Monitoring

Manual

for Grassland,

Shrubland and

Savanna Ecosystems

Volume II:

by

Jeffrey E. Herrick, Justin W. Van Zee,

Kris M. Havstad, Laura M. Burkett and Walter G. Whitford

with contributions from

Brandon T. Bestelmeyer, Ericha M. Courtright, Alicia Melgoza C.,

Mike Pellant, David A. Pyke, Marta D. Remmenga, Patrick L. Shaver,

Amrita G. de Soyza, Arlene J. Tugel and Robert S. Unnasch

Design, supplementary methods and interpretation

Reprinted 2009
Monitoring

Manual

for Grassland,

Shrubland and

Savanna Ecosystems

Volume II: Design, supplementary methods and

interpretation

by

Jeffrey E. Herrick, Justin W. Van Zee,

Kris M. Havstad, Laura M. Burkett and Walter G. Whitford

with contributions from

Brandon T. Bestelmeyer, Ericha M. Courtright, Alicia Melgoza C.,

Mike Pellant, David A. Pyke, Marta D. Remmenga, Patrick L. Shaver,

Amrita G. de Soyza, Arlene J. Tugel and Robert S. Unnasch

USDA - ARS Jornada Experimental Range

Las Cruces, New Mexico
ii

Printed 2009

Publisher:

USDA-ARS Jornada Experimental Range

P.O. Box 30003, MSC 3JER, NMSU

Las Cruces, New Mexico 88003-8003

http://usda-ars.nmsu.edu

ISBN 0-9755552-0-0

Distributed by:

The University of Arizona Press

Tucson, Arizona, USA

800-426-3797

www.uapress.arizona.edu

Cover:

RB Design & Printing

Las Cruces, New Mexico 88001

Cover illustration:

Collecting Line-point intercept data

in a south-central New Mexico desert grassland.
iii

Table of contents

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

Section I: Monitoring program development in six easy steps .............................................. 5

Chapter 1 Step 1: Define management and monitoring objectives ........................... 9

Chapter 2 Step 2: Stratify land into monitoring units............................................. 13

Chapter 3 Step 3: Assess current status................................................................ 18

Chapter 4 Step 4: Select indicators and number of measurements ......................... 21

Chapter 5 Step 5: Select monitoring plot locations ................................................ 27

Chapter 6 Step 6: Establish monitoring plots ........................................................ 30

Section II: Supplementary methods................................................................................... 37

Chapter 7 Compaction test ................................................................................. 38

Chapter 8 Single-ring infiltrometer ...................................................................... 44

Chapter 9 Plant production................................................................................. 51

Chapter 10 Plant species richness ......................................................................... 57

Chapter 11 Vegetation structure ............................................................................ 61

Chapter 12 Tree density ....................................................................................... 65

Chapter 13 Riparian channel vegetation survey ..................................................... 69

Chapter 14 Riparian channel and gully profile....................................................... 74

Chapter 15 Density, frequency and Line-point intercept alternative methods ............. 79

Section III: Indicator calculation and interpretation ............................................................ 83

Chapter 16 Calculate indicators........................................................................... 84

Chapter 17 Interpret results ................................................................................. 86

Section IV: Special Topics ............................................................................................... 107

Chapter 18 Riparian .......................................................................................... 109

Chapter 19 Livestock production ......................................................................... 111

Chapter 20 Wildlife habitat ................................................................................ 113

Chapter 21 Off-road vehicle use and other recreational land uses ......................... 115

Chapter 22 Fire ................................................................................................. 117

Chapter 23 Invasive species ............................................................................... 120

Chapter 24 State and transition models: an introduction ....................................... 122

Chapter 25 Remote sensing ................................................................................ 125

Chapter 26 Soil carbon...................................................................................... 128

Appendices

Appendix A Monitoring tools............................................................................... 131

Appendix B Conversion factors ........................................................................... 141

Appendix C How many measurements? ..................................................................142

Appendix D Soil Quality Information Sheets ......................................................... 172

Appendix E Soil texture chart .............................................................................. 173
iv

Glossary ....................................................................................................................... 174

References and Additional Resources ............................................................................. 189

Authors and Contributors............................................................................................... 200

Data forms

Monitoring Program Design Checklist........................................................................ 8

Monitoring Program Design Form I ......................................................................... 12

Monitoring Program Design Form II ........................................................................ 26

Monitoring Plot Description Form ....................................................................... 35-36

Soil Compaction – Impact Penetrometer Data Form .................................................. 43

Infiltration Data Form ............................................................................................. 50

Plant Production Data Form .................................................................................... 56

Plant Species Richness Data Form ........................................................................... 60

Vegetation Structure Data Form .............................................................................. 64

Tree Density and Size Data Form ............................................................................ 68

Riparian Channel Vegetation Survey Data Form....................................................... 73

Riparian Channel Profile Data Form ........................................................................ 78

Line-point Intercept with Height Data Form .............................................................. 82
1

T

he Monitoring Manual for Grassland,

Shrubland and Savanna Ecosystems is divided

into two volumes: Quick Start (Vol. I) and

Volume II. This two-volume document is intended

to assist a wide range of users, including

technicians (data collectors), field crew leaders,

ranchers and landowners, land managers,

rangeland professionals, and researchers.

Quick Start (Vol. I) includes basic methods and

instructions for establishing photo points and

completing four basic measurements. Volume II

provides more detailed guidance on monitoring

program design, data analysis and interpretation.

It also includes a number of supplementary

methods.

Section I describes how to design a monitoring

program in six steps.

Section II includes eight supplementary

monitoring methods and alternatives to the Linepoint intercept method.

Section III describes how to organize, analyze

and interpret monitoring data.

Section IV provides specific recommendations

for designing monitoring programs to address the

following issues:

- Riparian

- Livestock production

- Wildlife habitat

- Off-road vehicle use and other recreational

land uses

- Fire

- Invasive species

- State and transition models

- Remote sensing

- Soil carbon

Section IV also explains how state and

transition models can help you design monitoring

programs that are more sensitive to significant

changes, including thresholds. It also describes

how to improve monitoring, using remote sensing.

Finally, it discusses the relationship between soil

carbon and monitoring.

Do I have to read the whole thing?

No. Begin by completing the checklist on the first

page of Quick Start (Vol. I). This will help identify

the chapters that are relevant to you. In many

cases, you will not need to read Volume II at all.

However, we do recommend that you familiarize

yourself with Section I (to improve the quality of

the monitoring program design) and Section III (to

help with interpretation) of this volume.

Are these manuals all I need?

Possibly. Depending on your background,

experience and monitoring objectives, Volumes I

and II may provide enough guidance to design and

implement a monitoring program. However, we

strongly recommend consulting with other

information sources and local experts to design a

monitoring program that best suits your needs. A

number of excellent references are included in the

“References and Additional Resources” section at

the end of this volume.

Electronic data forms

Additional information and electronic data forms

can be downloaded from the following website:

http://usda-ars.nmsu.edu. We are committed to

continuously improving this document and will

periodically provide online updates.

Monitoring for management

Monitoring is part of a broader process in which

we use data to test and refine management

decisions. Monitoring data allow the collective

knowledge of scientists and land managers to be

applied to improve resource management.

Monitoring is designed to support a diverse set

of goals required by various societal interests (the

upper triangle in Fig. Intro.1). The monitoring

procedures described in this manual provide data

on three key attributes of landscape and ecosystem

sustainability: soil and site stability, hydrologic

function and biotic integrity. These data provide

the foundation for assessing and evaluating the

degree to which societal goals and/or values are

being met by current landscape management.

They also provide the basis for management

options that meet specific goals (Fig. Intro.1).

Introduction to Volume II
2

Adaptive management: management by

hypothesis or prediction. Every time we change

management or decide to continue with the same

management, we are making a prediction.

Sometimes these predictions are explicit.

Predictions are more likely to be explicit when

management requires a significant financial

investment (e.g., fencing) or is believed to increase

risk (e.g., fire). Frequently the predictions are

implicit, because most management decisions are

assumed to lead to improvements in the status of

the land, the quantity and quality of goods and

services provided by the land, or both.

These predictions are identical to scientific

hypotheses, and monitoring data allow us to

explicitly test our prediction(s). While we may not

be able to collect as much data as a researcher

would, the data are likely to be more useful for

adjusting management because they reflect the

unique characteristics of the land we are

managing. For example, we may decide to

maintain a stocking strategy because we suspect

that it does no harm to grass and soils. Our

hypothesis, then, is that basal cover and soil

stability will not deteriorate. We can test this

hypothesis using monitoring data.

In order to accurately test our predictions, we

need to carefully select both the types of indicators

and the monitoring locations. To do so, we also

must take into account the effects of other

influences on rangelands, such as climate and soil

variations. An informed selection of monitoring

sites and sufficient replication are essential to

producing useful data.

Additional tools

Three types of tools are extremely helpful in

designing monitoring programs, interpreting the

results and applying them to management.

Ecological sites are used to stratify landscapes into

similar units so that we can extrapolate our results.

State and transition models are used to help evaluate

the current status of an area relative to its

potential, to identify areas that are at risk of

crossing a relatively irreversible threshold, and to

understand the factors that may contribute to the

degradation or recovery of an area. Qualitative

indicators are used together with state and

transition models to evaluate current status and

identify critical processes.

Landscapes and ecological sites. The landscapes

that we manage are often highly variable. This is

because managed areas encompass differences in

geology, topography, soils and climate at several

spatial scales. Site characteristics that define the

potential of part of the landscape to support

different types and amounts of vegetation, and

therefore its potential response to management,

are used to stratify the area to be monitored into

monitoring units. Site characteristics in many

parts of the United States are described in

Ecological Site Descriptions (available from

National Resource Conservation Service, NRCS)

(Ch. 2). Ecological sites (landscape units) occur

together as a mosaic in landscapes. The units can

be further stratified, based on current status (Ch. 3)

and management.

State and transition models. In many countries,

conceptual models of how vegetation and soils

change due to different kinds of drivers (such as

drought or grazing) are being developed for each

ecological site or similar landscape unit. These

state and transition models describe changes in

community composition that are easy to reverse,

as well as those that are not (i.e., transitions to

new states) (Ch. 24). State and transition models

can help to indicate the potential risk of difficultto-reverse transitions and the potential

effectiveness of different management options.

Foundation

Soil & Site 

Stability

Hydrologic 

Function

Biotic 

Integrity

• Air 

quality 

• Recreation 

• Wildlife habitat 

• Minerals, oil & gas 

• Livestock production 

• Military testing & training 

• Aesthetic, open space & wilderness values 

• Invasive, threatened & endangered species

Figure Intro.1. Monitoring the three key attributes

(primary monitoring objective) serves as the

foundation for sustaining the potential to support

diverse management objectives.
3

Within a given ecological site, use vegetation and

soil surface properties to identify the ecological

state in the state and transition model. Identifying

the ecological state helps define both future

degradation risks and recovery options. Projections

in each model are based on the collective

observations of experienced managers, research

data, monitoring data and simulation models.

Qualitative indicators. Qualitative indicators

(Ch. 3) are important tools for matching patterns

observed on the ground to those described in the

state and transition models. Properties and

processes that cannot be easily measured

quantitatively can often be evaluated qualitatively.

This is particularly true for patterns occurring at

coarser scales, such as assessing the spatial extent

of runoff and run-on areas, and the relationship of

these areas to soils and current vegetation. Other

examples of qualitative indicators include platy

structure and horizontal root growth as indicators

of compaction in soils that do not normally

exhibit platy structure, and pedestalling of rocks

and plants as soil erosion indicators.

It is important to recognize that snapshot

observations do not provide absolute certainty

about how rangelands may change in the future.

By their nature, qualitative indicators can help

direct your attention to several ecological

processes across a broad area (with or without

monitoring). They are thus well suited for

snapshot inventories that indicate problems,

potential causes and potential management

remedies.

Making it work

In the long term, the data collected and

interpreted on each type of monitoring unit or

ecological site can help to refine ecological models

and how rangelands are managed. But it is of

limited value to learn only that a particular

management strategy resulted in persistent loss of

grass or soil. Both short-term and long-term

monitoring data should be used, together with

qualitative observations, to evaluate hypotheses

frequently—especially as environmental

conditions (such as rainfall) vary. If it begins to

look like a management strategy does not conform

to expectations, the strategy can be adjusted.

Successful feedback between monitoring and

management helps make land use more

sustainable.
4
5

Section I: Monitoring program

development in six easy steps

T

his section describes how to design and

implement a long-term ecosystem-based

monitoring program at the landscape level

(an area > 400 ha or 1000 acres; Fig. 0.1). It is

based on the assumption that one of the primary

objectives of the monitoring program will be to

detect long-term changes in the

status of three basic attributes of

grassland, shrubland and savanna

ecosystems: soil and site stability,

hydrologic function and biotic

integrity (Fig. Intro.1).

The six steps

Each of the first six steps illustrated

in the flow chart (Fig. 0.2) and

listed in the Monitoring Program

Design Checklist (found at the end

of this Introduction to Section I) is

described in its own chapter

(Chs. 1-6). The steps are listed in

the order they are normally

completed. Because there is no

“single” way to design a

monitoring program, revisiting

earlier steps is often helpful. For

example, the assessments

completed in Step 3 often reveal

issues that lead to new

management and monitoring

objectives (Step 1). State and

transition models can be helpful

here by focusing attention on areas

that are at risk, or have a high

potential for recovery. It is also

helpful to redefine management

and monitoring objectives (Step 1)

for specific monitoring units

identified in Step 2.

Use the Monitoring Program

Design Forms I (Ch. 1) and II

(Ch. 4) to organize information

about your monitoring program.

Use the Monitoring Program

Design Checklist to ensure that you have

completed each step. The system allows maximum

flexibility to address objectives and long-term

changes, including monitoring for adaptive

management, additional objectives, short-term

monitoring, and monitoring threats and drivers.

Figure 0.1. Landscape-scale monitoring programs should be responsive

to the most important drivers, and sensitive to interactions among

landscape units.

by Rob Wu
6

Figure 0.2. Monitoring program design and implementation (Steps 1-6) and integration with

management (Steps 7-10).

Monitoring for adaptive

management and management

by hypothesis

In addition to long-term monitoring data, adaptive

management requires three types of information:

short-term monitoring data, knowledge of potential

threats or drivers, and clearly defined hypotheses

(predictions) of management effects (Steps 1, 3 and 7

of the checklist). State and transition models (Ch.

24) can be used to integrate assessment and

monitoring data with current knowledge about

potential management effects (based on

management experience, scientific studies and

simulation models) to generate these predictions.

Monitoring for additional

objectives

Monitoring for the three basic attributes can serve

as the foundation for use-specific monitoring, as

illustrated in Figure Intro.1.

The basic measurements (described in Quick

Start) were selected in part because they also can

be used to generate indicators related to specific

uses. For example, the Line-point intercept

generates vegetation cover and composition

indicators that are related to the quantity and

quality of forage production. These indicators,

together with spatial structure indicators from the

Gap intercept method, can be used to assess and
7

monitor wildlife habitat quality, as well as plant

community changes in response to fire.

The value of the basic measurements can often

be increased at a relatively low cost through slight

modifications (see Section IV). For example,

vertical vegetation structure can be measured by

adding plant height measurements to the Linepoint intercept protocol (Ch. 15), or by adding the

Vegetation structure method (Ch. 11). In some

cases, such as riparian monitoring, supplementary

measurements (Section II) may be required.

Section IV also provides recommendations for

addressing specific monitoring objectives.

Short-term monitoring (Annual

Use Records)

Short-term monitoring data (listed at the end of

Quick Start) are used to make short-term

management changes (Steps 7 and 8). For

example, information on residual cover or biomass

is often used to decide when to move livestock to a

new pasture. This information is also used to

interpret long-term monitoring data.

Monitoring threats and drivers

Information on potential threats and drivers, such

as development of new roads or a change in fire

frequency, is used to help identify areas where a

change in management and/or monitoring will be

required. Threats and drivers are identified in Step 3.

What if I don’t have enough

time?

Nearly any monitoring is better than no monitoring.

Using management and monitoring objectives to

guide monitoring program design can reduce

monitoring costs. A few days of careful planning

often can reduce monitoring costs by 50 percent or

more and result in much more useful data.

• Use photo points where few changes are

expected (see description of state and

transition models in Ch. 24) or where you

require only a qualitative record.

• Select measurements that are sensitive to

changes defined in the management and

monitoring objectives.

• Select measurements that generate indicators

that are relevant to multiple objectives. The

measurements included in Quick Start were

selected in part because they are sensitive to

changes in the three key attributes, while

generating numerous indicators that are

relevant to many other objectives.

• Match monitoring frequency to expected rates

of change based on minimum detectable

change. If the smallest change in basal cover

you can detect is five percent (Ch. 4) and it

takes at least five years for this change to

occur, it’s a waste of time to repeat

measurements more frequently.

Using State and Transition

Models for Monitoring Design

State and transition (S&T) models (Chapter 24) are

conceptual models that describe the soil and

vegetation dynamics for a particular type of land

with similar soils and climate. Applying S&T

models to monitoring program design helps a)

define ecological potential, benchmarks, or

reference conditions and b) specify predictions

about the possible future change of different land

units in a landscape. This approach allows

monitoring site selection to be based on objectives

and the ecological processes involved in land

change. Designing a monitoring program within a

state and transition model framework helps

specify the ecosystem attributes to be monitored

and other details that may vary among states and

ecological sites.

Applying S&T conceptual models to monitoring site selection minimizes monitoring expenditures in highly degraded states where all available

evidence suggests they will not change; and focuses

monitoring efforts in ‘at risk’ states and plant

communities where management has the potential

to limit degradation or promote recovery. With this

logic in place, monitoring can be treated as a series

of tests matched to specific parts of a landscape.

Key components of this test are the steps used to

apply S&T models to a monitoring program design.

Steps for S&T Monitoring Design

First, stratify the landscape (Chapter 2) into ecological sites or potential-based land classes. This is

done using soil surveys, landform maps, digital

elevation models and knowledge of key soil gradients. Next, stratify each ecological site into states

based on S&T models using aerial photography,

remote sensing and/or field surveys. Finally, select

monitoring methods that detect changes in focal

patterns and processes within each specific ecological site and state.
8

Monitoring Program Design Checklist

Step* Task Completed?

Develop monitoring program

1 Define management and monitoring objectives

Define management objectives ..................................................................................... ________________

Define monitoring objectives.......................................................................................... ________________

2 Stratify land into monitoring units (areas with similar characteristics)

Assemble background information (maps, photos, management history) .................... ________________

Define stratification criteria (e.g., soils, vegetation, management units) ....................... ________________

Complete stratification and list monitoring units on Monitoring Program

Design Forms I and II (Chs. 1 & 4). ........................................................................ ________________

3 For each monitoring unit, assess current status; identify threats and drivers; refine longterm management and monitoring objectives; and develop/modify management strategy

Select assessment system (e.g., Pellant et al. 2005) .................................................... ________________

Verify that personnel have relevant qualifications ......................................................... ________________

Complete assessments.................................................................................................. ________________

Identify and record threats, drivers and opportunities ................................................... ________________

Refine long-term management and monitoring objectives ............................................ ________________

Develop/modify management strategy .......................................................................... ________________

4 Select monitoring indicators, number of monitoring plots, number of measurements, and

measurement frequency based on objectives and resource availability

Select monitoring indicators ........................................................................................... ________________

Define number of monitoring plots ................................................................................. ________________

Define measurement frequency ..................................................................................... ________________

Estimate time requirements ........................................................................................... ________________

5 Select monitoring plot locations

Choose and apply site selection approach .................................................................... ________________

Select “rejection criteria” and use to eliminate unsuitable locations .............................. ________________

6 Establish and describe monitoring plots, and record long-term monitoring data (baseline)

Establish and permanently mark monitoring plots ......................................................... ________________

Describe monitoring plots and record GPS locations, including coordinate

system, datum and zone......................................................................................... ________________

Record long-term data ................................................................................................... ________________

Error-check and copy data and keep copies in different locations ................................ ________________

Short-term monitoring (all years)

7 Record short-term monitoring data (at least 1x/year) (Quick Start) ..................................... ________________

8 Adjust management, if necessary (Quick Start) ..................................................................... ________________

Repeat long-term monitoring (every 1-5 years)

9 Repeat long-term monitoring measurements (Ch. 6), compare data with Year 1 and

interpret changes (Ch. 17)

Repeat long-term monitoring measurements ................................................................ ________________

Copy data and keep copies in different buildings .......................................................... ________________

Calculate indicators........................................................................................................ ________________

Compare with Year 1 (or previous years) ...................................................................... ________________

Interpret changes using short-term monitoring data and Section III.............................. ________________

10 Refine management strategy, if necessary ............................................................................. ________________

*Steps 1-6 correspond to Chapters 1-6, except where noted.
9

Why monitor?

Monitoring data are used to:

• evaluate the effects of past management;

• confirm effective management practices;

• identify trends that can be used to predict

future changes so management can be adapted

accordingly;

• learn more about how different factors

(drought, fire, management) affect the land.

The most useful monitoring programs help

managers achieve long-term management

objectives by generating relevant data.

Consequently, it is essential to clearly define both

management and monitoring objectives before

designing a monitoring program.

Use the Monitoring Program Design Form I

(end of Ch. 1) to record your objectives as you

develop them. You may find it easier to complete

the stratification process (Ch. 2) before defining

specific short- and long-term objectives.

Step 1.1. Define management

objectives

(a) List the general long-term management

objective(s) for the area to be monitored on the

first line in Monitoring Program Design Form I.

What do you want the land to look like? What

goods and services do you want it to be able to

provide now and 100 years from now?

(b) List specific long-term management objectives for

each monitoring unit or type of land in the

fifth column of the Monitoring Program

Design Form I (see Ch. 2 for a discussion of

monitoring units). The long-term monitoring

program will be designed to measure progress

towards meeting these objectives. For example,

Chapter 1

Step 1: Define management and

monitoring objectives

Checklist

1.1. Define management objectives ................................................................... ___________

1.2. Define monitoring objectives ...................................................................... ___________

the specific objectives may include

maintaining or increasing the production of

particular products (e.g., forage for livestock)

or services (e.g., filtering water before it

reaches streams). State and transition models

(Ch. 24) can be used to help define what types

of changes are possible in different areas.

(c) List short-term management objectives that are

necessary to achieve each of the long-term

objectives for each type of monitoring unit in

the same (fifth) column of the Monitoring

Program Design Form I. Use of short-term

monitoring indicators helps ensure the shortterm objectives are being met, and helps

interpret long-term monitoring data.

Examples of management objectives are listed

in Table 1.1.

Step 1.2. Define monitoring

objectives

Monitoring objectives follow directly from the

management objectives. Additional monitoring

objectives may result from plot assessments (Ch. 3).

Where possible, the monitoring objectives should

be quantitative. Use Appendix C to help decide if

monitoring objectives are realistic.

(a) List the general long-term monitoring objectives for

the area to be monitored in the second row of

the Monitoring Program Design Form I. These

should be based on the general long-term

management objectives. There are three

general types of monitoring objectives: (i)

change in average status, (ii) change in the

status of areas with a high degradation risk, and

(iii) change in the status of areas that have a

high recovery potential. Monitoring programs
10

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Table 1.1. Examples of management and monitoring objectives for a mid-elevation ranch in an area dominated

by sagebrush and perennial bunchgrasses. Similar objectives can be generated for areas in which recreation,

mining and/or biodiversity conservation are the primary land uses.

 Objectives

designed to primarily address the first

objective are usually the least cost-effective

because a lot of effort is devoted to monitoring

areas with a low probability of change.

Selecting one or both of objective types (ii)

and (iii) allows resources to be focused on areas

where management is most likely to have an

effect. See Chapter 5 for more information on

site selection.

(b) List the specific long-term monitoring objectives

for each type of monitoring unit in the sixth

column of the Monitoring Program Design

Form I. The potential for degradation and

recovery varies both within and among

monitoring units. State and transition models

(Ch. 24) can be used to help select appropriate

monitoring objectives for each type of

monitoring unit.
11

 Objectives

(c) List the short-term monitoring objectives

necessary to ensure the management plan is

being followed and to document management

changes. Record objectives in the same sixth

column of Monitoring Program Design Form I.

Figure 1.1. Tallgrass prairie functioning at its highest potential, Kansas, USA. Arrow reflects lack of significant

change over time. Long-term management objective(s): Maintain biodiversity and productivity. Long-term

monitoring objective(s): Detect changes in plant cover and production by plant functional group; detect changes

in plant species richness.

Figure 1.2. Overgrazed rangeland on left side of fence (b), and appropriately grazed rangeland on right side of

fence (c), and conversion to rain fed agriculture (a), Zacatecas, Mexico. Arrows reflect desirable and undesirable

changes from a long-term ecological sustainability perspective. Long-term management objectives: (1) Increase

grass cover for livestock forage production. (2) Avoid cultivation, which leads to a relatively irreversible threshold

due to increased soil degradation and erosion. Long-term monitoring objectives: (1) Detect changes in plant

cover and production by plant functional group and vegetation spatial distribution. (2) Collect sufficient data to

detect 5% change in bare ground.

X

a b c

Examples of monitoring objectives are listed in

Table 1.1. Figures 1.1 and 1.2 show two additional

examples, where arrows indicate desirable changes.
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13

Chapter 2

Step 2: Stratify land into

monitoring units

Checklist

2.1. Assemble background information (maps, photos, management history) ___________

2.2. Define stratification criteria (e.g., soils, vegetation, management units) .. ___________

2.3. Complete Stratification ............................................................................... ___________

2.4. Complete Monitoring Program Design Forms I (Ch. 1) and II (Ch. 4) ...... ___________

T

his chapter describes how to stratify the area

into monitoring units and decide which units

to monitor. Data from individual monitoring

plots can be more reliably extrapolated to represent

larger areas if the area of interest is stratified.

Because rangelands are among the most

diverse ecosystems in the world, it is impossible to

design a monitoring system that perfectly reflects

changes in all landscape units. However, the

accuracy and precision of any monitoring system

can be improved by carefully dividing the area

into relatively uniform monitoring units.

Monitoring units are areas located in a

particular part of the landscape (e.g., flood basin

or hill summit), within which vegetation, soil

type, management and current status are relatively

similar. All sections within a given monitoring

unit are expected to respond similarly to changes

in management and to catastrophic disturbances,

such as a combination of drought and fire.

Monitoring units may range in size from less than

an acre to several square miles or more.

Multiple monitoring units of the same type

(e.g., hill backslope in Fig. 2.1) often repeat across

the landscape, geographically separated from one

another by other monitoring units. Figure 2.1

shows how a landscape unit (floodplain) was

divided into two types of monitoring units based

on management (grazed vs. ungrazed).

Not all monitoring units will necessarily be

monitored (Fig. 2.1). For example, highly stable

types of monitoring units (such as bedrock) might

not be included in a monitoring program if the

primary objective is to monitor for degradation

risk or recovery (see Ch. 1). Use Monitoring

Program Design Forms I (Ch. 1) and II (Ch. 4) to

keep track of potential monitoring units.

Figure 2.1. Example of how monitoring units are

defined using landscape, soil, vegetation and

management criteria. In this example, three

monitoring plots, shown here as three sets of three

transects (spokes), were located in the summergrazed floodplain monitoring unit, which has a high

potential for both degradation and recovery. No

monitoring plots were located on monitoring units on

the adjacent slopes because they did not meet the

selection criteria, which included livestock use.

Stratification: How to do it

Landscape stratification is a three-step process:

2.1 Collect background information, maps and

photographs.

2.2 Define stratification criteria.

2.3 Divide the area into monitoring units:

(a) divide the area into soil-landscape units;

(b) subdivide the soil-landscape units into soillandscape-vegetation units (if necessary);

(c) subdivide the soil-landscape-vegetation

units into monitoring units based on type

of management.

Record each type of monitoring unit from 2.3 in

Monitoring Program Design Forms I and II.
14

Step 2.1. Collect background

information

The following resources are helpful in stratifying

the landscape into monitoring units and selecting

the units to monitor. See Table 2.1 for sources of

background information (regularly check http://

usda-ars.nmsu.edu for the most up-to-date list). In

some instances, there is a fee for these resources,

but many of them can be downloaded free from

the Internet. New sources are constantly becoming

available.

Aerial photographs. One of the easiest ways to

organize information is on a map or recent aerial

photograph of the area, or through a Geographic

Information System (GIS). Ideally, use one or more

aerial photographs with fences and roads marked

on them.

If you want to be able to locate yourself on the

aerial photo using GIS and a GPS (Global

Positioning System) unit, you will need a digital

image that has been modified so that the distances

on the photo correspond directly to distances on

the ground (orthorectified). The most widely

available photographs of this type are the USGS

Digitial Orthophoto Quarter Quadrangles, or

DOQQs. Each of these images covers one quarter

of a 7.5 minute USGS topographic map.

Satellite imagery. High resolution satellite imagery

can be used for stratification. See Chapter 25 for

more information on the use of remote sensing in

monitoring.

Written and oral histories. Information on

historic changes can help predict which parts of

the landscape are most likely to change in the

future. Sources of information on historic changes

include old monitoring records (often stored in

the local Bureau of Land Management [BLM] or

United States Forest Service [USFS] offices), old

aerial photographs and survey records. Interviews

with current and previous land managers are

among the most valuable sources of information.

Property maps. Conservation plan maps (available

from NRCS offices) locating current and historic

homesteads, fence lines, corrals, roads, watering

holes, supplemental feeding locations, and areas

seeded, herbicided or where vegetation was

removed are valuable when stratifying the

landscape into monitoring units. All of these have

the potential to affect the way land will respond to

future management.

Species lists. Lists of plant species commonly

found in the area are helpful. Vegetation

measurements are usually recorded to the species

level. At a minimum, lists of potential invasives

and exotics should be acquired for all monitoring

programs.

Ecological Sites and Site Descriptions (ESDs).

Each ecological site includes several similar soils.

Each ESD includes partial species lists and basic

soils information and state and transition models

that can be used to help plan and interpret

monitoring (see end of Introduction).

Soil maps. Soil maps are commonly available in

the form of county soil surveys. Soil maps are

often drawn on aerial photos. In addition to maps,

soil surveys have a wealth of information on soil

properties and the suitability of soils for different

uses. GIS layers of soil surveys can be obtained for

most counties from the local NRCS office.

Soil maps of pastures and rangelands rarely

include map units named with a single soil series

due to the complexity of most rangeland

landscapes (a soil series is like a plant species).

Instead, individual areas are mapped as

“complexes” or “associations” of two or more soil

map unit components. Soil map unit components

are phases of soil series. Phases of soil series are

usually identified based on features important for

management, such as slope, soil surface texture,

surface rockiness and salinity. A soil map unit

component is like a plant subspecies. The soil

survey (or a professional soil scientist) can help

you decide if the components in a particular map

unit are sufficiently similar to be treated uniformly

for monitoring purposes.

Soil series are distinguished based on soil

profile characteristics. These characteristics are

usually, but not always, directly related to soil

function. Soil series allow us to access reference

information included in Ecological Site

Descriptions and other databases.

Land stratification
15

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16

Land stratification

Step 2.2. Define stratification

criteria

There is virtually an infinite number of strategies

for stratifying the landscape into functionally

similar monitoring units. Three criteria useful for a

wide variety of ecosystems are: soil-landscape,

current vegetation and management.

Soil-landscape criteria include topography,

landscape position and soils. These criteria

determine the potential of the unit to support

different plant communities. Incorporating soillandscape criteria is a very important step,

especially in areas where the same plant

community currently dominates much of the

land. In these areas, knowledge of the underlying

soils can help identify locations where there is a

high recovery potential.

In most systems, historic differences in

management and disturbance have generated

variability in current vegetation within soillandscape units. Historic management and

disturbance can be used as stratification criteria, as

can current and planned future management.

While stratification may sound complex, in

reality it is relatively simple.

Step 2.3. Complete stratification:

divide the area into monitoring

units

This step is often broken into separate parts, based

on the number of stratification criteria. In the

following example, three criteria were used.

Remember that a single type of monitoring unit

may include many individual units scattered

across a landscape.

Step 2.3(a) Divide the area into soil-landscape

units (NRCS ecological sites or functionally

similar units such as the unit used in the USFS

Terrestrial Ecosystem Survey). Landscape units are

areas that are relatively homogeneous with respect

to slope, aspect and parent material (material from

which the soil was formed). As a result, they

generally have similar soil series, or similar soil

components. Where soil series or soil components

in a landscape unit are functionally similar, they

are included in the same soil-landscape unit.

Functionally similar soils have relatively

equivalent potentials to produce a particular type

and amount of vegetation under the same climate.

Soil-landscape units generally correspond to

NRCS “ecological sites” (previously referred to as

“range sites”). These are also similar to the units

used in the USFS Terrestrial Ecosystem Survey

system and to soil-landscape-based land

classification systems developed in New Zealand,

Australia and other countries, although some of

these systems also use current vegetation (see Step

2.3b). The grouping of functionally similar soils

into ecological sites has already been completed in

most areas of the United States, although the

specific criteria used to create unique ecological

sites varies somewhat among different states.

Soil-landscape units repeat across the

landscape (Fig. 2.2). For example, multiple areas

on south-facing 10-15% slopes, with 30-50 cm

(12-20 in) of soil over granitic bedrock, would be

classified as the same soil-landscape unit.

Step 2.3(b) Subdivide the soil-landscape units

into soil-landscape-vegetation units (if

necessary). Vegetation is generally correlated with

landscape position and soil type, but historic

differences in land use can lead to the

development of different plant communities on

the same soil-landscape unit (Fig. 2.3; see also

Ch. 24). Vegetation subdivisions are normally

based on the current dominant plant species that

define the community. They can also be based on

the presence of critical species, such as exotic or

invasive plants, or by habitat type for a particular

animal. Keep in mind that while soil-landscape

units are relatively persistent and useindependent, soil-landscape-vegetation units can

and do change rapidly.

Step 2.3(c) Subdivide the soil-landscapevegetation units into monitoring units based on

management (soil-landscape-vegetationmanagement units). A monitoring unit is the

largest contiguous area with the same soil type and

plant community that is expected to respond

similarly to management changes. Pasture borders,

distance from water, prescribed fire, woody

vegetation removal and recreational use can be
17

 Land stratification

Figure 2.3. Example of the subdivision of landscape

units (box in Fig. 2.2) into landscape-vegetation units.

Here one of the Hills landscape units was subdivided

into landscape-vegetation units.

Figure 2.2. Example of landscape unit stratification.

This type of stratification can only be done with aerial

photos. Subdivision into soil-landscape units was not

possible due to lack of soil survey information. The

use of Soil Survey Maps can make this process

easier and more accurate.

Figure 2.4. Example of the subdivision of landscapevegetation units into different types of monitoring

units (1-4) based on management. In this case, one

of the Hills-Pinyon-Juniper Savanna units was

subdivided based on the presence or absence of

prescribed fire; and the Hills-Blue grama Grassland

unit was subdivided based on whether or not

woodcutting is planned.

used to delineate monitoring units. Similar

monitoring units (same type) often repeat across

the landscape (Figs. 2.1 and 2.4). Figure 2.4 shows

four types of monitoring units.

Step 2.4. Record each type of

monitoring unit in the Monitoring

Program Design Forms I and II

(Chs. 1 and 4)

Each type of monitoring unit is recorded only

once, even if it repeats across the landscape. Leave

extra rows on Monitoring Program Design Form II

below monitoring units in which you expect to

include more than one monitoring plot.

Hills Monitoring Units

1 = Pinyon-Juniper Savanna

No prescribed fire

2 = Pinyon-Juniper Savanna

Prescribed fire

3 = Blue grama Grassland

No woody removal

4 = Blue grama Grassland

Woody removal
18

Chapter 3

Step 3: Assess current status

Checklist

3.1. Select assessment system ............................................................................. _________

3.2. Verify that personnel have relevant qualifications ..................................... _________

3.3. Complete assessments ................................................................................. _________

3.4. Identify and record drivers, threats and opportunities .............................. _________

3.5. Refine long-term management and monitoring objectives ....................... _________

3.6. Develop/modify management strategy ....................................................... _________

Where possible, the status of each area of

each monitoring unit (or at least each

type of monitoring unit) should be

evaluated and recorded in the Monitoring

Program Design Form I (Ch. 1). This evaluation

helps determine the relative usefulness of

establishing transects in each monitoring unit

based on the objectives identified in Step 1.

Assessments can be qualitative or quantitative.

Assessments can use current status, apparent

trend, or trend based on existing monitoring data.

All assessments require some kind of reference.

Where trend is used, the reference is the status at

some previous time. The reference for the current

status is generally the site potential, which is

defined based on soil and climate (e.g., in NRCS

Ecological Site Descriptions as discussed in Ch. 2).

Step 3.1. Select assessment

system

There are a number of protocols currently

available for assessing rangelands. We have

included brief descriptions of two we consider

useful: Interpreting Indicators of Rangeland Health

(IIRH) for uplands (Pellant et al. 2005; see also

Pyke et al. 2002) and Process for Assessing Proper

Functioning Condition (PFC) for riparian areas

(Prichard et al. 1998a, b). These protocols were

selected because they emphasize the capacity of

the system to function relative to its potential. In

other words, they reflect the current status of the

same fundamental ecosystem attributes that this

monitoring protocol is designed to address. They

are both at present (2004) widely applied by

governmental and non-governmental

organizations in the United States. IIRH has been

translated into Spanish and applied in Mexico.

Both of these protocols, like all qualitative

systems, should be applied by a team of trained

personnel with a working knowledge of the local

ecosystem. Links to PDF (portable document

format: documents in a format easily downloaded,

viewed and printed from the World Wide Web)

files of these protocols and training information

are available on the Internet (http://usdaars.nmsu.edu).

Upland areas. Interpreting Indicators of Rangeland

Health (Pellant et al. 2005) (Fig. 3.1). This

publication describes a process for using 17

qualitative indicators to generate assessments of

the same three attributes addressed by this

monitoring manual: soil and site stability,

hydrologic function and biotic integrity. A

standard or reference is established for each

ecological site (type of soil-landscape unit).

Reference information for each of the 17

indicators is summarized in a “Reference Sheet.”

Each indicator is placed into one of five categories

based on its relative departure from its reference

status (none to slight, slight to moderate, etc...).

Specific combinations of the 17 indicators are then

used to evaluate each of the three attributes.

Reference Sheets for some ecological sites have

already been developed in the United States and

Mexico. In the U.S., they are included in the

updated NRCS Ecological Site Descriptions.

Instructions for developing Reference Sheets where

they do not already exist are included in the latest

version of IIRH (version 4.0). This method is

included only to assist in the identification and
19

selection of potential monitoring sites (Ch. 5). The

indicators described should not be used to replace the

quantitative monitoring indicators described in this

manual. For additional information on how to apply

this method, please refer to the IIRH publication.

 Assessment

Riparian areas. Process for Assessing Proper

Functioning Condition (Prichard et al. 1998a, b)

(Fig. 3.2). This publication describes a process for

developing riparian qualitative assessments. It is

also based on 17 indicators. There are two primary

differences, though, to the upland areas

assessment protocol (IIRH). The first is that,

instead of generating a “degree of departure” from

that expected for the ecological site, the evaluation

is designed to rate a stream reach as functional, at

risk or non-functional. The second difference is

that there is no standard reference. The team

completing the evaluation must develop a unique

standard for each area to be evaluated. For this

reason it is essential that a diverse team of trained,

knowledgeable and experienced individuals

complete the evaluations for riparian areas.

Figure 3.1. Cover of Interpreting Indicators of

Rangeland Health (Pellant et al. 2005).

Step 3.2. Verify that personnel

have relevant qualifications

Relevant evaluator qualifications are listed in each

document. It is important to recognize that

experience and long-term knowledge of the

ecosystem is often as important as academic

qualifications. Academically trained individuals

with little field experience will find it difficult to

accurately and consistently apply assessment

protocols.

Step 3.3. Complete assessments

Paper and electronic forms are available for

completing the assessments.

Where? It is more important to complete

assessments in areas where the value of

monitoring and/or a change in management is

uncertain. If you already know that an area is in a

relatively stable state, it’s usually not worth

completing an assessment. Be sure to justify all

assessments with comments and observations.

Figure 3.2. Cover of Process for Assessing Proper

Functioning Condition (Prichard et al. 1998a, b).
20

Assessment

Both the upland and riparian assessment

systems are designed to evaluate individual

locations. Record additional notes of off-site effects

and impacts to describe relationships among

monitoring units. For example, excessive runoff in

one monitoring unit may reflect problems in an

upslope monitoring unit, or the presence of

invasive species on one monitoring unit may pose

a risk to adjacent monitoring units.

Step 3.4. Identify and record

drivers, threats and opportunities

A critically important part of the assessment

process is identifying drivers, and current and

future threats and opportunities. Both of the

assessment protocols are limited to current status

only. Areas likely to be threatened by future

activities, or where future activities present new

opportunities, should be considered for

monitoring because of their potential for change.

