The Greater Sage-Grouse Monitoring Framework

THE GREATER

SAGE-GROUSE

MONITORING

FRAMEWORK

Bureau of Land Management

U.S. Forest Service

Developed by

the Interagency

Greater

Sage-Grouse

Disturbance

and Monitoring

Subteam

May 30, 2014

The Greater Sage-Grouse Monitoring Framework

Developed by the Interagency Greater Sage-Grouse Disturbance and Monitoring Subteam

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

I. Broad and Mid Scales .................................................................................................................. 7

A. Implementation (Decision) Monitoring ..................................................................................... 7

B. Habitat Monitoring ..................................................................................................................... 8

B.1. Sagebrush Availability (Measure 1) ....................................................................................... 10

a. Establishing the Sagebrush Base Layer .......................................................................... 11

b. Monitoring Sagebrush Availability ................................................................................ 19

B.2. Habitat Degradation Monitoring (Measure 2) ................................................................. 22

a. Habitat Degradation Datasets and Assumptions ............................................................. 22

b. Habitat Degradation Threat Combination and Calculation ............................................ 26

B.3. Energy and Mining Density (Measure 3)......................................................................... 26

a. Energy and Mining Density Datasets and Assumptions ................................................. 28

b. Energy and Mining Density Threat Combination and Calculation ................................ 28

C. Population (Demographics) Monitoring .................................................................................. 29

D. Effectiveness Monitoring ......................................................................................................... 29

II. Fine and Site Scales ................................................................................................................. 35

III. Conclusion ............................................................................................................................... 37

IV. The Greater Sage-Grouse Disturbance and Monitoring Subteam Membership ...................... 37

Figure 1. Map of Greater Sage-Grouse range, populations, subpopulations, and Priority

Areas for Conservation as of 2013.................................................................................... 5

Table 1. Indicators for monitoring implementation of the national planning strategy,

RMP/LUP decisions, sage-grouse habitat, and sage-grouse populations at the

broad and mid scales. ........................................................................................................ 6

Table 2. Relationship between the 18 threats and the three habitat disturbance measures

for monitoring. ................................................................................................................ 9

Table 3. Datasets for establishing and monitoring changes in sagebrush availability. ................. 13

Table 4. Ecological Systems in BpS and EVT capable of supporting sagebrush vegetation

and capable of providing suitable seasonal habitat for Greater Sage-Grouse. ................ 13

Table 5. Ecological Systems with conifers most likely to encroach into sagebrush vegetation. .. 18

Table 6. Geospatial data sources for habitat degradation (Measure 2). ........................................ 27

Literature Cited .............................................................................................................................. 39

Attachment A: An Overview of Monitoring Commitments ......................................................... 43

Attachment B: User and Producer Accuracies for Aggregated Ecological Systems within

LANDFIRE Map Zones ....................................................................................... 45

Attachment C: Sagebrush Species and Subspecies Included in the Selection Criteria for

Building the EVT and BpS Layers ...................................................................... 47

INTRODUCTION

The purpose of this U.S. Bureau of Land Management (BLM) and U.S. Forest Service (USFS)

Greater Sage-Grouse Monitoring Framework (hereafter, monitoring framework) is to describe

the methods to monitor habitats and evaluate the implementation and effectiveness of the BLM’s

national planning strategy (attachment to BLM Instruction Memorandum 2012-044), the BLM

resource management plans (RMPs), and the USFS’s land management plans (LMPs) to

conserve the species and its habitat. The regulations for the BLM (43 CFR 1610.4-9) and the

USFS (36 CFR part 209, published July 1, 2010) require that land use plans establish intervals

and standards, as appropriate, for monitoring and evaluations based on the sensitivity of the

resource to the decisions involved. Therefore, the BLM and the USFS will use the methods

described herein to collect monitoring data and to evaluate implementation and effectiveness of

the Greater Sage-Grouse (GRSG) (hereafter, sage-grouse) planning strategy and the conservation

measures contained in their respective land use plans (LUPs). A monitoring plan specific to the

Environmental Impact Statement, land use plan, or field office will be developed after the

Record of Decision is signed. For a summary of the frequency of reporting, see Attachment A,

An Overview of Monitoring Commitments. Adaptive management will be informed by data

collected at any and all scales.

To ensure that the BLM and the USFS are able to make consistent assessments about sage-

grouse habitats across the range of the species, this framework lays out the methodology—at

multiple scales—for monitoring of implementation and disturbance and for evaluating the

effectiveness of BLM and USFS actions to conserve the species and its habitat. Monitoring

efforts will include data for measurable quantitative indicators of sagebrush availability,

anthropogenic disturbance levels, and sagebrush conditions. Implementation monitoring results

will allow the BLM and the USFS to evaluate the extent that decisions from their LUPs to

conserve sage-grouse and their habitat have been implemented. State fish and wildlife agencies

will collect population monitoring information, which will be incorporated into effectiveness

monitoring as it is made available.

This multiscale monitoring approach is necessary, as sage-grouse are a landscape species and

conservation is scale-dependent to the extent that conservation actions are implemented within

seasonal habitats to benefit populations. The four orders of habitat selection (Johnson 1980) used

in this monitoring framework are described by Connelly et al. (2003) and were applied

specifically to the scales of sage-grouse habitat selection by Stiver et al. (in press) as first order

(broad scale), second order (mid scale), third order (fine scale), and fourth order (site scale).

Habitat selection and habitat use by sage-grouse occur at multiple scales and are driven by

multiple environmental and behavioral factors. Managing and monitoring sage-grouse habitats

are complicated by the differences in habitat selection across the range and habitat use by

individual birds within a given season. Therefore, the tendency to look at a single indicator of

habitat suitability or only one scale limits managers’ ability to identify the threats to sage-grouse

and to respond at the appropriate scale. For descriptions of these habitat suitability indicators for

each scale, see “Sage-Grouse Habitat Assessment Framework: Multiscale Habitat Assessment

Tool” (HAF; Stiver et al. in press).

Monitoring methods and indicators in this monitoring framework are derived from the current

peer-reviewed science. Rangewide, best available datasets for broad- and mid-scale monitoring

will be acquired. If these existing datasets are not readily available or are inadequate, but they are

necessary to inform the indicators of sagebrush availability, anthropogenic disturbance levels,

and sagebrush conditions, the BLM and the USFS will strive to develop datasets or obtain

information to fill these data gaps. Datasets that are not readily available to inform the fine- and

site-scale indicators will be developed. These data will be used to generate monitoring reports at

the appropriate and applicable geographic scales, boundaries, and analysis units: across the range

of sage-grouse as defined by Schroeder et al. (2004), and clipped by Western Association of Fish

and Wildlife Agencies (WAFWA) Management Zone (MZ) (Stiver et al. 2006) boundaries and

other areas as appropriate for size (e.g., populations based on Connelly et al. 2004). (See Figure

1, Map of Greater Sage-Grouse range, populations, subpopulations, and Priority Areas for

Conservation as of 2013.) This broad- and mid-scale monitoring data and analysis will provide

context for RMP/LMP areas; states; GRSG Priority Habitat, General Habitat, and other sage-

grouse designated management areas; and Priority Areas for Conservation (PACs), as defined in

“Greater Sage-grouse (Centrocercus urophasianus) Conservation Objectives: Final Report”

(Conservation Objectives Team [COT] 2013). Hereafter, all of these areas will be referred to as

“sage-grouse areas.”

Figure 1. Map of Greater Sage-Grouse range, populations, subpopulations, and Priority Areas for

Conservation as of 2013.

This monitoring framework is divided into two sections. The broad- and mid-scale methods,

described in Section I, provide a consistent approach across the range of the species to monitor

implementation decisions and actions, mid-scale habitat attributes (e.g., sagebrush availability

and habitat degradation), and population changes to determine the effectiveness of the planning

strategy and management decisions. (See Table 1, Indicators for monitoring implementation of

the national planning strategy, RMP/LMP decisions, sage-grouse habitat, and sage-grouse

populations at the broad and mid scales.) For sage-grouse habitat at the fine and site scales,

described in Section II, this monitoring framework describes a consistent approach (e.g.,

indicators and methods) for monitoring sage-grouse seasonal habitats. Funding, support, and

dedicated personnel for broad- and mid-scale monitoring will be renewed annually through the

normal budget process. For an overview of BLM and USFS multiscale monitoring commitments,

see Attachment A.

Table 1. Indicators for monitoring implementation of the national planning strategy, RMP/LMP

decisions, sage-grouse habitat, and sage-grouse populations at the broad and mid scales.

Implementation

Habitat

Population

(State Wildlife

Agencies)

Geographic

Scales

Availability

Degradation

Demographics

Broad Scale:

From the

range of sage-

grouse to

WAFWA

Management

Zones

BLM/USFS

National planning

strategy goal and

objectives

Distribution and

amount of

sagebrush within

the range

Distribution and

amount of

energy, mining,

and

infrastructure

facilities

WAFWA

Management

Zone

population

trend

Mid Scale:

From

WAFWA

Management

Zone to

populations;

PACs

RMP/LMP

decisions

Mid-scale habitat

indicators (HAF;

Table 2 herein,

e.g., percent of

sagebrush per

unit area)

Distribution and

amount of

energy, mining,

and

infrastructure

facilities (Table 2

herein)

Individual

population

trend

I. BROAD AND MID SCALES

First-order habitat selection, the broad scale, describes the physical or geographical range of a

species. The first-order habitat of the sage-grouse is defined by populations of sage-grouse

associated with sagebrush landscapes, based on Schroeder et al. 2004, and Connelly et al. 2004,

and on population or habitat surveys since 2004. An intermediate scale between the broad and

mid scales was delineated by WAFWA from floristic provinces within which similar

environmental factors influence vegetation communities. This scale is referred to as the

WAFWA Sage-Grouse Management Zones (MZs). Although no indicators are specific to this

scale, these MZs are biologically meaningful as reporting units.

Second-order habitat selection, the mid-scale, includes sage-grouse populations and PACs. The

second order includes at least 40 discrete populations and subpopulations (Connelly et al. 2004).

Populations range in area from 150 to 60,000 mi

and are nested within MZs. PACs range from

20 to 20,400 mi

and are nested within population areas.

Other mid-scale landscape indicators, such as patch size and number, patch connectivity, linkage

areas, and landscape matrix and edge effects (Stiver et al. in press) will also be assessed. The

methods used to calculate these metrics will be derived from existing literature (Knick et al.

2011, Leu and Hanser 2011, Knick and Hanser 2011).

A. Implementation (Decision) Monitoring

Implementation monitoring is the process of tracking and documenting the implementation (or

the progress toward implementation) of RMP/LMP decisions. The BLM and the USFS will

monitor implementation of project-level and/or site-specific actions and authorizations, with

their associated conditions of approval/stipulations for sage-grouse, spatially (as appropriate)

within Priority Habitat, General Habitat, and other sage-grouse designated management areas, at

a minimum, for the planning area. These actions and authorizations, as well as progress toward

completing and implementing activity-level plans, will be monitored consistently across all

planning units and will be reported to BLM and USFS headquarters annually, with a summary

report every 5 years, for the planning area. A national-level GRSG Land Use Plan Decision

Monitoring and Reporting Tool is being developed to describe how the BLM and the USFS will

consistently and systematically monitor and report implementation-level activity plans and

implementation actions for all plans within the range of sage-grouse. A description of this tool

for collection and reporting of tabular and spatially explicit data will be included in the Record of

Decision or approved plan. The BLM and the USFS will provide data that can be integrated with

other conservation efforts conducted by state and federal partners.

