BLM

Developing a Resource Management

and Monitoring Protocol for a

Semiarid Landscape with Extensive

Oil and Gas Development Potential

Technical Note 439

July 2011 
Suggested citation:

Boone, R.B., J.J. Taylor, D.M. Swift, P.H. Evangelista, and E. Hollowed. 2011. Developing a Resource 

Management and Monitoring Protocol for a Semiarid Landscape with Extensive Oil and Gas 

Development Potential. Technical Note 439. U.S. Department of the Interior. Bureau of Land 

Management, National Operations Center, Denver, Colorado. 52 pp. 

Production services provided by:

Bureau of Land Management 

National Operations Center 

Information and Publishing Services Section 

P.O. Box 25047 

Denver, CO 80225

BLM/OC/ST-11/002+1734
Developing a Resource Management

and Monitoring Protocol for a

Semiarid Landscape with Extensive

Oil and Gas Development Potential

Technical Note 439

by

Randall B. Boone 

Natural Resource Ecology Laboratory 

Colorado State University 

Fort Collins, Colorado

Jason J. Taylor

Bureau of Land Management 

National Operations Center 

Branch of Assessment and Monitoring 

Denver, Colorado

David M. Swift

Natural Resource Ecology Laboratory 

Colorado State University 

Fort Collins, Colorado

Paul H. Evangelista

Natural Resource Ecology Laboratory 

Colorado State University 

Fort Collins, Colorado

Ed Hollowed

Bureau of Land Management 

White River Field Office

Meeker, Colorado

July 2011 

iii

Acknowledgments

While the opinions expressed herein remain 

those of the authors, we would like to extend 

our sincere appreciation to those who participated 

in the resource management and monitoring 

protocol (RMMP) workshop at Colorado State 

University and would like to acknowledge the 

efforts of those who provided independent, 

external review of this document, especially 

Drs. Tasha Carr (U.S. Geological Survey, 

Fort Collins), Mark Paschke (Colorado State 

University), and Peter Weisberg (University 

of Nevada, Reno). Thoughtful comments and 

questions helped to improve the RMMP’s 

contents. The authors would also like to 

acknowledge the efforts of Bureau of Land 

Management (BLM) personnel, especially 

Kent Walter, Dennis Sylvia, Matt Bobo, 

Dennis Zachman, and Craig MacKinnon for 

their contributions in championing the RMMP 

in its initial, conceptual stages within the 

BLM. Lastly, funding (via a Rocky Mountain 

Cooperative Ecosystem Studies Unit assistance 

agreement) and project development support 

were provided by the BLM’s Assessment, 

Inventory, and Monitoring Initiative.

v

Contents

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Focal Landscape . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Potential Effects of Oil and Gas Extraction and Other Activities on Ecosystem Services . . . . . . . . . . . . . 5

Approach to Monitoring Oil and Gas Extraction Activities and Associated Landscape Change . . . . . . . . 7

Approach to Defining Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Approach to Identifying Metrics to Monitor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Conceptual Diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Theoretical Rationale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Practical Considerations and Steps Taken . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Proposed Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

Literature Cited . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

vii

Abstract

Energy extraction activities in the Intermountain 

West doubled from 1990 to 2007. Within the 

Bureau of Land Management’s (BLM’s) White 

River Field Office (WRFO) management area, 

95 percent of new oil and gas development 

activities are anticipated to occur in the Piceance 

Creek Basin. Oil and gas development activities 

are leading to changes in the landscapes 

administered by the BLM. These landscapes 

provide habitats for many species, including 

some that are rare, and ecosystem services 

important to the public, including recreation, 

grazing, and energy production. WRFO personnel 

are developing a resource management plan 

amendment (RMPA) related to oil and gas 

extraction activities, with a goal to manage for 

long-term ecological viability, as defined by the 

Federal Land Policy and Management Act. A 

timely, cost-effective, scientifically valid, and 

publicly accepted approach to monitoring the 

effectiveness of land management decisions and 

practices was desired as part of the RMPA. To 

meet this need, the BLM, in collaboration with 

Colorado State University, and with input from 

the U.S. Geological Survey, developed this RMPA 

companion document, which proposes a resource 

management and monitoring protocol (RMMP). 

Once all phases are complete, the RMMP will 

include specific protocols and models for using 

remote sensing and other geospatial technologies, 

a series of metrics judged by experts as likely to 

reflect important changes in landscapes over time, 

and a means to report the results of the RMMP to 

the public, to operators, and within the BLM. 

1

Introduction

of energy reserves or incomplete reclamation, 

the WRFO recognized the need for a timely, 

cost-effective, scientifically valid, and publicly 

accepted monitoring approach to evaluate the 

effectiveness of the RMPA and resultant oil and 

gas extraction activities in the area. To meet this 

need, the BLM, in collaboration with personnel 

from Colorado State University, and with input 

from the U.S. Geological Survey (USGS), 

developed this RMPA companion document. This 

Technical Note describes a resource management 

and monitoring protocol (RMMP) relative to 

oil and gas extraction activities, including the 

rationale and recommended approach. The 

RMMP has an intermediate goal of integrating 

the results of this effort into the BLM planning 

process and a long-term goal of integrating 

management aspects beyond oil and gas activities 

(e.g., grazing effects), but both of those goals are 

beyond the scope of this document. 

From a fundamental perspective, the RMMP is 

an oil and gas RMPA implementation-monitoring 

protocol that has two immediate objectives. The 

first objective is to determine and describe the 

most efficient and effective way to inventory, 

monitor, and report surface disturbance and 

reclamation activities, with the inventorying 

and monitoring being completed via some 

combination of geospatial technologies and 

fieldwork. The second objective is to develop an 

assessment protocol, using landscape metrics 

and results from fieldwork to quantify and 

understand landscape change related to oil and 

gas development, including the effects of surface 

disturbance and reclamation activities, as well as 

changes due to other land uses and natural causes. 

The RMMP will be developed in a two-stage 

effort. The first phase is referred to as the “ideas” 

Energy extraction activities in the Intermountain 

West doubled from 1990 to 2007 (Naugle et al. 

2011). These activities are leading to changes 

in the landscapes administered by the Bureau of 

Land Management (BLM). These BLM-managed 

lands provide habitat for a variety of species, 

some of which are rare, and ecosystem services

(see “Glossary” for words in italics) important 

to the public (MEA 2005). Ecosystem services 

provided to the public include direct uses, such as 

energy production, hunting, hiking, livestock and 

wild horse grazing, and other resource uses of the 

federal lands, and indirect services, such as snow 

and water storage and nutrient cycling. Oil and 

gas development activities may lead to changes 

that degrade the condition, sustainability, and 

resilience of BLM lands (Rapport et al. 1998; 

de Soyza et al. 2000) potentially affecting 

important ecosystem services. One aspect 

of BLM’s multiple use mandate, as defined 

by the Federal Land Policy and Management 

Act (FLPMA), is to achieve “harmonious and 

coordinated management of the various 

resources without permanent impairment of the 

productivity of the land and the quality of the 

environment . . . .” In part to meet this FLMPA 

mandate, the White River Field Office (WRFO) 

of the BLM is developing a resource management 

plan amendment (RMPA) related to oil and gas 

development activities. The RMPA will include 

novel resource-management practices to monitor 

oil and gas related development and landscape 

change expected in the WRFO over the next 

several decades. 

To adhere to FLPMA, achieve the BLM vision 

of balanced stewardship, and ensure that some of 

the aforementioned uses (i.e., ecosystem services) 

are not unduly compromised by development 
2

phase, which is described in this document, and 

proposes a data management system. The second 

phase is referred to as “implementation” and 

will include development of detailed processes 

and models to achieve project objectives and 

analyses to quantify notable change in metrics. 

Once both phases are complete, the RMMP will 

include specific protocols and models for using 

remote sensing and other geospatial technologies, 

a series of metrics judged by experts as likely to 

reflect important changes in landscapes over time, 

and a means to report the results of the RMMP 

to the public, to operators, and within the BLM. 

Ultimately, an effective monitoring protocol 

will allow the WRFO to adopt a more rigorous 

approach to management. Land managers will be 

able to identify undesirable trends in a changing 

landscape and practice adaptive management, 

altering approaches to improve conditions.

The authors acknowledge the tremendous 

importance of energy extraction as a revenue 

source for private, local, state, and national 

interests, and the value of energy to citizens of 

Colorado and the nation. We also acknowledge 

the disruption that oil and gas extraction may 

have on landscapes and residents, and its potential 

negative effects on ecosystems. This document is 

not intended to sway those involved in political 

decisions. Instead, we seek to summarize 

scientific information in an unbiased way, and to 

suggest measures that will improve the monitoring 

of selected ecological and physical attributes in 

areas where oil and gas are being extracted.
3

Focal Landscape

by Lake Uinta, similar in size to modern-day 

Lake Michigan. Organic matter accumulated 

in the lake and formed oil shale, natural gas, 

and other energy resources that are now of great 

interest. Today the region is semiarid. In Meeker, 

Colorado, the average temperature in January 

ranges from 7-37 °F, and in July, from 47-86 °F 

(BLM 2006). Precipitation (as rain and snow) is 

well distributed throughout the year with annual 

totals of between 12 and 20 inches (CDOW and 

USFWS, undated). Some precipitation events 

can be intense, causing flooding and erosion. 

Soils are diverse, and the topography is complex. 

Vegetation composition has been relatively stable, 

comprising sagebrush (Artemisia spp.), shrublands 

and grasslands, Colorado pinyon pine (Pinus 

edulis), and Utah juniper (Juniperus osteosperma) 

communities, with deciduous forests along the 

riparian areas and midslopes of mountains and 

conifer forests at higher elevations. Livestock 

grazing has been a dominant land use in the area. 

Invasion of the region by exotic plant species, 

especially cheatgrass (Bromus tectorum), and 

increased representation of introduced grazingtolerant grasses, such as Kentucky bluegrass 

(Poa pratensis), have modified the ecology of the 

system, reduced its capacity to support herbivores, 

and compromised other ecological functions.

Public lands in Colorado’s Western Slope are 

undergoing rapid transformations in land use 

and land cover, largely resulting from extraction 

of oil and gas resources. This process of land 

cover change is expected to continue for decades. 

The WRFO encompasses 2.6 million acres of 

publicly and privately owned land in northwest 

Colorado. The region includes the Northern 

Piceance (or Piceance Creek) Basin, roughly 

700,000 acres, which is expected to support over 

95 percent of new oil and gas development in 

the area of concern to the WRFO. The Piceance 

Creek Basin is a nationally important natural 

gas reserve, but is also home to large herds of 

migratory elk (Cervus canadensis) and mule 

deer (Odocoileus hemionus), populations of 

greater sage-grouse (Centrocercus urophasianus), 

threatened plants such as the Dudley Bluffs 

bladderpod (Lesquerella congesta) and Dudley 

Bluffs twinpod (Physaria obcordata), significant 

cultural and paleontological resources, and a wild 

horse herd management area, and it is grazed 

by livestock and used by people for outdoor 

recreation. 

The landscapes throughout the majority of the 

BLM-managed portions of the WRFO are part 

of the Colorado Plateaus Ecoregion. Almost 

50 million years ago, the region was covered 

5

Potential Effects of Oil and Gas Extraction and 

Other Activities on Ecosystem Services

Across the western United States, the quantity of 

sagebrush habitat has been declining for 50 years 

(Connelly et al. 2000). Sagebrush decline is also 

occurring in the WRFO, where several sagebrushobligate species occur, such as the greater sagegrouse (Centrocercus urophasianus), Brewer’s 

sparrow (Spizella breweri), and sagebrush vole 

(Lemmiscus curtatus). Populations of deer, elk, 

and other animals may decline if developmentrelated activities, such as traffic, cause behavioral 

changes reducing the likelihood that animals will 

use forage and cover resources near roads. Oil 

and gas development activities may also alter 

the behavior of livestock as well, and livestock 

grazing is another important use of landscapes 

managed by the WRFO. Lastly, fugitive dust 

from oil and gas extraction activities can decrease 

air quality and coat nearby vegetation, including 

threatened and endangered species, reducing their 

productivity and possibly affecting their long-term 

existence.

Potential effects of oil and gas extraction on 

ecosystem attributes that provide services are 

varied, and include changes to soils, water, 

vegetation, wildlife, and the atmosphere. 

