United States

Department of 

Agriculture

Forest Service

Pacific Northwest

Research Station

General Technical

Report

PNW-GTR-255

February 1990

SAMM: A Prototype

Southeast Alaska

Multiresource Model

About This File: 

This file was created by scanning the printed publication. Misscans identified

by the software have been corrected; however, some mistakes may remain.
Technical Editors ROGER D. FlGHT is a principal economist and DALE L. WEYERMANN is a programmer

analyst, USDA Forest Service, Pacific Northwest Research Station, Forestry Sciences

Laboratory, P.O. Box 3890, Portland, OR 97208-3890; and LAWRENCE D. GARRETT is a

forest economist, Northern Arizona University, School of Forestry, Flagstaff, AZ 86001.
SAMM: A Prototype Southeast

Alaska Multiresource Model

Roger D. Fight,

Lawrence D. Garrett, and 

Dale L. Weyermann 

Technical Editors

Pacific Northwest Research Station

Forest Service

U.S. Department of Agriculture 

General Technical Report PNW-GTR-255 

February 1990
Fight, Roger D.; Garrett, Lawrence D.; Weyermann, Dale L., tech. eds.1990. SAMM: a 

southeast Alaska multiresource model. Gen. Tech. Rep. PNW-GTR-255. Portland, OR:

U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. 109 p.

The adaptive environmental assessment method was used by an interdisciplinary team of

forest specialists to gain an understanding of resource interactions and tradeoffs resulting

from forest management activities in southeast Alaska. A forest multiresource projection

model was developed in the process. The multiresource model, “SAMM,” is capable of

characterizing and displaying interactions of four major resources over a 150-year rotation:

timber, wildlife (Sitka black-tailed deer), hydrology (streams), and fisheries (anadromous).

SAMM is in a prototype stage of development; final testing is required before it can be used

by managers for quantitative analysis. Sufficient development and evaluation has been

done by the team of specialists to permit its use in qualitative assessments for planning.

Keywords: Multiresource, forest management, systems, models, southeast Alaska, simulation.

lnterdisciplinary forest specialists from southeast Alaska were brought together to develop

an integrated multiresource forest management model. The mission of the group was to

increase understanding of multiresource interactions resulting from forest management

actions in the Tongass National Forest. They used the adaptive environmental assessment

method.

A simulation modeling effort that used a procedure developed by the board-of-experts was

meant to accomplish several objectives: to improve multiagency understanding of forest

resource interactions; to enhance cooperative relations among agency specialists; to

develop analytic relations to represent the best knowledge of individual-resource dynamics; to specify critical linkages, variables, and interactions among multiresources and to

identify high-priority research needs.

Simulation modeling is an appropriate analytic approach for describing multiresource interaction in a forest environment. Analytic relations can also be appropriately defined by

boards-of-experts with simulation models, which generically are not data dependent and

offer flexibility in defining first approximations.

We developed four major process models and their interdependent linkages: timber,

hydrology, fisheries, and deer. Each model has descriptors of the important natural processes to drive each resource and to link that resource to other resources represented.

Even with the rather complex integrated algorithms developed for each resource, the 

models still present only abstractions of the real-world processes involved.

The overall linkage design in the model is cascade; that is, all information flow is in one

direction with no negative or positive feedback loops. The timber submodel is the first component in the overall model because of the impact management actions related to timber

have on the other resource models; for example, information flows from the timber model

through the hydrology and soils algorithms to the fisheries submodel, and information

moves directly from the timber model to the deer submodel.

The complete Southeast Alaska Multiresource Model, or SAMM, was developed for application on watershed areas of 5,000 to 20,000 acres. Three general types of input files are

needed to operate the model: data to describe physical and biological resources in the

Abstract

Summary
watershed area where the model will be applied, data to characterize management actions

applied to the watershed and its resources, and data describing economic and social characteristics of the area.

The timber submodel uses a stand algorithm based on site index class and age to simulate growth in the relatively even-aged Sitka spruce-western hemlock forests of southeast

Alaska. The algorithm regenerates and grows stands through 150 one-year increments.

Stands older than 250 years are considered old growth.

Timber removals occur through precommercial and commercial thinning, final harvest, or

mortality. The normal management practices simulated for a stand begin with a clearcutting and subsequent regeneration of the stand, a precommercial thinning, and then a 

series of one or more commercial thinnings input by the user.

The hydrology and soils submodel provides the primary link between road building, overstory management activities, and fish and deer habitat. The submodel is driven by inputs for

precipitation, sediment, and air and water temperature. Precipitation regimes are developed for a watershed area, whereas sediment and temperature regimes are developed for

spatial units within a watershed. The interaction of snow precipitation with timber overstory

manipulation has a significant impact on deer population, and sediment production from

road construction and use can have a critical impact on anadromous fish populations.

The fisheries submodel computes production and harvest of anadromous fish species and

currently has algorithms for pink, chum, and coho salmon. The submodel traces critical 

natural processes in the life history of each species, including spawning and rearing. Water

temperatures, flow levels, and siltation are taken from the timber and hydrology submodels

and are critical in determining fisheries dynamics.

The deer submodel simulates changes in Sitka black-tailed deer populations in response 

to vegetative manipulation, snowfall and hunting mortality. The submodel has three major

components: deer energy intake, energy cost, and population dynamics. Timber overstory

density and snowfall are critical variables in the submodel and are derived as outputs from

the timber and hydrology submodels.

Economic evaluations are developed for various timber management activities. Social 

inputs are represented in user days of recreational hunting and fishing and in jobs produced by harvesting timber and building roads.
1 Chapter 1: Development of SAMM, a Southeast Alaska Multiresource

Model 

1 lntroduction

2 Objectives of Model Development 

3 Evaluating the Developmental Process 

4 General Model Assumptions 

5 Submodel Assumptions

5 Timber 

5 Hydrology 

6 Fisheries 

7 Deer

8 Chapter 2: Specification of the Model 

8 lntroduction

9 Bounding the Model

9 Actions 

9 lndicators 

10 Spatial Structure 

11 Time 

11 Looking-Outward Matrix 

11 Submodel Definitions 

13 Chapter 3: The Timber Submodel 

13 lntroduction

15 Scope

16 Model Parameters

16 Basal Area

19 Mortality 

20 Tree Height 

20 Volume

21 Canopy Cover 

21 Slash

22 Dynamics of Old Growth 

22 Dynamics of Streamside Overstory 

22 lmplementing Actions

Contents
22 Overstory Harvest Actions and Methods 

22 Timber Management Plan 

24 Timber Management Economics and Employment 

24 Timber Revenues 

25 Timber Costs 

26 Old-Growth Logging Costs 

26 Road Costs 

26 Stand Establishment and Thinning 

27 Timber Employment 

27 Model Documentation 

28 Chapter 4: The Hydrology and Soils Submodel 

28 lntroduction

28 Sediment and Debris lnput 

33 Routing 

34 Debris Torrents 

36 Bedload Stability 

36 Undercut Banks 

36 Streamflow 

36 Water Balance 

40 Water Temperatures 

42 Snow

45 Model Documentation 

46 Chapter 5: The Fisheries Submodel 

46 lntroductlon

50 Model Stages 

50 Spawning 

51 Survival From Egg to Fry 

54 Freshwater Rearing of Coho 

57 Migration of Smolt 

57 Ocean Survival 

57 Fish Harvest 

62 Model Documentation
64 Chapter 6: The Deer Submodel 

64 lntroduction

67 Model Stages 

67 Understory Relations 

70 Energy Availability 

71 Effects of Slash

71 Effects of Snow Cover

74 Feeding

75 Energy Balance 

76 Reproduction 

76 Natural Survival 

77 Hunting Mortality 

77 Model Documentation 

79 Chapter 7: Using the Model SAMM: Implications for Management 

79 lntroduction

80 Model Applicability 

81 Model Transportability 

82 Model Verification and Validation 

82 Evaluating Predictive Capability 

83 A Final Note on Model Evaluation 

84 Status of SAMM and Example of Outputs 

84 Status 

84 Example of Outputs 

90 English and Metric Equivalents 

92 Literature Citations

97 Appendix 1: Economics of Forest Management

99 Appendix 2: Documentation of the Timber Submodel

102 Appendix 3: Documentation of the Hydrology Submodel 

105 Appendix 4: Documentation of the Fisheries Submodel 

107 Appendix 5: Documentation of the Deer Submodel
Development of SAMM,a Southeast 

Alaska Multiresource Model

Lawrence D. Garrett, Roger D. Fight, and Peter T. McNamee1

The Southeast Alaska Multiresource Model, SAMM, is a prototype model used to project

multiresource interactions in the spruce (Picea spp.) and hemlock (Tsuga spp.) forests of

southeast Alaska. The model has passed through two general stages of development: 

conceptual and prototype. One additional stage, testing, is necessary before it can be 

sanctioned for quantitative assessments in management. The following descriptions relate,

in general, the developmental stages needed to produce an operational model for management use.