B. Habitat Monitoring

The U.S. Fish and Wildlife Service (USFWS), in its 2010 listing decision for the sage-grouse,

identified 18 threats contributing to the destruction, modification, or curtailment of sage-grouse

habitat or range (75 FR 13910 2010). The BLM and the USFS will, therefore, monitor the

relative extent of these threats that remove sagebrush, both spatially and temporally, on all lands

within an analysis area, and will report on amount, pattern, and condition at the appropriate and

applicable geographic scales and boundaries. These 18 threats have been aggregated into three

broad- and mid-scale measures to account for whether the threat predominantly removes

sagebrush or degrades habitat. (See Table 2, Relationship between the 18 threats and the three

habitat disturbance measures for monitoring.) The three measures are:

Measure 1: Sagebrush Availability (percent of sagebrush per unit area)

Measure 2: Habitat Degradation (percent of human activity per unit area)

Measure 3: Energy and Mining Density (facilities and locations per unit area)

These three habitat disturbance measures will evaluate disturbance on all lands, regardless of

land ownership. The direct area of influence will be assessed with the goal of accounting for

actual removal of sagebrush on which sage-grouse depend (Connelly et al. 2000) and for habitat

degradation as a surrogate for human activity. Measure 1 (sagebrush availability) examines

where disturbances have removed plant communities that support sagebrush (or have broadly

removed sagebrush from the landscape). Measure 1, therefore, monitors the change in sagebrush

availability—or, specifically, where and how much of the sagebrush community is available

within the range of sage-grouse. The sagebrush community is defined as the ecological systems

that have the capability of supporting sagebrush vegetation and seasonal sage-grouse habitats

within the range of sage-grouse (see Section I.B.1., Sagebrush Availability). Measure 2 (see

Section I.B.2., Habitat Degradation Monitoring) and Measure 3 (see Section I.B.3., Energy and

Mining Density) focus on where habitat degradation is occurring by using the footprint/area of

direct disturbance and the number of facilities at the mid scale to identify the relative amount of

degradation per geographic area of interest and in areas that have the capability of supporting

sagebrush and seasonal sage-grouse use. Measure 2 (habitat degradation) not only quantifies

footprint/area of direct disturbance but also establishes a surrogate for those threats most likely to

have ongoing activity. Because energy development and mining activities are typically the most

intensive activities in sagebrush habitat, Measure 3 (the density of active energy development,

production, and mining sites) will help identify areas of particular concern for such factors as

noise, dust, traffic, etc. that degrade sage-grouse habitat.

Table 2. Relationship between the 18 threats and the three habitat disturbance measures for monitoring.

Note: Data availability may preclude specific analysis of individual layers. See the detailed methodology

for more information.

USFWS Listing Decision Threat

Sagebrush

Availability

Habitat

Degradation

Energy and

Mining

Density

Agriculture

Urbanization

Wildfire

Conifer encroachment

Treatments

Invasive Species

Energy (oil and gas wells and development

facilities)

Energy (coal mines)

Energy (wind towers)

Energy (solar fields)

Energy (geothermal)

Mining (active locatable, leasable, and saleable

developments)

Infrastructure (roads)

Infrastructure (railroads)

Infrastructure (power lines)

Infrastructure (communication towers)

Infrastructure (other vertical structures)

Other developed rights-of-way

The methods to monitor disturbance found herein differ slightly from methods used in Manier et

al. 2013, which provided a baseline environmental report (BER) of datasets of disturbance across

jurisdictions. One difference is that, for some threats, the BER data were for federal lands only.

In addition, threats were assessed individually, using different assumptions from those in this

monitoring framework about how to quantify the location and magnitude of threats. The

methodology herein builds on the BER methodology and identifies datasets and procedures to

use the best available data across the range of the sage-grouse and to formulate a consistent

approach to quantify impact of the threats through time. This methodology also describes an

approach to combine the threats and calculate each of the three habitat disturbance measures.

B.1. Sagebrush Availability (Measure 1)

Sage-grouse populations have been found to be more resilient where a percentage of the

landscape is maintained in sagebrush (Knick and Connelly 2011), which will be determined by

sagebrush availability. Measure 1 has been divided into two submeasures to describe sagebrush

availability on the landscape:

Measure 1a: the current amount of sagebrush on the geographic area of interest, and

Measure 1b: the amount of sagebrush on the geographic area of interest compared with

the amount of sagebrush the landscape of interest could ecologically support.

Measure 1a (the current amount of sagebrush on the landscape) will be calculated using this

formula: [the existing updated sagebrush layer] divided by [the geographic area of interest]. The

appropriate geographic areas of interest for sagebrush availability include the species’ range,

WAFWA MZs, populations, and PACs. In some cases these sage-grouse areas will need to be

aggregated to provide an estimate of sagebrush availability with an acceptable level of accuracy.

Measure 1b (the amount of sagebrush for context within the geographic area of interest) will be

calculated using this formula: [existing sagebrush divided by [pre-EuroAmerican settlement

geographic extent of lands that could have supported sagebrush]. This measure will provide

information to set the context for a given geographic area of interest during evaluations of

monitoring data. The information could also be used to inform management options for

restoration or mitigation and to inform effectiveness monitoring.

The sagebrush base layer for Measure 1 will be based on geospatial vegetation data adjusted for

the threats listed in Table 2. The following subsections of this monitoring framework describe

the methodology for determining both the current availability of sagebrush on the landscape and

the context of the amount of sagebrush on the landscape at the broad and mid scales.

a. Establishing the Sagebrush Base Layer

The current geographic extent of sagebrush vegetation within the rangewide distribution of sage-

grouse populations will be ascertained using the most recent version of the Existing Vegetation

Type (EVT) layer in LANDFIRE (2013). LANDFIRE EVT was selected to serve as the

sagebrush base layer for five reasons: 1) it is the only nationally consistent vegetation layer that

has been updated multiple times since 2001; 2) the ecological systems classification within

LANDFIRE EVT includes multiple sagebrush type classes that, when aggregated, provide a

more accurate (compared with individual classes) and seamless sagebrush base layer across

jurisdictional boundaries; 3) LANDFIRE performed a rigorous accuracy assessment from which

to derive the rangewide uncertainty of the sagebrush base layer; 4) LANDFIRE is consistently

used in several recent analyses of sagebrush habitats (Knick et al. 2011, Leu and Hanser 2011,

Knick and Hanser 2011); and 5) LANDFIRE EVT can be compared against the geographic

extent of lands that are believed to have had the capability of supporting sagebrush vegetation

pre-EuroAmerican settlement [LANDFIRE Biophysical Setting (BpS)]. This fifth reason

provides a reference point for understanding how much sagebrush currently remains in a defined

geographic area of interest compared with how much sagebrush existed historically (Measure

1b). Therefore, the BLM and the USFS have determined that LANDFIRE provides the best

available data at broad and mid scales to serve as a sagebrush base layer for monitoring changes

in the geographic extent of sagebrush. The BLM and the USFS, in addition to aggregating the

sagebrush types into the sagebrush base layer, will aggregate the accuracy assessment reports

from LANDFIRE to document the cumulative accuracy for the sagebrush base layer. The

BLM—through its Assessment, Inventory, and Monitoring (AIM) program and, specifically, the

BLM’s landscape monitoring framework (Taylor et al. 2014)—will provide field data to the

LANDFIRE program to support continuous quality improvements of the LANDFIRE EVT layer.

The sagebrush layer based on LANDFIRE EVT will allow for the mid-scale estimation of the

existing percent of sagebrush across a variety of reporting units. This sagebrush base layer will

be adjusted by changes in land cover and successful restoration for future calculations of

sagebrush availability (Measures 1a and 1b).

This layer will also be used to determine the trend in other landscape indicators, such as patch

size and number, patch connectivity, linkage areas, and landscape matrix and edge effects (Stiver

et al. in press). In the future, changes in sagebrush availability, generated annually, will be

included in the sagebrush base layer. The landscape metrics will be recalculated to examine

changes in pattern and abundance of sagebrush at the various geographic boundaries. This

information will be included in effectiveness monitoring (See Section I.D., Effectiveness

Monitoring).

Within the USFS and the BLM, forest-wide and field office–wide existing vegetation

classification mapping and inventories are available that provide a much finer level of data than

what is provided through LANDFIRE. Where available, these finer-scale products will be useful

for additional and complementary mid-scale indicators and local-scale analyses (see Section II,

Fine and Site Scales). The fact that these products are not available everywhere limits their utility

for monitoring at the broad and mid scale, where consistency of data products is necessary across

broader geographies.

Data Sources for Establishing and Monitoring Sagebrush Availability

There were three criteria for selecting the datasets for establishing and monitoring the change in

sagebrush availability (Measure 1):

 Nationally consistent dataset available across the range

 Known level of confidence or accuracy in the dataset

 Continual maintenance of dataset and known update interval

Datasets meeting these criteria are listed in Table 3, Datasets for establishing and monitoring

changes in sagebrush availability.

LANDFIRE Existing Vegetation Type (EVT) Version 1.2

LANDFIRE EVT represents existing vegetation types on the landscape derived from remote

sensing data. Initial mapping was conducted using imagery collected in approximately 2001.

Since the initial mapping there have been two update efforts: version 1.1 represents changes

before 2008, and version 1.2 reflects changes on the landscape before 2010. Version 1.2 will be

used as the starting point to develop the sagebrush base layer.

Sage-grouse subject matter experts determined which of the ecological systems from the

LANDFIRE EVT to use in the sagebrush base layer by identifying the ecological systems that

have the capability of supporting sagebrush vegetation and that could provide suitable seasonal

habitat for the sage-grouse. (See Table 4, Ecological systems in BpS and EVT capable of

supporting sagebrush vegetation and capable of providing suitable seasonal habitat for Greater

Sage-Grouse.) Two additional vegetation types that are not ecological systems were added to the

EVT: Artemisia tridentata ssp. vaseyana Shrubland Alliance and Quercus gambelii Shrubland

Alliance. These alliances have species composition directly related to the Rocky Mountain

Lower Montane-Foothill Shrubland ecological system and the Rocky Mountain Gambel Oak-

Mixed Montane Shrubland ecological system, both of which are ecological systems in

LANDFIRE BpS. In LANDFIRE EVT, however, in some map zones, the Rocky Mountain

Lower Montane-Foothill Shrubland ecological system and the Rocky Mountain Gambel Oak-

Mixed Montane Shrubland ecological system were named Artemisia tridentata ssp. vaseyana

Shrubland Alliance and Quercus gambelii Shrubland Alliance, respectively.

Table 3. Datasets for establishing and monitoring changes in sagebrush availability.