Other activities, such as livestock grazing, 

off-road vehicle recreation, and mining also 

alter ecosystem attributes. Some changes are 

relatively long-term, such as broad-scale erosion 

or projected climate change. Other changes 

are short-term, such as year-to-year changes in 

wildlife populations or weekly changes in air 

quality. The RMMP will highlight changes in 

selected ecosystem attributes that occur in the 

short-term, as well as quantify long-term changes 

in ecosystem attributes. For example, shortterm changes include the amount of bare ground, 

traffic, fugitive (i.e., airborne) dust, and pointsource pollution. Long-term changes include, for 

example, loss of soil crusts, erosion, land cover 

conversion, changes in stream temperatures, and 

invasion of nonnative plant species.

7

Approach to Monitoring Oil and Gas Extraction 

Activities and Associated Landscape Change

of a percent-cover data stack (including percent 

bare ground, percent litter, percent herbaceous, 

percent shrub by species, and percent tree by 

species); a discrete-class land-cover map; and a 

change-detection model based on comparisons 

of these data products through time. This 

combination of efforts will allow for baseline 

disturbance and vegetation to be mapped, and 

monitored. The validation of future site-level 

disturbances will provide a utility for tracking 

reclamation, coordinating reclamation-compliance 

field activities, and calculating many of the 

landscape measures (e.g., habitat totals, habitat 

isolation, etc.) that will be integral to the RMMP. 

While these efforts are largely underway, the 

second phase of the RMMP will fully evaluate 

and explain how current remote sensing efforts 

will be employed in the RMMP. Furthermore, 

processes and models will be detailed as to how 

remote sensing and geographic information 

systems (GIS) will be used for disturbance and 

reclamation mapping to calculate each of the 

landscape measures of interest.

Although acquiring information on development 

activities (via leaseholder reporting requirements 

and remote sensing) could be relatively 

straightforward, managing and visualizing that 

information is not. To aid in visualization and 

reporting (for the BLM, leaseholders, and the 

public, simultaneously) the USGS Fort Collins 

Science Center has created a data management 

system that has proven useful in tracking and 

reporting oil and gas development activities in 

Wyoming. This web-based system, originally 

called the Jonah Infill Data Management System, 

allows managers to map the state of well pads 

An approach to monitoring proposed oil and 

gas extraction activities, reclamation, and the 

infrastructure that supports these activities could 

be relatively straightforward because of reporting 

requirements placed upon lease holders. To drill 

on public lands where energy companies have 

leased mineral rights from the BLM, companies 

must submit the proposed well locations, plans 

for access roads, and other details to the BLM 

for review. As plans are approved and drilling 

and oil and gas extraction commences, the BLM 

requires updated reports from the companies. 

Lastly, as well pads, roads, and pipeline paths 

are revegetated, the BLM is informed of the state 

of reclamation. Schematics collected via these 

reporting requirements could serve as a basis for 

tracking development activities with a sample 

of the information checked for accuracy and 

consistency by the BLM using either high spatial 

resolution imagery or field visits. 

In addition to oil and gas reporting assessments, 

remote sensing and other geospatial technologies 

will provide the foundation for mapping and 

monitoring related landscape changes. Multiple 

imagery sources (at different spatial, spectral, and 

temporal resolutions) including NAIP (National 

Agriculture Imagery Program), QuickBird, 

and RapidEye will be employed in concert to 

address management questions. Staff from 

BLM’s National Operations Center (NOC), 

have been working in conjunction with the 

WRFO to develop a protocol for disturbance and 

reclamation mapping based on the aforementioned 

imagery. The protocol includes: field sampling 

for remote sensing imagery development and 

classification accuracy assessments; development 
8

across an energy field, and to perform complex 

queries to extract required reporting information. 

Changes in oil and gas development activities 

may be mapped through space and time using the 

system.

The web- and GIS-based data management system 

(DMS) is being adapted by the USGS to apply 

to WRFO-managed lands. This application is a 

foundational component of the RMMP. The DMS 

will allow surface disturbance, reclamation, and 

other oil and gas extraction activities to be planned 

and potential effects to be previewed, with both 

activities and potential effects to be expediently 

tracked and reported, which meets several parts 

of the first objective of the RMMP. The DMS 

will be helpful with the second objective of the 

RMMP as well. The landscape and population 

attributes to be monitored under the RMMP 

include information gathered through space and 

time, and can be visualized and reported in the 

DMS. The DMS should include visual indicators 

summarizing the degree of change in the 

landscape attributes or measures. For example, 

the indicator may be a graphical element that is 

“green” when change from one period to the next 

is small, “yellow” when the change is moderate, 

and “red” when the change is large. Most of the 

proposed metrics have a strong spatial component, 

and the same color codes may be used to show, 

for example, areas of small (green), moderate 

(yellow), or large (red) changes in vegetation 

cover. Green, yellow and red indicators designed 

to show varying levels of change in traffic or 

pollution would also be useful. The suite of 

comparisons incorporated into the RMMP may be 

summarized on a single page in the DMS. 
9

Approach to Defining Change

Second, any thresholds used or derived in the 

RMMP do not advocate management action by 

the BLM or have regulatory authority. Lastly, 

just as landscapes can be adaptively managed, 

the RMMP itself will be subject to adaptive 

modifications. For example, under an adaptive 

management model, metrics may be added 

or modified as new insights are gained, while 

protecting the integrity of the data that allows 

comparisons through time (Kotliar et al. 2008).

The RMMP will quantify selected landscape 

changes specifically related to disturbances by 

oil and gas extraction activities and will not 

specifically address changes due to other land 

uses, such as livestock grazing and recreational 

use, and regional or global-scale events, such 

as changes in climate associated with increased 

concentrations of greenhouse gases. The role of 

the RMMP is to determine methods to highlight 

notable change and to report those changes within 

the DMS graphical user interface and databases. 

As changes are indicated, managers can use the 

DMS, plus other resources, to explore underlying 

causes and adapt management as appropriate. 

Conceptual diagrams linking human land use and 

other stressors to environmental change (such 

as those in Miller 2005) may be referenced by 

researchers and land managers to understand 

relationships between observed landscape changes 

and potential outcomes. For example, an analyst 

may recognize an area showing large changes 

in vegetative cover as having the footprint of 

a recent wildland fire or a broad-scale decline 

in vegetation cover during a time of drought. 

But other changes may require more intensive 

review. Landscapes within the WRFO that have 

experienced relatively little disturbance may be 

delineated and used as “baseline” or “control” 

Defining what may be considered notable changes 

in ecosystems is contentious. Sustainability 

science is a new discipline and much remains 

unknown (Parrish et al. 2003; Rindfuss et al. 

2004). Specifically, changes in ecosystem 

attributes that are sufficient to destabilize an 

ecosystem, or threaten sustainability, are mostly 

unknown. Ecosystem responses may be nonlinear 

and with unknown thresholds, meaning that very 

rapid changes may occur with little warning. 

Progress in sustainability science is being made 

(e.g., Clark et al. 2001) and scientists have had 

some success in determining the causes of past 

collapses, but quantifying thresholds of change 

that will cause future collapses remains difficult 

for all but the simplest ecosystems, and the degree 

to which thresholds from one system inform 

others is poorly known. 

A further consideration is the frequent difference 

between changes that threaten the long-term 

functioning of the ecosystem and changes that 

might be unacceptable to the public. Consider 

the hundreds of plant and animal species that 

occur in the lands managed by WRFO. If some 

species of concern to the public were extirpated, 

the ecosystem in which they occurred is likely to 

remain stable; there are many examples where 

species have gone extinct and the ecosystems in 

which they had occurred continued to maintain 

stable function (reviewed in McCann 2000). 

Nonetheless, the outcome may not be acceptable 

to the public.

Difficulty in defining change, and the public 

perception of change, is addressed in three ways. 

First, the focus will be on change that reflects 

both long-term sustainability and ecosystem 

attributes describing services provided to humans. 
10

areas. Managers can compare changes in areas of 

interest with changes noted in these areas, to see 

if they are likely associated with disturbance, or 

if the changes are seen regardless of disturbance. 

Lastly, managers can compare notable changes 

with long-term landscape dynamics. For example, 

managers expect vegetation cover to decline in 

drought, but if the severity of drought and decline 

in vegetation cover had been related in past 

analyses, the current change could be compared to 

the long-term changes to see if it was more or less 

extreme.

Where possible, change thresholds will be defined 

based on their historical range of variation

(Landres et al. 1999). Most landscape attributes 

continually vary. For example, vegetation 

cover is sensitive to the amount and timing of 

precipitation in a given year. Change alone is 

insufficient to be highlighted as a concern in the 

DMS—change is ubiquitous. In one aspect, the 

RMMP seeks to highlight changes in vegetation 

cover that are “relatively large.” To define 

what relatively large may be, typical changes 

in vegetation production over years must be 

quantified. Historical ranges of variation will 

be calculated based on satellite imagery where 

imagery is available over a sufficiently long 

period, and for changes in wildlife populations, 

where long-term surveys have been conducted. 

Standard deviations in responses, and changes that 

exceed a multiplier of the standard deviation, may 

be identified. Numerous concerns, idiosyncratic 

to each type of change, must be addressed when 

judging historic variability, but some satellite data 

are available back to the 1970s, for example, and 

aerial photographs are available for decades prior, 

and together they may provide ample data for 

determining historical variation for some kinds of 

change.

For some types of change, a historical range 

of variability approach is inappropriate. For 

example, oil and gas development in the lands 

managed by WRFO has been progressing at 

unprecedented rates. For development-based 

change measures, future landscape change will 

be simulated and thresholds quantified based on 

the degree of change judged by experts to be of 

interest to landscape managers. Two degrees of 

notable change may be used, such as a change 

from year to year greater than a given amount 

(e.g., 3 percent) and change across any number of 

years of some larger value (e.g., 6 percent). With 

that, yearly changes that were below the lower 

threshold would still raise flags suggesting notable 

change across several years. 

Analyses to identify reasonable thresholds 

for the measures of change to be adopted will 

be done in the second phase of the RMMP 

development. Issues regarding sampling design, 

including spatial and temporal needs, will also be 

explored. Either through analyses of historical 

ranges of variation or expert assessment about 

rates of change through time, acceptable ranges 

of variation (Parrish et al. 2003) will be defined, 

and responses that exceed those ranges will raise 

alerts in the RMMP. Based on new information, 

or a better understanding of related systems, 

threshold values may be changed at a later date. 

Also, ecosystems experience gradual change 

irrespective of anthropomorphic stressors (Miller 

2005), and after careful review, managers may 

shift the central value around which ranges of 

variation are defined. Although more cautious 

users may choose to adopt different thresholds 

that signal concern, the RMMP will report 

quantifiable levels of change using the process 

described, which should help make monitoring 

more consistent.
11

Approach to Identifying Metrics to Monitor

or indirectly reflect the ability of the system to 

provide those ecosystem services. For example, 

attributes related to direct effects include bare 

ground and fragmentation, while attributes related 

to indirect effects include vegetation and animal 

population responses. Metrics will be one of 

several types: landscape spatial-pattern metrics 

derived from remote imagery; direct measures 

(i.e., counts) of vegetative variables or species 

abundance; or general descriptive or spatial 

statistics used to describe abundances.

Figure 1 is a conceptual diagram adapted from 

Miller (2005) and Kotliar et al. (2008) that 

Conceptual Diagram

The landscapes managed by the BLM WRFO 

are working landscapes. They provide important 

services, ecosystem-based (including wildlife 

habitat) and otherwise, to residents of the area, 

visitors from outside, and users of energy 

throughout the country. It is important that oil 

and gas extraction proceeds in the area, but it is 

also important that the ability of the landscapes 

to provide other key ecosystem services is not 

unduly degraded. As part of the monitoring 

process, the BLM intends to track metrics 

describing ecosystem attributes that either directly 

Figure 1. A conceptual diagram illustrating key structural components (rectangles) and drivers of change (ovals) on lands 

managed by the BLM WRFO. Functional relationships are indicated by arrows. Example components or relationships 

are shown in a smaller font (adapted from Kotliar et al. 2008 and Miller 2005). “Other disturbances” refer to those that are 

anthropogenic, including oil and gas extraction and livestock grazing. “Vertebrates” refer to wild animals.