1. Conceptual stage. Deriving a general notion or idea.

2. Prototype stage. Analysis, formulation, or framework for conceptual relation or idea.

3. Test stage. Fully documented algorithms having operational capability but without being

formally verified, tested, or validated.

4. Operational stage. Techniques, equipment, and algorithms used in accomplishing normal tasks.

The developmental process for SAMM is in the testing stage. Several complete models

have been drafted, each an improved design. In its current stage, the model can assess 

relative qualitative changes in resources in response to defined management actions.

1LAWRENCE D. GARRETT is a forest economist, Northern Arizona University, Flagstaff, AZ 86001; ROGER D. FlGHT is a principal economist, Forestry Sciences Laboratory, Portland, OR 

97208-3890; and PETER J. McNAMEE is a system ecologist, 

ESSA Ltd., Toronto, ON M4Y 1N3.

1

1

lntroduction
The model is at a suitable level of development to begin evaluations of its predictive 

capability and use in management. Characteristics of the model that should be evaluated include model stability, predictive effectiveness, and management usefulness 

and efficiency.

In general, most equations included in SAMM are first approximations of the biological

relations defined. For some of the relations, these approximations are suited to the outputs

desired. In general, variables or parameters changing little, or causing little to change, do

not require explicit specification. Variables needing explicit specification and testing are

those significantly altering model outputs.

The following brief overview of the SAMM developmental process is an assessment of the

original objectives of the project and reflects how the objectives have changed over time

and the effects the changes have had on model development.

Objectives for developing a model can range from very specific to general and can be singular or multiple. The development of SAMM was predicated at the outset on general

objectives that became more specific over time. The modeling process originally was

selected to improve general interagency communication and provide a common focus for

multiresource tradeoffs in southeast Alaska. During development, the model has been

given more technical capability, objectives have become more focused, and specific use

of the model for information synthesis, research planning, and describing resource impacts

have been identified. Currently, the prototype model is viewed as an analytic system to

conduct qualitative impact assessments.

lnitial objectives reflected the needs of the Alaska Region of the USDA Forest Service 

to assess the general benefits of modeling for forest planning and management. The

Tongass National Forest area guide (USDA 1977) and the Tongass forest plan (USDA

1979) identified resource conflicts requiring assessment; these included conflicts of timber

harvest and road building with habitat for deer and anadromous fisheries.

A method was needed to increase communication and working relations among the several agencies in Alaska involved with these resources. As the modeling process evolved

objectives and the people involved changed. lnteragency communication was greatly

improved by the modeling process, and resource and management issues were identified

and agreed on. As analysts became active in the process, a focus on specific elements of

the original problems began to occur. Management, in general, became enthusiastic about

the potential information and policy benefits obtainable by focusing on specific objectives.

As the developmental process progressed, original objectives were maintained but

received less emphasis; instead, greater focus was on information synthesis, research

planning, and policy analysis. lncreased attention was devoted to model refinement,

including evaluation of individual algorithms and sensitivity analysis.

The modeling effort eventually became a cooperative process to address questions on

specific management actions and their impact on multiresource outcomes. Because algorithm strength for quantitative predictions was lacking, the modeling objective focused on

predicting relative qualitative resource changes. Whereas the original emphasis was to

enhance communication, the emphasis now became analysis.

2

Objectives of Model

Development
Continuing development of SAMM might result in yet another change in the primary 

emphasis. With refinement and effective testing, the model might eventually be used for

quantitative analysis and prediction rather than qualitative analysis. Accomplishment of this

final objective will require an intense effort to define accuracies in both algorithm projections and linkages among algorithms. Additional model development, policy recommendations, and clarifying of the official status of SAMM are the responsibility of the interagency

Model Management Committee.2

One difficulty in the modeling effort occurred in the evolution of the objectives. Whereas 

the general communication objectives specified in the beginning were agreed on and 

understood, the group moved toward analysis objectives without identifying or clarifying the

shift in emphasis. As a result, confusion and misunderstanding existed among some cooperators before the changed direction was both understood and accepted. These issues 

were resolved through the close working relations that had evolved.

The process used in developing SAMM required, by design, extensive interpersonal communication, which was repeated to define resource interactions and eventually to specify

and refine quantified relations.

The approach used was adapted from Holling (1978) and is defined as the “adaptive environmental assessment and management technique.” This technique involves scientists,

managers, and policy specialists in designing qualitative assessment procedures for 

resolving resource conflicts. The extent to which the analytic procedure is made specific

depends on the interest of the parties involved. The development group pursued SAMM to 

a detailed analytic stage for assessing relative qualitative resource changes resulting from

management action.

The SAMM project addressed the objectives that evolved during the modeling process. 

The process identified issues, problems, and concerns; improved interagency communication; synthesized a large volume of multiresource information; and identified important

resource impacts. The current model has applicability for research planning and policy

analysis. It is capable of qualitative assessments of resource interactions resulting from

management actions. It is not yet appropriate for quantifying resource impacts resulting 

from management action; further testing and validation are required to assure its appropriateness in this use.

The model evaluates multiresource impacts of management action on tree growth and 

mortality, understory dynamics, snow regimes, deer habitat, deer energy cycles and mortality, streamflow volumes, sediment loading by silt and debris, gravel overturn, spawning

rates and survival of pink salmon (Oncorhynchus gorbuscha (Walbaum)), chum salmon 

(O. keta (Walbaum)), and coho salmon (O. kisutch (Walbaum)), smolt survival, commercial

salmon harvest, and sport fishing and hunting success. Economic algorithms predict costs

for implementing various management activities such as harvesting and road building. We

2Material taken from: lnteragency Agreement, Memorandum of 

Understanding No. PNW-85-413, 1985, among USDA Forest Service, Pacific Northwest Research Station; USDA Forest Service, 

Alaska Region; U.S. Department of Commerce, National Marine 

Fisheries Service. Northwest and Alaska Fisheries Center; U.S. 

Department of Commerce, National Marine Fisheries Service 

Regional Office; and Alaska Department of Fish and Game.

3

Evaluating the

Developmental

Process
emphasize that the intended use of SAMM is to increase understanding of the interrelations between resources and thus assist in resource planning, policy analysis, and 

qualitative assessments of resource interactions. As such, the model has promise as a

valuable tool for managers and decisionmakers in projecting relative changes among

resources after various methods of land management have been applied. The SAMM

model is not appropriate, however, either for quantifying resource impacts of management

actions or as a basis for economic tradeoffs. Although fish and wildlife values; are considerable, the model does not have the capability to assign dollars to either of these values.

Also, economic projections for timber can be made based on the model as outlined in

this paper, but we emphasize that no projections of costs and benefits for other resources

are included.

To permit efficient operation. S/\MM has many assumptions programmed into algorithms.

These assumptions represent the best judgments of professional managers and resource

analysts in southeast Alaska. The assumptions are based on a more holistic accounting of

observed relations as opposed to accurate but often narrow perspectives permitted in individual research studies and sample data sets.

Many assumptions in the model are not based on existing data. Because of the way they

were developed and the structure of the model, testing and validation will be difficult.

Selected assumptions can be isolated and tested, however, as data become available.

Three assumptions about the general model structure were made. These assumptions

partially define the model and how it functions, and they also control the amount and type

of information available to the manager. The three assumptions are as follows:

1. The watershed area is a useful geographic scale in southeast Alaska for evaluating

resource interactions.

2. Division of a watershed into subareas by stream-reach boundaries, elevation and slope

establish criteria that remain basically stable over time.

3. About 250 years or more are needed to regrow forests to old-growth condition as 

specified by timber specialists in Alaska. During this time, the dynamics of resources

differ and require different time intervals for changes in resources to occur; for example,

1 year for tree growth, fisheries, and sediment transport; 6 months for deer; and 3 days

for weather.

The general model assumptions may somewhat limit the usefulness of the model in

selected planning applications. Natural boundaries, such as a watershed, although useful

for modeling, are not always representative of the actual boundaries for resource interacions. Use of the watershed level as the area constraint and of stream reaches as subarea

delineations can cause difficulties during analyses of stand tradeoffs in traditional timber

types. Current timber land management planning approaches require resource evaluations

delineated by analysis area, or similar ecotypes, which are not fully compatible with the

chosen spatial design. Wildlife populations, such as deer, are not always confined to a

watershed. Their natural range may extend beyond the watershed, particularly in summer

when deer migrate to alpine ranges.

4

General Model

Assumptions
The above concerns aside, we believe the model structure is appropriate for southeast

Alaska. Because of the particular topography of the area, much forest area planning and

management does occur on or within watersheds. Simplistic summation procedures also

can be programmed into the model to relate interaction at the stand or analysis-area level.

The units of measure used in this paper are a mix of English and metric. They are used in

most cases in the same form as they are in the computer code to make comparing the 

documentation and the code easier. The units used in the code are generally the ones that

the model developers are most comfortable with. A table of conversion factors begins on

page 90.