Dataset

Source

Update

Interval

Most Recent

Version Year

Use

BioPhysical Setting

v1.1

LANDFIRE

Static

2008

Denominator for

sagebrush availability

Existing Vegetation

Type v1.2

LANDFIRE

Static

2010

Numerator for

sagebrush availability

Cropland Data Layer

National

Agricultural

Statistics Service

Annual

2012

Agricultural updates;

removes existing

sagebrush from

numerator of

sagebrush availability

National Land Cover

Dataset Percent

Imperviousness

Multi-Resolution

Land

Characteristics

Consortium

(MRLC)

5-Year

2011 (next

available in 2016)

Urban area updates;

removes existing

sagebrush from

numerator of

sagebrush availability

Fire Perimeters

GeoMac

Annual

2013

< 1,000-acre fire

updates; removes

existing sagebrush

from numerator of

sagebrush availability

Burn Severity

Monitoring

Trends in Burn

Severity

Annual

2012 (2-year delay

in data

availability)

> 1,000-acre fire

updates; removes

existing sagebrush

from numerator of

sagebrush availability

except for unburned

sagebrush islands

Table 4. Ecological systems in BpS and EVT capable of supporting sagebrush vegetation and capable

of providing suitable seasonal habitat for Greater Sage-Grouse.

Ecological System

Sagebrush Vegetation that the Ecological System has

the Capability of Producing

Colorado Plateau Mixed Low Sagebrush

Shrubland

Artemisia arbuscula ssp. longiloba

Artemisia bigelovii

Artemisia nova

Artemisia frigida

Artemisia tridentata ssp. wyomingensis

Columbia Plateau Low Sagebrush Steppe

Artemisia arbuscula

Artemisia arbuscula ssp. longiloba

Artemisia nova

Columbia Plateau Scabland Shrubland

Artemisia rigida

Columbia Plateau Steppe and Grassland

Artemisia spp.

Great Basin Xeric Mixed Sagebrush

Shrubland

Artemisia arbuscula ssp. longicaulis

Artemisia arbuscula ssp. longiloba

Artemisia nova

Artemisia tridentata ssp. wyomingensis

Inter-Mountain Basins Big Sagebrush

Shrubland

Artemisia tridentata ssp. tridentata

Artemisia tridentata ssp. xericensis

Artemisia tridentata ssp. vaseyana

Artemisia tridentata ssp. wyomingensis

Inter-Mountain Basins Big Sagebrush

Steppe

Artemisia cana ssp. cana

Artemisia tridentata ssp. tridentata

Artemisia tridentata ssp. xericensis

Artemisia tridentata ssp. wyomingensis

Artemisia tripartita ssp. tripartita

Artemisia frigida

Inter-Mountain Basins Curl-Leaf Mountain

Mahogany Woodland and Shrubland

Artemisia tridentata ssp. vaseyana

Artemisia arbuscula

Artemisia tridentata

Inter-Mountain Basins Mixed Salt Desert

Scrub

Artemisia tridentata ssp. wyomingensis

Artemisia spinescens

Inter-Mountain Basins Montane Sagebrush

Steppe

Artemisia tridentata ssp. vaseyana

Artemisia tridentata ssp. wyomingensis

Artemisia nova

Artemisia arbuscula

Artemisia tridentata ssp. spiciformis

Inter-Mountain Basins Semi-Desert Shrub-

Steppe

Artemisia tridentata

Artemisia bigelovii

Artemisia tridentata ssp. wyomingensis

Northwestern Great Plains Mixed Grass

Prairie

Artemisia cana ssp. cana

Artemisia tridentata ssp. vaseyana

Artemisia frigida

Northwestern Great Plains Shrubland

Artemisia cana ssp. cana

Artemisia tridentata ssp. tridentata

Artemisia tridentata ssp. wyomingensis

Rocky Mountain Gambel Oak-Mixed

Montane Shrubland

Artemisia tridentata

Rocky Mountain Lower Montane-Foothill

Shrubland

Artemisia nova

Artemisia tridentata

Artemisia frigida

Western Great Plains Floodplain Systems

Artemisia cana ssp. cana

Western Great Plains Sand Prairie

Artemisia cana ssp. cana

Wyoming Basins Dwarf Sagebrush

Shrubland and Steppe

Artemisia arbuscula ssp. longiloba

Artemisia nova

Artemisia tridentata ssp. wyomingensis

Artemisia tripartita ssp. rupicola

Artemisia tridentata ssp. vaseyana

Shrubland Alliance (EVT only)

Artemisia tridentata ssp. vaseyana

Quercus gambelii Shrubland Alliance (EVT

only)

Artemisia tridentata

Accuracy and Appropriate Use of LANDFIRE Datasets

Because of concerns over the thematic accuracy of individual classes mapped by LANDFIRE, all

ecological systems listed in Table 4 will be merged into one value that represents the sagebrush

base layer. With all ecological systems aggregated, the combined accuracy of the sagebrush base

layer (EVT) will be much greater than if all categories were treated separately.

LANDFIRE performed the original accuracy assessment of its EVT product on a map zone

basis. There are 20 LANDFIRE map zones that cover the historical range of sage-grouse as

defined by Schroeder (2004). (See Attachment B, User and Producer Accuracies for Aggregated

Ecological Systems within LANDFIRE Map Zones.) The aggregated sagebrush base layer for

monitoring had user accuracies ranging from 57.1% to 85.7% and producer accuracies ranging

from 56.7% to 100%.

LANDFIRE EVT data are not designed to be used at a local level. In reports of the percent

sagebrush statistic for the various reporting units (Measure 1a), the uncertainty of the percent

sagebrush will increase as the size of the reporting unit gets smaller. LANDFIRE data should

never be used at the 30m pixel level (900m

resolution of raster data) for any reporting. The

smallest geographic extent for using the data to determine percent sagebrush is at the PAC level;

for the smallest PACs, the initial percent sagebrush estimate will have greater uncertainties

compared with the much larger PACs.

Agricultural Adjustments for the Sagebrush Base Layer

The dataset for the geographic extent of agricultural lands will come from the National

Agricultural Statistics Service (NASS) Cropland Data Layer (CDL)

(http://www.nass.usda.gov/research/Cropland/Release/index.htm). CDL data are generated

annually, with estimated producer accuracies for “large area row crops ranging from the mid

80% to mid-90%,” depending on the state

(http://www.nass.usda.gov/research/Cropland/sarsfaqs2.htm#Section3_18.0). Specific

information on accuracy may be found on the NASS metadata website

(http://www.nass.usda.gov/research/Cropland/metadata/meta.htm). CDL provided the only

dataset that matches the three criteria (nationally consistent, known level of accuracy, and

periodically updated) for use in this monitoring framework and represents the best available

agricultural lands mapping product.

The CDL data contain both agricultural classes and nonagricultural classes. For this effort, and in

the baseline environmental report (Manier et al. 2013), nonagricultural classes were removed

from the original dataset. The excluded classes are:

Barren (65 & 131), Deciduous Forest (141), Developed/High Intensity (124), Developed/Low

Intensity (122), Developed/Med Intensity (123), Developed/Open Space (121), Evergreen Forest

(142), Grassland Herbaceous (171), Herbaceous Wetlands (195), Mixed Forest (143), Open

Water (83 & 111), Other Hay/Non Alfalfa (37), Pasture/Hay (181), Pasture/Grass (62), Perennial

Ice/Snow (112), Shrubland (64 & 152), Woody Wetlands (190).

The rule set for adjusting the sagebrush base layer for agricultural lands (and for updating the

base layer for agricultural lands in the future) is that once an area is classified as agriculture in

any year of the CDL, those pixels will remain out of the sagebrush base layer even if a new

version of the CDL classifies that pixel as one of the nonagricultural classes listed above. The

assumption is that even though individual pixels may be classified as a nonagricultural class in

any given year, the pixel has not necessarily been restored to a natural sagebrush community that

would be included in Table 4. A further assumption is that once an area has moved into

agricultural use, it is unlikely that the area would be restored to sagebrush. Should that occur,

however, the method and criteria for adding pixels back into the sagebrush base layer would

follow those found in the sagebrush restoration monitoring section of this monitoring framework

(see Section I.B.1.b., Monitoring Sagebrush Availability).

Urban Adjustments for the Sagebrush Base Layer

The National Land Cover Database (NLCD) (Fry et al. 2011) includes a percent imperviousness

dataset that was selected as the best available dataset to be used for urban adjustments and

monitoring. These data are generated on a 5-year cycle and are specifically designed to support

monitoring efforts. Other datasets were evaluated and lacked the spatial specificity that was

captured in the NLCD product. Any new impervious pixel in NLCD will be removed from the

sagebrush base layer through the monitoring process. Although the impervious surface layer

includes a number of impervious pixels outside of urban areas, this is acceptable for the

adjustment and monitoring for two reasons. First, an evaluation of national urban area datasets

did not reveal a layer that could be confidently used in conjunction with the NLCD product to

screen impervious pixels outside of urban zones. This is because unincorporated urban areas

were not being included, thus leaving large chunks of urban pixels unaccounted for in this rule

set. Second, experimentation with setting a threshold on the percent imperviousness layer that

would isolate rural features proved to be unsuccessful. No combination of values could be

identified that would result in the consistent ability to limit impervious pixels outside urban

areas. Therefore, to ensure consistency in the monitoring estimates, all impervious pixels will be

used.

Fire Adjustments for the Sagebrush Base Layer

Two datasets were selected for performing fire adjustments and updates: GeoMac fire

perimeters and Monitoring Trends in Burn Severity (MTBS). An existing data standard in the

BLM requires that all fires of more than 10 acres are to be reported to GeoMac; therefore, there

will be many small fires of less than 10 acres that will not be accounted for in the adjustment and

monitoring attributable to fire. Using fire perimeters from GeoMac, all sagebrush pixels falling

within the perimeter of fires less than 1,000 acres will be used to adjust and monitor the

sagebrush base layer.

For fires greater than 1,000 acres, MTBS was selected as a means to account for unburned

sagebrush islands during the update process of the sagebrush base layer. The MTBS program

(http://www.mtbs.gov) is an ongoing, multiyear project to map fire severity and fire perimeters

consistently across the United States. One of the burn severity classes within MTBS is an

unburned to low-severity class. This burn severity class will be used to represent unburned

islands of sagebrush within the fire perimeter for the sagebrush base layer. Areas within the other

severity classes within the fire perimeter will be removed from the base sagebrush layer during

the update process. Not all wildfires, however, have the same impacts on the recovery of

sagebrush habitat, depending largely on soil moisture and temperature regimes. For example,

cooler, moister sagebrush habitat has a higher potential for recovery or, if needed, restoration

than does the warmer, dryer sagebrush habitat. These cooler, moister areas will likely be detected

as sagebrush in future updates to LANDFIRE.