Drivers. Drivers include all factors that can

contribute to changes in the properties and

processes to be monitored. Typical drivers in

rangeland ecosystems are listed in Figure 0.1.

Drivers may or may not be threats.

Threats. Threats are drivers that might negatively

impact the land in the future. Future threats might

include increased off-road vehicle activity, invasive

plants that have been identified in the area,

cultivation (see Fig. 1.2), overgrazing by wildlife/

livestock associated with a change in

management, or drought and insect damage. The

level of each threat usually varies among

monitoring units. For example, off-road vehicle

activity is less likely to be a threat on isolated

mesas, and the threat of insect damage is

frequently greater in grass-dominated ecological

sites. Gully formation is more likely to occur in

monitoring units located downslope of areas

where an increase in runoff (e.g., associated with

road construction) is anticipated.

Invasive species sometimes pose a high threat

in particular soil types. Disturbance can favor the

establishment of invasive species. For example,

road graders can disperse African rue (Peganum

harmala) rhizomes. Additionally, cheatgrass

(Bromus tectorum) seeds are often dispersed by

grazing animals. Thus it pays to consider all

potential threats and drivers when designing a

monitoring program.

Opportunities. New opportunities are often more

difficult to predict than threats, but are at least as

important to address in a monitoring program.

Opportunities might include grants for restoration

that can only be applied to particular areas (e.g.,

riparian). A new neighbor or the development of a

grass bank in the region might bring new

opportunities for cooperative livestock

management. Climate change and even short-term

weather patterns can be viewed as both threats

and opportunities.

Identifying known or potential future

opportunities for a monitoring unit may influence

your decision to monitor. Knowledge of such

opportunities can allow flexible management to

use them. If monitoring data are collected prior to

and following a management change, the effects

of the new management can be quantitatively

evaluated.

3.5. Refine long-term

management and monitoring

objectives

New information can be provided by on-site

assessments and the development of a list of

threats and opportunities for each monitoring

unit. This information can be used to refine

management and monitoring objectives. These

changes should be recorded in the Monitoring

Program Design Form I (Ch. 1).

3.6. Develop/modify

management strategy

The management plan should be finalized (to the

extent possible) before beginning site and

indicator selection. At the risk of redundancy, we

repeat that in order for monitoring to be cost-effective,

it must focus on those areas, properties and processes

that are likely to change in response to management

(including lack of active management).
21

Chapter 4

Step 4: Select indicators and

number of measurements

Checklist

4.1. Select monitoring indicators ....................................................................... _________

4.2. Define number of monitoring plots ............................................................ _________

4.3. Define measurement frequency .................................................................. _________

4.4. Estimate time requirements......................................................................... _________

I

ndicator selection should be based on the

objectives defined in Step 1 (see Ch. 1). It is

important to think carefully about what you

need to learn from your monitoring program, and

how precise the data need to be.

Types of indicators

Two basic types of monitoring indicators are

addressed in this manual: short-term and longterm. Some (like plant cover) can serve as shortand long-term indicators. The difference between

short- and long-term indicators is discussed in

Quick Start and in Step 4.1.

In addition to the short- and long-term

indicators described in this manual, you may want

to include indicators of potential threats and new

opportunities. These are briefly described in

Chapter 3. Information on threats and

opportunities can be used to anticipate future

changes and adapt monitoring and management

accordingly.

Reducing monitoring costs

The most effective way to reduce monitoring costs

is to minimize the number of measurements.

Selecting measurements that generate indicators

addressing multiple objectives can minimize costs.

For example, the Line-point intercept method

described in Quick Start can be used to generate

Note: Steps 4 and 5 (Chs. 4 and 5) are often completed simultaneously. The number of transects that

can be monitored often depends on where they are and how many different types of measurements are

to be made on each transect. Different types of monitoring units sometimes require different

measurements. We suggest reading through Chapter 5 before actually beginning the tasks listed in

Chapter 4.

ground cover indicators that are important (1) for

erosion prediction; (2) for plant cover and species

composition; and (3) as an indicator of wildlife

habitat structure. Habitat structure requires the

addition of height measurements to the Line-point

intercept method (Ch. 15).

The measurements described in Quick Start are

sufficient to generate all of the indicators required

for most monitoring objectives. In many cases,

indicators generated from the Quick Start

measurements can substitute for the more timeconsuming measurements described in the

following chapters. For example, the Single-ring

infiltrometer (Ch. 8) is a direct measurement of

how quickly water will soak into the soil

(infiltration capacity), but it is very time

consuming. The Soil stability test (Quick Start) is

less time consuming and, together with indicators

calculated from the Line-point and Gap intercept

measurements, can generate information relevant to

the infiltration capacity of the soil (see Section III).

Another option is to make the more timeconsuming measurements (generally Level 4 in

Table 4.1) at a few high-priority locations.

Monitoring Intensity (Table 4.1). Where only

qualitative documentation of change is required,

photographs (Level I) are often sufficient. Level II

monitoring intensity (semi-quantitative) is
22

appropriate where only the core indicators

included in Quick Start are required, and where

the data will always be collected by the same

person. Level III monitoring intensity is the same

as Level II (i.e., Quick Start methods), except that

the measurements are more precise and repeatable.

In many cases, only a subset of Level II or III

measurements is necessary. For example, where

the primary concern is a change in woody shrub

cover, Line-point intercept (Level III) or step-point

(Level II) alone is often sufficient if woody species

comprise at least five percent of the foliar cover.

The Belt transect (Level II or III) is appropriate

where the only concern is early detection of

undesirable plant establishment, or when the

species/ functional group you wish to monitor is

very sparse (less than five percent cover).

Level IV measurements are usually included to

address specific concerns or objectives that cannot

be addressed using the basic measurements.

Step 4.1. Select monitoring

indicators

The monitoring indicators selected will determine

which measurements are needed. Selecting

measurements that generate multiple indicators,

or that generate indicators that address multiple

objectives, can often reduce costs.

Table 4.2 lists the measurements described in

both volumes of this manual and briefly describes

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Indicator selection

the relevant monitoring objectives for each. It also

includes some of the indicators that can be

generated from each measurement. Use

Monitoring Plot Design Form II (end of Ch. 4) and

Table 4.2, together with your objectives (outlined

in Monitoring Program Design Form I, Ch. 1) and

the results from your assessment, to select the

appropriate measurements for each monitoring unit.

Short-term. Short-term indicators should reflect

short-term management objectives. Most

management plans require very few short-term

indicators. For example, if management calls for

eliminating off-road vehicle traffic from an area,

the only indicator you need to monitor is vehicle

tracks (modified Belt transect, Gap intercept or

simply recording the number of tracks per 100

paces). For livestock grazing in arid and semi-arid

ecosystems, residual ground cover (step-point

transect), together with stocking rate information,

is often sufficient. Typical short-term indicators

are listed on the form at the end of the Quick Start

volume.

Long-term. Long-term indicators should reflect

long-term changes in the landscape caused by

changes in management, climate and so on.

Monitoring objectives (Ch. 1), together with

assessment results (Ch. 3) and state and transition

models (Ch. 24), can be used to help identify

appropriate indicators.
23

 Indicator selection

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Indicator selection

For example, many land managers in the

western United States need to identify and

monitor grass-dominated states that are at risk of

changing to shrub-dominated states, which are

associated with higher erosion rates. State and

transition models define the states and transitions

for the area of interest. The assessment would help

identify areas potentially at risk of a change in

state. The assessment, as well as the state and

transition model, assist in identifying indicators

associated with a state change (e.g., grass

mortality, reduced infiltration and/or shrub

establishment). The qualitative indicators included

in the Interpreting Indicators of Rangeland Health

protocol help focus attention on processes and the

associated properties that should be monitored

(Pellant et al. 2005).

Step 4.2. Define number of

monitoring plots

Defining the number of monitoring plots is a

balancing act between what changes need to be

detected (benefits) and the resources available

(costs). Use the factors listed below, along with

Appendix C, to determine the number of plots

needed. The number of short-term monitoring

plots should be determined separately from the

number of long-term monitoring plots. After

determining time estimates (Step 4.4), it may be

necessary to revisit this step to reduce costs.

Short-term. Use the recommendations listed for

long-term measurements (below and in Appendix

C) as a general guide for how many measurements

you need. As with long-term measurements,

monitoring more locations (plots) is generally

better than increasing the number of

measurements at each plot.

Long-term. The number of measurements required

depends on four factors:

(1) the amount of variability within the

ecological site (lower variability requires

fewer measurements);

(2) the size of the change you want to detect

(larger minimum changes require fewer

measurements for detection);

(3) how sure you want to be that if you say a

change has occurred (or has not occurred),

you’ll be right (statistical certainty – less

certainty requires fewer measurements);

(4) whether you want to detect change at the

plot scale (a plot selected to represent the

soil-landscape-vegetation management

unit) or at the landscape scale (ranch or

watershed level). Fewer measurements are

required to detect change at the plot scale

than at the landscape scale. However, to

detect change at the landscape scale, fewer

measurements are required per plot

because multiple plots are used.

Appendix C describes three options for

estimating the number of vegetation transects and

soil measurements you will need. It includes tables

that allow you to create unique recommendations

based on each of the four factors listed above.

These tables are based on spreadsheets that allow

even more flexibility in monitoring program design.

The downloadable (http://usda-ars.nmsu.edu)

spreadsheets will allow you to change transect

length and number of points per transect, as well

as minimum detectable change and statistical

parameters.

Table 4.3 lists one set of recommendations for

a semiarid grassland monitoring unit, based on

Option 2 in Appendix C. Each of the long-term

factors listed above affects measurement

recommendations. For example, referring to the

information presented in Table 4.3, if we wanted

to detect a minimum change of five percent bare

ground we would need four plots, while for a

change of ten percent, only two plots are needed.

Step 4.3. Define measurement

frequency

Measurement frequency should be matched to

expected rates of change based on minimum

detectable change selected in Step 4.2. If the

smallest change in basal cover you can detect is

five percent and it takes at least five years for this

change to occur, it’s a waste of time to repeat

measurements more frequently.
25

 Indicator selection

Step 4.4. Estimate time

requirements

Use Monitoring Program Design Form II to

estimate total time requirements. Time

requirements can vary by a factor of four or more,

depending on vegetation structure, species

identification requirements, weather, and observer

experience and condition. Some people prefer to

work by themselves, while others prefer a data

recorder. Expect to double total time requirements

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and characterization. Double them again if it is the

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If the time requirements seem too high, don’t

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have made about the indicators needed and

statistical precision required. Review your

objectives. Many indicators are interesting, but

often just a few are essential.
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27

Advantages

• Can be representative of all areas (if sufficient

number of plots included).

• Easy to apply.

• Statistically valid.

• Is clearly “unbiased.”

Disadvantages

• Not very cost-effective.

• Rarely includes locations in sensitive areas or

areas of special concern because they usually

represent a relatively small proportion of the

total land area.

• Not sensitive enough to monitor degradation

and recovery except where changes are

occurring throughout all parts of the

monitoring unit.

Chapter 5

Step 5: Select monitoring plot

locations

Checklist

Step 5.1. Choose and apply site selection approach .......................................... _________

Step 5.2. Select “rejection criteria” and use to eliminate unsuitable locations. _________

Step 5.1. Choose and apply site

selection approach

There are three approaches to selecting monitoring

plot locations: (a) random, (b) stratified random

and (c) subjective. Each approach has advantages

and disadvantages. The one you select depends on

your monitoring objectives, knowledge of the area

to be monitored and the number of plots you can

afford to monitor. In most cases, we recommend

the stratified random approach for developing

cost-effective, statistically valid monitoring

programs.

Regardless of the site selection approach you

choose, use Monitoring Program Design Form II

(Ch. 4) to record information for each plot

selected. Describe the approach used to select the

transects, and any rejection criteria, on the form.

Step 5.1(a) Random Plot Selection. Plots can be

randomly selected using any map or aerial

photograph. Simply create a fine-scale grid and

place it on top of the map or photo. This can be

easily done by placing one ruler on the bottom of

the map with the “0” end in the lower left corner

and a second ruler perpendicular to it along the

left edge, again with the “0” end in the lower left

corner. Randomly select two distances on each

ruler (e.g., 6.1 and 10.7 in Fig. 5.1) and find the

point where the two lines intersect. Repeat until

you have selected all plot locations. Make sure

each plot is at least 200 m from the closest

neighboring plot.

If a DOQQ or other orthorectified image is

available, the same process can be applied using a

grid of UTMs instead of the ruler. These coordinates

can then be entered directly into a GPS unit.

Figure 5.1. Random selection of monitoring locations

using rulers and an aerial photo. The numbers 10.7

and 6.1 were randomly selected (see text).

cm

10.7 cm

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14

12 11 10 9 8 7 6 5 4 3 2 1 0

6.1 cm --.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.->

-.-.-.-.-.-.-.-.-.-.-.>
28

Step 5.1(b) Stratified Random Plot Selection.

Stratified random sampling is identical to random

sampling except the number of plots in each type

of monitoring unit is predetermined. Plot location

within each type of monitoring unit is randomly

selected. This allows monitoring to focus on areas

with a high degradation risk or recovery

opportunity.

For example, in Figure 5.1, if the primary

objective is to monitor for degradation risk and

the primary degradation process is tree invasion,

then a higher proportion of the plots should be

located in the blue grama grassland, even though

most of the monitoring unit is pinyon-juniper

savanna.

Calculating indicator averages is slightly more

complicated with stratified random than with

random. For stratified random, calculate the

average value for each type of monitoring unit.

Then multiply each average value by the

proportional area covered for the corresponding

type of unit (e.g., 0.3 for a type of unit covering

30% of the total area monitored). The average for

the total area monitored is the sum of all the

products (monitoring unit x proportion of area).

The example in Table 5.1 shows that it’s easier

than it sounds.

Advantages

• Can be representative of all areas (if sufficient

number of plots included, plots are located in all

types of monitoring units, and the total area of

each type of monitoring unit is known).

• Statistically valid.

• Cost-effective.

• More sensitive to areas with a high probability

of change (degradation and/or recovery).

Disadvantages

• Requires pre-stratification (this should already

be done as part of the design process. See

Ch. 2).

Step 5.1(c) Subjective Plot Selection. Subjective

plot selection includes all approaches in which the

person designing the monitoring program decides

where to locate the plots without using a grid

system. This nonrandom approach has been used

to select a majority of existing monitoring plots.

Most historic USFS and BLM monitoring transects

were selected subjectively by experienced range

conservationists using the “key area” concept

discussed below.

Subjective site selection can result in much

more sensitive and representative monitoring

programs. However, this is only possible where

qualified personnel with a good understanding of

local soil and vegetation patterns and processes

design such monitoring programs.

Advantages

• Sensitive to local patterns and land use.

• Does not necessarily require access to maps

and photographs.

• Inexpensive.

Disadvantages

• High potential for bias.

• Difficult to extrapolate.

Key areas

A key area is a tract of land that is assumed to be

representative of much larger areas and is likely to

reflect the effects of management changes on

these larger areas. Key areas are often used in

subjective plot selection.

Site selection

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Key areas have been used in the design of

many monitoring programs throughout the world.

Key areas, like any subjective approach, can be

extremely effective when applied by qualified

personnel.

Where used for monitoring livestock grazing

effects, key areas are usually placed in an area that

reflects typical livestock use. They are not located

near watering points, mineral supplements, fences,

trails or isolated areas of a pasture that are

infrequently visited. The recommended distance

from water varies with topography, vegetation and

species or class of livestock.

Step 5.2. Select “rejection

criteria” and use to eliminate

unsuitable locations

List the rejection criteria in the space at the

bottom of the Monitoring Program Design Form II

(Ch. 4). Thoroughly describe the reasoning used to

select these criteria. This is important because the

criteria are used to help define how the

monitoring data will be extrapolated and because

what seems intuitive to us today may not seem

intuitive to other individuals, or even to ourselves,

many years later.

Rejection criteria can be based on almost

anything. Many monitoring programs exclude

areas that are thought to be anomalous because

they receive unusually high or low levels of

disturbance. Examples of rejection criteria include:

(1) plots must be located a minimum of 100 yards

from a road or watering point (to avoid

unrepresentative high disturbance areas); (2) no

plots on rock outcrops or slopes greater than

50 percent (these areas are unlikely to be

disturbed).

Specific locations may also be anomalous

because of landscape position. For example, areas

that receive unusual amounts of runoff or have

unusually dense stands of trees in a savanna may

be rejected because they are not representative of

larger areas.

Large areas that are not expected to change

because they have crossed a threshold are also

often omitted from monitoring programs. The

state and transition model and indicators used to

justify omission of these areas should be listed.

Rejection criteria should be carefully selected

to ensure areas that should be monitored are not

omitted. Also, the most unusual areas are often

those that change the most quickly and may serve

as early-warning indicators of degradation or

recovery in other parts of the landscape. Rather

than excluding these anomalous areas, we suggest

that a stratified random site selection approach be

used where possible. This allows apparently

anomalous areas to be clearly identified as part of

the monitoring program and potentially included

in a future expansion of the monitoring program.

Where there are areas of less interest (e.g., the

post-threshold areas), monitoring may be limited

to a few photo points.

It is highly recommended that a list of

rejection criteria be developed prior to selecting

and visiting monitoring locations. Deciding to

reject areas after visiting them because they “don’t

look right” introduces bias. See Chapter 17 for

additional guidance on the use of soil and

landscape features to improve monitoring data

interpretation.

 Site selection
30

Chapter 6

Checklist

6.1. Establish and permanently mark plots and transects ............................... _________

6.2. Describe monitoring plots and record GPS locations, including

coordinate system, datum and zone .......................................................... _________

6.3. Record long-term data................................................................................ _________

6.4. Error check and copy the data and keep copies in different locations ..... _________

After you have gone through the previous

five steps, this one should seem easy. It’s

important to carefully mark and describe

each monitoring plot for two reasons: so you can

find it again and so you can compare your data

against data collected on plots with similar soils,

topography and climate — all of the things that

determine site potential. Use the equipment

checklist for pre-field planning.

Step 6.1. Establish and

permanently mark plots and

transects

By now you should have already selected where

the plots are to be located (Ch. 5). Be sure to verify

that the site is suitable by checking it against the

“rejection criteria” you list on the back of the

Monitoring Program Design Form II (Ch. 4).

Step 6.1(a) Upland spoke design plots (Fig. 6.1).

Place a permanent stake into the ground at the

center of the monitoring plot. This stake will also

serve as the photo point (Quick Start).

Using a randomly selected azimuth (compass

direction: 1° to 360°), extend a tape in the azimuth

direction to a distance of 5 m (15 ft) further than

the length of the transect. Install a stake at the 5 m

mark. This will serve as the 0 m end of your

transect, because the transect begins 5 m from the

center point (Fig. 6.1). Mark the far end of the

transect with a stake.

Repeat transect establishment at regular

intervals in a circle around the plot. The interval

depends on the number of transects. For most

applications, there will be three transects, with

120° between each.

Figure 6.1. (a) Three spoke design plots located

within an upland monitoring unit. The starting point of

each transect is 5 m from the plot’s center. (b) Single

transect design maximizes spatial distribution across

the landscape.

Step 6.1(b) Single transect upland plots. Anchor

and mark the 0 m end of the transect. Using a

randomly selected azimuth (compass direction:

1° to 360°), extend a tape in that direction the

length of the transect. Mark the far end of the

transect with a stake.

Step 6.1(c) Single transect riparian plots (Fig. 6.2).

Anchor and mark the 0 m end of the transect.

Ensure the 0 m end is placed such that the transect

will cross the riparian channel perpendicular to

the channel, and the 0 m end is 5 m beyond the

riparian zone. Extend the tape perpendicular to

the riparian channel. Mark the far end of the

transect with a stake.

Step 6: Establish monitoring plots

(a)

(b)
31

 Plot establishment

Border of riparian channel 

(water flow area)

Border of riparian zone 

(defined by vegetation)

Riparian channel veg 

survey

Riparian channel profile

Standard transect 

anchored outside riparian 

zone

Figure 6.2. Single-transect plots crossing a stream

within a riparian monitoring unit. Note that transects

are anchored outside the riparian zone. See Chapters

13 and 14 for the Riparian channel vegetation survey

and the Riparian channel profile measurements

associated with riparian transects. Single-transect

plots can be used in upland areas.

Step 6.2. Describe monitoring

plots and record GPS locations,

including coordinate system,

datum and zone

At a minimum, fill out the Required section of the

Monitoring Plot Description Form (found at the

end of this chapter) when you establish each

monitoring plot. This will ensure the same

physical location is always monitored.

The Recommended section provides

information regarding the plot’s potential to

support a given plant community, and enables you

to verify the plot is on the mapped soil and

ecological site. The data in this section allow you

to determine how similar this plot is to other plots

within the same ecological site. The information

gathered here can help identify potential offsite

influences. Data gathered in this section also assist

in determining applicability of extrapolating plot

data to the landscape level.

The Optional section addresses plot

disturbances and management history. This

information is valuable for data interpretation. It

can help identify potential causes of trends and

assist with important land management decisions.

The Recommended and Optional sections of

the data form are important for data interpretation.

It is best to fill out this information when you

establish the plot, as you need to be on the plot to

complete these sections. However, if you have

time constraints, these sections can be completed

during a second plot visit.

Required Section. Record the site, management

unit and/or pasture name where the monitoring

plot is located. A site, management unit or pasture

is a distinct geographic unit typically with the

same landowner and a relatively homogeneous

management/disturbance regime. There are often

multiple plots located within the same site,

management unit or pasture.

Complete the remaining portions of the

Required section to ensure a permanent record of

plot and transect locations. Record the locations

(e.g., GPS coordinates) on the Monitoring Program

Design Form II and the Monitoring Plot Description

Form. If you are using GPS coordinates, make sure to

record the coordinate system, datum and zone,

and whether the data are in English or metric

units. Record geographic locations of the plot’s

center and the beginning and end of each transect.

Document each transect’s compass azimuth and

record the declination used, if any.

Recommended Section. Record the average longterm precipitation under “Avg. Precip,” indicating

whether the units are English or metric. Determine

the soil series by comparing soil observations

(recorded in the table) with a soil survey. Do not

rely on soil maps alone, although they can be used

as guides. Soils can be extremely variable within

broadly defined map units.

Dig a small pit at the plot’s center and at the

end of each transect. The pit should be deep

enough to detect soil horizons that significantly

affect plant growth. An auger or soil probe can be

used instead of a pit if you are already familiar

with the soils in the area. Any soil information is
32

Plot establishment

helpful. Don’t worry if you are not sure how to

distinguish soil horizons.

If you suspect there may be more than one

type of soil within your plot boundaries, be sure to

locate at least one of your mini pits within each

soil type. It is very important to record the soil

depth. Record the depth to any horizon that is

likely to restrict water movement or root growth.

If no restricting horizon is encountered, record the

maximum depth of the pit and write “no

restricting horizon.”

Record the upper and lower boundary of each

major horizon under soil depth (e.g., “Btk

horizon” or “horizon with clay and carbonate

accumulation” from 23 to 40 cm) (refer to USDANRCS 1999). Record information about each

horizon in a separate row on the data form. Refer

to The Field Book for Describing and Sampling Soils,

Version 2.0 (Schoeneberger et al. 2002), for

additional guidance.

If available, the soil survey will also provide

information on soil parent material. For areas in

the United States that have been mapped by the

NRCS, the ecological site can be determined by

looking up the soil series online (http://

esis.sc.egov.usda.gov) or by consulting the Field

Office Technical Guide in your local NRCS office

or NRCS website. If ecological sites have not been

developed, this space can be used to record any

other land classification information (e.g., USFS

Terrestrial Ecosystem Unit) that may assist with

data interpretation.

Record the slope using a clinometer or other

instrument. Record the slope’s aspect, looking

downslope, in compass degrees (e.g., 108°) or

cardinal direction (SE). Record the slope shape as

convex ( ), concave ( ) or linear (straight, not

curved) (Lal 2003).

Record the landscape unit on which your plot is

located. If the plot is located on a hill/mountain,

select the appropriate hillslope profile component

(see list on data form). If the plot is located on a

terrace, indicate whether it is on the riser (fairly

short, steep, linear slope that forms the sideslope of

the terrace) or the tread (a broad, relatively level

planar portion forming the top of the terrace that

can extend laterally for great distances). Refer to any

of the following for assistance: Encyclopedia of Soil

Science (Lal 2003), the Field Book for Describing and

Sampling Soils (Schoeneberger et al. 2002), Landforms

of the Basin and Range Province (Peterson 1981),

Geomorphology of Soil Landscapes (Wysocki and

Zanner 2003) and National Soil Survey Handbook,

Part 629, Glossary of Landform and Geologic

Terms, online at http://soils.usda.gov/technical/

handbook/detailedtoc.html#629 (USDA-NRCS 2003).

Optional Section. Record recent weather patterns

for the previous 12 months and the year prior.

Record any disturbances and management

information that might impact the plot. Describe

all known or observed wildlife and livestock use,

including utilization, seasonality, intensity and

residual cover. Describe previous management

history dating as far back as possible. Describe

offsite influences, including, but not limited to

(a) unusually high runoff or erosion from upslope

areas, (b) management practices, (c) presence of

invasives in the area, and (d) roads. Document any

other pertinent information under “Other

comments.” Draw a depiction of the plot and

record all potential drivers and other influences.

Step 6.3. Record long-term data

There are four options:

(1) Enter data onto paper data forms

photocopied from each chapter.

(2) Enter data onto paper Microsoft® Excel

spreadsheet forms printed from the website

listed below.

(3) Enter directly onto downloaded Excel

spreadsheet forms using a laptop or tablet

computer.

(4) Enter directly into a database (see website

for future download at http://usdaars.nmsu.edu).

Step 6.4. Error check and copy

the data and keep copies in

different locations

All data should be checked for errors, using the

steps below. This process can and often should

take as long as data entry.

Step 1. Before leaving the plot, review all forms for

completeness and legibility. If you used a digital
33

 Plot establishment

camera, review all of the photos to be sure that

plot information is visible. If the data were entered

directly into a computer, open and inspect each

file and make a backup copy on a separate device.

Step 2. Immediately upon returning from the field,

copy the data and store in at least two different

buildings. This is especially important if you are

using electronic data entry. Unless you have at

least one backup copy on media that you are sure

will be readable in the future (remember 51/2 in

disks, punch cards and tape drives?), then we

recommend printing copies of your data for

archiving. Be sure that someone else knows where

the extra copies are.

Step 3. Review all data for obvious errors. For

example, check to see that each column on the

Line-point intercept form includes only those

codes assigned to that column. Gap intercepts

should not overlap. Soil stability values should be

between 1 and 6. See Table 6.1 for an example of a

Compaction test (Ch. 7) dataset that includes

potential outliers (extreme values) at positions 3

and 6 that may or may not be errors. If we know

that this site had shallow soil or large rocks or

near-surface bedrock, we could justify deleting the

two bolded data points by assuming that the

penetrometer struck a rock. We could also delete

them if we were fairly certain that the data

recorder had lost count. However, it is also

possible that both measurements were made on a

game or livestock trail. In this case, they should be

retained. When in doubt, retain the data and make

a note.

Step 4. If the data were originally recorded on

paper forms, re-check each value after computer

entry. One way to do this is for one person to read

the values aloud from the data form while another

checks the values in the spreadsheet or database.

Your data are now ready for indicator calculation

(Ch. 16 and individual methods chapters) and

interpretation (Ch. 17).

“Outliers”

Values that are clearly outside the range of expected variation may be omitted if it is clear that

these values were due to measurement or data recording errors. Extreme care should be taken to

ensure that these values are in fact mistakes before omitting them.

A famous ecologist once said, “There are data that are outliers (meaning that they lie outside the

normal range of variation) and then there are “out-and-out liars.” Be sure that you only exclude the

“out-and-out liars.” The other outliers may be extremely important in defining the current status of

the system.

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37

Section II: Supplementary

methods

Quick Start (Vol. I) describes the four basic monitoring methods relevant to most monitoring

programs. This section describes supplementary methods that address more specific objectives.

They are generally used in addition to the basic measurements described in the Quick Start. This

section also includes alternative Line-point intercept methods.

Supplementary Methods (in Section II) Core Methods

Compaction test (Ch. 7) Vegetation structure (Ch. 11)

Infiltration test (Ch. 8) Tree density (Ch. 12)

Plant production (Ch. 9) Riparian vegetation (Ch. 13)

Species richness (Ch. 10) Channel/gully profiles (Ch. 14)

(Quick Start)

Photo points

Line-point intercept

(also in Vol. II, Ch. 15)

Gap intercept

Soil stability test

Belt transect
38

Chapter 7

Compaction test

T

he impact penetrometer is used to monitor

changes in soil compaction that can limit

water infiltration, root growth and

microorganism activity. Because penetrometer

measurements are very sensitive to soil moisture,

measurements can be compared among years only

if soil moisture content is the same during each

sampling period. Use Table 7.1 to decide whether

or not to include this measurement.

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The penetrometer can help determine whether

or not a soil is currently compacted, if reference

data for similar soils with the same moisture

content are available. However, qualitative

methods (e.g., Pellant et al. 2005) are generally

more reliable for determining whether soil is

compacted. For example, platy soil structure and

abrupt changes in root growth patterns not related

to a texture change are good indicators of

compaction.

Caution!

• Never use this instrument near buried power

or pipelines.

• Wearing earplugs and heavy leather gloves is

highly recommended.

• Always keep hands away from the strike plate

when operating the penetrometer.

Materials

• The same transect(s) used for Line-point and

Gap intercept

• Impact penetrometer (see Appendix A for

specifications)

• Thick leather gloves

• Clipboard, Soil Compaction - Impact

Penetrometer Data Forms and pencil(s)

Standard methods (rule set)

1. Define hammer drop height and record at

the top of the form.

Rules

1.1 Standard drop height is 40 cm. Drop height

can be increased for compacted soils and

decreased for loose (low bulk density) soils.

2. Define maximum depth.

Rules

2.1 Maximum depth should be at least 10 cm and

include qualitatively identified compaction

zones (e.g., lateral root growth).

Figure 7.1. Impact penetrometer with sliding hammer

elevated.
39

 Compaction test

3. Randomly select the sample locations you

plan to measure.

Rules

3.1 Use randomly selected points along the

transects used for Line-point and Gap intercept

measurements.

3.2 Record sampling locations (positions) on the

data form in the “Position on line” column.

3.3 Make measurements at least 1 m (3 ft) from the

transect to avoid affecting vegetation

measurements.

3.4 Penetrometer resistance cannot be measured

on plant bases or surface rocks. If you

encounter a rock or plant base, move

measurement 1 m (1 yd) down the transect.

The sample points have to be at least 1 m from

each other.

3.5 In areas with duff or embedded litter (e.g., under

coniferous trees), clearly define a standard depth

to which litter will be removed, based on soil

and litter characteristics (e.g., depth at which

there is 80 percent mineral soil by volume), OR

leave litter in place, OR exclude these areas.

Exclude sample points where a stick is embedded

in the soil.

3.6 Clearly record which of the three options listed

in Rule 3.5 was applied.

4. Determine soil moisture.

Rules

4.1 Check at least three different locations on the

plot for soil moisture by digging a small pit or

using an auger and assessing soil moisture by

touch.

4.2 Record soil moisture for each depth by circling

the appropriate category on the Soil

Compaction - Impact Penetrometer Data Form.

4.3 If possible, determine soil moisture

quantitatively by measuring wet and oven-dry

weights of at least three soil samples. Percent

soil moisture is: wet weight minus oven-dry

weight divided by oven-dry weight and

multiplied by 100% or

(wet wt) – (oven-dry wt)

(oven-dry wt)

x 100%

5. Record the dominant vegetation cover class

in the “Veg class” column of the Soil

Compaction - Impact Penetrometer Data

Form.

Rules

5.1 The area to be classified is a circle with the

same diameter as the top of the penetrometer

cone (see Appendix A).

5.2 Use one of the following cover classes:

NC = no perennial grass, shrub or tree canopy

cover

G = perennial grass canopy and grass/shrub

canopy mixture

F = perennial forb

Sh = shrub canopy

T = tree canopy

6. Check hammer drop height.

Rules

6.1 Measure the distance from the bottom of the

hammer to the stop collar (Fig. 7.2).

6.2 Be sure that the distance is identical to the

height recorded at the top of the data form.

6.3 Adjust stop collar if necessary.

6.4 There should be an average of at least three

strikes per depth increment. Lower drop

heights (more strikes) increase sensitivity.

Higher heights increase efficiency by reducing

the number of strikes per depth increment.

Figure 7.2. Hammer height is the

distance from the hammer to the

strike plate.
40

7. Determine the cumulative number of strikes

required for each 5 cm (2 in) depth

increment.

Rules

7.1 Wearing thick leather gloves and ear

protection is highly recommended. Always

keep hands away from strike plate when

operating the penetrometer.

7.2 Press the cone into the soil so the top of the

cone is flush with the soil surface (Fig. 7.3).

7.3 Keep the penetrometer vertical at all times. On

slopes, this means that the penetrometer will

be at less than a 90o angle to the soil surface.

7.4 Raise the hammer to the stop collar and release

(Fig. 7.1). Do not exert any downward pressure

on the hammer while releasing it.

7.5 Repeat until the penetrometer rod is inserted

5 cm (2 in) into the soil (Fig. 7.4), the first

increment.

7.6 Record the number of strikes to 5 cm on the Soil

Compaction - Impact Penetrometer Data Form.

7.7 If a strike pushes past a 5 cm (2 in) mark, record

it as a half strike (e.g., 9.5 strikes instead of 10).

Compaction test

7.8 Repeat for the next increment and record the

cumulative (total) number of strikes.

7.9 A change in tone, together with sudden

increased resistance in stony soils, indicates a

stone or other hard object has been

intercepted. Stop hammering and record

“rock” for that depth on the Soil Compaction -

Impact Penetrometer Data Form.

8. Remove the penetrometer.

Rules

8.1 Pull straight up on the penetrometer.

8.2 If this doesn’t work, try tapping the

penetrometer at the soil surface with a rubber

mallet, or rotating it in an increasing radius

circle (Fig. 7.5), being careful not to bend it.

Then pull straight up.

8.3 At least one of the manufacturers (Synergy) will

include a second sliding hammer below the

strike plate to assist with removal.

9. Tighten cone if necessary.

Rules

9.1 If cone loosens from rod, apply LoctiteTM or a

similar material to the cone threads and tighten.

9.2 Because the cone has been hardened, it is more

brittle than the rod. It can break at the threads

if it becomes loose.

10. Repeat steps 2 through 9 for all sample

positions.

Figure 7.3. The top of the cone is

flush with the soil surface.

Figure 7.4. Record the number of strikes

required to reach each 5 cm (2 in) increment

(marked by the scribed marks on the rod).
41

 Compaction test

Compaction test indicator

calculations

These instructions are used to calculate the average

number of strikes, which are linearly related to

resistance. For example, twice as many strikes are

the same as twice the resistance. For equations to

convert the number of strikes to resistance, see

Herrick and Jones (2002), Minasny and McBratney

(2005) and Herrick (2005). To make this

conversion, you will need the drop height and the

mass (weight) of the hammer.

1. Calculate the average number of strikes for

each depth (Average No. of Strikes, All).

Rules

1.1 Add all values in each column and record the

total in the “Sum no. of Strikes, All” row of the

Soil Compaction - Impact Penetrometer Data

Form.

1.2 Count the number of values in each column

and record that number in “Measurement no.,

All” row.

1.3 For each column, divide “Sum no. of Strikes,

All” in rule 1.1 by “Measurement No., All” in

rule 1.2, and record in “Average no. of Strikes,

All” row.

2. Calculate the average number of strikes for

each depth, using measurements with no

vegetation cover (NC).

Rules

2.1 Add all values with no vegetation cover in each

column and record the total in the “Sum no.

of Strikes, NC only” row of the Impact

Penetrometer Data Form (Veg class = NC).

2.2 Count the number of values in each column

and record that number in “Measurement no.,

NC only” row.

2.3 For each column, divide “Sum no. of Strikes,

NC only” in rule 2.1 by “Measurement No.,

NC only” in rule 2.2, and record in “Average

no. of Strikes, NC only” row.

3. Calculate the average number of strikes for

each depth, using measurements under

vegetation cover (G, F, Sh, T).

Rules

3.1 Add all values with vegetation cover in each

column and record the total in the “Sum no.

of Strikes, Veg only” row of the Impact

Penetrometer Data Form (Veg class = G, F, Sh

or T).

3.2 Count the number of values in each column

and record that number in “Measurement no.,

Veg only” row.

3.3 For each column, divide “Sum no. of Strikes,

Veg only” in rule 3.1 by “Measurement No.,

Veg only” in rule 3.2, and record in “Average

no. of Strikes, Veg only” row.

4. Calculate the ratio of the number of strikes

in areas without and with vegetation (ratio

of interspaces: under-plant canopies), and

record in the last row.

Rules

4.1 For each depth, divide the average number of

strikes for samples with no cover by the

average number of strikes for samples with

cover.

4.2 Using data from the example data form, 5 cm

depth, we divide 5.3 by 4.8 to get a Ratio of

NC / Veg of 1.1.

Figure 7.5. Gently rotating or

tapping the penetrometer at the soil

surface can help remove it.
42

mroFataDretemortenePtcapmI-noitcapmoClioS

_________________________:tolpgnirotinoM ________________:etaD

__________________:redroceR_________________:revresbO )?niromc(_______:thgiehremmaH

enoelcric

:ssalCgeV :)htpedhcaerofenoelcric(erutsiomlioS

CN = yponaclainnerepoN S y ecafru D t r M t sio eW

G = ximburhs/ssargrossarglainnereP 0 y mc5- D t r M t sio eW

F = broflainnereP 5 y mc01- D t r M t sio eW

hS = burhS 1 y mc51-0 D t r M t sio eW

T = eerT 1 y mc02-5 D t r M t sio eW

_ y __________ D t r M t sio eW

eniL noitisoP

enilno ssalcgeV )lanoitposhtpedrewol(sekirtsevitalumucfo).on(rebmuN

5 m mc 1 m c0 1 m c5 c02

llA,sekirtsfo.onmuS

llA,.ontnemerusaeM

)llA.gvA(llA,sekirtsfo.on.gvA

ylnoCN,sekirts.onmuS

ylnoCN,.ontnemerusaeM

)CN.gvA(ylnoCN,sekirts.on.gvA

ylnogeV,sekirts.onmuS

ylnogeV,.ontnemerusaeM

)geV.gvA(ylnogeV,sekirts.on.gvA

CN.gvA=(geV/CNfooitaR ÷ )geV.gvA

1

1

1

1

1

1

5.5

15.5

25.5

35.5

45.5

49.5

NC

G

NC

Sh

G

NC

5

4

6.5

5

5.5

4.5

9.5

8

11

10

10.5

9

23

Rock

26

15.5

16

22

30

Rock

32

21

Rock

27

30.5 58.0 102.5 110.0

6 6 5 4

5.1 9.7 20.5 27.5

16.0 29.5 71.0 89.0

3 3 3 3

5.3 9.8 23.7 29.7

14.5 28.5 31.5 21.0

3 3 2 1

4.8 9.5 15.8 21.0

5.3/4.8=

1.1 1.0 1.5 1.4

5

Jane Smith Dave Miller 20

Example

4 June 2003
43

mroFataDretemortenePtcapmI-noitcapmoClioS

_________________________:tolpgnirotinoM ________________:etaD

__________________:redroceR_________________:revresbO )?niromc(_______:thgiehremmaH

enoelcric

:ssalCgeV :)htpedhcaerofenoelcric(erutsiomlioS

CN = yponaclainnerepoN S y ecafru D t r M t sio eW

G = ximburhs/ssargrossarglainnereP 0 y mc5- D t r M t sio eW

F = broflainnereP 5 y mc01- D t r M t sio eW

hS = burhS 1 y mc51-0 D t r M t sio eW

T = eerT 1 y mc02-5 D t r M t sio eW

_ y __________ D t r M t sio eW

eniL noitisoP

enilno ssalcgeV )lanoitposhtpedrewol(sekirtsevitalumucfo).on(rebmuN

5 m mc 1 m c0 1 m c5 c02

llA,sekirtsfo.onmuS

llA,.ontnemerusaeM

)llA.gvA(llA,sekirtsfo.on.gvA

ylnoCN,sekirts.onmuS

ylnoCN,.ontnemerusaeM

)CN.gvA(ylnoCN,sekirts.on.gvA

ylnogeV,sekirts.onmuS

ylnogeV,.ontnemerusaeM

)geV.gvA(ylnogeV,sekirts.on.gvA

CN.gvA=(geV/CNfooitaR ÷ )geV.gvA
44

Chapter 8

Single-ring infiltrometer (for water

infiltration)

I

nfiltration rate is a measure of how fast water

enters the soil. Water entering too slowly may

lead to ponding on level areas or to erosion

from surface runoff on sloped areas. A Single-ring

infiltrometer provides a relative indication of

infiltration capacity under saturated conditions.

Infiltration cannot be measured with this

method on very rocky/gravelly sites, steep slopes

or areas with dense root mats at the surface.