The submodels in SAMM are arranged as separate components, each feeding into the 

next in succession. The timber submodel is the first component, and its assumptions 

impact all other components because of the cascade design. Assumptions for timber 

therefore relate directly to timber model outputs and indirectly to outputs of other submodels. Within the timber component, these assumptions are made:

1. Tree diameter growth occurs annually, although in the model it is computed from 

10-year stocking tables.

2. All tree diameters are equal in any given age class and site index class.

3. Crown cover in unthinned stands less than 30 feet in height is a function of stand height;

whereas cover in all thinned stands and unthinned stands > 30 feet in height is a function of stand basal area.

4. Slash deposited after precommercial thinning is 36 inches deep and 18 inches deep 

after a clearcut harvest commercial thinning. Each have a half-life of 10 to 15 years.

5. Old-growth is 250 or more years old and will remain static with no growth increment or

mortality until harvested.

The hydrology submodel is the primary link between the timber and fisheries submodels.

These assumptions are made in the hydrology submodel:

1. Water yield is based on the entire watershed area, whereas sediment yield is primarily

confined to a spatial unit and stream reach.

2. Two classes of inorganic sediment are recognized: < 3 mm and 3-9.5 mm. One class of

organic debris is recognized: 10 cm and larger.

3. The major sources of sediment are from constructing and using roads and from logging

operations in the form of mass wasting.

4. All sediment sources are 60 percent fine and 40 percent coarse material.

5

Submodel

Assumptions

Timber 

Hydrology
5. A background probability of debris torrents occurs only in first-order streams.

6. Undercut banks along the harvested part of the streambank are instantaneously

reduced by 60 percent at the time of logging. Undercut banks return to predisturbance

levels within 10 years.

7. Precipitation is entered as a yearly average of southeast Alaska data modified by a

long-term cycle.

8. Whether precipitation is rain or snow is controlled by the temperature being above or

below 32 °F.

9. Total surface water is determined by infiltration, surface runoff, and evapotranspiration.

10. Groundwater temperature is 40 °F.

11. Eight inches of snow is equivalent to 1 inch of water.

12. Eight-tenths of a percent of the inventory of large organic debris in streams is decomposed each year.

The fisheries submodel characterizes the various biological cycles of pink, chum, and coho

salmon. These assumptions are made in the fisheries submodel:

1. The sex ratio of retuning spawners is 1:1.

2. Spawners distribute themselves among stream reaches according to available gravel

areas.

3. Summer stream temperatures above 20.8 °C kill all eggs of pink and chum salmon.

4. Average stream temperatures below 2 °C in October and November reduce egg survival by 50 percent.

5. The proportion of eggs lost as a result of fall gravel bed overturn is directly proportional to the gravel overturn.

6. Pink and chum salmon remain in stream reaches one season, whereas coho salmon

remain 1 or 2 years depending on growth rate.

7. Coho fry distribute themselves among stream reaches based on rearing potential 

of habitat.

8. Stream temperatures exceeding 25 °C result in 100 percent fry mortality.

9. Mortality occurs year round, but the model calculates it only once a year. 

10. Streams with 30 percent or more protected habitat (pools) yield 25 percent first-year

survival of coho.

6

Fisheries
11. Without fish harvesting, annual survival rates in the ocean are 0.02 (pinks), 0.20

(chums), and 0.10 (coho).

12. Coho and pink salmon spawn after 1 year at sea; chum spawn at 2, 3, and 4 years. 

13. In the mixed-stock fishery, a user-specified percentage of commercial harvest occurs 

in each species.

14. Freshwater and subsistence fisheries do not operate if escapement is not sufficient to

fully seed the watershed.

15. Effects of canopy closure on fish production assume bank cutting occurs equally on 

both sides of the stream.

The deer submodel characterizes changes in deer populations resulting from changes in 

the timber submodel. The impacts of management actions on timber and understory and

their relative occurrence with snow cycles are the major driving relations in the deer submodel. These assumptions are made in the deer submodel: 

1. Deer receive all their energy from shrubs, forbs, and lichens.

2. Canopy cover is the primary control parameter on understory production in summer.

3. Snow and slash are the primary control parameters on forage in the winter. 

4. No shrub growth takes place in winter, and summer shrub biomass is reduced by 40

percent in winter to reflect leaf drop.

5. Forbs do not carry over from year to year. Only 50 percent of summer forbs are carried into winter.

6. Slash reduces forage availability.

7. Potential forage consumed is constrained by potential forage available.

8. Forage consumed per deer is directly proportional to the forage requirements for the

deer relative to total herd requirements.

9. Fawns and adults forage 11 hours per day and search about 1.00 hectare per day.

10. Deer are allocated to a stand based on the net energy available in each stand. 

11. Thirty percent of adult deer weight and 15 percent of fawn deer weight can be 

expended before starvation occurs.

12. Hunter populations include local residents, loggers and road builders, and hunters 

from outside the area.

13. Only nonlocal hunters exhibit a success response; that is, hunt more days after a 

previous successful season.

14. Killed deer are removed from herds in direct proportion to the population in each age

class within each herd.

7

Deer
Specification of the Model

Peter J. McNamee, Lawrence D, Garrett, Roger D. Fight, and 

Joseph R. Mehrkens1

There were two primary objectives in specification of the model. The first was to provide

analytic definition to interrelations among timber, hydrology, fisheries, and wildlife in southeast Alaska and provide insight to the relative changes occurring in these resources as a

result of management action. The second objective was to specify the complete analytic

structure of a model capable of displaying multiresource interactions and tradeoffs.

Satisfying the first objective was accomplished through interaction among specialists and

scientists from disciplines representing the four resource areas under study. Available

science and data permitted some definition of general interaction in each resource area.

Accomplishing the second objective required development of resource data and collaborative science. It also required repeated interaction among managers, scientists, and

specialists to evaluate individual model components most suited for displaying resource

interactions.

The research procedure used in specifying the model is defined by Holling (1978) as the

adaptive environmental assessment and management technique. The technique brings

together interdisciplinary scientists, analysts, and managers in a workshop environment to

define parameters and processes best representing biologic and economic relations in a

particular resource area, such as timber or anadromous fisheries. For SAMM, the same

group was asked to define the most appropriate linkages among the various resource

areas and the processes and relations represented in these linkages.

1PETER J. MCNAMEE is a system ecologist, ESSA Ltd., Toronto,

ON M4Y 1N3; LAWRENCE D. GARRETT is a forest economist, 

Northern Arizona University, Flagstaff, AZ 86001; ROGER D. 

FlGHT is a principal economist, USDA Forest Service, Forestry 

Sciences Laboratory, Portland, OR 9720&3890; and JOSEPH R. 

MEHRKENS was a regional economist, USDA Forest Service, 

Alaska Region, Juneau, AK 99802 and is currently with The Wilderness Society, Juneau, AK 99802.

8

2

lntroduction
Quantitative, rather than qualitative, relations were defined, which required use of concepts

of statistics, probability, and math to define both intraresource and interresource relations.

The resulting design represents an integrated multiresource model. Although some feedback mechanisms are employed among resource models, most linkages are in one 

direction, making the design cascade in both definition and function.

The primary objective was to develop a model that could project relative changes in

resources as a result of management action. To do this, certain constraints were necessary to describe feasible management actions as were measures to evaluate the impact of

actions on outputs of the model. The model was further defined by identifying actions and

indicators or outputs in a manageable spatial and temporal framework.

In the context of this model, actions are the feasible human interventions that can alter the

characteristics of the forest, fish, and wildlife. It is important to define the actions as a single intervention (high-lead logging) rather than as multiple interventions or a class of 

interventions (even-age management). Particular components of an environment may

respond differently to specific actions that are often grouped into one generic category. 

Most of the actions included in the model are related to forest management practices,

although some are related to fisheries, wildlife, and stream management (table 1).

Driving variables are, in a sense, special actions in that they affect the system dynamics

and are a kind of intervention. The key difference is that driving variables are exogenous to

the system and represent the links between the internal aspects of the model and the 

external world the model is not considering in any great detail.

Outputs or indicators, as defined here, are those measurements used to evaluate the state

or health of the system. They are the links between the simulation model and the manager’s mental model of the system being defined. Because managers have different 

measures of system performance, it is important to include in the model a comprehensive

set of outputs representing the system being described. The outputs projected by the 

model reflect the areas of major interest to managers with defined interactions and yields

represented. The following list gives examples of outputs produced by the model:

• Miles of road constructed 

• Employment in forest industry 

• Number of deer 

• Number of deer harvested 

• Percentage of fines in sediment 

• Monthly streamflows 

• Minimum summer and winter streamflows 

• Volume of large debris in streams 

• Total sediment volume by reach 

• Volume of timber harvested 

• Catch of pink, chum, and coho salmon 

• Production of pink, chum, and coho smolt

9

Bounding the

Model

Actions

lndicators
The spatial structure of the model determines whether the model will be used to examine

highly specific hypotheses or more general classes of hypotheses. In establishing the spatial structure, two questions are important: (1) What spatial area will be considered internal

to the model? and (2) What is the spatial resolution within the areas modeled?

