., and Aldridge, C.L., 2011, The importance of within-year repeated counts and the 

influence of scale on long-term monitoring of sage-grouse: Journal of Wildlife Management, 

vol. 75, no.5, p. 1022-1033.

Fielding, A. H., and Bell, J.F. 1997. A review of methods for the assessment of prediction errors 

in conservation presence/absence models: Environmental Conservation, v. 24, p. 38–49.

Herrick, J.E., van Zee, J.W., Havstad, K.M., Burkett, L.M., and Whitford, W.G., 2005, 

Monitoring Manual for Grassland, Shrubland and Savanna Ecosystems, Vol. II: Las Cruces, 

New Mexico, Jornada Experimental Range.

Herrick, J.E., van Zee, J.W., Havstad, K.M., Burkett, L.M., and Whitford, W.G., 2009, 

Monitoring Manual for Grassland, Shrubland and Savanna Ecosystems, Vol I.: Las Cruces, 

New Mexico, Jornada Experimental Range.
32

Herrick, J.E., van Zee, J.W., Havstad, K.M., and Whitford, W.G., 2001, Monitoring Manual for 

Grassland, Shrubland and Savanna Ecosystems: Las Cruces, New Mexico, Jornada 

Experimental Range.

Homer, C.G., Aldridge, C.L., Meyer, D.K., Coan, M.J., and Bowen, Z.H., 2008, Multiscale 

Sagebrush Rangeland Habitat Modeling in Southwest Wyoming. U.S. Geological Survey 

Open-File Report 2008-1027.

Homer, C.G., Aldridge, C.L., Meyer, D.K., and Schell, S., in press, Multi-scale remote sensing 

sagebrush characterization with regression trees over Wyoming, USA; laying a foundation for 

monitoring: Applied Earth Observation and Geoinformation: International Journal of Applied 

Earth Observation and Geoinformation

Hosmer, D.W., and Lemeshow, S., 2000, Applied logistic regression: New York, New York, 

USA, John Wiley and Sons, Inc.

Jasmer, G.E., and Holechek, J.L., 1984, Determining Grazing Intensity on Rangeland: Journal of 

Soil and Water Conservation, v. 39, no. 1, p. 32-35.

Johnson, A.E., James, L.F., and Spillet, J., 1976, The abortifacient and toxic effects of big 

sagebrush (Artemisia tridentata) and juniper (Juniperus osteosperma) on domestic sheep: 

Journal of Range Management, v. 29, p. 278-280

Liu, C., Berry, P.M., Dawson, T.P., and Pearson, R.G., 2005, Selecting thresholds of occurrence 

in the prediction of species distribution: Ecography, v. 28, p. 385-393.

Manel, S., Williams, H.C., and Ormerod, S.J., 2001, Evaluating presence-absence models in 

ecology: the need to account for prevalence: Journal of Applied Ecology, v. 38, no. 5, p. 921-

931.

Miller, R.F., Knick, S.T., Pyke, D.A., Meinke, C.W., Hanser, S.E., Wisdom, M.J., and A.L., H., 

2011, Ch. 11: Characteristics of sagebrush habitats and limitations to long-term conservation, 

Studies in Avian Biology, Ecology and conservation of greater sage-grouse: a landscape 

species and its habitats: Universit of California Press.

Nichols, M.H., Ruyle, G.B., and Nourbakhsh, I.R., 2009, Very-High-Resolution Panoramic 

Photography to Improve Conventional Rangeland Monitoring: Rangeland Ecology & 

Management, v. 62, no. 6, p. 579-582.

Nielsen, S.E., Munro, R.H.M., Bainbridge, E.L., Stenhouse, G.B., and Boyce, M.S., 2004, 

Grizzly bears and forestry II. Distribution of grizzly bear foods in clearcuts of west-central 

Alberta, Canada: Forest Ecology and Management, v. 199, no. 1, p. 67-82.

Northern Eagle/Southern Routt Work Group. 2004, Greater Sage-Grouse Conservation Plan for 

Northern Eagle County and Southern Routt County, Colorado.

Pellant, M., Shaver, P., Pyke, D.A., and Herrick, J.E., 2005, Interpreting Indicators of Rangeland 

Health-Version 4: Bureau of Land Management, National Science and Technology Center 

Technical Reference 1734-6.

Pyke, D.A., Herrick, J.E., Shaver, P., and Pellant, M., 2002, Rangeland health attributes and 

indicators for qualitative assessment: Journal of Range Management, v. 55, p. 584-597.

Rango, A., Laliberte, A., Herrick, J.E., Winters, C., Havstad, K., Steele, C., and Browning, D., 

2009, Unmanned aerial vehicle-based remote sensing for rangeland assessment, monitoring, 

and management: Journal of Applied Remote Sensing, v. 3, p. 1-15.

Schroeder, M.A., Aldridge, C.L., Apa, A.D., Bohne, J.R., Braun, C.E., Bunnell, S.D., Connelly, 

J.W., Deibert, P.A., Gardner, S.C., Hilliard, M.A., Kobriger, G.D., McAdam, S.M., McCarthy, 

C.W., McCarthy, J.J., Mitchell, D.L., Rickerson, E.V., and Stiver, S.J., 2004, Distribution of 

sage-grouse in North America: Condor, v. 106, no. 2, p. 363-376.
33

Smith, S.D., Bunting, S.C., and Hironaka, M., 1986, Sensitivity of Frequency Plots for Detecting 

Vegetation Change: Northwest Science, v. 60, no. 4, p. 279-286.

Stoddart, L.A., Smith, A.D., and Box, T.W., 1995, Range management, 3rd edition: New York, 

NY, McGraw-Hill.

Swets, J.A., 1988, Measuring the Accuracy of Diagnostic Systems: Science, v. 240, no. 4857, p. 

1285-1293.

Task Group on Unity in Concepts and Terminology Committee Members. 1995, New concepts 

for assessment of rangeland condition: Journal of Range Management, v. 48, p. 271-282.

Thurow, T.L., and Taylor, C.A., 1999, Viewpoint: The role of drought in range management: 

Journal of Range Management, v. 52, no. 5, p. 413-419.

Tongway, D.J., 1994, Rangeland soil condition assessment manual, CSIRO Publication, 

Melbourne, p. 1-16.

U.S. Department of Agriculture, Natural Resources Conservation Service, 2006, Chapter 4: 

Inventorying and monitoring grazing land resources, National range and pasture handbook.

U.S. Department of Agriculture, Natural Resources Conservation Service, 2009a, Chapter 3: 

Ecological sites and forage suitability groups, National range and pasture handbook, accessed 

January 3, 2011, at http://www.glti.nrcs.usda.gov/technical/publications/nrph.html.

U.S. Department of Agriculture, Natural Resources Conservation Service, 2009b, Chapter 4: 

Inventorying and monitoring grazing land resources, National range and pasture handbook, 

accessed January 3, 2011, at http://www.glti.nrcs.usda.gov/technical/publications/nrph.html.

U.S. Department of Agriculture, Natural Resources Conservation Service, 2009c, National range 

and pasture handbook, accessed January 3, 2011, at 

http://www.glti.nrcs.usda.gov/technical/publications/nrph.html.

Vallentine, J.F., 1990, Grazing Management: San Diego, CA, Academic Press, Inc.

West, N.E., 2003, History of rangeland monitoring in the USA: Arid Land Research & 

Management, v. 17, p. 495-545.

Westoby, M., Walker, B., and Noy-Meir, I., 1989, Opportunistic management for rangelands not 

at equilibrium: Journal of Range Management, v. 42, no. 4, p. 266-274.

Williams, B.K., Szaro, R.C., and Shapiro, C.D., 2007, Adaptive management: the U.S. 

Department of the Interior technical guide: Washington, D.C., U.S. Department of the Interior, 

Adaptive Management Working Group.

