Late-Successional and Old-Growth

Forest Effectiveness Monitoring

Plan for the Northwest Forest Plan

Miles Hemstrom, Thomas Spies, Craig Palmer, Ross Kiester,

John Teply, Phil McDonald, and Ralph Warbington

United States

Department of

Agriculture

Forest Service

Pacific Northwest

Research Station

General Technical

Report

PNW-GTR-438

December 1998
Authors MILES HEMSTROM is a regional ecologist and JOHN TEPLY is an inventory

program manager, U.S. Department of Agriculture, Forest Service, Pacific Northwest

Region, P.O. Box 3623, Portland, OR 97208; THOMAS SPIES is a research forester

and ROSS KIESTER is a mathematical statistician, U.S. Department of Agriculture,

Forest Service, Pacific Northwest Research Station, Forestry Sciences Laboratory,

3200 Jefferson Way, Corvallis, OR 97331; CRAIG PALMER is a program director,

Harry Reid Center for Environmental Studies, University of Nevada—Las Vegas, 505

South Maryland Parkway, Las Vegas, NV 89154-4009; PHIL McDONALD is a

research forester, U.S. Department of Agriculture, Forest Service, Pacific Southwest

Experiment Station, 2400 Washington Ave., Redding, CA 96001; and RALPH

WARBINGTON is a section head, U.S. Department of Agriculture, Forest Service,

Pacific Southwest Region, Remote Sensing Laboratory, 1920 20th St., Sacramento,

CA 95814.
Late-Successional and Old-Growth

Forest Effectiveness Monitoring

Plan for the Northwest Forest Plan

Miles Hemstrom, Thomas Spies, Craig Palmer, Ross Kiester,

John Teply, Phil McDonald, and Ralph Warbington

Published by:

U.S. Department of Agriculture

Forest Service

Pacific Northwest Research Station

Portland, Oregon

General Technical Report PNW-GTR-438

December 1998
Abstract Hemstrom, Miles; Spies, Thomas; Palmer, Craig; Kiester, Ross; Teply, John;

McDonald, Phil; Warbington, Ralph. 1998. Late-successional and old-growth

forest effectiveness monitoring plan for the Northwest Forest Plan. Gen. Tech. Rep.

PNW-GTR-438. Portland, OR: U.S. Department of Agriculture, Forest Service,

Pacific Northwest Research Station. 37 p.

This report presents options for long-term effectiveness monitoring of late-successional

and old-growth forests under the Northwest Forest Plan. It describes methods to

answer questions about how much late-successional forest exists on Federal land, its

pattern, how it’s changing, and if the Forest Plan is providing for its conservation and

management. It specifies data needed, analytic methods for using remotely sensed

and grid plot data, and implementation options. A periodic process for reporting the

status and trend of late-successional and old-growth forests on Federal lands is described, and links to finer scale monitoring of silvicultural and salvage effects on latesuccessional and old-growth forests are provided.

Keywords: Northwest Forest Plan, effectiveness monitoring, late-successional and oldgrowth forest, vegetation map, remote sensing, grid plot, GIS, landscape, stand-scale,

trend model.

Preface This report is part of a series describing the approach for monitoring effectiveness of

the Northwest Forest Plan that has been approved by the Intergovernmental Advisory

Committee. Other reports present the plans for monitoring the northern spotted owl,

marbled murrelet, and aquatic and riparian ecosystems. Future reports may address

survey-and-manage species and biodiversity of late-successional and aquatic ecosystems, socioeconomics, and tribal resources. These reports follow the framework

for effectiveness monitoring described in “The Strategy and Design of the Effectiveness Monitoring Program for the Northwest Forest Plan.” The purpose of this report is

to present a range of options for monitoring late-successional and old-growth forests

from which the Federal agencies responsible for the Forest Plan can select an approach meeting their respective information needs given current and expected resource availability. This report responds to the assignment from the Federal resource

agencies through the Intergovernmental Advisory Committee and incorporates responses to all comments and peer reviews, as requested. The options, recommended

by the authors and the interagency Effectiveness Monitoring Team, have been

selected for implementation in fiscal year 1998. Manuals, protocols, specific tasks,

and annual funding allocations will be provided in individual agency work plans. All

these documents, including manuals and work plans, will comprise the full set of

guidance for conducting the effectiveness monitoring program for the Forest Plan.
Executive Summary How much late-successional forest is there on Federal land? What is its pattern

across the landscape? Is the amount of late-successional and old-growth (LSOG)

forests on Federal land changing? If so, from what causes and at what rates? Is the

Northwest Forest Plan providing for conservation and management of LSOG forests

as anticipated?

The LSOG effectiveness monitoring plan provides methods to answer those questions. It specifies data needed, analytic methods, and implementation options with

estimates of necessary resources.

Data Sources This plan relies on two kinds of information:

• Permanent grid plot data, on at least a 3.4-mile plot spacing, remeasured at

least every 10 years

• Existing and potential vegetation maps

If these data sources are not implemented or developed, this monitoring plan cannot

be implemented as designed, which will affect other monitoring plans dependent on

forest vegetation and habitat data.

Approach Vegetation maps and data from permanent grid plots will be stratified and analyzed to

provide answers to the monitoring questions and estimates of future trends. Statistical

reliability of these analyses will be estimated. A monitoring report will be prepared

every 5 years, describing the status and trend of LSOG forests in the Northwest Forest Plan area. A panel of experts will review the report and recommend actions to the

regional interagency executives. Links are provided to finer scale monitoring of silvicultural and salvage effects on LSOG forests, but a sample design is not provided.

Costs This plan relies on collection of information from permanent grid plots and on the

existence of vegetation maps, which are produced through other agency programs.

Monitoring costs are for data analysis, additional modeling of expected trends, a pilot

effort to refine methods, and report generation but not for installing permanent grid

plot or building vegetation maps. Continued existence and updating of vegetation

maps and grid plot data are critical to this plan.

Timelines The monitoring approach depends on continuation of existing agency programs for

the production of vegetation maps and data, and thus the schedule for monitoring

depends on the provision of data from these programs. The intent is to conduct trend

analyses in 1999 and produce the first monitoring report at the beginning of 2000.
Contents 1 Introduction

1 Goals and Objectives

3 Background

3 FEMAT and FSEIS Basis

4 Two Definitions of LSOG

7 Monitoring Questions

8 Monitoring Approach

8 Stressors

9 Required Information

10 Plan Components

10 Conceptual Model

12 Indicators

12 Large Landscape-Scale Indicators

13 Stand-Scale Indicators

14 Change-Agent Indicators

15 Predictive Models

15 Stand-Scale Silviculture- and Salvage-Effect Indicators

16 Data Acquisition Methods

16 Large Landscape-Scale Indicators

16 Stand-Scale Indicators

16 Change-Agent Indicators

17 Silviculture- and Salvage-Effects Indicators

18 Detecting Change and Trend

18 Large Landscape-Scale Indicators

18 Stand-Scale Indicators

18 Change-Agent Indicators

19 Silviculture- and Salvage Effects-Indicators

19 Expected Values and Trends
20 Long-Term Averages

21 Uncertainty

22 Abundance and Ecological Diversity Thresholds

24 Process and Function Thresholds

24 Connectivity Thresholds

26 Baselines

26 Current Conditions Baseline

27 Long-Term Average Baseline

27 Quality Assurance

27 Data Analysis and Reporting

27 Link to Decisionmaking

28 Organizational Infrastructure

28 Estimated Costs

28 Alternatives

29 Pilot LSOG Effectiveness Monitoring Report

29 Refined Trend Estimates

29 Implementation Schedule

30 Research Needed

32 Acknowledgments

32 References

35 Appendix A: Vegetation Strike Team Standards

36 Appendix B: Alternative Approaches Considered
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Introduction The record of decision (ROD) for the Northwest Forest Plan (also Forest Plan) was

developed and signed to address managing and conserving late-successional and

old-growth forests (LSOG) on Federal lands in the range of the northern spotted owl

(USDA and USDI 1994b: 2). The Forest Ecosystem Management Assessment Team

report (FEMAT 1993) and the final supplemental environmental impact statement

(FSEIS; USDA and USDI 1994a) provide the scientific assessment the ROD was

based on and focus on the status and management of late-successional and oldgrowth forests. The ROD requires monitoring of the effectiveness of Forest Plan

implementation (USDA and USDI 1994b: app. E, p. 8 and 9). In addition, latesuccessional and old-growth forests provide habitat for northern spotted owls (Strix

occidentalis caurina), marbled murrelets (Brachyramphus marmoratus), and many

other species.

This plan provides a conceptual framework and methods for monitoring LSOG forests

on Federal lands under the Forest Plan. It focuses on regional and large landscape

scales (fig. 1) (FEMAT 1993: 27) and provides links for finer scale monitoring of silviculture and salvage effects. The analysis for this monitoring plan will include all

lands in the region of the Forest Plan to provide a context for LSOG on Federal lands.

Any management decisions resulting from this analysis will apply only to Federal

lands. Maps and data will be used to assess and evaluate forest vegetation and habitat for effectiveness monitoring of other Forest Plan resources, such as the spotted

owl (Lint et al., in press), marbled murrelet (Madsen et al., in press), and other

resources.

This effectiveness monitoring plan is part of a larger effort to monitor the effectiveness

of the Forest Plan (Mulder et al., in press). That report describes seven steps, which

are addressed in this effectiveness monitoring plan: (1) specify goals (refer to the section “Goals and Objectives”), (2) identify stressors (refer to “Monitoring Approach”),

(3) develop conceptual models (refer to “Monitoring Approach”), (4) select indicators

(refer to “Indicators”), (5) establish sampling design (refer to “Monitoring Approach”),

(6) define methods of analysis (refer to “Data Analysis and Reporting”), and (7)

ensure links to decisionmaking (refer to “Link to Decisionmaking”).

Goals and

Objectives

This plan will provide information that will allow managers to evaluate the success of

the Forest Plan in reaching the desired amount and distribution of LSOG on Federal

lands. Specific objectives include:

• Assess changes in the status and trends of late-successional and old-growth

forest on Federal lands at large landscape scales in the region of the Forest

Plan

• Provide a scientifically credible process for answering those questions, based

on a conceptual model

• Provide a process to evaluate those answers and provide a link to management decisions

1
Figure 1—Physiographic provinces in the Northwest Forest Plan region.

2
Background The FEMAT report (1993: 49-52) and the FSEIS (USDA and USDI 1994a: 35-40) list

three attributes against which the quantity and quality of late-successional ecosystems

may be judged: abundance and ecological diversity, process and function, and

connectivity. Descriptions in the FSEIS provide a starting point for developing specific

monitoring questions.