Conifer Encroachment Adjustment for the Sagebrush Base Layer

Conifer encroachment into sagebrush vegetation reduces the spatial extent of sage-grouse habitat

(Davies et al. 2011, Baruch-Mordo et al. 2013). Conifer species that show propensity for

encroaching into sagebrush vegetation resulting in sage-grouse habitat loss include various

juniper species, such as Utah juniper (Juniperus osteosperma), western juniper (Juniperus

occidentalis), Rocky Mountain juniper (Juniperus scopulorum), pinyon species, including

singleleaf pinyon (Pinus monophylla) and pinyon pine (Pinus edulis), ponderosa pine (Pinus

ponderosa), lodgepole pine (Pinus contorta), and Douglas fir (Pseudotsuga menziesii) (Gruell et

al. 1986, Grove et al. 2005, Davies et al. 2011).

A rule set for conifer encroachment was developed to adjust the sagebrush base layer. To capture

the geographic extent of sagebrush that is likely to experience conifer encroachment, ecological

systems within LANDFIRE EVT version 1.2 (NatureServe 2011) were identified if they had the

capability of supporting both the conifer species (listed above) and sagebrush vegetation. Those

ecological systems were deemed to be the plant communities with conifers most likely to

encroach into sagebrush vegetation. (See Table 5, Ecological systems with conifers most likely

to encroach into sagebrush vegetation.) Sagebrush vegetation was defined as including sagebrush

species or subspecies that provide habitat for the Greater Sage-Grouse and that are included in

the HAF. (See Attachment C, Sagebrush Species and Subspecies Included in the Selection

Criteria for Building the EVT and BpS Layers.) An adjacency analysis was conducted to identify

all sagebrush pixels that were directly adjacent to these conifer ecological systems, and these

pixels were removed from the sagebrush base layer.

Table 5. Ecological systems with conifers most likely to encroach into sagebrush vegetation.

EVT Ecological Systems

Coniferous Species and Sagebrush Vegetation that

the Ecological System has the Capability of

Producing

Colorado Plateau Pinyon-Juniper Woodland

Pinus edulis

Juniperus osteosperma

Artemisia tridentata

Artemisia arbuscula

Artemisia nova

Artemisia tridentata ssp. tridentata

Artemisia tridentata ssp. wyomingensis

Artemisia tridentata ssp. vaseyana

Artemisia bigelovii

Artemisia pygmaea

Columbia Plateau Western Juniper Woodland and

Savanna

Juniperus occidentalis

Pinus ponderosa

Artemisia tridentata

Artemisia arbuscula

Artemisia rigida

Artemisia tridentata ssp. vaseyana

East Cascades Oak-Ponderosa Pine Forest and

Woodland

Pinus ponderosa

Pseudotsuga menziesii

Artemisia tridentata

Artemisia nova

Great Basin Pinyon-Juniper Woodland

Pinus monophylla

Juniperus osteosperma

Artemisia arbuscula

Artemisia nova

Artemisia tridentata

Artemisia tridentata ssp. vaseyana

Northern Rocky Mountain Ponderosa Pine

Woodland and Savanna

Pinus ponderosa

Artemisia tridentata

Artemisia arbuscula

Artemisia tridentata ssp. vaseyana

Rocky Mountain Foothill Limber Pine-Juniper

Woodland

Juniperus osteosperma

Juniperus scopulorum

Artemisia nova

Artemisia tridentata

Rocky Mountain Poor-Site Lodgepole Pine Forest

Pinus contorta

Pseudotsuga menziesii

Pinus ponderosa

Artemisia tridentata

Southern Rocky Mountain Pinyon-Juniper

Woodland

Pinus edulis

Juniperus monosperma

Artemisia bigelovii

Artemisia tridentata

Artemisia tridentata ssp. wyomingensis

Artemisia tridentata ssp. vaseyana

Southern Rocky Mountain Ponderosa Pine

Woodland

Pinus ponderosa

Pseudotsuga menziesii

Pinus edulis

Pinus contorta

Juniperus spp.

Artemisia nova

Artemisia tridentata

Artemisia arbuscula

Artemisia tridentata ssp. vaseyana

Invasive Annual Grasses Adjustments for the Sagebrush Base Layer

There are no invasive species datasets from 2010 to the present (beyond the LANDFIRE data)

that meet the three criteria (nationally consistent, known level of accuracy, and periodically

updated) for use in the determination of the sagebrush base layer. For a description of how

invasive species land cover will be incorporated in the sagebrush base layer in the future, see

Section I.B.1.b., Monitoring Sagebrush Availability.

Sagebrush Restoration Adjustments for the Sagebrush Base Layer

There are no datasets from 2010 to the present that could provide additions to the sagebrush base

layer from restoration treatments that meet the three criteria (nationally consistent, known level

of accuracy, and periodically updated); therefore, no adjustments were made to the sagebrush

base layer calculated from the LANDFIRE EVT (version 1.2) attributable to restoration

activities since 2010. Successful restoration treatments before 2010 are assumed to have been

captured in the LANDFIRE refresh.

b. Monitoring Sagebrush Availability

Monitoring Sagebrush Availability

Sagebrush availability will be updated annually by incorporating changes to the sagebrush base

layer attributable to agriculture, urbanization, and wildfire. The monitoring schedule for the

existing sagebrush base layer updates is as follows:

2010 Existing Sagebrush Base Layer = [Sagebrush EVT] minus [2006 Imperviousness Layer]

minus [2009 and 2010 CDL] minus [2009/10 GeoMac Fires that are less than 1,000 acres] minus

[2009/10 MTBS Fires that are greater than 1,000 acres, excluding unburned sagebrush islands

within the perimeter] minus [Conifer Encroachment Layer]

2012 Existing Sagebrush Update = [2010 Existing Sagebrush Base Layer] minus [2011

Imperviousness Layer] minus [2011 and 2012 CDL] minus [2011/12 GeoMac Fires < 1,000

acres] minus [2011/12 MTBS Fires that are greater than 1,000 acres, excluding unburned

sagebrush islands within the perimeter]

Monitoring Existing Sagebrush post 2012 = [Previous Existing Sagebrush Update Layer] minus

[Imperviousness Layer (if new data are available)] minus [Next 2 years of CDL] minus [Next 2

years of GeoMac Fires < 1,000 acres] minus [Next 2 years of MTBS Fires that are greater than

1,000 acres, excluding unburned sagebrush islands within the perimeter] plus

[restoration/monitoring data provided by the field]

Monitoring Sagebrush Restoration

Restoration after fire, after agricultural conversion, after seedings of introduced grasses, or after

treatments of pinyon pine and/or juniper are examples of updates to the sagebrush base layer that

can add sagebrush vegetation back into sagebrush availability in the landscape. When restoration

has been determined to be successful through rangewide, consistent, interagency fine- and site-

scale monitoring, the polygonal data will be used to add sagebrush pixels back into the broad-

and mid-scale sagebrush base layer.

Measure 1b: Context for Monitoring the Amount of Sagebrush in a Geographic Area of

Interest

Measure 1b describes the amount of sagebrush on the landscape of interest compared with the

amount of sagebrush the landscape of interest could ecologically support. Areas with the

potential to support sagebrush were derived from the BpS data layer that describes sagebrush

pre-EuroAmerican settlement (v1.2 of LANDFIRE).

The identification and spatial locations of natural plant communities (vegetation) that are

believed to have existed on the landscape (BpS) were constructed based on an approximation of

the historical (pre-EuroAmerican settlement) disturbance regime and how the historical

disturbance regime operated on the current biophysical environment. BpS is composed of map

units that are based on NatureServe (2011) terrestrial ecological systems classification.

The ecological systems within BpS used for this monitoring framework are those ecological

systems that are capable of supporting sagebrush vegetation and of providing seasonal habitat for

sage-grouse (Table 4). Ecological systems selected included sagebrush species or subspecies that

are included in the HAF and listed in Attachment C.

The BpS layer does not have an associated accuracy assessment, given the lack of any reference

data. Visual inspection of the BpS data, however, reveals inconsistencies in the labeling of pixels

among LANDFIRE map zones. The reason for these inconsistencies is that the rule sets used to

map a given ecological system will vary among map zones based on different physical,

biological, disturbance, and atmospheric regimes of the region. These variances can result in

artificial edges in the map. Metrics will be calculated, however, at broad spatial scales using BpS

potential vegetation type, not small groupings or individual pixels. Therefore, the magnitude of

these observable errors in the BpS layer will be minor compared with the size of the reporting

units. Since BpS will be used to identify broad landscape patterns of dominant vegetation, these

inconsistencies will have only a minor impact on the percent sagebrush availability calculation.

As with the LANDFIRE EVT, LANDFIRE BpS data are not designed to be used at a local level.

LANDFIRE data should never be used at the 30m pixel level for reporting.

In conclusion, sagebrush availability data will be used to inform effectiveness monitoring and

initiate adaptive management actions as necessary. The 2010 estimate of sagebrush availability

will serve as the base year, and an updated estimate for 2012 will be reported in 2014 after all

datasets become available. The 2012 estimate will capture changes attributable to wildfire,

agriculture, and urban development. Subsequent updates will always include new fire and

agricultural data and new urban data when available. Restoration data that meet the criteria for

adding sagebrush areas back into the sagebrush base layer will be factored in as data allow.

Given data availability, there will be a 2-year lag (approximately) between when the estimate is

generated and when the data used for the estimate become available (e.g., the 2014 sagebrush

availability will be included in the 2016 estimate).

Future Plans

Geospatial data used to generate the sagebrush base layer will be available through the BLM’s

EGIS web portal and geospatial gateway or through the authoritative data source. Legacy

datasets will be preserved so that trends may be calculated. Additionally, accuracy assessment

data for all source datasets will be provided on the portal either spatially, where applicable, or

through the metadata. Accuracy assessment information was deemed vital to help users

understand the limitation of the sagebrush estimates; it will be summarized spatially by map zone

and will be included in the portal.

LANDFIRE plans to begin a remapping effort in 2015. This remapping has the potential to

improve the overall quality of data products greatly, primarily through the use of higher-quality

remote sensing datasets. Additionally, the BLM and the Multi-Resolution Land Characteristics

Consortium (MRLC) are working to improve the accuracy of vegetation map products for broad-

and mid-scale analyses through the Grass/Shrub mapping effort. The Grass/Shrub mapping effort

applies the Wyoming multiscale sagebrush habitat methodology (Homer et al. 2009) to depict

spatially the fractional percent cover estimates for five components rangewide and West-wide.

These five components are percent cover of sagebrush vegetation, percent bare ground, percent

herbaceous vegetation (grass and forbs combined), annual vegetation, and percent shrubs. A

benefit of the design of these fractional cover maps is that they facilitate monitoring “within”

class variation (e.g., examination of declining trend in sagebrush cover for individual pixels).

This “within” class variation can serve as one indicator of sagebrush quality that cannot be

derived from LANDFIRE’s EVT information. The Grass/Shrub mapping effort is not a substitute

for fine-scale monitoring but will leverage fine-scale data to support the validation of the

mapping products. An evaluation will be conducted to determine if either dataset is of great

enough quality to warrant replacing the existing sagebrush layers. At the earliest, this evaluation

will occur in 2018 or 2019, depending on data availability.