Xian, G., Homer, C.G., and Aldridge, C.L., in press, Assessing Long-Term Variations of 

Sagebrush Habitat – Characterization of Spatial Extents and Distribution Patterns Using Multitemporal Satellite Remote Sensing Data: International Journal of Remote Sensing.
34

Figure 1. Sample sites in Harney County, Oregon where production was estimated with NRCS reconstruction methods. 
35

Production: predicted ranges and field estimates

ESD

F B J C H E I D A

lb/acre

0

500

1000

1500

2000

2500

3000

3500

(4) (7) (2) (4) (6) (4) (2) (4) (9)

Figure 2. Predicted ranges and field estimates of production. Filled-circles are field data samples of 

production ±1SE (estimated using NRCS reconstruction methods), and boxes indicates the predicted 

range of production values according to Ecological Site Descriptions (ESD). A= JD Mountain Claypan 

12-16PZ, B=SR Mountain Clayey 12-16 PZ, C=JD Shrubby Mountain Clayey 12-16PZ, D=SR 

Mahogany Mountain Loam 14-18PZ, E=SR Mountain South 12-16PZ, F=SR Mountain North 12-16PZ, 

H= SR Clayey 9-12PZ, I=SR Mountain Loamy 9-12 PZ, J=SR Mountain North 9-12PZ. Parentheses 

indicate sample sizes.
36

Figure 3. Schematic of (1) the BLM allotment evaluation process which is based on quantitative 

monitoring data and (2) the Land Health Standards (LHS) Evaluation process which is based on 

rangeland health indicators. Red arrows indicate feedbacks between the two processes.
37

Actual Use Utilization Trend Photo Points

% of files containing data

0

20

40

60

80

100

Figure 4. Box plot showing percentage of BLM files containing Actual Use, Utilization, Trend and Photo 

Point data. Plots indicate median, 25/75 percentile, and 10/90 percentile for 13 field offices. ANOVA 

results indicate data availability differed significantly among the four data types (F3,36=7.56, p=.0005)
38

Billed Use

0 1000 2000 3000 4000 5000 6000

Actual Use

0

1000

2000

3000

4000

5000

6000

7000

y = 0.8695x + 40.146

R2

 = 0.7469

Figure 5. Relationship between yearly AUMs billed (Billed Use) by the Bureau of Land Management 

and AUMs (Actual Use) reported by permittees on 171 BLM allotments. Data points (n=906) represent 

allotment-year combinations between 1998 and 2007. Data points (n=42) from ten allotments that 

permit ≥ 5000 AUMs/year (n=42) were excluded due to under-reporting associated with large 

allotments used by multiple permittees. 
39

Figure 6. Spatial representation of whether BLM allotments have met Land Health Standards (LHS) 

(Upland, Riparian and Biodiversity) and whether livestock have contributed to unmet LHS. Data 

provided by BLM.
40

Figure 7. Predicted probability (risk) of any given BLM allotment not meeting Land Health Standards

(LHS) across Montana (Billings BLM Field Office, Panel A), Wyoming (State-wide, Panel B), and 

Colorado (Gunnison Basin, Panel C). The model was developed using available data for 1,131 LHS 

assessments in the region, for which 798 met LHS and 333 did not meet LHS. A total of 1,510 of 3,564 

allotments are at risk of not meeting LHS (crosshatching), based on an optimal model probability 

classification point of 0.271.
41

Figure 8. Predicted probability (risk) of not meeting standards, with livestock being the primary cause. 

Predictions are shown for all BLM allotments not meeting Land Health Standards (LHS) across 

Montana (Billings BLM Field Office, Panel A), Wyoming (State-wide, Panel B), and Colorado (Gunnison 

Basin, Panel C). A total of 1,131 LHS assessments in the region, for which 798 met LHS and 333 did 

not meet LHS. 
42

Table 1. Top table summarizes office file results from 310 allotments selected at random across 13 Bureau 

of Land Management (BLM) field offices. 

[Bottom table summarizes results from 62 of 310 allotments that cited livestock grazing as reason for not meeting at 

least one Land Health Standard. In both tables, allotments are divided into those being managed to “Maintain” vs. 

“Improve” rangeland condition. For each data type, “Freq.” indicates the percentage of allotments across the region 

with at least 1 year of data between 1997 and 2007 (although completeness of data within a given allotment is 

variable, for example, some allotments may have data for only a subset of key areas or pastures). The “mean # 

years” column indicates the average number of years for which data exist ± 1 SE (excluding allotments that had no 

data). AMP = Allotment Management Plan]

ALL SAMPLED ALLOTMENTS

Data type

Maintain (n=109) Improve ( n=201)

Freq. mean # yrs Freq. mean # yrs 

1) Actual Use 59% 6.3±0.46 77% 6.8±0.29

2) Utilization 51% 4.4±0.47 52% 4.7±0.33

3) Vegetation Trend 34% 1.0±0 38% 1.04±0.03

4) Photo Points 53% 1.3±0.06 61% 1.7± 0.09

AMP or Grazing Plan 17% -.- 26% -.-

Allotment Evaluation 15% -.- 8% -.-

ALLOTMENTS CITING LIVESTOCK ISSUES

Data type

Maintain (n=17) Improve (n=45)

Freq. mean # yrs Freq. mean # yrs 

1) Actual Use 47% 5±1.22 84% 3.66±0.59

2) Utilization 53% 2.56±1.07 51% 4.43±0.69

3) Vegetation Trend 35% 1.0±0 36% 1.01±0.03

4) Photo Points 65% 1.6±0.19 71% 2.02±0.22

All 4 data types 35% -.- 24% -.-

Data types 1,2,3 35% -.- 27% -.-

Data types 1, 2 42% -.- 49% -.-

No data 29% -.- 9% -.-
43

Table 2. Types of data (collected between 1997 and 2007) contained in a randomly selected sample of 310 allotment files from 13 Bureau of Land 

Management (BLM) offices across 6 States. 

[All Frequency, Cover, and Production techniques are described in the 1996 Interagency Technical Reference 1734-4, except Line-point, which is a variation of

the point-intercept method. All Utilization techniques are described in the 1996 Interagency Technical Reference 1734-3, except the Utilization Gauge method 

which is a US Forest Service stubble height method. Both “State D” offices also collected Observed Apparent Trend data, a subjective numerical rating that

considers vigor, seedlings, surface litter, pedestals and gullies. Offices A-1, C-1, D-1 and D-2 also used 3× 3 feet or 5× 5 feet Range Trend Plots for visual 

estimates of key species attributes such as cover, frequency, density, and vigor; specific methodology varied across BLM offices]

Office

Frequency (and ground 

cover) Cover Production (and composition) Utilization

Photo 

quadrat points

freq. 

nested 

freq.

pace 

freq.

DaubenmireLine 

intercept

Linepoint Step 

point

Method 

not 

specified 

Dryweightrank

Comparative 

yield 

Grazedclass Heightweight

Utilization 

gauge

Key 

species

A-1 x x x x x x x

B-1 x x x x

B-2 x x

C-1 x x x x

C-2 x x x x x x

D-1 x x x

D-2 x x x x x x

E-1 x x x x x

E-2 x x x

F-1 x x

F-2 x x x

F-3 x x x

F-4 x x x x
44

Table 3. Results of Land Health Standards (LHS) evaluations conducted by Bureau of Land Management (BLM) allotments between 1997 and 2007. 

[Allotments are divided into those managed to “Maintain” vs. “Improve” rangeland condition. For allotments that had “Not met” a standard, the “Livestockcaused” column indicates the percentage of “Not met” due to livestock. Top table summarizes whether allotments met all of their state standards (three to eight, 

depending on state). Other tables summarize three standards common to all states (Upland Soil, Riparian, and Biodiversity). ANOVA indicates significant 

differences in meeting of “all standards” between Maintain and Improve allotments (F1,18=7.74, p=0.02) and across states (F9,18=31.27, p=<0.0001). Standards 

that were “Not met” due to livestock differed significantly across states (F9,18=3.14, p=0.02) and among Upland, Riparian and Biodiversity standards (F2,18=5.18, 

p=0.02), and there was a significant interaction between standards (Upland, Riparian, Biodiversity) and allotment status (Maintain, Improve) (F2,18== 21.09, 

p<.0001). Raw LHS data supplied by BLM]

ALL STANDARDS

"MAINTAIN" ALLOTMENTS "IMPROVE" ALLOTMENTS

NO DATA

State

All 

stds 

met

≥ 1 std 

Not met

LivestockcausedAll stds 

met

≥ 1 std 

Not 

met

Livestockcaused

A n=67 73% 27% 11% n=83 66% 34% 14% n=189 56%

B n=182 71% 29% 42% n=461 64% 36% 47% n=292 31%

C n=62 35% 65% 55% n=57 25% 75% 72% n=409 77%

D n=204 61% 39% 56% n=262 52% 48% 46% n=353 43%

E n=140 79% 21% 52% n=246 82% 18% 43% n=565 59%

F n=385 70% 30% 23% n=352 47% 53% 30% n=862 54%

G n=100 63% 37% 14% n=107 34% 66% 34% n=71 26%

H n=371 63% 37% 45% n=469 39% 61% 60% n=583 41%

I n=1463 87% 13% 47% n=670 68% 32% 56% n=124 5%

J n=130 89% 11% 14% n=180 85% 15% 41% n=1093 78%

TOTAL n=3104 77% 23% 41% n=2887 59% 41% 48% n=4541 43%
45

Table 3. Results of Land Health Standards (LHS) evaluations conducted by Bureau of Land Management (BLM) allotments between 1997 and 

2007.—Continued. 