Abundance and ecological diversity—

Abundance of late-successional and old-growth communities and ecosystems refers to the total acreage of forest meeting structural, functional,

or minimum age criteria based ecological conditions and definitions for

each physiographic province. The standards that define forests are based

on the extent of three stages of late-successional and old-growth forest.

The three stages are the (1) maturation, (2) transition, and (3) shifting

small gap stages of late-successional and old-growth forest development.

Ecological diversity is also indicated by the distribution of late-successional

and old-growth communities on the landscape, and the interrelationships

among a variety of geographic, climatic, elevational, topographic, soil

distributions [USDA and USDI 1994a: 35].

Process and function—

Process refers to ecological changes or actions that lead to the development and maintenance of late-successional and old-growth ecosystems at

all spatial and temporal scales. Examples include: (1) tree establishment,

maturation, and death, (2) gap formation and filling, (3) understory development, (4) small and large scale disturbances such as fire and wind, (5)

decomposition, (6) nitrogen fixation, and (7) canopy interceptions of energy

and matter, and (8) energy and matter transfers between the forest and

atmosphere [USDA and USDI 1994a: 37].

Functions . . . refer to ecological values of the late-successional ecosystem

or its components that (1) maintain or contribute to the maintenance of

populations of species that use these ecosystems, and (2) contribute to

the diversity and productivity of other ecosystems (such as carry over of

large dead trees to early successional ecosystems, and storage of carbon

in the global ecosystem). Examples of ecosystem function include habitat

for organisms, climatic buffering, soil development and maintenance of soil

productivity through inputs of large woody debris, nitrogen fixation, spread

of biotic and abiotic disturbances through landscapes, and nutrient cycles

production, storage, utilization, and decomposition [USDA and USDI

1994a: 37].

FEMAT and FSEIS Basis

3
Connectivity—

Connectivity is a measure of the extent to which the large landscape

pattern of the late-successional and old-growth ecosystem provides for

biological and ecological flows that sustain late-successional and oldgrowth animal and plant species across the range of the northern spotted

owl. Connectivity does not necessarily mean that late-successional and oldgrowth areas have to be physically joined in space, because many latesuccessional and old-growth species can move (or be carried) across areas

that are not in late-successional ecosystem conditions. Large landscape

features affecting connectivity are (1) distance between late-successional

and old-growth areas, and (2) forest conditions in areas between latesuccessional and old-growth areas [USDA and USDI 1994a: 38].

Two Definitions of LSOG This effectiveness monitoring plan uses two perspectives of LSOG, one from remotely

sensed characteristics and one from plot data at the stand scale. It establishes the

statistical relation between them and evaluates trends in each (Czaplewski and Catts

1992). Remote sensing information allows answers to one set of questions (spatial

features), and plot data provide information about stand-scale structure and composition. Because permanent plot methods are easier to repeat over time, they provide

the most accurate estimators of LSOG amount.

Late-successional and old-growth forests can be described from remotely sensed

information about upper canopy features, such as canopy cover, the size of tree

crowns and inferences about tree diameter, canopy structure (single versus multiple

layers), and to some extent, tree species (Cohen and Spies 1992). Maps depicting

LSOG that use these features also can be used to examine large landscape extent,

patterns, and amounts. Remote sensing, however, cannot detect some critical features

(dead wood, canopy layers below the top layer, understory vegetation, elements of

the spatial distribution of these features) that determine LSOG condition at the stand

scale.

The Regional Interagency Executive Committee adopted a standard, minimum set of

vegetation attributes for large landscape-scale mapping. These standards (appendix

A), from the Vegetation Strike Team (1995), are more than sufficient for the LSOG

effectiveness monitoring outlined in this plan. Development of remotely sensed information about the structure and composition of forests in the Forest Plan region is

critical for implementation of this monitoring plan. An assessment of the accuracy of

remotely sensed attributes will be necessary.

Late-successional and old-growth forests also can be defined from stand-scale,

ground-based measurements of vegetation features (such as species, sizes, canopies,

and amount of dead material). A uniform grid plot system that measures these attributes has been established in parts of the Forest Plan area (Max et al. 1996). Estimates of LSOG amounts by stratum can be derived from these data. Because definitions, attributes, and scale differ between remotely sensed and plot-based analyses of

LSOG, estimated amounts of LSOG also are likely to differ.

4
Remote sensing definitions—Forest classes for LSOG analysis can be defined from

remotely sensed attributes, as was done by FEMAT (1993) and the FSEIS (USDA

and USDI 1994a). This LSOG effectiveness monitoring plan adds supplemental

classes to allow description of a full range of potentially forested conditions and to

identify stands dominated by trees more than 30 inches in diameter, which often are

favored habitat for old-growth-related species. Class breaks are slightly modified from

those in FEMAT (1993: 9) because the Vegetation Strike Team (1995) used standard

diameter classes with breaks every 10 inches. Under these definitions, LSOG stands

are dominated by trees 20 inches in diameter and larger. In addition, FEMAT and

FSEIS definitions distinguish stands with one canopy layer from stands with two or

more canopy layers. Determining canopy layers from remotely sensed information

may be impossible (Cohen and Spies 1992), but simple canopy structure (even upper

canopies with relatively uniform crowns and few canopy breaks) can be distinguished

from complex structure (uneven canopies with variable crown sizes and common

canopy breaks).

Forest classes interpreted from remote sensing—The effectiveness monitoring

plan uses nine forest class strata that can be combined into 16 forest vegetation

mapping units (table 1). These classes parallel those in the FEMAT report and the

FSEIS, except as noted below:

Potentially forested—Land with tree canopy cover less than 10 percent. Ten percent canopy cover is a long-established standard for distinguishing forested from

nonforested land. This class is restricted to lands that have been forested recently

and that likely will be forested again in the near future, but where trees cannot be

detected in imagery. This class is in addition to those specified by FEMAT and the

FSEIS.

Seedling and sapling forest—Land with average dominant and codominant tree

diameters between 0 and 10 inches.

Small single-storied stands—Land with average dominant and codominant tree

diameters of 10 and 20 inches and simple canopy structure.

Table 1—Forest vegetation mapping units for use in LSOG effectiveness

monitoring analysis

Composition class

Structure class Deciduous (D) Mixed (M) Conifer (C)

Potentially forested (PF) PF PF PF

Seedling and sapling

(SS) SS-D SS-M SS-C

Small single-storied (SSS) SSS-D SSS-M SSS-C

Medium and large singlestoried (MSS) MSS-D MSS-M MSS-C

Medium and large

multistoried (MMS) MMS-D MMS-M MMS-C

Large multistoried (LMS) LMS-D LMS-M LMS-C

5
Medium to large single-storied stands—Land with average dominant and

codominant tree diameters between 20 and 30 inches and simple canopy structure. These stands qualify as late-successional forest.

Medium to large multistoried stands—Land with average dominant and codominant

tree diameters between 20 and 30 inches and complex canopy structure. These

stands qualify as late-successional or old-growth forest.

Large multistoried stands—Land with average dominant and codominant tree diameters of 30 inches or more and complex canopy structure. These stands qualify as

late-successional or old-growth forest. This class, which is in addition to those specified by FEMAT, separates stands that are more likely to be optimum habitat for

LSOG-dependent species.

Conifer, deciduous, and mixed stands—Each of the forested land classes above

will be assigned one of three categories: conifer dominated (hardwood canopy tree

cover less than 20 percent of total tree cover), deciduous dominated (conifer canopy

tree cover less than 20 percent of total tree cover), and mixed (conifer and hardwood

canopy tree cover are both more than 20 percent of total tree cover). These classes

are in addition to those specified by FEMAT and the FSEIS.

Plant communities—To deal with FEMAT specifications that the acreage and variety

of plant communities are important characteristics of LSOG, land will be additionally

stratified by potential vegetation series (Vegetation Strike Team [1995] standard) at

the province scale. If series maps are not available for California, Society of American

Foresters (SAF) cover type will be used. This stratification is called plant community

in this document.

Stand-scale definitions—The FEMAT report also discusses the structure and

composition of LSOG forests at the stand scale. The four major structural elements

are live old-growth trees, standing dead trees, fallen trees or logs on land, and logs

in streams (FEMAT 1993: 28, 29; Franklin et al. 1981). Additional important elements

typically include multiple canopy layers, smaller understory trees, canopy gaps, and

patchy understory. Structural characteristics of old forests differ with vegetation type,

disturbance regime, and developmental stage. Structural and compositional characteristics differ among physiographic provinces, so standards and guidelines intended to

promote the desired conditions also will differ. A partial set of stand-scale definitions

of old-growth conditions is available for USDA Forest Service lands in Washington,

Oregon, and California (USDA Forest Service 1992, 1993b). These definitions continue to evolve and currently cover only part of the Forest Plan area. Additional research is needed on definitions or descriptions of LSOG across the region (see “Research Needed” below). Note that “old growth” is a subset of LSOG as defined by

FEMATand the FSEIS. The large multistoried forest class closely represents old

growth (Franklin et al. 1981), and the medium large single-storied and medium large

multistoried classes represent mature forest conditions. As defined by FEMAT and the

FSEIS, LSOG includes mature and old forests. Because site-scale permanent plots

measure stand-scale features upon which old-growth definitions are based, they may

provide a more reliable estimate of the total area and kinds of LSOG than will remote

sensing analysis. Field plots unfortunately cannot adequately address large landscape

spatial distribution issues.

6
Monitoring

Questions

A set of monitoring questions focuses on important LSOG characteristics that will

result from implementing the Forest Plan. Each includes a subset of questions related

to measurable characteristics of LSOG forests described in the ROD (USDA and

USDI 1994b), FSEIS (USDA and USDI 1994a), and FEMAT (1993).

What are the distribution and amount of forest classes, including LSOG, at the

large landscape scale?

What are the distribution (map) and amount (acreage) of forest classes from remote

sensing information for the region? For the region by land management allocation?

Within each province by plant community (potential vegetation series [Pacific Northwest Region] or SAF cover type [Pacific Southwest Region])? Within each province

by plant community and land management allocation?

What is the amount (acreage) of forest classes from stand-scale (permanent grid plot)

samples for Federal lands in the region? For the region by land management allocation? Within each province by plant community (potential vegetation series [Pacific

Northwest Region] or SAF cover type [Pacific Southwest Region])? Within each

province by plant community and land management allocation?

What are the structure and composition characteristics (for example, tree diameter

distribution, snags, and down woody debris) of forest classes from stand-scale

samples for each stratum?

How much have the structure and composition characteristics changed since the last

measurement cycle? How much are they likely to change in the foreseeable future?

What is the error associated with these estimates?

Are trends within expectations derived from the FSEIS and FEMAT reports?

What are the stand-size distribution, stand interior area distribution, and

interstand distance distribution of LSOG at the large landscape scale?

For the region as a whole? For the region by land management allocation? For each

province? For each province by land management allocation? For each province by

plant community? For each province by plant community and land management

allocation?