Regional climatic and

atmospheric conditions

Precipitation amount and timing,

wind, radiant energy, CO2

Landscape Structure

Spatial configuration, composition

Physical conditions

Hydrologic and geomorphic setting, soils, aspect, slope

Natural

disturbances

Drought, flood, fire

Biological

soil crusts

Mosses, lichens,

cyanobacteria

Soil resources

Water, nutrients, organic matter, biota

Other

disturbances

Drilling, roads

Vertebrates

Big game, nongame,

endangered, predators

Vegetation

Grasses, forbs,

shrubs, trees Invertebrates

Pollinators, granivores,

detritivores
12

encapsulates key ecosystems components and 

relationships that describe or influence mesolevel 

ecological structure or function and that helps 

identify metrics to monitor. Regional climatic 

conditions influence almost all components of the 

ecosystem. For example, the amount and timing 

of precipitation, combined with the physical 

conditions of landscapes, are important in defining 

site potential. Disturbances may be natural (e.g., 

fire, drought, or flood) or anthropogenic (e.g., oil 

and gas development activities, roads, or grazing), 

either of which may alter vegetation, soil crusts, 

and landscape structure. Soil resources support 

vegetation growth and the persistence of soil 

crusts. In turn, vegetation and crusts promote 

soil stability and contribute to soil resources 

through additions of organic matter. Landscape 

structure is primarily determined by the physical 

conditions and site potential of an area plus the 

vegetation, which is itself influenced by landscape 

structure. Vertebrates and invertebrates influence, 

and are influenced by, the landscapes they inhabit, 

including soils and soil resources, vegetation, and 

landscape structure. Effects of seasonality and 

severe weather events on vertebrates complete 

this sketch of the most important components and 

functions of these ecosystems. 

Although all the attributes in the conceptual 

diagram are considered important, financial and 

staff limitations within the field office dictate that 

a parsimonious, meaningful, and achievable suite 

of metrics be selected to describe key ecosystem 

attributes that are tied to essential ecosystem 

benefits. Attributes that fit well with ongoing 

efforts by the BLM and other organizations were 

also sought. 

Theoretical Rationale

The simplicity of the conceptual diagram 

(figure 1) masks the innumerable components and 

relationships that exist in the focal ecosystem. 

For instance, typical small riparian plots that 

include 50 plant species and dozens of bird 

species occur in the project area (e.g., Baker 

1990; Kingery 1998). To identify a manageable 

suite of metrics for monitoring, logic analogous 

to an approach now common in conservation 

planning, the coarse-filter approach was used. In 

the 1980s, The Nature Conservancy adopted the 

coarse—filter/fine-filter approach to conserving 

communities, which was later expanded to include 

patterns of physical distribution and disturbance 

(Noss 1987; Hunter et al. 1988). The coarse-filter 

approach was a response to the species-centric 

conservation efforts of the 1970s. In conserving 

biological diversity, the coarse-filter approach 

protects representative communities, such that the 

vast majority of species are conserved. For any 

species not conserved by the coarse filter, specific 

measures of conservation are adopted—the fine 

filter component of the approach. A related idea 

is that of umbrella species (reviewed in Roberge 

and Angelstam 2004), where the conservation 

of a species with broad-ranging requirements 

may help conserve many other species. Classic 

examples of umbrella species are large carnivores, 

which require large areas of relatively undisturbed 

areas. By putting in place conservation-network 

designs so that large carnivores persist, species 

with less expansive requirements—the species 

under the umbrella—will be conserved. A plan to 

conserve biodiversity is outside the scope of the 

RMMP, but the logic of using a limited number 

of well-chosen metrics to measure change in 

important ecosystem attributes applies here. For 

example, recognizing that the greater sage-grouse 

is a species of management concern in the lands 

managed by the WRFO, metrics may be proposed 

that reflect changes in the vegetation sage-grouse 

use most often—sagebrush. Monitoring changes 

in the sagebrush vegetation type can serve as 

an umbrella for habitat used by a suite of other 

sagebrush obligate species, such as the sagebrush 

vole and Brewer’s sparrow. 
13

In considering what metrics to select through the 

filter approach, one aspect of note is that metrics 

used to reflect landscape structure are often highly 

correlated (Cushman et al. 2008). For example, 

the mean size of habitat patches and patch density 

(common landscape metrics) are redundant 

because they represent the same information 

(McGarigal et al. 2002) measured from different 

perspectives; measuring one or the other is 

reasonable, but measuring both is typically 

unnecessary. Rarely is the question about the 

relatedness of potential metrics as clear cut as 

in this size/density example, but it reasonable 

to expect that other attributes may be related. 

For example, measuring a select number of air 

pollutants produced during oil and gas extraction 

activities would presumably reflect concentrations 

of other pollutants known to co-occur. 

Practical Considerations and 

Steps Taken

Expert input, information from the literature, 

workshop discussions, a field visit, and peer 

review were used to create and refine this ideas 

proposed in this document. In September 2009, a 

group of 22 experts in a diversity of domains (e.g., 

landscape, big game, and avian ecologists and 

air quality, riparian, and spatial analysis experts) 

gathered for a 1-day workshop at Colorado State 

University in Fort Collins (see table 1 for a full 

list of participants). Participants were provided 

background information on issues of concern in 

the Piceance Creek Basin. Speakers were then 

asked to review potential monitoring metrics 

for attributes within their area of expertise (e.g., 

mule deer, sage-grouse, air quality, acoustics, 

and disturbance). The workshop concluded with 

a lengthy freeform discussion. Core CSU team 

members (Swift, Boone, Evangelista) then made 

a site visit to the Piceance Basin in November 

2009, hosted by the WRFO staff. The group 

toured a variety of lands disturbed by oil and gas 

development, where activities were at different 

stages. The information gathered during these 

activities influenced the content of the RMMP, in 

terms of general direction, specific metrics to be 

monitored, and metrics that were not incorporated. 

For example, the CSU team had planned to 

incorporate suggestions for acoustic monitoring 

into the RMMP, but after witnessing the relatively 

low noise levels associated with modern oil and 

gas development activities, the team determined 

that limited resources would be better used for 

monitoring other ecosystem attributes. Also, the 

team learned that sources of permanent noise are 

localized, most noise is transitory, monitoring 

acoustics in the area is not practical, and lastly, 

linking changes in noise to ecological effects is 

Table 1. Participants in a workshop September 23, 2009, 

at Colorado State University, Fort Collins, where attributes 

of a monitoring protocol for western Colorado lands were 

discussed.

Participant Affiliation

Chuck Anderson Colorado Division of Wildlife

Matt Bobo Bureau of Land Management

Randall Boone Colorado State University

Tasha Carr U.S. Geological Survey

Paul Evangelista Colorado State University

Maria Fernández-Giménez Colorado State University

Ed Hollowed Bureau of Land Management

Danielle Johnston Colorado Division of Wildlife

Kim Kaal Colorado Division of Wildlife

Megan Kram The Nature Conservancy

David Mack U.S. Geological Survey

Al Maki Exxon Mobil Corporation

Jana Milford University of Colorado

Terri Schulz The Nature Conservancy

Dave Swift Colorado State University

Jason Taylor Bureau of Land Management

Janice Thomson Wilderness Society

Gordon Toevs Bureau of Land Management

Brett Walker Colorado Division of Wildlife

Kent Walter Bureau of Land Management

Christine Wiedinmyer University of Colorado

Ken Wilson Colorado State University
14

difficult. That said, further research into acoustic 

effects on animal behavior is suggested.

The RMMP approach intends to integrate with, 

and take advantage of, an existing constellation 

of BLM national agendas, standards, and ongoing 

fieldwork within the WRFO (e.g., long-term 

vegetative trend monitoring) and other related 

activities, both current and past. For example, 

assessment, inventory, and monitoring (AIM) 

and rapid ecoregional assessments (REAs) are 

two national-level BLM initiatives that were 

considered in the RMMP design. The AIM 

Strategy describes indicators and methods for 

monitoring landscape changes at multiple scales 

and for improving coordinated quantitative 

monitoring for detecting changes in land health. 

Efforts to define terrestrial indicators for the 

AIM Strategy were described by Herrick and 

others (2010). A thorough review of this effort 

was done as the RMMP was designed to find the 

points of convergence between national and local 

needs and to promote both efforts simultaneously. 

The REAs intend to identify landscape-scale, 

ecologically based conservation and restoration 

needs for developing future resource management 

and use objectives. The RMMP is considered, 

at least partially, an implementation/monitoring 

mechanism for REA-like results. Because 

the RMMP will be completed prior to when 

the Colorado Plateaus REA is completed, the 

authors recognize the need for the RMMP to 

remain flexible to adapt to REA results as they 

become available. The standards for public land 

health (e.g., Pellant et al. 2005) guide aspects of 

stewardship on BLM lands as well as resource 

management planning in Colorado. Although 

the RMMP may be more inclusive or broader 

in scale, it should not be in conflict with the 

standards, but rather, where applicable, should 

harness fieldwork already being completed to 

assess public land health and long-term vegetation 

trends. Other pertinent efforts are either currently 

ongoing or are recently completed. The Nature 

Conservancy is working through a conservation 

action plan (CAP) for the White River watershed, 
15

the Wilderness Society has recently completed 

a fragmentation mapping study (Wilbert et 

al. 2008), and the USGS designed a regional 

approach to monitoring related to wildlife and 

energy development (Kotliar et al. 2008). Each of 

these efforts was reviewed prior to completing the 

first phase of the RMMP in an effort to learn from 

and take advantage of the work. 

During metric selection, priority was given to 

metrics that are: (1) ecologically or socially 

relevant and illuminate changes of interest to 

BLM WRFO land managers, especially related to 

implementation of the RMPA; (2) demonstrated in 

the literature to be effective and have the potential 

for being successfully and reliably calculated 

over the long-term, considering both technical 

and financial limitations; (3) interpretable to the 

extent that decisionmakers and the general public 

can understand the meaning of changes in the 

metrics; and, (4) built upon data currently being 

collected, such as long-term vegetative trend 

monitoring . In some cases, new field data will 

need to be collected to monitor specific landscape 

attributes. An attempt was made to ensure that 

any newly suggested field-collection efforts could 

be tiered to the existing, seasonal field campaign. 

In addition, metrics that use remotely sensed 

data to provide local-to-regional views of change 

and that are calculated based on the geometry of 

landscapes were favored. Continuous variables 

were favored over those that yield categories of 

change, as they are more flexible when merged 

into larger monitoring efforts and more helpful as 

coarse filters. Lastly, whereas some monitoring 

plans call for modeling from which cumulative 

effects of changes on landscape attributes or 

animal populations can be inferred, metrics 

representing observable changes on the landscape 

were sought for the RMMP. That is, rather 

than combining the effects of several landscape 

changes into a synthetic metric (e.g., what would 

be a dependent variable in such a model, such 

as “sage-grouse habitat”), the RMMP seeks to 

monitor the changes that would be input into such 

syntheses (e.g., sagebrush cover and disturbance).

17

Proposed Metrics

effects captured in the conceptual diagram 

(figure 1). For each metric, the following 

information is included: the name of the metric 

and its units (“Indicator”); the general ecosystem 

attribute the metric is meant to address (“Use”) 

with its corresponding number in figure 2; the 

The proposed metrics are summarized in 

table 2. It is a minimal set of metrics, such that 

key attributes of each of the main resources or 

effects related to important ecosystem services 

are monitored. Figure 2 provides an iconic 

interpretation of the ecosystem resources or 

Table 2. Summary of resource-monitoring metrics proposed to be incorporated into the RMMP. The metrics are sorted 

from those considered most important or readily available, to those least pressing, most challenging to monitor, or already 

monitored by other organizations and referenced in the RMMP. “Geospatial” includes spatial analyses in a GIS and analyses 

of remotely sensed images.

Resource 

or Effect Proposed metric Primary sources Significance

Soil Bare ground Geospatial Excessive exposed soil may indicate poor range conditions, 

erosion potential, overgrazing, or disturbance.

Vegetation

Sagebrush cover Geospatial Declines in sagebrush land cover may highlight risks to 

species that depend upon that land cover type. Old-growth 

pinyon-juniper is an uncommon cover type important for 

some species and of conservation interest in its own right. 

More generally, changes in vegetation composition may indicate long-term changes in environmental conditions, use, 

and range condition. 