The spatial area chosen for modeling is a watershed, which can range from about 5,000 to

20,000 acres. A watershed-level model can represent a wide range of silvicultural practices and harvest patterns as well as the population dynamics of the fish and wildlife

species being modeled. Three main advantages of a watershed-level model are:

1. It allows representation of biophysical processes for which data and conceptual understanding exist.

2. Much forest- and area-level planning by interdisciplinary terms occurs on similar-size

areas.

10

Spatial Structure
3. Many management-area delineation in southeast Alaska use natural watershed 

boundaries.

The spatial resolution desired is obtained by dividing a watershed into subareas of irregular

boundaries with topography, stream-reach boundaries, and elevation as criteria. This representation allows the user to adapt the model to other watersheds by altering the subarea

characteristics in the initial conditions; for example, area, site index class, and tree age per

volume. Each spatial unit within the watershed of interest is associated with the above collection of characteristics that do not change with time. These characteristics are detailed in

the submodel descriptions in later chapters.

Development of a dynamic simulation model requires specifying a time horizon over which

model projections are of interest. A time step must also be specified over which the change in

value of the state variables will be calculated and displayed. Because the model does not

allow for harvesting young-growth stands, the model time horizon is limited to 150 years.

The basic time step for the model is 1 year. Selected processes in each submodel are simulated on short or long time frames, as in the following tabulation:

Process Time step

Timber growth 1 year

Timber management activities 1 year 

Sediment dynamics 1 year 

Streamflow, temperature, and 

snowfall 3 days 

Fisheries 1 year

Wildlife 6 months

The looking-outward matrix in figure 1 defines the set of information a particular submodel

requires from all other submodels. Each set of information listed in the matrix implies a 

specific hypothesis; for example, the fisheries submodel requiring summer low flows for

each stream reach means that fisheries scientists hypothesize that fish survival is influenced by water flows. Similar arguments can be made for each information transfer.

The interaction matrix encourages workshop participants from different resource disciplines

to concentrate on identifying common variables, units of measure, and time frames and to

focus on important functional relations. The result is mutual understanding of issues and 

system dynamics and the creation of a positive atmosphere for mutual problem solving.

The major criteria for proper division of the model into submodels was minimizing the 

transfer of information between submodels. Each submodel simulates a distinct selfcontained component of the whole system.

The four submodels of the simulation model are timber, hydrology and soils, fisheries, and

deer. The cascade design of SAMM results in an integrated rather than interactive structure. And, because overstory manipulation is the primary cause of change in outputs to all

submodels, documentation of the timber submodel is presented first.

11

Time

Looking-Outward

Matrix

Submodel Definitions
Figure 1—A looking-outward matrix illustrating submodel linkages in SAMM.

12
The Timber Submodel

Wilbur A. Farr, Peter J. McNamee, Robert L. Gerdes, William H. 

Wilson, Joseph R. Mehrkens, Roger D. Fight, and Lawrence D.

Garrett1

The timber submodel is a dynamic representation of timber growth in southeast Alaska. It

includes a growth and yield model for the hemlock and spruce forests predominating 

there. The model also simulates management actions applied to these forests and the 

associated economic costs and revenues. Stand dynamics in the model are responsive to

timber management strategies other than just those attempting to maximize timber yields

or economic returns.

A relatively simple whole-stand model based on site index (SO class and age is used to

depict the development of even-aged stands of western hemlock (Tsuga heterophylla

(Raf.) Sarg.) and Sitka spruce (Picea sitchensis (Bong.) Carr.). Stands are grown in 10-

year age classes from age zero to age 250 years. Stands more than 250 years old are 

classified as old growth.

Removal of trees from the stand occurs during precommercial thinning, commercial thinning, or final harvest and as regularly occurring natural mortality. Timber actions and 

indicators and the outputs fed to other submodels are listed in figure 2.

1WlLBUR A. FARR is a principal research mensurationist, USDA

Forest Service, Forestry Sciences Laboratory, Juneau, AK 99802; 

PETER J. McNAMEE is a systems ecologist, ESSA Ltd., Toronto, 

ON M4Y 1N3; ROBERT L. GERDES and WlLLlAM H. WlLSON are 

timber specialists, USDA Forest Service, Alaska Region, Petersburg, AK 99833 and Juneau, AK 99802, respectively; JOSEPH R. 

MEHRKENS was a regional economist, USDA Forest Service, 

Alaska Region, Juneau, AK 99802, and is currently with The Wilderness Society, Juneau, AK 99802; ROGER D. FlGHT is a principal economist, USDA Forest Service, Forestry Sciences Laboratory, Portland, OR 97208-3890 and LAWRENCE D. GARRETT is a 

forest economist, Northern Arizona University, Flagstaff, AZ 86001.

13

3

lntroduction
Figure 2—lnformation flow and system linkages for the timber submodel.

Basal area, mean stand height of dominants and codominants, and number of stems per

acre are the most important variables influencing growth and yield of the well-stocked

stands (fig. 3). Stand age is equal to the number of years since an area was clearcut.

Basal area increment is a function of existing basal area, potential growth of well-stocked

stands of similar site and age, and growth adjustments if stands are less than wellstocked. Mean stand diameter is calculated from basal area and number of trees per acre

Mean stand height of sawtimber trees (> 9.0 inches diameter at breast height [DBH]) is a

function of mean height of dominant and codominant trees. Mortality, in number of trees

per acre, is calculated as a function of site, age, and density.

14
Figure 3—Basic structure and driving parameters of the timber submodel.

Management actions in the timber submodel are scheduled annually, and stand growth is

computed yearly. Actions and indicators in the submodel are presented in figure 2. Linkages in the model are represented as information received from and directed to the 

various submodels. the acreage in each spatial unit may be subdivided into as many as 

five SI classes (70, 90, 100, 110 and 130). Site index is based on the mean total height of

dominant and codominant hemlock and spruce at a total age of 100 years.

Within each SI class are 26 age classes of forest. The first 25 represent 10-year age classes

between 0 and 250 years. The 26th class is for stands older than 250 years of age.

Developing a detailed distance-independent, individual-tree forest growth model like

PROGNOSlS (Stage 1973) would be one way to examine the effects of alternative timber

management strategies, such as precommercial and commercial thinning for timber production, or to assess the probable effects of repeated heavy thinnings on understory 

forage production for wildlife. Another approach would be to develop a detailed wholestand model similar to DFSlM (Curtis and others 1981). Such models unfortunately are not

yet available for the hemlock-spruce forest type of southeast Alaska.

A PROGNOSlS variant known as SEAPROG, for southeast Alaska prognosis, is under

development for western hemlock and Sitka spruce in southeast Alaska.2 Field work 

underway for the past 10 years is contributing to this effort and will also contribute to development of a whole-stand growth and yield model for the area.

2Development plan on file: Forestry Sciences Laboratory, P.O. Box 

909, Juneau, AK 99802.

15

Scope
The relatively simple whole-stand model developed for SAMM uses Taylor’s (1934) 

normal-yield tables to project yields for western hemlock and Sitka spruce. The tables are

based on SI at a total stand age of 100 years. Adjustments have been made to the statistics for well-stocked stands to allow for the probable effects of intensive timber management such as precommercial and commercial thinning.

Farr’s (1984) total height function for Sitka spruce was used to estimate height and height

growth of dominant trees. Equations were also developed to convert these estimates of

height and height growth based on SI at breast height age 50 years to Taylor’s (1934) estimates based on a total age of 100 years.

Total basal area and basal area growth, and total number of trees and tree mortality for wellstocked stands by SI and age were estimated from Taylor’s (1934) tables 7 and 4, respectively. Estimates of the effects of precommercial and commercial thinning on basal area and

diameter growth were derived from British yield models (Edwards and Christie 1981).

Basal area at 10-year intervals for normally stocked stands (adapted from Taylor 1934,

table 7), is given in table 2. Annual basal area increment (∆BA) for natural stands is 

calculated by:

∆BASI,j = (NBAGSI,j✶PBAGj

) ✶ [1 + IBAGSI✶ (1 - BASI,j/NBASI,j)],

where 

j = age in years, 

SI = site index class, 

∆BASI,j = annual basal area increment for site index class SI between ages j and j+1,

NBAGSI,j = net annual basal area growth for normal stands, 

PBAGj = percentage of net annual basal area growth, 

lBAGSI = increase in basal area growth for stands with less than normal stocking, 

BASI,j = stand basal area per acre, and 

NBASI,j = normal stand basal area per acre.

16

Model Parameters

Basal Area
Figure 4—Percentage of maximum rate of basal area growth as a function of stand age and percentage of

normal basal area.

Percentage of maximum rate of basal area growth (PBAG) is a function of stand age and

percentage of normal basal area (fig. 4) and reflects the fact that stands become less 

capable of responding to thinning as they get older.

A potential increase in basal area growth (PBAG) takes place in less-than-weIl-stocked

stands and is a function of SI:

IBAGSI = 0.4 + 0.01 ✶ SI.