What is the error associated with these estimates?

How have these attributes changed since the last measurement cycle? How much

are they likely to change in the foreseeable future?

Is the trend within expectations derived from the FSEIS and FEMAT?

What changes are produced by stressors in distribution and amount of forest

classes, starting with the year of the FEMAT analysis (1993), from stand-scale

data?

What are the gains from growth and succession?

What are the losses from logging, fire, wind, insects, and disease?

What are the ramifications of these changes to future trends, especially in LSOG

classes?

7
For the region as a whole? For the region by land management allocation? For each

province? For each province by land management allocation? For each province by

plant community? For each province by plant community and land management

allocation?

What are the effects of silvicultural treatment and salvage on the development

of LSOG structure and composition at the stand scale?

Because these attributes must be sampled at the stand scale by using a different set

of plots (treated versus control or case study experimental plots), this regional and

provincial-scale effectiveness monitoring plan does not provide a sampling or analysis

scheme.

Is the relation of forest structure and composition (in stands at various ages

and at multiple scales) to ecological processes and biological diversity

assumed by FEMAT, the FSEIS, and the conceptual model (fig. 2) accurate?

This is a validation monitoring question not addressed in this effectiveness monitoring

plan. It is included as a reminder for those developing a validation monitoring plan. It

also helps distinguish effectiveness monitoring from validation monitoring while

establishing a link between them.

Monitoring Approach Change agents and dynamic factors that may alter the amount, distribution, structure,

and composition of LSOG forests at regional and provincial scales include a variety of

disturbance elements. The following list is not complete, but includes major change

agents. Other elements also stressing LSOG structure and composition (for example,

air pollution, and global climate change) will not be considered because their effects

are difficult to assess accurately.

Stressors

Figure 2—Conceptual model of late-successional and old-growth forest effectiveness monitoring plan.

8
Human development and use—Human use changes LSOG through a wide variety

of mechanisms (for example, timber harvest, recreation, mining). Such changes may

or may not be discernible in Landsat thematic mapper (TM) and other remotely

sensed information, but they will be detectable at the stand and site scales.

Fire—Wildfire and prescribed fire can cause significant changes in the amount, distribution, structure, and composition of LSOG. Fire can play many roles, ranging from

stand replacement to slight alteration of understory structure and composition. Fire

causing complete or partial stand replacement has effects that should be visible at

large landscape scales. Underburns may not produce visible change at large landscape scales but may generate significant changes at the stand scale through

changes in understory vegetation and the generation and composition of dead

standing trees and logs.

Insects and pathogens—Insect outbreaks and pathogen epidemics can play a role

similar to fire, though effects generally are more selective and of finer scale, especially on the west side of the Cascade Range. Changes range from stand replacement (rarely) to mortality of selected species and size classes within species. Effects

may be hard to detect from Landsat TM data but may be visible from other remote

sensing platforms. Insects and pathogens often generate considerable dead and

down woody structure in stands and may actually move stands farther along the

successional continuum.

Wind—Wind can have effects similar to those of fire, including partial to complete

mortality of the overstory canopy. Severe blowdown often results in releases of understory vegetation from shade and competition, resulting in changed canopy structure

and composition over time. Wind effects often are difficult to detect in Landsat TM

data because they tend to be patchy and partial.

Growth, regeneration, and succession—Although these processes are not generally considered stressors, they do produce changes in the amount, distribution,

composition, and structure of LSOG forests. They tend generally to reduce fragmentation, interstand distance, and edge while increasing stand size, stand structure, and

connection. At one extreme, in late-seral forests, succession can cause a decline in

composition and structural diversity as stands lose long-lived, early seral species and

move to climax condition. This circumstance is rare, however, given the time required

and rates of disturbance. Analysis of early seral stages and projections of growth will

be necessary to project trends.

Exotic species—Some exotic species are highly competitive or pathogenic and

could have long-term effects on LSOG.

Required Information The LSOG effectiveness monitoring plan uses data and information from several

sources, including permanent grid plot data and remotely sensed information. Remotely sensed information on potential vegetation and existing vegetation structure

and composition, summarized in remotely sensed forest classes, above, is key. If

permanent grid plots or map information on existing and potential vegetation are not

developed or maintained as planned, this LSOG effectiveness monitoring plan cannot

be implemented as designed.

9
Information on stressors (see below) and models that project both stand and large

landscape development of forest classes also are required.

Plan Components The LSOG effectiveness monitoring plan includes analysis of large landscape-scale

and stand-scale indicators of amounts and distributions of LSOG and stressors that

might alter amounts, distributions, and trends of LSOG; it also provides links to analysis of silviculture and salvage effects at stand scales (fig. 3).

Conceptual Model The conceptual model (fig. 2) for LSOG effectiveness monitoring relies on the structure and composition of stands, from large landscape to stand scales, as intrinsic parts

of and reasonable indicators of process, function, and biological diversity. This model

presumes that structure and composition both influence and are influenced by process

and function (Edmonds et al. 1989, Franklin et al. 1989, Peterken 1996, Spies and

Franklin 1988). For example, disturbance processes change stand- and landscapescale structure and composition by killing trees, changing amounts of snags and down

woody debris, and changing stand size and shape (Forman and Godron 1986,

Morrison and Swanson 1990). Stand size and shape influence the spread of disturbances across the landscape because some kinds of stands transmit the disturbance

more readily than others (Turner et al. 1989, White 1979). Stand structure and composition also influence disturbance; some stand conditions are usually more favorable to

the generation and spread of particular disturbances than others (Turner et al. 1989).

Certainly, changes in composition, structure, and landscape pattern can influence

ecological process and function (Allen and Hoekstra 1992, Spies and Franklin 1996,

Turner 1989), and changes in structure and composition may indicate changes in

underlying process and function. This conceptual model is implicitly stated in FEMAT

(1993: 36): “In many respects, the test of providing a functional, interacting latesuccessional and old-growth forest ecosystem subsumes the test of viability for the

system’s component species and groups of species.” From this perspective, the structure and composition of vegetation at large landscape and stand scales is a reasonable indicator of the processes, functions, and diversity of ecological systems (fig. 2).

This conceptual framework may apply most clearly to plants. Measures of viability for

some individual species of plants and animals, especially the northern spotted owl

and marbled murrelet, will need to be monitored (see Lint et al., in press; Madsen et

al., in press).

A similar relation exists between structure and composition, and biological diversity.

Species diversity is the biological foundation on which structure and composition are

built. In turn, structure and composition determine habitat conditions that may support

species diversity. This feedback loop is highly complex. Certainly, changes in structure

and composition of forest stands indicate direct changes in biological diversity and

may indicate habitat alteration and change in the composition of associated species.

Relations of many wildlife species to habitat in Douglas-fir (Pseudotsuga menziesii

[Mirbel] Franco) dominated forests are summarized by Ruggiero et al. (1991).

10
The relation between biological diversity and structure has been a major focus of

ecology for the last 30 years. In general, two major classes of functional factors determine the biological diversity in a given large region. The first is the amount of energy

that the ecosystem can capture, which in turn is governed by latitudinal variation in

solar radiation and a host of climatic and soil factors (often called site quality by

foresters) that affect carbon fixation and primary productivity. The second major

class is structure: structure is defined as the physical arrangement of objects in threedimensional space and their complexity. Much of the recent work on the relation of

structure and biological diversity is summarized by Bell et al. (1991), who consider

structure as having three components: scale, heterogeneity, and complexity. In a

general way, all these components are related to biological diversity, but the details

in any particular ecosystem can be very different.

Structure depends on spatial scale and can be described at any scale in the biological

hierarchy, but it may have different associations and correlations with biological diversity at each scale; for example, at the scale of biomes, mammal species richness in

the United States is strongly correlated with topographic relief: more species live in

mountainous areas. At the opposite end of the spatial scale, the number of lizard species on a single tree is well correlated with canopy complexity: trees with more complex canopies have more species. At the scales of stands and large landscapes,

which are the foci of LSOG effectiveness monitoring, general relations exist between

vegetation structural complexity and bird species diversity. Perhaps the best known

of these relations is between foliage height diversity and bird species diversity first described by MacArthur and MacArthur (1961). In these studies, a correlation was found

between the number and evenness of layers of vegetation in a canopy and the number of bird species and the evenness of distribution of individuals among those species. A general summary of most work is that structural complexity and heterogeneity

are positively correlated with diversity for many groups of organisms.

Figure 3—Components of the late-successional and old-growth forest effectiveness monitoring plan.

11
Studies of the Pacific Northwest’s Douglas-fir and western hemlock (Tsuga

heterophylla [Raf.] Sarg.) forests show that the general pattern holds at both the

landscape and stand scales. Hansen et al. (1995) show that, at the landscape scale,

greatest diversity results from a mix of successional stages each with a different canopy structure. Some bird species, for example, prefer open clearcuts while others prefer LSOG forests. Thus, a diversity of stand types including an important component

of LSOG promotes diversity at the landscape scale. These different stand types and

the changes in their distribution can be estimated relatively accurately from remotely

sensed imagery. At the stand scale, greater canopy structural complexity is correlated

with a great abundance in some bird species. Further, the response of the abundance

of some bird species is nonlinear, with a distinct threshold (Hansen et al. 1995); that

is, as structural complexity increases, a point is reached where abundance for LSOG

associated species increases dramatically. These results are species specific in the

shape of the relations, in thresholds that may exist, and in possible causal factors. Results from other groups of organisms are lacking, but all indications are that the Pacific

Northwest forests roughly follow this general pattern. Stand structure thus should be a

useful indicator of biological diversity. Further, Cohen and Spies (1992) found that

stand structural differences of a kind related to biological diversity can be estimated

with remotely sensed data and in particular with Landsat TM data, which means that

the detection of change of an important biological diversity indicator is logistically

feasible.

Indicators Indicators developed in this effectiveness monitoring plan hinge on the relation of

structure and composition to ecological system process and function and to biological diversity (fig. 4). Four kinds of indicators are considered: (1) large landscapescale indicators that can be addressed through analysis of remotely sensed imagery,

(2) stand-scale indicators that can be addressed through analysis of field plots, (3)

change agent indicators, and (4) stand-scale silviculture and salvage effect indicators.

This list is a starting point, based on existing knowledge. Future indicator development

will follow the process described in the overall effectiveness monitoring plan (Mulder

et al., in press).

Large Landscape-Scale

Indicators

Several indicators for large landscape-scale LSOG distribution can be estimated and

summarized.

Amount—The acreage of land meeting forest class definitions will be estimated by

physiographic province by plant community and land management allocation.

Distribution—The distribution of land by forest class will be depicted in a map, which

for some analyses will be a polygon map with attributes and for others will be a pixel

map.