Old-growth 

pinion-juniper Field/ Geospatial 

Composition Field

Surface 

Disturbance

Road density Geospatial

Species of special concern in the area (e.g., sage grouse, 

mule deer) as well as others avoid areas of disturbance. 

Recreational users select less disturbed areas as well. 

More generally and collectively, these indicators reflect the 

direct ecological footprint of oil and gas extraction activities 

in the region.

Well-pad density Geospatial

Distance to roads and 

wells Geospatial

Pipeline density Geospatial

Area disturbed/section Geospatial

Total area disturbed Geospatial

Area reclaimed/section Geospatial

Total area reclaimed Geospatial

Well-pad status Reports/Field

Road traffic Field

Atmospheric

Particulate matter Field Excessive levels of particulates or ground-level ozone can 

damage plants, reduce productivity, harm wildlife, and be a 

danger to human health. Ground-level Ozone Field

Landscape 

spatial pattern

Shape complexity Geospatial

Wildlife species are sensitive to the amount of habitat available, but also to its spatial pattern. Some species favor 

edges, some the interior of habitat patches. Some species 

are migratory, and require suitable habitat along their migratory pathways. Collectively, these landscape spatial pattern 

metrics quantitatively describe patterns relevant to wildlife.

Edge contrast Geospatial

Core area Geospatial

Large patch dominance Geospatial

Like adjacencies Geospatial

Nearest neighbor 

distance Geospatial

Patch isolation 

Connectance Geospatial
18

Table 2. (continued) Summary of resource-monitoring metrics proposed to be incorporated into the RMMP. The metrics 

are sorted from those considered most important or readily available, to those least pressing, most challenging to monitor, or 

already monitored by other organizations and referenced in the RMMP. “Geospatial” includes spatial analyses in a GIS and 

analyses of remotely sensed images.

Resource 

or Effect Proposed metric Primary sources Significance

Vegetation Invasive plants species Field

Invasive plant species, including exotic weeds, displace 

native vegetation, and are often unpalatable to wild and 

domestic grazing animals. Tracking changes in their abundance influences management practices, including control 

of early invasions.

Water Quality

Dissolved solids Field Water quality is an integrative measure of conditions within 

watersheds. Changes in water quality have affects on 

aquatic ecosystems and may affect wildlife, but also may 

indicate problems associated with oil and gas extraction.

Dissolved oxygen Field

Metals concentration Field

Suspended sediments Field

Focal species

Mule deer fawn:doe ratio Field Mule deer provide recreational opportunities to hunters and 

wildlife viewers. Sage-grouse and song birds are of conservation concern; declines in their populations may alter 

management practices.

Sage-grouse lek counts Field

Song bird abundances Field

Soil Biological soil crusts Field Biologically important in stabilizing soils, etc., but unlikely to 

be disturbed on a broad scale by oil and gas extraction.

Figure 2. Ecosystem resources or effects of interest include: 1) disturbance, such as from oil and gas development or natural 

disturbances like fire, 2) soil resources, 3) vegetation, including invasive plants, 4) species of special concern, such as sage 

grouse, 5) wildlife, such as mule deer and livestock, 6) water resources, 7) landscape structure, and 8) atmospheric resources.

1

2

3 4

5

6 7

8

“Spatial scale” over which individual values 

will be reported (e.g., a section, road corridor, or 

cell in a satellite image); the “Temporal scale” 

of the metric, indicating how often new values 

will be generated or stored; a description of why 

the metric is important to include in the RMMP 
19

(“Justification”); the data that may be used 

to create the metric and its source (“Potential 

basis”); any “Field data” required to calculate the 

proposed metric; and, “Alternatives” considered 

in place of the metric. 

The proposed metrics are agreed upon by 

experts to be useful; however, they are subject 

to change or modification. The RMMP will 

follow an adaptive monitoring approach, and as 

more information becomes available, changes 

may be made if appropriate. As technology 

advances, or as BLM mandates change, the 

RMMP will be modified to capture new goals. 

Such modifications will be done with an eye 

toward keeping temporal comparisons valid. 

Also, the proposed metrics reflect what can be 

adequately measured at this time and should not 

limit future research initiatives. For example, the 

RMMP does not incorporate a metric for small 

mammal biodiversity because of the difficulty 

in quantifying changes in populations of less 

common species. Regardless of this difficulty, 

research to improve small mammal monitoring 

remains important to the BLM. Also, the effects 

of lights used to illuminate drilling sites at night 

may have important implications and deserve 

more research.

The metrics are sorted from those considered 

most important or readily available, to those least 

pressing, most challenging to monitor, or already 

monitored by other organizations and referenced 

in the RMMP. The metrics proposed for inclusion 

in the RMMP are:

Indicator: Bare ground (percent)

Use: To characterize the broad state of soil stability (2 in figure 2)

Spatial scale: 30-m resolution or finer

Temporal scale: Repeated annually, characterized in the second quarter of the year

Justification: The quantity of bare ground in a landscape is an important ecosystem health indicator 

(de Soyza et al. 2000; Pyke et al. 2002; Pellant et al. 2005). Ground cover such 

as green or standing dead vegetation, litter, mosses, and lichens are important 

components of functioning western Colorado ecosystems, and the soil exposed in 

their absence is classed as bare ground. Vegetation provides forage and cover for 

wildlife and livestock, secures soils to reduce erosion, helps slow snow drift and 

runoff to replenish groundwater, and provides aesthetic and economic value to 

humans. In contrast, bare ground exposes soils to erosion from wind and water and 

provides habitat for relatively few wildlife species. The percent of bare ground may 

be increased directly due to human activities, such as road and pipeline construction 

for oil and gas exploration or expansion of urban areas, or indirectly, such as through 

loss of vegetation resulting from fine dust settling on leaves. Increases in livestock 

grazing or decreases in their movements across landscapes may lead to increased 

offtake of forage in an area and increased bare ground. Lastly, changes in hydrologic 

cycles may reduce vegetative cover, and increase bare ground. The amount of bare 

ground is a reliable indicator of rangeland condition that has been used extensively 

(NRC 1994; Whiteford et al. 1998; Booth and Tueller 2003; O’Brien et al. 2003). 
20

Potential basis: The percent of bare ground has been successfully quantified using remotely sensed 

images (Booth and Tueller 2003; Washington-Allen et al. 2006). Imagery-based 

percent of bare ground calculations are regularly produced at coarse resolutions for 

the globe (500-m-square cells) (Hansen et al. 2004). Those data include significant 

mixing within the large raster cells, in that a cell may contain a mix of vegetation, 

bare ground, structures, etc. Images with finer resolution (between 5-m- and 

30-m-square pixels) reduce this difficulty, although for this and other reasons, 

identifying the percent of bare ground in western rangelands remains nontrivial. 

Classification accuracy is also linked to the amount of bare ground in an area. For 

areas with more than 50 percent bare ground, classification accuracies can exceed 

80 percent, but for areas with much less bare ground, accuracies decrease (Weber et 

al. 2009). The WRFO is using 5-m-resolution RapidEye imagery in ongoing efforts 

to map land cover, including bare ground. 

Field data: BLM personnel conduct qualitative field surveys through their areas of interest 

(e.g., Pellant et al. 2005) and long-term quantitative surveys. These surveys include 

estimates of the percent of bare ground and may be used, in part, as assessment 

information. A statistically valid sampling schema for quantitative measurement of 

bare ground across the basin may be required to accurately map this layer. Such a 

schema is currently being considered at national levels within the BLM.

Alternatives: Percent of vegetative cover is an alternative to percent of bare ground. For natural 

areas, the two are easily converted (percent vegetative cover = 100 – percent of bare 

ground). However, for areas with structures or other nonvegetative land cover, that 

conversion is not appropriate. Given the common use of the percent of bare ground 

as a metric of rangeland health, it is preferred here.

Indicator: Sagebrush cover (percent)

Use: To characterize big game winter forage base, successional status, and the quantity of 

habitat for a species of concern (3 and 4 in figure 2)

Spatial scale: 30-m resolution or finer

Temporal scale: Repeated annually

Justification: Sagebrush as a habitat supports several species of management interest, including, 

for example, mule deer, sage-grouse, Brewer’s sparrow, and sagebrush vole. Of 

particular management interest are sage-grouse, which use a variety of habitats, but 

those most critical to their yearlong survival are expanses of big sagebrush (Artemisia 

tridentata). Sagebrush provides cover for nesting birds, and forbs in sagebrush 

understories are used by hens and their broods to provide sufficient nutrients early in 

life (Barnett and Crawford 1994). Sage-grouse most often nest under sagebrush and 

those that do are more successful than sage-grouse that nest under other plant species 

(Connelly et al. 1991). Moreover, sagebrush leaves are the primary diet for the birds 
21

in winter and an important dietary component in other months (Wallestad et al. 1975). 

If sagebrush habitats are eliminated through activities or events such as energy 

development, including roads, pads, and pipelines, large-scale fires, overgrazing, 

herbicide application, or invasion by annual grasses (e.g., cheatgrass), the areas are 

no longer suitable for sage-grouse (Beck and Mitchell 2000; Leonard et al. 2000). 

In summary, the relationship between sage-grouse and sagebrush is so close that the 

birds are classified as sagebrush obligates (Connelly et al. 2000).

Sage-grouse respond favorably to increasing sagebrush cover only up to a point. The 

birds select areas with moderate cover (15-35 percent); in areas with more cover, 

understory plants may be shaded out, and the area becomes progressively less suitable 

for sage-grouse (Connelly et al. 2000). Also, the leks where males display to attract 

females are in more open places, surrounded by sagebrush. Sage-grouse biology 

suggests that the spatial connectedness of habitat patches is less critical than the 

quantity of habitat; some birds will migrate 75 km or more to reach suitable habitat 

(Leonard et al. 2000). However, sage-grouse show high fidelity to nesting locations 

(Fischer et al. 1993), so broad-scale sagebrush conversion or reconfiguration may 

alter nesting success.

Potential basis: Sagebrush cover has been characterized using remotely sensed data (Knick et al. 

1997; Sivanpillai et al. 2009). There are ongoing efforts within the WRFO using 

5-m-resolution RapidEye imagery to map land cover, including sagebrush cover. 

More advanced datasets may aid in mapping sagebrush cover and canopy height, 

such as hyperspectral data and LiDAR remote sensing technology (Mundt et al. 

2006), but those relatively uncommon datasets are not initially proposed, as they 

complicate analyses and may limit the areas over which the metric would be adopted. 

The sagebrush cover type supports a variety of ecosystem services in the region, so 

rather than focusing on a specific service, a general change in sagebrush cover should 

be noted. Also, recovery of disturbed or destroyed sagebrush communities can take 

decades (e.g., Cooper et al. 2007). Therefore, relatively small changes in sagebrush 

cover will cause concern. 

Field data: BLM personnel conduct qualitative and quantitative field surveys through their areas 

of interest (e.g., Pellant et al. 2005). These surveys include estimates of the percent 

of cover of shrubs and listing of dominant and subdominant land cover types. These 

data may contribute to estimating the percent of sagebrush cover.

Alternatives: Alternatives that would classify habitat suitable to sage-grouse with the relative ease 

and effectiveness of the percent of sagebrush cover are not available—the species is a 

sagebrush obligate. More detailed mapping of sagebrush cover, by different species 

or by size categories for example, is a possibility. Lek counts are an important 

indicator of sage-grouse population status. Extensive surveys would estimate sagegrouse density directly and reflect population status but are beyond the scope of the 

RMMP. 
22

Indicator: Old-growth pinyon-juniper woodlands (patch number and acres)

Use: To characterize the quantity of habitats of special concern (3 in figure 2)

Spatial scale: Basinwide

Temporal scale: Intermittently updated as mapped or modified

Justification: Pinyon-juniper woodlands are an important land cover in the Colorado Plateau, 

including on the lands managed by the WRFO. Two concerns are cited by managers 

and others. Pinyon-juniper woodlands have expanded in range in response to fire 

suppression, release from past harvests, and land uses such as livestock grazing. 

This expansion decreases the amount of sagebrush and grassland habitat available 

(BLM, undated). Pinyon-juniper-expansion is unlikely to be associated with oil 

and gas development activities. The other concern, the preservation of very old 

stands of pinyon-juniper, is more likely related to oil and gas development activities. 