Materials

• The same transect(s) used for Line-point and

Gap intercept

• Six infiltrometer rings (see Appendix A,

diameter = 12.5 cm)

• Six 25 x 50 cm (10 x 20 in) terrycloth towels

• Two 370 ml (12.5 oz) cups*

• Two 30 x 30 cm (12 x 12 in) sheets of plastic

(e.g., grocery bags)

• Five gallons of water

• One 15 cm (6 in) ruler

• Stopwatch

• Six infiltration bottles full of water (diameter

= 8.7 cm) (see Appendix A for construction

instructions)

• Clipboard, Single-ring Infiltrometer Data

Forms and pencil(s)

*Based on volume required for 3 cm depth in a

12.5 cm diameter ring. For other ring diameters,

volume = 9.4 x r2

, where radius equals one-half the

diameter (r = 1/2 d).

Standard methods (rule set)

1. Determine locations for the tests.

Rules

1.1 Randomly select points along the transects

used for Line-point and Gap intercept

(Fig. 8.1).

1.2 Record sampling locations (positions) on the

data form in the “Position on line” column.

1.3 If you are also making vegetation

measurements, move the infiltration

measurements at least 1 m (1 yd) from the

transect, and move the infiltration

measurement at least 1 m from any

penetrometer measurement(s).

2. Record the vegetation class for the sample

point in the “Veg class” column of the

Single-ring Infiltrometer Data Form.

Rules

2.1 Lay down the infiltrometer ring on the sample

point and record the dominant cover class for

the sample area:

NC = no perennial grass, forb, shrub or tree

canopy cover

G = perennial grass canopy and grass/

shrub canopy mixture

F = perennial forb canopy

Sh = shrub canopy

T = tree canopy

2.2 If the soil surface is protected by a rock or

embedded litter that prevents ring insertion,

select another sample point 1 m (1 yd) down

the transect and note the move.

Figure 8.1. Infiltrometer supplies and sample

location.
45

 Infiltration

3. Remove the aboveground vegetation

(Fig. 8.2).

Rules

3.1 If the sample point is located on a plant,

carefully remove aboveground vegetation to

within 1 cm of ground level, using a serrated

knife and cutting with a sawing motion.

3.2 Do not disturb the soil crust in or around the

plant.

3.3 Gently remove loose (not embedded) litter

obstructing the edge of the ring.

3.4 In areas with duff (e.g., under coniferous trees),

clearly define a standard depth to which litter

will be removed, based on soil and litter

characteristics (e.g., depth at which there is 80

percent mineral soil by volume), OR leave litter

in place and insert ring to standard depth in

the mineral soil, OR exclude these areas. If

pieces of litter create a visible hole in the soil

when the ring is inserted, select another

sample point at least 1 m (1 yd) down the

transect and note the move. This is necessary

because the ring will not seal.

3.5 Clearly record which of the three options in

rule 3.4 was applied.

Figure 8.2. Remove aboveground vegetation.

4. Pre-wet the soil to a depth of at least 4 cm

(1.5 in) (Fig. 8.3).

Rules

4.1 Fold a moistened towel in half and lay over the

sample area.

4.2 Using the 370 ml cup, pour water slowly on

the towel in a series of applications.

4.3 Wait several minutes between applications.

4.4 Minimize water runoff from under the towel.

4.5 Continue adding water until soil is wet to 4 cm

(1.5 in). The required volume varies with soil

texture and structure, but should be

approximately 740 ml (25 oz), or two cupfuls.

Figure 8.3. Pre-wet the soil to a 4 cm depth.

5. Insert the infiltration ring to a depth of

3 cm (13/16 in) (Fig. 8.4).

Rules

5.1 Distribute pressure evenly on as much of the

ring as possible. If necessary, twist the ring

very slightly while pushing.

5.2 Test if the ring is set securely in the soil by

gently wiggling the sides. If there is any

movement, push the ring into the ground an

additional 0.5 cm (3/16 in).

Figure 8.4. Insert infiltration ring to 3 cm.
46

6. Add water to the ring without disturbing

the soil surface (Fig. 8.5).

Rules

6.1 Line the bottom and sides of the ring with the

plastic sheet.

6.2 Pour sufficient water onto the sheet to bring the

water depth to approximately 3 cm (1.25 in) and

gently pull out the plastic sheet (Fig. 8.5). For a

12.5 cm ring, this is 370 ml water.

Infiltration

Figure 8.5. Add 370 ml water, using plastic sheet.

7. Watch for leaks (Fig. 8.6).

Rules

7.1 Observe the ring, watching for obvious leaks.

Wetting at the soil surface around the ring is

normal and does not constitute a leak.

7.2 Water should not pond on the soil surface or

glisten around the outside edge of the ring

(Fig. 8.6). If either of these occurs, the ring is

leaking.

7.3 If a leak occurs, gently push the ring in 0.5 cm

(3

/16 in) more and see if the leak stops.

7.4 If the leak persists, remove the ring and

relocate the sample at least 1 m (1 yd) away in

the same vegetation class (up or down the

transect line). Note the move.

8. Place bottle in ring (Fig. 8.7).

Rules

8.1 Push the infiltration pipette almost all the way

into the bottle.

8.2 Open the cap on the bottle so that water will

come out when it is upside down, but the cap

will not fall off. The cap should be very loose.

Figure 8.6. Water leaking from the ring.

Leaking

ring

8.3 Gently place the infiltration bottle in the ring

so the silicone beads on the bottle catch on

the ring suspension wires.

8.4 The bottle should be suspended in the ring

with the cap end completely submerged but

not touching the soil surface.

9. Adjust the pipette to maintain the water in

the ring at 3 cm (1.25 in) depth (Fig. 8.8).

Rules

9.1 It is crucial to keep the water level inside the

ring at 3 cm (13/16 in) or a similar standard

depth, such as 5 cm (2 in). For 3 cm, a range of

2.5 to 3.5 cm is allowed. The bottle will do this

automatically after the pipette is adjusted. To

increase depth follow rules 9.2 through 9.6. To

reduce depth follow rules 9.7 through 9.11.

Figure 8.7. Suspend bottle inside the ring.
47

 Infiltration

9.2 To increase the depth, raise the pipette.

Supporting the bottle carefully with one hand,

gently twist and pull the pipette upwards with

the other hand until air bubbles come from

the lower end of the pipette.

9.3 At this point, stop pulling up on the pipette

and start pushing down a tiny distance, until

the bubbles stop. This often amounts to less

than 1 mm of movement.

9.4 Wait several seconds for the bubbles to start

again. Bubbles should emerge at a constant

rate within 10 seconds to one minute.

9.5 If no bubbles appear within one minute,

slowly pull the pipette upwards and readjust

its level (i.e., repeat 9.2 through 9.4).

9.6 Measure water depth. If too shallow, repeat 9.2

through 9.6. If too deep, follow 9.7 through

9.10.

9.7 To reduce water depth, push the pipette down.

Supporting the bottle carefully with one hand,

gently twist and push the pipette downwards.

Caution: Grasp the side of the pipette only.

Do not place your palm on top of the pipette.

9.8 Wait until bubbles appear. This often takes

several minutes, because water must drain

from the ring into the soil.

9.9 If no bubbles appear within several minutes,

measure the water depth. If the desired depth

has been reached, pull the pipette upwards and

follow steps 9.2 through 9.6.

9.10 When bubbles appear, measure water depth.

If too shallow, repeat steps 9.2 through 9.6. If

too deep, follow steps 9.7 through 9.10.

10. Move the rubber band to mark the

water level and record the start time

(Hours:Minutes:Seconds) in the

“Start Time” column.

Rules

10.1 The top of the rubber band should mark the

bottom of the meniscus where it intersects the

vertical line of the bottle.

10.2 The “meniscus” is the bottom of the curved

line formed by the surface of the water inside

the bottle.

10.3 Record the start time.

10.4 Check for leaks during the run (defined in 7.2

above).

10.5 If a leak occurs, you must start over.

11. Wait for the water level in the bottle to drop

at least 50 mm (2 in).

Rules

11.1 Make sure the water level inside the ring stays

at a 3 cm (13/16 in) depth (± 0.5 cm or 3/16 in).

11.2 If water inside the ring drops below the

allowable level, carefully pour water into the

ring and adjust the pipette if necessary.

12. Record the infiltration end time and

measure the distance the water level has

dropped.

Rules

12.1 Simultaneously record the infiltration end

time and the distance.

12.2 Record infiltration end time as

Hours:Minutes:Seconds.

12.3 Record infiltration distance as the distance

between the top of the rubber band and the

meniscus (in mm or 16ths of an inch).

12.4 Measure infiltration distance along the

straight portion of the bottle only. Use the

vertical line on the infiltration bottle as a

guide.

12.5 You can safely make measurements as far

down the bottle as you like, as long as the final

measurement is greater than 50 mm (2 in) and

the water level inside the bottle does not go

past the curve in the bottle.

Figure 8.8. Adjust the pipette.
48

Infiltration

Bottleless Infiltration Method

(Semi-quantitative Alternative)

Pre-wet sample point, insert ring, and pour

water into the plastic bag just as with the

standard Single-ring infiltration test (Steps

1-6). Carefully remove bag and record start

time. Allow water to infiltrate. When 95% of

the soil surface inside the ring is not shining,

quickly insert the plastic bag and add

another cup of water. Record the start time

when the bag is removed. Record the end

time when 50% of the soil surface is not

shining. The difference between the start

and end time is the time required for 3 cm

(1.25 in) to infiltrate the soil.

Single-ring infiltrometer indicator

calculations

If you use the ring and bottle sizes specified in

Appendix A, your correction factor is 0.48 and you

may skip to step 4. Otherwise please begin at step 1.

1. Calculate the cross-sectional area of the bottle.

Rules

1.1 The formula for area is: Area =  x r2 3.14 x r2

Area 3.14 x (d ÷ 2) x (d ÷ 2)

r = radius = 1/2 d

d = diameter (width)

1.2 If you don’t know the bottle diameter, you can

calculate if from the circumference, C.

d C ÷ 3.14

1.3 Record on the Single-ring Infiltrometer Data Form.

2. Calculate the cross-sectional area of the ring

(see Step 1).

Rules

2.1 The formula for ring area is: Ring area = 3.14 x r2

or = 3.14 x r x r

or = 3.14 x (d ÷ 2) x (d ÷ 2)

2.2 Bottle area = 3.14 x r2

2.3 Record on the Single-ring Infiltrometer Data Form.

3. Calculate the correction factor for the

difference between the area of the bottle

and the area of the ring.

Rules

3.1 Correction factor = bottle area ÷ ring area.

3.2 Record on the Single-ring Infiltrometer Data Form.

4. Calculate the infiltration time in hours.

Rules

4.1 Subtract the end time from the start time.

4.2 Record in “Total time (min).”

4.3 Convert to hours by dividing by 60.

4.4 Record in “Total time (hr).”

4.5 Example: Start time = 12:55:01, End time =

1:04:31. Time elapsed (min) = 1:04:31 –

12:55:01 = 9.5 min. Time elapsed (hr) = (9.5

min) ÷ (60 min/hr) = 0.1583 hr.

5. Calculate the bottle infiltration rate in mm/hr.

Rules

5.1 Infiltration rate = distance the water dropped

(in mm) divided by the amount of time it took

to drop (in hours).

5.2 Record the bottle infiltration rate in “Bottle

rate” column of data form.

5.3 Example:

Distance traveled was 5.1 cm.

Convert 5.1 cm to mm:

(5.1 cm) x (10 mm/cm) = 51 mm.

Divide distance traveled by time:

51 mm ÷ 0.1583 hr = 322.17 mm/hr.

6. Calculate the soil infiltration rate (corrected

for the difference in area between the ring

and the bottle).

Rules

6.1 Multiply the infiltration rate (from step 5) by

the correction factor (from step 3).

322 mm/hr x 0.42 = 135 mm/hr.

6.2 Record in “Infil rate (mm/hr)” column of data

form.

Single-ring vs. double ring

infiltrometers

While double ring infiltrometers are

sometimes recommended, it has been clearly

shown (both theoretically and

experimentally) that they provide little

advantage over single-ring infiltrometers

(Bouwer 1986), and the measurements take

much longer. The best way to improve the

accuracy of ponded infiltration measurements

is to increase ring diameter, provided that this

does not increase the risk of leaks (e.g., in

soils with gravel or woody litter).
49

Example

12.5

122.7

8.7

59.4

East Pasture 1

Tara North

15 September 2003

Tim White

1

1

1

1

1

1

2.5

12.5

22.5

32.5

42.5

49.5

G

NC

NC

G

T

NC

12:55:01

12:58:05

13:01:29

13:04:53

13:08:24

13:11:05

13:04:31

13:12:02

13:16:59

13:11:33

13:19:30

13:25:05

52

48

55

60

58

50

9.5

13.9

15.5

6.7

11.1

14

0.16

0.23

0.26

0.11

0.19

0.23

328

206

213

540

314

222

158

99

102

259

150

107

0.48

146

3
50
51

Chapter 9

Plant production

T

otal annual production, which includes

woody material, is an expression of all

aboveground plant production during a

single growing year, regardless of accessibility or

palatability to grazing animals.

Total annual forage production is the amount

of total annual production composed of forage

species, or species likely to be used by grazing

animals.

Annual production can be divided into many

different classes, such as herbage production for

herbaceous species (grasses, sedges, rushes and

forbs) or woody plant production for woody

species (trees and shrubs). For woody plant

production (trees, shrubs and half-shrubs), annual

growth includes only leaders, leaves and fruit, or

seed production for the current growing season,

not the entire plant.

Annual production is an attribute of rangeland

vegetation that is very difficult to quantify, but is

important for management. There can be

tremendous variation in annual production within

a single pasture or management unit. As plants

grow at different times of the year, determining

when to quantify annual production and how to

adjust for material that has not yet been produced

or has been removed can be very difficult. In

addition, total aboveground production can vary

tremendously from year to year due to climatic

variations (especially seasonal differences in

precipitation), irrespective of management actions.

Because of these challenges, and the time involved

in data collection, most monitoring programs do

not include annual or forage production methods.

When estimates of annual production are

needed or desired, there are three basic methods

for collecting data: (1) estimating (by weight

units); (2) double sampling (an approach that

includes estimating and harvesting to correct

estimates); and (3) harvesting, an approach that

uses clipping of plots and air drying harvested

material to obtain a measure of dry matter

production. Double sampling is recommended

because it combines the efficiency of estimation

with the accuracy of harvesting. All three methods

are detailed in the NRCS National Range and Pasture

Handbook, Chapter 4, Inventory and Monitoring

Grazing Land Resources, pages 4-3 through 4-13

(USDA-NRCS 1997). The double sampling method

is described below.

The methods described here:

• Follow standard USDA-NRCS national

protocols.

• Are based on English units, in order to

maintain consistency with USDA-NRCS

protocols. For metric conversions, please

see Appendix B.

• Allow the inclusion of correction factors

for material that has not yet been

produced or has been removed.

• Generate production estimates for a single,

user-determined (usually calendar) year.

Materials

• The same transect(s) used for other measurements

• 1 pair grass clippers

• 1 pair pruning shears for woody vegetation

• Quadrat frames (1.92, 4.8 or 9.6 ft2)

• Paper bags for weighing samples

• Gram spring scales: 0-60 g; 0-100 g x 1 g,

0-300 g x 2 g; 0-600 g x 5 g

• Plant identification guides

• Ecological Site Descriptions

• Clipboard, Plant Production Data Forms and

pencil(s)

Figure 9.1. Weighing a clipped sample.
52

Production

Standard Methods (Rule Set)

1. Establish subplots. (For this example we

chose 10 – any number is possible.)

Rules

1.1 Randomly locate 10 sample locations. These

can be located on the transect(s) used for other

measurements.

1.2 The number of subplots commonly

recommended is 10. The formulas in

Appendix C can be used to calculate the

optimum number of subplots. Additional

guidance will be posted on the Internet when

available (http://usda-ars.nmsu.edu).

1.3 Separate sample locations by at least 10 m (33 ft).

1.4 Record the sample location for each subplot on

the data form under “Subplot position.”

1.5 Place subplots with the edge of the sampling

frame adjacent to the transect.

1.6 Locate subplots on the side of the transect not

walked along for other vegetation

measurements.

1.7 Determine production of herbaceous and half

shrub species using 1.9, 4.8 or 9.6 ft2

 subplots.

In most arid and semi-arid areas, 9.6 ft2 is the

best size. As production and plant density

increases, smaller frame sizes are appropriate.

For example, the 9.6 ft2 is more appropriate in

the desert, while the 1.9 ft2 or 4.8 ft2 would be

more appropriate in tallgrass prairie and

pasture ecosystems.

1.8 Where total production and/or woody

production is of interest, expand a subset of

subplots to 0.01 acre to measure tree and shrub

production. The 0.01 acre expanded subplot is

usually a circle with an 11 ft 10 in radius (3.6 m

radius). However, you can also use a 21 by 21 ft

square (6.4 m sides).

1.9 Woody production is more variable than

herbaceous production. Where woody

production is of interest, include a minimum

of two expanded plots.

2. Record all species in a subplot.

Rules

2.1 At least 50 percent of the plant base must be

located within a subplot to be recorded.

2.2 Record each species within a subplot once.

2.3 Record the species in the “Species code”

column of the Plant Production Data Form,

using one of the following: the PLANTS

database species code (http://plants.usda.gov);

a four-letter code based on the first two letters

each of the genus and species; or the common

name.

2.4 Record the subplot size for each species (see 1.6

and 1.7 for options).

3. Determine the weight unit for each species

(for the first subplot) or determine the

weight unit for each species not previously

recorded (for the remaining subplots).

Rules

3.1 Within a species, a weight unit can consist of a

plant part, an entire plant or a group of plants.

3.2 Grams are the unit of measure for herbaceous

and half shrub species.

3.3 Pounds are the unit of measure for tree species.

Grams or pounds may be used for shrubs.

3.4 Determine a weight unit appropriate for each

species. Select a weight unit that is easy to

identify, count and remember. Be careful not

to select a weight unit that is too small, nor

too large.

3.5 Select the equivalent of the weight unit and

harvest it.

3.6 Determine the actual weight of the weight

unit.

3.7 Repeat steps 3.4 through 3.6 until the weight

unit can be accurately estimated.

3.8 Record the weight unit weight in the “Wt unit

wt” column of the Plant Production Data

Form.

3.9 Enter the unit of measure (grams or pounds) in

the “Wt unit g or lb” column.

4. Estimate the number of weight units by

species.

Rules

4.1 Enter the number of weight units located in

each subplot for each species in the

appropriate column of the form.

4.2 If only a trace amount of a species is detected,

record “T” for that subplot.

4.3 At least 50 percent of the plant base must be

located within a subplot to be recorded.
53

 Production

5. Repeat for all subplots and expanded plots.

Rules

5.1 Repeat steps 2 through 4 for all herbaceous

subplots.

5.2 Repeat steps 2 through 4 for all woody

expanded plots.

6. Clip species to allow for later calculation of

the double sampling correction factor.

Rules

6.1 Select at least two of the ten subplots in which

to clip and weigh each species. These subplots

should include all or most of the species found

in all the subplots.

6.2 Circle the subplots on the data form.

6.3 Record the clipped weight for each species in

the “Clip wt” column.

6.4 Record a clipped weight for any species not

found in your selected subplot(s) using a

sample from another subplot. Make sure to

note where the sample was collected.

6.5 Enter the appropriate values under “Clipped

subplots Est wt” and “Clipped subplots Clip wt.”

7. Record the subplot size conversion factor.

Rules

7.1 Record subplot size conversion factor in the

“Plot Size CF” column for each species.

7.2 Convert the sampled weight to pounds per

acre using the appropriate conversion factor:

CF = 50 where subplot size is 1.92 ft2 with

grams as the unit of measure

CF = 20 where subplot size is 4.8 ft2 with

grams as the unit of measure

CF = 10 where subplot size is 9.6 ft2 with

grams as the unit of measure

CF = 0.22 where subplot size is 0.01 acre with

grams as the unit of measure

CF = 100 where subplot size is 0.01 acre with

pounds as the unit of measure.

8. Enter the air-dry weight adjustment for each

species.

Rules

8.1 Enter the appropriate air-dry weight (ADW)

proportion in decimal form in the “ADW adj”

column.

8.2 If available, use established charts and tables

that convert green weight to dry weight based

on various stages of growth. If local charts or

tables are not available, vegetation can be air

dried.

8.3 Repeat for each species.

9. Enter the utilization adjustment for each

species where livestock and/or wildlife

grazing has occurred.

Rules

9.1 Enter the proportion of the plant remaining

after utilization, in decimal form, in the “Util

adj” column.

9.2 Utilization can vary among subplots, so make

sure to use the average utilization for the

entire plot.

9.3 Example: if a plant averages 40 percent

utilization, then 60 percent remains and you

enter 0.60 in the “Util adj” column.

10. Enter the growth adjustment for each plant

species.

Rules

10.1 Enter the cumulative proportion of growth

(in decimal form) that has occurred during the

current year in the “Gwth adj” column.

10.2 This proportion is relative to the total

production expected for that year, regardless of

climatic variation. The growth adjustment

corrects for how much the plant has grown for

that year, against its potential for the year

(100 percent). For example, if growth

adjustment on July 1 is 60 percent during a dry

year, it is also 60 percent on July 1 during a

wet year, even though the total amount of

growth on July 1 of a dry year may be much

less than that of a wet year.

10.3 Growth curves are available for most major

rangeland species in the United States. These

growth curves show the typical cumulative

proportion of growth by calendar date. These

curves are approximate, as they do not

account for annual variability in rainfall

distribution. Contact your local NRCS office or

Extension office for further assistance.
54

11. Enter the weather (climate) adjustment for

each plant species.

Rules

11.1 Enter the weather (climate) adjustment in

decimal form in the ”Wthr adj” column.

11.2 The weather adjustment is used to describe

the kind of growing conditions that have

occurred or are expected. This includes

precipitation amount, intensity and timing, as

well as temperature, and their relationships to

one another.

11.3 Enter a value between 0.1 and 2.0.

11.4 This adjustment can be different for different

species, depending on the moisture and

temperature requirements of the plants.

11.5 Example: An adjustment of 1.0 would

indicate that the growing conditions were

normal for the site that growing year. An entry

of 1.2 would indicate that the growing

conditions exceeded normal by an amount

sufficient to increase species productions by

20 percent. An adjustment of 0.75 would

indicate that the growing conditions were only

sufficient to support 75 percent of normal

species productions.

Plant production calculations

1. Add the total weight units for each species.

Rules

1.1 Add the weight units in each subplot by

species and enter this in the “Total wt units”

column.

1.2 Record weight units to the nearest decimal.

1.3 Ignore trace amounts, or “T’s.”

Production

2. Calculate the double-sampling correction

factor.

Rules

2.1 For the clipped subplots only, enter the total

estimated weight for each species in the

“Clipped subplots Est wt” column.

2.2 Total estimated weight = total weight units

(Total wt units) in the clipped subplot,

multiplied by the weight unit weight (Wt unit

wt).

2.3 Enter total clipped weight for each plant

species for the clipped subplots in the “Clipped

subplots Clip wt” column.

2.4 Calculate the double sampling correction

factor by dividing the “Clipped subplots Clip

wt” by the “Clipped subplots Est wt.”

2.5 Enter the double sampling correction factor in

the “Clip/Est CF” column.

3. Calculate pounds per acre for each plant

species.

Rules

3.1 Use the following equation to calculate air-dry

reconstructed weight in pounds per acre,

where s = the number of subplots:

3.2 Enter this value in the “Total wt (lb/ac)”

column.

lb/ac = ________________________________

(Total wt units x Wt unit wt x 1/s x

Plot size CF x ADW adj x Clip/Est CF)

 (Util adj x Gwth adj x Wthr adj)
55

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North Pasture 1 Renee Smith Ken Fields 1 1

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56
57

Chapter 10

Plant species richness (modified

Whittaker approach)

“Plant species richness” is the total number of

species in an area. It is one indicator of

biodiversity. This Plant species richness method is

based on Stohlgren et al. (1995) and Bull et al.

(1998). The Plant species richness method is very

time intensive. The number of measurements may

be reduced, depending on information

requirements and time availability. A minimum

estimate of species richness can be calculated from

Line-point intercept data. The Line-point intercept

estimate of species richness can be supplemented

by a thorough search for exotics and other species

of interest throughout the plot area.

Note: precise unit conversions are used in this

chapter to facilitate calculations.

Materials

• The same transect(s) used for Line-point and

Gap intercept

• 100 m (328 ft) tape

• Metal stakes and hammer for marking plot

corners

• At least 120 m (400 ft) of twine to mark plot

borders

• Clipboard, Plant Species Richness Data Forms,

pencil(s)

Standard methods (rule set)

1. Set up plots.

Rules

1.1 Lay out the 10 by 30 m plot.

1.2 Anchor 100 m (328 ft) tape at point “a” on the

plot, 5 m (15 ft 5 in) and 90° away from the

“0” end of the transect (Fig 10.1).

1.3 Pull the tape out, crossing the “0” end of the

transect, to 10 m (32 ft 10 in) (point “b”) and

wrap it around another stake (Fig. 10.2).

1.4 Continue pulling the tape out, parallel to the

transect. At 30 m (98 ft 5 in), insert another

stake (point “c”). The tape will read 40 m

(131 ft 3 in) at this stake. Continue to point “d.”

1.5 Finish by pulling the tape back to point “a.”

1.6 The tape should read 80 m (262 ft 6 in) once

you are done.

1.7 Pull in the tape, but leave all the stakes in

place.

1.8 Anchor twine at one of the stakes and string it

out where the tape was.

1.9 Continue laying out the smaller plots, using

twine, as in Figure 10.1.

Figure 10.1. Species richness plots and their layout

with respect to a monitoring plot. Drawn to scale.

Figure 10.2. Pulling out tape to set up the species

richness plot.

40 m

10 m

0 m

a, 0 m b, 10 m

d, 50 m c, 40 m

0.5 x 2 m plots

10 x 30 m plot

2 x 5 m plot, centered at 15 m

vegetation transect

spoke center, photopoint location X 5 m radius, buffer zone

50 m
58

(Relatively) rapid alternatives

The simplest alternative is to use the minimum

estimate provided by the Line-point intercept.

However, this will miss most species. Another

alternative is to search the 10 x 30 m plot

without subplots. This is appropriate if the

species-area curve is not required.

Species richness

1.10 Center the 2 x 5 m (6 ft 7 in x 16 ft 5 in) plot

at the 15 m (49 ft 3 in) position on the

vegetation transect.

1.11 Place four 0.5 x 2 m (1 ft 7 in x 6 ft 7 in) plots

in the corners of the large (10 x 30 m or 32 ft

10 in x 98 ft 5 in) plot.

1.12 Repeat steps 1.1 through 1.11 for the two

remaining vegetation transects, if desired. Due

to the high sampling costs, it is generally more

cost-effective to sample fewer transects (one

can be sufficient) at more locations.

2. Record number of species in each plot.

Rules

2.1 Make all observations on all species richness

plots on one transect at a time. Complete

observations on all transects within a plot.

Then move to the next transect.

2.2 Use one data form for each transect.

2.3 Start with the smallest (0.5 x 2 m or 1 ft 7 in x

6 ft 7 in) plots.

2.4 Record all species that occur in a small plot

under the appropriate column on the data

form (Fig. 10.3).

2.5 At least half of a plant base must be inside the

plot boundary to be recorded. Plants with less

than half their bases in the plot are not recorded.

2.6 Record the species in the “Species code” column

of the Plant Species Richness Data Form, using

one of the following: the PLANTS database

species code (http://plants.usda.gov); a four-letter

code based on the first two letters each of the

genus and species; or the common name.

2.7 Move to the next small plot and record all

species in that plot in the next column.

2.8 Repeat 2.1 through 2.7 until all four small

plots are sampled.

2.9 Search the 2 x 5 m (6 ft 7 in x 16 ft 5 in) plot

and record all species detected.

2.10 Search the 10 x 30 m (32 ft 10 in x 98 ft 5 in)

plot and record all species detected.

2.11 Make sure to include all species already found

in the smaller plots in the list for the 10 x 30 m

(32 ft 10 in x 98 ft 5 in) plot.

Plant species richness

calculations

1. Measure species richness.

Rules

1.1 Count all species encountered in all the plots.

1.2 Each species is counted only once, no matter

how many plots it occurs in.

2. Estimate species richness (not included on

data form).

Rules

2.1 This should only be calculated by someone

with an understanding of linear regression. It

is based on the assumption that there is a

linear relationship between the number of

species and the log of the area for uniform

areas.

2.2 Graph the number of species found in each

plot against the log of the area of each plot

(0, 1 and 2.5 for the 1, 10 and 300 m2

 plots).

2.3 The equation below can be used to predict

species richness in a larger area provided that

the area is relatively uniform and that the plot is

representative of the area.

Species richness = intercept + (constant) x (log [area])

2.4 For monitoring, it is strongly recommended

that only measured species richness be used.

Figure 10.3. Record each plant species within each of

the four small (0.5 x 2.0 m or 1 ft 7 in x 6 ft 7 in) plots.
59

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61

T

he Vegetation structure method provides

information on visual obstruction and

habitat structure (and thus suitability) for

various wildlife species. Visual obstruction

methods have also been used to estimate plant

biomass. A large amount of literature exists related

to various uses of this method and associated

indicators (e.g., Flather et al. 1992, Interagency

Technical Reference 1996, MacArthur and

MacArthur 1961, Robel et al. 1970, Nudds 1977).

There are many ways to measure vegetation

structure based on visual obstruction. There is no

standard method, nor is there a standard set of

indicators. The Vegetation structure method

described here is similar to methods that have

been used historically for research and monitoring,

such as a Robel pole, cover board, vegetation

profile board or density board.

The dimensions of the cover pole can be easily

modified to address different objectives.

Materials

• The same transect(s) used for Line-point and

Gap intercept

• Cover pole (see Appendix A for construction)

• 1 m (3 ft) PVC sighting pole

• Clipboard, Vegetation Structure Data Forms,

pencil(s)

Standard methods (rule set)

Before beginning the measurements, record the

length of each segment on your cover pole at the

top of the data form. The four segments are

numbered from the top to the bottom of the pole.

Each segment is subdivided into five equal bands.

A typical segment length is 0.5 m (1 ft 8 in) on a 2 m

(6 ft 8 in) pole. Each band is then 10 cm (4 in).

1. Randomly select five positions along each

transect.

Rules

1.1 Record the transect or line number under

“Line” on the data form.

1.2 Record each position under “Position” on the

data form.

Chapter 11

Vegetation structure

1.3 Positions must be at least 7 m (22 ft) apart.

2. Place the cover pole at the first position.

Rules

2.1 The recorder places the cover pole at the

sample position.

3. Collect Vegetation structure data.

Rules

3.1 The observer stands 5 m (15 ft) from the cover

pole, along the transect.

3.2 Using the “sighting pole” to maintain a

constant observation height, the observer

records whether or not each band is covered

by vegetation.

3.3 A band is considered covered by vegetation if

at least 25 percent of the band is visually

obstructed by vegetation.

3.4 Record “1” on the data form if the band is

visually obstructed. Record “0” if the band is

not obstructed.

3.5 The observer repeats steps 3.1 through 3.4,

standing 5 m (15 ft) from the cover pole in the

opposite direction, along the transect.

4. Repeat steps 1 through 3 for all sample

positions along a transect and for all

transects.

Figure 11.1. Observer stands 5 m (15 ft) from the

cover pole, along the transect.
62

Vegetation structure indicator

calculations

1. Calculate the average percent of visual

obstruction.

Rules

1.1 Add the number of bands within each segment

obstructed by vegetation.

1.2 Percent visual obstruction = 100% x (number

of bands obstructed ÷ total number of bands)

1.3 Calculate the plot average for each segment.

Add up all percent visual obstructions (Vis.

obst.). Then divide this total by the number of

Vis. obst.

2. OPTIONAL. Calculate the foliage height

diversity (FHD, the vertical structural

diversity). Note: This indicator requires a

calculator or computer, so it is not included

on the field data form.

Rules

2.1 The formula for foliage height diversity is:

FHD =   pi ln pi

2.2 For each segment at each observation, add the

number of bands obstructed by vegetation.

2.3 Sum the number of bands in each segment for

the entire plot.

2.4 Calculate the proportion of total hits found in

each segment:

pi = proportion of hits in the ith segment,

where i = 1 to 5.

2.5 Multiply the proportion of hits in each

segment (from rule 2.4) by its natural log

pi * ln pi

2.6 Add up all pi * ln pi.

2.7 Multiply the sum obtained in Rule 2.6 by -1.

Figure 11.2. Example of a cover pole

with some visual obstruction.

Vegetation structure
63

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65

Chapter 12

Tree density

I

t is important to quantify the density and size

of trees in savannas and grazed woodlands in

order to understand the structural diversity of

the plant community. Structural diversity at a site

can provide protection from elements and cover

for wildlife. Increased density of trees in savannas

and grazed woodlands could indicate a trend

toward an important community change.

The method described here is extracted from

the USFS Forest Inventory and Analysis (FIA) National

Core Field Guide Volume I: Field Data Collection

Procedure for Phase 2 Plots, Version 1.7 (USDA

Forest Service 2003). The FIA protocol includes a

large number of additional requirements (e.g.,

assigning a unique record number to each tree)

and indicators not needed for our monitoring

objectives. For more information on the FIA

protocol, please see http://www.srs.fs.usda.gov/fia/

data_acquisition/field_guide/p2manual.htm.

Materials

• The same transect(s) used for Line-point and

Gap intercept

• Extending range pole

• Steel pins for anchoring tape

• Additional tape (for defining subplots)

• Diameter or DBH tape

• Clipboard, Tree Density and Size Data Forms

and pencil(s)

Standard methods (rule set)

1. Define measurement area for trees and

saplings (>2.5 cm [1 in] in DBH [Diameter at

Breast Height] or DRC [Diameter at Root

Collar]).

Rules

1.1 Establish four subplots, one with its center

located at the center of the spoke and the

remaining three located at 36.6 m (120 ft) on

each of the three transects (Fig. 12.1).

1.2 Subplots should have a 7.3 m (24 ft) radius (see

subplots in Fig 12.1).

1.3 Other subplot sizes may also be used. If using a

different subplot size be sure to record the size

and adjust indicator calculations accordingly.

2. Determine for which species DRC will be

used instead of DBH.

Rules

2.1 DRC is normally used on multi-stemmed

species.

2.2 A list of species that the USFS classifies as

multi-stemmed can be found in Appendix 4 of

the FIA protocol (USDA Forest Service 2003).

Figure 12.1. USFS Forest Inventory and Analysis plot

diagram (modified from USDA Forest Service 2003).

3. Record the species or common name for

each tree that falls within each subplot.

Rules

3.1 Include only those individuals with at least

50 percent of the plant base inside the plot.

3.2 Use the same codes or names used for the

Line-point intercept method.

3.3 Record the species code in the “Species”

column of the Tree Density and Size Data

Form.

1

2

4 3

•

•

•

• •

• •

• 1

2

4 3

•

• •

•

Azimuth 1-2 = 360°

Azimuth 1-3 = 120°

Azimuth 1-4 = 240°

Subplot:

7.3 m (24.0) ft 

radius

Distance between

subplot centers is

36.6 m (120 ft)
66

Tree density

Figure 12.2. Savanna/woodland ecosystem showing

relatively low tree density.

4. Record the DBH or DRC in the appropriate

column.

Rules

4.1 Measure DBH at 1.4 m (4.5 ft) using a diameter

tape (Fig. 12.3).

4.2 If a diameter tape is not available, measure

with a standard tape measure and convert to

diameter with the following formula (3.14):

diameter = circumference  

4.3 Measure DRC as illustrated in Fig. 12.4.

For multi-stemmed individuals,

DRC for the tree = SQRT (SUM [DRC2]).

5. Record each tree’s height.

Rules

5.1 Measure the maximum height of the tree as

the distance from the bottom of the trunk to

the highest point of the canopy.

Figure 12.3. Measuring DBH.

Figure 12.4. How to measure DRC (modified from

USDA Forest Service 2003).

5.2 If the tree is too tall to measure with a meter/

yard stick, use an extendable range pole

(Fig. 12.5), visually estimate the height, or

use a clinometer and trigonometry.

5.3. Record tree height in the “Height” column on

the data form.

Figure 12.5. Measuring tree height with an

extendable range pole.

a. Measure at ground line

 when reasonable. b. Measure above root collar.

c. Multistemmed above

 diameter.

d. Excessive diameter below stems.

 Measure stems. Compute DRC.

e. Measure missing stem(s)

 Compute DRC.

f. Multistemmed at or below

 ground. Compute DRC.
67

Tree density calculations

1. Calculate the plot area in acres.

Rules

1.1 Plot area = (4 x  x plot radius x plot radius) ÷

conversion factor ( 3.14).

1.2 The metric conversion factor is 10,000

(converts square meters to hectares).

1.3 The English conversion factor is 107,639

(converts square feet to hectares).

 Tree density

2. Sum the number of trees and saplings.

Rules

2.1 Count all trees detected on all four subplots

(trees have a DBH or DRC  12.7 cm or 5 in).

2.2 Count all saplings detected on all four subplots

(saplings have a DBH or DRC  2.5 cm or 1 in

and  12.7 cm or 5 in).

3. Calculate densities.

Rules

3.1 Tree density = (total no. of trees) ÷ (plot area).

3.2 Sapling density = (total no. of saplings) ÷ (plot

area).

Subplot 1 (plot center) Subplot 2 (Line 1) Subplot 3 (Line 2) Subplot 4 (Line 3)

Species DBH DRC Ht. Species DBH DRC Ht. Species DBH DRC Ht. Species DBH DRC Ht.

Tree Density and Size Data Form

Monitoring plot: Date: Line length:

(m or ft?)

Observer: Recorder:

Subplot radius Diameter units: Height units:

(m or ft?)

(m or ft?)

circle one

circle one

circle one circle one (cm or in?)

plot radius plot radius

plot radius plot radius

Total plot area (all subplots) = (4 x 3.14 x

Total plot area (all subplots) = (4 x 3.14 x

m x

ft x

m) ÷ 10,000 =

ft) ÷ 107,639 = hectares

hectares

Tree

density

Sapling

density

No. of trees

Plot area

No. of saplings

Plot area = = = = = = 

= Total number of TREES (DBH ≥ 12.7 cm [5 in])

= Total number of SAPLINGS (2.5 cm [1 in] < DBH < 12.7 cm [5 in])

0.07

1

0.07

14.3

7.3 7.3

3

1

3

0.07

42.9

POFR 40 6.5 POFR 52 11.5 POFR 4 3

FRVE 35 10

3 36.6

Mark Second Tara Third

22 July 2003

7.3

Example
68

Subplot 1 (plot center) Subplot 2 (Line 1) Subplot 3 (Line 2) Subplot 4 (Line 3)

Species DBH DRC Ht. Species DBH DRC Ht. Species DBH DRC Ht. Species DBH DRC Ht.

Tree Density and Size Data Form

Monitoring plot: Date: Line length:

(m or ft?)

Observer: Recorder:

Subplot radius Diameter units: Height units:

(m or ft?)

(m or ft?)

circle one

circle one

circle one circle one (cm or in?)

plot radius plot radius

plot radius plot radius

Total plot area (all subplots) = (4 x 3.14 x

Total plot area (all subplots) = (4 x 3.14 x

m x

ft x

m) ÷ 10,000 =

ft) ÷ 107,639 = hectares

hectares

Tree

density

Sapling

density

No. of trees

Plot area

No. of saplings

Plot area = = = = = = 

= Total number of TREES (DBH ≥ 12.7 cm [5 in])

= Total number of SAPLINGS (2.5 cm [1 in] < DBH < 12.7 cm [5 in])
69

Chapter 13

Riparian channel vegetation

survey

T

he Riparian channel vegetation survey

provides a general assessment of plant cover

and composition along both sides of the

channel. It is appropriate for systems and reaches

with the potential to support a continuous band of

riparian vegetation on the banks.

This approach is based on the “greenline”

method developed by Alma Winward (2000),

except that Winward’s greenline method is based

on identifying the plant community at each point

along the channel. Winward’s method is superior

to the method presented here and is

recommended if the expertise is available to

identify plant communities.

Winward’s definition of the greenline is

critical: “the first line of perennial vegetation that

forms a lineal grouping of community types on or

near the water’s edge.” Winward adds, “Most often

the greenline is located at or near the bank-full

stage (Fig. 13.1). Or, as flows recede and the

vegetation continues to develop summer growth,

it may be located part way out on a gravel or

sandbar (Fig. 13.2). At times when banks are

freshly eroding or when a stream has become

entrenched, the greenline may be located several

feet above bank-full stage (Fig. 13.3). In these

Figure 13.2. Location of the greenline

(Winward 2000).

Figure 13.1. Location of the greenline at or

near bank-full stage (Winward 2000).