Stand size—The areal extent of stands of vegetation that meet tree size and canopy layering criteria for LSOG forest classes will be analyzed from remotely sensed

imagery. The unit of measure is acres. Stands will be aggregates of pixels from remotely sensed images. A frequency distribution or cumulative distribution function will

display the abundance of stands by size; be stratified by region, province, and plant

community; and be stratified by physiographic province, plant community, and land

management allocation.

12
Stand interior area—Indices of stand shape (edge to area, fractal dimension, and so

forth) often are hard to relate to specific habitat issues. Interior core area of stands,

after sharp edges are buffered, will be used as an index of stand shape. Interior core

area will be analyzed from remotely sensed data by using a buffer of 330 feet from

LSOG edges with nonforested and small, single-storied forest classes. Other edge

conditions may be analyzed, if needed. Chen et al. (1992) found edge effects extending more than 1300 feet in some cases. Most effects seem to occur within 330 feet,

however. Edge effects will be projected into LSOG stands from potentially forested

and seedling and sapling forest classes. Stands of the small single-storied forest class

are likely tall enough to substantially reduce or eliminate microclimatic edges that

might extend into LSOG stands. It may be necessary to analyze stand interior area

given other edge effect distances. Pixel data will be available for analysis, because

some species find appropriate habitat in much smaller stands. A frequency distribution

or cumulative distribution function of stand interior area will be produced for the region; for each province by plant community; and by province, plant community, and

land management allocation.

Interstand distance—The distance between LSOG stands for the region and for

each province by plant community will be displayed in a frequency distribution or

cumulative distribution function. A frequency distribution or cumulative distribution

function of interstand distances will be produced for the region; for each province by

plant community; and by province, plant community, and land management allocation.

Stand-Scale Indicators Stand-scale structural and compositional attributes will be summarized from permanent grid plots and compared with LSOG definitions. Comparison of grid plot data

with remotely sensed information will allow development of statistical relations between remotely sensed and stand-scale definitions of LSOG (Czaplewski and Catts

1992). The following attributes will be analyzed.

Figure 4—Late-successional and old-growth forest effectiveness monitoring plan indicators.

13
Amount—This is the forested area that meets forest class definitions and groundbased definitions of LSOG (for example, USDA Forest Service 1992, 1993b) by

stratum. In addition, the amount of forested area that meets other indicators of oldgrowth characteristics will be estimated as new descriptions of old growth evolve.

Stand-scale structure and composition—The stratumwide averages and variation

for the stand-scale structural and compositional characteristics given below will be

calculated for each LSOG definition used at the stand scale by physiographic province and plant community.

The following attributes should be summarized for each grid plot to provide data for

analysis of stand-scale LSOG amount and structure.

• Tree diameter class distribution by species, per acre. The diameter class

distribution of all trees, and of selected species, separately, in all the permanent grid plots within each province, plant community, LSOG class, and land

management allocation stratum.

• Canopy structure and height class distribution by species, per acre. The

height class distribution of all trees, and of selected species, separately, in all

the permanent grid plots in each province, plant community, LSOG class, and

land management allocation stratum. Tree height distributions can be used to

separate single-storied and multistoried stands.

• Snag (standing dead tree) height and diameter distribution by species,

per acre. The height class distribution by decay class and diameter class distribution will be determined for snags in all the permanent grid plots within each

province, plant community, LSOG class, and land management allocation

stratum.

• Down woody debris (fallen trees), per acre. Tons, linear feet, and pieces per

acre of down wood by diameter class and decay class in all the permanent grid

plots in each province, plant community, LSOG class, and land management

allocation stratum.

Other attributes that may be useful for characterizing LSOG will likely emerge from

research on stand-scale characteristics, ecological process, and function. Stand age,

where it can be determined, may be important for some species (Halpern and Spies

1995). Ongoing research indicates that the spatial arrangement of structure at the

stand scale may be important for old-growth function.1 The existing permanent grid

plot design does not track spatial distribution of trees, snags, or down wood. There

may be a need to add this to a subset of permanent grid plots.

Change-Agent Indicators Change agents that alter remotely sensed forest classes will be tracked from both remotely sensed information and permanent grid plot data. Remote sensing will allow

detection of changes in amount and distribution of forest classes and of changes in

stand size, stand interior area, and interstand distance for LSOG classes. Remotely

sensed information will not generally allow determination of the change agent, except

1 Personal communication. 1997. Jerry Franklin, professor,

University of Washington, College of Forest Resources,

Seattle, WA 98195.

14
possibly for timber harvest and large, intense fires. Most information about change

agents will come from examining permanent grid plots. Data recorded on each grid

plot include causes of tree damage (such as fire, logging, insects, and disease).

These data will be summarized by stratum to provide a summary of the sources of

change from mortality or loss.

Increases in area of remotely sensed forest classes are generally caused by growth

and succession. Overall increases in amounts, distributions, and large landscape patterns will be depicted and analyzed from remotely sensed information. Change in

LSOG amounts and spatial distribution as detected in remotely sensed images also

can come from changes in methods used to process remotely sensed data. It will be

important to quantify changes in spatial attributes due to methods alone. However, the

inherently variable nature of interpreting remotely sensed information produces apparent change that must be examined by a professional panel or workshop that uses the

analyses and reports developed in the monitoring process. In this sense, remotely

sensed information about LSOG attributes is less statistically reliable than information

from permanent grid plot remeasurement.

Large landscape change indicators—

• Change in amount and distribution by forest class and stratum

• Change in stand size distribution, stand interior area, and interstand distance

by LSOG class and stratum

• Change in amount, distribution, stand size, stand interior area, and interstand

distance due solely to change in remote sensing interpretation and analysis

methods

Stand change indicators—

• Amount of forested area in each stratum that has changed forest class as a

result of human use (for example, logging), fire, wind, insects, disease, including transitions among forest classes by change agent and stratum

• Amount of forested area that has changed forest class as a result of growth or

succession including transitions by forest class and stratum

Predictive Models Forest growth and development can be projected in the grid plot data by using stand

growth simulation models. Models can project growth trends for each forest class by

stratum. Current growth models unfortunately are oriented toward tree growth and

volume (and not well calibrated for stands beyond age 100) or toward detailed stand

succession models that may not work well for large landscapes. Models will have to

be calibrated for each stratum (see “Research Needed” below).

Stand-Scale Silvicultureand Salvage-Effect

Indicators

The ROD (USDA and USDI 1994b: app. C, p. 12) allows silvicultural (such as thinning, under planting, prescribed fire, and killing trees to produce snags or down

woody debris) and salvage activities in late-successional reserves, as long as treatments benefit, or at least do not retard, development of late-successional forest conditions. For the most part, these activities are confined to younger stands within latesuccessional reserves. The set of attributes listed above (tree diameter class distribution, canopy structure and tree height, snag height and diameter, down woody debris

15
amounts) could be analyzed to track changes in structure and composition in stands

manipulated by silvicultural or salvage activities. Tracking these elements at the stand

scale over time will test the effectiveness of silvicultural and salvage activities in maintaining or enhancing LSOG stand-scale attributes. Because these attributes must be

sampled at the stand scale by using a different set of plots (treated versus control experimental plots or case studies), this regional- and provincial-scale effectiveness monitoring plan does not provide a sampling or analysis scheme. Each province should

develop a sampling plan that uses the stratifications developed in this plan to allow

aggregation of results. Minimum attributes to be sampled or studied should include

those listed above, though others may be added as necessary. Sample areas might

be drawn at random from sites chosen to monitor Forest Plan implementation. This is

an adaptive management process that should provide feedback from management experiments to future management activities.

Data Acquisition

Methods

Amount, distribution, stand size, stand interior area, and interstand distance of forest

classes will be analyzed from maps and data derived from remotely sensed images.

Remote sensing analyses and processes exist for producing map data in polygon and

raster form and for preparing accuracy assessments. Mapping standards identified by

the Vegetation Strike Team (1995) are more detailed than needed for this analysis and

can be simplified through aggregation into the required stratification, including physiographic provinces, forest classes, and plant communities. Forest classes will include

at least those called for in FEMAT (1993: 49-50) and the ROD (USDA and USDI

1994b) for characterizing LSOG forests. Analysis of earlier seral stages will be necessary to estimate future trends, hence the addition of earlier seral stage land classes. A

forest class with dominant trees more than 30 inches in diameter was added because

many LSOG-dependent species find optimum habitat in stands with large trees.

The minimum polygon size required by the Vegetation Strike Team (1995) standards

(2 acres) is smaller than feasible for large landscape-scale LSOG analysis. Ten acres

seems a reasonable minimum polygon size for stand analysis, as it allows for some

interior core area with an edge effect of 330 feet. Polygons will be lumped to a minimum of 10 acres. It may be necessary to analyze smaller stand sizes.

Stand-Scale Indicators The diameter of trees below the emergent canopy, tree heights, snags, and down

wood cannot be reliably estimated from remotely sensed data, but they have been

sampled on a grid of permanent plots. Measurement errors on permanent grid plots

are known from a remeasurement of randomly selected plots (Max et al. 1996). Standscale attributes will be characterized from grid plots falling in each stratum. The

3.4-mile grid is the common, minimum grid plot density across Federal lands in the

Forest Plan region. Some areas may have grid plots at higher density (1.7 miles and

0.85 mile between plot centers). These plot data will be included where they are

available.

Change-Agent Indicators Maps and databases derived from remotely sensed information will be analyzed for

changes in amounts, distributions, and large landscape patterns of forest classes.

These data are the same as needed for large landscape-scale indicators. Change due

to different remotely sensed data analysis methods in subsequent time steps will be

quantified.

Grid plot information will be analyzed for changes in forest class by stratum. Stand

development or succession models (for example, Forest Vegetation Simulator) will

be used to project growth and development. Data needed are the same as for standscale indicators.

Large Landscape-Scale

Indicators

16
Silviculture- and

Salvage-Effects

Indicators

This regional and provincial monitoring plan does not develop a sampling scheme for

stand-scale silviculture or salvage effects. Large local variation in silviculture and salvage treatment kind and intensity make sampling stratification at the regional and provincial scales difficult. Sufficient numbers of permanent grid plots are unlikely to fall in

areas treated by silvicultural manipulation or salvage logging to characterize effects of

silviculture and salvage activities on LSOG attributes. Monitoring the effects of silviculture and salvage treatments on the development of LSOG forests should begin as

soon as possible, however. The following discussion provides links (common attributes, sampling schemes, and stratifications) that can help tie results of silviculture and

salvage monitoring to regional and provincial effectiveness monitoring.