Stands with trees that are hundreds of years old are more structurally diverse than 

younger stands, are biologically diverse, and provide unique habitats, such as hollows 

for cavity-nesting birds. They also provide quantities of nuts and berries that are 

important food sources. These old stands tend to occur where the risk of fire is low 

and at sites of low productivity (Weisberg et al. 2008). Tracking changes in the 

amount and distribution of old-growth pinyon-juniper and including an old-growth 

stand map in the RMMP would be useful for planning.

Potential basis: Old-growth pinyon-juniper stands have proven difficult to map using computer 

algorithms that seek to identify patches within remotely sensed images. The most 

effective way of identifying old-growth pinyon-juniper stands is through fieldwork. 

Any existing digital spatial data for old-growth stands should be used and updated to 

include all mapped stands, with new stands added as they are located. 

Field data: The perimeter of old-growth pinyon-juniper stands will be delineated using a global 

positioning system or indications on a map. The composition of the stand, such as 

mix of species, understory, and ages of trees, should be noted as well.

Alternatives: Alternatives to field mapping of stands are not known, although efforts to map oldgrowth pinyon-juniper stands using remotely sensed images will continue to develop. 

In general, the height and spectral distinction of the stands are not distinct enough to 

enable mapping. Very high-resolution images that are now available may allow for 

the complex structure of old-growth stands to be distinguished.

Indicator: Vegetation composition (percent cover and richness)

Use: To characterize long-term vegetation changes (3 in figure 2)

Spatial scale: < 100 m 
23

Temporal scale: Repeated every 3-5 years (per site) and annually with remote sensing

Justification: Plant inventories are an important component of managing any terrestrial landscape 

and are mandated for federal lands under several pieces of legislation (e.g., Federal 

Land Policy and Management Act of 1976, Public Rangelands Improvement Act of 

1978). Vegetation composition, aerial estimates, and heterogeneity provide critical 

measures of ecosystem conditions, processes, and relationships. These measures 

include biodiversity (Tilman et al. 1997), productivity (Kahmen et al. 2005), nutrient 

cycling (Hooper and Vitousek 1998), wildlife habitat (Sauls et al. 2006), invasions 

(Stohlgren et al. 2001), and fire risk (Evangelista et al. 2004). Most plant species 

are sparse, rare, and patchily distributed; as a result, detecting species occurrences 

and monitoring changes in vegetation composition generally requires frequent and 

intensive field surveys. For example, in a 6-year vegetation assessment that included 

intensive field surveys in southern Utah, Stohlgren and others (2005) found that more 

than 60 percent of the known flora was not detected in over 330 established field 

plots. This is especially concerning given the need to monitor plant diversity and 

successional trends, detect invasive species early, and manage ecosystem function. 

A systematic approach for conducting baseline inventories, monitoring along 

disturbance gradients, and ensuring reclamation success is needed. Initially, this 

approach will require intensive field surveys to record quantitative data on vegetation 

composition and site conditions. These data will be entered into the data management 

system cited previously. Once collected, these data can support multiple analyses 

that will better facilitate adaptive management strategies, support advanced sampling 

alternatives (i.e., remote sensing), and highlight spatial and temporal changes related 

to energy development while distinguishing them from other agents of change (e.g., 

climate, livestock grazing, and fire suppression). 

Potential basis: Conducting systematic field surveys should include measurements that quantify 

species richness and percent foliar cover to maximize data utility (Stohlgren 2007). 

Average height by species may also be considered for estimating productivity, 

biomass, and fuel loads. Multiscaled nested plot designs have been demonstrated 

in numerous studies to be the most effective for detecting species occurrence and 

spatial trends (Stohlgren et al. 1995). Most field plots include measures of other 

site conditions and ancillary data that would be useful for additional proposed 

metrics. These measurements should include bare ground, standing dead, surface 

litter, biological soil crusts, and wildlife use, each providing important elements in 

monitoring vegetation composition and also contributing information that is valuable 

to other metrics related to oil and gas development. Plot locations and data analyses 

can be stratified by major vegetation types (e.g., sagebrush, pinyon-juniper), land use 

(e.g., grazing regimens, recreation), disturbance types (e.g., roads, pads), topography 

(e.g., slope, aspect), and others. Potential statistical analyses may be as simple as 

comparing species occurrences (by plot) over time to more complex geospatial 

models that model distributions across the landscape. Furthermore, georeferenced 
24

field data on plant composition will be required to test the applications of emerging 

remote sensing technologies, which are expected to be an alternative to field sampling 

for future monitoring. 

Field data: Field sampling to inventory and monitor vegetation composition can be conducted 

using a number of potential methods and integrated into current BLM survey 

activities. Nested plot designs of at least two scales should be considered to 

maximize data quantity and quality (Barnett and Stohlgren 2003; USDA 2004). Field 

measurements and estimations should follow standard sampling protocols (Herrick 

et al. 2009; Stohlgren 2007) and include additional site conditions and ancillary data 

that may support different types of analyses (e.g., remote sensing). Consideration 

should be given to methods adopted by the Forest Service’s Forest Inventory and 

Analyses (FIA) program (USDA 2004) or the National Resources Inventory (NRI) 

program (USDA 2009). The BLM has several vegetation monitoring projects already 

in place (e.g., rangeland health, long-term Daubenmire transects). Such datasets will 

provide valuable information on historic and baseline conditions on the landscape, but 

the methods may require modifications to meet increasing data needs and priorities 

related to oil and gas development. 

Alternatives: Using remote sensing for detailed inventories and monitoring of vegetation 

composition is not currently a viable alternative to field surveys. The number of 

plants found in the Piceance Basin and high variability in densities and phenology 

can simply not be measured in detail using current remote sensing technology. 

However, a number of new sensors and methods currently being developed suggest 

that remote sensing applications may soon meet the proposed needs (Rocchini et al. 

2005; Gillespie et al. 2008). Of particular interest is the use of rarefaction curves to 

measure spectral variation and estimate species richness within a given study area 

(Rocchini et al. 2009). These and other remote sensing approaches can be improved 

with high-resolution imagery, multiple bands, and seasonal data acquisition, all 

recently developed by the BLM using multiple platforms (i.e., QuickBird, RapidEye, 

and NAIP). New technologies in remote sensing, coupled with new innovative 

analyses, will likely provide a superior means to surveying vegetation across spatial 

and temporal scales in the near future.

Indicator: Surface disturbance, consisting of:

Use: To characterize disturbance by oil and gas extraction activities, levels of fragmentation, 

and reclamation associated with oil and gas extraction (1 and 7 in figure 2)

Road density (mile/section)

Well-pad density (pads/section)

Distance to roads and wells (per pixel)

Pipeline density (mile/section)

Area disturbed (acres/section)

Total area disturbed (acres)

Area reclaimed (acres/section, interim and final)

Total area reclaimed (acres, interim and final)

Well-pad status (categorical, per well)
25

Spatial scale: Variable, depending upon metric

Temporal scale: Updated with each proposed and approved use

Justification: Oil and gas development activities may have direct or indirect effects on the 

behaviors of wildlife and other ecosystem attributes. Roads may be barriers to 

animal movement, subdivide populations, and reduce habitat both directly and 

through disturbance. Roads and the traffic they bring may disturb wildlife or alter 

roadside habitats, limit the movements of animals, increase access to landscapes 

for recreationists (with results that are positive or negative, depending upon 

perspectives), and reduce the aesthetics of landscapes to humans (reviewed in 

Forman and Alexander 1998; Spellerberg 1998). Research into response of wildlife 

to nearby roads is extensive (e.g., Oxley et al. 1974; Rost and Bailey 1979; Cole et 

al. 1997; Forman 2000; Lodé 2000; Dyer et al. 2002; Epps et al. 2005; Jaeger et al. 

2005; Thomson et al. 2005; Ouren et al. 2007; Frair et al. 2008; Sawyer et al. 2009). 

Road networks can become quite dense, with the ecological footprint of their effects 

overlapping (Frair et al. 2008). The effects of changes in access include increased 

disturbance, weed spread (Lonsdale and Lane 1994; Zwaenepoel et al. 2006), and 

decreased opportunities for solitude during recreational activities. 

The commonalty among these metrics is that they may be calculated directly from 

spatial information that will be stored within a GIS. As oil and gas extraction plans 

are reviewed by the WRFO, the proposed infrastructure may be incorporated into a 

GIS and these metrics recalculated to help judge potential effects. 

Potential basis: An extensive spatial database is available for the study area and is currently being 

updated, and new or proposed infrastructure may be added easily. Status reports 

come from oil and gas production companies or from field visits by BLM personnel. 

Calculating these metrics is then a spatial database management exercise.

Field data: Infrastructure is incorporated into the geodatabase in use by the BLM. New and 

proposed infrastructure, with attributes reflecting their type and frequency of use, will 

be provided by energy enterprises or digitized from high-resolution remotely sensed 

images.

Alternatives: Road networks and other oil and gas extraction infrastructure are frequently used 

in wildlife and landscape ecology research as a direct measure of disturbance and 

fragmentation or a surrogate. Given that spatial data are developed by the BLM as a 

routine part of oil and gas development authorization and other development in the 

Piceance Creek Basin, better alternatives are not available. However, weightings may 

be assigned to different kinds of disturbances as their potential effects are synthesized 

(e.g., a main roadway versus a rarely used two-track). Alternative weightings and 

buffers of disturbance may be explored. 
26

Indicator: Road traffic (vehicles/day)

Use: To characterize disturbance (1 and 7 in figure 2)

Spatial scale: Road traffic summarized for monitored corridors

Temporal scale: Reported continuously

Justification: Road traffic may have direct or indirect effects on the behaviors of wildlife. Roads 

increase mortality through vehicle collisions and reduce habitat both directly and 

through disturbance. Conover and others (1995) estimated that in the United States, 

vehicle collisions with deer caused $1.1 billion in damage. Accidents involving 

fatalities have steadily increased (119 per year from 1993 to 1997, 155 per year 

from 1998 to 2002, and now are estimated at 210 deaths per year) (IIHS 2009). 

Many deer, elk, and other animals die from collisions, in some areas so often that 

population effects are evident. In areas with oil and gas extraction activities, the 

risk of disturbance from vehicles may be quite high; Kuhn (2006) estimated that in 

northeastern Utah, each new well required trucking between 375 and 1,375 loads of 

materials, supplies, and equipment. Additionally, operating wells must be visited, 

although advances in technology make those visits less frequent (Sawyer et al. 2009).

Road traffic may disturb wildlife or alter roadside habitats and reduce the aesthetics 

of landscapes to humans (reviewed in Forman and Alexander 1998; Spellerberg 

1998). Research into the response of wildlife to nearby roads is extensive (e.g., 

Oxley et al. 1974; Rost and Bailey 1979; Cole et al. 1997; Forman 2000; Lodé 2000; 

Dyer et al. 2002; Epps et al. 2005; Jaeger et al. 2005; Thomson et al. 2005; Ouren et 

al. 2007; Frair et al. 2008; Sawyer et al. 2009), and the effects of dust on vegetation 

from nearby roads have been explored in other areas (e.g., Walker and Everett 

1987; Ndibalema et al. 2007; Fahrig and Rytwinski 2009). The effects of changes 

in access include increased disturbance, the spread of weeds (Lonsdale and Lane 

1994, Zwaenepoel et al. 2006), and decreased opportunities for solitude as part of 

recreational activities.

Potential basis: Road traffic monitoring sensors are proposed for key travel corridors within 

the Piceance Creek Basin. Advanced monitoring systems are available that use 

components such as in-car GPS receivers, but if the devices were placed in industry 

vehicles, the approach would only quantify a portion of traffic. Systems using 

cameras may be effective (Atkočiūnas et al. 2005), and traffic may even be quantified 

from satellite images. Regardless, the use of a standard cabled vehicle counter and 

classifier system is proposed. These systems provide information on axle count, 

speed, and other data, which may be used to infer vehicle type.

Field data: Road traffic will require monitoring devices and reports of vehicle control features 

(e.g., gating) from the field.
27

Alternatives: The travel management plan for the region is being updated by the WRFO, which 

may alter the nature of monitors and will influence the travel corridors proposed for 

monitoring.