B.2. Habitat Degradation Monitoring (Measure 2)

The measure of habitat degradation will be calculated by combining the footprints of threats

identified in Table 2. The footprint is defined as the direct area of influence of “active” energy

and infrastructure; it is used as a surrogate for human activity. Although these analyses will try to

summarize results at the aforementioned meaningful geographic areas of interest, some may be

too small to report the metrics appropriately and may be combined (smaller populations, PACs

within a population, etc.). Data sources for each threat are found in Table 6, Geospatial data

sources for habitat degradation. Specific assumptions (inclusion criteria for data, width/area

assumptions for point and line features, etc.) and methodology for each threat, and the combined

measure, are detailed below. All datasets will be updated annually to monitor broad- and mid-

scale year-to-year changes and to calculate trends in habitat degradation to inform adaptive

management. A 5-year summary report will be provided to the USFWS.

a. Habitat Degradation Datasets and Assumptions

Energy (oil and gas wells and development facilities)

This dataset will compile information from three oil and gas databases: the proprietary IHS

Enerdeq database, the BLM Automated Fluid Minerals Support System (AFMSS) database, and

the proprietary Platts (a McGraw-Hill Financial Company) GIS Custom Data (hereafter, Platts)

database of power plants. Point data from wells active within the last 10 years from IHS and

producing wells from AFMSS will be considered as a 5-acre (2.0ha) direct area of influence

centered on the well point, as recommended by the BLM WO-300 (Minerals and Realty

Management). Plugged and abandoned wells will be removed if the date of well abandonment

was before the first day of the reporting year (i.e., for the 2015 reporting year, a well must have

been plugged and abandoned by 12/31/2014 to be removed). Platts oil and gas power plants data

(subset to operational power plants) will also be included as a 5-acre (2.0ha) direct area of

influence.

Additional Measure: Reclaimed Energy-related Degradation. This dataset will include

those wells that have been plugged and abandoned. This measure thereby attempts to

measure energy-related degradation that has been reclaimed but not necessarily fully

restored to sage-grouse habitat. This measure will establish a baseline by using wells that

have been plugged and abandoned within the last 10 years from the IHS and AFMSS

datasets. Time lags for lek attendance in response to infrastructure have been documented

to be delayed 2–10 years from energy development activities (Harju et al. 2010).

Reclamation actions may require 2 or more years from the Final Abandonment Notice.

Sagebrush seedling establishment may take 6 or more years from the point of seeding,

depending on such variables as annual precipitation, annual temperature, and soil type and

depth (Pyke 2011). This 10-year period is conservative and assumes some level of habitat

improvement 10 years after plugging. Research by Hemstrom et al. (2002), however,

proposes an even longer period—more than 100 years—for recovery of sagebrush habitats,

even with active restoration approaches. Direct area of influence will be considered 3 acres

(1.2ha) (J. Perry, personal communication, February 12, 2014). This additional

layer/measure could be used at the broad and mid scale to identify areas where sagebrush

habitat and/or potential sagebrush habitat is likely still degraded. This layer/measure could

also be used where further investigation at the fine or site scale would be warranted to: 1)

quantify the level of reclamation already conducted, and 2) evaluate the amount of

restoration still required for sagebrush habitat recovery. At a particular level (e.g.,

population, PACs), these areas and the reclamation efforts/success could be used to inform

reclamation standards associated with future developments. Once these areas have

transitioned from reclamation standards to meeting restoration standards, they can be

added back into the sagebrush availability layer using the same methodology as described

for adding restoration treatment areas lost to wildfire and agriculture conversion (see

Monitoring Sagebrush Restoration in Section I.B.1.b., Monitoring Sagebrush Availability).

This dataset will be updated annually from the IHS dataset.

Energy (coal mines)

Currently, there is no comprehensive dataset available that identifies the footprint of active coal

mining across all jurisdictions. Therefore, point and polygon datasets will be used each year to

identify coal mining locations. Data sources will be identified and evaluated annually and will

include at a minimum: BLM coal lease polygons, U.S. Energy Information Administration mine

occurrence points, U.S. Office of Surface Mining Reclamation and Enforcement coal mining

permit polygons (as available), and U.S. Geological Survey (USGS) Mineral Resources Data

System mine occurrence points. These data will inform where active coal mining may be

occurring. Additionally, coal power plant data from Platts power plants database (subset to

operational power plants) will be included. Aerial imagery will then be used to digitize manually

the active coal mining and coal power plants surface disturbance in or near these known

occurrence areas. While the date of aerial imagery varies by scale, the most current data

available from Esri and/or Google will be used to locate (generally at 1:50,000 and below) and

digitize (generally at 1:10,000 and below) active coal mine and power plant direct area of

influence. Coal mine location data source and imagery date will be documented for each

digitized coal polygon at the time of creation. Subsurface facility locations (polygon or point

location as available) will also be collected if available, included in density calculations, and

added to the active surface activity layer as appropriate (if an actual direct area of influence can

be located).

Energy (wind energy facilities)

This dataset will be a subset of the Federal Aviation Administration (FAA) Digital Obstacles

point file. Points where “Type_” = “WINDMILL” will be included. Direct area of influence of

these point features will be measured by converting to a polygon dataset as a direct area of

influence of 3 acres (1.2ha) centered on each tower point. See the BLM’s “Wind Energy

Development Programmatic Environmental Impact Statement” (BLM 2005). Additionally, Platts

power plants database will be used for transformer stations associated with wind energy sites

(subset to operational power plants), also with a 3-acre (1.2ha) direct area of influence.

Energy (solar energy facilities)

This dataset will include solar plants as compiled with the Platts power plants database (subset to

operational power plants). This database includes an attribute that indicates the operational

capacity of each solar power plant. Total capacity at the power plant was based on ratings of the

in-service unit(s), in megawatts. Direct area of influence polygons will be centered over each

point feature representing 7.3ac (3.0ha) per megawatt of the stated operational capacity, per the

report of the National Renewable Energy Laboratory (NREL), “Land-Use Requirements for

Solar Power Plants in the United States” (Ong et al. 2013).

Energy (geothermal energy facilities)

This dataset will include geothermal wells in existence or under construction as compiled with

the IHS wells database and power plants as compiled with the Platts database (subset to

operational power plants). Direct area of influence of these point features will be measured by

converting to a polygon dataset of 3 acres (1.2ha) centered on each well or power plant point.

Mining (active developments; locatable, leasable, saleable)

This dataset will include active locatable mining locations as compiled with the proprietary

InfoMine database. Aerial imagery will then be used to digitize manually the active mining

surface disturbance in or near these known occurrence areas. While the date of aerial imagery

varies by scale, the most current data available from Esri and/or Google will be used to locate

(generally at 1:50,000 and below) and digitize (generally at 1:10,000 and below) active mine

direct area of influence. Mine location data source and imagery date will be documented for each

digitized polygon at the time of creation. Currently, there are no known compressive databases

available for leasable or saleable mining sites beyond coal mines. Other data sources will be

evaluated and used as they are identified or as they become available. Point data may be

converted to polygons to represent direct area of influence unless actual surface disturbance is

available.

Infrastructure (roads)

This dataset will be compiled from the proprietary Esri StreetMap Premium for ArcGIS. Dataset

features that will be used are: Interstate Highways, Major Roads, and Surface Streets to capture

most paved and “crowned and ditched” roads while not including “two-track” and 4-wheel-drive

routes. These minor roads, while not included in the broad- and mid-scale monitoring, may

support a volume of traffic that can have deleterious effects on sage-grouse leks. It may be

appropriate to consider the frequency and type of use of roads in a NEPA analysis for a proposed

project. This fine- and site-scale analysis will require more site-specific data than is identified in

this monitoring framework. The direct area of influence for roads will be represented by 240.2ft,

84.0ft, and 40.7ft (73.2m, 25.6m, and 12.4m) total widths centered on the line feature for

Interstate Highways, Major Roads, and Surface Streets, respectively (Knick et al. 2011). The

most current dataset will be used for each monitoring update. Note: This is a related but

different dataset than what was used in BER (Manier et al. 2013). Individual BLM/USFS

planning units may use different road layers for fine- and site-scale monitoring.

Infrastructure (railroads)

This dataset will be a compilation from the Federal Railroad Administration Rail Lines of the

USA dataset. Non-abandoned rail lines will be used; abandoned rail lines will not be used. The

direct are of influence for railroads will be represented by a 30.8ft (9.4m) total width (Knick et

al. 2011) centered on the non-abandoned railroad line feature.

Infrastructure (power lines)

This line dataset will be derived from the proprietary Platts transmission lines database. Linear

features in the dataset attributed as “buried” will be removed from the disturbance calculation.

Only “In Service” lines will be used; “Proposed” lines will not be used. Direct area of influence

will be determined by the kV designation: 1–199 kV (100ft/30.5m), 200–399 kV (150ft/45.7m),

400–699 kV (200ft/61.0m), and 700-or greater kV (250ft/76.2m) based on average right-of-way

and structure widths, according to BLM WO-300 (Minerals and Realty Management).

Infrastructure (communication towers)

This point dataset will be compiled from the Federal Communications Commission (FCC)

communication towers point file; all duplicate points will be removed. It will be converted to a

polygon dataset by using a direct area of influence of 2.5 acres (1.0ha) centered on each

communication tower point (Knick et al. 2011).

Infrastructure (other vertical structures)

This point dataset will be compiled from the FAA’s Digital Obstacles point file. Points where

“Type_” = “WINDMILL” will be removed. Duplicate points from the FCC communication

towers point file will be removed. Remaining features will be converted to a polygon dataset

using a direct area of influence of 2.5 acres (1.0ha) centered on each vertical structure point

(Knick et al. 2011).

Other Developed Rights-of-Way

Currently, no additional data sources for other rights-of-way have been identified; roads, power

lines, railroads, pipelines, and other known linear features are represented in the categories

described above. The newly purchased IHS data do contain pipeline information; however, this

database does not currently distinguish between above-ground and underground pipelines. If

additional features representing human activities are identified, they will be added to monitoring

reports using similar assumptions to those used with the threats described above.

b. Habitat Degradation Threat Combination and Calculation

The threats targeted for measuring human activity (Table 2) will be converted to direct area of

influence polygons as described for each threat above. These threat polygon layers will be

combined and features dissolved to create one overall polygon layer representing footprints of

active human activity in the range of sage-grouse. Individual datasets, however, will be

preserved to indicate which types of threats may be contributing to overall habitat degradation.

This measure has been divided into three submeasures to describe habitat degradation on the

landscape. Percentages will be calculated as follows:

Measure 2a. Footprint by geographic area of interest: Divide area of the active/direct

footprint by the total area of the geographic area of interest (% disturbance in geographic

area of interest).

Measure 2b. Active/direct footprint by historical sagebrush potential: Divide area of the

active footprint that coincides with areas with historical sagebrush potential (BpS

calculation from habitat availability) within a given geographic area of interest by the

total area with sagebrush potential within the geographic area of interest (% disturbance

on potential historical sagebrush in geographic area of interest).

Measure 2c. Active/direct footprint by current sagebrush: Divide area of the active

footprint that coincides with areas of existing sagebrush (EVT calculation from habitat

availability) within a given geographic area of interest by the total area that is current

sagebrush within the geographic area of interest (% disturbance on current sagebrush in

geographic area of interest).