Annual basal area increment (∆BA) for thinned stands is calculated by:

∆BASI,j = (NBAGSI,j ✶ PBAGj ✶:1.1) ✶ (1 + IBAGSI,j ✶ [1 - (BASI,j/NBASI,j) ✶ 1.1]).

17
This equation differs from the previous equation for basal area growth of natural stands in

that thinned stands are allowed to grow at a relatively faster rate and eventually accumulate up to 10 percent more basal area than a natural stand. This is because thinning

reduces stand composition to more vigorous, well-spaced trees.

18
A stand with 100 percent of normal basal area is modeled to have a given number of trees

per acre based on SI and age (table 3). Mortality rates are derived from these values, and

model outputs follow these expected values.

Tree mortality in thinned stands and in natural stands that are less than well-stocked is typically low until stand basal area approaches that of a normal stand. The tree mortality rate

for these stands is a function of normal number of trees based on SI, age, normal mortality

rates, and percentage of normal basal area (fig. 5). It is calculated from:

MORTSI,j = STSI,j ✶ (STSI,j/NSTSI,j) ✶ (1 - NSTSI,j+1/NSTSI,j) ✶ MULT, 

where 

MORTSI,j = annual stem mortality for site index class SI between ages j and j+ 1 if

MORTSI,j is greater than 0, otherwise MORTSI,j = 0; 

STSI,j = number of stems per acre; 

NSTSI,j = number of stems in a normal stand of the same site index class and age; and

MULT = multiplier between ) and 1 to reduce mortality in stands having less than normal

basal area (obtained from fig. 5).

Table 3—Number of trees per acre in normal stocked stands of western hemlock 

and Sitka spruce in southeast Alaska by age at breast height and site index

19

Mortality
Figure 5— Fraction of normal annual tree mortality related to percentage of normal basal area, to adjust 

mortality in understocked stands.

Overstory height in the model is the mean height of dominant and codominant hemlock 

and spruce in a stand. Overstory height is computed indirectly by using Farr’s (1984) 

height equation for Sitka spruce based on SI at a breast height age of 50 years. Heights 

are converted to a 100-year base, and this equation is solved to give patterns of height

growth such that at total age 100 years mean height of dominant and codominant trees is

70, 90, 100, 110, or 130 feet, thereby satisfying conditions required to enter Taylor’s (1934)

normal yield tables.

Timber volume, Scribner rule, is calculated for trees 9.0 inches and larger DBH between a 

1-foot stump and a 6-inch top. The relation between mean stand height and mean height 

of dominant and codominant trees in the stand is calculated with the equation:

MHSI,j = -4.0 + 0.9833 ✶ OHSI,j, 

where 

MHSI,j = mean height of trees 9.0 inches DBH and larger; and 

OHSI,j = overstory height; that is, mean height of dominant and codominant spruce and 

hemlock.

Volume per acre, Scribner rule for 32-fool logs to a 8inch top, is calculated for sawtimber

trees (9.0 inches DBH and larger) in the stand. Number of trees per acre that are 9.0 

inches DBH and larger is a function of the total number of stems and mean diameter of 

the stand and is given by:

20

Tree Height

Volume
ST9SI,j = STSI,j ✶ [-78.29 + 18.041 ✶ DSI,j - 0.5229 ✶ D2

SI,j + 0.00335 ✶ D3SI,j],

where 

ST9SI,j = number of stems per acre 9.0 inches DBH and larger for site index class SI at 

age j if DSI,j is greater than 5.1 inches; otherwise ST9SI,j = 0;

STSI,j = total number of stems per acre; and

DSI,j mean stand diameter. 

Mean stand diameter of trees 9.0 inches DBH and larger (D9SI,j) a function of mean stand

diameter, is calculated from:

D9SI,j = 6.4 + 0.6506 ✶ DSI,j.

Total volume per acre is a product of total number of stems 9.0 inches DBH and larger and

mean volume per tree. Mean volume per tree for diameter D9SI,j and mean height MHSI,j

is calculated from the Scribner volume equation given in Chambers and Foltz (1979).

Canopy cover (a fraction between 0 and 1) in young, unthinned even-aged stands with an

overstory height less than 5 feet is calculated as a function of overstory stand height:

CCj = 0.015 ✶ OH,

where

CCj = fraction of canopy cover at age j if overstory height <5 feet, and

OH = overstory height.

Canopy cover for stands with an overstory height greater than 5 feet is computed by using

two equations. The first equation estimates mean crown width for trees of mean stand 

diameter; the second uses estimated mean crown width and total number of trees taller 

than 4.5 feet per acre to estimate canopy cover.

Mean crown width is a function of mean stand diameter:

CWj = 4.04 +1.98 ✶ DBHj

0.87,

where 

CWj = mean crown width for trees of mean diameter at age j, and 

DBHj = mean stand diameter at breast height for trees taller than 4.5 feet.

Slash dynamics are modeled by using a time-dependent decay function to reduce slash

height from three types of harvesting: clearcutting, commercial thinning, and precommercial thinning. Each action results in a given height of slash being left in a stand. lnitial 

depths of slash are 18 inches for areas clearcut or commercially thinned and 36 inches for

areas precommercially thinned. Any remaining slash is reduced by 5 percent per year.

21

Canopy Cover

Slash
In this model, any stand more than 250 years old is classified as old growth. A key assumption is that stands initially classified as old growth will remain in a steady-state condition 

until they are harvested. Younger even-aged stands will move toward the steady-state condition of old growth. When an even-aged stand is 250 years old, it will be classified as old

growth and will then, over time, decay to the steady-state condition.

The timber submodel keeps track of overstory stand height and canopy cover along 

streambanks so that stream temperatures can be calculated for the hydrology submodel 

and food availability for the fisheries submodel. Overstory height and canopy cover for 

each stream reach in a spatial unit are calculated as follows: 

26 

ATSi,m = (Σ ATi,SI,j ✶ TAi,SI,j) / TASi,SI , 

j=1 

where 

ATSi,m = either overstory height or canopy cover for stream reach m in spatial unit i, ATi,SI,j =

either overstory height or canopy cover for the spatial unit i through which the 

stream reach flows,

TAi,SI,j - area in spatial unit i of site index class SI and age class j, and 

TASi,SI = total area in acres in spatial unit i and site index class SI.

Four types of forest management actions are allowed in the model:

1. Clearcutting.

2. Road construction.

3. Stand establishment and precommercial thinning. 

4. Commercial thinning.

Forest management actions may be implemented through a designed timber management plan providing the details considered in most timber sales and giving projections of 

revenues and costs. The specific design of the procedure also makes it adaptable to geographic information systems.

When the timber management plan begins is time dependent; that is, the user must specity the year or years when each specific activity is to occur. To accomplish this, a separate

matrix must be set up for each type of action (table 4). All actions scheduled for a particular year are carried out when the appropriate simulated year is reached. Each entry in the

matrix includes information on how that action will be implemented.

Table 4—Example of a partial matrix of management actions that must be entered 

in the timber submodel to implement the timber management procedure

22

Dynamics of Old

Growth

Dynamics of

Streamside

Overstory

lmplementing

Actions

Overstory Harvest 

Actions and Methods

Timber Management

Plan
Clearcutting—The following information is required for each area to be clearcut:

1. Acres cut. 

2. Spatial unit. 

3. Year to cut.

4. Percentage of streambank vegetation to be removed. 

5. Harvest method.

The acreage that is clearcut in the model can be allocated to each SI class by two 

methods. In the first method, it is allocated in proportion to the relative area of the SI class

in the spatial unit. For example, a 70 percent of the land in a spatial unit has an SI of 110,

then 70 percent of the cut will come from SI class 110. In the second method, acreage cut 

in each SI class is allocated by the user. All acreage that is cut is put into a pool, which is

moved into the youngest age class.

The percentages of streambank vegetation removed from each spatial unit are summed

over all cuts each year to give a total percentage cut in each spatial unit.

Road construction—The following information is required for each occurrence of road 

construction:

1. Year road is constructed.

2. Spatial unit where the road is located. 

3. Miles of mainline road to be built. 

4. Miles of temporary road to be built.

Retirement of roads is not considered in the model.

Site preparation, planting, and precommercial thinning—The following information is

required if intensive timber management actions are to occur after clearcutting:

1. Year action is to be implemented. 

2. Spatial unit affected. 

3. Acres of site preparation. 

4. Acres to plant.

5. Acres to be precommercially thinned.

Consequences of implementing site preparation or planting are reflected only in the 

economic costs of carrying out the actions. The economic costs of these actions are 

discussed in the section on economics of forest management.

Commercial thinning of even-aged stands—The following information is needed to 

implement commercial thinning of even-aged stands:

1. Year action is to be implemented. 

2. Spatial unit affected.

3. Age class of stand at time of cutting. 

4. Residual basal area left after cutting. 

5. Harvesting method.

23
Repeated commercial thinnings are implemented by specifying a series of thinnings in a

particular stand. All acreage in a given age class in a spatial unit is thinned, irrespective of

site class. It is assumed that any old-growth acreage harvested early in a run of the model

will be managed as a unit and not be broken down into acreages by SI class.