Minimum attributes to be measured include those necessary to use existing and

emerging old-growth definitions (tree diameter class distributions, canopy structure

and tree height class distributions, snag height and diameter class distributions, and

down woody debris amounts). Over several decades, remotely sensed images and

grid plot data may begin to show the effects of these treatments. In the nearer term,

stand growth models can be used to predict trends in stand structure and composition

given measured plot data and silvicultural treatment. Stand growth models could be

used to predict development patterns on different sites, which could be used to guide

the location of field sample plots to cover the variability displayed in models. The field

units (National Forests, Bureau of Land Management [BLM] Districts) will have to develop a statistically reliable process for selecting manipulated and control stands for

sampling, by using the provincial, plant community, and forest class stratification provided in this effectiveness monitoring plan as a starting point. Finer stratifications may

be necessary for field unit purposes, but results should be aggregated to the strata

proposed in this plan. Administrative studies may be required.

Several possible sampling schemes exist for documenting the effects of silvicultural

and salvage treatments at the stand scale. Two general approaches could be taken.

A statistical design could be developed that would provide reliable, quantitative information. Alternatively, qualitative information could be developed. The following sample

designs illustrate possibilities within a range of statistical robustness and attendant

cost. These designs are merely examples to illustrate possible ranges of effort, cost,

and information reliability. They are not suitable for implementation without additional

study and refinement. The Regional Inventory Advisory group, which has assisted in

designing the permanent grid plot system, could help establish a sampling design,

once decisions have been made about the degree of statistical reliability needed and

the items to be sampled. It might be possible to select control and manipulated plots

from those selected for Forest Plan implementation monitoring.

• Establish a randomly selected set of permanent plots and controls in

stands manipulated by silvicultural and salvage treatments that are

stratified by province, plant community, and treatment type. Plots should

be selected at random before treatments are applied. Treatment should be

applied to one of either the control or treated pair at random. The sample design for this alternative could become huge. The 12 physiographic provinces

each have 4 to 10 or more plant community strata and many possible kinds of

silvicultural and salvage treatment. Each sample unit would consist of a treated

plot and an untreated control. Here is a simple scenario: in each of the 12

provinces, five plant communities would each receive four management treatments (two rates of removal from silviculture and salvage). Each combination

17
would be sampled 10 times, yielding a total of 2,400 sample plots. The addition

of 10 untreated controls per stratum would add another 600 plots, bringing the

total number of sampled plots to 3,000. The number of plots could easily be

much higher if stand age or structural condition before treatment were an additional stratification criterion. This process would allow statistical analysis of

whether treated stands differ from control stands in meeting old-growth definitions and providing LSOG structures by stratum.

• Establish fewer plots in a stratified random sample design of less complexity. Strata might include physiographic province and general treatment

type (precommercial thinning, commercial thinning, and others as appropriate).

A set of treatment and control plots could be established for a reduced stratified

random sample design, which might distribute treated and untreated pairs of

plots by physiographic province. This design would be considerably less expensive and time consuming to install than the one above. For example, 10 pairs

of treated and untreated plots might be selected at random from areas used for

Forest Plan implementation monitoring across the 12 physiographic provinces,

producing a sample of 240 plots. This design would not allow separation of

effects by province, plant community, or kind of treatment, but would allow statistical analysis of whether treated stands differ from control stands in meeting

old-growth definitions and providing LSOG structures.

• Establish a set of case studies to examine the effects of various silvicultural or salvage treatment intensities. Ongoing studies of stand growth and

development should be examined for opportunities to track the development of

LSOG after treatment. If appropriate sites are available, a time series might be

established that allows retrospective analysis of treatments. This would shorten

the lag time in producing results. No statistical analyses would be possible. This

design would not allow separation of effects by province, plant community, or

kind of treatment. No quantitative extrapolation of results would be possible.

Qualitative inferences could be provided.

Detecting Change

and Trend

Several ways of organizing change and trend detection in LSOG large landscapescale attributes are possible. Generally speaking, the more confidence needed in the

reality of apparent change, the more costly the process. Because the state of remote

sensing science continues to evolve, analysis at each subsequent time step likely will

involve different techniques and result in apparent change in LSOG from changes in

methods alone. To account for changing methods, both old (previous time step)

methods and new methods will be applied at each time step, thereby producing an

estimate of change due to methods alone.

Stand-Scale Indicators The permanent grid plot system is designed for systematic remeasurement. This

process will provide measurements with known measurement error for LSOG elements

on the plots. Change detection and rates of change can be quantified with known

reliability for the stand-scale indicators.

Change-Agent Indicators Maps and data derived from remotely sensed information will be analyzed for changes

in amounts, distributions, and large landscape patterns for forest classes. These data

are the same as needed for large landscape-scale indicators.

Large Landscape-Scale

Indicators

18
Changes in remotely sensed LSOG attributes may be due to changed remote sensing

interpretation or analysis methods. If remote sensing interpretation or analysis methods have changed from the previous time step, two analyses will be performed. The

new remotely sensed data will be analyzed by using procedures from the previous

time step and the new procedures. Differences in spatial attributes due to new methods will be quantified for each spatial element (amount, stand size, stand interior

area, and interstand distance) for each stratum.

Grid plot information will be analyzed for changes in forest class by stratum. Stand

development or succession models (such as the Forest Vegetation Simulator) will be

used to project growth and development. Grid plot data needed are the same as for

stand-scale indicators.

Silviculture- and

Salvage-Effects

Indicators

Plots established to track the effects of silvicultural and salvage treatment should be

permanently marked and remeasured periodically to allow updates in conditions and

trends. Standard methods, used on permanent grid plots or in stand exams, should

be used. Stand development or succession models will be used to project growth and

development.

Expected Values

and Trends

FEMAT (1993: 49-53) and the FSEIS (USDA and USDI 1994a: 36-43) provide a

beginning basis for thresholds. They discuss abundance and ecological diversity,

process and function, and connectivity outcomes for LSOG. Thresholds provided in

these documents are general, regionwide, and apply only to Federal lands. Outcomes

in FEMAT and the FSEIS link to the likelihood of maintaining both the viability of

LSOG-related species (FEMAT 1993: 28) and the likelihood of maintaining a functional, interacting LSOG forest ecosystem on Federal lands (FEMAT 1993: 25).

The following discussion translates outcomes supplied in FEMAT and the FSEIS into

thresholds against which change can be evaluated. The outcomes were used to help

rank alternatives considered in the FSEIS and ROD (USDA and USDI 1994b). Alternative 9, which was chosen with slight modification in the ROD, was among the more

highly ranked of the alternatives in terms of the probability that its implementation

would produce a functional, interacting LSOG forest ecosystem on Federal lands.

Alternative 9 has a 75-percent, or better, chance of providing abundance and diversity, process and function, and connectivity within the long-term average conditions in

the moist physiographic provinces over the next 100 years (USDA and USDI 1994a:

44; table 2). All the alternatives were more likely to produce functional, interacting

LSOG forest ecosystems in the moist physiographic provinces (Washington Olympic

Peninsula, Washington Western Lowlands, Washington Western Cascades, Oregon

Coast Range, Oregon Willamette Valley) than in the intermediate and dry physiographic provinces (Oregon Western Cascades, California Coast Range, Washington

Eastern Cascades, Oregon Eastern Cascades, Oregon Klamath, California Klamath,

California Cascades).

The FEMAT report (1993) and the FSEIS (USDA and USDI 1994a: 43) do not project reaching these outcomes for a considerable time, because it takes decades or

centuries for young stands to develop into LSOG. Changes in the first several decades should be projected for 100 years or more to evaluate likely outcomes. FEMAT

and the FSEIS provide some initial estimates of long-term trends. These initial estimates should be improved with a more comprehensive large landscape-scale stand19
development modeling effort (see “Research Needed” below). Tracking sources of

projected change (for example logging, fire, insect epidemics, and disease) will be

important. Although the outcome elements provided in FEMAT and the FSEIS represent the state of science at the time, several are qualitative or difficult to measure.

The outcomes from FEMAT and the FSEIS may not reflect current thinking, but they

do provide a starting point for analysis.

Long-Term Averages The FEMAT report (1993) and the FSEIS (USDA and USDI 1994a) call for developing

long-term average reference conditions, ranges of LSOG by province and plant community (potential vegetation series).

Long term is defined as a period of at least 200 to 1,000 years, or the time

over which the full potential range of late-successional and old-growth communities can develop following severe disturbance [USDA and USDI

1994a: 36].

The following discussion of presumed long-term average conditions is taken from the

FSEIS [USDA and USDI 1994a: 37].

The long-term average regional abundance of late-successional and oldgrowth communities can only be approximated from a few local studies of

fire history. Assuming that the average regional natural fire rotation was

about 250 years for severe fires (those removing 70 percent of more of the

basal area), then 60 to 70 percent of the forested area of the region was

typically dominated by late-successional and old-growth forests, depending

on the age at which “mature” forest conditions develop (assume a range

Table 2—Likelihood of achieving a functional, interacting late-successional

and old-growth forest ecosystem on Federal lands for alternative land

management strategies considered in the Forest Plan

Moist provinces Dry provinces

Alternative Aa Pa Ca Average A P C Average

- - - - - - - - - - - - - - - - - - Percent - - - - - - - - - - - - - - - - - -

1 86b 52 92 77 66 34 76 59

3 92 71 90 85 75 53 78 69

4 93 62 90 82 75 46 76 65

5 80 59 80 73 69 47 66 60

7 66 50 68 62 64 41 51 52

8 69 59 74 68 64 38 53 51

9 76 75 80 77 69 53 66 63

a Attributes: A = abundance and ecological diversity; P = process and function; C = connectivity.

b Numbers of at least 80 percent represent the likelihood that the given alternative will meet minimum

requirements for these attributes.

20
of 80 to 100 years). Converting this to a single number, 65 percent, provides an estimate of the long-term average percentage of the regional

landscape covered by late successional forest. This average percentage

would certainly differ by physiographic province; moist, northerly provinces

would have higher averages than drier provinces with higher fire

frequencies.

The total percentage of late-successional and old-growth forest would

apply to a wide range of stand sizes, from less than 1 acre, to hundreds

of thousands of acres. Most of the total percentage (perhaps 80 percent

or more) would probably have occurred as relatively large (greater than

1,000 acres) areas of connected forest.

The average centurial-low coverage (average of the lows that occur in

100-year periods) by late-successional forest is defined as setting the

lower limit of the “typical” range. There is no data from which to estimate

the average low from the preceding millennium. Consequently, this value

was estimated based on the subjective opinions of the ecosystem experts.

The Forest Ecosystem Management Assessment Team hypothesized that

the average low amounts might be about 40 percent coverage by latesuccessional forests, with lower values expected for individual provinces.

Long-term averages have not been well documented for much of the Forest Plan region. In addition, it is important to recognize expected variation around the average,

because a single average condition may never have actually occurred. The following

set of thresholds addresses the intent of FEMAT (1993) until long-term averages by

province and plant community can be developed. The Forest Plan does not project

achieving threshold amounts for at least 100 years because current conditions are

substantially below these amounts and stand development takes considerable time.