B.3. Energy and Mining Density (Measure 3)

The measure of density of energy and mining will be calculated by combining the locations of

energy and mining threats identified in Table 2. This measure will provide an estimate of the

intensity of human activity or the intensity of habitat degradation. The number of energy

facilities and mining locations will be summed and divided by the area of meaningful geographic

areas of interest to calculate density of these activities. Data sources for each threat are found in

Table 6. Specific assumptions (inclusion criteria for data, width/area assumptions for point and

line features, etc.) and methodology for each threat, and the combined measure, are detailed

below. All datasets will be updated annually to monitor broad- and mid-scale year-to-year

changes and 5-year (or longer) trends in habitat degradation.

Table 6. Geospatial data sources for habitat degradation (Measure 2).

Degradation Type

Subcategory

Data Source

Direct Area of

Influence

Area

Source

Energy (oil & gas)

Wells

IHS; BLM (AFMSS)

5.0ac (2.0ha)

BLM WO-

300

Power Plants

Platts (power plants)

5.0ac (2.0ha)

BLM WO-

300

Energy (coal)

Mines

BLM; USFS; Office of Surface

Mining Reclamation and

Enforcement; USGS Mineral

Resources Data System

Polygon area

(digitized)

Esri/

Google

Imagery

Power Plants

Platts (power plants)

Polygon area

(digitized)

Esri Imagery

Energy (wind)

Wind Turbines

Federal Aviation

Administration

3.0ac (1.2ha)

BLM WO-

300

Power Plants

Platts (power plants)

3.0ac (1.2ha)

BLM WO-

300

Energy (solar)

Fields/Power

Plants

Platts (power plants)

7.3ac

(3.0ha)/MW

NREL

Energy

(geothermal)

Wells

IHS

3.0ac (1.2ha)

BLM WO-

300

Power Plants

Platts (power plants)

Polygon area

(digitized)

Esri Imagery

Mining

Locatable

Developments

InfoMine

Polygon area

(digitized)

Esri Imagery

Infrastructure

(roads)

Surface Streets

(Minor Roads)

Esri StreetMap Premium

40.7ft (12.4m)

USGS

Major Roads

Esri StreetMap Premium

84.0ft (25.6m)

USGS

Interstate

Highways

Esri StreetMap Premium

240.2ft

(73.2m)

USGS

Infrastructure

(railroads)

Active Lines

Federal Railroad

Administration

30.8ft (9.4m)

USGS

Infrastructure

(power lines)

1-199kV Lines

Platts (transmission lines)

100ft (30.5m)

BLM WO-

300

200-399 kV Lines

Platts (transmission lines)

150ft (45.7m)

BLM WO-

300

400-699kV Lines

Platts (transmission lines)

200ft (61.0m)

BLM WO-

300

700+kV Lines

Platts (transmission lines)

250ft (76.2m)

BLM WO-

300

Infrastructure

(communication)

Towers

Federal Communications

Commission

2.5ac (1.0ha)

BLM WO-

300

a. Energy and Mining Density Datasets and Assumptions

Energy (oil and gas wells and development facilities)

(See Section I.B.2., Habitat Degradation Monitoring.)

Energy (coal mines)

(See Section I.B.2., Habitat Degradation Monitoring.)

Energy (wind energy facilities)

(See Section I.B.2., Habitat Degradation Monitoring.)

Energy (solar energy facilities)

(See Section I.B.2., Habitat Degradation Monitoring.)

Energy (geothermal energy facilities)

(See Section I.B.2., Habitat Degradation Monitoring.)

Mining (active developments; locatable, leasable, saleable)

(See Section I.B.2., Habitat Degradation Monitoring.)

b. Energy and Mining Density Threat Combination and Calculation

Datasets for energy and mining will be collected in two primary forms: point locations (e.g.,

wells) and polygon areas (e.g., surface coal mining). The following rule set will be used to

calculate density for meaningful geographic areas of interest including standard grids and per

polygon:

1) Point locations will be preserved; no additional points will be removed beyond the

methodology described above. Energy facilities in close proximity (an oil well close

to a wind tower) will be retained.

2) Polygons will not be merged, or features further dissolved. Thus, overlapping

facilities will be retained, such that each individual threat will be a separate polygon

data input for the density calculation.

3) The analysis unit (polygon or 640-acre section in a grid) will be the basis for counting

the number of mining or energy facilities per unit area. Within the analysis unit, all

point features will be summed, and any individual polygons will be counted as one

(e.g., a coal mine will be counted as one facility within population). Where polygon

features overlap multiple units (polygons or pixels), the facility will be counted as one

in each unit where the polygon occurs (e.g., a polygon crossing multiple 640-acre

sections would be counted as one in each 640-acre section for a density per 640-acre-

section calculation).

4) In methodologies with different-sized units (e.g., MZs, populations, etc.) raw facility

counts will be converted to densities by dividing the raw facility counts by the total

area of the unit. Typically this will be measured as facilities per 640 acres.

5) For uniform grids, raw facility counts will be reported. Typically this number will

also be converted to facilities per 640 acres.

6) Reporting may include summaries beyond the simple ones above. Zonal statistics

may be used to smooth smaller grids to help display and convey information about

areas within meaningful geographic areas of interest that have high levels of energy

and/or mining activity.

7) Additional statistics for each defined unit may also include adjusting the area to

include only the area with the historical potential for sagebrush (BpS) or areas

currently sagebrush (EVT).

Individual datasets and threat combination datasets for habitat degradation will be available

through the BLM’s EGIS web portal and geospatial gateway. Legacy datasets will be preserved

so that trends may be calculated.

C. Population (Demographics) Monitoring

State wildlife management agencies are responsible for monitoring sage-grouse populations

within their respective states. WAFWA will coordinate this collection of annual population data

by state agencies. These data will be made available to the BLM according to the terms of the

forthcoming Greater Sage-Grouse Population Monitoring Memorandum of Understanding

(MOU) (2014) between WAFWA and the BLM. The MOU outlines a process, timeline, and

responsibilities for regular data sharing of sage-grouse population and/or habitat information for

the purposes of implementing sage-grouse LUPs/amendments and subsequent effectiveness

monitoring. Population areas were refined from the “Greater Sage-grouse (Centrocercus

urophasianus) Conservation Objectives: Final Report” (COT 2013) by individual state wildlife

agencies to create a consistent naming nomenclature for future data analyses. These population

data will be used for analysis at the applicable scale to supplement habitat effectiveness

monitoring of management actions and to inform the adaptive management responses.

D. Effectiveness Monitoring

Effectiveness monitoring will provide the data needed to evaluate BLM and USFS actions

toward reaching the objective of the national planning strategy (BLM IM 2012-044)—to

conserve sage-grouse populations and their habitat—and the objectives for the land use planning

area. Effectiveness monitoring methods described here will encompass multiple larger scales,

from areas as large as the WAFWA MZ to the scale of this LUP. Effectiveness data used for

these larger-scale evaluations will include all lands in the area of interest, regardless of surface

ownership/management, and will help inform where finer-scale evaluations are needed, such as

population areas smaller than an LUP or PACs within an LUP (described in Section II, Fine and

Site Scales). Data will also include the trend of disturbance within these areas of interest to

inform the need to initiate adaptive management responses as described in the land use plan.

Effectiveness monitoring reported for these larger areas provides the context to conduct

effectiveness monitoring at finer scales. This approach also helps focus scarce resources to areas

experiencing habitat loss, degradation, or population declines, without excluding the possibility

of concurrent, finer-scale evaluations as needed where habitat or population anomalies have been

identified through some other means.

To determine the effectiveness of the sage-grouse national planning strategy, the BLM and the

USFS will evaluate the answers to the following questions and prepare a broad- and mid-scale

effectiveness report:

1) Sagebrush Availability and Condition:

a. What is the amount of sagebrush availability and the change in the amount

and condition of sagebrush?

b. What is the existing amount of sagebrush on the landscape and the change in

the amount relative to the pre-EuroAmerican historical distribution of

sagebrush (BpS)?

c. What is the trend and condition of the indicators describing sagebrush

characteristics important to sage-grouse?

2) Habitat Degradation and Intensity of Activities:

a. What is the amount of habitat degradation and the change in that amount?

b. What is the intensity of activities and the change in the intensity?

c. What is the amount of reclaimed energy-related degradation and the change in

the amount?

3) What is the population estimation of sage-grouse and the change in the population

estimation?

4) How are the BLM and the USFS contributing to changes in the amount of sagebrush?

5) How are the BLM and the USFS contributing to disturbance?

The compilation of broad- and mid-scale data (and population trends as available) into an

effectiveness monitoring report will occur on a 5-year reporting schedule (see Attachment A),

which may be accelerated to respond to critical emerging issues (in consultation with the

USFWS and state wildlife agencies). In addition, effectiveness monitoring results will be used to

identify emerging issues and research needs and inform the BLM and the USFS adaptive

management strategy (see the adaptive management section of this Environmental Impact

Statement).

To determine the effectiveness of the sage-grouse objectives of the land use plan, the BLM and

the USFS will evaluate the answers to the following questions and prepare a plan effectiveness

report:

1) Is this plan meeting the sage-grouse habitat objectives?

2) Are sage-grouse areas within the LUP meeting, or making progress toward meeting, land

health standards, including the Special Status Species/wildlife habitat standard?

3) Is the plan meeting the disturbance objective(s) within sage-grouse areas?

4) Are the sage-grouse populations within this plan boundary and within the sage-grouse

areas increasing, stable, or declining?

The effectiveness monitoring report for this LUP will occur on a 5-year reporting schedule (see

Attachment A) or more often if habitat or population anomalies indicate the need for an

evaluation to facilitate adaptive management or respond to critical emerging issues. Data will be

made available through the BLM’s EGIS web portal and the geospatial gateway.

Methods

At the broad and mid scales (PACs and above) the BLM and the USFS will summarize the

vegetation, disturbance, and (when available) population data. Although the analysis will try to

summarize results for PACs within each sage-grouse population, some populations may be too

small to report the metrics appropriately and may need to be combined to provide an estimate

with an acceptable level of accuracy. Otherwise, they will be flagged for more intensive

monitoring by the appropriate landowner or agency. The BLM and the USFS will then analyze

monitoring data to detect the trend in the amount of sagebrush; the condition of the vegetation in

the sage-grouse areas (MacKinnon et al. 2011); the trend in the amount of disturbance; the

change in disturbed areas owing to successful restoration; and the amount of new disturbance the

BLM and/or the USFS has permitted. These data could be supplemented with population data

(when available) to inform an understanding of the correlation between habitat and PACs within

a population. This overall effectiveness evaluation must consider the lag effect response of

populations to habitat changes (Garton et al. 2011).