[Allotments are divided into those managed to “Maintain” vs. “Improve” rangeland condition. For allotments that had “Not met” a standard, the “Livestockcaused” column indicates the percentage of “Not met” due to livestock. Top table summarizes whether allotments met all of their state standards (three to eight, 

depending on state). Other tables summarize three standards common to all states (Upland Soil, Riparian, and Biodiversity). ANOVA indicates significant 

differences in meeting of “all standards” between Maintain and Improve allotments (F1,18=7.74, p=0.02) and across states (F9,18=31.27, p=<0.0001). Standards 

that were “Not met” due to livestock differed significantly across states (F9,18=3.14, p=0.02) and among Upland, Riparian and Biodiversity standards (F2,18=5.18, 

p=0.02), and there was a significant interaction between standards (Upland, Riparian, Biodiversity) and allotment status (Maintain, Improve) (F2,18== 21.09, 

p<.0001). Raw LHS data supplied by BLM]

UPLAND SOIL STANDARD

"MAINTAIN" ALLOTMENTS "IMPROVE" ALLOTMENTS

NO DATA

State Met 

Not 

met

Livestockcaused Met Not 

met

Livestockcaused

A n=54 96% 4% 0% n=67 96% 4% 0% n=218 64%

B n=182 87% 13% 39% n=457 79% 21% 48% n=296 32%

C n=57 81% 19% 73% n=55 60% 40% 68% n=416 79%

D n=204 87% 13% 50% n=260 85% 15% 35% n=355 43%

E n=140 95% 5% 71% n=246 91% 9% 43% n=565 59%

F n=375 91% 9% 34% n=336 85% 15% 31% n=888 56%

G n=96 98% 2% 50% n=88 93% 7% 50% n=94 34%

H n=371 95% 5% 71% n=464 79% 21% 67% n=588 41%

I n=1455 95% 5% 57% n=656 93% 7% 73% n=146 6%

J n=127 93% 7% 0% n=178 87% 13% 48% n=1098 78%

TOTAL n=3061 93% 7% 50% n=2807 86% 14% 53% n=4664 44%
46

Table 3. Results of Land Health Standards (LHS) evaluations conducted by Bureau of Land Management (BLM) allotments between 1997 and 

2007—Continued. 

[Allotments are divided into those managed to “Maintain” vs. “Improve” rangeland condition. For allotments that had “Not met” a standard, the “Livestockcaused” column indicates the percentage of “Not met” due to livestock. Top table summarizes whether allotments met all of their state standards (three to eight, 

depending on state). Other tables summarize three standards common to all states (Upland Soil, Riparian, and Biodiversity). ANOVA indicates significant 

differences in meeting of “all standards” between Maintain and Improve allotments (F1,18=7.74, p=0.02) and across states (F9,18=31.27, p=<0.0001). Standards 

that were “Not met” due to livestock differed significantly across states (F9,18=3.14, p=0.02) and among Upland, Riparian and Biodiversity standards (F2,18=5.18, 

p=0.02), and there was a significant interaction between standards (Upland, Riparian, Biodiversity) and allotment status (Maintain, Improve) (F2,18== 21.09, 

p<.0001). Raw LHS data supplied by BLM]

RIPARIAN STANDARD

"MAINTAIN" ALLOTMENTS "IMPROVE" ALLOTMENTS

NO DATA

State Met 

Not 

met

Livestockcaused Met Not 

met

Livestockcaused

A n=54 94% 6% 33% n=67 96% 4% 67% n=218 64%

B n=182 94% 6% 73% n=457 88% 12% 72% n=296 32%

C n=56 70% 30% 65% n=47 36% 64% 83% n=362 78%

D n=200 75% 25% 66% n=260 66% 34% 49% n=359 44%

E n=139 91% 9% 77% n=246 93% 7% 82% n=566 60%

F n=371 86% 14% 40% n=324 75% 25% 49% n=904 57%

G n=96 89% 11% 9% n=87 70% 30% 77% n=95 34%

H n=358 85% 15% 68% n=436 66% 34% 72% n=629 44%

I n=1459 93% 7% 61% n=656 77% 23% 68% n=142 6%

J n=130 100% 0% 0% n=180 98% 2% 100% n=1093 78%

TOTAL n=3045 90% 10% 59% n=2760 78% 22% 66% n=4664 45%
47

Table 3. Results of Land Health Standards (LHS) evaluations conducted by Bureau of Land Management (BLM) allotments between 1997 and 

2007.—Continued 

[Allotments are divided into those managed to “Maintain” vs. “Improve” rangeland condition. For allotments that had “Not met” a standard, the “Livestockcaused” column indicates the percentage of “Not met” due to livestock. Top table summarizes whether allotments met all of their state standards (three to eight, 

depending on state). Other tables summarize three standards common to all states (Upland Soil, Riparian, and Biodiversity). ANOVA indicates significant 

differences in meeting of “all standards” between Maintain and Improve allotments (F1,18=7.74, p=0.02) and across states (F9,18=31.27, p=<0.0001). Standards 

that were “Not met” due to livestock differed significantly across states (F9,18=3.14, p=0.02) and among Upland, Riparian and Biodiversity standards (F2,18=5.18, 

p=0.02), and there was a significant interaction between standards (Upland, Riparian, Biodiversity) and allotment status (Maintain, Improve) (F2,18== 21.09, 

p<.0001). Raw LHS data supplied by BLM]

BIODIVERSITY STANDARD

"MAINTAIN" ALLOTMENTS "IMPROVE" ALLOTMENTS

NO DATA

State Met 

Not 

met

Livestockcaused Met Not 

met

Livestockcaused

A n=68 84% 16% 36% n=55 93% 7% 25% n=216 64%

B n=459 74% 26% 50% n=182 75% 25% 40% n=294 31%

C n=55 40% 60% 67% n=59 51% 49% 52% n=414 78%

D n=260 65% 35% 40% n=204 74% 26% 50% n=355 43%

E n=245 87% 13% 31% n=140 86% 14% 45% n=566 60%

F n=341 77% 23% 33% n=375 88% 12% 30% n=883 55%

G n=88 72% 28% 36% n=96 79% 21% 20% n=94 34%

H n=466 54% 46% 61% n=367 74% 26% 43% n=590 41%

I n=665 88% 12% 58% n=1460 94% 6% 32% n=132 6%

J n=178 87% 13% 43% n=128 91% 9% 18% n=1097 78%

TOTAL n=2825 75% 25% 50% n=3066 87% 13% 39% n=4641 44%
48

Table 4. Results of informal conversations with federal and university rangeland science experts on how 

best to prioritize monitoring of rangeland condition and livestock impacts. 

[Experts were presented with a hypothetical monitoring scenario (appendix 5). Although we spoke with 22 

university scientists, three participated in a group conversation and expressed consensus opinions; they are therefore 

counted as a single expert]

Monitoring priority Federal (n=20) University (n=20)

cover 55% 70%

bare ground 25% 15%

gap 5% 5%

production 10% 10%

frequency 5% 0%

density 10% 10%

utilization 35% 25%

cattle and/or wildlife condition 5% 10%

soils 25% 10%

reference areas or ecological sites 30% 40%

photos 30% 15%

remote sensing 30% 35%

identification of at-risk areas 25% 15%

Additional insights:

Perceived disincentive to report under-grazing 5% 0%

Photo points compelling in court 5% 0%

Effectiveness of monitoring teams 0% 5%
49

Table 5. Mean, standard deviation (Std) and t-test comparisons of sagebrush vegetation characteristics for 

allotments in Montana (Billings BLM Field Office), Wyoming (State-wide), and Colorado (Gunnison Basin) that 

have “Met” (798) versus “Not met” (333) Land Health Standards (LHS) assessments. 