Figure 13.3. Location of the greenline on an

eroded bank. Following the definition of

greenline, “the first line of perennial vegetation

that forms a lineal grouping of community types

on or near the water’s edge,” the eroded nonriparian portion of the stream bank serves as

the current greenline (Winward 2000).

situations, the vegetation is seldom represented by

hydrophilic species and, in fact, may be composed

of non-riparian species...” (Winward 2000).
70

2. Measure the channel vegetation.

Rules

2.1 Begin in the channel at the point the Linepoint or Gap intercept tape crosses the

channel.

2.2 Indicate the direction of the “walk” (upstream

or downstream) and record the side of the

channel (e.g., NE, NW, SE or SW) in the blank

next to “Stream side” on the Riparian Channel

Vegetation Survey Data Form.

2.3 Select a stream side and direction and begin

the survey, keeping in mind the standard pace

length. Place the “L” tool at the first pace

(“Pt.” on the data form) along the edge of the

greenline with the “scope” end pointing away

from the center of the channel (Fig. 13.5).

2.4 For the point defined by the center of the Ltool scope, record the uppermost top layer

species intercept under “Top layer” on the data

form.

2.5 Look up and down the scope, if necessary, to

ensure that all species are recorded. Record

additional species intercepts in the appropriate

“Lower layers” column.

2.6 Record the appropriate soil surface code in the

last column of the data form.

2.7 If desired, record the height of the tallest plant

intercepted in the “Ht.” column.

Riparian vegetation

Figure 13.4. Checking pace length.

Materials

• “L” tool or dual-ended laser pointer (see

Appendix A for construction and suppliers)

• 50 or 100 m tape (150 or 300 ft)

• Meter (or yard) stick

• Extending range pole

• Clipboard, Riparian Channel Vegetation

Survey Data Forms and pencil(s)

Standard Methods (rule set)

Note: Due to the difficulty in defining the

greenline, and the physical impossibility of

defining a permanent transect, on-site trainings

are particularly important for this method. Where

possible, this method should be repeated by the

same person each time it is completed.

1. Determine pace length.

Rules

1.1 In order to increase the repeatability of this

method, observers should try to calibrate their

pace 1 m or 3 ft.

1.2 Determine pace length by repeatedly walking

along a measuring tape and counting the

number of paces required for a particular

distance (e.g., 100 m or 100 yd) (Fig. 13.4).

1.3 Divide distance by pace number to determine

pace length (e.g., 100 m ÷ 125 paces = 0.8 m

[80 cm] per pace).

Figure 13.5. Conducting the Riparian channel

vegetation survey.
71

3. Repeat the measurement for all four sides of

the channel.

Rules

3.1 Return to the start point for each of the four

“walks,” two upstream and two downstream

on each side of the channel, and repeat steps

2.2 through 2.7.

 3.2 The stopping points on one bank may not

coincide with those on the other bank due to

differences in the lengths of meanders on each

side of the channel.

Riparian channel vegetation

survey indicator calculations

See the Line-point intercept section in Quick Start

for foliar cover, basal cover and bare ground.

Instructions are provided here for calculating three

additional indicators.

Site-specific indicators based on functional

groups can be extremely valuable in riparian areas.

Winward (2000) includes specific suggestions for

developing indicators of greenline successional

status and greenline bank stability, based on

community types that have been defined for the

U.S. Intermountain Region.

1. Calculate percent stabilizing species cover.

Rules

1.1. Acquire or develop a list of bank stabilizing

species. Winward (2000) includes lists for the

U.S. Intermountain region.

1.2. Count the total number of sample points at

which a stabilizing species was recorded.

1.3. Multiply the number of stabilizing species

sample points (from rule 1.2) by 2* and record

your “% stabilizing spp. cover” in the blank

provided on the data form.

2. Calculate stabilizing species as a percent of

total species cover**.

Rules

2.1 Acquire or develop a list of bank stabilizing

species (see Winward 2000).

2.2 Count the total number of times that a

stabilizing species was intercepted (“Top layer”

and “Lower layers” columns). Where more

than one stabilizing species is intercepted at a

point, all are counted. Record this on the data

form as the numerator or “Total no. of

stabilizing spp intercepts.”

2.3 Count the total number of plant intercepts, to

include species and litter intercepts (“Top

layer” and “Lower layers” columns). Record

this on the data form as the denominator or

“Total no. of plant intercepts.”

2.4 Divide the total number of stabilizing species

intercepts by the total number of plant

intercepts. Multiply this value by 100 and

record in the blank provided.

3. Calculate percent woody species cover.

Rules

3.1 Acquire or develop a list of woody species.

3.2 Count the total number of sample points at

which a woody species was recorded and

record in the blank provided on the data form.

3.3 Multiply the number of samples points with

woody species (from 3.2) by 2* and record your

“% woody spp cover” on the data form.

*For 50 points per line. Multiply by 1 for 100 points per

line. Multiply by 4 for 25 points per line.

**Note that this is total species cover, not total cover.

Total cover would require that multiple plant

intercepts of the same species be recorded.

Riparian vegetation
72

1 1

X

Cathy Berger Tom Marshall

15 September 2002

NE

100

FEID

NONE

NONE

NONE

NONE

POPR

NONE

NONE

ARTRT

NONE

ARTRT

ARTRT

NONE

NONE

NONE

NONE

ARTRT

NONE

NONE

ARTRT

ARTRT

ARTRT

ARTRT

ARTRT

ARTRT

ARTRT

ARTRT

ARTRT

COSE

COSE ARTRT

COSE

ROWO

FEID

FEID

FEID

COSE

COSE

NONE

COSE

COSE

ARTRT

ARTRT

COSE

ROWO

ARTRT

ARTRT

ARTRT

POFR

POFR

40

180

155

10

220

205

220

190

160

210

240

220

L

L

L

FEID

FEID

FEID

L

L

L

L

L

L

L

L

L

L

ARTRT

FEID

FEID

ARTRT

ARTRT

85

155

200

105

190

170

145

90

10

5

5

120

130

210

190

160

180

220

100

200

90

150

700

550

FEID

S

R

R

R

S

S

R

S

R

S

ARTRT

R

R

R

R

S

R

R

S

S

S

ARTRT

S

ARTRT

S

ARTRT

S

S

S

S

S

ROWO

S

S

S

S

S

S

S

COSE

S

S

S

S

ARTRT

S

S

S

S

36

3

8

72

6

16

8

31

8

44

18.2

62

16

Example

North
73
74

Chapter 14

Riparian channel and gully

profile

T

he Riparian channel and gully profile

provides a description of channel shape. This

method can also be used to record the shape

of the soil surface (e.g., covered by rills and gullies)

in uplands.

Caution!

• Stream currents can be dangerous.

• Use this method only when and where it can

be safely applied.

Materials

• Two 1.5 m (5 ft) rebar stakes

• 100 m (300 ft) roll of nylon string

• Hacksaw

• Hand sledge

• String line level

• Meter stick

• 100 m (300 ft) tape

• Clipboard, Riparian Channel Profile Data

Forms and pencil(s)

Standard Methods (rule set)

1. Determine the location for the profile.

Rules

1.1 Measure the profile where the Line-point or

Gap intercept crosses the channel.

1.2 Determine where the edge of the greenline is

on each side of the channel.

2. Erect rebar, string and tape.

Rules

2.1 Using the hacksaw, make a notch on both

pieces of rebar about 3 cm from the end.

2.2 Pound one rebar in on one side of the channel

at least 2 m (6 ft 7 in) in from the edge of the

greenline, leaving 25-50 cm (10-20 in) exposed

(Fig. 14.1). Install with the notch end up.

2.3 Tie the nylon string at the notch and pull it

tight across the channel.

2.4 Determine how high the other rebar should be

in order for the line to be level on the opposite

side of the channel, and then pound in the

other rebar.

2.5 Pull the string tight and tie off at the notch on

the second rebar.

2.6 Install the line level and fine-tune the depth of

either rebar until the line is perfectly level

(Fig. 14.2).

2.7 Stretch the tape between the pieces of rebar

with the 0 end on the left as you are looking

upstream.

Figure 14.1. Installing rebar 2 m from edge of

greenline.

Figure 14.2. Level the string across the channel.
75

Channel profile

3. Record the channel profile.

Rules

3.1 Beginning at the rebar at the 0 m end of the

measuring tape, measure the distance from the

soil surface to the string, using a meter stick

(Fig. 14.3).

3.2 Record the position along the tape under “Tape

distance” and the channel depth measurement

under “Channel depth” on the data form.

3.3 Repeat these measurements at 50 cm (1 or 2 ft)

intervals.

3.4 Make the final measurement at the rebar on

the opposite end of the channel.

3.5 For riparian systems only (not gullies), record

the location of the greenline (bank-full) on

each side of the channel.

Figure 14.3. Record the channel depth every 50 cm

(1 to 2 ft) for the length of the tape crossing the

channel.

Riparian channel profile and

soil surface contour indicator

calculations

Note: Due to the difficulty in defining channel

width, the same person should calculate these

indicators each time. Save the raw data so the

indicators can be recalculated in the future. These

indicators can be used to monitor relative changes.

Interpretation requires a trained professional who

is familiar with the area. Please see Chapter 17 for

more information.

1. Graph the channel profile.

Rules

1.1 Mark a line at the top of the graph to represent

the string. Make sure it is parallel to the “x”

(horizontal) axis, and set it to “0”.

1.2 Graph heights relative to the “y” (vertical)

axis, creating a graph that looks like the shape

of the channel.

1.3 Always draw the graph as if you are looking at

the profile from downstream of it.

1.4 Graph each measurement as a negative

number against the distance along the

measuring tape.

1.5 If measurements are not evenly spaced or a

measurement is missing and you are using a

computer, be sure that the “x” axis is correct.

In Microsoft® Excel, you must use the “scatter”

(not the “line”) graph option.

2. Calculate the bank angle.

Rules

2.1 On the graph, mark the base and top of the

bank on the side of the channel marked by the

0 end of the tape.

2.2 Measure the horizontal distance between these

two points.

2.3 Measure the vertical distance between these

two points.

2.4 Divide the vertical distance by the horizontal

distance.

2.5 To express the angle in percent, multiply the

result of Rule 2.4 by 100.

2.6 To express the angle in degrees, use a

calculator to calculate the arctangent of the

result of Rule 2.4. Excel and some calculators

report the result in radians. To convert from

radians to degrees, multiply by 57.3. In Excel,

the formula is:

=DEGREES(ATAN(result of Rule 2.4)).

2.7 Record this as the “bank angle (0-end)” in the

blank provided on the data form. Include

appropriate units (percent or degrees).

2.8 Repeat 2.1 through 2.7 for the other bank on

the non-zero end of the tape and record as the

“bank angle (non-0 end).” Ensure you subtract

the base from the top at the non-0 end.
76

Channel profile

3. Calculate the width:depth ratio.

Rules

3.1 The width is the horizontal (parallel to the “x”

axis) distance between the points used for the

bank angle at the top of each bank.

3.2 The depth is the greatest vertical distance from

a straight line drawn between these two points

to the bottom of the channel. The straight line

between the two points will not necessarily be

horizontal.

3.3 Divide the width by the depth and record as

the “width:depth ratio” in the blank provided

on the data form.

Sinuosity

The level of sinuosity is an excellent indicator

of stream status, particularly in relatively low

gradient systems. Sinuosity is most easily

quantified using aerial photography. A simple

index of sinuosity is the ratio of distance along

the streambed to the straight-line distance

between two points.
77

Depth

Distance

Bank angle (0 end) = vertical distance

horizontal distance x 100% = x 100% = ______ %

Bank angle (non-0 end) = x 100% = ______ %

Width:depth ratio = = ______

width

depth =

vertical distance

horizontal distance x 100% =

Tape

distance

Channel

depth (cm)

Tape

distance

Channel

depth (cm)

Tape

distance

Channel

depth (cm)

Tape

distance

Channel

depth (cm)

Riparian Channel Profile Data Form

Date: _____________________________ Shaded cells are for calculations

Monitoring plot: ____________________ Line: _______

Observer: _________________________ Recorder: _________________________

Side of channel where line starts (N, S, E, W): ______________

Tape distance always starts at "0" with a reading from where the string is tied to the rebar.

The last reading should be where the string is tied to the rebar on the opposite side of the channel.

0

50

100

150

200

250

300

350

400

450

500

550

600

650

700

750

800

850

900

950

1000

1050

1100

1150

1200

1250

1300

1350

1400

1450

1500

1550

1600

1650

1700

1750

1800

1850

1900

1950

2000

2022

33.0

33.0

42.5

45.5

46.5

43.5

46.0

47.0

48.5

51.0

52.5

56.0

58.5

62.0

62.5

64.5

65.0

62.5

64.5

70.5

70.5

72.0

70.0

76.5

71.5

75.5

77.5

80.0

78.5

79.0

78.5

75.0

67.5

71.0

74.0

75.5

71.0

57.5

7.0

3.5

3.0

3.0

5 October 2002

North

Ken Fields

1

Ken Fields

E

47

1300

3.6

77

650

11.8

1950

77

25.3

Remember for

this end:

2000 - 1350 = 650

◆

◆ ◆

◆ ◆ ◆

◆ ◆ ◆ ◆ ◆ ◆ ◆ ◆ ◆ ◆ ◆ ◆ ◆ ◆

◆ ◆ ◆ ◆

◆

◆

◆ ◆ ◆ ◆ ◆ ◆

◆

◆

◆ ◆ ◆

◆

◆

◆

◆ ◆

top

base

string

0

-10

-20

-30

-40

-50

-60

-70

-80

-90

0 500 1000 1500 2000 2500

(c m)

(c m)

top

Example
78Depth

Distance

Bank angle (0 end) = vertical distance

horizontal distance x 100% = x 100% = ______ %

Bank angle (non-0 end) = x 100% = ______ %

Width:depth ratio = = ______

width

depth =

vertical distance

horizontal distance x 100% =

Tape

distance

Channel

depth (cm)

Tape

distance

Channel

depth (cm)

Tape

distance

Channel

depth (cm)

Tape

distance

Channel

depth (cm)

Riparian Channel Profile Data Form

Date: _____________________________ Shaded cells are for calculations

Monitoring plot: ____________________ Line: _______

Observer: _________________________ Recorder: _________________________

Side of channel where line starts (N, S, E, W): ______________

Tape distance always starts at "0" with a reading from where the string is tied to the rebar.

The last reading should be where the string is tied to the rebar on the opposite side of the channel.
79

Chapter 15

Density, frequency and Line-point

intercept alternative methods

T

his chapter includes a brief discussion of

density and frequency methods, and

alternative Line-point intercept methods.

Density and frequency are generally used for

individual species of interest, although it is

possible to use them for all species encountered in

an area. For more information on density and

frequency, see Elzinga et al. 2001.

Density

Plant density is simply the number of individuals

per unit area. It is particularly useful for

monitoring vegetation where cover varies widely

during the season (e.g., annuals). It is not

appropriate where individuals are difficult to

distinguish (e.g., many rhizomatous grasses).

Method. Count the number of individuals of the

species of interest that have at least 50 percent of

their base in a subplot (quadrat) or other plot of

defined size. The subplots should be large enough

so that most of them include more than one

individual of each species that is being monitored.

Multiple noncontiguous subplots are randomly or

systematically located in the plot.

Calculations. Add the number of individuals

found in each subplot. Divide this sum by the area

of subplots to generate the average density

(number per square meter or square feet). To

convert to the number per hectare, multiply the

density by 10,000 (if working with square meters).

To convert the density in number per square feet

to the number per hectare, multiply by 107,639.

Frequency

Plant frequency is the proportion of subplots out

of all subplots of a specified size that contain a

particular species. It is a rapid and useful indicator

of the spatial distribution of different species, and

is appropriate for the same types of species as

density (above). Two methods for collecting

frequency data are the rapid method and the

intensive method. The rapid method generates

data for just one species. The intensive method

produces data for many species. Data collected

with the intensive method (below) can generate

information about fine-scale associations among

species.

Rapid method. Define and use only one subplot

size. The subplot should be small enough to ensure

that the species of interest does not occur in all

subplots. This is because if the species occurs in all

subplots, frequency will always be 1.0. Randomly

or systematically locate and establish subplots.

Count the number of subplots in which at least

one individual of the target species is located. A

species must have at least 50 percent of its base in

a subplot to be considered present.

Intensive method. Define and use only one

subplot size. Subplot size should be selected based

on the species of greatest interest. Randomly or

systematically establish subplots. Make a

comprehensive species list. For each subplot,

record whether or not each species occurs in that

subplot. A species is recorded for a subplot if at

least 50 percent of at least one plant base falls

within the subplot.

 To increase speed, use a species list with tally

marks or dot boxes. A dot box consists of four dots

in a square connected by four lines with an “X” in

the middle. Each dot and each line represents a

plot in which the species occurs, for a total of 10

individuals per complete dot box.

Calculations. Divide the number of subplots in

which the species occurs by the number of

subplots searched. This is frequency.
80

Alternative vegetation methods

Line-point intercept alternatives

Line-point intercept can be used to generate more

indicators than virtually any other monitoring

method. Adding height measurements (Option B

in Table 15.1) generates additional information on

vegetation structure. The height of first (top layer)

intercept is recorded under ‘Ht’. If litter or woody

litter is the tallest element (i.e., taller than the

plant at that point), record this in the notes. An

alternative method (used by the USDA National

Resources Inventory) is to record the tallest

element within a 15 cm radius of the point. If

species information is required, the species of this

element (or WL for woody litter) should be

recorded in a separate column. Options D through

H take less time, but generate fewer indicators.

Figure 15.1. Line-point intercept with height

alternative.

Typical applications. Line-point intercept (Table

15.1: A-E) should be used where precise, repeatable

measurements are required. Options D and E can

reduce time where changes in species composition

(e.g., grass to shrub, or annuals to perennials) are

not important. Option D is ideal where the

primary objective is to document changes in

erosion resistance.

Step-point intercept methods (Table 15.1: F-H)

require less time because no tape is required. They

can be relatively accurate provided that a pin is used

in place of the toe of your boot. Using the toe can

significantly overestimate cover because plants are

pushed over by the foot, which artificially

increases measured cover data.

Quadrat-point intercept. Where quadrats (or

subplots) are being used along a line (e.g., to

monitor frequency or density), points on the four

corners of the frame can sometimes be used to

replace four points along the line, provided that

the points are sufficiently far apart. The minimum

distance varies with plant community. To

determine whether or not this method is

appropriate, randomly select six transects and

compare means and variability for both methods.

For example, a 50 m transect with 100 points with

a point every 50 cm would be compared with a 50

m transect with 25 frames (four points each), one

frame located every 2 m.
81

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82
83

Section III: Indicator calculation

and interpretation

S

ection III explains how to calculate

monitoring indicators, and how to interpret

 monitoring results. Section III includes two

chapters.

Chapter 16 discusses three options for

calculating indicators: by hand using a calculator,

with Microsoft® Excel spreadsheets containing

automated calculations, or with a Microsoft®

Access database. Chapter 16 also introduces basic

statistics, which can be used to detect changes in

monitoring data over time.

Chapter 17 provides an overview of how to

interpret monitoring indicators. This chapter links

monitoring indicators to three ecological

attributes: soil and site stability, hydrologic

function and biotic integrity. Chapter 17 reviews

each method, some of the indicators that can be

calculated, and how the indicators relate to the

three ecological attributes, as well as how they

relate to important ecosystem processes.

References and additional resources are provided

for each method. Finally, this chapter introduces a

variety of approaches for extrapolating monitoring

data to the monitoring unit or landscape scale.
84

T

his manual (Volume I: Quick Start and

Volume II) includes instructions for the

calculation of basic indicators for each

measurement. All of the measurements can be

used to generate many additional indicators. Some

are listed in Table 4.2 and discussed in Chapter 17.

Three options for basic indicator calculation are

described below. See the “Extrapolation” section at

the end of Chapter 17 for a discussion of different

approaches for combining and extrapolating

results from multiple plots.

Option 1: Hand or calculator

calculation

The data forms were designed to facilitate rapid

indicator calculation in the field. Instructions are

provided in each chapter. While this is the least

efficient method, it is useful where data are

required to make an immediate field assessment

(e.g., to improve the quality of qualitative

assessments made using one of the systems

described in Chapter 3) and a field computer is not

available. It is also the most subject to error

because calculations can only be checked by reentering all of the data into a calculator or recalculating by hand.

Option 2: Spreadsheets

Spreadsheet versions of each data form are available

for download from http://usda-ars.nmsu.edu. To

use these spreadsheets, you will need Microsoft®

Excel 2000 or above, or a compatible program.

Spreadsheets automatically calculate the basic

indicators listed at the bottom of the data forms.

This method has the advantage of allowing data to

be re-checked after you enter the data. To calculate

indicators from more than one plot, simply copy

the blank data forms to new pages in the

spreadsheet, or to new spreadsheets.

Caution. The formulas are written for specific line

lengths, number of measurements and units

(English vs. metric). Some of the variables can be

modified; others cannot. Be sure to re-check the

values in all yellow boxes at the top of the form

before entering your data. It is also a good idea to

check the calculated indicators against your best

estimate.

Option 3: Database

A Microsoft® Access database will be available for

downloading from http://usda-ars.nmsu.edu. The

database is designed for field data entry using a

tablet PC or laptop and can also be used on a

desktop PC. The database calculates the basic

indicators for selected methods. Future versions

will include additional indicator options and

supplementary methods. The database is userfriendly, so you do not need to be a database

expert to use it. However, spending a few hours

learning what databases are and how they work

will help you take advantage of the many optional

features, such as designing your own queries to

extract different types of information.

Data entry is similar to the spreadsheets,

except that there are a number of enhancements,

such as choice lists, that can increase speed and

accuracy. The biggest advantage of the database is

that it automatically stores and organizes data

from multiple plots, and from multiple visits to

each plot. It also allows data to be combined and

compared in many different ways.

Caution: Formulas in the database, like those in

the spreadsheets, are based on specific line lengths

and number of measurements. Where possible, we

have included automatic checks in the database.

However, the inherent flexibility within the

database leaves it vulnerable to certain errors. For

example, if your transect length is 25 m and you

enter 50 m, your gap indicators will be off by a

factor of two.

Chapter 16

Calculate indicators
85

A recommendation: As with any software

package, we strongly recommend that you

check the formulas the first time you use them

by comparing with hand calculated indicators

and your own best estimates. If the values do

not match, begin by checking the fixed

variables (e.g., transect length and number of

measurements), then the data, and finally the

formulas.

Indicator calculation

85 percent bare ground, the mean is 25 percent

and the median is 10 percent. The median is more

representative of the area. However, both the

median and the mean fail to reflect that while most

of the area (four of five plots) has relatively high

cover, at least some of the area (represented by one

plot) has extremely low cover. It is often these areas

that are of greatest interest from a management

perspective. For this reason, it is useful to record the

maximum and minimum values in order to report

the range (e.g., 10 – 85 percent) of values.

In addition to the range, the standard

deviation (s) is often used to help describe how

variable the data are. The standard deviation is

also used to determine whether or not there is a

statistically significant difference between two

values. The formula for calculating standard

deviation is:

Detecting differences

Use a statistical computer program or the formulas

listed in Appendix C, Option 3 to make statistical

comparisons between years. It is best to consult

someone with statistical training before applying

these tests for the first time. Additional guidance is

provided in a number of texts, including Bonham

(1989) and Elzinga et al. (2001).

	X

n X = Monitoring Technical support

The monitoring web page (http://usdaars.nmsu.edu) will include responses to

Frequently Asked Questions (FAQs). We are

committed to continuing to improve the

quality of these tools, as resources permit.

Unfortunately, we do not have funding

available to provide direct technical support.

How to report mean (average),

median, range or standard

deviation for each indicator of

interest

Depending on the question you wish to answer,

you can report indicator statistics by plot,

ecological site, pasture, monitoring unit,

management unit, etc. These statistics are

calculated from data collected during the same

year, not from data collected over multiple years.

They are used to monitor changes through time.

The mean (X) is the most commonly reported

statistic. The mean or average is simply the sum

(j) of all values (X) divided by the number (n) of

values. It is useful as a general description but can

be extremely misleading if the data are not

normally distributed (bell curve) or thresholds

exist. The formula for calculating the mean is:

The median is the middle value. An equal

number of values are greater and less than the

median. This is often more useful than the mean

in characterizing a typical value for non-normally

distributed data, particularly if there are extreme

values. For example, if there are four plots with

10 percent bare ground and one plot with

	
  2

n1 s =
86

Chapter 17

Interpret results

I

f you have not already done so, calculate your

indicators using the data form at the end of

the relevant methods chapters, or automatically

generate them using the applicable excel

spreadsheet or the database (see Chapter 16). Then

review the five parts of this chapter.

Combining indicators is discussed first.

Options for interpreting your calculated indicators

are described in the second part (Interpretation

options).

The third part of this chapter (Attributes)

describes the three attributes (soil and site stability,

hydrologic function and biotic integrity). It

provides background information linking the

indicators to each attribute, allowing the user to

monitor the status of each attribute.

Each measurement and indicator are discussed

individually in the fourth section of this chapter

(Measurements and indicators), which is

organized by measurement. Scientific publications

and technical references relevant to specific

indicators calculated from each measurement are

listed at the end of each method.

The fifth section (Extrapolation) describes

how to interpret your data based on where the

plot is located in the landscape. This section also

explains how to extrapolate your results to larger

areas, where relevant.

Combining indicators

We recommend that the indicators not be

combined into an index unless you have extensive

expert knowledge of the system, and the index is

flexible enough to incorporate thresholds.

National inventories represent a situation in which

indices may be appropriate because they are often

the only way to integrate large volumes of data. In

this case, the risk of using an index is outweighed

by the benefit of making these data interpretable.

The problem with using simple indices (e.g.,

averages) in complex ecosystems is that they tend

to homogenize the data. Key indicators that a

system is at risk of crossing a threshold (e.g., the

presence of one individual of an exotic species)

can be easily disregarded if other indicators convey

stability. Instead, we recommend that the

preponderance of evidence be used for each of the

three attributes (soil and site stability, hydrologic

function and biotic integrity). In this approach, all

of the indicators for each attribute are considered

individually and an evaluation is justified based

on an understanding of how each indicator is

related to the functioning of that particular system.

If a key early warning indicator suggests that

the system is at risk of degradation, a change in

management should be considered, even if other

indicators do not reflect a change in the status of

the system. On the other hand, some indicators,

such as an unusually high density of annuals in an

otherwise degraded area, may suggest opportunities

to manage for recovery.

For more discussion on the “preponderance of

evidence” approach, see Pellant et al. (2005).

Interpretation options

There are three options for interpreting your

results. The option you choose depends on your

objectives, and on how much information you

have about your monitoring unit(s).

Option 1: Trend

Trend simply involves looking at the direction of

change in each indicator: whether it is positive,

negative or static.

Appropriate applications. Looking at trend is

appropriate if the objective is simply to determine

whether or not an area is changing. Trend can be

used to identify areas for more careful

management based on the rate and direction of

change. Careful examination of the indicators that

are changing can provide insight into the

management changes that are most likely to be

effective. Trend analysis provides little information

that can be used to predict whether or not a

change in management will be effective.

Information required. Most of the information

necessary for trend interpretation is included in

the “Attributes” and the “Measurements and
87

indicators” sections. In many cases, additional

knowledge of the ecosystem is necessary to

determine whether a change in an indicator is

large enough to represent a significant change in

ecosystem function.

Option 2: Comparison to a standard

This involves comparing the indicator value to an

optimum value. The similarity indices used by

land management agencies represent an example

of this approach. The species composition of a

landscape unit is compared to that expected for a

similar landscape unit at or near its ecological

potential.

Appropriate applications. Like Option 1, Option

2 is also appropriate if the objective is to

determine trend. The quantitative departure from

the standard can be used to prioritize areas for

management intervention and to more precisely

define relative improvement. Unlike Option 1,

Option 2 can generate an assessment from

measurements made at a single point in time. Like

Option 1, however, it cannot be used to determine

if a change in management is likely to be effective.

Information required. In addition to the

information provided in the “Attributes” and

“Measurements and indicators” sections, an

optimum range of values must be identified for

each indicator. Optimum values are different for

each ecological site or monitoring unit.

Option 3: Comparison to a state and transition

model

Indicator values or the range of indicator values

associated with the reference state in a state and

transition model (Ch. 24) are often used as a

reference. Alternatively, comparison to indicator

values associated with a threshold can be used.

Thresholds between ecological states are

defined in terms of the status of a large number

of interacting properties and processes.

Consequently, there is no unique threshold for a

particular indicator. Declines in one indicator can

be compensated for by increases in another. For

hydrologic function, for example, an increase in

the amount of time it takes for water to soak into

the soil can be compensated for by a reduction in

the distance between plant bases. A decrease in the

distance between plant bases increases the amount

of time water is retained on the site, and therefore

the amount of time water has to soak in.

Ideally, a range should be established for

critical indicators of states or thresholds for each

ecological site or equivalent functional unit.

Where threshold ranges are used, they should be

established with the understanding that additional

information must be used to make evaluations

when the indicator nears the threshold. For

example, a typical threshold range for canopy gaps

in arid grasslands susceptible to wind erosion is

50-75 cm. This assumes that the soil in the gaps

has been recently disturbed. Where gravel or

lichen crusts protect the surface, the threshold gap

size may be much larger or may not even exist.

This type of quantitative information is

increasingly being incorporated into NRCS

Ecological Site Descriptions (see Glossary) and

associated IIRH Reference Sheets (see Ch. 4).

The NRCS, TNC (The Nature Conservancy),

BLM and other organizations began developing

and publishing state and transition models in

2001, and are continuing this process. These

Interpretation

Guidelines for Selecting and

Using Reference Sites as

Standards

• Use areas that are geographically close to

monitoring sites, are located at a similar

landscape position, and have similar soils.

Landscape position is particularly important

in areas with differences in runoff or solar

exposure.

• Livestock and wildlife exclosures are

essentially small “islands” and hence are not

necessarily representative of processes that

occur across larger areas. Be very cautious

about using them as reference sites.

• Roadsides are generally associated with

additional runoff and nutrients, and the soil

is usually modified during road construction.

They are not recommended as reference sites.

• Ideal reference sites are those in which

anthropogenic disturbance is naturally

limited by distance from roads and/or water.
88

models may be useful in helping to identify

possible thresholds and suitable indicators.

Appropriate applications. Option 3 (comparison

to a state and transition model) is the ideal

approach for most ecosystems and objectives. It

allows areas that are more likely to be at risk of

serious degradation (e.g., crossing a threshold) to

be identified. Where warranted, land managers

can target areas at risk of serious degradation for

intensive management intervention. Option 3 also

helps land managers avoid wasting resources on

areas that have crossed an ecological threshold

and are therefore unlikely to respond to typical

management inputs.

Information required. This approach requires

that a threshold range be identified for each

indicator and each ecological site or equivalent

functional unit. It is also helpful to identify

optimum and worst possible ranges for the

indicator, as described under Option 2. For more

information on state and transition models, please

see Chapter 24.

How can qualitative indicators help?

In addition to assisting with site selection,

qualitative indicators can be extremely helpful for

interpreting quantitative indicators. They can also

help identify additional quantitative indicators to

calculate from the existing data. For example, if

increased pedestalling or rills are observed, it may

be worthwhile to look more closely at the Gap

intercept data for both plant canopies and plant

bases. Such an assessment may lead to the

calculation of additional indicators (e.g., percent

of the line covered by canopy gaps >75 cm).

Attributes

Three attributes (soil and site stability, hydrologic

function and biotic integrity) define the

foundation of most terrestrial ecosystems. Nearly

all of the human values supported by grassland,

shrubland and savanna ecosystems depend on

minimizing soil erosion, controlling the flow of

water through the system, and maintaining biotic

recovery processes. This section includes a brief

definition and a general description of each

attribute, and a discussion of the types of factors

Interpretation

that affect each attribute. In addition to the

information below, please see Interpreting Indicators

of Rangeland Health (Pellant et al. 2005) for a list of

easily observed indicators of each attribute.

Rangeland Soil Quality Information Sheets provide

additional information about some indicators and

the three attributes (see Appendix D or http://

soils.usda.gov/sqi/management/gl_mgmt.html).

Monitoring the Vegetation Resources in Riparian

Areas (Winward 2000) includes quantitative

indicators for a similar set of riparian system

attributes.

1. Soil and site stability

Soil and site stability are defined as the capacity of

the site to limit redistribution and loss of soil

resources (including nutrients and organic matter)

by wind and water. Grassland, shrubland and

savanna ecosystems are affected by both wind and

water erosion.

How can I tell if erosion is occurring? The best

way to learn about the different types of erosion in

your area is to make observations during an

intense rainstorm and on a very windy day. Look

especially for whether or not different types of

surfaces (under and between vegetation, disturbed

and undisturbed) erode.

Determining which type of erosion (wind or

water) is most important on a site can be difficult.

For example, wind erosion is clearly important in

the conversion of grasslands to mesquite coppice

dunelands in the southwestern United States and

northern Mexico. Water erosion also plays an

important role in soil loss and redistribution (Fig.

17.1), although its effects are often hidden by

subsequent redistribution by wind.

Fortunately, it is not necessary to determine

which type of erosion is most important in order

to monitor changes in the ability of different sites

to resist degradation. Most of the core indicators

calculated from the four basic measurements

reflect resistance to both wind and water erosion.

Some indicators are related to wind and water

erosion, while others are more relevant to only

one type of erosion.
89

What factors affect erosion? The susceptibility of

a site to wind and water erosion depends on static

and dynamic factors. Static factors are generally

independent of management. Slope and soil

parent material are static factors. Dynamic factors

change over relatively short periods of time and

are generally more influenced by management.

Plant cover and soil aggregate stability are

dynamic factors.

The indicators focus on dynamic factors

because management can affect them. It is

important to understand how the relatively static

erosion factors affect these indicators. These

inherent factors ultimately determine the extent

to which erosion can be controlled through

management on a particular site.

Water erosion: static factors. Factors influencing

erosion that cannot be controlled by management

include slope, aspect, soil depth, soil parent

material and climate.

Slope: Water running off steep slopes has more

energy to detach and carry soil particles to streams

and lakes. Lower parts of longer slopes are more

susceptible to rill and gully erosion because runoff

concentrates downslope.

Aspect: South-facing slopes in arid and semiarid areas in the northern hemisphere tend to

have lower vegetative cover than north-facing

slopes. This is due to greater evaporation and

higher temperatures from the south-facing slopes,

which are exposed to more of the sun’s energy.

The reverse applies in the southern hemisphere.

Soil depth: In higher rainfall areas, there is

often greater erosion from shallower soils,

particularly over bedrock, because these soils

become saturated more quickly. Water that cannot

soak into the soil evaporates or runs off, carrying

exposed soil with it.

Soil parent material: Parent material and soil age

affect soil erosion, primarily because of their

effects on soil texture at different depths in the

profile. Soil age is important because soils change

over time: soil particles become smaller and

vertical stratification of soil horizons increases.

Infiltration is usually, but not always, faster in

coarse-textured soils, such as sands. Texture also

affects soil erodibility, or how easily particles detach

from the soil surface. Poorly aggregated soils, such

as those with a high amount of sand and low

amount of organic matter, disperse readily from

raindrop impact. Soil organic matter binds soil

particles together, producing porous soils that soak

up and hold water, and thus resist erosion.

Climate: Climate is another factor influencing

erosion that cannot be controlled by management,

although it is temporally variable. Three of the

most important climatic factors are rainfall

amount, intensity and erosivity. The amount of

rainfall determines how much water is potentially

available to cause erosion or to increase plant

cover (limiting erosion).

Rainfall intensity is the rate at which rain

reaches the ground. When the intensity exceeds

the rate at which water can soak into the soil,

runoff begins. Rainfall intensity is often expressed

in units of inches or millimeters per hour, and is

often reported for periods as short as 5 minutes.

This is because runoff can be generated during

very short, intense storms.

Rainfall erosivity is related to intensity because

it is a measure of the energy of the rain. Clearly,

the higher the intensity, the more energy there is.

However, the size of the drops is also important, as

larger drops are able to dislodge more soil than

smaller drops.

The timing of precipitation events in relation

to cover is also important. Intense storms

occurring when cover is low are more likely to

cause severe erosion than when cover is high.

Interpretation

Figure 17.1. Runoff and erosion in a shrubdominated community in the Chihuahuan Desert.
90

Water erosion: dynamic factors. Factors affecting

erosion that can be influenced by management

include total cover, plant basal cover, spatial

distribution of plant bases, soil structure and soil

disturbance.

Total cover is the single most important factor

affecting water erosion. Soil that is covered by

plants, litter, gravel, lichens or mosses is protected

from raindrop impact. In order to be effective,

though, the materials must be relatively close to

the soil surface. Water that drips from tree

canopies onto an exposed soil surface can dislodge

soil as effectively as rain directly striking the soil.

Plant basal cover, as well as the number and

type of other obstructions to water flow, impacts

water erosion. Water that remains on a site longer

has more time to soak in. Anything that increases

the length of time water must travel to get to the

bottom of the slope (i.e., path length) will increase

water retention time. Plant basal obstructions also

reduce the energy of the water by slowing it down.

In addition, the rate of infiltration into the soil is

often higher around plant bases, due to root

channels and the activity of soil organisms

(increased micro- and macropores).

Spatial distribution of plant bases and other

obstructions is also important. Obstructions that are

uniformly or randomly distributed across the surface

generally have a more positive effect on reducing

water erosion than clumped obstructions (Fig. 17.3

versus Fig. 17.4). One exception occurs in arid

environments when plant cover is so low that the

only way to slow water, and to accumulate enough

water for plant production, is by concentrating the

vegetation in bands along the contour. These bands

are a common feature in large areas of Australia, as

well as parts of North America and Africa (Fig. 17.5).

Soil structure affects soil susceptibility to

erosion. Soil erodibility is reduced by soil organic

matter, which helps glue soil particles together.

The glue can include byproducts of litter and root

decomposition and the decomposer microorganisms

themselves (Fig. 17.6). In arid ecosystems, soil

Interpretation

Figure 17.4. Clumped vegetation.

Figure 17.3. Relatively uniform vegetation.

Figure 17.2. Effect of vegetation structure on

infiltration (figure modified from Martinez-Meza and

Whitford 1996).
91

lichens and photosynthetic cyanobacteria that live

in the top few millimeters of soil play an

important role in stabilizing soil. Where they are

sufficiently dense to be visible, they can form a

biological soil crust.

For a good overview of the role of other soil

microbiota in creating soil structure, cycling

nutrients and increasing infiltration, see Tugel et al.

(2000). Additional information on soil microbiotic

crusts, including mosses, lichens and

cyanobacteria, is available at www.soilcrust.org.

Soil structure is also important because it

affects the rate at which water soaks into the soil.

Well-structured soils have a more stable soil

surface, which limits soil dispersion, sealing and

physical crusting. In addition, well-structured soils

tend to have more continuous pores for

conducting water into the soil, thereby limiting

runoff.

Soil disturbance is the other factor that

significantly affects soil and site stability.

Disturbance of the soil surface breaks the bonds

that hold soil particles together, and exposes the

more erodible soil below. Nearly every study has

demonstrated that disturbance of the soil surface

potentially increases soil erosion for some length

of time, particularly where plant canopy or litter

does not protect the soil surface.

Wind erosion: static factors. The amount of soil

lost or redistributed by wind is a function of soil

erodibility and the velocity of the wind at the soil

surface (Fig. 17.7). Soil erodibility for wind is

different than that for water. For water, it is a

function of how tightly soil particles are glued

together and their ability to resist detachment by

water. The ease with which soil particles are

carried by wind depends on their size, shape and

density.

Soil erodibility: In general, soils with a high

proportion of fine sand are the most susceptible to

wind erosion. This is because the particles are light

Interpretation

Figure 17.5. Banded vegetation on the Jornada

Experimental Range, New Mexico.

Figure 17.6. Fungal hyphae entanglement of soil

particles.

Figure 17.7. Plants buried by wind-deposited soil.
92

enough to become carried by the wind, but large

enough to prevent becoming tightly bound into

larger particles, as occurs with clay soils. Soils that

are very gravelly or stony tend to be more resistant

to wind erosion, particularly after some erosion

has occurred (wind erodes the lighter particles,

concentrating these heavier materials at the

surface).

Wind velocity at the soil surface tends to be

lower in landscapes with a lot of uneven surfaces

(e.g., boulders and narrow ravines). However,

topographic complexity can lead to locally

increased wind erosion associated with

concentrated airflow over ridges and around

isolated obstructions.

Wind erosion: dynamic factors. Factors affecting

wind erosion that can be influenced by

management include plant cover, plant density,

soil structure and soil disturbance.

Plant cover: Like water erosion, the most

important factor for wind erosion is cover. Unlike

water erosion, tall vegetation usually provides

better protection than short vegetation, provided

that both are arranged in approximately the same

spatial distribution. Vegetation directly protects

the soil surface beneath it. It also protects nearby

soil by reducing wind velocity at the soil surface.

Plant density: Where vegetation is widely

spaced, as in areas with planted windbreaks, the

density of the vegetation is also important. A band

of vegetation that is too dense can actually

increase wind erosion on the lee side due to

increased turbulence.