Revenues, costs, and employment associated with management activities are derived in

the model by using several equations (given below) and tabular data on revenue, cost,

and employment, which are given in appendix 1. Economic evaluations are currently

restricted to capabilities related in these functions and data. Much of the procedure is

taken from the Alaska Region timber sale appraisal procedure (Mifflin and Lysons 1979,

USDA 1979). All the unit costs and revenues given in appendix 1 are in data files in the

model and should be changed by the user to correspond to the situation being simulated.

Employment, an area significantly impacted by timber operations, is developed in the timber submodel for use in the fisheries and deer submodel. Employment is also used as a

social measure of community impacts.

Timber could be valued at various points in the production process; that is, on the stump,

at the terminal transport facility, delivered at a mill, or as finished product, FOB mill. In this

discussion and in the data shown in appendix 1, timber is valued as delivered at a mill. The

timber prices used are therefore mill pond values, and the costs include all the costs of

harvesting, including delivering the logs to a mill. Pond values are approximations generated by applying time- and site-specific assumptions to the Alaska Region timber sale

appraisal procedures (USDA 1979). The model could be used with values at some other

point, as long as the prices and costs that are put in the model are for the same point in

the process.

The annual revenue, REV, from wood harvest in an entire watershed in year k is calculated by:

ns nc

REVk = Σ Σ

i=1 n=1

5

An Σ {(ARi,SI,26/TAi) ✶ [Vi,SI,26 ✶ (PCi,SI ✶ RC) + (1-PCi,SI) ✶ RvSI]}

Sl=1

5 25

+ {Σ Σ ARi,SI,j ✶ VTSI,j ✶ [1-(BATi,SI,j/BAi,SI,j)] ✶ Vi,SI,j ✶ RvSI} ,

Sl=1 j=1

where 

n = cut number;

nc = number of cuts made in spatial unit i in year k;

ns = number of spatial units in the watershed; 

An, = acreage harvested in cut n; 

TAi, = total area of spatial unit i;

Vi,SI,j = volume per acre (thousand board feet) in age class j, site index class SI, and

spatial unit i;

24

Timber

Management

Economics and

Employment

Timber Revenues
ARi,SI,j = area of age j in site index class SI in spatial unit i; 

PCi,SI = proportion of volume in cedar, site index class SI, spatial unit i; 

RC = revenue from cedar (per mbf); 

RvSI = revenue for the particular volume of noncedar wood in site index class SI; 

BAi,SI,j = basal area per acre before treatment; 

BATi,SI,j = basal area to which the stand is thinned; and 

VTSI,j = second-growth revenue multiplier.

The first part of the right side of the equation calculates revenue from old growth harvest;

the second part calculates revenue from second-growth commercial thinning.

The value of Vt varies with the type of young-growth entry being made. It equals 0.46 for

second-growth thinnings, 0.40 for second-growth final harvest on unthinned stands, and 

0.64 for final harvest on previously thinned stands.

Unit revenue varies with timber quality. In the model, timber quality is assumed to be

related to stand volume, and unit revenues are used for both old-growth and secondgrowth timber (table 12).

The following list presents the cost items that should be included in the model. lncluded 

are all costs for harvesting and transporting timber to a mill. Each item should include 

overhead costs customarily charged to these activities.

Administration—Costs include planning and management of sales, roads, and facilities. 

In general, these represent the costs of resident forest managers and specialists and 

specific costs for designated activities, such as particular sale layout or an environmental

impact statement.

Road construction—Costs include general overhead plus specific costs associated with

road segments. Treatment of both mainline and temporary road systems is included.

Harvesting, stump to truck—Harvest system costs cover harvesting logs and moving 

them from the stump to the loading site. Eleven different harvest systems are specified.

These systems and the associated costs for logging different volume classes are given in

appendix 1.

Hauling to a terminal transportation facility (TTF)—Hauling costs include two 

elements, unit hauling cost and unit maintenance costs for roads. Hauling cost is 

expressed in dollars per thousand board feet (mbf) per mile hauled from landing to TTF. 

The road maintenance cost is a dollar cost per mbf per mile from landing to the TTF.

Hauling from TTF to manufacturing point—The transfer cost from the TTF to the mill is

expressed as dollars per mbf per mile from TTF to mill site.

Timber management—Costs relate primarily to regrowth of stands. Costs for site

preparation, planting, and precommercial thinning are required in many stands and are 

listed in appendix 1.

Support facilities—Costs include construction of facilities such as logging camps or TTF.

These costs are allocated over an expected total sale volume serviced and are expressed 

in dollars per mbf.

25

Timber Costs
Environmental protection—Costs directly relate to preventing or controlling environmental damage. Stream cleaning, grass seeding, undercut bank protection, directional

felling, and differential road alignment costs are examples of costs incurred in timber 

management. To account for these costs, the manager inputs incremental costs of 

management activities such as road construction and harvesting.

Some of the costs described above are combined to enter them in the model. The following sections show what cost categories are used in the model and how the costs are

summed for the watershed.

The total annual costs associated with old growth logging (CL) are given by:

ns nc 5

CL = Σ Σ { [CPMBFCTi,n + (DFLDi) ✶ (CHAUL) + OCOST] ✶ Σ VCi,SI,n },

i=1 n=1 SI=1

where 

CTi,n = harvest method used for cut n in spatial unit i; 

CPMBFCT = cost per mbf of using harvest method CT; 

CHAUL = hauling costs per mbf per mile; 

VCi,SI,n = volume of wood cut in spatial unit i, site index class SI, and cut n (mbf); 

DFLDi = mean distance of each spatial unit from the mouth of the watershed, the assumed

log storage site; and 

OCOST = other costs per mbf.

Eleven different logging methods can be specified (table 14).

The total costs for roads in a given year (CR) are given by: 

ns 

CR = Σ [(CBMR ✶ MMRi) + (CBSR ✶ MSRi) + (CMR ✶ TVi ✶ DFLDi)] , 

i=1 

where 

MMRi = miles of mainline road built in spatial unit i,

MSRi = miles of temporary road built in spatial unit i, 

CBMR = unit cost for constructing mainline road, 

CBSR = unit cost for constructing temporary road, 

CMR = unit cost for road maintenance, 

TVi = total volume harvested in spatial unit i (mbf), 

DFLDi = mean distance of each spatial unit from the mouth of the watershed, the assumed

log storage site.

This equation considers new road construction (both mainlines and spurlines), and maintenance on roads constructed up to the current year.

The total cost of stand establishment and precommercial thinning in a particular year (TM)

is given by:

ns 3

TM = Σ Σ CPMm ✶ AMm,i ,

i=1 m=1

26

Old-Growth Logging

Costs

Road Costs

Stand Establishment

and Thinning
where 

AMm,j = the acreage in spatial unit i subject to management practice m (1 = site preparation; 2 = planting; 3 = precommercial thinning), and 

CPMm = the unit cost of management practice m.

Employment is determined for each watershed from volume of old growth harvest, miles of

road built, and volume of commercial thinnings. It is also affected by management actions

such as site preparation, planting, and precommercial thinning in young even-aged stands.

Employment (E) on a watershed is calculated by: 

3

E = R1 ✶ TVC + R2 ✶ TRB + R3 ✶ TVT + Σ R3+i ✶ ASi , 

i=1 

where 

Ri-6 = employment rate parameters (person years per unit of management), 

TVC = total volume clearcut, 

TRB = miles of road built, 

TVT = volume of second-growth cut, 

ASi.=.acreage of each management action in young even-aged stands.

A summary of the sources of documentation for the timber submodel is given in appendix 

2. Most of the relations in the growth and yield model were derived from normal yield 

tables for western hemlock and Sitka spruce (Taylor 1934). Effects of thinning were extrapolated from the British forest management tables (Bradley and others 1966) or were based

on professional judgment. Data on crown cover, slash depth and decay, and the dynamics

of streamside overstory are based on the professional judgment of the participants 

involved in model development. Data on revenue and costs in appendix 1 are mostly from

published sources.

27

Timber Employment

Model

Documentation
The Hydrology and Soils Submodel

Douglas N. Swanston, Timothy M. Webb, Louie Bartos, 

William R Meehan, Tom Sheehy, and Ann Puffer1

The hydrology submodel provides a primary link among road building, overstory management activities, and impacts on fish and deer habitat. This linkage is specific to measured

and predicted changes in streamflow, sediment and debris transport, water temperature,

and snow accumulation. The submodel is driven by precipitation, sediment, and air and

water temperature inputs, with feedback from and to other submodels (fig. 6).

The submodel structure and basic ordering of the rules for change within the submodel are

presented in figure 7. Precipitation information for driving the flow model is developed for

an entire watershed. lnformation on sediment and temperature inputs is developed for and

maintained within each spatial unit and within each stream reach making up a watershed.

Each spatial unit drains into only one stream reach.