[Cover variables include sagebrush (sb), herbaceous (hb), litter (lt), and bare (ba); metrics assessed include mean, 

median and standard deviation (std)]

Met Not Met t-test means comparison

Variable/metric Mean Std Mean Std t-value df p-value

sb_mean 9.882 4.060 8.167 4.073 6.460 619.971 0.0000

sb_std 3.520 1.153 3.129 1.313 4.722 555.894 0.0000

sb_median 9.830 4.464 8.117 4.447 5.896 624.049 0.0000

hb_mean 24.320 11.037 18.236 9.881 9.112 689.802 0.0000

hb_std 7.510 3.003 6.778 2.878 3.849 646.623 0.0001

hb_median 23.972 11.533 17.697 10.519 8.885 677.587 0.0000

lt_mean 21.013 6.792 18.233 7.332 5.936 581.274 0.0000

lt_std 6.149 1.871 5.825 1.750 2.779 661.487 0.0056

lt_median 21.060 7.453 18.060 7.976 5.876 585.625 0.0000

ba_mean 42.677 14.128 52.357 16.035 -9.575 557.569 0.0000

ba_std 11.828 3.374 11.426 3.338 1.840 627.959 0.0663

ba_median 41.711 15.429 52.306 17.544 -9.583 556.733 0.0000

Table 6. Mean, standard deviation (Std) and t-test comparisons of sagebrush vegetation characteristics for 

allotments in Montana (Billings BLM Field Office), Wyoming (State-wide), and Colorado (Gunnison Basin) 

that did not meet Land Health Standards (LHS) assessments due to livestock (132) versus other causes 

(201). 

[Cover variables include sagebrush (sb), herbaceous (hb), litter (lt), and bare (ba); metrics assessed include mean, 

median and standard deviation (std)]

Other causes Livestock as cause t-test means comparison

Variable/metric Mean Std Mean Std t-value df p-value

sb_mean 7.592 4.029 9.042 3.998 -3.226 281.887 0.0014

sb_std 3.017 1.370 3.300 1.207 -1.982 303.930 0.0484

sb_median 7.557 4.407 8.970 4.388 -2.869 281.211 0.0044

hb_mean 16.988 8.796 20.136 11.104 -2.740 235.078 0.0066

hb_std 6.546 2.429 7.132 3.430 -1.704 216.167 0.0899

hb_median 16.498 9.322 19.523 11.924 -2.462 232.715 0.0145

lt_mean 17.425 7.547 19.465 6.838 -2.554 299.060 0.0111

lt_std 5.604 1.708 6.163 1.767 -2.860 273.593 0.0046

lt_median 17.294 8.166 19.227 7.560 -2.211 295.149 0.0278

ba_mean 54.831 16.069 48.591 5.288 3.570 290.048 0.0004

ba_std 10.990 3.335 12.091 3.244 -2.995 285.738 0.0030

ba_median 54.721 17.506 48.629 17.018 3.159 285.895 0.0018
50

Table 7. Candidate models for each metric [mean, median and standard deviation (std)] within each 

variable subgroup comparing sagebrush vegetation characteristics for allotments in Montana (Billings BLM 

Field Office), Wyoming (State-wide), and Colorado (Gunnison Basin) that have “Met” (798) versus “Not 

met” (333) Land Health Standards (LHS) assessments. 

[See table 5 for a definition of model variables. State is a categorical variable with Wyoming as the indicator. Log 

likelihood (LL) and number of parameters in the model (K) are shown. Models are ranked by ΔAICc within each 

variable subgroup. In all cases, the top AICcselected model was carried forward for candidate model building]

Model LL K AICc ΔAICc

Sagebrush

sb_mean state -625.632 4 1259.334 0.000

sb_mean sb_std state -625.492 5 1261.091 1.756

sb_median state -629.120 4 1266.310 6.976

sb_median sb_std state -628.609 5 1267.325 7.990

sb_std state -645.055 4 1298.180 38.846

Litter

lt_mean state -636.669 4 1281.41 0.000

lt_median state -637.550 4 1283.17 1.761

lt_mean lt_std state -636.652 5 1283.41 2.002

lt_median lt_std state -637.454 5 1285.02 3.606

lt_std state -649.426 4 1306.92 25.514

Herbaceous

hb_mean hb_std state -616.028 5 1242.16 0.000

hb_median hb_std state -619.871 5 1249.85 7.685

hb_mean state -620.943 4 1249.96 7.794

hb_median state -622.534 4 1253.14 10.975

hb_std state -649.540 4 1307.15 64.987

Bare

ba_mean ba_std state -607.959 5 1226.03 0.000

ba_mean state -610.153 4 1228.38 2.351

ba_median ba_std state -609.372 5 1228.85 2.825

ba_median state -611.533 4 1231.14 5.113

ba_std state -651.163 4 1310.40 84.372
51

Table 8. Final candidate models comparing sagebrush vegetation characteristics for allotments in Montana 

(Billings BLM Field Office), Wyoming (State-wide), and Colorado (Gunnison Basin) that have “Met” (798) 

versus “Not met” (333) Land Health Standards (LHS) assessments. 

[See table 5 for a definition of model variables. State is a categorical variable with Wyoming as the indicator. Log 

likelihood (LL) and number of parameters in the model (K) are shown. Models are ranked by ΔAICc with Akaike 

weight (wi) indicating the weight of evidence for each model within the candidate set]

Model LL K AICc ΔAICc wi

sb_mean hb_mean hb_std state -594.716 6 1201.582 0.000 1.000

sb_mean ba_mean ba_std state -603.873 6 1219.896 18.315 0.000

ba_mean ba_std state -607.959 5 1226.025 24.444 0.000

lt_mean hb_mean hb_std state -610.437 6 1233.024 31.443 0.000

hb_mean hb_std state -616.028 5 1242.164 40.582 0.000

sb_mean state -625.632 4 1259.334 57.753 0.000

sb_mean lt_mean state -625.306 5 1260.719 59.138 0.000

Table 9. The coefficients (β) and standard errors (SE) of the variables in the top AICc-selected models 

comparing sagebrush vegetation characteristics for allotments in Montana (Billings BLM Field Office), 

Wyoming (State-wide), and Colorado (Gunnison Basin) that have “Met” (798) versus “Not met” (333) Land 

Health Standards (LHS) assessments. 

[Model variables include sagebrush (sb), herbaceous (hb), and state as categorical variable with Wyoming as the 

indicator. See Table 5 for a definition of model variables. State is a categorical variable with Wyoming as the 

indicator; state_1 is Colorado and state_2 is Montana]

Parameter β SE

sb_mean -0.118 0.019

hb_mean -0.065 0.01

hb_std 0.044 0.034

state_1 3.468 0.746

state_2 -0.146 0.217

constant 1.198 0.283
52

Table 10. Candidate models for each metric [mean, median and standard deviation (std)] within each 

variable subgroup comparing sagebrush vegetation characteristics for allotments in Montana (Billings BLM 

Field Office), Wyoming (State-wide), and Colorado (Gunnison Basin) that did not meet Land Health 

Standards (LHS) assessments due to livestock (132) versus other causes (201). 

[See table 5 for a definition of model variables. State is a categorical variable with Wyoming as the indicator. Log 

likelihood (LL) and number of parameters in the model (K) are shown. Models are ranked by ΔAICc within each 

variable subgroup. In all cases, the top AICc-selected model was carried forward for candidate model building]

Model LL K AICc ΔAICc

Sagebrush

sb_mean state -209.818 4 427.880 0.000

sb_mean sb_std state -209.439 5 429.246 1.366

sb_median state -210.981 4 430.205 2.325

sb_median sb_std state -210.243 5 430.854 2.974

sb_std state -212.020 4 432.285 4.405

Litter

lt_std state -212.738 4 433.719 0.000

lt_mean lt_std state -212.245 5 434.857 1.138

lt_mean state -213.422 4 435.088 1.369

lt_median lt_std state -212.404 5 435.174 1.455

lt_median state -213.812 4 435.867 2.148

Herbaceous

hb_mean state -213.746 4 435.736 0.000

hb_median state -214.100 4 436.444 0.708

hb_std state -214.336 4 436.916 1.180

hb_mean hb_std state -213.712 5 437.791 2.055

hb_median hb_std state -213.962 5 438.291 2.555

Bare

ba_mean ba_std state -208.917 5 428.200 0.000

ba_median ba_std state -209.503 5 429.374 1.173

ba_std state -211.468 4 431.180 2.980

ba_mean state -212.533 4 433.310 5.110

ba_median state -213.214 4 434.673 6.473
53

Table 11. Final candidate models comparing sagebrush vegetation characteristics for allotments in 

Montana (Billings BLM Field Office), Wyoming (State-wide), and Colorado (Gunnison Basin) that failed to 

meet Land Health Standards (LHS) due to livestock (132) versus other causes (201). 