Soil structure affects wind erosion both by

increasing surface roughness and by reducing

erodibility. Soils with better structure tend to be

rougher. An exception is physical crusts.

Degradation of fine-textured soils can lead to the

development of dense, physical crusts that are

relatively resistant to wind erosion (Fig. 17.8). The

resistance of physically crusted soils to wind

erosion is primarily due to the strong physical

bonds that form when the soil dries. Although

these bonds are destroyed when the soil is rewetted (making these same soils highly susceptible

to water erosion), they effectively limit removal of

particles from the surface while dry and

undisturbed.

Unfortunately, soils with physical crusts also

reduce water infiltration relative to soils without

physical crusts. Reduced water infiltration leads to

lower plant production. Lower plant production

(and lower plant cover) reduces surface roughness

and increases wind velocity at the soil surface. The

beneficial effects of physical crusts on soil

erodibility are negated by increased water erosivity

at the soil surface. Consequently, in the long run,

physical crusts can increase both wind and water

erosion.

Soil disturbance is an extremely important

factor for wind erosion. This is especially true in

areas with low vegetative cover, or where there are

relatively large non-vegetated patches (Fig. 17.9).

Interpretation

Figure 17.8. Dry lakebed (playa) with saline

physical crusts in the Great Salt Lake, Utah.

Inset: Non-saline physical crust on a playa in

southern New Mexico.

Figure 17.9. Wind erosion in the Mojave Desert.

Photo by Arlene Tugel
93

Disturbances occurring during seasons with high

winds cause greater wind erosion than

disturbances occurring at other times of the year,

particularly where vegetative cover is low.

Studies completed throughout the western

United States have consistently shown that

erosion is inevitable on disturbed, bare surfaces.

Wind erosion is significantly reduced where the

soil is protected by a physical crust (fine-textured

soils) or biological crust (all soils), provided that

there is no source of loose soil upwind. The latter

point is extremely important and often ignored

when interpretations are made for an individual

plot. Loose sand grains that become airborne can

easily slice through even the most resistant

physical crust, and can cover (and thereby kill)

biological crusts.

2. Hydrologic function

Hydrologic function is defined as the capacity of

the site to capture, store and safely release water

from rainfall, run-on and snowmelt. This

definition can be scaled up or down to any spatial

level, from an individual plant to the Missouri

River watershed. A properly functioning system

captures and controls the release of as much water

as possible from a site through infiltration,

evapotranspiration, and slow movement of water

(across the surface or laterally through the soil).

Deep percolation to replenish the water table also

occurs in most properly functioning systems.

Rapid runoff creates flashy, intermittent streams

and generates large amounts of sediment. Too

much sediment can reduce stream water quality

and rapidly fill lakes and reservoirs with sediment.

Factors affecting hydrologic function. The ability

of the system to capture water depends on (1) how

much water arrives at the soil surface (as rainfall,

snowmelt and runoff from higher landscape

positions); (2) how fast it arrives; (3) when it

arrives; and (4) how quickly it can soak into the

soil. The ability to store water depends on soil

depth and other soil properties. The ability to

release water that does not enter the soil depends

on vegetation and soil surface characteristics. The

ability to release water once it is in the soil

depends on the properties of the soil and

underlying materials (if the water is released to

groundwater or streams via subsurface flow). The

ability to release water once it is in the soil also

depends on complex interactions between plant

roots, soil organisms and the physical and

chemical characteristics of the soil (if the water is

released through evaporation or transpiration).

Factors affecting the ability of the system to

capture water and to release water that does not

soak into the soil are discussed within this chapter

under “soil and site stability.” The remainder of

this section focuses on the storage and release of

water that has already soaked into the soil.

Relatively static factors. The amount of water that

can be stored by the soil depends on soil texture,

structure and depth. Soil texture and depth are both

inherent soil properties, although both can be

affected by erosion. Soil structure is strongly

affected by soil texture. Sandier soils generally

hold the least water because the pores between the

sand grains are large, and because they tend to

have minimal structure. Rock, stones and gravel in

the soil profile also reduce storage capacity.

These factors, together with the slope and

structure of the material underlying the soil, also

affect transmission of surface water vertically to

groundwater or laterally to springs and streams.

Water moves vertically through the soil until it

encounters an impervious layer (such as

unfractured bedrock). Then it moves laterally,

following the slope, eventually reappearing in a

seep, spring or stream. This is the invisible source

of water that keeps ephemeral streams running for

weeks after a rainstorm, even in relatively arid

environments. In areas without an impervious

layer, any water that cannot be stored continues to

move down through the soil, eventually ending up

in the groundwater. The groundwater may also

move laterally, eventually reappearing as surface

water downslope.

Relatively dynamic factors. Both soil structure and

vegetation have large effects on infiltration (see soil

and site stability within this chapter). The ability

of the soil to store and release water also depends

on soil structure and vegetation.

Soil structure: While larger pores (0.003 to 5 mm)

transmit water, smaller pores store water. Water in

the smallest pores (<0.005 mm) is not accessible to

Interpretation
94

most plants (Brady and Weil 2002). The volume

accounted for by the smallest pores depends

primarily on the amount of clay in the soil and is

affected little by management.

The volume of pores that hold water accessible

to plants depends in part on soil structure.

Vegetation and soil biota, along with wetting,

drying, freezing and thawing cycles, rearrange soil

particles and glue them together, forming the

water-holding pores. Consequently, the type and

distribution (horizontal and vertical) of both plant

roots and soil biota can affect soil structural

development over time.

Vegetation has a more direct effect on the

amount of water that is released to surface and

groundwater after water has soaked into the soil.

Plants, and the litter they produce, shade the soil,

limiting evaporation. Green plants also serve as

pipelines, carrying water from deep in the soil into

the atmosphere. The effect of a plant on total

evaporation from a site depends on the depths

from which its roots are drawing water, how much

of the year it is green and photosynthesizing, and

how easily water is lost from its leaves. All three of

these vary widely among plant species, within

the same plant species growing in different

environments, and even within the same plant

species in different microenvironments in the

same watershed. Generally, in arid environments,

more deeply rooted species with greater leaf area,

such as trees and shrubs, will conduct more water

into the atmosphere on an annual basis than

shallow-rooted grasses and forbs.

Spatial pattern: This manual focuses on factors

that affect the capture and retention of water at

the landscape scale. The hydrologic function of a

watershed depends on these site-based factors, and

how the ecological sites are distributed across the

watershed. If surface water quality and quantity

are significant issues, the spatial distribution of

landscape units within a watershed and the status

of each need to be considered. The effects of a

degraded watershed on stream water quality can

often be partially limited by careful management

of the riparian zone and of the area immediately

surrounding this zone. Long-term sustainability of

the watershed, however, depends on careful

management of riparian and upland areas.

3. Biotic integrity

Biotic integrity “reflects the capacity of a site to

support characteristic functional and structural

communities in the context of normal variability;

to resist loss of this function and structure due to a

disturbance; and to recover following

disturbance(s)” (Pellant et al. 2005). The emphasis

of the third attribute is on the long-term

sustainability of the system, in contrast to the first

two, which focus more specifically on current

function.

The relative importance of resistance and

resilience varies among ecosystems, and depends

Interpretation

Figure 17.10. Resistance is the ability of a system to resist a disturbance over time. Resilience is

the ability of a system to rebound after a disturbance (adapted from Seybold et al. 1999).
95

on the type of stress or disturbance (Fig. 17.10).

For example, blue grama grasslands are very

resistant to overgrazing by cattle. With heavy

grazing, much of their biomass and growing

points become concentrated close to the ground

where they are protected to a great degree.

However, they are not as resilient as many annual

grasslands. Both resistance and resilience are

relative terms: there is a threshold beyond which

no system can resist or recover from degradation.

In general, ecosystems will be more resistant and

resilient in response to disturbances that are most

similar to those with which they have evolved.

Mechanisms of resistance and resilience are

extremely complex and vary in response to

different combinations of disturbances. This

explains why it is so difficult to identify universal

indicators of biotic integrity.

In addition to resistance and resilience, biotic

integrity reflects the capacity “to support

characteristic functional and structural

communities in the context of normal variability”

(Pellant et al. 2005). The obvious indicator is the

presence of plant functional groups on the plot.

However, the absence of these groups does not

necessarily mean that the site is currently

incapable of supporting them. In some cases they

have been removed from the site chemically

(herbicides), mechanically or due to overgrazing,

but the site is still able to support them.

Conversely, some perennial species can persist

long after a site has degraded to the point where

establishment of new individuals is impossible

without extensive intervention. In this case, the

presence of a functional group on a site can be a

false indicator of biotic integrity. In state and

transition model terminology, the site has crossed

a threshold into a new state (Ch. 24).

Our objective in the development of this

monitoring system has been to select

measurements that generate data that can be

applied to a wide variety of indicators. We have

selected a few indicators that appear to be useful

for many ecosystems and types of disturbance

regimes. We discuss other general types of

indicators that could be calculated and applied to

specific situations. In all cases, it is important to

carefully interpret the indicators in the context of

as much local information as possible.

Measurements and indicators

This section includes a discussion of the basic

indicators. It also includes selected additional

indicators that can be calculated from the data.

The indicators were selected because they

provide information on the status of the three

basic ecosystem attributes: soil and site stability,

hydrologic function and biotic integrity. We

encourage the users of this manual to be creative

in their development of additional indicators and

to consult ongoing projects designed to generate

sets of nationally and internationally recognized

indicators (e.g., the Sustainable Rangelands

Roundtable in the United States).

Photo points

Photographs are extremely useful for providing

visual documentation of where change has

occurred, and for providing an independent check

on changes indicated by the quantitative data.

They usually cannot be used as a substitute for

quantitative data. It is extremely difficult to

generate reliable quantitative data from photos,

except under very controlled conditions.

References

Coulloudon et al. 1999a

Hall 2002a

Hall 2002b

Howery and Sundt 1998

Line-point intercept

The Line-point intercept method measures the

proportion of the soil surface that is covered by

different species of vascular plants, as well as

rocks, litter, mosses and lichens.

Total cover is the proportion of the soil surface

that is covered by vascular plant parts, litter, rocks

larger than 5 mm in diameter, mosses and lichens.

Total cover is positively correlated with soil and

site stability and hydrologic function. It protects

the soil surface from raindrop impact, thereby

limiting detachment of soil particles and physical

crusting of the soil surface. Additionally, higher

cover generally means there are more obstructions

to water flow.

Basal and foliar cover are more sensitive

indicators of biotic integrity. They are more closely

Interpretation
96

related to production, energy flow and nutrient

cycling because, unlike total cover, they don’t

include rock cover. Basal cover is simply the area

covered by plant bases. It is generally a more

reliable long-term indicator than foliar cover

because it is less affected by growing season,

drought, grazing or other short-term disturbances.

Changes in total basal cover should be

interpreted in the context of changes in species

composition. In areas with the potential to

support perennial grassland, an increase in basal

cover due to a change in species composition

usually (but not always) indicates an improvement

in biotic integrity. This is because perennial grasses

tend to have higher basal cover than shrubs.

Sometimes an increase in basal cover can

improve soil and site stability, while reducing

biotic integrity. An example is the replacement of

a cool season (C3) bunchgrass-blue grama

community by a predominantly blue grama

community (decreased species richness and a

change in dominant functional/structural group).

Blue grama develops high basal cover, and

therefore enhances soil and site stability. It is also

very resistant to some types of disturbances, such

as grazing, which can maintain or improve biotic

integrity. Cool season bunchgrasses, on the other

hand, increase resistance and resilience through

their diversity of reproductive strategies (they

reproduce more easily from seed). They also

increase resistance and resilience by extending the

range of climatic conditions to which the

community is adapted (they are more efficient at

lower temperatures).

Foliar cover is often used as an indicator of

changes in plant community composition. Due to

its variability, however, data should be compared

across several years with consideration for yearly

climatic variability. In order to make these

comparisons, it is critical that the same method be

used. As used here, it is limited to the area

physically covered by plant parts (leaf, stem,

flower, etc.).

There are an almost infinite number of

additional indicators that can be calculated from

the Line-point intercept data. Minimum estimate of

species richness, or the total number of species

detected on a plot, is perhaps one of the most

useful. However, it needs to be applied very

carefully. Line-point intercept generally yields the

lowest estimate for species richness of any method.

Line-point intercept usually detects only those

species that represent a relatively high proportion

of the total cover. Species with <5% cover on a site

are often not detected with Line-point intercept,

or are underestimated. For more accurate estimates

of species richness, nested plot methods should be

used, such as the modified Whittaker method

described in Chapter 10.

The area covered by species resistant to

catastrophic disturbances is also a potentially useful

indicator of both soil and site stability and biotic

integrity. It provides some estimate of how the

system will respond to potential degradation. This

indicator can be sensitive to changes (i.e.,

resilience), particularly if it is based on basal cover.

Specifying the types of disturbance that are

expected for the site is therefore important.

Dead and decadent vegetation contribute

positively to foliar cover protection of the soil

surface. However, excessive increases in standing

dead cover can be a sign of higher than normal

mortality rates or reduced decomposition. It can

also reflect reduced fire frequency, or grazing

frequency or intensity. Therefore, it is related to

biotic integrity. Proportion of dead plant intercepts is

an indicator of the amount of dead and decadent

vegetation for a given species.

Invasive plant cover is an extremely important

indicator of change in many ecosystems and is

consistently associated with a decline in biotic

integrity. Exotic species invasions often lead to

declines in soil and site stability and hydrologic

function. These effects are documented with other

indicators, such as woody plant cover. Woody plant

cover generally increases as invasive species

increase.

References

Anderson 1974

Benkobi et al. 1993

Blackburn 1975

Blackburn and Pierson 1994

Cerda 1999

Gutierrez and Hernandez 1996

Huenneke 1995a,b

Johnson and Gordon 1988

Morgan 1986

Interpretation
97

Pierson et al. 1994

Smith and Wischmeier 1962

Thurow et al. 1988a,b

Warren 2001

Weltz et al. 1998

Whitford 1988

Gap intercept

The spatial pattern of vegetation is correlated with

soil and site stability, hydrologic function and

biotic integrity. The Canopy Gap intercept method

does not measure spatial pattern directly, but does

provide an indication of the extent to which plant

cover is aggregated (forming a few large gaps) or

dispersed (forming many small gaps). A reduction

in total plant foliar cover will usually, but not

always, increase the area encompassed by larger

gaps. The distance between plant bases (basal gaps)

increases when plants become more aggregated

and when basal cover declines (e.g., when shrubs

replace grasses).

The proportion of line covered by canopy gaps

exceeding a designated length (e.g., 50 cm) is a useful

indicator. Canopy gaps affect soil erosion,

hydrologic function and biotic integrity. The area

covered by large gaps can vary tremendously. This

indicator can vary even across sites with the same

total foliar cover (as measured by the Line-point

intercept method), depending on how the

vegetation is arranged (see Figs. 17.11 and 17.12).

The susceptibility of disturbed soil to wind

erosion depends on the wind velocity at the soil

surface. Wind velocity is higher in large gaps than

it is in small gaps, because vegetation reduces wind

speed. Research has shown that for typical desert

grasslands, soil redistribution by wind from a

disturbed surface occurs when gap diameter (the

diameter of the spaces between the vegetation)

exceeds approximately 50 cm (20 in). On average,

this is equivalent to a gap intercept of

approximately 39 cm (15 in).

The minimum gap diameter for wind erosion

to occur varies, depending on other factors. The

minimum gap diameter is larger where the

vegetation is taller, or the height of the vegetation

is more variable. Greater variability in vegetation

height creates greater surface roughness, which

reduces wind velocity near the surface.

Larger gaps generally indicate greater spatial

variability in soil organic matter inputs (organic

matter decreases as you get further from

vegetation). This means that soil structure is

typically poorer in large gaps than in small gaps.

Consequently, soil in the gaps is more erodible by

both wind and water. Water erosion is further

increased in areas with large gaps because these

gaps tend to be more highly connected (less

vegetative obstructions to water flow). This means

that once a soil particle is detached, there is little

to prevent it from continuing to move downslope.

Hydrologic function is similarly affected by

large gaps: water moves more quickly offsite and

therefore has less time to soak in. However, there

Interpretation

Figure 17.11. Large canopy gaps.

Figure 17.12. Small canopy gaps.
98

are at least two exceptions to this statement.

Infiltration at the ecological site level can actually

increase on some sites with greater vegetation

patchiness. This generally occurs in areas with

extremely low precipitation relative to plant water

requirements. Water from a relatively large area

must be concentrated in order to provide enough

moisture for these species to grow. The plants, in

turn, increase infiltration capacity in the patches

where they do become established by increasing

soil organic matter. This soil organic matter

protects the soil surface from raindrop impact and

supports an active soil biotic community. In some

areas, these patches eventually form bands of

vegetation across the slope (Fig. 17.13). This

pattern effectively increases the amount of water

that is intercepted, increasing infiltration.

The second exception occurs when grasses

with dense near-surface roots, such as blue grama,

replace bunchgrasses without any change in gap

sizes. Infiltration through these root mats can be

quite slow, especially at the beginning of storms,

because the mats repel water. Conceivably, if gap

sizes increased in this situation you could see an

increase in infiltration rate at the landscape level

(infiltration is slower through blue grama root

mats than in the interspaces).

Patchiness is also highly correlated with biotic

integrity. As gaps open in the existing vegetation,

susceptibility to invasion by exotic species

generally increases. Also, the ability of existing

species to become re-established in the larger plant

interspaces following disturbance often declines

due to changes in both the soil and microclimate.

The proportion of line covered by basal gaps

exceeding a designated length (e.g., proportion of line

covered by gaps exceeding 50 cm). The relationship

between basal gaps and the three ecosystem

attributes is similar to that for canopy gaps. The

primary difference is that basal gaps vary less in

response to short-term disturbances (see discussion

of basal cover under Line-point intercept). Another

difference is the relative strength of the

relationship to the attributes. Wind erosion is

more sensitive to changes in canopy gap size,

while water erosion and hydrologic function are

strongly linked to changes in basal gap

dimensions. There is little research comparing the

effects of basal versus canopy gap dimensions on

exotic plant invasions, or on basal versus canopy

gap recovery following disturbance. The few

existing studies have focused on canopy gaps.

Standard gap dimensions are 25-50 cm, 51-100

cm, 101-200 cm and >200 cm. The proportion of

the line covered by gaps of other sizes can also be

calculated. In addition, it may be of interest to

know what species are associated with the large

gaps. For example, do all large gaps occur at the

perimeter of invading shrubs? The Gap intercept

data can be combined with the Line-point

intercept data to generate relevant indicators.

Relevance to pastures and other high foliar cover

systems. Canopy and basal gap indicators are

clearly less sensitive to changes in high cover plant

communities, such as wet meadows, where gaps

rarely occur. However, it is worth including the

measurement because it takes very little time (less

than 5 minutes) and may detect changes missed

by casual observation. In these situations, you may

Interpretation

Figure 17.13. Large gaps between banded

vegetation patches shown in an aerial photo.
99

want to reduce the minimum gap intercept from

20 or 30 cm (8 to 12 in) to 10 cm (4 in),

particularly if invasive species are linked to

increases in gap sizes.

References

Kuehl et al. 2001

Schlesinger et al. 1990

Tongway and Ludwig 1997

Tongway 1994

Whitford et al. 1998

Soil stability test

The Soil stability test is a relatively simple test that

is sensitive to complex changes in physical,

chemical and biological processes. These are the

processes that glue soil particles together.

Two core indicators are calculated from this

test: the average surface stability value and the

average sub-surface stability value. The percentage of

the surface samples tested that are equal to 6 (very

stable) is another useful indicator that is easy to

calculate. Both are correlated with all three

ecosystem attributes. Higher stability has been

directly correlated with reductions in erosion. It is

more difficult for individual soil particles to

become detached as the soil stability value

increases.

More stable soils are less likely to form

physical crusts, which soak up water more slowly.

Thus, hydrologic function tends to be better on

soils with high stability values. However, there are

some cases in which soil surfaces stabilized by

microbiotic crusts (high stability values) actually

have lower infiltration rates than similar soils

without crusts. Infiltration rates are also decreased

when soils become hydrophobic or “afraid of

water.” This can occur in at least two situations.

One is immediately following a very hot fire, such

as in forested areas. The other is in areas with high

densities of fungi. In both cases, the soil is

relatively stable because water cannot penetrate

into the soil (and therefore cannot dislodge soil

particles) but as slope increases, runoff

concentrates and rills and gullies can form.

Soil stability values generally are positively

correlated with biotic integrity, because biotic

activity is required to bind the soil particles

together. The smallest soil particles are bound

together by physical and chemical forces, and by

soil organic matter that formed long ago. These

microaggregates are then glued to each other and

to larger sand-sized particles, and become

aggregates that are too large to fall through the

screen in the soil stability test kit. The glue that

binds these larger aggregates is primarily recently

produced live and dead soil organic matter. This

organic matter includes fungi, bacteria that feed

on decomposing roots and plant litter, root

exudates (material that is produced by roots), and

the feces of soil organisms that feed on the fungi,

bacteria and root exudates. These compounds

degrade fairly rapidly in the soil, so high stability

values are an indication that biotic recovery

mechanisms are functioning.

Soil stability at different depths and under

different types of vegetation can be used to reflect

changes in organic matter cycling.

Changing the rating system. The rating system is

arbitrary and can be adjusted to increase its

sensitivity in different ecosystems. For example, in

areas with very high aggregate stability, classes 5

and 6 can be split into several classes, based on the

amount of material that remains on the sieve.

Where possible, however, the original rating

system should be followed to facilitate comparisons

among different datasets.

References

Blackburn and Pierson 1994

Herrick 1999

Herrick et al. 2001

Seybold and Herrick 2001

Warren 2001

Whitford 1996

Belt transect (woody and invasive plants)

The density (number of plants per hectare) of woody

and invasive plants is a very sensitive indicator of

biotic integrity in many areas. This is particularly

true for systems that are at risk of changing from a

native grass-dominated system to one that is

dominated by shrubs, trees, exotic grasses or forbs.

In some cases, the size of the woody/invasive is

also important, especially where fire can kill small

individuals. In these cases, individual indicators

Interpretation
100

should be calculated for each size class (plant

density by size class).

The probability that woody/invasive plants

will be encountered is higher with larger plots. If

invasive species are a threat, and few or none have

been detected in the area, much larger areas

should be systematically searched.

References

Bonham 1989

Sheley et al. 1999

Compaction test (impact penetrometer)

The impact penetrometer is used to detect changes

in soil compaction. When soils become denser, or

compacted, the number of hammer strikes needed

to push a rod down through the soil profile

increases. This measurement is normally only used

when a compaction problem already exists, or a

change in management or vegetative cover is

likely to result in a change in soil density.

Compaction is a natural phenomenon that

occurs in all ecosystems. Compaction becomes a

problem when recovery processes, including

freeze-thaw, root expansion and soil movement by

soil biota and animals, fail to balance the

compacting effects of vehicles, livestock, wildlife

and other factors.

Compaction affects hydrologic function

because it reduces pore sizes, causing water to

move more slowly through compacted layers than

through non-compacted layers. Compaction can

reduce the amount of water that soaks into the soil

and increase runoff. Consequently, it can indirectly

reduce soil and site stability. Compaction makes it

more difficult for roots to access water, both

because water already in the soil moves more

slowly to refill depleted zones around roots, and

because it is more difficult for the roots to

penetrate the compacted soil. Compaction can

restrict the movement of soil organisms,

consequently limiting the release of plant

nutrients.

Compaction also affects the amount of water

that can be stored by the soil. It reduces soil water

storage capacity in most soils, but can increase

storage capacity in extremely coarse-textured soils.

The number of penetrometer strikes required to

reach a particular depth can be a very sensitive and

precise indicator of soil compaction. It is much easier

to consistently generate this indicator than to

directly measure the density of the soil. The results

must be carefully interpreted because other factors

can cause changes in the resistance of the soil to

penetration. The most important factor is soil

moisture content. It takes less energy (fewer strikes)

to penetrate moist or wet soil than dry soil.

Consequently, the penetrometer is best used to make

repeated comparisons on dry soils, rather than to

compare different soils, or soils at different moisture

contents. At a minimum, the moisture content of

the soil should always be described or, if possible,

measured for each of the depths evaluated.

A second important factor is soil texture. It is

generally more difficult to penetrate soils with

high clay content.

Ratios can be used to help determine if a

compaction layer exists and to monitor changes in

compaction. In order to make these comparisons,

the soil must have uniform texture and moisture

content throughout the measurement depth for

the area of interest. In most cases, this means the

soil must be dry because soil moisture varies with

both depth and plant cover.

The ratio of strikes in the interspaces vs. under

plant canopies can also be helpful. As for all

comparisons, however, the fact that the soil in the

interspace is more resistant to the penetrometer

does not necessarily mean that compaction is

having a negative effect on root growth or

infiltration. Qualitative indicators can often be

used to assess the effects of compaction on root

growth. The infiltrometer (Chapter 8 and the next

section) can be used to evaluate the effects of

compaction on infiltration.

References

Blake and Hartge 1986

Bradford 1986

Campbell and Hunter 1986

Herrick and Jones 2002

Larson and Pierce 1993

Thurow et al. 1995

Warren et al. 1986

Webb and Wilshire 1983

Willatt and Pullar 1983

Interpretation
101

Single-ring infiltrometer (water infiltration)

Water infiltration rate in a cylinder is an indicator

of how quickly water soaks into the soil during

rainstorms. It is important to remember that

infiltration rate calculated from the Single-ring

infiltrometer is simply a relative indicator and does

not measure actual infiltration rates during

rainstorms or snowmelt. Single-ring infiltration

rates are significantly higher (sometimes as much

as 10x) than natural infiltration rates. This is

primarily because during the test, water can move

horizontally as well as vertically after it enters the

soil. Consequently, while the test is fairly sensitive

to changes in the soil surface, it is not very

sensitive to subsurface compaction unless the

cylinder is inserted deep into the compacted layer.

Note that if the cylinder is inserted more deeply,

more time and water are required for the

infiltration to equilibrate because the soil must

become fully saturated to a depth below the

bottom of the cylinder.

There are two other important differences

between the Single-ring infiltrometer and

infiltration during natural precipitation events.

The first is that the test does not include the

effects of raindrop impact. Raindrops can

rearrange bare soil particles and contribute to the

formation of a physical crust, thereby reducing

infiltration rates. The second difference is that

with the Single-ring infiltrometer there is no

opportunity for water redistribution to occur from

areas with low infiltration rates, such as plant

interspaces, to areas with higher infiltration rates,

such as under plant canopies.

Single-ring infiltration data for areas with deep

layers of embedded litter or duff should be

carefully interpreted. This material is usually

removed to a standard depth prior to beginning

the measurements, or the ring is inserted deeply

enough so that the bottom extends into mineral

soil. Both duff and embedded litter are often

hydrophobic. By repelling water, they initially

reduce infiltration rates. However, they also have

high porosity and can significantly reduce runoff

after they have been wetted. Infiltration rings

often artificially reduce hydrophobicity of intact

layers of embedded litter or duff, resulting in an

even greater overestimate of infiltration rates. If

the litter or duff is removed prior to measurement,

infiltration rates can be underestimated.

Despite these limitations, the rate of infiltration

recorded with the Single-ring infiltrometer can be a

valuable indicator of change in the hydrologic

characteristics of the soil surface.

References

Abu-Awaad 1997

Bouwer 1986

Morin and Van Winkel 1996

Pierson et al. 1994

Thurow et al. 1988a,b

Thurow et al. 1995

Warren et al. 1986

Webb and Wilshire 1983

Plant production

Total plant production is one of the most important

indicators of biotic integrity because plants reflect

changes in resource availability, including water

and nutrients, and because they respond rapidly to

changes in the disturbance regime. It also reflects

the amount of energy potentially available to

herbivores. The annual production of specific

species or specific groups of species (e.g.,

functional groups) is often used to estimate

carrying capacity for both livestock and wildlife.

The number of species recorded in all production

subplots can be used as a minimum estimate of

species richness.

The value of plant production data is often

limited by various factors. Both the precision and

accuracy of the data can be quite low, variable and

difficult to define. Individuals vary widely in their

abilities to estimate biomass. One way to alleviate

this limitation is by double sampling (comparing

estimated weights to clipped weights). Data from

clipped plots help standardize data for herbaceous

species, but are less useful for woody species.

Another source of error is in estimating the

correction factors for plant material that has been

removed or has not yet been produced. Individuals

vary widely in their ability to select correction

factors. Accurately estimating correction factors

depends on correctly predicting future weather

and plant growth responses to weather and other

conditions.

Production data are often used to calculate a

similarity index. This requires a standard, such as

one or more of the plant communities found in

Interpretation
102

the reference state. Most of the indicators

discussed for the Line-point intercept method can

also be calculated using production instead of

cover.

Reference

USDA-NRCS 1997

Plant species richness (modified Whittaker

approach)

Species richness is simply the number of species

that occur in an area. It is one of many

biodiversity indicators. No method will detect all

species. A minimum estimate of species richness

can be calculated by counting the number of

species recorded on the Line-point intercept data

form. The modified Whittaker nested plot

approach described in Chapter 10 has been shown

to be more effective than other methods in

measuring species richness. Plant species richness

allows the maximum number of species on the

plot to be predicted. This is done by plotting the

number of species found in each subplot against

the area searched. Data points are then connected

with a line. The line is then extrapolated to predict

the maximum number of species (horizontal axis;

Fig. 17.14).

For more information on the modified

Whittaker approach, please see recent publications

by Tom Stohlgren and others listed here in the

References.

References

Huenneke et al. 2002

Stohlgren et al. 1995

Stohlgren et al. 1997

Vegetation structure

The cover pole is used to quantify changes in

vegetation structure. Higher vertical structure

indicator values are caused by the presence of

vegetation at many different heights (i.e., nonuniform vegetation height). Vertical structure is

related to wildlife habitat quality and reducing

wind speeds near the soil surface. It also affects the

aesthetic value of the land. Vertical structure often

determines where recreational activities are most

likely to occur on a landscape.

Vegetation structure indicators are most often

correlated with vegetation biomass and wildlife

habitat quality. The two indicators included here,

visual obstruction (Robel 1966) and Foliage Height

Diversity (FHD; MacArthur and MacArthur 1961),

have both been related to habitat quality for

various wildlife species.

As yet unpublished studies in New Mexico

have shown that cover pole indicators are

correlated with foliar cover and height, and with

Gap intercept indicators.

References

Benkobi et al. 2000

MacArthur and MacArthur 1961

Mills et al. 1991

Nudds 1975

Robel 1966

Robel et al. 1970

Tree density

Tree density is a useful indicator of biotic integrity

in savanna and woodland plant communities.

Changes in tree density are also often associated

with changes in soil erosion. This is because they

affect wind velocity at the soil surface, and the

distribution of herbaceous plants and litter. As

with the belt transect, the precision of tree density

estimates is very sensitive to plot size. If this is an

important indicator, and density is low, larger

plots should be used.

In addition to total density, the data collected

with this method can be used to calculate density

Interpretation

Predicted number of species

Cumulative number of species

Cumulative plot area

Figure 17.14. Species area curve.
103

by species and by size class. Size classes can be based

on tree height and/or diameter. The amount of

wood that could be potentially harvested can also

be estimated using species-specific conversion

tables (Wenger 1984).

References

USDA Forest Service 2003

Bonham 1989

Wenger 1984

Riparian-specific measurements

The Riparian channel vegetation survey and

Riparian channel and gully profile are basic

supplementary methods that should be added

when monitoring plots fall in riparian areas. For

more intensive riparian monitoring (e.g.,

following intensive restoration work), or where the

characteristics of the stream itself are of interest,

additional measurements should be included.

Sources for other measurements are included at

the end of this section.

Interpretation of riparian data is extremely

complex because the potential of riparian areas

depends on many factors that are not readily

observable, including geology of the watershed

and of the channel itself. Participation in one or

more riparian assessment course is strongly

encouraged before attempting to interpret the

indicators described here. The information below

simply serves as a basic introduction to some of

the indicators that can be calculated with the

measurements described in this manual.

Riparian channel vegetation survey

The Riparian channel vegetation survey is designed

to provide the same type of information generated

by the Line-point intercept method. The same basic

indicators can be calculated. Please see the Linepoint intercept discussion within this chapter.

Additional indicators can be used to determine

the relative effectiveness of the plant community

in protecting the streambank from erosion.

Indicators can be added to monitor changes in

woody species cover. Woody species can be

important for creating favorable conditions for

both terrestrial and aquatic animal species. This

survey can also be used to characterize plant

Interpretation

community structure using the height

measurements.

Stabilizing species cover is often the most

important indicator for both hydrologic function

and biotic integrity. Stabilizing species generally

have an extensive, deep, fibrous root system that

helps hold the soil together, resisting the erosive

action of the stream and promoting sinuosity. In

riparian areas dominated by herbaceous species,

the same indicator can be calculated using basal

cover.

The stabilizing species as a percent of total species

cover is related to the relative dominance of bank

stabilizing species. It is particularly useful where

multiple species are intercepted at each point.

Higher values are associated with areas where a

higher proportion of the species intercepted are

stabilizing species.

Production or biomass measurements can be

used to generate a more accurate estimate of

relative dominance. Recording multiple intercepts

of the same species at each point can also be used

to generate a more accurate indicator of relative

dominance.

An additional indicator is woody cover. The

presence of woody species, particularly trees, is an

indicator of a healthy riparian system in many

regions. In order to effectively interpret this

indicator with respect to hydrologic function, it is

important to know something about the species

that are contributing to woody cover. The age

distribution is important to biotic integrity.

Younger trees are an indication that regeneration

is occurring. However, the negative effects on

hydrologic function and biotic integrity of some

invasive trees (such as tamarisk) can outweigh

their positive stabilizing effects.

References

Briggs 1996

Prichard et al. 1998a

Prichard et al. 1998b

Winward 2000

Riparian channel and gully profile

The Riparian channel and gully profile is used to

describe changes in the shape of the channel. It

can also be used to monitor recovering (or
104

deepening) gullies. A number of indicators can be

calculated. Two of the most common are described

below. Please note that the interpretation of these

indicators is context-dependent. A trained riparian

specialist who is familiar with local soils, hydrology

and vegetation should be consulted regarding

interpretations.

Bank angle or slope gradient is the slope of the

bank. In riparian systems, the optimal bank angle

for a functional stream depends on geomorphology

and soil. For gullies, a reduction in the angle is

nearly always indicative of a recovering system.

Changes in the width-depth ratio indicate

changes in the stability of the stream. The ideal

width-depth ratio depends on a number of site

characteristics. Healthier streams generally have

lower width-depth ratios, except where incision

and/or a reduction in sinuosity have occurred. If

significant changes in this indicator occur, consult

a riparian expert with knowledge of the hydrology

of the streams in your area. Note that the widthdepth ratio calculated from the channel profile

method will not necessarily be the same as one

based on bank-full. Bank-full is defined based on

the water level during typical high flow events.

Width-depth ratios based on bank-full are

potentially most closely related to the functioning

of riparian systems. The two types of width-depth

ratios are correlated.

A reduction in width-depth ratio in gullies is

generally a sign that active cutting is occurring,

while an increase can be an indicator of recovery

through deposition or stabilization of the gully

edges. However, changes in gully morphology also

can be due to changes in upslope processes

(sediment sources) and subsurface properties (e.g.,

a very gravelly or highly erodible layer of soil).

References

Briggs 1996

Prichard et al. 1998a

Prichard et al. 1998b

Winward 2000

Extrapolation

Careful extrapolation of the results from

individual measurements is important for most

Interpretation

monitoring programs. Extrapolation allows the

results to be interpreted throughout much larger

areas than the monitoring plots themselves.

There are three general approaches for

extrapolation: (1) non-spatial; (2) spatially

implicit; and (3) spatially explicit (Peters et al.

2004). Non-spatial extrapolation is used where

plots are randomly selected. Spatially implicit and

explicit extrapolations require stratified random

plot selection. These two approaches can be

applied to randomly selected plots if the plots are

subsequently stratified. The third approach,

spatially explicit extrapolation, requires knowledge

about where the plot is relative to other types of

monitoring units.

Information from non-randomly selected plots

(e.g., key areas and other subjective systems)

cannot be quantitatively extrapolated. However,

expert knowledge can often be used to make

qualitative inferences about other larger areas

based on data from subjectively selected plots on

key areas.

Non-spatial extrapolation

Non-spatial extrapolation is the simplest

approach. Here you simply average the values

from all plots and use this value to represent the

entire area sampled. This is generally only

appropriate where the land is so homogeneous

that there is only one type of monitoring unit. In

other words, the soil, climate, topography,

vegetation and management are functionally

similar throughout the area being monitored and

interactions with adjacent areas are insignificant

(or do not vary).

Spatially implicit extrapolation

In this approach the average of all plots within a

single type of monitoring unit is used to reflect

typical conditions throughout the unit. This

approach is also quite simple and the level of

certainty associated with the estimate of each

indicator can be easily calculated using standard

statistical methods (see Appendix C).

Spatially explicit extrapolation

In spatially explicit extrapolation, interpretations

for each plot are modified based on attributes of

adjacent plots. In the case of wind erosion, an area
105

that is classified as highly susceptible to wind

erosion based on canopy gap data might be

reclassified as only moderately susceptible because

it is surrounded by dense woodland that reduces

wind speed. Alternatively, it could be reclassified

as very highly susceptible if it is downwind of a

large sand source, such as an area recently cleared

of vegetation. This is because mobile sand can

erode through protective crusts even in the

absence of disturbance by vehicles or animals.

Spatially explicit extrapolation generally

requires some kind of model, or at least a set of

clearly defined rules.

Spatial context

While the spatial context is used only in the

spatially explicit extrapolation approach, it should

be considered in the interpretation of all

monitoring data, even if no spatial extrapolation is

planned. Information about where a plot is located

in the landscape can be used to improve the

quality and value of data interpretation at both

plot and landscape scales.

The spatial context must be considered in

order to determine (1) whether data from the plot

truly reflect the status of the area that it was

selected (randomly or subjectively) to represent,

and (2) whether the indicators measured at the

plot scale are adequate to reflect the status of the

area.

Anomalous plots. Both subtle differences in the

relatively static properties of a plot (e.g., slope and

soil texture) and the location of the plot in the

landscape can confound extrapolation.

Soil texture significantly affects plant

production potential and soil erodibility. Climate

also varies significantly across the landscape. For

example, south-facing slopes are subjected to

higher evaporation rates and generally have

shallower soils than north-facing slopes. Both

higher evaporation rates and shallower soil depth

result in lower soil moisture availability on southfacing slopes, increasing bare ground and the

potential for rill formation even on sites that are at

or near their potential.

Ecological sites that are located lower on the

landscape (downslope) may receive runoff water

during intense storms or snowmelt. The effect of

increased runoff can be positive if additional water

is retained on site and becomes available for plant

growth (concave microsite). Increased runoff can

be negative if it results in greater erosion (convex

microsite). Microsites that capture wind-driven

snow generally have a higher production potential

than sites that are free of snow most of the time,

except where the snow persists long enough that it

significantly limits the length of the growing

season. Sometimes these microsite differences are

reflected in different ecological sites, but most

ecological sites include a broad range of microsites

with variable potential.

We recommend avoiding locating plots in

anomalous sites. Using a random or stratified

random plot selection approach can significantly

minimize the effects of these plots on the

interpretation. If you cannot avoid anomalous

sites, increase the level of replication beyond the

minimum recommended. The effects of these

anomalous plots on average values decline as the

number of plots included increases. Please see

Chapter 5 for a discussion of how to deal with

potentially anomalous plots during the plot

selection process.

Adequacy of plot-scale indicators. Determining

whether the indicators measured at the plot scale

are adequate to reflect the current status of the

area can be extremely difficult. Both larger scale

patterns and processes, and the status of adjacent

areas that may affect the area represented by the

monitoring plot(s), must be considered.

The importance of larger scale patterns and

processes is reflected in attempts to monitor the

urban-wildland interface using small plots. High

vegetation structural diversity measured at the plot

scale is an indicator of good habitat quality for

many species. However, its value as an indicator

declines if the plot is located in the middle of a

sprawling subdivision of 1-5 acre lots (i.e., habitat

structural diversity is suitable, but habitat size is

too small to be used).

The status of adjacent areas is particularly

important when considering monitoring site

susceptibility to runoff and erosion. High ground

cover and soil surface stability are generally good

indicators of soil erosion resistance. However, high

Interpretation
106

ground cover and soil stability are insufficient to

resist gully formation by concentrated runoff from

roads located on adjacent land. Even if landscapelevel indicators are unavailable, qualitative

information about the surrounding area can be

used in both cases to improve indicator

interpretation.

Temporal context

The temporal context is also important,

particularly when using the data to make

management decisions. In arid and semi-arid

environments, time since grazing, as well as

timing, amount and intensity of precipitation,

affects many of the indicators. Foliar cover and

production are particularly variable, but all of the

indicators are sensitive to these factors.