The FSEIS (USDA and USDI 1994a: 43) projects that achieving about half the threshold acres will occur in the first 50 years (fig. 5). Analyses in FEMAT and the FSEIS

were preliminary and general. Additional research is needed to refine expected

trends. Simple succession models (seral-stage transition models) will be used to project growth from LSOG amounts measured at each monitoring point to estimated 50-

and 100-year conditions. Model projections at each monitoring time step should be

evaluated against the following thresholds.

Uncertainty The following thresholds should be used with caution because LSOG forest development takes many decades, long-term reference conditions by stratum are poorly

understood, climatic conditions for the long-term average conditions may have been

significantly different than they are now, and scientific understanding about forest

development and succession is not well developed for many strata. The thresholds

provide an indication of the anticipated direction of change in LSOG forests under the

Forest Plan. Findings from this effectiveness monitoring plan should be reviewed by

a panel of experts from management and science who have full appreciation for the

inherent variability in projected trends.

21
Abundance and

Ecological Diversity

Thresholds

These outcomes are from FEMAT (1993: 50 and 51) and the FSEIS (USDA and

USDI 1994a: 35-43), with interpretations that allow quantification where possible.

Outcome 1—

Late-successional and old-growth ecosystem abundance and ecological

diversity on Federal lands at least as high as the long-term average...prior

to logging and extensive fire suppression....Relatively large areas (50,000

to 100,000 acres) would still contain levels of abundance and distribution of

late-successional forests which are well below the regional average for long

periods. However, within each physiographic province, abundance would

be at least as high as province-level long-term averages, which might be

higher or lower than the regional long-term average [USDA and USDI

1994a: 36].

The long-term average proportion of LSOG for the entire Forest Plan area was

estimated at 65 percent in FEMAT (1993: 51). Because this criterion is the same as

LSOG cover in connectivity (below), the same number (60 percent) was used for

outcome for both abundance and connectivity (table 3). Most (more than 80 percent)

of the LSOG in the long-term average was assumed to have occurred in large blocks

(more than 1,000 acres).

Outcome 2—

Late-successional and old-growth ecosystem abundance and ecological diversity on Federal lands is less than the long-term conditions (prior to logging and extensive fire suppression) but within the typical range of conditions that occurred during previous centuries [USDA and USDI 1994a: 36].

Late-successional and old-growth is present in all provinces and at all elevations but

with larger gaps in distribution than in outcome 1. The average of the low end of the

range for LSOG amount in the long-term average was assumed to be 40 percent in

FEMAT (1993: 51). The range in amounts under outcome 2 is between 40 and 65

percent. Less than 80 percent of the LSOG would be in stands of more than 1,000

acres (table 3).

Figure 5—Expected trends in amount

of late-successional and old-growth

forest after implementing the Northwest Forest Plan for the next 150

years

22
Outcome 3—

Late-successional and old-growth abundance and ecological diversity on

Federal lands is considerably below the typical range of conditions that

have occurred during the previous centuries, but some provinces are within

the range of variability....The ecological diversity (age-class diversity) may

be limited to the younger stages of late-successional ecosystems. Latesuccessional and old-growth communities and ecosystems may be absent

from some physiographic provinces and/or occur as scattered remnant

patches within provinces [USDA and USDI 1994a: 36].

Amounts of LSOG would be less than the average century lows from the long term

(40 percent; FEMAT 1993: 51), but some LSOG would still exist (for example, more

than 1 percent of the Federal land area). Less than 80 percent of the LSOG would

be in stands of more than 1,000 acres (table 3). The LSOG may be absent from

some physiographic provinces or elevations within provinces and occur as scattered

remnant stands within provinces.

Outcome 4—

Late-successional and old-growth ecosystems are very low in abundance

and may be restricted to a few physiographic provinces or elevation bands

or localities within provinces. Late-successional and old-growth communities and ecosystems are absent from most physiographic provinces or

occur only as small remnant patches [USDA and USDI 1994a: 36].

Late-successional and old-growth ecosystems covers less than 1 percent of the

Federal lands. Less than 80 percent of the LSOG is in stands of more than 1,000

acres (table 3). The LSOG is absent from most provinces or occurs only as small

remnant forest stands.

Amounts of LSOG will be estimated from both remotely sensed and grid plot data.

Spatial patterns (stand size) will be analyzed from remotely sensed information. Currently existing LSOG definitions (for example, USDA Forest Service 1992, 1993b)

emphasize the old-growth component. Amounts of old-growth, by these definitions,

will be less than amounts of LSOG determined from remotely sensed information.

Table 3—Outcomes and thresholds for late-successional and old-growth

forests used in ranking alternative land management strategies considered

in the Forest Plan

Lands in stands of Provinces meeting

Land covered by more than 1,000 both amount and

Outcome LSOG acres stand size

- - - - - - - - - - - - - Percent - - - - - - - - - - - - - - -

1 60 to 100 80 to 100 100

2 40 to 60 5 to 80 100

3 5 to 40 1 to 5 50 to 100

4 less than 5 less than 1 less than 50

23
Existing stand-scale old-growth definitions also provide discontinuous answers to

whether stands are old growth, while real world structure and composition differ in

gradients (Franklin and Spies 1991). Revised indicators of LSOG characteristics at

stand scales are needed (see “Research Needed” below).

Process and Function

Thresholds

The FSEIS (USDA and USDI 1994a: 38) provides descriptions of process and

function outcomes that could result from implementing the Forest Plan.

Outcome 1—“The full range of natural disturbance and vegetative development

processes and ecological functions are present at all spatial scales from microsite to

large landscapes.”

Outcome 2—“Natural disturbance and vegetative development processes and

ecological functions occur across a moderately wide range of scales but are limited at

large landscape scales through fire suppression and limitation of areas where

late-successional ecosystems can develop.”

Outcome 3—“Natural disturbance and vegetative development processes are limited

in occurrence to stand and microsite scales. Many stands may be too small or not

well developed enough to sustain the full range of ecological processes and functions

associated with LSOG ecosystems.”

Outcome 4—“Natural disturbance and vegetative development processes associated

with LSOG ecosystems are extremely restricted or absent from most stands and large

landscapes. Most LSOG stands are too small or not well developed enough to sustain

the full range of processes and ecological functions associated with late-successional

and old-growth ecosystems.”

No quantitative criteria are provided in FEMAT (1993), the FSEIS (USDA and USDI

1994a), or the ROD (USDA and USDI 1994b) for process and function thresholds. In

the near term, process and function will be assumed to be provided to the extent that

ecological abundance and diversity outcomes are met. Thresholds for abundance and

ecological diversity will be used. In the first 20 years of implementing the Forest Plan,

a series of studies should be undertaken to document the long-term expected amounts

of forested lands affected by fire, wind, insects, and pathogens.

Connectivity Thresholds The following outcomes are from FEMAT (1993: 52) and the FSEIS (USDA and USDI

1994a: 40), with interpretations that allow quantification where possible. Strong relations exist between connectivity and abundance of LSOG, as these are defined in

FEMAT and the FSEIS.

Outcome 1—

Connectivity is very strong, characterized by relatively short distances (less

than 6 miles on average) between late-successional and old-growth areas.

Smaller patches of late-successional and old-growth forest frequently

occur....The proportion of the landscape covered by late-successional and

old-growth conditions of all stand sizes exceeds 60 percent, a threshold

when many measures of connectivity increase rapidly. At regional scales,

physiographic provinces are connected by the presence of landscapes

containing areas of late-successional and old-growth forests [USDA and

USDI 1994a: 40].

24
Mean distances of less than 6 miles between LSOG stands of 1,000 acres or larger

and LSOG cover greater than 60 percent indicate outcome 1 (table 4). Small stands

of LSOG (riparian buffers, green tree retention in harvest units, etc.) are common, as

indicated by cumulative frequency distributions of LSOG stand sizes. Large LSOG

stands connect between adjacent provinces.

Outcome 2—

Connectivity is strong, characterized by moderate distances (less than 12

miles on average) between large late-successional and old-growth areas.

Smaller patches of late-successional forest occur as described in outcome

1. At regional scales, physiographic provinces are connected by the presence of landscapes containing areas of late-successional and old-growth

forests. The total proportion of landscape in late-successional and oldgrowth conditions, including smaller patches, is at least 5[0] percent, so

that the late-successional condition is still the dominant cover type (USDA

and USDI 1994a: 40).

Mean distances of 6 to 12 miles between LSOG stands of 1,000 acres or larger and

LSOG cover greater than 50 percent indicate outcome 2 (table 4). Small stands of

LSOG (riparian buffers, green tree retention in harvest units, etc.) are common as

indicated by cumulative frequency distributions of LSOG stand sizes. Large LSOG

stands connect between adjacent provinces.

Outcome 3—

Connectivity is moderate, characterized by distance[s] of 12 to 24 miles

between large old-growth areas. There is limited occurrence of smaller

patches of late-successional forest in the matrix. The late-successional

forest is at least 25 percent of the landscape, and the matrix contains

some smaller areas for dispersal habitat [USDA and USDI 1994a: 40].

Mean distances of 12 to 24 miles between LSOG stands of 1,000 acres or larger and

LSOG cover greater than 25 percent indicate outcome 3 (table 4). Small stands of

LSOG occur in matrix lands.

Table 4—Connectivity thresholds for late-successional and old-growth forest used when ranking land

management alternatives considered in the Forest Plan

Mean distance Adjacent provinces

between stands of LSOG stands less connected with large

Outcome more than 1,000 acres LSOG cover than 1,000 acres LSOG stands

Miles Percent Percent

1 less than 6 60 to 100 common 100

2 6 to 12 50 to 60 common 100

3 12 to 24 25 to 50 present less than 100

4 more than 24 less than 25 absent to few less than 100

25
Outcome 4—

Connectivity is weak, characterized by wide distances (greater then

24 miles) between old-growth areas. There is a matrix in which latesuccessional and old-growth conditions occur as scattered remnants

or are completely absent [USDA and USDI 1994a: 40].

Mean distances of over 24 miles between LSOG stands of 1,000 acres or larger and

LSOG cover less than 25 percent indicate outcome 4 (table 4). The LSOG occurs as

small remnant stands or is absent in matrix lands.

Baselines Detection of trends depends on a baseline for comparison. Baselines will be

established for remotely sensed and stand-scale indicators.

A remotely sensed vegetation baseline for large landscape-scale indicators should be

established from vegetation maps used in the FEMAT analysis (1993), from remotely

sensed imagery from 1994-95 (the beginning of Forest Plan implementation), or from

1997-98 imagery used in the first monitoring report. Vegetation maps used for baselines could be one of the following:

• FEMAT vegetation maps. The vegetation maps used in the FEMAT analysis

(1993) are poorly suited for use as a monitoring baseline because they were

compiled from a variety of information sources, all using different mapping

methods. Future mapping efforts cannot replicate those methods. Changes

detected when comparing new maps to the FEMAT maps would contain large

and unknown variation due to differing methods alone. The maps exist, however, and could be used if their limitations are recognized.