[See table 5 for a definition of model variables. State is a categorical variable with Wyoming as the indicator. Log 

likelihood (LL) and number of parameters in the model (K) are shown. Models are ranked by ΔAICc with Akaike 

weight (wi) indicating the weight of evidence for each model within the candidate set]

Model LL K AICc ΔAICc wi

sb_mean ba_mean ba_std state -206.157 6 424.829 0.000 0.416

sb_mean lt_std ba_mean ba_std state -205.560 7 425.810 0.980 0.255

sb_mean state -209.818 4 427.880 3.051 0.091

ba_mean ba_std state -208.917 5 428.200 3.371 0.077

sb_mean hb_mean state -208.969 5 428.306 3.477 0.073

sb_mean lt_std state -209.300 5 428.966 4.137 0.053

lt_mean ba_mean ba_std state -208.897 6 430.310 5.481 0.027

lt_std state -212.738 4 433.719 8.890 0.005

hb_mean state -213.746 4 435.736 10.907 0.002

lt_mean hb_mean state -212.925 5 436.216 11.387 0.001

Table 12. The coefficients (β) and standard errors (SE) of the variables in the top AICc –selected models 

comparing sagebrush vegetation characteristics for allotments in Montana (Billings BLM Field Office), 

Wyoming (State-wide), and Colorado (Gunnison Basin) that failed to meet Land Health Standards (LHS)

assessments due to livestock (132) versus other causes (201). 

[Model variables include sagebrush (sb), bare (ba), and litter (lt), and state as categorical variable with Wyoming as 

the indicator. See table 5 for a definition of model variables. State is a categorical variable with Wyoming as the 

indicator; state_1 is Colorado and state_2 is Montana]

Top Model Second Model

Parameter β SE β SE

sb_mean 0.084 0.036 0.099 0.039

ba_mean -0.005 0.011 -0.006 0.011

ba_std 0.096 0.036 0.132 0.049

lt_mean -0.109 0.1

state_1 1.154 0.41 1.193 0.413

state_2 0.952 0.44 1.016 0.446

Constant -2.238 0.913 -2.084 0.926
54

Appendix 1. Spatial Allotment Data

Introduction

The overall objective of this exercise was to assess the available spatial data for Bureau 

of Land Management (BLM) grazing allotments and determine if it was feasible to combine 

spatial data with tabular data to describe important attributes of allotments. The second goal was 

to join the spatial data with tabular data describing the distribution of Land Health Standards

(LHS) assessed on grazing allotments. Finally, we wanted to use the compiled spatial datasets to 

investigate relationships among livestock grazing, land health status, and sagebrush cover.

Once we determined that use of the existing data would not be feasible, the primary 

objective was adapted to develop a usable topologically enforced coarse dataset of grazing 

allotments administered by the BLM. In particular, this exercise sought to limit/eliminate 

problems associated with gaps, slivers, edge matching issues, duplicate polygons and attribution 

and incorporate recently updated spatial data where possible. Appendix table 1-1 defines the 

topology issues referenced in this report.

Data Origination

The BLM provided a national dataset that was housed within the BLM’s National 

Integrated Land System (NILS). However, these data were out-of-date and contained numerous 

topological errors. Topology is a set of integrity rules that define the behavior of geographically 

integrated features (Arctur and Zeiler, 2004). We established two topology rules: polygons must 

not overlap and must not have gaps. Once topology rules were enforced on the 2002 National 

Data Set, 3,160 errors in the form of slivers, overlapping polygons, and duplications were found 

covering an area of 5,635 km2. Many gaps also were identified, but it is not reasonable to tally 

numbers or area covered because some were not true gaps. These areas were outside of BLM 

jurisdiction and therefore were likely not considered a gap until adjacent datasets were merged 

together to form the national dataset. Our analysis sought to differentiate true gaps, such as nonBLM land, reservoirs, canyons, etc., from the false gaps. Overall, errors in topology were largely 

the result of poor edge matching between state and field office boundaries, although not 

exclusively.

Methodology

Data Collection

Although a national dataset existed, individual field offices, and in some cases, state 

offices, maintain and update spatial allotment information on a regular basis. However, many of 

these data are not integrated into the NILS database, for various reasons. Beginning in late 2007, 

we contacted BLM State Offices for updated allotment data. At this time, we learned that each 

State followed a unique protocol regarding the management of allotment data. In some cases, 

State offices post updated statewide data on their website or State GIS clearinghouse on a regular 

basis. Other states maintain a fairly regularly updated dataset, but only internally. Alternatively, 

in some States, field offices are responsible for maintaining their own spatial allotment data, and 

no state-wide integration is in place. Regardless of the storage location and management level, 

we contacted appropriate offices to obtain updated data. 
55

Data were collected over the course of 3 years. Spatial data are often dynamic in nature 

and updates have likely taken place in some offices since we obtained the data. For instance, 

several field offices provided us with data that were more current than data posted on their 

website. However, we had to establish a deadline in order to move forward and assemble data 

into a national dataset. Appendix table 1-2 outlines the dataset and the approximate date that it 

was collected. Our project area of interest (appendix fig. 1-1) is congruent with the boundary for 

the conservation assessment of Greater Sage-Grouse and sagebrush habitat conducted by the 

Western Association of Fish and Wildlife Agencies (Connelly and others, 2004) and is based on 

the pre-settlement distribution of Greater Sage-Grouse (Schroeder and others, 2004). Therefore, 

we omitted several field offices from Arizona and New Mexico. The State of Nebraska, assigned 

to the Newcastle, Wyoming Field Office did not contain any allotments and also was omitted 

from the analyses.

Initially, we planned to collect spatial data at the pasture level, but decided to focus on 

the allotment level for two reasons. Spatial data at the pasture level was only readily available at 

select State and field offices. Second, tabular data on LHS, Billed Use, and most allotment 

information obtained from field office visits only currently exists at the allotment level. 

Assessment of Data

In order to create a national dataset that met our needs, we established a protocol to 

efficiently create a topologically enforced dataset. The methodology incorporated a series of 

decision rules and ancillary data such as State and county boundaries, land ownership, 1:24,000 

7.5’ USGS quadrangles and color-infrared photographs (only used in Wyoming). The protocol 

intended to minimize subjectivity, but given the scale of the project, some arbitrary decisions 

were made. The protocol applied to both state level and field office level datasets. Field office 

datasets from Arizona, Colorado, New Mexico, The Dakotas, and Wyoming were first compiled 

into State datasets, then loaded into the new, USGS national geodatabase. 

An initial assessment of each dataset was undertaken once it was loaded into the 

geodatabase and subject to topology rules. First, a unique identifier (IDENT in the attribute 

table) was created by concatenating the two letter state abbreviation with the five digit allotment 

number. Currently, BLM allotment numbers are unique within a state, therefore the IDENT field 

created a unique identifier in the national dataset. For example, allotment #00001 in Arizona 

(IDENT = AZ00001) is differentiated from allotment #00001 in California (IDENT = CA00001) 

by this attribute. The remaining attributes from the source dataset were converted to match our 

established scheme. At this time, polygons identified in the attributed table as non-allotments 

were labeled as “OUT by BLM” to be differentiated from additional out polygons generated by 

our analysis (“OUT by USGS”).

Topology Analysis

Next, topology issues contained within a dataset (that is, spatially internal errors that 

were not associated with edges of the dataset; appendix fig. 1-2) were identified and corrected 

using the methodology outlined below. It is important to note that only some state and field 

office datasets contained topology problems that needed correction. States that maintain more 

rigorous standards did not have topology issues within the dataset. 

Most topology errors were associated with edge matching along State and Field Office 

boundary lines (appendix fig. 1-2c). Ancillary data such as a shapefiles of state boundaries were 

obtained from State GIS clearinghouses and BLM State Offices. The boundary dataset with the 
56

assumed higher accuracy was used to determine which allotment dataset should be used if there 

was a conflict. For example, a 1:24,000 boundary dataset would trump a 1:100,000 boundary 

dataset. If both boundary datasets were developed at the same scale, then 7.5’ USGS quadrangles 

were assessed to identify the most appropriate boundary and which dataset was more accurate. 

This resulted in a series of decision rules that were used to objectively edge match adjacent State 

datasets. On occasions where two different allotments occupied the same boundary area and 

space (for example, Allotment #00001 from State A and Allotment #02050 from State B), the 

BLM Rangeland Administration System (RAS) database was consulted. If only one of the 

allotments existed in RAS, then it was retained and the other polygon was deleted. This ensured 

maximum alignment of potential tabular data when subsequently joining to spatial allotment 

data. Field office boundaries, or more importantly range allotment management boundaries, are 

not always congruent with state lines. This was taken into consideration and was occasionally 

reflected in the adjacent input datasets. For example, the attribute of the polygon in the State A 

dataset might have indicated that the allotment is actually managed by the adjacent field office in 

State B. This information was taken into account when it existed.