A long historical record can be extremely

helpful. Information on historical use and

management can help when interpreting the rate

and direction of trends.

Interpretation

Pulling it all together: the big picture

Perhaps the most useful tools for interpreting

monitoring data are the state and transition

models described in Chapter 24. These are used to

help define the status of each monitoring plot

relative to potential thresholds, and to identify

potential future drivers of change.

Additional reading

For more information on the three types of spatial

extrapolations described here, see Peters et al.

(2004). Ludwig et al. (1997) discuss many of the

issues that are important for defining landscape

context. The use of aerial photographs in

identifying historical manipulations is described in

Rango et al. (2002).
107

Section IV: Special topics

Riparian (Ch. 18)

Wildlife habitat (Ch. 20)

Fire (Ch. 22)

Livestock production (Ch. 19)

Vehicles/Recreation (Ch. 21)

Invasive species (Ch. 23)

State and transition models (p. Ch. 24) Remote sensing (Ch. 25)

Grassland

State

Shrubland

State

Shrub-invaded

Grassland

State

Photo by Bob Gibbens Photo by Jayne Belnap

Photo by Patrick Shaver

Photo by Mike Pellant

Photo by Doug Burkett

Soil carbon (Ch. 26)
108

T

he monitoring methods included in the

previous chapters of this manual were

selected because they generate indicators

relevant to the three key attributes: soil and site

stability, hydrologic function and biotic integrity.

These attributes represent the foundation for

nearly every land management objective,

including livestock production, wildlife habitat,

recreation and watershed protection. The first six

chapters, and the ninth chapter, of this section

provide additional guidance on how to adapt these

protocols to address more specific management

and monitoring objectives.

Each of these chapters is organized into four

sections: an introduction, a summary table,

methods notes and additional resources. Notes are

included only for methods that require

modifications or for which there are additional

indicators that are not described in the methods

chapters (Section II, Chs. 7 through 15). The

additional resources portion describes printed and

online resources. When possible, local experts

(NRCS, USFS, Extension, etc.) should be consulted,

particularly for projects involving multiple

objectives in complex systems.

Each chapter addresses three strategies. The

first and simplest strategy is to calculate additional

indicators from the core measurements described

in Quick Start (Vol. I). Many of the measurements

included in this manual were selected in part

because they can be used to easily generate a large

number of indicators. For example, Line-point

intercept was selected instead of vegetation

frequency or density because it can be used to

generate cover and composition indicators, as well

as information on soil surface properties such as

rock and lichen cover. Unlike Daubenmire

quadrats, Line-point intercept data can easily

generate vegetation structure indicators. Linepoint intercept also can quantify ground cover in

plant interspaces.

The second strategy involves making relatively

simple modifications or additions to the core

measurements, such as adding height to the Linepoint intercept measurements.

The third and most expensive strategy is to

incorporate supplementary measurements.

Each of these strategies increases monitoring

costs. The first six chapters, and the ninth chapter

of Section IV, include tables defining the relative

priority of each measurement for typical

applications. These tables can be used, together

with the time estimates in Quick Start, to compare

the relative costs and benefits of each

measurement for the particular management or

monitoring objective. Because each situation is

unique, these rankings should be used only as a

rough guide for selecting measurements.

The lists of additional resources are by no

means complete. There are hundreds of

monitoring guides available now and many more

are becoming available on the Internet. Most are

specific to particular uses or values, and most can

be adapted to and integrated with the flexible

monitoring system described here.

Please note that there is potential for overlap

among the special topics. The first (Riparian) is a

type of land. The next three (Livestock production,

Wildlife habitat and Off-road vehicles) are most

commonly thought of as land uses or values. The

fifth (Fire) is often applied as a management tool

but it, like the sixth (Invasive species), can also be

viewed as a threat. It is increasingly common to

find that all six topics need to be addressed

simultaneously. For example, fire is used to control

invasive species in riparian zones that are

simultaneously managed for livestock, wildlife,

recreation and carbon sequestration. The

advantage of using an integrated system is that the

data are relevant to all six topics. While the time

allocated to different measurements may vary

depending on the relative importance of each

topic, the basic structure should remain constant.

Chapters 24 and 25 provide a brief

introduction to state and transition models, and

remote sensing. Both of these tools can be

extremely useful in monitoring program design

and data interpretation.
109

Chapter 18

Riparian

I

mportant indicators in most riparian systems

are plant community composition and

structure. These can be monitored using a

combination of one or more of the following

methods: Line-point intercept, Riparian channel

vegetation survey, Belt transect and Tree density.

Additional long-term monitoring methods can

provide more complete information on

relationships between changes in vegetation and

channel morphology.

Figure 18.1. Riparian vegetation along Rio Peñasco,

New Mexico

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P s stniopotoh I a otohplennahcedulcn n h / giH

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yratnemelppuS

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Riparian notes

Line-point intercept. The Line-point intercept

method can be used to effectively monitor

changes in cover and composition across the

width of the riparian zone. Install transects

perpendicular to the channel with at least three

transects per area of interest. Set transect ends at

least 5 m (15 ft) outside the maximum potential

riparian zone. Transects should be extended

further in areas where the riparian area is expected

to expand. For extremely wide riparian areas,

reduce the frequency of measurements along the

transect. Adding height measurements at each

point provides useful information on vegetation

structure. Use the Line-point Intercept with Height

Data Form in Chapter 15 to record height

measurements.
110

In addition to the standard indicators,

calculate the proportion of the transect covered by

woody riparian species, and average plant height.

Standard deviation of height measurements and

the average number of species recorded at each

point are useful indicators of structural diversity.

Changes in the width of riparian zones can be

monitored by noting where riparian vegetation

begins and ends along the transect. Line-point

intercept data also can be used for this purpose,

but will often underestimate riparian zone width.

Soil stability test. The Soil stability test can be a

useful indicator of changes in soil structural

development, but results are often difficult to

interpret in riparian systems. Interpretation of data

is limited by the fact that soil texture often varies

widely within a riparian zone. Texture also may

change during the year as flood events deposit

new material. Sediment deposition may result in a

negative change in average soil stability following

a flood. However, sediment deposition by floods is

often a positive indicator of riparian zone

recovery, despite its initially low stability.

Belt transect and Tree density. Either of these

methods can be used to monitor woody plant

density by size class. Belt transects are more

appropriate for monitoring recruitment of new

individuals, while the Tree density method is more

applicable for areas with a few highly dispersed

individuals. The Belt transect can be applied either

along the greenline (edge of the channel) or on

the Line-point intercept transect that crosses the

channel.

Compaction and Infiltration. High rates of

recreational or grazing use, especially on moist or

wet soil, can cause degradation of soil structure,

including compaction. Where compaction appears

to have resulted in reduced infiltration, both the

infiltrometer and the penetrometer may be used.

Infiltration measurements are usually low priority

because they are relatively time consuming (high

cost-benefit ratio). Compaction test measurements

are relatively rapid, but the data are difficult to

interpret unless the measurements are made in soil

with the same moisture content each year. This is

more likely to be possible in arid ecosystems when

measurements can often be made following a

period with no precipitation.

Plant species richness. Plant species richness can

be a valuable indicator of riparian recovery and

degradation. It is useful when biodiversity is a

management objective. The method generally has

a low priority because a minimum estimate of

richness can be calculated from the Line-point

intercept and Riparian channel vegetation survey.

The method is also quite time consuming, adding

significantly to costs in most cases.

Additional resources

A large number of riparian monitoring systems

have been developed for perennial streams. Many

systems focus on specific stream characteristics

believed to be important for fish habitats,

including water temperature and chemistry. One

of the most widely applied riparian vegetation

methods is described in Winward (2000). This

method, like the Riparian channel vegetation

survey, depends on identifying the greenline.

Researchers are continuing to develop appropriate

methods for intermittent streams, washes and

arroyos where the greenline is often difficult to

identify. Using aerial photography and

videography to monitor (Prichard et al. 1996) is

becoming increasingly popular, particularly where

dense vegetation and accessibility make ground

measurements difficult or impossible.

Riparian
111

Chapter 19

Livestock production

Figure 19.1. Herding cattle in the Chihuahuan

Desert.

L

ong-term sustainability of livestock

production in upland areas depends on the

three key ecosystem attributes: soil and site

stability, hydrologic function and biotic integrity.

The Quick Start measurements should be adequate

for monitoring these attributes, except where there

is a specific problem such as compaction, or a

concern such as biodiversity (species richness).

Where the flexibility exists to make short-term

changes in stocking rates or grazing patterns (e.g.,

by moving water, salt blocks or supplemental

feed), conduct short-term monitoring (Quick

Start).

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Livestock production notes

Plant production. Annual forage production is

sometimes considered to be one of the most

important indicators for livestock management.

This is generally calculated as part of total plant

production. It can be used to plan annual stocking

rates. It is also a long-term indicator of changes in

land status. Plant production is one of the most

difficult and costly indicators to accurately

monitor, especially in arid and semi-arid

ecosystems. It can also be difficult to interpret,

particularly in areas with highly variable

precipitation.

Photo by Bob Gibbens
112

An aboveground standing biomass of forage

species is a useful short-term indicator, which can

be used to determine how many animals a pasture

will support for a particular period of time. It can

be calculated from the plant production data

collected by simply setting utilization to 0 and the

growth adjustment factor to 1.0 (Rules 9 and 10 in

Chapter 9). Residual (standing) biomass, like

residual cover, is also an excellent short-term

indicator for determining when to remove

livestock from a pasture.

Additional resources

Most monitoring systems used by federal agencies

in the United States were designed to monitor

livestock grazing impacts on plant communities

and, to a lesser extent, production. They often

include a mixture of short-term indicators (such as

stubble height and estimated utilization) and longterm indicators (such as similarity to a

hypothesized historic plant community). The

Natural Resources Conservation Service (NRCS)

and Bureau of Land Management (BLM) both

currently rely on the NRCS National Range and

Pasture Handbook (USDA-NRCS 1997) for

monitoring guidance. However, there is significant

variability at both the state and local level. The

United States Forest Service (USFS) has relied on

the Parker Three Step method for monitoring in

most regions, although other methods are

increasingly used. The basic approach is described

in Parker (1951). The local office should be

consulted to find out exactly how the method was

and is being applied in each forest. In addition to

federal handbooks, most state extension services

have developed and published rangeland

monitoring guides. Again, these generally focus on

effects of livestock grazing and include a mix of

short- and long-term indicators. Contact your

local extension office or land-grant university for

current versions.

Livestock production
113

Chapter 20

Wildlife habitat

I

mportant characteristics for wildlife

management are vegetation composition and

structure. These can be monitored using

augmented versions of the Line-point intercept

and Belt transect methods, as well as adding a

cover pole or cover board measurement

(Vegetation structure).

Every species has unique habitat requirements.

These requirements may be poorly understood and

they can change during the year. Therefore, please

read the Wildlife habitat notes section below to

determine which combination of methods best

suits your needs.

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Wildlife habitat notes

Line-point intercept. Line-point intercept can be

used to assess plant composition. In savannas and

other systems with widely scattered trees, it should

be supplemented with the Belt transect and/or

Tree density methods.

Where vertical vegetation structure is of

interest, height estimates should be included for at

least every fifth point. Use the Line-point Intercept

with Height Data Form in Chapter 15. Line-point

intercept (with height) should be combined with

Canopy Gap intercept to best understand

vegetation structure.

Figure 20.1. Mule deer habitat.

Photo by Doug Burkett
114

In addition to the standard indicators,

calculate the proportion of the line covered by

woody species and average height. The standard

deviation of the height measurements and the

average number of species recorded at each point

are useful indicators of structural diversity. The

distribution of species along a transect can be

used, together with Gap intercept from the same

transect, to characterize individual vegetation

patches.

Gap intercept. Canopy Gap intercept is generally

more useful than Basal Gap intercept for

addressing horizontal vegetation structure with

respect to wildlife. Canopy Gap intercept should

be combined with Line-point intercept (with

height) to best understand vegetation structure.

Basal Gap intercept may be used as a surrogate in

systems where canopy cover is extremely dynamic.

The standard Gap intercept indicators can be used

to estimate the proportion of area in which an

animal would be exposed to aerial predators or to

direct sunlight. The standard indicator classes (25-

50 cm, 51-100 cm, etc.) were selected based on

erosion criteria. The proportion of land in even

larger gaps (e.g., > 500 cm, approximately 15 ft)

can be used to examine the extent to which

vegetation is clumped or dispersed.

The Gap intercept method can be modified to

examine gaps between tall clumps of vegetation by

establishing a minimum height or by recording

two separate gap types (greater than x cm and less

than x cm tall) for canopy intercepts. The

indicator calculations and data forms are identical

to the standard technique.

Soil stability test. While not directly related to

habitat for most wildlife species, soil stability is

essential to the sustainability of the system. It is

also an important indicator of the integrity of soil

processes, including the activity of soil-dwelling

animals responsible for root and plant litter

decomposition.

Belt transect. Belt transects can be used to assess

plant species composition in communities

containing widely scattered trees. Additional size

classes can be included for species measured with

the Belt transect to better estimate vertical

vegetation structure (but see the Vegetation

structure method discussed below).

Plant production. Please see discussion in

“Livestock production notes” in Chapter 19.

Plant species richness. Please see “Riparian”

discussion in Chapter 18.

Vegetation structure (cover pole). Cover poles and

boards are among the most widely used tools for

characterizing habitat structure. The proportion of

the pole that is obscured by vegetation in each

height increment when viewed from a specified

distance reflects the proportion of an animal that

would be obstructed from view at that distance.

Additional resources

We found few generic resources for wildlife habitat

monitoring, although there are literally hundreds

of protocols available for individual species. A

wide range of literature does exist linking

vegetation structure to wildlife habitat, bird

diversity, visual obstruction and production (e.g.,

Robel 1970, Robel et al. 1970, and Harrell and

Fuhlendorf 2002).

If a particular species or group of species is of

concern, try contacting a local wildlife biologist or

searching the Internet. Keywords that may assist in

Internet searches include: foliage height diversity

(FHD), vegetation structure, vertical structural

diversity, wildlife habitat structure, cover pole,

cover board and Robel Pole. Krebs (1998) lists a

number of techniques for measuring animal

populations directly. Measuring and Monitoring

Plant and Animal Populations (Elzinga et al. 2001)

also has information on animal population

monitoring, although the primary focus is on

vegetation monitoring. Research and Management

Techniques for Wildlife and Habits (1994) is another

resource for wildlife habitat methods.

Wildlife habitat
115

Chapter 21

Off-road vehicle use and other

recreational land uses

Areas impacted by off-road vehicles and

other recreational land uses are often

characterized by linear surface

disturbances. While these disturbances can cover a

relatively small portion of the landscape, their

effects on ecosystem function can be significant,

especially in steeply sloping terrain and riparian

zones. Recent research (Herrick et al. unpublished

data) has shown that even a single pass of a

relatively small vehicle can compact some soils,

significantly reducing water infiltration and soil

stability for extended periods of time.

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Figure 21.1. Off-road vehicle trails north of Salt Lake

City.

Photo by Jayne Belnap
116

Recreation

In order to make a sufficient number of

measurements in tracked areas, it may be

necessary to pre-stratify soil measurements into

areas that both do and do not appear to be in

tracks. Randomly select locations for an equal

number of soil measurements (Soil stability test,

Compaction test and/or infiltration) in tracked

and non-tracked areas. If this approach is used, it

is essential that track intercepts be recorded on the

Line-point Intercept or Gap Intercept Data Form so

that a weighted average can be calculated for each

soil indicator.

Recreation notes

Line-point intercept. Where vehicle tracks are

relatively distinct, the proportion of area they

cover can be quantified by recording the number

of Line-point intercept points that fall on them.

Use the Line-point Intercept with Height Data

Form and change the “Height” column to “Track”

(or add another column). The track cover estimate

is likely to be less precise than other cover

estimates, such as bare ground, because of the

difficulty in defining what constitutes a track.

Observer ability varies, and tracks tend to be more

apparent early and late in the day. On some soils it

may be possible to define a minimum depth

required for tracks to be recorded.

Gap intercept. The Gap intercept method can also

be used to quantify the proportion of the area

covered by tracks. This is particularly useful in

areas where tracks cross the transects relatively

infrequently (e.g., less than five percent of the

transect). On the Gap Intercept Data Form simply

use the last few columns of the “Basal Gap

intercept” side of the page and record where each

track or contiguous set of tracks begins and ends

along the transect.

Belt transect. Belt transect measurements and

other strategies to monitor invasive species (see

“Invasive species,” Ch. 23) should be given high

priority due to the potential for vehicles to

transport invasive species relatively large distances.

It is important to train field workers to identify all

species that could potentially invade a site, based

on soil and climate requirements, whether or not

the species is already present in the area.

Plant species richness. Please see the discussion in

“Riparian notes” in Chapter 18.

Additional resources

David Cole of the USFS Rocky Mountain Forest

and Range Experiment Station has written a

number of publications on monitoring

recreational impacts. They are available on the

USFS websites (http://fsinfo.fs.fed.us/cgi-bin/gw/

chameleon, http://www.srs.fs.fed.us/pubs/

index.jsp or http://leopold.wilderness.net/

pubs.cfm). Most of these focus on the effects of

hikers, campers and mountain bikers. Richard

Knight of Colorado State University and others

have also published extensively on monitoring

recreational impacts. However, there are

surprisingly few protocols available for monitoring

off-road vehicle effects.
117

Chapter 22

Fire

T

here are two general types of fire monitoring:

fire risk and fire recovery. Fire risk

monitoring is a relatively well-developed

science based on estimates of fuel availability,

vertical and horizontal continuity of fuel, moisture

content and weather conditions. Fire risk

monitoring is not addressed here.

Fire recovery monitoring is generally initiated

following fire. Where possible (e.g., prescribed

burns), pre-fire baseline data should be collected at

the same time of year that monitoring will be

continued following fire. It is more important to

monitor at the same time of year before and after

than to take measurements immediately following

the fire.

Figure 22.1. Prescribed fire in an old world bluestem,

sideoats grama, little bluestem and blueberry juniper

grassland community.

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Photo by Pat Shaver
118

Fire

The most common post-fire and fire recovery

concerns are runoff, erosion and regeneration of

the plant community. Runoff and erosion are

expensive to measure directly. The Quick Start

methods and indicators reflect changes in plant

communities and in the risk of runoff and erosion.

Consequently, the basic measurements often can

be applied to post-fire recovery monitoring with

relatively little modification.

The method modifications described here are

based on experience from northern New Mexico in

grasslands invaded by piñon pine, juniper, oak

and/or ponderosa pine. Additional modifications

may be useful in other ecosystems.

Fire notes

Line-point intercept. The Line-point intercept can

be applied with little modification. For savannas

and woodlands with significant coarse woody

debris, it may be useful to split the woody litter

class (WL on the Line-point Intercept Data Form)

into multiple size classes. Where short-term

mortality estimates are required, the height

column from the Line-point Intercept with Height

Data Form (Ch. 15) can be changed to “Dead?”

and used as a checkbox. However, mortality may

be more precisely quantified using the Belt

transect method, especially for woody species.

Differentiating between dead and live herbaceous

plants is normally not recommended because of

the high level of uncertainty associated with these

assessments. In addition, plant mortality is usually

more accurately reflected in increased bare ground

and reduced plant cover the following year.

Gap intercept. Gap intercept is one of the more

useful measurements for monitoring post-fire

recovery. It distinguishes between recovery

occurring uniformly across a site, and recovery

concentrated in dense vegetation patches. Some

organizations have modified the Gap intercept

method to include embedded litter because of its

role in helping to slow runoff. Embedded litter is

assumed to have a similar effect on runoff as a

plant base does. While this may be true for

systems in which litter is firmly anchored to the

soil by fungal mats, it is probably not appropriate

in all cases. Coarse woody debris can also act like a

plant base where it is in direct contact with the

soil surface.

Soil stability test. Prescribed burns rarely cause

short-term changes in soil stability. Stability can

begin to decline over time, however, if plant

recovery is slow. This is due to reduced root,

fungal and litter inputs necessary for soil aggregate

formation.

Intense fires where a large amount of fuel is

burned at the soil surface can actually increase soil

stability by making it hydrophobic. Unfortunately,

because these surfaces repel water, they ultimately

increase erosion downslope because they increase

surface runoff. Other factors can contribute to

hydrophobicity, including high fungal

concentrations. Hydrophobicity can be easily

quantified by recording the number of soil

stability samples that float when they are placed in

water.

Belt transect and Tree density. The Belt transect

and Tree density methods can be used to quantify

mortality and recruitment by simply recording live

and dead individuals, and new seedlings, in

different columns. The Belt transect method is also

useful for monitoring the invasive plant

populations after they have become established

(see “Invasive species,” Chapter 23).

Compaction. Fire does not cause compaction.

However, fire-fighting activities often do,

especially when vehicles are driven off road. In

addition to the burned area itself, firebreaks and

access points for fire crews should be considered

for inclusion in post-fire recovery monitoring.

Where time permits, infiltration may also be

measured.

Plant production. Please see the discussion in

“Livestock production notes” (Chapter 19).

Plant species richness. Please see the discussion in

“Riparian notes” (Chapter 18).
119

Fire

Vegetation structure. Vegetation structure

indicators can be used as a relative indicator of the

presence of “ladder fuels” in savannas. Taller

herbaceous plant material and low branches

facilitate the movement of ground fires into tree

canopies.

Additional resources

In the past, fire recovery monitoring was

traditionally limited to photographs and

occasional quadrat or transect measurements.

Funding was rarely available for repeated

measurements, or to develop and test protocols.

Increased interest in response to large burned areas

has sparked the development of a large number of

monitoring systems, many of which are becoming

available on the Internet. Many of the systems

consist of separate methods for each monitoring

objective (runoff, erosion, wildlife, vegetation,

etc.). Where possible, the methods should be

combined in order to limit costs associated with

redundant measurements.
120

Chapter 23

Invasive species

I

nvasive species may be the most important,

ecologically sensitive and profitable single

factor to monitor in many areas. The amount of

money that can be saved through early detection

of a new population can often exceed the current

value of the land. Unfortunately, establishment

can be difficult to detect remotely, and it is

impossible to search every acre every year. The

following protocol can be used to reduce

monitoring costs while increasing the probability

of early detection. It is based on rapid assessment

of nonpermanent plots in areas with a high risk of

invasion.

The methods included in this manual can be

used to address two objectives related to invasive

species:

(1) To monitor changes in invasive species after

they have become established (Belt transect for

low cover and Line-point intercept for high

cover).

(2) To monitor changes in the susceptibility of a

site to invasion (Line-point intercept and Gap

intercept) where there is a high risk of seed

dispersal, or it is known that invasive species

already exist in the seed bank.

The “Invasive Species Detection Protocol” at

the end of this chapter is designed to detect

invasive species in the early stages of

establishment on a site.

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Figure 23.1. Cheatgrass grassland with sagebrush.

Photo by Mike Pellant
121

Invasive species notes

Line-point intercept. The Line-point intercept

method can be used to quantify invasive species

cover changes where the species is a significant

component of the plant community (generally

greater than five percent cover). Line-point

intercept cover and composition indicators also

often reflect the resistance of a site to invasive

species establishment.

The plant community can affect resistance

directly by competing with the invasive species. It

can affect resistance indirectly through its effects

on herbivore populations and soil microbial

communities. It can also indirectly affect

resistance to invasion through its effect on the

timing, frequency and intensity of disturbances,

which then modify conditions for establishment

of both invasive and non-invasive species.

Relevant indicators are site specific and may

include percent bare ground or percent cover of a

particular functional group.

Gap intercept. The proportion of the land covered

by large gaps in foliar or basal cover directly affects

invasive plant establishment through its effect on

competition and soil stability. It can indirectly

affect invasive plant establishment through its

effects on small herbivore activity and larger scale

disturbances such as fire. No new indicators are

required, but the gap sizes of interest may vary

depending on species.

Belt transect. The Belt transect is one of the most

rapid methods for monitoring invasive species that

cover too little area to be reliably detected with the

Line-point intercept method (generally less than

five percent cover). It can also be used to

quantitatively monitor the appearance of small

seedlings where it is known that the species

already exists in the seedbank, or where there is a

high risk of introduction.

Plant production. Please see the discussion in

Chapter 19, “Livestock production notes.”

Plant species richness. Please see the discussion in

Chapter 18, “Riparian notes.”

Invasive species

Invasive Species Detection

Protocol

(1) Use existing information to stratify the

landscape into areas that have an inherently

high invasion risk for each species, based on

soil and climate. Ecological Site Descriptions

(Chapter 2) can be extremely helpful and often

list potentially invasive species. Aerial

photographs and other remote sensing tools

can be extremely useful in developing riskbased landscape stratification.

(2) Within high risk monitoring units, identify

areas most susceptible to invasion. This

analysis should be based on risk of dispersal

(the risk that seeds will be brought to the site)

and risk of successful establishment (the

probability seeds will land in an area favorable

for establishment). For example, trails are

highly susceptible to invasion. Trails have an

increased risk of invasion because of the high

probability of dispersal from distant plant

populations. Trails are also at risk because trail

margins are often disturbed, reducing

competition against invasives.

(3) Identify additional high-risk areas each year.

For example, the establishment of a new

campground, road or mineral survey operation

can increase the risk of invasive species

establishment. Again, aerial photographs and

other remote sensing tools can be invaluable

in this process.

(4) Randomly select areas for ground-based surveys

based on risk analyses in 1 through 3 above.

(5) Visit each area and complete a rapid

assessment that includes the following:

• Estimate and record presence, number and

size of invasive species.

• Predict the probability that population size

will increase for all invasive species

encountered, based on site characteristics,

climate and disturbance regime.

• Evaluate future invasion risk, including the

need to return to the area within a

specified period of time.

• Record GPS locations of invasive plants

and populations.

(6) Revise risk analysis (steps 1 through 3) based

on field observations.
122

Chapter 24

State and transition models: an

introduction

State and transition (S&T) models (Figs. 24.1 and

24.2) illustrate possible changes in plant

communities and soil properties and their

interactions. They can be used, together with

assessments of the current status, to help decide

where to monitor based on where change is most

likely to occur. They can also be used to help

decide what to monitor, because they often

provide information on soil and vegetation

changes that are likely to precede a change in

state. States are distinguished by transitions, which

may be relatively irreversible, reflecting a

significant increase in energy required to shift

back to the previous state.

Individual S&T models are usually developed

for each ecological site. Ecological sites are defined

as land that has a similar potential to support a

particular range of plant communities based on

soils and climate. Land included in each ecological

site is expected to respond similarly to different

types of disturbance, climate and management.

State and transition models generally include

at least two states, and one or more plant

community within each state. Plant communities

within a state are similar in their species

compositions. Plant communities within a state

are generally functionally similar in their capacity

to limit soil loss, cycle water and produce

vegetative biomass. Changes among plant

communities within states are considered to be

reversible through simple changes in grazing

management (in grazed ecosystems) or fluctuating

climatic conditions. The S&T diagrams (Fig. 24.1)

show possible transitions between states. The

diagrams also illustrate the factors that increase

the probability that changes will occur. Transitions

between states are reversible only through

generally costly, intensive practices such as shrub

removal or soil modification.

The NRCS, BLM, The Nature Conservancy and

other organizations are currently developing state

and transition models, and similar types of

models. Many are available from NRCS. Please

contact your local NRCS field office or refer to the

NRCS website for state and transition models

pertaining to your ecological sites. All indicators

described in this manual can be used to help

quantitatively define states and the probability

that transitions will occur. For more information

on the development of these models, see

Bestelmeyer et al. (2003) and Stringham et al.

(2001).

Figure 24.1. Typical state and transition model structure (based on Bestelmeyer et al. 2003 and Stringham et al.

2003). Large boxes are states defined by relatively irreversible transitions. Small boxes within states represent

plant communities. Transitions (dashed lines) are relatively reversible. Single-state systems are possible where

no thresholds have been identified.

------

------

----------

------

----------

----------

----------
123

Sideoats grama

Shrub live oak

Blue grama

Hairy grama

Black grama

Sideoats grama

Hairy grama

Shrub live oak

Mixed grass savanna

Juniper

(Blue grama)

Sacahuista

Shrub live oak

Live oak

(Blue grama)

Woody/succulent dominated

Sparse

Blue grama

Blue grama/bare

1a

1b

2a 2b 3

1a. Heavy grazing, summer drought,

decreased fire

1b, Clearing, seeding, increased fire

frequency

2a. Heavy grazing, erosion, reduction

of A horizon

2b. Erosion control, gully destruction

3. Clearing after erosion is severe

Figure 24.2. State and transition conceptual model for the “Breaks” ecological site in west-central New Mexico

(Major Land Resource Area 36, Land Resource Unit WP-3). General structure follows Bestelmeyer et al. (2003)

and Stringham et al. (2001, 2003). See description on following page.

S & T models

----------

----------

----------

----------
124

Description for state and transition model for an ecological site (“Breaks”) in west-central New Mexico (MLRA WP-3)

(Fig. 24.2).

Overview

The Breaks sites intergrade with Hills sites and often contain Loamy sites occurring as narrow to broad drainageways. The

historic plant communities of the Breaks sites are dominated by black grama (Bouteloua eriopoda) and sideoats grama

(Bouteloua curtipendula) and/or blue grama (Bouteloua gracilis) among others, depending on soil types and aspect. Under

heavy grazing pressure, especially on steeper slopes and on soils with strong argillic (clay-rich) horizons, erosion may lead to a

persistent loss of vegetation. A decline in fire frequencies, or perhaps regional increases in the relative amount of winter rainfall

or grazing, may lead to significant increases in the abundance of woody plants and succulents including sacahuista (Nolina

microcarpa), shrub liveoak (Quercus spp.), and one-seed juniper (Juniperus monosperma). The established woody plants may

compete with grasses and lead to persistent reductions in grass abundance. No systematic studies of communities, states or

transitions have been performed in the Breaks site.

Catalog of states, community pathways, and transitions

Mixed-grass savanna: The expression of the community depends upon aspect and soil. On south-facing slopes, black grama

tends to dominate and there may be some sideoats grama among other grasses. On north-facing slopes, sideoats grama dominates, with blue grama and hairy grama (Bouteloua hirsuta) as subordinates; black grama occurs in smaller amounts. In some

cases (especially west of Silver City), sacahuista (Nolina microcarpa) may be dense enough to be considered a secondary

dominant. Live oak, sacahuista, and juniper exist in low densities giving the site a savanna aspect. Grazing and drought-induced

mortality may lead to reductions in black and sideoats grama and dominance by hairy grama, blue grama, or annuals.

Diagnosis: Sacahuista, oak and juniper are present and scattered; most of the ground surface is grassy, with few large bare areas.

Transition to woody/succulent-dominated state (1a): It is unclear why succulents or trees increase in abundance,

although it is likely that the subsequent decline in grasses is due to competition for water and nutrients. The formation

of bare ground patches due to grazing, decreases in fire frequency, and increases in winter precipitation, either

independently or in concert, may be responsible for the transition.

Key indicators of approach to transition: Increases in bare ground, decreases in litter cover and grass cover, increased

frequency of oak seedlings and small sacahuista (threshold may have been crossed), decreased fire frequency.

Transition to blue grama/bare state (2a): Heavy grazing, especially in drought conditions on steeper slopes and on

soils with shallow, strong argillic horizons (e.g., Lonti gravelly loam) may result in grass loss and subsequent erosion

of the organic matter-rich A horizon.

Key indicators of approach to transition: Increases in bare ground, decreases in litter cover and grass cover, surface soil

loss, water flow patterns, rills, pedestalling of plants and stones.

Woody/succulent-dominated: Grass cover is often highly reduced and shrubs, trees, or succulents become dominant. Bare

ground is extensive, and scattered, small blue grama or hairy grama plants represent the dominant grass cover. West of Silver

City, sacahuista tends to dominate in this state, and liveoak may or may not be a secondary dominant. In other cases, juniper or

oak may dominate.

Diagnosis: Oak, sacahuista, and/or juniper are the dominant perennial species and the bare ground areas between them are

interconnected. Grass clumps are small and scattered. Evidence of erosion (rills, water flow patterns, pedestalling) is common.

Transition to woody/succulent-dominated state (1b): Thinning of woody or succulent species may release grasses from

competitive suppression and grasses may colonize patches where trees or sacahuista were present. If erosion in

interspaces has not been severe, recolonization may take place there over several years.

Transition to blue grama/bare state (3): Tree and succulent removal, especially on slopes, may accelerate erosion if

grasses do not respond to the treatment and the soil is exposed to raindrop impact and erosion.

Blue grama/bare: This state is characterized by extreme erosion and tends to occur on steeper slopes. Bare ground cover is

extreme, gullies may be present, and few small perennial plants, usually blue grama, are present. Trees and succulents are not

especially abundant.

Diagnosis: Bare ground is interconnected, and trees and succulents are not especially abundant. Evidence of erosion is common,

the mollic A horizon is very shallow (a few cm) or missing.

Transition to mixed grass savanna state (2b): The placement of structures (e.g., terraces) to retard erosion and that

accumulate soil, in addition to the destruction of gullies, may be used to initiate the eventual recovery of perennial

grass dominance.

Information sources and theoretical background: Communities, states, and transitions are based upon information in the

Ecological Site Description and observations by Gene Adkins, NRCS and Brandon Bestelmeyer, USDA-ARS Jornada Experimental Range.

S & T models
125

Chapter 25

Remote sensing

Remote sensing includes any data that are

collected remotely, including aerial

photographs, satellite imagery and digital

elevation models generated from aircraft or

satellites.

Remote sensing can increase the quality and

cost-effectiveness of monitoring programs in a

number of different ways. It can be used to stratify

the landscape into relatively homogeneous units,

to extrapolate ground-based measurements and, in

some cases, to quantify properties and processes in

the absence of ground-based measurements using

previously established relationships.

Figure 25.1. Example of a color IR aerial photo of Increasing monitoring cost- Mimbres Watershed.

effectiveness with remote sensing

Incorporating remote sensing imagery into the

monitoring design process at an early stage can

dramatically increase cost-effectiveness and

reliability. It helps to focus monitoring on

representative areas with a high potential for

change, while avoiding areas that have already

crossed a threshold. Although imagery used for

this step should be as recent as possible, the actual

date is not as critical for the monitoring design

step as when used as monitoring data. Additionally,

variability in image quality is much less critical

than when the imagery is being used directly for

monitoring.

Options for incorporating remote sensing

into monitoring programs are summarized in

Table 25.1. Option 1 can be done with or without

GIS knowledge. Options 2 and 3 (Table 25.1)

require training or extensive experience in remote

sensing and GIS. Option 3 is difficult, but not

impossible, to apply to larger areas. It can be more

easily applied to relatively small areas (farms,

ranches or conservation areas). All three options

often can be applied together.

Option 1. It is appropriate to use remote sensing

imagery for monitoring unit stratification and

extrapolation where the imagery lends itself to

visual classification of geomorphic and vegetation

units. During stratification, use remote sensing

imagery (e.g., aerial photographs), together with

other available spatial data, to stratify the

landscape into relatively similar landscape units

(Figs. 2.1 through 2.4 in Ch. 2). Where possible,

further subdivide landscape units based on current

vegetation, management and the status of the

three ecological attributes (soil and site stability,

hydrologic function and biotic integrity).

The next step in stratification is to combine

these spatial data with state and transition models

(Bestelmeyer et al. 2003; Briske et al. 2003;

Stringham et al. 2001, 2003) and information on

current and potential drivers (Brown and Havstad

2004). All of this information can be used to

identify landscape units with a relatively high

potential for degradation or recovery.

Extrapolation using remotely sensed data

requires an adequate number of plots to represent

the landscape. Develop a good relationship

between these ground-based measurements and

remote-sensing indicators. If this is not feasible, it

is possible to extrapolate using remotely sensed

imagery if extensive, long-term knowledge of the

landscape, its ecological communities, and their

interactions and drivers exists.
126

Option 2. The ability to make coarse-scale

extrapolations based on repeated, ground-truthed

imagery depends on the scale of the imagery and

the scale of the vegetation heterogeneity and

dynamics. Imagery and ground-based data must be

collected in the same time frame (either within the

same month or within the same season). Expertise

in image classification is required for this option.

Option 3. Fine-scale extrapolation based on

repeated, ground-truthed imagery has the same

requirements as described in Option 2, but to a

higher degree. Fine-scale extrapolation requires the

highest level of GIS expertise, field sampling, and

image quality. Imagery must be at a fine enough

resolution to detect the same community level

changes as the ground-based measurements.

Defining the relationship(s) between the fieldbased indicators and remote-sensing indicators can

be challenging. It can even, at times, be impossible

(see “Monitoring with remote sensing alone”

below).

Monitoring with remote sensing

alone

A fourth option for incorporating remote sensing

into monitoring programs is to use predefined

relationships between remote sensing indicators

and ground-based measurements. This option is

problematic because of the high spatial and

temporal variability in soil and vegetation

relationships. Confounding this factor is the

relatively low vegetation cover typical for

rangelands. In addition, vegetation reflectance and

temperature change rapidly and unpredictably in

response to highly variable soil moisture. All of

these caveats make monitoring solely via remote

sensing in arid and semi-arid communities

challenging.

New techniques that take advantage of greater

computing power, higher resolution images and

integration of information using different types of

images are currently being developed at the

Jornada Experimental Range and elsewhere (Rango

et al. 2003). While these techniques are likely to be

more sensitive and reliable, it is unlikely that we

will ever be able to design comprehensive

monitoring programs based exclusively on remote

sensing. Periodic ground-truthing is likely to be

required for most applications.

Conclusions

By using remote sensing imagery primarily to

improve monitoring program design, we exploit

the strengths of remote sensing technologies.

Using remote sensing imagery only for

stratification allows us to avoid the pitfalls of overRemote sensing

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Remote sensing

reliance on relatively abstract indicators, many of

which require new ground-based calibration data

for each new set of imagery. By combining remote

sensing with qualitative assessments and state and

transition models, we can target both management

and monitoring to those parts of the landscape

with the highest probability of change. Where it is

possible to obtain repeated, concurrent groundbased and remote-sensing data, imagery can be

used to generate a more precise extrapolation than

is possible with the initial stratification alone.

However, the ability to make such extrapolations

is tightly linked to the type of vegetation

community and the resolution of the imagery.

Parts of this Chapter were adapted from Herrick et al.

(2003).
128

Chapter 26

Soil carbon

S

oil carbon can be a useful and accessible

long-term indicator of change in the

functioning of an ecosystem. Soil carbon is

directly related to soil organic matter content, a

key indicator of soil quality. Soil organic matter is

important for maintaining soil structure. Soils

with good soil structure generally have lower

erosion rates, higher water infiltration rates and

higher water-holding capacities. Soil organic

matter also serves as an important nutrient

reservoir.

Typically, increasing soil carbon has positive

effects on soil and ecosystem health. But simply

increasing soil carbon may not always be the land

management goal. For instance, replacement of

grasslands by woody-dominated plant communities

may increase total carbon sequestration at the

landscape level, but reduce soil quality near the soil

surface in plant interspaces. This reduction in soil

quality associated with woody plant invasion is

particularly common in arid ecosystems.

Figure 26.1 Soil organic matter and soil carbon

are usually higher near the soil surface.

In most cases, meeting land management

objectives will require tracking changes in soil

carbon over time. There are three options for

carbon monitoring: measurement, modeling, and

monitoring changes in vegetation cover,

composition and production.

Soil carbon measurement is currently too

expensive in most arid and semi-arid ecosystems.

This is due to a combination of high sampling and

analysis costs and the large number of samples

required to detect a change.

Soil carbon models predict changes in soil

carbon based on soil properties, current vegetation

and climate. However, most available carbon

models focus on agricultural, forest and grassland

ecosystems, and do not reliably predict soil carbon

dynamics in diverse arid and semi-arid ecosystems.

Given their drawbacks, carbon measurement and

modeling are not yet recommended as viable

monitoring options. However, the accuracy of

both measurement and modeling is improving.

Cost-effective rangeland carbon monitoring

systems integrating the two approaches should be

available within the next decade.

A third, more practical, option for the present

time is to simply monitor changes in vegetation

cover and composition (Line-point intercept

method) and production (Plant production

method). These indicators cannot currently predict

Carbon sequestration

In addition to being a good indicator of soil

quality, sequestering (storing for long periods)

carbon in the soil keeps it out of the

atmosphere, where it occurs as carbon dioxide

and contributes to the greenhouse effect and

global warming. The United States has

adopted a market-based approach to provide

incentives for reducing greenhouse gases in

the atmosphere. Efforts are currently

underway to establish standard systems for

“trading” carbon released into the atmosphere

(e.g., from fossil fuel combustion) for

additional carbon stored in the soil as soil

organic matter. From a practical perspective,

this means that carbon producers (e.g., power

plant operators) can purchase credits in a

market. Those credits may be supplied by a

variety of sources, including increased soil

carbon sequestration. However, formal trading

procedures are not currently in place and the

details are still uncertain.
129

soil carbon changes, but they are associated with

changes in carbon inputs. In general (but not

always), soil carbon increases with cover and

production. In systems in which a significant

portion of the production is consumed by

livestock or wildlife, utilization records (“Shortterm monitoring” in Quick Start) should also be

carefully maintained.