Indicator: Particulate matter (size-resolved concentrations)

Use: To characterize atmospheric pollution (8 in figure 2)

Spatial scale: Point-based 

Temporal scale: Reported continuously, every 24 hours, and annually

Justification: Particulate matter of greatest concern is generated by vehicles, roadside activities, 

industry, and other disturbances and is classified into two categories: fine and 

coarse particles. Fine particles are those in smoke and haze that are less than 

2.5 micrometers (PM2.5), and the greatest health concern. Particulate matter of 

this size can be inhaled deeply into the lungs and cause increased respiratory 

problems, such as irritation, coughing, bronchitis, and aggravated asthma. For 

those with compromised pulmonary or respiratory systems, exposure to high PM2.5

concentrations can cause premature death (EPA 2009). Coarse particles are those that 

are larger than PM2.5 but less than 10 micrometers (PM10). These remain a human 

health concern, but are not inhaled as deeply into the lungs. Particles greater than 

10 micrometers, such as sand and coarse dust, have not been related to human health 

concerns and are not monitored by the Environmental Protection Agency (EPA). 

In addition to problems related to human health, particulate matter makes the 

atmosphere hazy, reducing the aesthetic appeal of landscapes. Haziness at national 

parks is an ongoing problem (NPS 2002). Particulate matter can lift into the 

atmosphere and travel long distances. Particles may be inhaled by people distant 

from the pollution source or may settle on plants, reducing their productivity; in water 

sources, increasing their acidity; on buildings or other structures, causing damage to 

them; or on snow, increasing its melt rate (Flanner et al. 2009).

Potential basis: Particulate matter is measured at scattered locations throughout western Colorado. 

Monitors for PM2.5 or PM10 are in place in Delta, Parachute, Clifton, Rifle, Grand 

Junction, and Cortez, plus new sensors are in place in Meeker and Rangely. 

Standards for fine (PM2.5) and coarse (PM10) particulate matter concentration are 

defined by the EPA under the Clean Air Act. The standards are formally defined and 

include both annual (for PM2.5) and 24-hour averaging time. See EPA (2009) and 

documents therein for more detail, but in general, formulas are used to determine if 

PM10 exceeded 150 μg/m3

, averaged over 24-hour periods, and if PM2.5 exceeded 

15 μg/m3

 annually or 35 μg/m3 over 24 hours.

Field data: These data are gathered from EPA designated monitoring sites. No further field data 

are needed.
28

Alternatives: Particulate matter has a long history of being both a concern and a monitored 

pollutant in the United States. Alternative measures are not suggested.

Indicator: Ground-level ozone concentration (parts per million)

Use: To characterize atmospheric pollution (9 in figure 2)

Spatial scale: Point-based 

Temporal scale: Reported continuously, every 8 hours, and annually

Justification: Kilometers above the earth’s surface, in the stratosphere, ozone helps protect humans 

and ecosystems from the sun’s ultraviolet rays. But at ground level, ozone is a 

pollutant that is a danger to humans and other organisms (Royal Society 2008). 

Ground-level ozone is created through chemical reactions between the nitrogen 

oxides and volatile organic compounds found in vehicle exhaust, industrial emissions, 

and natural sources (EPA 2009). Ozone is a major component of smog and is a 

respiratory irritant, causing coughing, inflammation, and other breathing difficulties 

in humans. Ozone aggravates asthma, can permanently harm lungs with repeated 

exposure, and increases death rates with chronic exposure (Jerrett et al. 2009). Ozone 

also affects plants and animals. It can damage the leaves of plants, can reduce growth 

rates and crop yields, and may decrease biodiversity (e.g., Fuhrer and Booker 2003; 

Royal Society 2008).

Potential basis: In western Colorado, ozone sensors are operated in Rifle, Palisade, and Gothic 

(CDPHE 2008), and two newly established monitoring stations are located in Meeker 

and Rangely. Given the plans for oil and gas extraction in the Piceance Creek Basin, 

an ozone monitor that is equivalent to those used by the EPA would be beneficial near 

the center of oil and gas activities in the WRFO. 

Three metrics would be reported: instantaneous ozone concentration (stored through 

time), the number of measurements within each 8-hour period that exceed the 

threshold, and the number of times the threshold is exceeded annually. Standards for 

ground-level ozone concentration are defined by the EPA under the Clean Air Act, 

and were updated in 2008. The standards are formally defined and include multiyear 

averages of 8-hour peak concentrations, but in essence, they require that ozone should 

not exceed 0.075 ppm at peak periods. 

Field data: Additional field data, beyond the information reported by the monitors, is not 

required.

Alternatives: Other pollutants may be measured, rather than ozone. Nitrogen oxides and volatile 

organic compounds contribute to ozone production. However, ozone has been 

demonstrated to have direct effects on human health and a modest monitoring 

network is in place in Colorado. 
29

Indicator: Landscape spatial pattern, consisting of:

Use: To characterize landscape patterns and the patterns of habitats used by 

species of management concern (4, 5, and 7 in figure 2)

Spatial scale: 30-m resolution or finer

Temporal scale: Updated with each change, or proposed change, in infrastructure; land cover map 

updated annually

Justification: In addition to the composition of habitat areas available to wildlife (addressed under 

the “Vegetation composition” indicator), the spatial pattern (e.g., connectivity, patch 

complexity, etc.) of habitats within landscapes plays an important role in the survival 

and productivity of animals. Animals that migrate, such as mule deer in the Piceance 

Basin, have higher survival rates if suitable habitat is available along the migratory 

pathway (Berger 2004). An important class of landscape metrics, dealing with 

connectivity, addresses the concern of migratory pathways. Some wildlife species are 

edge species, meaning they are associated with areas where two kinds of habitat come 

together. A square landscape patch (of a given area) would provide edge species with 

less habitat (because of relatively minimal “edge”) as compared to a patch with a 

complex shape, resulting in a great deal of edge. Two more suites of metrics capture 

patch complexity (i.e., shape) and the related contrast between habitats at edges. 

Contrast indicates the difference in habitat types at a patch edge. For example, 

contrast between deciduous forest and mixed conifer-deciduous forest may be low, 

and contrast between deciduous forest and bare ground would be high. Lastly, some 

species favor large tracts of intact or undisturbed habitat, such as several species 

of nesting neotropical migratory birds. Landscape metrics that capture core area 

are therefore helpful to distinguish habitats (of a given area) that occur in scattered 

patches from habitats that may be in a single, contiguous large patch. Scientists and 

land managers seek landscape metrics that capture these and other habitat attributes 

and metrics that more generally provide an indication of changes in landscape pattern 

over space and time.

Landscape ecologists have devised many indices of landscape pattern, but most 

of these have been shown to be highly correlated; that is, two measures may be 

measuring the same thing in different ways (Riitters et al. 1995; Hargis et al. 1998; 

Cushman et al. 2008). Cushman and others (2008) tested 54 metrics on three 

landscapes, and judged 7 metrics to be both consistent and broadly applicable in 

capturing different aspects of the landscapes. Five of those metrics are appropriate 

Shape complexity (a dimensionless index) 

Edge contrast (index) 

Core area (acres)

Large patch dominance (percent)

Like adjacencies (percent) 

Nearest neighbor distance (meters) 

Patch isolation (meters) 

Connectance (index)
30

for inclusion into the RMMP, as are three additional measures that are correlated 

with the metrics studied by Cushman et al., but that are more easily interpreted. The 

metrics are grouped into four general classes and their definitions are drawn mostly 

from McGarigal and others (2002:

Complexity

Shape complexity is a widely used metric, where the complexity of each 

patch in the landscape is represented by the ratio of the perimeter of the 

patch compared to the perimeter of a patch of equal area, if it were a square 

(Patton 1975). The mean of those values will be calculated as well to 

yield a landscape-level metric. As the perimeter of a patch becomes more 

convoluted, this ratio increases, and in turn, as a patch shape approaches a 

square, the ratio approaches 1. 

Edge contrast uses spatial layers and additional information. Analysts assign 

similarity indices to land cover types. For example, a mixed oak-maple class 

may be assigned high similarity to a cottonwood class, moderate similarity 

to a juniper class, and very low similarity to an industrial class. The edge 

contrast index then summarizes the contrast between the edges of adjoining 

patches, with a landscape dominated by patches that are similar approaching 

0 percent, and a landscape with very different abutting classes approaching 

100 percent.

Core Area

Core area reports the “interior” area for individual patches, or the total core 

area for a landscape, where the interior area is defined as land within patches 

that is a given distance from either the edge of inhospitable patches or sources 

of disturbance.

Large patch dominance is the area of land in a landscape that comprises the 

largest contiguous patches of a given land cover type, divided by the entire 

area of that given type, converted into a percentage. For a given patch type, 

many small evenly spaced patches would yield values approaching 0 percent 

for the largest patch on the landscape, and if most of the area of a given patch 

type was in one patch, it would approach 100 percent for that large patch. 

Contagion

Like adjacencies is a metric that compares the number of adjacent landscape 

cells that are of the same type against the total number of cells adjacent to the 

focal type, regardless of their type. If landscape cells are clustered together 

(i.e., have high contagion) the percentage will be high. If cells are highly 
31

dispersed, the percentage will be low. In essence, the metric is reflecting 

fragmentation of different land cover types at the pixel level.

Connectivity

Nearest neighbor distance is a reflection of patch isolation, the mean 

distance between the centroid of a given habitat patch and its nearest neighbor. 

From patch-level distances, a landscape-level mean of nearest neighbor 

distance may be calculated. 

Patch isolation is a measure of the variability in nearest neighbor distances 

among patches of the same type, reflecting the degree to which patches 

are distributed across a landscape, converted to a percentage. For land 

cover types that have uniformly short nearest neighbor distances, the 

index approaches 0 percent. For those where the types are clumped on the 

landscape in dispersed patches, the index approaches 100 percent.

Connectance is based on the number of patches of a given type that are 

within a specified distance in a landscape, compared with the total number 

of patches of that type in the landscape. If patches of a given type are farther 

away, the connectance index declines.

Direct linkages have been made between changes in complexity, core area, contagion, 

and connectivity and changes in populations or behaviors of wildlife. For example, 

working in Austria, Moser and others (2002) demonstrated a linkage between patch 

shape complexity and plant species richness. Australian bird richness has been linked 

(weakly) to patch shape complexity (Radford et al. 2005). Core area metrics have 

been derived to quantify high-quality habitat for species that avoid edges, such as 

forest-interior nesting birds (e.g., Laurance and Yensen 1991). Species that occur 

within the Piceance Creek Basin have been shown (elsewhere) to avoid disturbed 

areas, such as sage-grouse avoiding oil and gas development activities (Aldridge and 

Boyce 2007; Doherty et al. 2008; Holloran et al. 2010; discussed in Kotliar et al. 

2008) and mule deer avoiding roads or well pads (Rost and Bailey 1979; Sawyer et al. 

2006; Sawyer et al. 2009). Connectivity has been explicitly considered in planning 

for greater sage-grouse in Oregon (Hagen 2005). Literature suggests that patches 

that are nearby one another (i.e., more connected) are expected to be used more than 

patches that are distant (MacArthur and Wilson 1967, reviewed in Bender et al. 2003). 

Regarding focal species in the Piceance Creek Basin, a report states that greater sagegrouse select for more complexly shaped patches for nest sites (Petersen et al. 2009), 

whereas general habitat quality for sage-grouse declines as edge-to-area increases 

(Hagen 2005, addressed more generally in Crawford et al. 2004). Elk use has also 

been shown to be positively related to the shape complexity of the meadows they use 

in summer (Stubblefield et al. 2006). More generally, large herbivores must access 
32

different patches to acquire sufficient forage (e.g., Boone 2007). Demonstrations of 

wildlife avoidance of roads, traffic (Dyer et al. 2002; Epps et al. 2005; Jaeger et al. 

2005; Gavin and Komers 2006; Frair et al. 2008), and oil and gas extraction activities 

(e.g., Bradshaw et al. 1997; Lyon and Anderson 2003; Berger 2004; Sawyer et al. 

2006; Aldridge and Boyce 2007; Walker et al. 2007; Copeland et al. 2009; Sawyer 

et al. 2009), or of the benefit that any landscape treatment may provide to mitigate 

the effects of fragmentation, provide justification for the utility of capturing habitat 

isolation metrics. 

Potential basis: Metrics can be calculated based on habitat maps created using two major sources. 