For small overlaps along edges, we used a majority rule, where for example when two 

States (or field offices) had an overlapping allotment in their respective datasets, the allotment 

was merged into the State which contained the majority of the allotment. For gaps along edges of 

States, the closest allotment on each side of the boundary was extended until meeting at the State 

boundary line. A similar method was used at the field office level where a State dataset did not 

exist (Wyoming, Colorado, New Mexico, and Arizona). The State field office boundaries were 

used to determine edge matching between adjacent field offices.

Land ownership also was used to identify areas that were not or should not be part of the 

allotment dataset (that is, areas representing true gaps). This was especially difficult along 

boundaries where one office may have identified it as an ‘out’ polygon, while the adjacent office 

displayed it as a gap (no polygon). This situation occurred on BLM lands adjacent to U.S.

Department of Defense lands, U.S. Forest Service Lands, Bureau of Indian Affairs Lands, and 

private lands (in a few instances), but also within allotments where reservoirs, steep canyons, or 

major streams and road allowances/right of ways may have been excluded (appendix fig. 1-2d).

We attempted to dissolve on the IDENT attribute, which would have represented

allotments as either a single or multi-part polygon. (Allotments are not always represented by 

continuous polygons and may be represented by more than one polygon in the database. For 

example, one allotment can be separated by landscape features or ownership patterns or 

represented as a conglomeration of pasture polygons if the input dataset delineated pastures). 

However, this exercise identified duplicate IDENT attributes within states because input datasets 

contained duplicate allotment numbers. In many cases the associated polygons likely represent 

more than one allotment, based on (long) distances between adjacent polygons.

We assumed that polygons with duplicate allotment numbers in close proximity likely 

belonged to the same (multi-polygon) allotment. We set an arbitrary proximity threshold of three 

kilometers. If any parts of two or more polygons with the same IDENT were within the three 

kilometer threshold, then they were considered one allotment. If they fell outside of the 

threshold, then these polygons were considered duplicate polygon allotments, flagged and moved 

to a separate feature class. There are likely false positives (actual allotments that have been 

removed) in this dataset, but we chose to be conservative in establishing the baseline proximity 

threshold.
57

Results and Discussion

We developed a final geodatabase that includes three feature classes; Allotments, OUT 

Polygons, and Duplicate_Polygon_Allotments. The Allotments feature class contains a total of 

17,162 allotments. The OUT_polygons feature class contains polygons created by the BLM and 

the USGS to identify areas within the dataset that do not represent a BLM allotment. These were 

created to ensure the topology rules were met. The Duplicate_Polygon_Allotments feature class 

contains 1,806 polygons, which were segregated from the allotment feature class based on our 

concern that they might represent errors in the dataset (Appendix table 1-3). The vast majority of 

these are found in the Montana State Office because we had to use the original 2002 National 

Data for Montana (as we were only able to obtain updated spatial data for one of the field offices 

administered by the Montana state office, the South Dakota Field Office; Appendix table 1-2).

Caution should be used when interpreting our results as false positives likely exist in this dataset, 

particularly in Montana. Furthermore, the 3 kmbuffer was set to establish baseline proximity, but

high variation in distance between adjacent polygons (likely managed as pastures) may exist

within some allotments. However, these issues provide an opportunity to address these issues in 

the future to ensure a high quality national dataset. 

This dataset was joined with billed and permitted use data, Land Health Standard data 

and sagebrush mapping product data for several other analyses referenced in the report. Three 

file geodatabase tables are included with the spatial dataset. The table “lhs_x_walk” includes the 

information synthesized by the Land Health Standard analysis (appendix 2). The tables 

“billed_use” and “permitted_use” were obtained from the BLM RAS database. All three tables

can be joined with the allotment feature class on the IDENT attribute.

Users should be aware of the limitations that exist in the dataset. This is a coarse dataset

intended for use in a landscape-scale context. The data should not be used as an official 

allotment demarcation tool. Updates to allotment datasets are made by the BLM at irregular 

intervals. Therefore this product should be considered a static dataset and treated accordingly. 

There is not 100 percent agreement between the spatial and tabular datasets. There are some 

allotments that exist in the spatial data that are not accounted for in the tabular data. Likewise, 

records exist for allotments in the tabular data that are not present in the spatial data. The partial 

disagreement between datasets is likely the result of allotment numbers that were changed by 

BLM, but this assessment provides a potential first step for rectifying these discrepancies to 

provide a more robust dataset in the future.

References Cited

Arctur, D. and Zeiler, M. 2004. Designing Geodatabases: Case Studies in GIS Data Modeling, 

ESRI Press.

Connelly, J. W., Knick, S. T. Schroeder, M. A. and Stiver, S. J. 2004. Conservation assessment 

of greater sage-grouse and sagebrush habitats: Western Association of Fish and Wildlife 

Agencies, Cheyenne, Wyoming, USA.

Schroeder, M.A., Aldridge, C.L., Apa, A.D., Bohne, J.R., Braun, C.E., Bunnell, S.D., Connelly, 

J.W., Deibert, P.A., Gardner, S.C., Hilliard, M.A., Kobriger, G.D., McAdam, S.M., McCarthy, 

C.W., McCarthy, J.J., Mitchell, D.L., Rickerson, E.V. and Stiver, S.J. 2004. Distribution of 

Sage-Grouse in North America: The Condor, vol, 106, p. 363-376.
58

Figure 1-1. Source of original BLM grazing allotment data information used to spatially summarize 

allotments across area of interest. Area coincides with the conservation assessment of Greater SageGrouse and sagebrush habitat conducted by the Western Association of Fish and Wildlife Agencies 

(Connelly and others, 2004; Schroeder and others, 2004). **Note we were unable to obtain data for the 

state of Montana so the 2002 National Data Set was used.
59

A. B.

C. D.

Figure 1-2. Examples of topology and attribute errors associated with edge matching from the BLM 

national grazing allotment dataset. (a) Sliver polygons (in red) and associated gaps (in white) between two 

state borders; (b) gaps (in yellow) between allotments bordering a highway; (c) partial duplication (overlap) 

between adjacent state offices of an allotment (in orange hatch); (d) inconsistency in dataset between field 

offices where the yellow polygon represents no data and light blue polygon (reservoir) represents a gap 

that should represent “no data” but is not accounted for in the dataset.
60

Table 1-1. Definitions of geospatial topology issues that were identified and corrected. 

[See appendix figure 1-2 for graphic examples of these common issues]

Issue Definition

Gap An empty space between adjacent allotments (appendix fig. 1-2a). False gaps represent a gap in an 

area where there should not be any space between adjacent allotments. True gaps represent areas 

where a grazing allotment does not exist (for example, reservoir, highway right of way, land 

administered by another agency, etc.). “Out polygons” were created for true gaps to ensure that 

topology could be enforced. 

Slivers A long, narrow polygon can be created during edge matching or some other processing event. These 

polygons are an artifact of geoprocessing and do not represent any part of an allotment on the 

ground.

Edge 

Matching

The edges of source datasets rarely match. These edges represent boundaries between state and/or 

field offices, and ancillary data must be used to rectify the edge (appendix fig.1-2b).

Duplicate 

Polygons

In some cases one allotment is represented by two or more polygons that are exact or close in size 

and shape and located in the same geographic area. 

Attributes Each source dataset has its own set of attributes and codes to describe the data. A consistent attribute 

scheme was created and employed in the new national dataset.
61

Table 1-2. Date of spatial allotment data collection from state or field offices across the conservation 

assessment area for Greater Sage-Grouse.

BLM State Office Date 

California 1/2009

Idaho 8/2008

Montana 2002*

Nevada 4/2008

Oregon 1/2008

Utah 4/2008

*The original national dataset was used for Montana because the state office was unable to provide such data for this 

project.