Calculating Question 1, National Planning Strategy Effectiveness: The amount of sagebrush

available in the large area of interest will use the information from Measure 1a (I.B.1., Sagebrush

Availability) and calculate the change from the 2012 baseline to the end date of the reporting

period. To calculate the change in the amount of sagebrush on the landscape to compare with the

historical areas with potential to support sagebrush, the information from Measure 1b (I.B.1.,

Sagebrush Availability) will be used. To calculate the trend in the condition of sagebrush at the

mid scale, three sources of data will be used: the BLM’s Grass/Shrub mapping effort (Future

Plans in Section I.B.1., Sagebrush Availability); the results from the calculation of the landscape

indicators, such as patch size (described below); and the BLM’s Landscape Monitoring

Framework (LMF) and sage-grouse intensification effort (also described below). The LMF and

sage-grouse intensification effort data are collected in a statistical sampling framework that

allows calculation of indicator values at multiple scales.

Beyond the importance of sagebrush availability to sage-grouse, the mix of sagebrush patches on

the landscape at the broad and mid scale provides the life requisite of space for sage-grouse

dispersal needs (see the HAF). The configuration of sagebrush habitat patches and the land cover

or land use between the habitat patches at the broad and mid scales also defines suitability. There

are three significant habitat indicators that influence habitat use, dispersal, and movement across

populations: the size and number of habitat patches, the connectivity of habitat patches (linkage

areas), and habitat fragmentation (scope of unsuitable and non-habitats between habitat patches).

The most appropriate commercial software to measure patch dynamics, connectivity, and

fragmentation at the broad and mid scales will be used, along with the same data layers derived

for sagebrush availability.

The BLM initiated the LMF in 2011 in cooperation with the Natural Resources Conservation

Service (NRCS). The objective of the LMF effort is to provide unbiased estimates of vegetation

and soil condition and trend using a statistically balanced sample design across BLM lands.

Recognizing that sage-grouse populations are more resilient where the sagebrush plant

community has certain characteristics unique to a particular life stage of sage-grouse (Knick and

Connelly 2011, Stiver et al. in press), a group of sage-grouse habitat and sagebrush plant

community subject matter experts identified those vegetation indicators collected at LMF

sampling points that inform sage-grouse habitat needs. The experts represented the Agricultural

Research Service, BLM, NRCS, USFWS, WAFWA, state wildlife agencies, and academia. The

common indicators identified include: species composition, foliar cover, height of the tallest

sagebrush and herbaceous plant, intercanopy gap, percent of invasive species, sagebrush shape,

and bare ground. To increase the precision of estimates of sagebrush conditions within the range

of sage-grouse, additional plot locations in occupied sage-grouse habitat (Sage-Grouse

Intensification) were added in 2013. The common indicators are also collected on sampling

locations in the NRCS National Resources Inventory Rangeland Resource Assessment

(http://www.nrcs.usda.gov/wps/portal/nrcs/detail/national/technical/nra/nri/?&cid=stelprdb10416

20).

The sage-grouse intensification baseline data will be collected over a 5-year period, and an

annual sage-grouse intensification report will be prepared describing the status of the indicators.

Beginning in year 6, the annual status report will be accompanied with a trend report, which will

be available on an annual basis thereafter, contingent on continuation of the current monitoring

budget. This information, in combination with the Grass/Shrub mapping information, the mid-

scale habitat suitability indicator measures, and the sagebrush availability information will be

used to answer Question 1 of the National Planning Strategy Effectiveness Report.

Calculating Question 2, National Planning Strategy Effectiveness: Evaluations of the amount of

habitat degradation and the intensity of the activities in the area of interest will use the

information from Measure 2 (Section I.B.2., Habitat Degradation Monitoring) and Measure 3

(Section I.B.3., Energy and Mining Density). The field office will collect data on the amount of

reclaimed energy-related degradation on plugged and abandoned and oil/gas well sites. The data

are expected to demonstrate that the reclaimed sites have yet to meet the habitat restoration

objectives for sage-grouse habitat. This information, in combination with the amount of habitat

degradation, will be used to answer Question 2 of the National Planning Strategy Effectiveness

Report.

Calculating Question 3, National Planning Strategy Effectiveness: The change in sage-grouse

estimated populations will be calculated from data provided by the state wildlife agencies, when

available. This population data (Section I.C., Population [Demographics] Monitoring) will be

used to answer Question 3 of the National Planning Strategy Effectiveness Report.

Calculating Question 4, National Planning Strategy Effectiveness: The estimated contribution by

the BLM or the USFS to the change in the amount of sagebrush in the area of interest will use

the information from Measure 1a (Section I.B.1., Sagebrush Availability). This measure is

derived from the national datasets that remove sagebrush (Table 3). To determine the relative

contribution of BLM and USFS management, the current Surface Management Agency

geospatial data layer will be used to differentiate the amount of change for each management

agency for this measure in the geographic areas of interest. This information will be used to

answer Question 4 of the National Planning Strategy Effectiveness Report.

Calculating Question 5, National Planning Strategy Effectiveness: The estimated contribution by

the BLM or the USFS to the change in the amount of disturbance in the area of interest will use

the information from Measure 2a (Section I.B.2., Monitoring Habitat Degradation) and Measure

3 (Section I.B.3., Energy and Mining Density). These measures are all derived from the national

disturbance datasets that degrade habitat (Table 6). To determine the relative contribution of

BLM and USFS management, the current Surface Management Agency geospatial data layer

will be used to differentiate the amount of change for each management agency for these two

measures in the geographic areas of interest. This information will be used to answer Question 5

of the National Planning Strategy Effectiveness Report.

Answers to the five questions for determining the effectiveness of the national planning strategy

will identify areas that appear to be meeting the objectives of the strategy and will facilitate

identification of population areas for more detailed analysis. Conceptually, if the broad-scale

monitoring identifies increasing sagebrush availability and improving vegetation conditions,

decreasing disturbance, and a stable or increasing population for the area of interest, there is

evidence that the objectives of the national planning strategy to maintain populations and their

habitats have been met. Conversely, where information indicates that sagebrush is decreasing

and vegetation conditions are degrading, disturbance in sage-grouse areas is increasing, and/or

populations are declining relative to the baseline, there is evidence that the objectives of the

national planning strategy are not being achieved. Such a determination would likely result in a

more detailed analysis and could be the basis for implementing more restrictive adaptive

management measures.

With respect to the land use plan area, the BLM and the USFS will summarize the vegetation,

disturbance, and population data to determine if the LUP is meeting the plan objectives.

Effectiveness information used for these evaluations includes BLM/USFS surface management

areas and will help inform where finer-scale evaluations are needed, such as seasonal habitats,

corridors, or linkage areas. Data will also include the trend of disturbance within the sage-grouse

areas, which will inform the need to initiate adaptive management responses as described in the

land use plan.

Calculating Question 1, Land Use Plan Effectiveness: The condition of vegetation and the

allotments meeting land health standards (as articulated in “BLM Handbook 4180-1, Rangeland

Health Standards”) in sage-grouse areas will be used to determine the LUP’s effectiveness in

meeting the vegetation objectives for sage-grouse habitat set forth in the plan. The field

office/ranger district will be responsible for collecting this data. In order for this data to be

consistent and comparable, common indicators, consistent methods, and an unbiased sampling

framework will be implemented following the principles in the BLM’s AIM strategy (Taylor et

al. 2014; Toevs et al. 2011; MacKinnon et al. 2011), in the BLM’s Technical Reference

“Interpreting Indicators of Rangeland Health” (Pellant et al. 2005), and in the HAF (Stiver et al.

in press) or other approved WAFWA MZ–consistent guidance to measure and monitor sage-

grouse habitats. This information will be used to answer Question 1 of the Land Use Plan

Effectiveness Report.

Calculating Question 2, Land Use Plan Effectiveness: Sage-grouse areas within the LUP that are

achieving land health stands (or, if trend data are available, that are making progress toward

achieving them)—particularly the Special Status Species/wildlife habitat land health standard—

will be used to determine the LUP’s effectiveness in achieving the habitat objectives set forth in

the plan. Field offices will follow directions in “BLM Handbook 4180-1, Rangeland Health

Standards,” to ascertain if sage-grouse areas are achieving or making progress toward achieving

land health standards. One of the recommended criteria for evaluating this land health standard is

the HAF indicators.

Calculating Question 3, Land Use Plan Effectiveness: The amount of habitat disturbance in sage-

grouse areas identified in this LUP will be used to determine the LUP’s effectiveness in meeting

the plan’s disturbance objectives. National datasets can be used to calculate the amount of

disturbance, but field office data will likely increase the accuracy of this estimate. This

information will be used to answer Question 3 of the Land Use Plan Effectiveness Report.

Calculating Question 4, Land Use Plan Effectiveness: The change in estimated sage-grouse

populations will be calculated from data provided by the state wildlife agencies, when available,

and will be used to determine LUP effectiveness. This population data (Section I.C., Population

[Demographics] Monitoring) will be used to answer Question 4 of the Land Use Plan

Effectiveness Report.

Results of the effectiveness monitoring process for the LUP will be used to inform the need for

finer-scale investigations, initiate adaptive management actions as described in the land use plan,

initiate causation determination, and/or determine if changes to management decisions are

warranted. The measures used at the broad and mid scales will provide a suite of characteristics

for evaluating the effectiveness of the adaptive management strategy.

II. FINE AND SITE SCALES

Fine-scale (third-order) habitat selected by sage-grouse is described as the physical and

geographic area within home ranges during breeding, summer, and winter periods. At this level,

habitat suitability monitoring should address factors that affect sage-grouse use of, and

movements between, seasonal use areas. The habitat monitoring at the fine and site scale (fourth

order) should focus on indicators to describe seasonal home ranges for sage-grouse associated

with a lek or lek group within a population or subpopulation area. Fine- and site-scale monitoring

will inform LUP effectiveness monitoring (see Section I.D., Effectiveness Monitoring) and the

hard and soft triggers identified in the LUP’s adaptive management section.

Site-scale habitat selected by sage-grouse is described as the more detailed vegetation

characteristics of seasonal habitats. Habitat suitability characteristics include canopy cover and

height of sagebrush and the associated understory vegetation. They also include vegetation

associated with riparian areas, wet meadows, and other mesic habitats adjacent to sagebrush that

may support sage-grouse habitat needs during different stages in their annual cycle.

As described in the Conclusion (Section III), details and application of monitoring at the fine and

site scales will be described in the implementation-level monitoring plan for the land use plan.

The need for fine- and site-scale-specific habitat monitoring will vary by area, depending on

proposed projects, existing conditions, habitat variability, threats, and land health. Examples of

fine- and site-scale monitoring include: habitat vegetation monitoring to assess current habitat

conditions; monitoring and evaluation of the success of projects targeting sage-grouse habitat

enhancement and/or restoration; and habitat disturbance monitoring to provide localized

disturbance measures to inform proposed project review and potential mitigation for project

impacts. Monitoring plans should incorporate the principles outlined in the BLM’s AIM strategy

(Toevs et al. 2011) and in “AIM-Monitoring: A Component of the Assessment, Inventory, and

Monitoring Strategy” (Taylor et al. 2014). Approved monitoring methods are:

 “BLM Core Terrestrial Indicators and Methods” (MacKinnon et al. 2011);

 The BLM’s Technical Reference “Interpreting Indicators of Rangeland Health”

(Pellant et al. 2005); and,

 “Sage-Grouse Habitat Assessment Framework: Multiscale Assessment Tool” (Stiver

et al. in press).