• New baseline from 1994-95 imagery. Baseline vegetation maps could be developed by using 1994-95 remotely sensed imagery. This approach would provide vegetation maps from imagery dating from the beginning of Forest Plan

implementation. No attempt would be made to reconcile vegetation maps used

in the FEMAT analysis with the new baseline. Although several sources of vegetation maps from this imagery exist, complete coverage of the region does not

exist, and methods used in existing work differ.

• New baseline from 1997-98 imagery. Baseline maps could be developed from

1997-98 imagery, which will be used in the first monitoring report. In this case,

the 1997-98 imagery would be accepted as approximating the conditions at the

start of Forest Plan implementation even though some change would have occurred in the 3 to 4 years between the date of Forest Plan implementation and

the date of the remotely sensed imagery. No attempt would be made to reconcile vegetation maps used in the FEMAT analysis with the new baseline.

A stand-scale baseline will be established from the permanent grid plots established

across the region by the end of 1998. In addition, maps and descriptions of the strata

used in the first report (physiographic provinces, plant communities, land allocations)

will be archived for future use. These baseline information sets should be housed in a

permanent facility, accessible to anyone who needs to use them.

Current Conditions

Baseline

26
Long-Term Average

Baseline

FEMAT (1993: 51) and the FSEIS (USDA and USDI 1994a: 37) call for comparing

existing LSOG conditions (amounts, distribution, connection, process, function) to

long-term averages and long-term average lows. These are defined as the LSOG

conditions in the last several centuries to the last 1,000 years. The FEMAT report and

the FSEIS suggest developing this information through fire history studies, stratified

by province and plant community, but these studies do not exist for much of the region. An initial attempt, using expert opinion and existing information, was compiled

for National Forest lands in Oregon and Washington (USDA Forest Service 1993a).

This report could be used as a starting point, but it is not complete. The FEMAT report and the FSEIS provide estimates of long-term average conditions based on expert opinion, but they call for better estimates. This monitoring plan does not require

a retrospective analysis of conditions prior to FEMAT for the first report. Analysis of

the long-term averages could be used in subsequent reports as research is

completed.

Quality Assurance Accuracy assessments must be performed on any maps used in LSOG analysis to

understand the confidence interval associated with any results. Because the 3.4-mile

grid plots are independent of vegetation maps (assuming the plots are not used

for training sites in developing vegetation maps), they could be used to perform

an accuracy assessment of the vegetation map as aggregated for LSOG analysis

(Czaplewski and Catts 1992).

To establish a new baseline for LSOG forests, or to compare results with older baseline map information, the imagery used for developing the vegetation map should be

of the same date (usually within 2 to 3 months) across the province being analyzed.

Data Analysis and

Reporting

Analysis of the large landscape-scale attributes proposed in this effectiveness monitoring plan will be done every 5 years, starting in 1998. After the first measurement

cycle and report (1999), updates to forest class maps should be relatively inexpensive

and quick. Several other effectiveness monitoring efforts will use these data (for example, northern spotted owl, marbled murrelet, aquatic and riparian, and other monitoring efforts that use forest-vegetation data). This plan assumes remotely sensed

information on existing vegetation, sufficient to analyze forest classes defined above,

will be available in early 1999 for the entire Forest Plan region.

Grid plot data are collected through standardized efforts, on fixed remeasurement

cycles. Grid plots system have been installed for most National Forest lands in the

region. The Oregon State Office of the BLM has started installing 3.4-mile grid of

plots under the same methodology. It is possible that some other permanent plots on

other ownerships could be used (for example, forest inventory and analysis plots) if

methodologies are sufficiently similar. This LSOG effectiveness monitoring plan does

not propose collection of additional permanent plots. It does depend on the initial

installation and systematic remeasurement of the existing permanent grid plots.

Link to

Decisionmaking

A report will be generated every 5 years, more often if needed, detailing the status

of threshold attributes and their projected 50- and 100-year trends in comparison to

FSEIS (USDA and USDI 1994a) expectations (see Mulder et al. [in press] for details

of the reporting process for the effectiveness monitoring program). Attributes that depart significantly from projected trends (more than 10 percent) will be highlighted. Attributes that fall below projected trends (LSOG amounts more than 10 percent below

27
projected trends or for which the projected trends fall 10 percent below reference conditions) trigger a variety of actions, ranging from review of stand succession models

and mapping methods, to an examination of the Forest Plan and its implementation.

Because results may require interpretation, a panel of scientists, managers, and others

(as necessary) will review results and develop interpretations and recommendations

for regional executives. The agencies will decide how to proceed.

Organizational

Infrastructure

This monitoring plan proposes analyses of existing data from permanent grid plots and

the availability of existing and potential vegetation maps at the beginning of the LSOG

effectiveness monitoring plan analysis period. Plans for implementing those activities

have been developed elsewhere and are not reviewed here (for example, Max et al.

1996). Analysis in the LSOG effectiveness monitoring plan of those data will not be

extremely expensive or time consuming.

An interagency cooperative geographic information system (GIS) should be put in

place as a foundation for analysis. A GIS and database staff of two should be sufficient to do the LSOG effectiveness monitoring analysis. The first report will be the

most difficult and time consuming, because methods likely will require some adjustment and finalizing. A pilot LSOG effectiveness monitoring report should be done for

a portion of the Forest Plan region to determine methodologies, report formats, and

cost estimates.

Estimated Costs Several possible approaches to collecting the necessary information were considered

(appendix B). Alternative approaches for generating a monitoring report include two

possibilities (table 5).

• Analyze grid plot data only. Make no attempt to analyze large landscape

patterns or generate maps. This alternative supposes that either vegetation

maps are not available or that a decision is made not to fund additional analyses of those data to produce maps of forest classes. This alternative would

allow calculating acres of forest classes by stratum and establishing statistical

confidence the estimates. It would not allow analyzing large landscape-scale

indicators (LSOG distribution maps, stand size, stand interior area, and

interstand distance). Changes in forest classes, including LSOG, could be

tracked through remeasurement of the grid plots over time, only in terms of

acreage and stand-scale structural indicators.

• Analyze vegetation maps and grid plots. Permanent grid plot data would be

analyzed as proposed. A baseline forest class map would be compared with the

map for the first reporting period. This would provide estimates of change in

large landscape-scale LSOG indicators and stand-scale structural conditions. If

baseline vegetation map data from 1994-95 imagery are available, quantitative

information about amounts, stand-scale structures, and large landscape patterns could be produced. This is the recommended alternative (see appendix B

for comparison of alternatives for trend monitoring). Cost estimates for

producing the first monitoring report are based on this alternative (table 6).

Costs for remote sensing analysis of large landscape-scale features for the first monitoring report assume that vegetation maps suitable for delineating forest classes and

potential vegetation series are available. Costs for producing those maps are not

included. Note that the costs for LSOG monitoring are primarily associated with reporting, because the data are collected and managed through other agency programs,

such as the Forest Service’s current vegetation survey.

Alternatives

28
Pilot LSOG

Effectiveness

Monitoring Report

Although the methods used to generate the first monitoring report are not new, the

integration of them into one effort needs considerable work. In addition, development

of the actual format and character of the first report, which will set the tone for subsequent efforts, will require substantial effort. A pilot monitoring effort and report is

proposed to test and refine analysis methods, refine estimates of work and cost, and

provide executives with a product on which to base funding decisions.

The cost for a pilot LSOG monitoring effort and report would be about $40,000 in

fiscal year 1998. A task for producing an LSOG effectiveness monitoring report has

been added to an existing pilot effort (Plumley et al. 1996); the pilot report should be

available by fall 1998.

Table 5—Costs of alternatives for large landscape and permanent grid plot

data for late-successional and old-growth forest effectiveness monitoring

Alternative Funding and staff needs GIS database Total

Grid plots only $30,000 (1/2 person-year) $ 5,000 $ 35,000

Vegetation baseline

and grid plots $120,000 (2 person-years) $20,000 $140,000

Table 6—Proposed costs for LSOG effectiveness monitoring

Fiscal year

Monitoring activity 1998 1999 Total

1997 image and grid plot

analysis $140,000 $140,000

Pilot monitoring analysis

and report $40,000 $ 40,000

Trend projection by stratum

(for interpretive report) $30,000 $ 30,000 $ 60,000

Table 7—Proposed implementation schedule for LSOG

effectiveness monitoring

Fiscal year

Monitoring activity 1998 1999 2000

Build vegetation maps X X

Image and grid plot

analysis X X

Pilot monitoring analysis

and report X

Trend projection by stratum X X

Interpretive report X

29
Refined Trend Estimates The FEMAT report (1993) and the FSEIS (USDA and USDI 1994a) use broad, regionwide estimates of expected trends in LSOG (abundance, distribution, stand interior

area, interstand distance). Both recommend analyses to project trends for 100+ years,

stratified by province and plant community. Some initial work in estimating expected

trends will be necessary to evaluate change. Although this work is listed as a research need (see below), refined trend estimates will be necessary to evaluate results

beyond the very broad regional generalization of LSOG amounts provided in FEMAT

and the FSEIS. Developing refined trend estimates will take about 1 year and cost

about $60,000. After the first analysis, costs will decline substantially because subsequent analysis will require fine tuning rather than model development. This effort

should be cooperative, involving research and management.

Implementation

Schedule

An implementation schedule illustrates accomplishment of the proposed LSOG

effectiveness monitoring effort for the first 3 years of the monitoring program (table 7).

Because completion of vegetation mapping is crucial to the effectiveness monitoring

effort, a more detailed schedule of work for the first time period, including vegetation

mapping, shows the necessary flow of tasks:

Task Expected completion date

Complete pilot LSOG report End of fiscal year 1998

Completion of grid plot data processes through 1998 End of fiscal year 1998

Completion of expected trend analysis End of fiscal year 2000

Provide baseline and initial vegetation maps and data March 2000

Analysis of large-landscape and permanent grid plot data Fiscal year 2000

First report (1988-89 to 1998 period) December 2000

Administrative review of report Fiscal year 2001

Analyses and reports should be generated every 5 years (table 8). Although changes

in vegetation may not be striking over 5-year time intervals, an ongoing monitoring

effort with frequent updates to vegetation maps for planning purposes should be developed. This will allow maintenance of skills and organization to perform effectiveness monitoring.

Research Needed Several issues will require research investment in the near future. Priorities and

budgets will have to be established when work plans for these research items are

available. Among the most critical research issues are:

• The use of remotely sensed information to detect forest structure and

composition. In addition, analysis of the sources of error in remote sensing

classifications and change detection is needed.