State BLM Field Office Date 

Arizona Phoenix 7/2008

Arizona Arizona Strip 12/2007

Colorado Dolores/Columbine/Pagosa Springs 10/2008

Colorado Glenwood Springs 10/2008

Colorado Grand Junction 10/2008

Colorado Gunnison 10/2008

Colorado Kremmling 9/2009

Colorado Little Snake 10/2008

Colorado Royal Gorge 10/2008

Colorado Saguache/Del Norte/La Jara 10/2008

Colorado Uncompaghre 10/2008

Colorado White River 11/2008

New Mexico Farmington 3/2009

New Mexico Taos 3/2009

South Dakota South Dakota (treated as a MT field office; included 

North Dakota data)

6/2009

Wyoming Buffalo 10/2007

Wyoming Casper 10/2007

Wyoming Cody 10/2007

Wyoming Kemmerer 11/2007

Wyoming Lander 12/2007

Wyoming Newcastle 11/2007 (from BLM website)

Wyoming Pinedale 11/2007

Wyoming Rawlins 10/2007

Wyoming Rock Springs 7/2008

Wyoming Worland 12/2007
62

Table 1-3. Final geodatabase summary of total BLM grazing allotments (# of Allotments) and number of 

duplicate allotments described for each state within the Greater Sage-Grouse Conservation Assessment 

area. Duplicates are not included in the allotment count. 

State # of Allotments # of Duplicates

Arizona 300 4

California 689 0

Colorado 2,429 37

Idaho 2,122 47

Montana (including the Dakotas) 3,761 1,366

New Mexico 541 0

Nevada 750 18

Oregon (including Washington) 1,878 157

Utah 1,275 143

Wyoming 3,417 34

TOTAL 17,162 1,806
63

Appendix 2. Refinement of BLM Land Health Standards Dataset

Purpose

Our objectives were to (1) examine the status of Land Health Standards (LHS) on Bureau 

of Land Management (BLM) allotments and (2) in cases of unmet standards, examine whether 

failure to meet standards was due to livestock. 

Data Origination

In 2008, the BLM compiled a dataset which contained results of the most recent LHS 

evaluations for all BLM allotments; LHS are region-specific (table 2-1), and evaluations are 

performed by BLM personnel. The original dataset contained the following attributes: 

administrative state, BLM office code, allotment number, authorization number, date of most 

recent LHS determination, standards not met and their significant causal factor(s), and 

authorization status (table 2-2).

We were interested in information at the level of individual grazing allotments. However, 

raw LHS evaluation data were reported for individual grazing authorizations, and multiple 

authorizations can be associated with a single allotment.1 We therefore needed to condense the 

data to create a one-to-one relationship between allotments and LHS data. 

Methodology

We used three decision rules: (1) as long as LHS evaluation data existed for at least one 

authorization in a given allotment, any other authorizations missing LHS evaluation data were 

ignored, (2) if there were date discrepancies among authorizations for a given allotment, we 

conservatively assumed the evaluation was performed on the earliest date, and (3) in some cases 

where there was conflicting data among authorizations (for whether standards were met on a 

given allotment). Therefore, if any one of the authorizations indicated a standard was “Not met”, 

the allotment was considered to have “Not met” that standard.

Land Health Standards vary according to region (table 2-1). To make comparisons across 

states and analyze standards range-wide, we grouped and assigned similar LHS to three main 

categories of interest: Upland, Riparian, and Biodiversity. In some cases, more than one standard 

fit into a category (for example, for Colorado, “Native Plant and Animal Communities” and 

“Threatened and Endangered Species” fell into our Biodiversity category). In those cases, the 

category was considered “Met” only if all standards were met (for example, in Colorado, both

“Native Plant and Animal Communities” and “Threatened and Endangered Species” met 

standards), and was considered “Not met” if at least one standard was not met (e.g., in Colorado, 

if either “Native Plant and Animal Communities” or “Threatened and Endangered Species” did 

not meet standards)

All allotments were evaluated for these three (Upland, Riparian and Biodiversity) 

categories, with the exception of eighty allotments (<0.4 percent of all allotments administered 

by the BLM) in the Mojave area of Nevada that did not have a Riparian standard. The attributes 

contained in our resulting (standardized) dataset are described in table 2-1. 

 1 Allotments represent actual spatial delineations on the ground, whereas authorizations reflect the legal grazing 

description associated with the allotment.
64

Table 2-1. Bureau of Land Management regional Land Health Standards (LHS), number of allotments in each region, and sources outlining LHS. 

[Parentheses indicate which LHS standards were placed into Upland (U), Riparian (R), or Biodiversity (B) categories. We did not include water quality, air 

quality, seedings, exotic plant communities, ecosystem components, wild horse/burro, or cultural resources in our categorization or analyses]

BLM LHS Regions LHS Standards Number of 

Allotments 

Source

Arizona Uplands (U)

Riparian (R)

Biodiversity – native species, special status species, desired 

species (B)

795 http://rangelandswest.arid.arizona.edu/ra

ngelandswest/jsp/hottopics/legal/policy/az

standards/azstandardsstandards.jsp

Northwestern 

California and Central 

California Regions

Soils (U)

Species (B)

Riparian (R)

Water Quality

331 http://www.blm.gov/ca/st/en/prog/grazing

.html

Northeastern 

California and 

Northwestern Nevada 

Regions

Upland Soils (U)

Streams (R)

Water Quality

Riparian and Wetland Sites (R)

Biodiversity (B)

116 http://www.blm.gov/ca/st/en/prog/grazing

.html

California Desert 

Region

Upland Soils (U)

Riparian and Wetland (R)

Stream Channel Morphology (R)

Native Species (B)

51 Appendix 4: Fundamentals of Rangeland 

Health and Standards and Guidelines for 

Grazing Administration (43 CFR 4180), 

Section 4180.2 (f)

Colorado Upland Soils (U)

Riparian Systems (R)

Native Plant and Animal Communities (B)

Threatened and Endangered Species (B)

Water Quality

2088 http://www.blm.gov/co/st/en/BLM_Progra

ms/grazing/rm_stds_guidelines.html

Idaho

Idaho cont.

Watersheds (U) 

Riparian and Wetlands (R)

Stream Channel/Floodplain (R)

Native Plant Communities (B)

Seedings

Exotic Plant Communities 

Water Quality

Threatened and Endangered Plant and Animal Species (B)

1945 http://www.blm.gov/pgdata/etc/medialib/b

lm/id/publications.Par.91993.File.dat/SG

Final.pdf

Montana (including 

North Dakota and 

South Dakota)

Uplands (U)

Riparian and Wetlands (R)

Water Quality

Air Quality

Native Plant and Animal Habitat or Biodiversity (B)

5000 http://www.blm.gov/mt/st/en/prog/grazing

.1.html
65

Table 2-1. Bureau of Land Management regional Land Health Standards (LHS), number of allotments in each region, and sources 

outlining LHS.—Continued

[Parentheses indicate which LHS standards were placed into Upland (U), Riparian (R), or Biodiversity (B) categories. We did not include water 

quality, air quality, seedings, exotic plant communities, ecosystem components, wild horse/burro, or cultural resources in our categorization or 

analyses]

New Mexico Upland Sites (U)

Biotic Communities including Threatened and Endangered 

Species (B)

Riparian Sites (R)

2152 http://www.blm.gov/pgdata/etc/medialib/b

lm/nm/field_offices/nmso/nmso_planning/

nmso_misc_planning.Par.47309.File.dat/

memo-RMPA.pdf

Nevada – Mojave and 

Southern Great Basin

Soils (U)

Ecosystem Components

Habitat/Biota (B)

Wild Horse/Burros

80 http://www.blm.gov/nv/st/en/prog/grazing

/grazing_s_gs.html

Nevada – Sierra Front 

and Northwestern 

Nevada

Soils (U)

Riparian/Wetlands (R)

Water Quality

Plant /Animal Habitat (B)

Special Status/Threatened and Endangered Species (B)

184 http://www.blm.gov/nv/st/en/prog/grazing

/grazing_s_gs.html

Nevada – Northeastern 

Great Basin

Uplands (U)

Riparian/Wetlands (R)

Habitat (B)

Cultural Resources

Healthy Wild Horse/Burros

482 http://www.blm.gov/nv/st/en/prog/grazing

/grazing_s_gs.html

Oregon Uplands (U)

Riparian (R)

Ecological Processes (B)

Water Quality

Habitat for Threatened and Endangered Species (B)

1810 http://www.blm.gov/or/resources/recreati

on/csnm/files/rangeland_standards.pdf

Utah

Utah cont.