A primary purpose of this submodel is to predict the quantities of sediment and organic

debris transported through and deposited within anadromous fish habitat. Fisheries biologists in the North Pacific region have defined three classes of inorganic sediment and

organic debris that have major influence on egg, alvin, and fry survival, rearing habitat, and

food sources:

1. Debris less than 3.0 mm (fine sediment). 

2. Debris 3.0 mm to 9.5 mm (coarse sediment). 

3. Debris greater than 10.0 cm.

To provide output directly compatible with input requirements of the fisheries submodel,

(levels of each of these classes are simulated in each stream reach.

1DOUGLAS N. SWANSTON is a geologist and WlLLlAM R. 

MEEHAN is a fisheries biologist, USDA Forest Service. Forest Sciences Laboratory, Juneau, AK 99802; TlMOTHY M. WEBB is a 

hydrologist, ESSA Ltd., Vancouver, BC V6Z 2H2; LOUlE BARTOS 

is a watershed specialist, USDA Forest Service, Tongass National 

Forest, Ketchikan. AK 99901; and TOM SHEEHY and ANN 

PUFFER are watershed specialists. USDA Forest Service, Alaska 

Region, Juneau, AK 99802.

28

4

lntroduction

Sediment and

Debris lnput
29

Figure 6—lnformation flow and system linkages for the hydrology submodel
30

Figure 7—Basic structure and driving parameters of the hydrology submodel.

The total input of a single class of sediment into a stream reach is calculated by accumulating (1) the natural input per acre multiplied by the delivery ratio for that spatial unit, (2)

estimated input from roads and logging in each spatial unit feeding the reach, and (3) input

from stream reaches feeding the reach. The delivery ratio accounts for not all sediment 

reaching the stream. For purposes of this submodel, natural sediment loading from soil 

mass movements was estimated at a rate of (0.16 m3/acre)/ year. Loading rate is based 

on data obtained for areas in the Pacific Northwest having similar topography and climate

(Swanston and Swanson 1976).

In areas disturbed by logging activities, additional sediment loading is derived from debris

avalanches, slumps from clearcuts and roads, road surface erosion, and road use. lnputs
Figure 8—Sediment inputs from clearcut areas.

of sediment from logging-related loading are a function of years since clearcut (fig. 8). 

lnputs of sediment from road erosion are a function of time since the road was constructed

(fig. 9). Construction of these functions is based on data from Swanston and Swanson

(1976), Reid and others (1981), Reid and Dunne (1984), Paustian (1987), and on professional judgment and experience of field personnel in southeast Alaska.2

All sources of sediment are assumed to be composed of 60 percent fine and 40 percent

coarse material. This ratio approximates the findings of sediment budget studies elsewhere in the Pacific Northwest and generally agrees with preliminary data from southeast

Alaska.3

Road sediment data for southeast Alaska are limited; however, preliminary measurements 

of sediment delivery from forest roads in two watersheds on Chichagof lsland (see footnote 2) indicate peak volumes of 160 to 170 (m3/mile)/year during and immediately after

construction. This volume is believed to diminish substantially as the road ages and

becomes more stable (see fig. 9).

2Paustian, S.J.; Marion, D.; Maki, S.; Kelliher, D. 1987. Kadashan 

sediment monitoring study. Juneau, AK: U.S. Department of Agriculture, Forest Service, Tongass National Forest. 29 p. Interim 

report. On file with: U.S. Department of Agriculture, Forest Service, 

Tongass National Forest-Chatham Area Fisheries, Wildlife, and 

Watershed Staff Office, Sitka, AK 99835.

3Personal communication, S.J. Paustian, hydrologist, USDA Forest 

Service, Chatham Area, Tongass National Forest, Sitka, AK 99835.

31
Figure 10—The decay function for sediment from constructed roads.

Sediment delivery to channels as the result of regular road use is believed to be lower in

southeast Alaska than elsewhere in the Pacific Northwest and is estimated to be about 50

percent below construction levels (83.0 [m3/mile]/year) for normal log-haul traffic (5-10

hauls per day). This is believed to be the case for several reasons. Most road construction

has a fairly low level of ground disturbance because of the occurrence of a thick organic

mat and muskeg and limited hillslope cuts and fills. Furthermore, there is a general lack of

fine sediment in surface and subsurface materials, and log-haul traffic is moderate and

Runs on less than full-year operating periods. Sediment delivery from light traffic on main

haul roads (two or less hauls per day) is believed to be low and is estimated at about the

same level as reported by Reid and Dunne (1984) for the Olympic Peninsula [1.30 [m3/

mile]/year]. Sediment delivery directly linked to traffic on spur roads is believed to be minimal because these roads are not generally close to viable channels; sediment delivery is

estimated at about the natural rate [0.15 [m3/mile]/year]. Estimated sediment inputs from

road use are shown in the following tabulation:

Type of use Annual sediment input 

m3/mile

Light use, spur road 0.15

Light use, main road (2 or less hauls/day) 1.30

Normal use, main road (5-10 hauls/day) 83.00

The total input of debris into a stream reach is calculated by (1) the natural inputs from vegetation next to a stream reach, (2) logging, and (3) inputs from streams feeding the reach.

32
Figure 10— Debris inputs from stands.

Addition of organic debris to stream channels is a continuing natural process in the oldgrowth spruce and hemlock forests of southeast Alaska. This input is estimated at 0.015

m3/m of streambank/year. The inputs of organic debris after logging are a function of stand

age and time since cutting. The curve in figure 10 reflects the debris input over the life of 

the stand. Changes in stream debris inventory resulting from logging are a user input. The

input from a reach entering a spatial unit is the sum generated in the spatial units the 

reach passes through. This routing of debris can have a significant impact on spawning 

and therefore is treated below in more depth.

The sediment-routing portion of the submodel uses a simple scheme based on the 

assumption that a fixed proportion of the sediment and debris in the various classes is

washed out of each reach each year. The basic equation for debris routing in year tn is:

Lt + 1 = (Lt + It

) ✶ (1 - P) ✶ (1 - D) ,

where 

Lt = total sediment (debris) level, 

It = total sediment (debris) input, 

P = proportion moving out of a reach each year, and 

D = 0.008 (proportion decomposing each year).

At the beginning of the simulation, the model calculates the proportion of debris moving to

the next stream reach (P). This implies:

P = (Ie + Iu - D)/Le ,

33

Routing
where 

Ie = annual input for equilibrium for this reach, 

Iu = annual input from upper reaches at equilibrium, 

D = 0.008 (proportion of debris decomposing each year), and 

Le = inventory of debris at equilibrium.

The determination of equilibrium conditions for fine and coarse sediment is slightly more

complex. The model calculates an initial condition based on the equilibrium ratio for fine to

coarse sediments (Le1/Le2):

Le1/Le2 = (I1/I2) ✶ [(1/P1 -1)/(1/P2 -1)] ,

where 

Le1 =: equilibrium level of fine sediment, 

Le2 - equilibrium level of coarse sediment, 

I1 = input of fine sediment, 

I2 - input of coarse sediment, 

P1 = movement rate of fine sediment, and 

P2 = movement rate of coarse sediment.

Given the ratio Le1/Le2, I1, I2, and the movement rate P1, for each reach, the model calculates P2 at the beginning of the simulation. Once this calculation is completed, equilibrium

levels for fine and coarse sediment are determined. Table 5 shows the sediment movement rates used in the calculations for different stream orders.

Table 5—Proportions of sediment washed out of each reach In each year, and proportion of fines at equilibrium, by stream order

Proportion washed out per year Equilibrium 

proportion of fines 

<3 mm > 3 mm < 3 mm

Stream order 

3; 4 0.20 0.026 0.30

2 .50 .097 .20

1 .90 .493 .15

Debris torrents are high-velocity, high-volume flows of heavily charged sediment and

organic debris in confined channels. They are usually triggered by debris avalanches into

upper slope gullies and canyons during major storms. Debris torrents create a major

source of short-term sediment and debris loading.

Data from the Pacific Northwest (Swanston and Swanson 1976) and professional judgment

of hydrologist in southeast Alaska indicate an increase in the frequency of occurrence of

debris torrents as a result of logging. For purposes of this submodel, the probability of

debris torrents was assumed to occur in first-order streams only. Logging increases this

probability (fig. 11). The delayed rise seen in figure 11 is due to a hypothesized gradual

reduction in strength of upper slope soils as the binding and reinforcing effect of tree roots

diminish through decay after logging.

34

Debris Torrents
35

Figure 11—Effect of logging on the probability of debris tonents in first-order streams.

Figure 12—Bedload overturn as a function of peak velocity in the largest fall storm (value set by user).
lnformation on bedload overturn is required by the fisheries submodel to predict potential

destruction of eggs and alvin during fall stormflow periods. Overturn is tied to calculated

peak velocities by using the relation shown in figure 12. The maximum possible bedload

overturn in each reach is assumed to be less than 100 percent because of various local

effects within the stream.