Carbon notes

Photo points. Soil profile photos showing nearsurface carbon accumulation where accumulation

rates are high can supplement vegetation photos.

They can help substantiate changes recorded in

the quantitative soil and vegetation data. They are

also useful communication tools.

Line-point intercept. Line-point intercept data are

used in models to estimate carbon inputs. Both

cover and species composition are required for

carbon models.

Gap intercept. Gap intercept may be used as an

index of soil erosion risk. The highest

concentration of soil organic carbon is usually in

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Soil carbon

the top ten centimeters (4 in), which is also the

layer that is most susceptible to soil erosion.

Soil stability test. Soil stability is closely related to

the creation of new soil organic matter, and may

be a good early warning indicator of changes in

total soil organic carbon. However, the

relationship between soil stability and soil organic

carbon is highly variable. It should only be used as

a general indicator to compare among

management systems. When testing for soil

stability as an indicator of soil carbon, be sure to

test soil from different depths. In many arid soils,

the stability of the top few millimeters of the soil

surface is stabilized by cyanobacteria. Changes in

cyanobacterial biomass are not necessarily related

to changes in root production, which is the

primary source of soil organic matter in most

rangeland ecosystems.

Belt transect and Tree density. Changes in both

the density and cover of shrubs and trees have the

potential to significantly modify soil carbon

sequestration. Either of these two methods, Belt

transect or Tree density, can be used to detect
130

changes in woody species when cover is below

that which can be monitored using Line-point

intercept methods (generally five percent).

Additional resources

Guidelines for soil carbon monitoring are

currently in the process of being established.

Because this field is so dynamic, the best approach

to locating the most current and relevant resources

is an Internet search, focusing on those resources

that include evaluations of the cost, accuracy and

precision of the proposed methods. A recent

Council on Agricultural Science and Technology

report (CAST 2004) provides a good overview of

many of the issues associated with soil carbon

sequestration. For information on CENTURY, one

of the models currently being applied in the

United States, see www.nrel.colostate.edu/projects/

century5/reference/html/Century/desc-intro.htm

(accessed June 23, 2008).

Soil carbon
131

Appendix A: Monitoring tools

Measuring tapes, stakes and flags are available

through most hardware stores and natural resource

supply catalogs. Manufacturers of, and instructions

to build, less widely available tools are listed

below. Mention of trade names or commercial

products in this publication is solely for the

purpose of providing specific information and

does not imply recommendation or endorsement

by the U.S. Department of Agriculture. The tools

are not patented. None of the authors receive any

compensation from manufacturers.

Caution. Fabrication of these tools, like any shop

fabrication project, can result in injury. If you are

not comfortable with the required procedures and

standard safety protocols, find someone who is.

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132

Soil stability kit

(Quick Start)

Materials needed for construction of stability

kits:

• Schedule 40 PVC pipe with a 3/4 in inside

diameter

• PVC pipe cutter and hacksaw

• Tape measure and pencil

• A section of metal window screen (mesh size

approx. 1.5 mm [1/16 in])

• A bottle of water-resistant contact cement

• A plastic trash bag

• 2 plastic “parts” boxes (21 x 11.5 x 3.5 cm

outside dimensions [8 1/2 x 4 15/32 x 1 5/16 in])

with 18 cells, approximately 3 x 3 x 3 cm each

(1 1/4 x 11/4 x 1 1/4 in each)

• One small tube of silicone sealant

• A small piece of sheet metal, handle of an old

spoon, or pocket knife

1. Stability basket construction.

1.1 Cut pieces of PVC pipe into 3 cm (1 1/4 in)

lengths.

1.2 Make a 2 cm deep, cross-sectional cut, 5 mm

from one end (Fig. A.1).

1.3 Make the second cut perpendicular to the first,

ending at the end of the cross-sectional cut in

step 1.2 (Fig. A.2). This forms the basket

handle.

1.4 Remove burrs from all cuts.

1.5 Lay out a section of window screen on top of a

plastic trash bag. Make sure the screen is flat

and has no creases.

1.6 Apply contact cement to the bottom of the cut

PVC and quickly place on top of the screen

(Fig. A.3). Apply glue only in areas with

adequate ventilation.

1.7 Allow contact cement to dry overnight.

1.8 After 24 hours, remove the plastic trash bag

from the screen. Cut out each basket from the

screen (Fig. A.4).

2. Plastic box preparation.

2.1 If the plastic box has removable parts, be sure

to glue them into place.

2.2 Use silicone caulk or sealant to seal all corners

inside the box.

3. Stability shovel construction.

3.1 Using tin snips, cut out the shape of the

stability shovel from a piece of thin sheet

metal, tin can, aluminum weighing tin or

similar material with a thickness of 1 mm

(1/32 in) or less (Fig. A.5).

Figure A.1. First cut on PVC. Figure A.2. Second cut on PVC.

Monitoring tools
133

Figure A.3. Baskets glued to screen.

Figure A.4. Cutting out baskets from screen.

Figure A.5. Cutting shovels with tin snips.

 Monitoring tools
134

Impact penetrometer

(Chapter 7)

Materials needed for construction of impact

penetrometers:

• 0.625 in (15.9 mm) diameter steel rod 60.0 in

(152.40 cm) long

• Removable steel cone, hardened and smoothed

to a shiny finish (see Fig. A.6 for dimensions)

• 2.0 kg sliding steel hammer, 15.75 in (40 cm)

long with internal diameter of 0.688 in

(17.5 mm) and external diameter of 2.0 in

(5.1 cm)

• 0.5 in (12.7 mm) thick steel striker plate with

1.25 in (3.18 cm) external diameter

• Adjustable steel collar

Note:

Most machine shops should be able to construct

the impact penetrometer from Figure A.6 and the

instructions below.

1. Steel penetrometer rod construction.

1.1 The 60 in (152.4 cm) steel rod is 0.625 in

(15.9 mm) in diameter and is constructed of

2 pieces (Fig. A.6).

1.2 Etch the lower rod at 5 cm (1 31/32 in)

increments, starting from the top of the cone

and ending at 50 cm (19 11/16 in).

1.3 Thread the bottom of the upper rod (male) to

join with the striker plate (female).

1.4 The striker plate extends 0.39 in (1 cm) out of

the rod.

1.5 The upper rod contains the sliding steel

hammer and adjustable steel collar.

2. Removable steel cone construction.

2.1 The removable steel cone is 0.015 in (0.38 mm)

in radius at its point and 0.8 in (20.3 mm) in

diameter at its base.

2.2 See Figure A.6 for cone angle specifications.

2.3 The base of the cone contains a threaded end

(male) that connects to the lower, etched rod.

2.4 The cone is one seamless, hardened unit, and

the cone is smoothed to a shiny finish

(Fig. A.6).

3. 2.0 kg (4.51 lbs) sliding steel hammer

construction.

3.1 The sliding steel hammer should be exactly

2.0 kg (4.51 lbs).

3.2 Stamp the exact weight on the hammer.

3.3 The steel hammer should be approximately

15.75 in (40.0 cm) in length, and must slide

over the upper rod (Fig. A.6).

Monitoring tools
135

 Monitoring tools

RADIUS .015 


[0.04cm]

30°

.125 
[0.32cm]

0.80 
[2.03cm]

0.5 
[1.27cm],

1.25
 [3.18cm]

STEEL STRIKER PLATE

3.94 
[10.00cm]

1.97 
[5.00cm]

28.5 
 [72.39cm] 31.0 
[78.74cm]

12345

12345

12345

ADJUSTABLE

COLLAR

2.00 kg 2
 [5.1cm] DIAM.

SLIDING HAMMER

2.0 
[5.08cm]

BUBBLE LEVEL (OPTIONAL)

MOUNTED ON 3/4 
 P.V.C.

60.0 
[152.40cm]

JORNADA IMPACT PENETROMETER

-----------------

-----------------

0.625
 [1.6cm] DIAM.

SOLID STEEL ROD

Figure A.6. Impact penetrometer design (Herrick and Jones 2002). All dimensions are in inches [cm]. Use steel

for all parts except for the bubble level mount. Weld the striker plate to the center of a single rod, or to the bottom

half of a two-piece rod. Thread the top half. Cut the cone, from steel, on a lathe and then harden. Shorten the

penetrometer for transport by threading the bottom end of the top section of the shaft into the striker plate.

Always wear protective clothing when using the penetrometer (ear plugs and heavy leather gloves).
136

Single-ring infiltrometer

(Chapter 8)

Materials needed for construction of six

infiltrometers:

• At least 72 cm (28 11/32 in) of 12.5 cm (5 in)

diameter thin-walled (max 1.5 mm [1/16 in]

thick) metal cylinders (irrigation pipe is ideal;

stovepipe is OK provided that seams [if any]

are sealed).

• 1 hacksaw

• 1 grinder or metal file

• Bath towels

• Two 20-oz plastic pop bottles

• Flexible plastic grocery bags

• One five-gallon bucket of water

• One 15 cm (6 in) ruler

• One stopwatch

• At least six (ideally 18) wide-mouthed,

smooth-sided, 32-oz plastic soda bottles

• 18 to 20 gauge steel wire

• A drill and 29/64 in or 7/16 in bit

• At least six (ideally 18) plastic serological

pipette tubes (10 ml in 1/10)

• Silicone caulking

• Six 0.5 in (1 5/16 cm) rubber stoppers with a

hole in the center that is slightly smaller than

the diameter of the pipettes.

• Six thin rubber bands, large enough to stretch

around the bottle

• Black felt-tip permanent marker

1. Infiltrometer ring construction.

1.1 Cut 12.5 cm (4 15/16 in) diameter irrigation pipe

(or stove pipe) into 12 cm (4 23/32 in) lengths

and remove burrs.

1.2 Sharpen the end that will be inserted into the

soil to a 45° angle, using a grinder or metal

file.

1.3 Drill 2 small holes, 5 mm (0.2 in) apart and

1 cm (0.4 in) below the top edge of the ring,

at three equally spaced locations around the

ring (Fig. A.7).

1.4 Thread wire through the rings so that a

triangle is formed inside the ring (Fig. A.8).

1.5 Pull the wire tight.

1.6 Using a permanent marker, draw an insertion

depth line around the outside of the ring, 3 cm

above the bottom.

1.7 Repeat steps 1.1 through 1.6 until six (ideally

18) rings are constructed.

2. Infiltration bottle construction.

2.1 Remove the label from the 32 oz, widemouthed, smooth-sided soda bottle.

2.2 Drill a hole in the bottom center of the bottle

that is slightly larger than the small end of the

rubber stopper. The hole should be 12 mm in

diameter. A 29/64 in drill bit works best. A 7/16 in

drill bit will work, but requires widening the

hole.

2.3 Position a rubber stopper in hole (Fig. A.9).

2.4 Slide a plastic pipette into the rubber stopper

so the end is almost to the cap. Lubricate

pipette with silicone grease.

2.5 Cut off the end of the pipette if it protrudes

more than 10 cm (3.9 in) outside the bottom

of the bottle.

2.6 Apply three 10 cm (3.9 in) beads of silicone

caulking at equally spaced intervals around the

bottle. Start at the top of the bottle and go to

about its mid-section (Fig. A.10).

2.7 Using a marker, draw a line down the smooth

portion of the bottle (Fig. A.11).

2.8 Slide the rubber band around the bottle.

2.9 Repeat steps 2.1 through 2.8 until six (ideally

18) bottles are constructed.

3. Accessories

3.1 Cut up bath towels into five 25 x 50 cm

(10 x 20 in) pieces.

3.2 Cut the tops off of the two 20 oz plastic soda

bottles. Draw a line indicating the 375 ml

water level, using a permanent marker.

3.3 Cut the flexible plastic grocery bags into two

30 x 30 cm (12 x 12 in) sheets.

Monitoring tools
137

 Monitoring tools

Figure A.7. Drill three equally spaced pairs of holes

1 cm from the top of the ring. Each hole should be

5 mm from its pair.

Figure A.8. Thread wire through holes and tighten.

Figure A.9. Drill hole in bottom of wide-mouthed,

32 oz soda bottle and insert stopper.

Figure A.10. Apply three 10 cm silicone beads from

top of bottle to mid-section.

Figure A.11. Draw line along smooth length of bottle.
138

Monitoring tools

Cover pole (Chapter 11:

Vegetation structure)

Materials needed for cover pole:

• 2 m (78 3/4 in) of 1-in (2.5-cm) diameter PVC

pipe

• One male-threaded PVC coupling

• One female-threaded PVC coupling

• PVC pipe cleaner, primer and glue

• One aluminum 3/4 in (1.9 cm) wide tent stake

• One 1 in (2.5 cm) diameter PVC tube cap

• Masking tape

• Enamel paint (white, fluorescent orange,

black)

• Spar-urethane glossy varnish

• Epoxy

• Drill with 1/4 in (6 mm) drill bit

• Hacksaw

Materials needed for sight pole:

• 1.1 m (43 5/16 in) of 1/2 in (1.3 cm) PVC pipe

• Two 1/2 in (1.3 cm) PVC tube caps

1. Cover pole construction:

1.1 Cut the 1 in (2.5 cm) diameter pipe into two

1 m (39 3/8 in) lengths.

1.2 Attach male coupling to pipe, using the

cleaner, primer and glue.

1.3 Repeat process with other pipe half and female

coupling (Fig. A.12).

1.4 Connect two halves at coupling.

1.5 Measure and trim the connected pipe back to a

2 m (78 3/4 in) length.

1.6 Drill one hole each at 1 in and at 2 in (2.5 and

5 cm) above the bottom of the pipe. Each hole

should be 1/4 in (6.4 mm) in diameter.

1.7 Cover all holes, except for the one at the 2 in

(5 cm) level, using the masking tape.

1.8 Drill two 1/4 in holes into the tent spike in the

area that will be between the end of pipe and

the 2 in hole.

1.9 Insert tent spike into the bottom of the pipe so

that at least 3 in of the spike protrudes below

the end of the pipe (in areas with very loose

topsoil increase protruding spike length).

1.10 Using masking tape, seal the bottom of the

pipe to hold the spike in place and inject

epoxy into one of the open holes until epoxy

reaches the 2 in level.

1.11 Let epoxy dry, with spike straight in the pipe

(Fig. A.13).

1.12 Using masking tape and paint, paint

alternate 10 cm segments white and black with

every 5th section painted fluorescent orange

(Fig. A.14).

1.13 Once dry, coat with spar-urethane to avoid

scratches and UV degradation of paint colors.

Figure A.12. Male and female couplings for cover

pole.

Figure A.13. Spike attached to cover pole.
139

 Monitoring tools

Figure A.14. Completed cover pole.

2. Sight pole construction:

2.1 Using the cleaner, primer and glue, attach one

cap to end of pipe.

2.2 Measure 1 m from end of cap and drill 1/4 in

hole through pipe.

2.3 Attach cap to other end of pipe without using

glue (can be used as storage for Line-point

intercept pin flags).
140

Monitoring tools

Riparian vegetation survey pole

(Chapter 13)

Materials needed for construction of L-tool for

Riparian channel vegetation survey:

• 2 m (78 3/4 in) of 3/4 in (1.9 cm) inside diameter

schedule 40 PVC pipe

• One 3/4 in PVC elbow joint

• One 3/4 in PVC T-joint

• PVC cement

• One hacksaw

• One meter stick

• One permanent marker

1. L-tool construction.

1.1 Cut four pieces of 3/4 in diameter PVC pipe, as

follows: one 50 cm (11 11/16 in) piece, one 100

cm (39 3/8 in) piece and two 10 cm (3 15/16 in)

pieces.

1.2 Connect and glue the 100 cm (39 3/8 in) pipe

to the 50 cm (11 11/16 in) pipe with the elbow

joint.

1.3 Connect the T-joint to the end of the 50 cm

(11 11/16 in) pipe and position it parallel with

the 100 cm (39 3/8 in) pipe. Glue in place.

1.4 Place a 10 cm (3 15/16 in) piece on each open

end of the T-joint and glue in place.

1.5 The finished product should look like

Figure A.15.

1.6 Mark and label lines every 10 cm (3 15/16 in) on

the 100 cm (39 3/8 in) pipe to determine plant

height.

2. Optional laser installation.

2.1 The L-tool works much better with a doubleended laser pointer in place of the “T”.

2.2 For sources, see Table A.1.

Figure A.15. L-tool design.
141

Appendix B: Conversion factors

English units are used wherever practical. Metric units are used for linear measurements. The advantage of

metric tapes is that a single number (cm) can be used to record distances. Decimal feet tapes may be

substituted; the calculations remain the same. Metric tapes are available from Forestry Supplier, Ben

Meadows, Gemplers and other catalog suppliers. They are also increasingly available at building supply

stores.

feet X 0.305 = meters meters X 3.281 = feet

inches X 2.54 = centimeters centimeters X 0.394 = inches

inches X 25.4 = millimeters millimeters X 0.039 = inches

miles X 1.609 = kilometers kilometers X 0.621 = miles

acres X 0.405 = hectares hectares X 2.471 = acres

quarts X 0.946 = liters liters X 1.057 = quarts

ounces X 29.57 = milliliters milliliters X 0.034 = ounces

pounds X 0.454 = kilograms kilograms X 2.205 = pounds

ounces X 28.35 = grams grams X 0.035 = ounces

lb/acre X 1.12 = kg/ha kg/ha X 0.891 = lb/acre

square meters X 10.764 = square feet square feet X 0.0929 = square meters
142

Appendix C: How many

measurements?

Introduction

This appendix describes three options for deciding

how many measurements to make for selected

indicators. Option 1 is the simplest. Option 3 is

the best.

Option 1

General recommendations for arid and semi-arid

grasslands and shrublands based on a study of

eight plant communities in southern New Mexico,

USA.

Option 2

Specific results for each of the eight plant

communities.

Option 3

Equations for calculating measurement

requirements based on available data for your area.

For Options 1 and 2, Type I error = 0.2 and Type II

error = 0.2; Power = 0.8; rho = 0.5. The Internet

version will allow for modification of these

parameters (select “Monitoring and Assessment”

from http://usda-ars.nmsu.edu). We will update

these recommendations as new information

becomes available for different ecological sites.

Definitions

Type I error. Type I error is the probability that

you will conclude that a change has occurred

when there has been no change.

Type II error. Type II error is the probability that

you will conclude that no change has occurred

when there really has been a change. Scientists

often set Type I error at 0.05 and ignore Type II

error, allowing it to exceed 0.5 in many cases. For

most monitoring studies, we set Type I = Type II =

0.2 because it is usually just as important to be

able to detect real change as it is to avoid falsely

concluding that change has occurred. Reducing

either type of error requires increasing the number

of measurements.

Power. Power is the probability that you will

conclude that a change has occurred when there

really has been a change. It is equal to 1 minus

Type II error.

Rho. Rho is the correlation between sampling unit

values in the first time period and sampling unit

values in the second time period. Increasing rho

reduces the number of replications required. For

example, sampling requirements decline by

50 percent if rho is increased from 0.5 to 0.75.

Returning as close as possible to the location of

the original transect increases rho. Remember,

however, that while it is important to place the

transect in approximately the same location each

year, it is unnecessary (and generally impossible)

to measure exactly the same points each year. Note

that for comparisons between two independent

plots, rho = 0.

Average. Average is the sum divided by the

number of values.

Median. Median is the value within a group of

numbers at which half the values are larger and

half are smaller.

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Plot scale. Plot scale is the scale at which we

normally collect monitoring data. An individual

plot is defined as one hectare (2.5 acres). One

hectare (2.5 acres) is the area covered by a spoke

plot with three 50 m transects starting 5 m from

the center. This scale is appropriate if the key area

concept is applied—that is, if you are confident

that change detected in an individual plot

accurately reflects changes occurring across larger

areas.

Landscape scale. Landscape scale is the scale at

which statistically based monitoring programs are

normally applied. Change is detected using

multiple plots located within relatively

homogeneous monitoring units (see Volume II,

Section I).

Data for Options 1 and 2

The recommendations listed under Options 1 and

2 are based on a study completed in southern New

Mexico, USA in 2000. Measurements were

completed on three transects in each of three plots

in eight plant communities (Fig. C.1; Table C.2).

The plant communities represented a relatively

degraded and non-degraded state in each of four

ecological sites.

Caution

While this is one of the more comprehensive

studies of replication requirements for a variety of

soil and vegetation measurements, it is still

extremely limited, particularly for the

recommendations at the landscape (monitoring

unit) scale. The Option 1 estimates are based on a

total of 72 transects and 24 plots. Each Option 2

estimate is based on just nine transects and three

plots. Furthermore, these data are specific to

southern New Mexico. The estimates will tend to

be more accurate in regions where plant

communities are similar in structure to the

Chihuahuan Desert (e.g., Great Basin and Sonoran

Desert), and less so in regions with different plant

community structure (e.g., annual grassland and

shortgrass prairie). We have included footnotes

highlighting specific characteristics of the plots

sampled that may explain unusually high values.

See our website for revised recommendations

based on more comprehensive data (“Monitoring

and Assessment” link at http://usda-ars.nmsu.edu).

Assumptions

The tables printed here are based on the following

assumptions:

• Type I error (p-value) = 0.2

• Type II error (1 – power) = 0.2

• Rho (between-date correlation) = 0.5

• The absolute minimum number of replications

is 2.

The “rho” assumption is extremely important.

Sampling requirements decline by 50 percent if

rho is increased to 0.75. The value of 0.5 is

conservative. The value will be higher (and

therefore sampling requirements lower) in most

monitoring programs. Rho = 0.5 was selected

based on the results of a different multi-year study

in a semi-arid environment where a 30 m transect

was shifted left or right at least 1 m (3 ft) within a

6 m (18 ft) band each year.

The absolute minimum number of transects

was set to two to ensure that the data can be

statistically analyzed. In some cases, our analyses

(for Options 1 and 2) generated replication

recommendations that were less than two.

These assumptions are for design purposes

only. Your ability to detect change depends on site

variability. If your site is more variable than the

sites used to generate these recommendations, the

minimum detectable change will be larger for a

given sample size. If your site is less variable, you

will be able to detect a smaller change. It is nearly

always better to establish more transects than you

think you need, as the number can later be

reduced without information loss.

When you analyze your data, you can calculate

rho, set Type I and II errors at any level, and

determine how many transects need to be remeasured in future years using the equations in

Option 3.

Instructions: Option 1

Choose Option 1 if you are comfortable using the

median sample size recommendations from all

eight communities combined. If you are not

How many measurements?
144

comfortable using the median of all eight

communities, use Option 2 or 3. Use Tables C.3

and C.4 to determine how many transects and

measurements are required at the plot scale. Use

Tables C.5 and C.6 to determine how many plots

(with the specified number of transects and

measurements) are required at the landscape scale.

Please see the “Caution” and “Assumptions”

sections in the Introduction above.

How many measurements?
145

Plant Community

Sandy

loam -

Loamy

sand

(Sandy

Ecological

Site)

Sandy

loam

(Shallow

Sandy

Ecological

Site)

Very fine

sandy

loam

(Gyp

Upland

Ecological

Site)

Clay

loam

(Clayey

Ecological

Site)

B. Large shrubs (Prosopis glandulosa

[honey mesquite])

D. Rhizomatous/stoloniferous grasses and subshrubs (Gutierrezia sarothrae [snakeweed])

F. Degraded salt desert shrubland

(A. canescens [fourwing saltbush])

H. Patchy stoloniferous grasses

(Scleropogon brevifolius [burrograss])

Figure C.1. Photographs of each plant community used to generate the sampling requirement estimates listed

for Options 1 and 2.

A. Stoloniferous grasses (Bouteloua eriopoda

[black grama])

C. Rhizomatous/stoloniferous/bunchgrasses

(B. eriopoda & B. gracilis [black & blue grama])

E. Salt desert shrubland (Atriplex canescens

[fourwing saltbush])

G. Banded (patchy) stoloniferous grasses

(Pleuraphis mutica [tobosa])

How many measurements?
146

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Instructions for Option 2: PLOT

scale

Choose this option to determine how many

vegetation transects and soil measurements you

need to detect change within a plot (plot scale).

Complete the Plot Scale Worksheet (see Table C.7)

to calculate the recommended number of

measurements for your plot-scale monitoring

project. The recommendations are based on data

from the southern New Mexico study (Tables C.8

through C.13). Before beginning, please see the

“Caution” and “Assumptions” sections in the

Introduction to this appendix.

Step 1. Using Figure C.1 and Table C.2, select at

least three plant communities that appear to be

most similar to your own. Record them under

“Plant communities” in the worksheet (Table C.7).

We strongly recommend that you select at least

three communities, even if your community

appears to have very similar characteristics to only

one of the eight.

Step 2. Define the minimum change you want to

be able to detect at the plot scale for each

indicator. Record this value under “Minimum

detectable change” in the Plot Scale Worksheet.

Step 3. Copy appropriate values for the number of

measurements and transects from Tables C.8

through C.13 for each community to the Plot

Scale Worksheet. Make sure to copy the values

from the correct rows and columns.

Step 4. Record the median or maximum number

of measurements and transects in the last column.

How many measurements?

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156

Instructions for Option 2:

LANDSCAPE scale

Choose this option to determine how many

vegetation transects and soil measurements are

required to detect change within a monitoring

unit (landscape scale). Complete the Landscape

Scale Worksheet (Table C.14) to calculate the

recommended number of measurements for your

landscape-scale monitoring project. The

recommendations are based on data from the

southern New Mexico study (Tables C.15 through

C.20). Before beginning, please see the

“Caution” and “Assumptions” sections in the

Introduction to this appendix, Appendix C.

Step 1. Using Figure C.1 and Table C.2, select at

least three plant communities that appear to be

most similar to your own. Record them under

“Plant communities” in the worksheet (Table C.14).

We strongly recommend that you select at least

three communities, even if your community

appears to have very similar characteristics to only

one of the eight.

Step 2. Define the minimum change you want to

be able to detect at the landscape scale for each

indicator. Record this value under “Minimum

detectable change” in the Landscape Scale

Worksheet.

Step 3. Circle the number of transects (1 vs. 3 for

vegetation) and measurements (6 vs. 18 for soil)

per plot for each method.

Step 4. Copy the appropriate values for the

number of measurements and transects from

Tables C.15 through C.20 for each community to

the Landscape Scale Worksheet. Make sure to copy

the values from the correct rows and columns.

Step 5. Record the median or maximum number

of plots required in the last column of Table C.14.

How many measurements?
157

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164

Instructions for Option 3:

Calculations based on available

data

In Option 3 you conduct your own research to

determine how many transects and measurements

are needed to detect change. This option requires

data from a pilot study within the monitoring unit

of interest. Sometimes data from a previous

monitoring study or research project can be used.

Use one of the equations provided below to

determine the necessary number of vegetation

transects or soil measurements required at the plot

scale, or for the number of plots at the landscape

scale.

A pilot study is a small-scale version of your

monitoring program. Conducting a pilot study

allows you to more accurately estimate the

population mean(s), the standard deviation(s) and

the degree of correlation between years. These

values are used in Equations 1 and 2.

Equation 1. Equation 1 is used to determine

requirements for detecting differences between

two means from permanent plots, using a paired ttest. This is similar to the equations used to

generate the recommendations listed in Options 1

and 2. It is the test used to detect change over

time.

If you wish to detect change at the plot scale,

use a plot’s transect data (or soil measurement

data) from a single sampling period to generate

the population mean and standard deviation.

If you wish to detect change at the landscape

(monitoring unit) scale use data from all of the

plots within the landscape unit (or monitoring

unit) from a single sampling period to generate the

population mean and standard deviation. At the

landscape scale, the number of replications is

equal to the number of plots, not the number of

transects or soil measurements.

Equation 2. Equation 2 is used to determine

requirements for detecting differences between a

single mean and a threshold value, using a onesample t-test. It is assumed that the means will be

compared using a paired t-test.

If you wish to detect a difference between a

plot’s mean and a threshold value (at the plot

scale), use data from the plot’s transects (or soil

measurements) to generate the population mean

and standard deviation.

If you wish to detect a difference between a

landscape unit’s (monitoring unit’s) mean and a

threshold value (landscape scale), use data from all

of the plots within the landscape unit (or

monitoring unit) to generate the population mean

and standard deviation.

For more detailed information, please see Elzinga

et al. (2001) or Bonham (1989).

How many measurements?
165

Option 3, Equation 1: determining the necessary sample size for

detecting a difference between two means with permanent sampling

units that will be evaluated with a paired t-test.

When paired sampling units are being compared, or when data from permanent plots or transects are

being compared between two time periods, then sample size determination requires a different procedure

than if samples are independent of one another. The equation for determining the number of samples

necessary to detect some true difference between two sample means generated at different times from the

same monitoring plot or transect is:

Where:

sdiff = Standard deviation of the differences between paired samples (see equation and examples

below).

Z = Z-coefficient for the false-change (Type I) error rate from Table C.21a below.

Z = Z-coefficient for the missed-change (Type II) error rate from Table C.21b below.

MDC = Minimum detectable change size. This needs to be specified in absolute terms. For example, if

you wanted to detect a 20 percent change in the sample mean from one year to the next and

your first year sample mean = 10 plants/plot or transect, then MDC = (0.20 x 10) = 2 plants/

plot or transect. Similarly, a change from 10 to 15 percent bare ground is a 5 percent change.

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How many measurements?

n = ’’’’ (sdiff)2

(Z+Z)2

(MDC)2 (Equation 1)
166

If the objective is to track changes over time with permanent sampling plots and only a single year of data

is available, then you will not have a standard deviation of differences between paired samples. If you have

an estimate of the likely degree of correlation between the two years of data, and you assume that the

among-sampling units standard deviation is going to be the same in the second time period, then you can

use the equation below to estimate the standard deviation of differences.

Where:

sdiff = Estimated standard deviation of the differences between paired samples.

s1 = Sample standard deviation among sampling units at the first time period.

corrdiff = Correlation coefficient, or rho, between sampling unit values in the first time period and

sampling unit values in the second time period (this is the square root of r2

 (=r) from a

regression and is the “rho” discussed in the introduction above).

Example 1:

Management objective:

Achieve at least a 20 percent higher density of Species F at Site Y in areas excluded from grazing as

compared to grazed areas in 1999 (landscape scale).

Sampling objective:

I want to be able to detect a 20 percent difference in mean plant density in areas excluded from grazing

versus adjacent paired grazed areas. I want to be 90 percent certain (power = 0.9) of detecting that

difference, if it occurs. I am willing to accept a 10 percent chance ( = 0.1) that I will make a false-change

error (concluding that a difference exists when it really does not).

Results from pilot sampling:

Five paired plots (a total of 10 plots) were sampled where one member of the pair was excluded from

grazing (with a small exclosure) and the other member of the pair was open to grazing.

.22.CelbaT .stolpfostesowtehtneewtebsecnereffidehtrofscitsitatsyrammuS

rebmuntolP

tolp/stnalpforebmuN dezargneewtebecnereffiD

dezargnudna G d dezar ezargnU

123 1

258 3

349 5

47 21 5

537 4

0 02.4=

29.1=s

0 08.7=

72.3=s

0 ffid 06.3=

s ffid 76.1=

How many measurements?

sdiff =(s1)(p&&&&&&&& (2(1-corrdiff)) )
167

Given:

The sampling objective specified a desired minimum detectable difference (i.e., equivalent to the MDC) of

20 percent. Taking the larger of the two mean values (ungrazed from Table C.22) and multiplying by

20 percent leads to: (7.80 x 0.20) = MDC = 1.56 plants/plot. The larger mean is used because the result

generates a more conservative test.

The appropriate standard deviation to use is 1.67 (from Table C.22), the standard deviation of the

differences between the pairs of plots.

The acceptable False-change error rate () is 0.10. Therefore the appropriate Zfrom Table C.21a is 1.64.

The desired Power is 90 percent (0.90), so the Missed-change error rate () = 0.10. The appropriate Z

coefficient from Table C.21b is 1.28.

Calculate the estimated necessary sample size using the equation provided below:

Round up 9.8 to 10 plots.

Thus, the final estimated sample size needed to be 90 percent certain of detecting a true difference of

1.56 plants/plot between the grazed and ungrazed plots with a false-change error rate of 0.10 = 10 plots.

Example 2:

Management objective:

Increase the density of species F at site Q by 20 percent between 1999 and 2002 (landscape scale).

Sampling objective:

I want to be able to detect a 20 percent difference in mean plant density of species F at site Q between

1999 and 2002. I want to be 90 percent certain of detecting that change, if it occurs. I am willing to accept

a 10 percent chance that I will make a false-change error (conclude that a difference exists when it really

does not).

The procedure for determining the necessary sample size for this example would be very similar to the

previous example. Replace “grazed” and “ungrazed” in Table C.22 with “1999” and “2002” and the rest of

the calculations would be the same. Because the sample size determination procedure needs the standard

deviation of the difference between two samples, you will not have the necessary standard deviation term

to plug into the equation until you have two years of data. However, the standard deviation of the

difference can be estimated in the first year. You will need an estimate of the correlation coefficient

between sampling unit values in the first time period and the sampling unit values in the second time

period (see the sdiff equation above).

How many measurements?

n = ’’’’’’ = 9.8 (1.67)2(1.64 +1.28)2

(1.56)2 n = ’’’’ (sdiff)2(Z

+Z)2

(MDC)2
168

Correction for sampling finite populations

The above formula assumes that the population is very large compared to the proportion of the

population that is sampled. If you are sampling more than five percent of the entire population area, you

should apply a correction to the sample size estimate. This correction incorporates the finite population

correction factor (FPC). This will reduce the sample size. The formula for correcting the sample size

estimate is as follows:

Where:

n’ = The new sample size based upon inclusion of the finite population correction factor.

n = The original sample size estimate from the equation:

N = The total number of possible plot locations in the population. To calculate N, determine the

total area of the population and divide by the size of each individual sampling unit (plot).

Example:

If the pilot data described above was gathered using a 1 m x 10 m (10 m2

) plot and the total population

being sampled was located within a 10 m x 50 m macroplot (500 m2), then N = 500 m2 ÷ 10 m2 = 50. The

corrected sample size would then be:

Round 8.3 to 8.

The new, FPC-corrected estimated sample size needed to be 90 percent confident of detecting a true

difference of 1.56 plants/plot between the grazed and ungrazed plots with a false-change error rate of

0.10 = 8 plots.

Note on the statistical analysis for two sample tests from finite populations

If you have sampled more than five percent of an entire population, you should apply the finite

population correction factor to the results of the statistical test. This procedure involves dividing the test

statistic by the square root of (1-n/N). For example, if your t-statistic from a particular test turned out to be

1.782 and you sampled n=8 plots out of a total N=50 possible plots, then your correction procedure would

look like the following:

Where:

t = The t-statistic from a t-test.

t’ = The corrected t-statistic using the FPC.

n = The number of plots or units sampled.

N = The total number of possible plot locations in the population. To calculate N, determine the

total area of the population and divide by the size of each individual sampling unit.

You would then need to look up the p-value of t’ = 1.944 in a t-table for the appropriate degrees of freedom

to obtain the correct p-value for this statistical test.

How many measurements?

n’ = ’’’’ n

(1 + (n / N))

n = ’’’’ (sdiff)2(Z

+Z)2

(MDC)2

n’ = ’’’’ n

(1 + (n / N)) n’ = ’’’’ = 8.3 10

(1 + (10 / 50))

t’ = ’’’’ t

p&&&&&&&& 1 - (n / N) t’ = ’’’’ = 1.944 1.782

p&&&&&&&& 1 - (8 / 50)
169

Option 3, Equation 2: Determining the necessary sample size for

detecting a difference between a single mean and a threshold value

that will be evaluated with a one-sample t-test.

Here is the equation for determining sample size when a single mean value is going to be compared with

some threshold value:

Where:

s = Standard deviation of the sample.

Z = Z-coefficient for the false-change (Type I) error rate from Table C.21a.

Z = Z-coefficient for the missed-change (Type II) error rate from Table C.21b.

MDC = Minimum detectable change from the threshold. This needs to be specified in absolute terms

rather than as a relative percentage. For example, if you wanted to detect a 20 percent

difference from a threshold density of 30 plants/plot then MDC = (0.20 x 30) = 6 plants/plot.

Similarly, a change from 10 to 15 percent bare ground is a five percent change.

Example 1:

Management objective:

Maintain a population of species Y in population Z with a density of at least 25 plants/plot for the next

10 years.

Sampling objective:

I want to be able to detect a 20 percent difference in mean plant density from a threshold density of 25

plants/plot. I want to be 90 percent certain of detecting this difference, if it occurs. I am willing to accept a

10 percent chance that I will make a false-change error (conclude that the mean is different from the

threshold when it really is not).

Results from pilot sampling:

Mean (0) = 31 plants/plot

Standard deviation (s) = 7 plants.

Given:

The acceptable False-change error rate () = 0.10, so the appropriate Z from Table C.21a = 1.64.

The desired Power is 90 percent (0.90) so the Missed-change error rate () = 0.10 and the appropriate Z

coefficient from Table C.21b = 1.28.

The Minimum Detectable Change (MDC) is 20 percent of the threshold value or (0.20 x 25)= 5 plants/plot.

How many measurements?

n = ’’’’ (Equation 2) (s)2

(Z+Z)2

(MDC)2
170

Calculate the estimated necessary sample size using the equation provided above:

Round up 16.7 to 17 plots.

Final estimated sample size needed to be 90 percent confident of detecting a difference of five plants from

the threshold density of 25 plants with a false-change error rate of 0.10 = 17 plots.

Correction for sampling finite populations

The above formula assumes that the population is very large compared to the proportion of the

population that is sampled. If you are sampling more than five percent of the entire population area, you

should apply a correction to the sample size estimate. This correction incorporates the finite population

correction (FPC) factor. This will reduce the sample size. The formula for correcting the sample size

estimate is as follows:

Where:

n’ = The new sample size based upon inclusion of the finite population correction factor.

n = The sample size from the equation:

N = The total number of possible plot locations in the population. To calculate N, determine the

total area of the population and divide by the size of each individual sampling unit.

Example 2:

If the pilot data described above was gathered using a 1 m x 10 m (10 m2

) plot and the total population

being sampled was located within a 20 m x 50 m macroplot (1000 m2) then N = 1000 m2/10 m2 = 100. The

corrected sample size would then be:

Round up 14.5 to 15.

The new, FPC-corrected estimated sample size needed to be 90 percent certain of detecting a difference of

five plants from the threshold density with a false-change error rate of 0.10 = 15 plots.

How many measurements?

n’ = ’’’’ n

(1 + (n / N))

n = ’’’’ (s)2(Z

+Z)2

(MDC)2

n’ = ’’’’ n

(1 + (n / N))

n’ = ’’’’’ = 14.5

17

(1 + (17 / 100))

n = ’’’’’ = 16.7 (7)2(1.64 +1.28)2

(5)2

n = ’’’’ (s)2(Z

+Z)2

(MDC)2
171

Note on the statistical analysis for two sample tests from finite populations

If you have sampled more than five percent of an entire population, you should apply the finite

population correction factor to the results of the statistical test. This procedure involves dividing the test

statistic by the square root of the finite population factor (1-n/N). For example, if your t-statistic from a

particular test turned out to be 1.645 and you sampled n=26 plots out of a total N=100 possible plots, then

your correction procedure would look like the following:

Where:

t = The t-statistic from a t-test.

t’ = The corrected t-statistic using the FPC.

n = The number of plots (or units) sampled.

N = The total number of possible plot locations in the population. To calculate N, determine the

total area of the population and divide by the size of each individual sampling unit.

You would then need to look up the p-value of t’ = 1.912 in a t-table at the appropriate degrees of freedom

to obtain the correct p-value for this statistical test.

How many measurements?

t’ = ’’’’ t

p&&&&&&&& 1 - (n / N) t’ = ’’’’’ = 1.912 1.645

p&&&&&&&&& 1 - (26 / 100)
172

Appendix D: Soil Quality

Information Sheets

Additional information on soil indictors is available in a series of information sheets about soil quality and

rangelands. Rangeland Soil Quality Information Sheets (USDA-NRCS 2001) address soil properties that

change in response to management and climate. Refer to these sheets for a description of soil properties

that can serve as indicators, factors affecting them and general management strategies to improve them.

Available on line at: http://soils.usda.gov/sqi/management/gl_mgmt.html (accessed June 23, 2008).