The first is the land cover map that is being developed by the WRFO, using 5-m 

resolution RapidEye satellite imagery. Habitat maps for selected species can be 

created from habitat associations agreed upon by collaborators, but will be similar 

to those used in Colorado Gap Analysis (CO-GAP 2001). Focal species include 

deer, elk, and sage grouse, with the possibility of list expansion if needed. For these 

habitat maps, areas that are within buffers (buffer distances yet to be determined) 

around sources of disturbance can be excluded from calculations to capture potential 

behavioral effects. 

Landscape indicators can also be calculated for the full land cover map. The results 

of these calculations can be helpful in two ways. First, they can characterize changes 

in landscape pattern that serve as habitat for other species not listed here (Boone et al. 

2008). Second, the results can identify changes in spatial patterning of selected plant 

communities. Communities of interest include pinyon-juniper, old-growth pinyonjuniper, and sagebrush, broken into the three predominant species of sagebrush in the 

WRFO. 

For the metric calculations specific to habitat, the focus is on habitat suitability (i.e., 

how suitable it is for an animal’s use); therefore, the different land cover types can 

be merged to create a “suitable” versus “unsuitable” habitat layer. Then for all maps, 

indicators can be calculated for suitable habitats at the patch level, yielding maps 

showing metric values spatially, and at the landscape level, yielding a single value for 

a landscape.

Field data: Landscape structure metrics are calculated based on the patterning of available spatial 

data and do not require further field-collected data for their creation. 

Alternatives: These metrics, like any, cannot capture all that is of interest regarding landscape 

pattern (e.g., Bender et al. 2003), but in concert with the other metrics in the RMMP, 

and with support from the literature, they can provide a comprehensive representation 

in the RMMP. In future versions of the RMMP, areas of interest may be defined that 

are subsets of the entire landscape (e.g., hilltops or particular management units), and 

landscape-level metrics can be calculated based on those subsets.
33

Indicator: Invasive plant species (percent cover and richness)

Use: To characterize extent and distribution of invasive species (3 in figure 2)

Spatial scale: < 100 m 

Temporal scale: Field sampling every 3-5 years (per site); annually with remote sensing

Justification: Direct and indirect effects of invasive species on native species and ecosystem 

processes are wide ranging and well documented in the scientific literature. Most 

invasive species exhibit unique physiological traits that provide them with a 

competitive advantage over their native counterparts. These traits may enhance 

an invader’s ability to exploit resources, survive under adverse conditions, 

successfully reproduce, or modify ecological conditions to their advantage. Road 

and infrastructure construction associated with oil and gas development has been 

implicated as a major conduit for new invasions (Bergquist et al. 2007) while 

providing conditions that facilitate the dominance of these species. Clearing of 

vegetation and topsoil creates ecological niches by improving accessibility to water, 

sunlight, and soil nutrients (Trombulak and Frissell 2000), and vehicles transport 

seeds that are dispersed along roadsides (Schmidt 1989; Lonsdale and Lane 1994). 

Once established, invasive species typically are more resilient than native species and 

continue to survive in disturbed areas and roadsides. Mowing, herbicide treatments, 

and soil compaction often prevent the reestablishment of natives while having only 

minimal effects on invaders (Forman and Alexander 1998, Gerbard and Belnap 2003). 

Of particular concern is cheatgrass (Bromus tectorum), a fast-growing annual grass 

that has invaded rangelands throughout the West and has already become moderately 

established in the Piceance Basin. Cheatgrass germinates during winter months, 

prior to most native annuals, allowing early root development that can quickly 

exploit soil nutrients and water (Skipper et al. 1996). The species is also tolerant to 

grazing, drought, poor soils, and other conditions that may be detrimental to native 

species, and most notably, is has a positive response to fire. The species produces 

considerably more aboveground litter than native species, increasing the probability, 

extent, and severity of fires (West 1983, D’Antonio and Vitousek 1992, D’Antonio 

2000). This in turn modifies site conditions to be more favorable for cheatgrass and 

often results in large-scale monocultures. Other high-priority invasive species are 

leafy spurge (Euphorbia esula) and Russian knapweed (Acroptilon repens), which 

have already become well established in the Piceance Basin. Like cheatgrass, further 

infestations of these species may be facilitated by activities related to oil and gas 

development.

Potential basis: Detection and monitoring of invasive species require vigilance, especially within 

close proximities to developing infrastructure and at reclamation sites. Field 

sampling (see the vegetation composition indicator section) can provide quantitative 
34

data that allows for plot-level comparisons across spatial and temporal scales and can 

be used for training of remotely sensed imagery. Species richness measurements can 

provide a means for early detection of new arrivals while allowing resource managers 

to monitor and predict spatial distributions of invaders that have already established 

(Stohlgren 2007). The percent of cover that an invasive species occupies can serve to 

measure species dominance (Crall et al. 2006) and provide baseline field data that can 

be used to determine spectral signatures for specific species of concern. Plot data can 

also be stratified by vegetation or disturbance types (including reclamation) and used 

to identify community-level changes and risks (Bergquist et al. 2007). Field sampling 

alone may not be adequate for early detection of invasive species. Occurrences of 

invasive species should be recorded whenever opportunity allows. Georeferenced 

occurrences (e.g., coordinates collected with GPS) can supplement datasets required 

for remote sensing applications and assess distribution patterns.

Only a few invasive species have been successfully measured using remote sensing 

techniques; of these, most either occur as large monocultures or are shrub and tree 

species that have greater visibility. Invasive grasses and forbs are more difficult to 

detect and require high-resolution imagery collected throughout the growing season. 

Populations and distributions of invasive species should be expected to respond 

positively to oil and gas development if left unchecked (Bergquist et al. 2007). In 

a few studies, cheatgrass was distinguished from other vegetation using time-series 

data collected from various remote sensing platforms (Bradley and Mustard 2005, 

Peterson 2005). In these cases, cheatgrass infestations were extensive. However, 

given the unique phenology of cheatgrass and the availability of high-resolution, 

multispectral imagery, the ability to detect invasive species using remote sensing 

is rapidly improving. Examples can be found with yellow starthistle (Miao et al. 

2006), spotted knapweed (Lass et al. 2002), tamarisk (Evangelista et al. 2009), and 

others. The methods may not yet be applicable for early detection or low densities, 

but they should be considered for monitoring infestations across the landscape, along 

disturbance gradients, and at reclamation sites.

Field data: Field surveys aimed at detecting and monitoring new invasions should be integrated 

with efforts to measure native vegetation composition (see the vegetation composition 

indicator section). Monitoring for invasive plant species can also occur during 

compliance visits in the field at production sites, reclamation sites, and pipeline 

rights-of-way. As previously mentioned, there are a number of plot designs and 

sampling strategies that can be used for collecting field data and for training of 

remotely sensed imagery (Barnett et al. 2007; Herrick et al. 2009). Sampling 

protocols should follow those outlined by Herrick and others (2009) and other widely 

accepted standards (e.g., North American Weed Management Association at www.

nawma.org; National Institute of Invasive Species Science at www.niiss.org).

Alternatives: Remote sensing alone is an alternative for monitoring large infestations, but it is 

currently inadequate for early detection of new invasive species. Remote sensing 
35

initiatives recently implemented by the BLM will greatly improve detection 

capabilities and can augment field sampling now and in the future. Spatial modeling 

has proven to be an effective tool for predicting the potential spread of new invaders 

and highlighting areas at risk on the landscape (Evangelista et al. 2008). These 

methods still require intensive field surveys initially, but surveys can be scaled down 

as new methods are developed. 

Indicator: Water quality, consisting of:

Use: To characterize water quality (6 in figure 2)

Spatial scale: Point- or reach-based

Temporal scale: Monthly and annually

Justification: Water quality is included here because it is an excellent integrator, reflecting the 

condition of the watershed and its underlying aquifers. The quality of the water in 

a creek or stream is a reflection of conditions in the watershed through which the 

water flowed. Water quality within the Piceance Basin is highly variable, largely 

due to differences in geology and flow regimes (EPA 2004). Base flows of annual 

streams come primarily from bedrock aquifers, which tend to reflect the chemistry 

and geologic parent material and are of low quality (EPA 2004; CDOW and USFWS 

2007). Surface runoff is generally from spring snowmelt and intense rain events. 

As a result, sediment loads are high (Andrews 1983) and of low quality due to 

the presence of sodium bicarbonate and salts (EPA 2004). Drinking water is only 

available from shallow waters and wells (< 200 feet), thus it is a critical resource for 

residents of the area (EPA 2004). Coproduced water from methane extraction can 

be particularly poor in quality (Rice et al. 2000; Stearns et al. 2005), and can only be 

disposed of in evaporation ponds or by reinjection in the Piceance Basin (EPA 2004).

Surface disturbances caused by various activities related to oil and gas development 

have been reported to alter soil chemistry and nutrient composition (Bergquist et 

al. 2007). Modifications at the soil surface can in turn be transported by surface 

runoff and accumulate in streams, wetlands, and lowlands and circulate with shallow 

groundwater. Because of the limited potable water and pollutants contributed by 

other natural and anthropogenic activities (e.g. agriculture), water quality should be 

monitored frequently and intensely.

Potential basis: A number of water quality studies are currently being facilitated by the BLM, USGS, 

Colorado Oil and Gas Conservation Commission (COGCC), and oil and gas operators 

in the Piceance Basin. Some of these studies have been implemented for several 

Dissolved solids (parts per million)

Dissolved oxygen (parts per million)

Metals concentration (parts per million)

Suspended sediments (turbidity units)
36

decades (http://waterdata.usgs.gov/nwis/nwis), while others have been recently 

developed to address baseline data needs (http://rmgsc.cr.usgs.gov/cwqdr/Piceance/). 

Most analyses include major cations and anions, water quality parameters, metals, 

volatile and semivolatile organic compounds, and other dissolved minerals and 

suspended sediments.

Field data: Currently, there are a number of water quality studies and monitoring programs for 

surface water and groundwater. Additional field data may be required; however, 

previously collected baseline data and existing studies may be sufficient. Notable 

changes in water quality data and proposed sites for development should serve as a 

guide for additional data collection.

Alternatives: Additional field sampling may be required to identify sources of dissolved minerals 

and suspended sediments. These efforts may be conducted in collaboration with the 

USGS or with oil and gas operators. Remote sensing applications are commonly used 

to detect changes in turbidity, sediment deposition, inundation, and lateral shifts in 

stream channels (see reviews by Smith 1997; Pavelsky and Smith 2009) and should 

be considered as a monitoring tool in the Piceance Basin.

Indicator: Mule deer fawn:doe ratio (number per 100 does)

Use: To characterize landscape suitability for big game (5 in figure 2)

Spatial scale: Colorado Division of Wildlife (CDOW) Data Analysis Unit (DAU)

Temporal scale: Annually 

Justification: Wildlife managers have identified different means of monitoring the performance of 

deer herds. Surveying the number of adults, especially the number of does, seems 

like a measure of success. However, female deer survival is generally fairly high 

and not very sensitive to year-to-year variations in conditions. One measure that has 

proven effective is the fawn:doe ratio. Does may produce fawns in good years, and in 

other years, they may not. A variety of conditions may affect doe productivity, such 

as forage availability, predation rates, weather, body condition, stress from raising 

a fawn the previous year, and disease. As such, the fawn:doe ratio is a metric that 

integrates a series of effects on populations.

Potential basis: The CDOW estimates fawn:doe ratios based on aerial and ground surveys. Winter 

flights are conducted, with observers counting, sexing, and aging deer observed. 

From these data, ratios are calculated. The completeness of counts is dependent, in 

part, on snow cover, which increases the visibility of deer. A negative of this metric is 

that changes in snow cover from year to year can increase its variability. 

Field data: Ratios can be calculated based on surveys and simulations conducted by the CDOW, 

using methods they deem most appropriate.
37

Alternatives: Buck:doe ratios may be helpful, although those are more closely related to harvest 

histories and management than to the suitability of landscapes to support females 

producing offspring, as in the fawn:doe ratio. Population modeling is conducted 

by the CDOW, and the fawn:doe ratio is a factor in the analysis. In years when the 

ratio is unavailable, simulation output may be used, with appropriate notation. More 

generally, these data are regularly collected by the CDOW, and provide an indication 

of changes in habitat quality for mule deer within the Piceance Creek Basin. Given 

that the data are available, other alternatives are not suggested. However, during 

years when the CDOW does not have funds for the basin to be surveyed aerially, 

financial support from the energy industry or the BLM may make a survey possible, 

which would be beneficial to the RMMP.