Upland Soils (U)

Riparian/Wetlands (R)

Desired Species (natives, threatened and endangered, 

special status) (B)

Water Quality

1380 http://www.blm.gov/ut/st/en/fo/vernal/gra

zing_/rangeland_health_standards.html

Wyoming Soils (U)

Riparian/Wetlands (R)

Upland Vegetation (U)

Habitat for Threatened and Endangered Species (B)

Water Quality

Air Quality

3433 http://www.blm.gov/wy/st/en/programs/gr

azing/standards_and_guidelines/standard

s.html
66

Table 2-2. Attribute table of original Bureau of Land Management Land Health Standards (LHS) dataset and 

description of attributes

Attribute Description

Administrative State

BLM State Office under which allotment 

jurisdiction falls

Office Code

BLM field office under which allotment 

jurisdiction falls

Allotment Number

unique identification number associated 

with allotment

Authorization Number

unique identification number for grazing 

permits associated with allotments

Date of most recent Land Health 

Determination

date of most recent evaluation of whether 

allotment is meeting state LHS

Land Health Standard(s) not achieved in the 

Allotment and significant causal factor(s) 

identified

indicates which, if any, LHS not achieved 

and identifies causal factor

Authorization Status

indicates grazing permits put on a “hold” 

status (i.e. grazing no longer authorized)
67

Table 2-3. Attributes associated with final standardized Land Health Standards (LHS) dataset .

Attribute(s) Description Values

Administrative State BLM State Office under which 

allotment jurisdiction falls

State Abbreviation (e.g. AZ, CO, 

WY, etc.)

Office Code BLM field office under which 

allotment jurisdiction falls BLM Administrative Office Code 

Allotment Number unique identification number 

associated with allotment Numerical 

Authorization Number unique identification number for 

grazing permits associated with 

allotments

Numerical 

Year LHS Performed

Year most recent LHS 

determination performed 

(according to USGS assumptions)

Year (e.g., 2002)

9999 = not completed

8888 = allotment exempted from 

LHS requirements

7777 = no information given

U, R, B Indicates whether following 

standards were met:

U = Upland, Upland Soils, Soils, 

Upland Vegetation, Watersheds 

LHS

R = Riparian, wetlands, streams, 

stream channel LHS

B = Biodiversity, Biotic 

Communities, Native Species, 

Native and Desired Plant 

Communities and Habitat, 

Ecological Processes, and Special 

Status Species Standards

N = No

Y = Yes

NA = Not Applicable

NC = Evaluation Not Completed

U_L, R_L, B_L

Indicates whether failure to meet 

a standard was due to livestock 

grazing 

N = No

Y = Yes

NA = Not Applicable

NC = Evaluation Not Completed

NI = Not Indicated

All_Stds_URB_LS

Indicates if one or more of the 

Upland, Riparian or Biodiversity 

standards were unmet due to 

livestock

PASS = if all standards met or 

not applicable

LS = if livestock caused at least 

one standard to be unmet

No_LS = if any standards were 

unmet and none due to livestock

NC = if all standards were not 

completed

Pct_Fail_URB_LS Ratio of number of standards not 

met to number of standards

Numerical

NC = LHS not completed
68

Table 2-3. Attributes associated with final standardized Land Health Standards (LHS) dataset .—Continued

NumStdsFailing_URB_LS Number of standards that indicate 

livestock as a causal factor for 

failure to meet standards

Numerical

Num_Stds_URB Number of U, R, B, standards 

present (i.e. a count of 3 means 

all are present)

Numerical 

9999 = LHS not completed

All_Stds_LS

If one or more regional allotment 

standards were unmet due to 

livestock

PASS = if all standards were met 

or not applicable

LS = if livestock caused at least 

one standard to be unmet

No_LS = if any standards were 

unmet and none due to livestock

NC = if all standards were not 

completed

Pct_Fail_LS Ratio of number of unmet 

regional allotment standards to 

total number of regional 

standards

Numerical (ratio)

NC = LHS not completed

NumStdsFailing_LS Number of regional standards that 

indicate livestock as a causal 

factor for not meeting standard

Numerical (count data)

Num_Stds Number of regional standards for 

evaluation 

Numerical (count data)

9999 = LHS not completed
69

Appendix 3. ESD Sites and Selection Protocol

We sought to field-validate estimates of potential vegetation production reported in the Natural 

Resources Conservation Service (NRCS) Ecological Site Information System (ESIS). Ecological Site 

Descriptions (ESDs) contained in ESIS report rangeland site information such as plant community types 

and site characteristics (for example, elevation, climate). Because ESDs are linked to soil map units, we 

first inspected soil survey data coverage across Bureau of Land Management (BLM) land. We in turn 

selected BLM land located in Harney County, Oregon, as our area of focus because it was an area 

within our study region with full coverage in the NRCS Soil Survey Geographic Database (SSURGO). 

We then identified soil map units within this area associated with a single ESD (as opposed to multiple 

ecological sites scattered throughout a map unit). Because growth curve information was required for 

our method of assessing production (see below), we eliminated ecological sites that did not contain 

growth curves in their ESDs. From the resulting dataset, we chose nine common ecological sites (table 

3-1) where we would compare our own field production estimates to the estimates found in the sitespecific Ecological Site Descriptions. 

In June 2010, we made estimates of production at forty-two randomly located points across the 

nine ecological sites (table 3-1) after verifying soil types and ecological sites. All occurred within the 

Central Rocky and Blue Mountain Foothill Major Land Resource Area (OR-MLRA 010). For ease of 

sampling, points were located 75–200 m from improved roads and where slope was <50. Points within 

an ecological site type were >250 m apart (fig. 1). Points were located >200 m from water features that 

were likely to have concentrated livestock activity in their immediate vicinities (perennial streams and 

water bodies, and Burns BLM District GIS point features identified as DAM, DIKE, DRAIN, 

DUGOUT, GUZZLER, POND, RESERVOIR, SILT BASIN, SPIGOT, SPRING, TANK, TROUGH, 

VALVE, VALVE/VENT, VENT, WATERHOLE, WELL). 

We used the NRCS Reconstruction Method (U.S. Department of Agriculture, Natural Resources 

Conservation Service, 2006), which entails making visual estimates of production-by-weight for 

individual plant species and correcting for variables such as herbivory, time of year, and rainfall. At 

each of the forty-two points we sampled along two 50 m transects, oriented north and east. We placed a 

1×1 m quadrat every 10 meters (between 10 and 50 m) along each transect where we made production 

estimates of all herbaceous species. We then compared those on-the-ground production estimates of 

herbaceous cover to estimates for those sites contained in the ESIS database (fig. 2).

References Cited

U.S. Department of Agriculture, Natural Resources Conservation Service, 2006, Chapter 4: 

Inventorying and monitoring grazing land resources, National range and pasture handbook.
70

Table 3-1. NRCS Ecological Sites sampled.

N= NRCS Site Name and Precipitation Elevation (m)

9 R010XB080OR JD MOUNTAIN CLAYPAN 12-16 PZ 3,000-5,700

7 R010XC032OR SR MOUNTAIN CLAYEY 12-16 PZ 4,800-6,000

4 R010XB028OR JD SHRUBBY MOUNTAIN CLAYEY 12-16 PZ 4,000-6,000

4 R010XC080OR SR MAHOGANY MOUNTAIN LOAM 14-18 PZ 4,000-7,500

4 R010XC047OR SR MOUNTAIN SOUTH 12-16 PZ 3,500-6,000

4 R010XC066OR SR MOUNTAIN NORTH 12-16 PZ 4,500-6,000

6 R010XC021OR SR CLAYEY 9-12 PZ 2,000-3,500

2 R010XC030OR SR MOUNTAIN LOAMY 9-12 PZ 3,500-4,200

2 R010XC065OR SR MOUNTAIN NORTH 9-12 PZ 3,200-4,500
71

Appendix 4. Fundamentals of Rangeland Health Outlined in 43 CFR 4180.1

1. (a) Watersheds are in, or are making significant progress toward, properly functioning physical 

condition, including their upland, riparian-wetland, and aquatic components; soil and plant 

conditions support infiltration, soil moisture storage, and the release of water that are in balance 

with climate and landform and maintain or improve water quality, water quantity, and timing 

and duration of flow.

2. (b) Ecological processes, including the hydrologic cycle, nutrient cycle, and energy flow, are 

maintained, or there is significant progress toward their attainment, in order to support healthy 

biotic populations and communities.

3. (c) Water quality complies with State water quality standards and achieves, or is making 

significant progress toward achieving, established BLM management objectives such as meeting 

wildlife needs.

4. (d) Habitats are, or are making significant progress toward being, restored or maintained for 

Federal threatened and endangered species, Federal proposed or candidate threatened and 

endangered species, and other special status species.
72

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For more information concerning the research in this report, contact the

 Director, Forest and Rangeland Ecosystem Science Center

 U.S. Geological Survey

 777 NW 9th Street, Suite 400

 Corvallis, Oregon, 97330

 http://fresc.usgs.gov/
Veblen and others—Range-Wide Assessment of Livestock Grazing Across the Sagebrush Biome—Open-File Report 2011–1263