Rangeland Soil Quality Information Sheets (accessed June 23, 2008)

1. Rangeland Soil Quality — Introduction

(http://soils.usda.gov/sqi/management/files/RSQIS1.pdf )

2. Rangeland Soil Quality — Indicators for Assessment and Monitoring

(http://soils.usda.gov/sqi/management/files/RSQIS2.pdf )

3. Rangeland Soil Quality — Aggregate Stability

(http://soils.usda.gov/sqi/management/files/RSQIS3.pdf )

4. Rangeland Soil Quality — Compaction

(http://soils.usda.gov/sqi/management/files/RSQIS4.pdf )

5. Rangeland Soil Quality — Infiltration

(http://soils.usda.gov/sqi/management/files/RSQIS5.pdf )

6. Rangeland Soil Quality — Organic Matter

(http://soils.usda.gov/sqi/management/files/RSQIS6.pdf )

7. Rangeland Soil Quality — Physical and Biological Soil Crusts

(http://soils.usda.gov/sqi/management/files/RSQIS7.pdf )

8. Rangeland Soil Quality — Soil Biota

(http://soils.usda.gov/sqi/management/files/RSQIS8.pdf )

9. Rangeland Soil Quality — Water Erosion

(http://soils.usda.gov/sqi/management/files/RSQIS9.pdf )

10. Rangeland Soil Quality — Wind Erosion

(http://soils.usda.gov/sqi/management/files/RSQIS10.pdf )
173

Appendix E: Soil texture chart

Place approximately 25 grams in palm. Add 

water dropwise and knead the soil to break 

down all aggregates. Soil is at the proper 

consistency when plastic and moldable, like 

moist putty.

Add dry soil to soak

up water

Does soil remain in a 

ball when squeezed? Is the soil too dry? Is the soil too wet? Sand

Place ball of soil between thumb and forefinger, gently push the soil with the thumb, 

squeezing it upward into a ribbon. Form a ribbon of uniform thickness and width. Allow 

the ribbon to emerge and extend over the forefinger, breaking from its own weight.

Does the soil form a ribbon? Loamy

Sand

Does soil make a weak 

ribbon less than 1 inch 

long before breaking?

Does soil make a 

ribbon 1 inch long 

before breaking?

Does soil make a strong 

ribbon 2 inches or longer 

before breaking?

Excessively wet a small pinch of soil in palm and rub with forefinger.

Sandy

Loam

Silt

Loam

Loam

Sandy

Clay 

Loam

Silty

Clay 

Loam

Clay 

Loam

Sandy 

Clay

Silty 

Clay

 Clay

Does soil 

feel very 

gritty?

Neither 

gritty nor 

smooth?

Does soil 

feel very 

gritty?

Does soil 

feel very 

smooth?

Neither 

gritty nor 

smooth?

Does soil 

feel very 

gritty?

Does soil 

feel very 

smooth?

Neither 

gritty nor 

smooth?

➪

➪

➪

➪

➪

➪

➪

➪

➪

➪

➪

➪

➪ ➪

➪

➪

➪

➪

➪

➪➪

➪

➪

➪

➪

➪

➪

➪

➪

➪

➪➪

➪ ➪

Yes

Yes Yes

Yes

Yes Yes Yes

Yes Yes Yes

Yes

Yes

Yes

Yes

Yes

Yes

No

No No

No

No

No

Does soil 

feel very 

smooth?

No No

No

No No No
174

Glossary

An asterisk (*) indicates a term used in this manual. Other terms commonly used in rangeland monitoring

are also included for reference.

Abundance The total number of individuals of a species in an area, population or community (SRM 1999).

Accuracy* The closeness of a measured or computed value to its true value (Elzinga et al. 2001). See also

precision*.

Active fraction The highly dynamic or labile portion of soil organic matter* that is readily available to

soil organisms. May also include the living biomass. Particulate organic matter (POM) and light fraction

(LF) are measurable indicators of the active fraction. POM particles are larger than other SOM and can be

separated from soil by sieving. LF particles are lighter than other SOM and can be separated from soil by

centrifugation.

Annual plant A plant that completes its life cycle and dies in one year or less. See also biennial plant and

perennial plant (SRM 1999).

Annual production* (syn. primary production) The conversion of solar energy to chemical energy

through the process of photosynthesis. It is represented by the total quantity of organic material produced

within a year (SRM 1999). See total annual production*.

ARS* Agricultural Research Service.

Aspect* The direction a slope faces.

Assessment* The process of estimating or judging the value or functional status of ecological processes

(e.g., rangeland health*).

Attribute* One of the three components—soil and site stability, hydrologic function, and integrity of the

biotic community—that collectively define rangeland health*.

Azimuth* Compass direction; from 0 to 360°.

Bank-full* The bank-full stage corresponds to the water level (stream discharge) just below the point that

water enters the floodplain. It is the most effective stage for maintaining the shape of stream channels.

The following features can be used to identify bank-full: top of the point bar, significant changes in

vegetation, topographic slope break, change in size, staining or color of substrate, and change in the

nature and amount of debris deposits. Definition and examples derived from Dunne and Leopold (1978),

Rosgen (1996) and Prichard et al. (1998a). Bank-full flow events generally occur an average of every 18

months (R. Baker, pers. comm.). They occur less frequently in incised channels.

Bank angle* The slope of the bank. See Chapter 14 for calculations.

Bare ground* (bare soil) All land surface not covered by vegetation, rock or litter (SRM 1999). As used in

this document, visible biological crusts and standing dead vegetation are included in cover estimates and

are not bare ground. Bare ground should always be clearly defined for each monitoring program because

there are many definitions.
175

Bare ground (%)* Bare ground occurs where there are no plant or litter intercepts, and mineral soil is

intercepted. Bare ground (%) is calculated by counting the number of points with “None” in the top layer

column, nothing in any of the “Lower layers” columns and “S” in the “Soil surface” column. Divide this

sum by the total number of points sampled. Multiply this value by 100.

Basal area* (plants) (syn. basal area) The cross-sectional area of the stem or stems of a plant or of all

plants in a stand. Basal area is measured at or near ground level (SRM 1999).

Basal cover (%)* Basal cover is the area covered by plant bases. Basal cover (%) is the percent of the soil

surface covered by plant bases in the Line-point intercept method. Basal cover (%) is calculated by

counting the number of basal intercepts in the “Soil surface” column. Divide this sum by the total number

points sampled. Multiply this value by 100.

Basal gap A space between plant bases that is not occupied by rooted vegetation when viewed as a

vertical projection from the transect tape edge to the ground. Minimum gap sizes are defined by the user,

and are usually 20 cm (0.7 ft). Vegetation stems of any diameter, even 1 mm, can start or stop a basal gap,

as long as they are rooted in soil. Vegetation may occur as live or standing dead. User must define if gaps

can be terminated by any plant, or by perennial plants only.

Biennial plant* A plant that completes its life cycle and dies in two years or less. See also annual plant

and perennial plant.

Biological crust* Microorganisms (e.g., lichens, algae, cyanobacteria, microfungi) and non-vascular plants

(e.g., mosses, lichens) that grow on or just below the soil surface.

Biomass* (plants) The total amount of living plants above and below ground in an area at a given time

(SRM 1999).

Biotic integrity* Capacity of a site to support characteristic functional and structural communities in the

context of normal variability, to resist loss of this function and structure due to a disturbance, and to

recover following such disturbance. See also soil and site stability*, hydrologic function* and rangeland

health*.

BLM* Bureau of Land Management (USDI*).

C-3 plant A plant employing the pentose phosphate pathway of carbon dioxide assimilation during

photosynthesis; a cool-season plant (USDA-NRCS 1997).

C-4 plant A plant employing the dicarboxylic acid pathway of carbon dioxide assimilation during

photosynthesis; a warm-season plant (USDA-NRCS 1997).

Canopy cover The amount of ground covered by the vertical projection of the outer foliage and branches

of a plant. Canopy cover overestimates the area of ground covered by vegetation because open spaces

within a plant’s canopy are not excluded from the canopy cover estimate. Compare to Foliar cover.

Glossary
176

Canopy gap A space between plant canopies that is not occupied by rooted vegetation when viewed as a

vertical projection from the canopy edge to the ground. Minimum gap sizes are defined by the user, and

are usually 20 cm (0.7 ft). Vegetation may occur as live or dead. Edges of canopy gaps are defined as a

point along a transect tape where at least 50% of a 3 cm segment of tape edge intercepts live or dead plant

canopy. User must define if gaps can be terminated by any plant, or by perennial plants only.

Climate* The average or prevailing weather conditions of a place over a period of years (SRM 1999).

Community pathway Shifts in plant species compositions among biological communities within a single

state.

Compaction layer* A layer of dense soil at or near the surface, caused by repeated impact on or

disturbance of the soil surface. When soil is compacted, soil grains are rearranged, pore spaces are

decreased and soil particles are brought into closer contact with one another, thereby increasing bulk

density (SSSA 1997).

Composition* The proportions of various plant species in relation to the total on a given area; it may be

expressed in terms of cover, density, weight, etc. (SRM 1999).

Cover* The proportion of the soil surface covered by a vertical projection of the cover class of interest,

regardless of what is above or below the object: plant parts (foliar cover*), plant bases (basal cover*),

woody and herbaceous litter (litter cover*), lichens, mosses, duff, etc. The opposite of bare ground*.

Cover by functional group (%)* A subset of foliar cover*. Cover by functional group is the proportion of

the soil surface covered by the vertical projection of plant canopies belonging to a specific functional

group. Calculate cover by functional group by first determining which plant species belong to the

functional group. Then count the total number of sample points where species in the designated

functional group are intercepted. Divide this sum by the total number of sample points. Multiply this

value by 100.

Cover by species resistant to fire, grazing, traffic, etc.* A subset of foliar cover*. Cover by species

resistant to fire, grazing, traffic, or cover by invasive species is the proportion of the soil surface covered by

the vertical projection of plant canopies belonging to the specific group. Calculate cover by species

resistant to fire, grazing, traffic, or cover by invasive species by first determining which plant species

belong to the designated group. Then count the total number of sample points where species in the

designated group are intercepted. Divide this sum by the total number of points sampled. Multiply this

value by 100. See also foliar cover*.

Culm A jointed grass stem. This may or may not be hollow.

DBH* See diameter at breast height*.

Declination* Angle, in degrees, between magnetic north and geographical north.

Decomposition* The biochemical breakdown of organic matter into its original compounds and

nutrients.

Diameter at Breast Height (DBH)* The diameter of a tree at breast height (1.4 m or 4.5 ft above the

ground), measured outside of the bark (USDA Forest Service 2003).

 Glossary
177

Diameter at Root Collar (DRC)* The diameter of a tree measured at the ground line or stem root collar,

measured outside of the bark.

DOQQ* Digital Orthophoto Quarter Quadrangle.

DRC* See diameter at root collar.

Duff* Partially decomposed plant litter. It consists of decomposing leaves and other organic material. You

should see NO recognizable plant parts. When moss is present, the top of the duff layer is just below the

green portion of the moss. The bottom of this layer is the point where mineral soil (A horizon) begins. See

also litter* and embedded litter*.

Ecological processes* Ecological processes include the water cycle (the capture, storage and redistribution

of precipitation), energy flow (conversion of sunlight to plant and animal matter) and nutrient cycle (the

cycle of nutrients, such as nitrogen and phosphorus, through the physical and biotic components of the

environment). Ecological processes functioning within a normal range of variation at an ecological site

will support specific plant and animal communities.

Ecological reference area An area representing a single ecological site in which ecological processes are

functioning within a normal range of variability, and the plant community has adequate resistance to and

resiliency from most disturbances. These areas do not need to be pristine, historically unused lands (e.g.,

climax plant communities or relict areas).

Ecological site* (syn. rangeland ecological site) A kind of land with specific physical characteristics,

which differs from other kinds of land in its ability to produce distinctive kinds and amounts of vegetation

and in its response to management. Apparently synonymous with ecological type used by USDA Forest

Service (SRM 1999).

Ecological site description* Description of the soils, uses and potential of land with specific physical

characteristics that produces distinctive kinds and amounts of vegetation.

Ecosystem* Organisms, together with their abiotic environment, that form an interacting system and

inhabit an identifiable space (SRM 1999).

Embedded litter* Non-decomposed, detached plant material (litter*) partially implanted or set in the soil

surface such that, if the litter is removed, it will leave an indentation in the soil’s surface. See also litter*,

duff*.

Energy flow Conversion of sunlight to plant and animal matter; one of the ecological processes*.

Erodibility* (syn. soil erodibility*) The degree or intensity of a soil’s state or condition of, or

susceptibility to, being eroded by wind or water (adapted from SSSA 1997).

Erosion* Detachment and movement of soil or rock fragments by water, wind, ice, gravity; the process

whereby the land surface is worn away by running water, wind, ice or other geological agents, including

such processes as gravitational creep (SRM 1999).

Erosivity* The capacity of rainfall to detach soil particles. This is a function of raindrop size and rainfall

intensity.

Glossary
178

Exotic plant* A plant growing or occurring in an ecosystem that is beyond its natural range of existence

or natural zone of potential dispersal (SRM 1999).

Flow pattern The path that water takes (i.e., accumulates) as it moves across the soil surface during

overland flow.

Foliage height diversity (FHD)* Foliage height diversity is the vertical structural diversity of vegetation. It

is calculated using the Shannon-Weiner diversity index. See Chapter 11 for calculations from the

Vegetation structure method. The same calculations can be applied to the Line-point intercept with Height

method (Ch. 15); given height classes are developed (such as 0-50 cm, 51-100 cm, 101-150 cm and 151-

200 cm). See also vegetation structure*.

Foliar cover* Proportion of the soil surface covered by a vertical projection of a plant cover. This is

effectively the area that is protected from raindrops and the area in shade when the sun is directly

overhead. This is the definition used in erosion models.

Foliar cover (%)* Foliar cover (%) is calculated by counting the number of plant intercepts (“Top layer” =

species code). Divide this sum by the total number of points sampled. Multiply this value by 100.

Forb* Any broad-leafed, herbaceous plant, other than those in the Poaceae (grasses), Cyperaceae (sedges)

and Juncaceae (rushes) families (SRM 1999). May or may not be woody.

Functional/structural groups* A suite or group of species that, because of similar shoot or root structure,

photosynthetic pathways, nitrogen-fixing ability, life cycle, etc., are grouped together.

GIS* Geographic Information System.

GPS* Global Positioning System.

Grassland* An area of vegetation dominated by herbaceous grasses. Grassland constitutes a major world

vegetation type and occurs where there is sufficient moisture for grass growth, but where the

environmental conditions, both climatic and anthropogenic, prevent tree growth. Its occurrence,

therefore, is correlated with a rainfall intensity between that of desert and forest.

Greenline* The first perennial vegetation that forms a lineal grouping of community types on or near the

water’s edge. It occurs most often at or slightly below the bank-full stage (Winward 2000).

Ground cover* The percentage of material (e.g., litter*, standing dead vegetation*, gravel/rocks,

vegetation and biological crust*) excluding bare soil*, covering the land surface. See also soil surface*.

Gully A furrow, channel or miniature valley, usually with steep sides, through which water commonly

flows during and immediately after rains or snow melt (SRM 1999).

Half-shrub (syn. sub-shrub) A perennial plant with a woody base, whose annually produced stems die

each year (SRM 1999).

 Glossary
179

Hillslope profile component* The landscape element along a hill’s slope. Distinguished by shape,

position and long-term erosion or sedimentation related to soil formation. These components include:

summit, shoulder, backslope, footslope and toeslope (Wysocki and Zanner 2003).

Horizontal slope shape* The geometric shape along an elevation contour across the hillslope (the

elevation contour). Slope shape can be convex, concave or linear. Compare with vertical slope shape*.

Humus (syn. stabilized organic matter) Usually a synonym for stabilized organic matter, but is

sometimes used to refer to all soil organic matter (SSSA 1997). See stabilized organic matter.

Hydrologic function* The capacity of a site to capture, store and safely release water from rainfall, run-on

and snowmelt (where relevant); to resist a reduction in this capacity; and to recover this capacity following

degradation (one of the three attributes of rangeland health*). See also biotic integrity* and soil and site

stability*.

Indicators* Components of a system whose characteristics (e.g., presence or absence, quantity,

distribution) are used as an index of an attribute (e.g., biotic integrity*) that is too difficult, inconvenient

or expensive to measure.

Infiltration* The entry of water into the soil (SSSA 1997).

Infiltration rate (mm/hr)* The rate (or speed) at which water infiltrates the soil during a specified time

period. See Chapter 8 for calculations. Ratio of interspace:under plant canopy* is calculated by dividing

the average infiltration rate in plant canopy interspaces (Veg class = NC) by the average infiltration rate

under plant canopies (Veg class = G, Sh, F or T). See also infiltration*.

INIFAP* Instituto Nacional de Investigaciones, Agricolas y Forestales (Mexico).

Interspace* An area between the canopies of two or more plants, or between the bases of two or more

plants. Interspaces can consist of bare soil, litter*, plants of a different functional group* (i.e., grass

between shrubs), or soil surface features, including rocks, biological crusts*, duff* and embedded litter*.

Invasive plant* Plants that are not part of (exotic) or a minor component of (native) the original plant

community or communities that increase above what’s expected given the normal range of variability of a

site.

Inventory (syn. rangeland inventory): (1) The systematic acquisition and analysis of resource information

needed for planning and management of rangeland; (2) the information acquired through rangeland

inventory (SRM 1999).

Landscape unit* A type of land that can be defined based on slope, aspect, landscape position (including

elevation) and soil. Landscape units repeat across the landscape for a particular region. Landscape unit is a

generic term that is generally equivalent to ecological sites used by the NRCS and ecological types used by

the USFS. The only difference is that ecological sites and types are defined at a particular scale (generally

including several soil series). Landscape units can be defined very coarsely (e.g., basin vs. range) or

extremely finely (e.g., Stellar fine sandy loam on east-facing 1-2% slopes), depending on objectives. It is

appropriate to substitute either the term “ecological site” or “ecological type” for “landscape unit”

throughout this manual.

Glossary
180

Lineal* Along a line.

Litter* The uppermost layer of organic debris on the soil surface; essentially, the freshly fallen or slightly

decomposed plant matter (SRM 1999). Includes persistent and non-persistent organic matter that is in

contact with the soil surface (i.e., not rooted in the soil). See also embedded litter*, duff*, woody litter*

and litter cover (%)*.

Litter cover (%)* The proportion of the soil surface covered by a vertical projection of litter*. Calculate

litter cover (%) by counting the total number of sample points where litter (L or WL) is intercepted. Divide

this sum by the total number of points sampled. Multiply this value by 100. See also litter*, woody litter*,

foliar cover*, bare ground* and basal cover*.

Minimum estimate of species richness* A minimum estimate of the total number of species in an area.

This can be estimated from the Line-point intercept or Plant production methods. Simply count the

number of species detected with either method, making sure not to count the same species twice. See also

species richness*.

Monitoring* The orderly and quantitative collection, analysis and interpretation of resource data to

evaluate progress toward meeting management objectives. The process must be conducted over time in

order to determine whether or not management objectives are being met (SRM 1999).

Monitoring plot* A transect or set of transects (plot) permanently located within a monitoring unit from

which monitoring data are collected.

Monitoring site* (syn. monitoring plot*) Locations selected for monitoring.

Monitoring unit* Areas located on a particular part of the landscape (e.g., valley bottom), within which

vegetation, soil type, use intensity, and the status of the soil and vegetation are relatively homogeneous.

Monitoring units may range in size from less than an acre to a square mile or more and may be repeated

across the landscape. Not all monitoring units will actually be monitored. A monitoring unit in riparian

zones is equivalent to a riparian complex. Monitoring units are typically smaller than, and subsets of, a

landscape unit*.

Normal variability or normal range of variability* The deviation of characteristics of biotic

communities and their environment that can be expected, given natural variability in climate and

disturbance regimes.

Noxious weed Any plant designated by a federal, state or county government to be injurious to public

health, agriculture, recreation, wildlife, or any public or private property (Sheley et al. 1999).

NRCS* National Resources Conservation Service (USDA*)

Number of strikes per depth increment* The number of impact penetrometer strikes for each depth

increment (0-5 cm, 5-10 cm, 10-15 cm, 15-20 cm, etc.). This is not the cumulative number of strikes per

depth (see Chapter 7). Ratio of interspace:under plant canopy* is calculated by dividing the average

number of strikes in plant canopy interspaces (Veg class = NC) by the average number of strikes under

plant canopies (Veg class = G, Sh, F or T) for each depth increment. See also compaction layer*.

 Glossary
181

Organic matter* Any material that is part of, or originated from, living organisms. Includes soil organic

matter*, plant residue, mulch, compost, and other materials (SSSA 1997). See also soil organic matter*.

Parent material The unconsolidated and more or less chemically weathered rocks, mineral matter or

organic accumulations (e.g., peat) from which soils are developed by pedogenic processes (simplified from

SSSA 1997).

PDF* Portable Document Format. Documents in a format easily downloaded, viewed and printed from the

Internet.

Pedestal (erosional)* Plants or rocks that appear elevated as a result of soil loss by wind or water erosion

(does not include plant or rock elevation as a result of non-erosional processes such as frost heaving).

Compare to terracette.

Pedon. A three-dimensional body of soil with lateral dimensions large enough to permit the study of

horizon shapes and relations. Its area ranges from 1 to 10 m2 (10 to 100 ft2) (see SSSA 1997 for more

detail).

Percent basal cover* See basal cover (%)*.

Perennial plant* A plant that has a life span of three or more years (USDA-NRCS 1997). See also annual

plant and biennial plant.

Physical crust* Impact of raindrops on bare soil causes the soil surface to seal and form a thin surface

layer that inhibits water absorption.

Plant base* Located above the roots and below the stems of a plant, at and just above the soil surface.

Plant decadence In a plant community, decadence refers to an overabundance of dead or dying plants

relative to what is expected for a site, given the natural range of variability in disease, climate and

management influences.

Plant density* The number of individuals per unit area (see Chapter 15).

Plant density by size class* The number of individuals, within a given size class, per unit area. See

Chapter 15 for calculations.

Plant production* See total annual production*.

Polypedon. A group of contiguous similar pedons. The limits of a polypedon are reached where there is

no soil, or where the pedons have characteristics that differ significantly (SSSA 1997).

Precision* The closeness of repeated measurements of the same quantity (Elzinga et al. 2001). See also

accuracy*.

Primary production See annual production*.

Glossary
182

Production by plant species* (or annual production by plant species) All aboveground plant biomass

produced during a single growing year for a given plant species. Production by plant species is expressed in

pounds per acre (lb/ac). See Chapter 9 for methods of estimating and calculating total annual production.

See also total annual production*.

Production by functional group* (or annual production by functional group) All aboveground plant

biomass produced during a single growing year for a given plant functional group*. Calculate production

by functional group by first determining which plant species belong to the functional group. Using the

methods described in Chapter 9 for estimating and calculating total annual production, measure and

calculate production by plant species. Then add together annual plant production for each individual

species in the functional group to determine plant production for the functional group. Production by

functional group is expressed in pounds per acre (lb/ac). See also total annual production*, production

by plant species* and functional/structural groups*.

Proportion of dead plant intercepts (by species)* For a given species, count the number of dead plant

intercepts (i.e., hit a dead plant part; the entire plant does not have to be dead). Divide this sum by the

total number of plant intercepts for the selected species. Multiply this value by 100.

PVC* Polyvinyl Chloride.

Qualitative data* Non-quantitative data derived from observations, commonly visual, and recorded

descriptively. Qualitative data is not measured (e.g., descriptive or non-numerical data).

Qualitative rangeland health assessment The determination of the functional status of an attribute(s)

through non-numerical observations of indicators. Qualitative assessments have an element of

subjectivity.

Quantitative data* Data derived from measurements, such as counts, dimensions, weights, etc., and

recorded numerically; may include ratios or other values. Qualitative numerical estimates, such as ocular

cover and production estimates, are often referred to as “semi-quantitative.”

Quantitative rangeland health assessment The determination of the functional status of an attribute(s)

through measurements of vegetation, soil or landscape characteristics that are indicators, or can be used to

derive indicators. Quantitative assessments have a known level of precision and accuracy, and require a

quantitative reference value for comparison.

Rangeland* Land on which the indigenous vegetation (climax or natural potential) is predominantly

grasses, grass-like plants, forbs or shrubs and is managed as a natural ecosystem. If plants are introduced,

they are managed similarly. Rangelands include natural grasslands, savannas, shrublands, many deserts,

tundra, alpine communities, marshes and wet meadows (SRM 1999). Oak and piñon-juniper woodlands

could also be included in this definition.

Rangeland ecological site See ecological site*.

Rangeland health* The degree to which the integrity of the soil, vegetation, water and air, as well as the

ecological processes of the rangeland ecosystem, are balanced and sustained. Integrity is defined as

maintenance of the structural and functional attributes characteristic of a locale, including normal

variability (SRM 1999) (see footnote at end of Glossary).

 Glossary
183

Ratio of interspace:under plant canopy* See infiltration rate (mm/hr)* for infiltration (Single-ring

infiltrometer) and number of strikes per depth increment* for compaction (Compaction test).

Reference area or site A landscape unit or monitoring unit at the reference state. Often used or referred

to for qualitative and quantitative assessments.

Reference state* The state where functional capacities represented by soil and site stability, hydrologic

function and biotic integrity are performing at an optimum level under the natural disturbance regime.

This state usually includes, but is not limited to, what is often referred to as the potential native plant

community (PNC).

Resilience* The capacity of ecological processes to recover following a disturbance. Resilience can be

defined in terms of the rate of recovery, the extent of recovery during a particular period of time, or both.

Resistance* The capacity of ecological processes to continue to function without change following a

disturbance.

Rhizomatous plant A plant that develops clonal shoots by producing rhizomes. Rhizomes are horizontal

underground stems that usually produce roots and shoots from nodes (SRM 1999).

Rill* A small, intermittent watercourse with steep sides, usually no more than several centimeters deep

(SSSA 1997). Rills generally are linear erosion features.

Riparian colonizing species* Plant species that become established in open, barren areas. They are often

among the first plants to occupy open sites. In riparian areas they “colonize” edges of bars or areas where

stream banks have freshly eroded. They are rhizomatous/stoloniferous in growth form, but the roots are

shallow and the stems are relatively weak. Although they are short-lived, they have a capacity to grow very

rapidly—up to one to four centimeters per day. They initiate shallow roots every few centimeters and, as

water force aligns their stems parallel to the water’s edge, they develop temporary bands/stringers of

vegetation along stream edges. Their primary function is to filter and catch very fine (flour-like) sediments

and build substrate for the stronger, more permanent “stabilizing” species (see definition for riparian

stabilizing species*). As such, they play a crucial role in initiating recovery/maintenance of stream banks.

Typical examples include brookgrass (Catabrosia aquatica) and water-cress (Rorippa nasturtium-aquaticum)

(Winward 2000).

Riparian complex* A unit of land characterized by a unique set of biotic and abiotic factors. Complexes

are identified on the basis of their topography, soils, stream gradient, associated water flow features, and

their general vegetation patterns. A riparian complex is similar in definition to a valley segment, except

that the valley segment refers to the stream channel proper, while the riparian complex is used to describe

the full width of the riparian area across a particular portion of a valley. Generally, a limited set of stream

reaches is nested within a given riparian complex (Winward 2000).

Riparian greenline* See greenline*.

Glossary
184

 Glossary

Riparian stabilizing species* Plant species that become established along edges of streams, rivers, ponds

and lakes. Although they generally require hydric settings for establishment, some may persist in drier

conditions once they become firmly established. They commonly have strong, cord-like rhizomes as well

as deep fibrous root masses. In addition, they have coarse leaves and strong crowns, which, along with

their massive root systems, are able to buffer stream banks against the erosive force of moving water. Along

with enhancing streambank strength, they filter sediments and, with the force of water, build/rebuild

eroded portions of stream banks. They likewise filter chemicals, which is important in improving water

quality. The presence of these species, thereby, plays a significant role in attaining/maintaining proper

functioning of riparian and aquatic ecosystems (based on Winward 2000).

Runoff* The portion of precipitation, snowmelt or irrigation on an area that does not infiltrate, but

instead is discharged by the area (SSSA 1997).

Savanna* A plant community found between a tropical, subtropical or temperate forest biome and a

desert biome; transitional in character between grassland or desert and forest, it typically has droughtresistant vegetation dominated by grasses with shrubs and scattered tall trees.

Shrub* A plant that has persistent, woody stems and a relatively low-growth habit, and that generally

produces several basal shoots instead of a single trunk. It differs from a tree in its low stature (generally less

than five meters, or sixteen feet) and non-arborescent form (SRM 1999).

Site* See monitoring site*. See also monitoring plot* and ecological site*.

Slope shape* The geometric shape of the hillslope (convex, concave or linear). See vertical slope shape*

and horizontal slope shape*.

Soil and site stability* The capacity of a site to limit redistribution and loss of soil resources (including

nutrients and organic matter) by wind and water; one of the three attributes* of rangeland health*.

Soil association Each delineation on the soil map shows the boundaries, shape and location of a

landscape unit* composed of two or more soil components*. The individual bodies of component soils

are large enough to be delineated at the scale of mapping. Several to numerous bodies of each kind of

component soil are apt to occur in each delineation, and they occur in a fairly repetitive and describable

pattern (SSSA 1997).

Soil complex Each delineation on the soil map shows the boundaries, shape and location of a landscape

unit* composed of two or more soil components*. The individual bodies of component soils are too small

to be delineated at the scale of mapping. Several to numerous bodies of each kind of component soil are

apt to occur in each delineation, although their pattern may not be apparent (SSSA 1997).

Soil component* A subdivision of a soil series based on features that affect its use and management. For

example, slope, stoniness and thickness (USDA-NRCS Soil Survey Staff 1993). See also soil inclusion and

soil map unit*.

Soil erodibility* See erodibility*.
185

Glossary

Soil inclusion One or more soil component* within a delineation of a soil map unit*, not identified by

the map unit name (i.e., is not one of the named component soils). Such soils or areas are either too small

to be delineated separately without creating excessive map or legend detail, occur too erratically to be

considered a named component, or are not identifiable by practical mapping methods. Inclusions reduce

the homogeneity of map units (SSSA 1997).

Soil organic matter (SOM)* The total organic matter in the soil. It can be divided into three general pools:

living biomass of microorganisms, fresh and partially decomposed residues (the active fraction), and the

well-decomposed and highly stable organic material (stabilized organic matter). Surface litter* is

generally not included as part of soil organic matter (SSSA 1997).

Soil map unit* A kind of soil, a combination of kinds of soil, or miscellaneous land type or types, that can

be shown at the scale of mapping for the defined purposes and objectives of the survey. Soil map units are

the basis for the delineations of a soil survey map (Donahue et al. 1977). See also soil component*, soil

inclusion, soil complex and soil association.

Soil particles (syn. soil separates) Mineral particles, <2.0 mm in equivalent diameter, ranging between

specified size limits. The names and size limits of separates recognized by the USDA are: very coarse sand

(1-2 mm); coarse sand (0.5-1 mm); medium sand (0.25-0.5 mm); fine sand (0.1-0.25 mm); very fine sand

(0.05-0.5 mm); silt (0.002-0.05 mm); and clay (<0.002 mm) (SSSA 1997).

Soil quality* The capacity of a specific kind of soil to function within natural or managed ecosystem

boundaries, to sustain biological productivity, maintain environmental quality, and promote plant and

animal health (SSSA 1997) (see footnote at end of Glossary).

Soil series* The lowest category of U.S. system of soil taxonomy; a conceptualized class of soil bodies

(polypedons) that have limits and ranges more restrictive than all higher taxa. Soil series are commonly

used to name dominant or codominant polypedons represented on detailed soil maps. The soil series

serve as a major vehicle to transfer soil information and research knowledge from one soil area to another.

Soil structure* The combination or arrangement of primary soil particles into secondary units or peds.

The secondary units are characterized on the basis of size, shape and grade (degree of distinctiveness) (SSSA

1997). See http://soil.gsfc.nasa.gov/pvg/prop1.htm for diagrams illustrating different types of structure

(accessed September 25, 2008).

Soil surface* Term used to indicate classes of material on or at the soil or land surface (e.g., plant base,

rock fragments, bedrock, embedded litter*, duff*, mosses, lichens, dark cyanobacteria, or soil that is

visibly unprotected by any of the above). See also ground cover*.

Soil surface in canopy gaps > __* The proportion (or percent) of the soil surface encompassed by gaps

between plant canopies (as measured along a vegetation transect) that are longer than a specified

minimum length. Determine the minimum gap size of concern, such as the minimum canopy gap at

which wind erosion or weed invasion becomes a significant risk. List all canopy gap sizes that are equal to,

or larger than the selected minimum canopy gap size. Add these gap sizes together. Divide this sum by the

total length of the vegetation transect. Be sure to keep units consistent; if gaps are recorded in centimeters

and line length in meters, convert line length to centimeters. Multiply this value by 100. See Volume I:

Quick Start and Chapter 17 for further discussion on the Canopy Gap intercept method and canopy gaps.
186

 Glossary

Soil surface in basal gaps > __* The proportion (or percent) of the soil surface encompassed by gaps

between plant bases (as measured along a vegetation transect) that are longer than a specified minimum

length. Determine the minimum gap size of concern, such as the minimum basal gap at which water

erosion or runoff becomes a significant risk. List all basal gap sizes that are equal to, or larger than the

selected minimum basal gap size. Add these gap sizes together. Divide this sum by the total length of the

vegetation transect. Be sure to keep units consistent; if gaps are recorded in centimeters and line length in

meters, convert line length to centimeters. Multiply this value by 100. See Volume I: Quick Start and

Chapter 17 for further discussion on the Basal Gap intercept method and basal gaps.

Soil survey* The systematic examination, description, classification and mapping of soils in an area. Soil

surveys are classified according to the kind and intensity of field examination (SSSA 1997).

Soil texture* The relative proportions of the various soil particles (sand, silt and clay) (SSSA 1997).

Species composition* The proportions of various species in relation to the total in a given area. Plant

species composition may be expressed in terms of cover, density or weight (SRM 1999).

Species richness* The total number of species in an area. This is one indicator of biodiversity. See Chapter

10 for calculations. See also minimum estimate of species richness*.

SRM* Society for Range Management.

SSSA* Soil Science Society of America.

Stabilized organic matter (syn. humus) The pool of soil organic matter that is resistant to biological

degradation because it is either physically or chemically inaccessible to microbial activity. These

compounds are created through a combination of biological activity and chemical reactions in the soil

(SSSA 1997).

Standing dead* (or standing dead vegetation) Dead plant material still attached to a rooted plant. All

standing dead vegetation produced in previous (not the current) growing season(s) not in contact with the

soil surface (Pellant et al. 2005). Compare to litter*, woody litter*, duff* and embedded litter*.

State* A state includes one or more biological (including soil) communities that occur on a particular

ecological site and that are functionally similar with respect to the three attributes (soil and site stability,

hydrologic function and biotic integrity). States are distinguished by relatively large differences in plant

functional groups, soil properties and ecosystem processes and, consequently, in vegetation structure,

biodiversity and management requirements. They are also distinguished by their responses to disturbance.

A number of different plant communities may be included in a state and the communities are often

connected by traditionally defined successional pathways (Pellant et al. 2005 based on Bestelmeyer et al.

2003 and Stringham et al. 2001, 2003).

Stratify* To separate, divide or delineate into classes.

Structure (soil)* See soil structure*.

Structure* (syn. vegetation structure*) The height and area occupied by different plants or life forms (and

spatial diversity thereof) in a community.
187

Glossary

Sub-shrub* See half-shrub.

Sub-surface stability (class)* The stability value ranging from one to six that is assigned to a sub-surface

stability sample during the Soil stability test. See Volume I: Quick Start for more details.

Surface stability (class)* The stability value ranging from one to six that is assigned to a surface stability

sample during the Soil Stability test. See Volume I: Quick Start for more details. Another indicator is the

proportion of surface values = class 6*. Calculate the proportion of surface values = class 6 by counting

the number of surface samples with a stability class equal to six. Then divide this sum by the total number

of surface samples taken. Multiply this value by 100.

T&E* Threatened and Endangered (species).

Terracette. “Benches” of soil deposition behind obstacles caused by water erosion, not wind erosion

(Pellant et al. 2005). May or may not reflect current erosion. Compare to pedestal.

Threshold* A transition boundary that an ecosystem crosses, which results in a new stable state* that is

not easily reversed without significant inputs of resources.

Total annual production* All aboveground plant biomass produced during a single growing year,

including woody material and regardless of palatability or accessibility to grazing animals. Total annual

production is expressed in pounds per acre (lb/ac). See Chapter 9 for methods of estimating and

calculating total annual production.

TNC* The Nature Conservancy.

Transition* A shift between two states. Transitions are not reversible by simply altering the intensity or

direction of factors that produced the change. Instead, they require new inputs such as revegetation or

shrub removal. Practices such as these that accelerate succession (USDA-NRCS 1997) are often expensive to

apply.

Tree* A woody perennial, usually single-stemmed plant that has a definite crown shape and reaches a

mature height of at least 4 meters (13.1 ft). The distinction between woody plants known as trees and

those called shrubs is gradual. Some plants, such as oaks (Quercus spp.), may grow as either trees or shrubs

(SRM 1999).

Trend* The direction of change in ecological status or resource value rating observed over time (SRM

1999).

USDA* United States Department of Agriculture.

USDI* United States Department of the Interior.

USFS* United States Forest Service (USDA).

USGS* United States Geological Survey (USDI).
188

Footnote: Together, rangeland health and soil quality focus on above - and below - ground soil and vegetation features important

for rangeland function. They can be thought of as the foundation upon which most land uses and values depend.

 Glossary

Vegetation class* Used in this document to distinguish between different types of vegetation for the

purpose of stratifying soil measurements. The vegetation class is defined as NC (no canopy) if there is less

than 50 percent canopy cover over the area measured or sampled. The vegetation class is defined as G if

greater than 50 percent canopy cover over the area measured is grass or a grass/shrub mixture; F for

perennial forb, Sh for shrub canopy and T for tree canopy.

Vegetation structure* (syn. structure*) Vegetation structure is the vertical and horizontal distribution of

vegetation in space. The height and area occupied by different plants or life forms (and spatial diversity

thereof) in a community. See also foliage height diversity* and visual obstruction*.

Vertical slope shape* The geometric shape of the vertical profile (up and down slope) of a hillslope. This

can be convex, concave or linear. Compare to horizontal slope shape*.

Vesicular crust A type of physical crust that contains numerous small, unconnected air pockets or pores

similar to a sponge. As with all physical crusts, they seal the soil’s surface, causing a reduction in

infiltration rate.

Visual obstruction* An indicator that reflects the vertical plant cover or the density of vegetation at

different heights. Observers determine what proportion of a cover pole, Robel pole, or cover board is

visually obstructed from view by plant cover at standard height intervals. Visual obstruction

measurements can be used to estimate standing crop biomass, vegetation structure* and foliage height

diversity*. See Chapter 11 for calculations from the Vegetation structure method. The same calculations

can be applied to the Line-point intercept with Height method (Ch. 15); given height classes are developed

(such as 0-50 cm, 51-100 cm, 101-150 cm and 151-200 cm). See also vegetation structure* and foliage

height diversity*.

Weather* The current state of the atmosphere with regard to wind, temperature, cloudiness, moisture,

pressure, etc.

Width-depth ratio* This is the width of a channel or gully divided by its depth. The width is the

horizontal distance between the points used for the bank angle (Riparian channel and gully profile) at the

top of each bank. The depth is the greatest vertical distance from a straight line drawn between the two

points used for the bank angle and the bottom of the channel. See Chapter 14 for more information on

calculations.

Woody litter* Detached plant material (litter*) that is greater than 5 mm (~1

/4 in) in diameter that is in

direct contact with the soil.
189

References and Additional Resources

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References and additional resources
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Authors

Jeffrey E. Herrick is a research scientist, Kris M. Havstad is the research leader, Justin W. Van Zee is a

technician, and Laura M. Burkett is a range technician with the United States Department of Agriculture –

Agricultural Research Service (USDA-ARS) Jornada Experimental Range in Las Cruces, New Mexico.

Walter G. Whitford was employed by the US Environmental Protection Agency while working on this

book and is now retired and based at the USDA-ARS Jornada Experimental Range.

Contributors

Brandon T. Bestelmeyer is a research scientist with the USDA-ARS Jornada Experimental Range in Las

Cruces, New Mexico.

Ericha M. Courtright is a College Assistant Professor with New Mexico State University and the Jornada

Experimental Range in Las Cruces, New Mexico.

Alicia Melgoza is an ecologist with Instituto Nacional de Investigaciones Forestales, Agricolas y Pecuarias

(INIFAP) in Chihuahua, Mexico.

Mike Pellant is the Great Basin Restoration Initiative Coordinator with the Bureau of Land Management

(BLM) in Boise, Idaho.

David A. Pyke is a research ecologist with the United States Geological Survey (USGS) in Corvallis, Oregon.

Marta D. Remmenga is a statistician and professor at New Mexico State University.

Patrick L. Shaver is a rangeland management specialist with the USDA-Natural Resources Conservation

Service (NRCS) West National Technology Support Center in Portland, Oregon.

Amrita G. de Soyza is a senior ecologist at the Nevada Desert Research Center in Reno, Nevada.

Arlene J. Tugel is a soil scientist with the USDA-NRCS Soil Survey Division in Las Cruces, New Mexico.

Robert S. Unnasch is an senior ecologist with The Nature Conservancy (TNC) in Boise, Idaho.