• Expected rates of transition among forest classes over time (succession

models, disturbance models) using plant community and province strata.

Existing models are probably more accurate for transitions and growth in young

stands than in LSOG. These models should be calibrated by series to give

more accurate predictions. Remeasured permanent grid plot data can be used

to help calibrate models, which should become more accurate over time and

with repeated measurements.

30
• Refined estimates of LSOG trends (abundance, stand core area,

interstand distance) by province and plant community for the next

100+ years. This will require work on succession and disturbance models.

• Baseline, long-term conditions for LSOG by province and plant community. This might include a combination of disturbance history analysis,

disturbance modeling, and stand growth modeling. It will be important to

describe ranges of conditions for LSOG attributes, not just average conditions,

for the last several centuries.

• Refined definitions or indices of LSOG forests to help in assigning plots

and remotely sensed stands to a position along a continuum of LSOG

structure and composition conditions by stratum. This includes the need

to examine within-stand spatial distribution of structural components (Franklin

and Spies 1991).

• Better stand-scale projection models for LSOG stand structure and

composition attributes (live trees, snags, down wood, species) that

allow long-term projections (for example, 100+ years) by stratum.

• Basic research on the relations among forest structure, stand age,

disturbance history, biological diversity, and ecosystem processes and

functions. This is needed to support (or modify) and refine the conceptual

model.

Table 8—Proposed LSOG monitoring schedule

Fiscal year

Activity 1998 1999 2000 2001 2002 2003 2004 2005

Implement monitoring

plan X X X X X X X X

Pilot test of methods X X

Develop refined trend

analysis X X X

Generate analysis and

interpretive report X X X X

31
Acknowledgments Thoughtful peer reviews were provided by Ray Czaplewski, Ken Denton, Tom

Hoekstra, and Hart Welsh, Forest Service; Jerry Franklin, University of Washington;

John Gordon, Yale University; Bill McComb, University of Massachusetts; Tony Olson,

EPA; and Marty Stapanian, BLM. We also are grateful for the assistance of Barry

Mulder (U.S. Fish and Wildlife Service) in finishing this report, the fine comments from

Martha Brookes, and the editorial and publication assistance from Karen Esterholdt.

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Bell, S.S.; McCoy, E.D.; Mushinsky, H.R., eds. 1991. Habitat structure: the physical

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environments in old-growth Douglas-fir forests. Ecological Applications. 2: 387-396.

Cohen, W.B.; Spies, T.A. 1992. Estimating structural attributes of Douglas-fir/western

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Franklin, J.F.; Spies, T.A. 1991. Ecological definitions of old-growth Douglas-fir

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ecosystem health. Portland, OR: Pacific Northwest Region. 109 p.

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34
Appendix A:

Vegetation Strike

Team Standards

The Regional Interagency Executives accepted a set of standard elements for vegetation map information for interagency use in western Oregon, western Washington,

and northern California (Vegetation Strike Team 1995). These are for use in remotely

sensed information, in particular. The standard elements include:

• Total tree canopy cover is the vertical projection of tree canopy (or the

canopy visible from above) as seen from above to the ground. Ten-percent

classes will be used. It may not be possible to distinguish each class, and

classes may be grouped as appropriate to the resolution of the data.

• Forest canopy structure indicates the layering of the canopy, as seen from

above. Remote sensing may separate only single layered from multilayered

canopies. Field data may distinguish additional layers.

• Tree overstory size class provides information about the typical or average

diameter class of the canopy trees visible from above. This is generally

interpreted from tree crown diameter. Size classes (in inches) specified include

0-5, 5-10, 10-20, 20-30, 30-50, and greater than 50. Remote sensing data

resolution may require lumping classes.

• Species can be either cover types according to Society of American Foresters

or Society for Range Management lists or a list of canopy species, when seen

from above, in decreasing order of abundance. This really represents two

attributes: cover type and species list.

• Stand year of origin comes from agency field records and pertains to

even-aged stands.

• Land cover class provides a field for the land class according to Federal

Geographic Data Committee standards (FGDC 1997), when they are finalized

or accepted for agency use.

• Potential vegetation information is really a separate map product that

provides a stratification of environments and an indication of vegetation at the

endpoint of succession in each polygon. In the short term, potential vegetation

at the series level is feasible. Over the longer term, plant association groups

could be mapped.

35
Appendix B:

Alternative

Approaches

Considered

Several possible approaches to collecting information on the large landscape-scale

and stand-scale characteristics of LSOG forests surfaced during team discussions

(table 9). These possibilities were evaluated regarding how well they would provide

necessary LSOG information, and to what extent they would provide vegetation data

for habitat analysis for northern spotted owls (Lint et al., in press) and marbled

murrelet (Madsen et al., in press); the use of these data in monitoring for aquatic and

riparian and survey-and-manage species is being evaluated. Based on this analysis,

the team pursued a combination of grid plot data and new remote sensing image

analysis.

• Field query. Poll field units for acres of harvest or catastrophic loss (fire,

wind, etc.) that removes LSOG. Tally acres by physiographic province and

land allocation. Presume that the remaining LSOG continues to exist and that

younger stands in late-successional reserves continue to grow into LSOG.

This is the lowest cost method of monitoring effects of the Forest Plan on

LSOG, though field units would have to compile necessary information. No

reliability estimates and no spatial analysis would be possible. No map for

northern spotted owl and marbled murrelet habitat estimation would be

generated.

• Grid plot data only. Analyze permanent grid plots by physiographic province

and land allocation. Calculate amounts of LSOG from stand-scale definitions.

Specify the reliability of these measures. No analysis of spatial distribution

beyond province and land allocation would be possible. No map generated for

northern spotted owl and marbled murrelet habitat estimation. Because grid

plot data analysis is not hugely expensive (estimated at less than one full time

staff person), it is included in all the following alternatives.

• FEMAT map. Use the FEMAT (1993) map as the baseline and compensate

for stand conversions through 1997. Grid plot data analysis could be added

at minimal expense. No accuracy assessment of map or spatial distribution of

LSOG. This map would not be sufficient for northern spotted owl and marbled

murrelet habitat work, or for field unit planning and operational uses.

• Existing maps. Combine existing maps. Coverage does not include the entire

landscape (for example, southwestern Washington). Accuracy assessment

would be possible on some parts of the map. Some parts would be suitable for

northern spotted owl and marbled murrelet habitat work. Maps might be useful

for unit planning but not consistently across administrative boundaries.

• New image analysis. Apply a consistent, peer reviewed, accuracy assessed

method to produce new maps. Maps suitable for northern spotted owl and

marbled murrelet habitat work and as a starting point for field unit planning or

operational work would be generated. This is the recommended alternative

because it provides information for all LSOG indicators, peer reviewed rigor, a

vegetation map for northern spotted owl and marbled murrelet habitat analysis,

and a starting point for unit planning or operational use.

36
Rationale for

Recommended LSOG

Methods

Late-successional and old-growth effectiveness monitoring, as proposed, consists of

analyzing vegetation maps and permanent plot data. It relies on vegetation maps for

spatial indicators and permanent plots for stand-scale indicators. It also includes a

spatially linked predictive model to project forest vegetation classes into the future,

thereby allowing relatively quick detection of vegetation trends that deviate from those

envisioned in the Forest Plan. Modeling also allows fine tuning of expected LSOG

trends to the particular environments in different physiographic provinces. The criteria

used (table 10) relate strongly to the development of consistent vegetation maps;

those are key to the proposed methods. There are really only two choices for LSOG

vegetation maps and grid plot data analysis: perform the data analysis and reporting,

or not.

Predictive trend modeling is necessary because the estimated LSOG trends in

FEMAT (1993) and the FSEIS (USDA and USDI 1994a) are one simple curve

showing an upward trend in the next 100 years. No refined estimates for different

provinces are provided, even though the development rates for LSOG differ with

environmental differences by province. The proposed methodology includes refined

trend estimates by province.

Table 9—Comparison of alternative methods for collecting data for LSOG

effectiveness monitoring analysis

Analysis of FEMAT

and FSEIS criteria

Spotted owl and

Accuracy murrelet Planning and

Method assessment Spatial Stand vegetation mapa operational use

Field query no no partial no partial

Grid plot data yes no yes no partial

FEMAT map no partial yes no accuracy no accuracy

Existing maps partial yes yes partial partial

New image

analysis yes yes yes yes yes

a The use of LSOG vegetation data in monitoring for aquatic and riparian, survey-and-manage species,

and other monitoring activities is being evaluated.

Table 10—Rationale for selecting recommended LSOG monitoring methods

Recommended method Option selection criteria

Vegetation map and grid plot

data analysis Landscape and stand-scale analysis— better information

Accuracy and detection of real change— reliable information

Synergism with spotted owl and murrelet vegetation map

needs

Tie to field unit planning and operational use

Trend model Allow quicker detection of change direction

Refine crude FSEIS estimates

Allow prediction of effects of management actions

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Hemstrom, Miles; Spies, Thomas; Palmer, Craig; Kiester, Ross; Teply, John;

McDonald, Phil; Warbington, Ralph. 1998. Late-successional and old-growth

forest effectiveness monitoring plan for the Northwest Forest Plan. Gen. Tech.

Rep. PNW-GTR-438. Portland, OR: U.S. Department of Agriculture, Forest

Service, Pacific Northwest Research Station. 37 p.

This report presents options for long-term effectiveness monitoring of late-successional

and old-growth forests under the Northwest Forest Plan. It describes methods to

answer questions about how much late-successional forest exists on Federal land, its

pattern, how it’s changing, and if the Forest Plan is providing for its conservation and

management. It specifies data needed, analytic methods for using remotely sensed

and grid plot data, and implementation options. A periodic process for reporting the

status and trend of late-successional and old-growth forests on Federal lands is described, and links to finer scale monitoring of silvicultural and salvage effects on latesuccessional and old-growth forests are provided.

Keywords: Northwest Forest Plan, effectiveness monitoring, late-successional and oldgrowth forest, vegetation map, remote sensing, grid plot, GIS, landscape, stand-scale,

trend model.

The Forest Service of the U.S. Department of

Agriculture is dedicated to the principle of multiple

use management of the Nation’s forest resources

for sustained yields of wood, water, forage, wildlife,

and recreation. Through forestry research, cooperation with the States and private forest owners, and

management of the National Forests and National

Grasslands, it strives—as directed by Congress—to

provide increasingly greater service to a growing Nation.

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disabilities who require alternative means of communication of program information (Braille, large print, audiotape,

etc.) should contact the USDA’s TARGET Center at

(202) 720-2600 (voice and TDD).

To file a complaint, write the Secretary of Agriculture,

U.S. Department of Agriculture, Washington, DC 20250,

or call (800) 245-6340 (voice), or (202) 720-1127 (TDD).

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