Other state-specific disturbance tracking models include: the BLM’s Wyoming Density and

Disturbance Calculation Tool (http://ddct.wygisc.org/) and the BLM’s White River Data

Management System in development with the USGS. Population monitoring data (in cooperation

with state wildlife agencies) should be included during evaluation of the effectiveness of actions

taken at the fine and site scales.

Fine- and site-scale sage-grouse habitat suitability indicators for seasonal habitats are identified

in the HAF. The HAF has incorporated the Connelly et al. (2000) sage-grouse guidelines as well

as many of the core indicators in the AIM strategy (Toevs et al. 2011). There may be a need to

develop adjustments to height and cover or other site suitability values described in the HAF;

any such adjustments should be ecologically defensible. To foster consistency, however,

adjustments to site suitability values at the local scale should be avoided unless there is strong,

scientific justification for making those adjustments. That justification should be provided.

WAFWA MZ adjustments must be supported by regional plant productivity and habitat data for

the floristic province. If adjustments are made to the site-scale indicators, they must be made

using data from the appropriate seasonal habitat designation (breeding/nesting, brood-rearing,

winter) collected from sage-grouse studies found in the relevant area and peer-reviewed by the

appropriate wildlife management agency(ies) and researchers.

When conducting land heath assessments, the BLM should follow, at a minimum, “Interpreting

Indicators of Rangeland Health” (Pellant et. al. 2005) and the “BLM Core Terrestrial Indicators

and Methods” (MacKinnon et al. 2011). For assessments being conducted in sage-grouse

designated management areas, the BLM should collect additional data to inform the HAF

indicators that have not been collected using the above methods. Implementation of the

principles outlined in the AIM strategy will allow the data to be used to generate unbiased

estimates of condition across the area of interest; facilitate consistent data collection and rollup

analysis among management units; help provide consistent data to inform the classification and

interpretation of imagery; and provide condition and trend of the indicators describing sagebrush

characteristics important to sage-grouse habitat (see Section I.D., Effectiveness Monitoring).

III. CONCLUSION

This Greater Sage-Grouse Monitoring Framework was developed for all of the Final

Environmental Impact Statements involved in the sage-grouse planning effort. As such, it

describes the monitoring activities at the broad and mid scales and provides a guide for the BLM

and the USFS to collaborate with partners/other agencies to develop the land use plan- specific

monitoring plan.

IV. THE GREATER SAGE-GROUSE DISTURBANCE AND MONITORING SUBTEAM

MEMBERSHIP

Gordon Toevs (BLM -WO)

Duane Dippon (BLM-WO)

Frank Quamen (BLM-NOC)

David Wood (BLM-NOC)

Vicki Herren (BLM-NOC)

Matt Bobo (BLM-NOC)

Michael “Sherm” Karl (BLM-NOC)

Emily Kachergis (BLM-NOC)

Doug Havlina (BLM-NIFC)

Mike Pellant (BLM-GBRI)

John Carlson (BLM-MT)

Jenny Morton (BLM -WY)

Robin Sell (BLM-CO)

Paul Makela (BLM-ID)

Renee Chi (BLM-UT)

Sandra Brewer (BLM-NV)

Glenn Frederick (BLM-OR)

Robert Skorkowsky (USFS)

Dalinda Damm (USFS)

Rob Mickelsen (USFS)

Tim Love (USFS)

Pam Bode (USFS)

Lief Wiechman (USFWS)

Lara Juliusson (USFWS)

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Oyler-McCance, F.R. Quamen, D.J. Saher, and A.J. Titolo. 2013. Summary of science, activities,

programs, and policies that influence the rangewide conservation of Greater Sage-Grouse (Centrocercus

urophasianus): U.S. Geological Survey Open–File Report 2013–1098. 170pp.

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http://www.nrel.gov/docs/fy13osti/56290.pdf.

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version 4. Technical Reference 1734-6. U.S. Department of the Interior, Bureau of Land Management,

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conservation of a landscape species and its habitats, edited by S.T. Knick and J.W. Connelly, 531–548.

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Attachment A. An Overview of Monitoring Commitments

Broad and Mid Scales

Fine and Site

Scales

Implemen-

tation

Sagebrush

Availability

Habitat

Degradation

Population

Effectiveness

How will

the data be

used?

Track and

document

implementation

of land use plan

decisions and

inform adaptive

management

Track changes

in land cover

(sagebrush) and

inform adaptive

management

Track changes in

disturbance

(threats) to sage-

grouse habitat

and inform

adaptive

management

Track trends in

sage-grouse

populations

(and/or leks; as

determined by

state wildlife

agencies) and

inform adaptive

management

Characterize the

relationship

among

disturbance,

implementation

actions, and

sagebrush

metrics and

inform adaptive

management

Measure seasonal

habitat,

connectivity at

the fine scale, and

habitat conditions

at the site scale,

calculate

disturbance, and

inform adaptive

management

Who is

collecting

the data?

BLM FO and

USFS Forest

NOC and NIFC

National datasets

(NOC), BLM

FOs, and USFS

Forests as

applicable

State wildlife

agencies

through

WAFWA

Comes from

other broad- and

mid-scale

monitoring

types, analyzed

by the NOC

BLM FO and SO,

USFS Forests and

RO (with

partners)

How often

are the

data

collected,

reported,

and made

available

USFWS?

Collected and

reported

annually;

summary report

every 5 years

Updated and

changes

reported

annually;

summary

report every 5

years

Collected and

changes reported

annually;

summary report

every 5 years

State data

reported

annually per

WAFWA

MOU;

summary report

every 5 years

Collected and

reported every 5

years (coincident

with LUP

evaluations)

Collection and

trend analysis

ongoing, reported

every 5 years or

as needed to

inform adaptive

management

What is

the spatial

scale?

Summarized by

LUP with

flexibility for

reporting by

other units

Summarized by

PACs (size

dependent)

with flexibility

for reporting by

other units

Summarized by

PACs (size

dependent) with

flexibility for

reporting by

other units

Summarized by

PACs (size

dependent)

with flexibility

for reporting by

other units

Summarized by

MZ and LUP

with flexibility

for reporting by

other units (e.g.,

PAC)

Variable (e.g.,

projects and

seasonal habitats)

What are

the

potential

personnel

and budget

impacts?

Additional

capacity or re-

prioritization of

ongoing

monitoring

work and

budget

realignment

At a minimum,

current skills

and capacity

must be

maintained;

data

management

costs are TBD

At a minimum,

current skills and

capacity must be

maintained; data

management and

data layer

purchase cost are

TBD

No additional

personnel or

budget impacts

for the BLM or

the USFS

Additional

capacity or re-

prioritization of

ongoing

monitoring work

and budget

realignment

Additional

capacity or re-

prioritization of

ongoing

monitoring work

and budget

realignment

Who has

primary

and

secondary

responsi-

bilities for

reporting?

1) BLM FO

& SO;

USFS

Forest &

2) BLM &

USFS

Planning

1) NOC

2) WO

1) NOC

2) BLM SO,

USFS RO,

appropriate

programs

1) WAFWA

& state

wildlife

agencies

2) BLM SO,

USFS RO,

NOC

1) Broad and

mid scale at

the NOC,

LUP at

BLM SO,

USFS RO

1) BLM FO &

USFS Forests

2) BLM SO &

USFS RO

What new

processes/

tools are

needed?

National

implementation

datasets and

analysis tools

Updates to

national land

cover data

Data standards

and rollup

methods for

these data

Standards in

population

monitoring

(WAFWA)

Reporting

methodologies

Data standards

data storage; and

reporting

FO (field office); NIFC (National Interagency Fire Center); NOC (National Operations Center); RO

(regional office); SO (state office); TBD (to be determined); WO (Washington Office)

Attachment B. User and Producer Accuracies for Aggregated Ecological Systems within LANDFIRE

Map Zones

LANDFIRE Map Zone Name

User

Accuracy

Producer

Accuracy

% of Map Zone

within Historical

Schroeder

Wyoming Basin

76.9%

90.9%

98.5%

Snake River Plain

68.8%

85.2%

98.4%

Missouri River Plateau

57.7%

100.0%

91.3%

Grand Coulee Basin of the Columbia Plateau

80.0%

89.3%

Wyoming Highlands

75.3%

85.9%

88.1%

Western Great Basin

69.3%

75.4%

72.9%

Blue Mountain Region of the Columbia Plateau

85.7%

88.7%

72.7%

Eastern Great Basin

62.7%

80.0%

62.8%

Northwestern Great Plains

76.5%

92.9%

46.3%

Northern Rocky Mountains

72.5%

89.2%

42.5%

Utah High Plateaus

81.8%

78.3%

41.5%

Colorado Plateau

65.3%

76.2%

28.8%

Middle Rocky Mountains

78.6%

73.3%

26.4%

Cascade Mountain Range

57.1%

88.9%

17.3%

Sierra Nevada Mountain Range

0.0%

12.3%

Northwestern Rocky Mountains

66.7%

60.0%

7.3%

Southern Rocky Mountains

58.6%

56.7%

7.0%

Northern Cascades

75.0%

2.6%

Mogollon Rim

66.7%

100.0%

1.7%

Death Valley Basin

0.0%

1.2%

There are two anomalous map zones with 0% user and producer accuracies, attributable to no

available reference data for the ecological systems of interest.

User accuracy is a map-based accuracy that is computed by looking at the reference data for a class and

determining the percentage of correct predictions for these samples. For example, if I select any

sagebrush pixel on the classified map, what is the probability that I'll be standing in a sagebrush stand

when I visit that pixel location in the field? Commission Error equates to including a pixel in a class

when it should have been excluded (i.e., commission error = 1 – user’s accuracy).

Producer accuracy is a reference-based accuracy that is computed by looking at the predictions produced

for a class and determining the percentage of correct predictions. In other words, if I know that a

particular area is sagebrush (I've been out on the ground to check), what is the probability that the digital

map will correctly identify that pixel as sagebrush? Omission Error equates to excluding a pixel that

should have been included in the class (i.e., omission error = 1 – producer’s accuracy).

Attachment C. Sagebrush Species and Subspecies Included in the Selection Criteria for Building the

EVT and BpS Layers

 Artemisia arbuscula subspecies longicaulis

 Artemisia arbuscula subspecies longiloba

 Artemisia bigelovii

 Artemisia nova

 Artemisia papposa

 Artemisia pygmaea

 Artemisia rigida

 Artemisia spinescens

 Artemisia tripartita subspecies rupicola

 Artemisia tripartita subspecies tripartita

 Tanacetum nuttallii

 Artemisia cana subspecies bolanderi

 Artemisia cana subspecies cana

 Artemisia cana subspecies viscidula

 Artemisia tridentata subspecies wyomingensis

 Artemisia tridentata subspecies tridentata

 Artemisia tridentata subspecies vaseyana

 Artemisia tridentata subspecies spiciformis

 Artemisia tridentata subspecies xericensis

 Artemisia tridentata variety pauciflora

 Artemisia frigida

 Artemisia pedatifida