Indicator: Lek counts (number)

Use: To characterize population trend of species of special interest (4 in figure 2)

Spatial scale: Basinwide

Temporal scale: Annually

Justification: Quantifying potential habitat availability for sage-grouse has been proposed. The 

RMMP should also include information already collected that provides a more direct 

estimate of trends for sage-grouse, such as results from lek counts in the Piceance 

Creek Basin. Leks are the breeding areas where male sage-grouse return each year 

to dance and attract females. Biologists within the range of sage-grouse count 

known leks, seek out new leks, and conduct several counts of the number of birds 

seen during the breeding period. There are numerous assumptions and issues that 

must be considered when interpreting lek counts (reviewed in CGSSC 2008), but in 

general, some areas have had counts in place for 50 years, and they have been shown 

to be useful indicators of population size, although standardization of methods is 

encouraged (Walsh et al. 2004).

Potential basis: Lek counts are conducted semiannually by CDOW personnel and supplemented by 

counts from BLM staff. These count results form the basis for this metric, or trend, 

within the RMMP. Total sage-grouse estimates, rather than actual counts, for the 

Piceance Creek Basin may be recorded in the RMMP. 

Field data: Population estimates are based on counts on leks, using methods established by the 

CDOW.

Alternatives: Alternative methods of estimating population size are available (CGSSC 2008), but 

deciding upon best practices is the purview of the CDOW. More generally, a direct 

estimate of the sage-grouse population over time for the basin is useful; these data are 

available without additional cost, so alternatives are not suggested.
38

Indicator: Selected songbird abundances (index)

Use: To characterize population trend of species of special interest (5 in figure 2)

Spatial scale: Point-based at areas of interest

Temporal scale: Annually, in the spring

Justification: A component of the BLM’s mission is to maintain biological diversity on the public 

lands it manages. Land managers therefore track the general trends of species 

populations, including songbirds. Three species of songbirds of conservation 

concern, as listed by the U.S. Fish and Wildlife Service, or management interest were 

selected by WRFO personnel for survey. In general, these species were selected 

because they are in sufficient abundance to allow typical point counts to yield 

abundance trends: gray vireo (Vireo vicinior), sage sparrow (Amphispiza belli), and 

Brewer’s sparrow. Gray vireos are small birds with subtle coloration that most often 

inhabit pinyon and juniper woodlands and other brushy habitats on midslopes. These 

vireos breed in western Colorado and other southwestern states and winter in Mexico. 

Sage sparrows are medium-sized sparrows that breed primarily in WRFO’s lower 

elevation sagebrush and salt desert communities. Sage sparrows breed and summer 

in Colorado and winter in areas of the Great Basin west and south of Colorado. 

Brewer’s sparrows are also midsized and breed in Colorado and other western states, 

extending into Canada. They migrate to the southern United States and Mexico 

in winter. Brewer’s sparrows are confined to various big-sagebrush habitats in 

northwest Colorado.

Results from the USGS North American Breeding Bird Survey (BBS) show gray 

vireos declining in Colorado from 1966 to 2003 at more than 1.5 percent per year 

(Sauer et al. 2008; the extremes in population change in these maps are -1.5 percent 

and +1.5 percent per year). Trends for Brewer’s sparrow are strongly negative overall 

in Colorado. Their population trend varies spatially, with populations declining at 

more than 1.5 percent per year for the western slopes of Colorado and the eastern 

plains, but increasing at greater than 1.5 percent per year in the Rocky Mountains. 

Populations of sage sparrow are stable overall in Colorado, according to the BBS, 

although the pattern of change is more complex than for the other species and shows 

declines in the northwestern part of the state (Sauer et al. 2008).

Potential basis: BLM personnel are conducting annual point counts for the three species of interest 

in areas of suitable habitat where they may be disturbed by oil and gas extraction 

activities. These surveys should continue and be expanded to the degree practical to 

capture potential effects of oil and gas extraction disturbance on these songbirds. 

Field data: For areas of interest identified by BLM managers, point counts are conducted in the 

spring. A person skilled in bird identification by sight, and especially by sound, looks 
39

and listens for a period (often 3 minutes), recording all the birds seen or heard during 

that time. 

Alternatives: There are five active BBS routes in the area managed by the BLM WRFO; one is fully 

within WRFO-managed lands and four that are partially included. In addition, two 

other surveys include other parts of the Piceance Basin that are in close proximity 

of the WRFO. These surveys are not ideal to capture potential changes in bird 

abundance related to oil and gas development activities. The routes are placed on 

secondary roads often far from oil and gas development. The surveys described here, 

which adopt BBS techniques for the points sampled, are placed in areas specifically 

because of the distribution of disturbance. Given that bird surveys are rapid and 

effective in indicating population changes over sufficient time, no alternatives to these 

metrics are suggested.

Indicator: Biological soil crusts (percent cover and age-class)

Use: To characterize changes in soil biology, extent of crusting, and disturbance (1 and 2 in 

figure 2)

Spatial scale: < 100-m field plots (incorporate with vegetation composition)

Temporal scale: Repeated every 3-5 years (per site)

Justification: An important property of soils found in semiarid regions throughout the western 

United States is the presence of biological soil crusts. Composition of soil crusts 

is a complex assemblage of microorganisms dominated by cyanobacteria, algaes, 

lichens, and mosses. Concentrated in the top 1 to 4 mm of soil, soil crusts primarily 

affect processes that occur at the land surface or soil-air interface (Belnap 1996). In 

semiarid ecosystems, soil crusts function to increase soil stability, control erosion 

from wind and water, fix atmospheric nitrogen, contribute nutrients to neighboring 

plants, provide positive soil-plant-water relations, enhance seed germination, 

and stimulate plant growth (Anderson et al. 1982a, 1982b; Belnap and Gardner 

1993; Belnap and Gillette 1998a, 1998b). Growth and development of soil crusts 

are generally slow processes, and crusts may take 80 to 100 years to reach full 

development (Anderson et al. 1982a). The fragile structure of soil crusts causes them 

to be extremely susceptible to surface disturbances, which puts them at risk from road 

development, pipelines, pad construction, and other activities related to oil and gas 

development. Depending on the type of disturbance and site conditions, recovery for 

well-developed crusts may take hundreds of years (Belnap 1996; Evangelista et al. 

2004). Disturbances to soil crusts can impede nitrogen fixation capabilities, which 

could potentially affect the nitrogen budget throughout an ecosystem, resulting in 

shifts in floral composition and invasion of nonnative plants (Belnap 1995, 1996; 

Evans and Johansen 1999). Loss or degradation of soil crusts may also result in 

decreased water availability to plants, accelerated wind and water erosion, decreased 
40

plant diversity and soil biota, and slowed decomposition of soil organic matter 

(Belnap 1995, 1996; Evans and Johansen 1999). 

Potential basis: West (1990) broadly divided the organism composition of soil crusts into two 

categories: those that are microscopic (e.g., algae, fungi, and bacteria), and those 

that are visible to the human eye (e.g., lichens and mosses). Although direct field 

observations of the microscopic organisms are limited, their presence creates smallscale topographic features at the soil surface that are visually apparent. Based 

on physical characteristics, several studies have developed and tested simple 

classification indices that can be used by resource managers to visually measure and 

assess the development and condition of biological soil crusts in the field (Evangelista 

et al. 2004; Belnap et al. 2008). Height, structure, and coloration of crusts have been 

found to be highly correlated with chlorophyll and pigment content and may be easily 

incorporated in the field without costly laboratory analyses. 

Field data: These measurements should focus on areas that are in the process of reclamation and 

within close proximity to infrastructure related to extraction activities. Because field 

observations can be conducted rapidly, additional measurements may be integrated 

with surveys targeting native vegetation composition (see vegetation composition 

indicator section) providing opportunities for comparative studies. The presence of 

crusts is already recorded with existing BLM projects (e.g., rangeland health, longterm Daubenmire transects). Modifications of existing field efforts would require 

additional measurements that quantify crust development and condition (e.g., percent 

cover, developmental stages). An increased number of sampling sites and monitoring 

periods will be needed in areas open to oil and gas development and following 

reclamation efforts.

Alternatives: There are few remote sensing alternatives for measuring and monitoring biological 

soil crusts for the Piceance Basin. Several studies using remote sensing techniques 

have had promising results (Anderson and Kuhn 2008; Ustin et al. 2009); however, 

most of these tests were conducted as controlled experiments or in arid regions 

with minimal vegetation. Despite high-resolution and multiband remote sensing 

capabilities, topography and vegetation structure will likely prevent these approaches 

from being considered as an effective alternative in the near term. Efforts to test new 

methods for measuring soil crusts using high-resolution remote sensing should be 

considered.
41

Glossary

Ecosystem: Living and nonliving elements in 

an environment, forming an interacting system 

inhabiting an identifiable space (adapted from 

Bedell 1998).

Ecosystem services: “The benefits people obtain 

from ecosystems. These include provisioning 

services such as food, water, timber, and fiber; 

regulating services that affect climate, floods, 

disease, wastes, and water quality; cultural 

services that provide recreational, aesthetic, and 

spiritual benefits; and supporting services such 

as soil formation, photosynthesis, and nutrient 

cycling” (MEA 2005). 

Ecosystem health: The “degree to which the 

integrity of the soil and the ecological processes 

of . . . ecosystems are sustained” (NRC 1994). 

An ecosystem may be called healthy if it is 

sustainable, remaining organized through time and 

resilient to stresses (Rapport et al. 1998). 

Historical range of variation: A method of 

judging whether measured changes are notable 

using an ecological perspective. Ecosystems 

are dynamic by nature. Past efforts to manage 

ecosystems as static entities, with any deviation 

from what is deemed “in balance” fought against, 

have not been successful. By looking at the 

historic range of variability in attributes of 

ecosystems, managers may compare metrics to 

these ranges, and judge if the observed responses 

are unusual (reviewed in Daigle and Dawson 

1996).

Metric: A measurable indicator. Indicators are 

quantitatively measurable aspects of a system 

that serve as surrogates for the status of the key 

ecological attributes. Indicators as surrogates are 

Adaptive management: “A decision process 

that promotes flexible decision making that can be 

adjusted in the face of uncertainties as outcomes 

from management actions and other events 

become better understood. Careful monitoring 

of these outcomes both advances scientific 

understanding and helps adjust policies or 

operations as part of an iterative learning process. 

Adaptive management also recognizes the 

importance of natural variability in contributing 

to ecological resilience and productivity” (USDI 

2008) 

Anthropogenic: Resulting from the influence of 

human beings, loosely meaning human caused or 

human made.

Classification: The assignment of items or 

landscape elements into classes based on shared 

similarities (adapted from Bedell 1998).

Classification accuracy: For satellite images 

classified into categories, the degree of agreement 

between the class assigned to a location in the 

image, and the class assigned to the location on 

earth through some means considered “truth,” 

such as through field visits. This is a crude 

measure of accuracy, with more refined measures 

incorporating errors such as omission and 

commission.

Cover: The proportion of a landscape area that 

is occupied by vegetation or a specific type of 

variation, living or dead (adapted from Bedell 

1998).

Disturbance: Changes in vegetation cover and 

the altering of top soil done by humans. For 

example, oil and gas extraction can include 

removal of vegetation or the temporary removal 

of top soil.
42

necessary because ecological attributes are not 

commonly directly measurable, and indicators can 

be selected specifically to be achievable. 

Reclamation: To restore vegetation and landform 

of a site. Two levels of reclamation are used, 

interim and final. In interim reclamation, well 

operators restore vegetation and landform to the 

site, except for those areas needed to maintain 

production. Areas restored should be sufficient 

to maintain healthy, biologically active topsoil, 

control erosion, and minimize habitat and forage 

loss, visual impact, and weed infestation. After 

a well is plugged, final reclamation commences. 

The objective for reclamation is to set the course 

for eventual ecosystem restoration, including of 

the landform, vegetation community, hydrologic 

systems, visual resources, and wildlife habitats 

(USDI and USDA 2007).

Spatial Resolution: The area of earth a single 

pixel or cell in a remotely sensed image is 

intended to represent, such as 1 km x 1 km or 

30 m x 30 m. Often resolution is identified by a 

single value with a square cell implied, such as 

30-m resolution.
43

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