To predict habitat changes, the fisheries submodel also requires information on the quantity of undercut banks along each reach. According to Murphy and others (1984, 1986) as

much as 200 ma of undercut bank per kilometer of main stream channel may occur In oldgrowth stands. In the model, area of undercut bank is calculated for each reach based on

estimated length and mean depth of undercut (user input). Logging along stream banks

reduces available area of undercut banks of affected portions by 60 percent (Murphy and

others 1986). Reestablishment of these bank undercuts begins almost immediately after

initial destruction as a result of frequent lateral cutting during annual fall and winter high

flows in the channel (storm periods). The time needed to develop fully stabilized undercut

banks is unknown. Experience of hydrologists and fisheries biologists in southeast Alaska

suggests that, although the available area of undercut banks is ephemeral and subject to

local collapse during storm flows, it generally returns to prelogging levels within 5 to 25

years after disturtbance. In the model, 10 years is used as an average return time.

To adequately model streamflows, temperatures, and the interaction between them, a

smaller time-step flow model is included within the main submodel. This model is structured to run with a 3-day time step. Each year, the flow model runs according to the 

appropriate time step, starting on November 1.

Factors considered in water balance are rain, snow, snowpack, evapotranspiration, snow

melt, infiltration, runoff, base flow, and water table (fig. 13).

ln the model, the precipitation rate is driven by average monthly precipitation similar to that

shown in figure 14. The average monthly precipitation is modified in two ways: a long-term

adjustment of the form shown in figure 15, and a random annual pattern drawn from a random normal distribution with a standard deviation selected by the user.

Air temperature is calculated by using average monthly temperature similar to that shown

in figure 16 and a standard altitude effect of 3.6 °F per thousand feet. This temperature distribution is then modified by using the following procedure:

1. Mean winter air temperature is modified by a normal distribution variant with mean of

zero and a user-input standard deviation.

2. Mean 3-day temperature is modified by a normal distribution variant with a mean of 

zero and an input standard deviation of 4 °F for summer and 6 °F for winter.

3. Mean day and night temperatures are on 12-hour intervals. Mean daily temperature Is

modified by a value reflecting the difference between day and night temperatures (7 °F

for summer and 6 °F for winter).

When the air temperature in a particular area is below 32 °F, precipitation falls as snow 

and is added to the snowpack. When the temperature is above 32 °F, all precipitation is

assumed to fall as rain.

36

Bedload Stability

Undercut Banks

Streamflow

Water Balance
37

Figure 13—Generalized model of water movement through a watershed system.
38

Figure 14—Typical mean precipitation pattern used in the hydrology submodel.
39

Figure 15—Forty-year mean adjustment for yearly precipitation.

Figure 16—Typical annual distribution of mean sea level air temperature.
Snow melt is driven by both temperature and rainfall. When the temperature is below 

freezing there is no melt. When temperature is above freezing, there are two sources of

snowmelt. Air temperature causes melt of 0.06 inch of snow (water equivalent) per degreeday. Rain causes melt of 0.007 inch of snow (water equivalent) per degree per inch of rain.

Three things happen to the total surface water (precipitation plus melt): evapotranspiration,

surface runoff, and infiltration. The percentage of evapotranspiration in an old-growth stand

is a function of time of year (fig. 17), which has two elements: a constant background 

evaporation of 10 percent, and an evapotranspiration effect that is active during summer

and rises to a peak adding up to an additional 15 percent. Figure 18 illustrates the way this

summer evapotranspiration effect is assumed to vary after clearcutting. It returns to a maximum at a stand age of 10 years.

The proportion of the water remaining to infiltrate the soil after evapotranspiration depends

on two things: a background rate hypothesized to be 20 percent and the remaining water

capacity of the soil. All watershed areas are assumed to have an available water capacity

of 5 inches; when this capacity is reached, no further water is allowed to infiltrate.

Water in the soil is assumed to enter streams as base flow in direct proportion to the

amount of water in the water table, or 0.5 percent per day. Flows into each reach consist of

surface runoff from surrounding areas and base flow from those same areas. Flows from

one reach to another move downstream as instream flows.

Water temperatures are hypothesized to depend on air temperature and solar radiation on

the stream surface. The initial water temperature in a stream reach (Tl is the flowweighted average of groundwater (40 °F), surface runoff, and the final temperature from

the previous time step:

Tl = [(TF ✶ Qs) + (TR ✶ QR)] + [(TB ✶ QB) ✶ (QS + QR + QB)] ,

where 

Tl = initial water temperature (°F), 

TF = final water temperature from the previous time step (°F), 

TB = groundwater temperature (40 °F), 

TR = surface runoff temperature (°F), 

QS = streamflow (cfs), 

QB = groundwater inflow (cfs), and 

QR = Surface runoff (cfs).

The effect of solar heating follows Brown’s (1970) model, which states that the heating

effect is described by:

T (°F) = (area of exposed stream surface (ft2) 

✶ rate of heat absorption [(BTU/ft2)/min] 

✶ 0.000267 }/discharge (ft3/sec) ,

where 

T(°F) = the incremental increase in stream temperature, and 

0.000267 = a constant for converting discharge (ft3/sec) to pounds of water per minute.

40

Water

Temperatures
41

Figure 17—Evapotranspiration over the year in oldgrowth stands.

Figure 18—Maximum increase in summer evapotranspiration above background level as a function of stand

age.
This relation is applied to the water in each reach as it flows through the system. Final 

temperatures are a flow-weighted average of final temperatures for water in a reach and

incoming water from upper reaches.

The area of exposed stream surface is the surface area of the reach not shaded by 

streamside vegetation. The amount of shading depends on the stream width, the height of

streamside vegetation, and the time of year.

Each reach has a user input of maximum possible shading,which is less than 100 percent,

that results from open areas of various sorts, such as rocky ground and muskegs. The

height of streamside vegetation required for maximum shade is calculated as shown in figure 19. Given the maximum solar angle for each month (fig. 20), the height of streamside

vegetation for maximum shade is calculated with the equation:

Height = stream width ✶ tan (solar angle). 

This height is used in the relation shown in figure 21, along with the height needed for 

maximum shade, to calculate the proportion of the shaded stream surface. this calculation

neglects the orientation of the stream.

The rate of heat absorption is hypothesized to depend on two factors: the maximum solar

angle and the residence time of water within a reach. The maximum solar angle, given in

figure 20 for each month, is used to generate a factor indicating the reduction in heat

absorption to compare with that for the maximum solar angle of the year (fig. 22).

The residence time of water within a reach is calculated as:

Residence time (hr) = reach volume (ft3)/discharge (ft3/hr).

This is used to calculate the rate of heat absorption by using the relation shown in figure

23.

The wildlife submodel requires estimates of the number of days in winter when snow accumulation in each spatial unit is between 2 and 20 inches and when it is greater than 20 

inches. Eight inches of snow is assumed to equal 1 inch of water.

The flow model keeps track of the mean depth of snow in each spatial unit as equivalent

inches of precipitation. Snow depth on the forest floor includes snow that accumulates in

openings between the trees and snow that accumulates under the trees. The proportion of

the stand that is between crowns, or at 0 percent crown closure, accumulates the same

amount of snow as does a measured opening. Under the remainder of the stand, snow

depth is modified as a function of the average basal area per stem in the stand:

Modified snow depth = 1 - (0.166 ✶ basal area/stem) 

x (unmodified snow depth).

Average snow depth in the stand is equal to:

Average snow depth = (modified snow depth ✶ percent crown closure) 

+ [(unmodified snow depth) 

✶ (1- percent crown closure)].

42

Snow
43

Figure I9—Method of calculating height of streamside vegetation required for maximum shade.

Figure 20—Maximum solar angle by month in southeast Alaska.
44

Figure 21—Effect or vegetation on shading of streams. The minimum is determined by the percentage of

bank having vegetation.

Figure 22—Effect of solar angle on heat absorbed by water.
Figure 23—Effect of residence time on heat absorbed by water.

A summary of documentation for the hydrology submodel is given in appendix 3. Most of 

the model reactions and parameters pertaining to streamflow and temperatures are documented either from scientific research in Alaska or the Pacific Northwest or from estimates

made by using extrapolations from related work. Many of the relations describing sediment

and debris routing and snow interception have not been documented by scientific study.

45

Model

Documentation
The Fisheries Submodel

Michael L. Murphy, K V. Koski, Steven T, Elliott, David R 

Gibbons, John R. Edgington, William R Meehan, Robert S. 

Benda, Robert R Everitt, John R. Hamilton, and Lewis E.

Queirolo1

The fisheries submodel calculates production and harvest of some anadromous fish species having recreational, personal use, or commercial importance in southeast Alaska. The

submodel is designed to do calculations for pink, chum, and coho salmon.

This chapter describes the calculations made to simulate the life history of these species,

beginning with spawning and ending with escapement of harvest. The responsibilities and

order of calculations of the fisheries submodel are summarized in figures 24, 25, and 26.

The submodel is driven by precipitation patterns, snowmelt conditions, morphological characteristics of the stream, vegetative manipulation and road building at the stream site or in

the watershed. It models several key processes, including escapement to a particular

stream reach, egg deposition, egg survival, smolting success, rearing success, and harvest.

1MlCHAEL L. MURPHY and K V. KOSKl are fisheries research 

biologists, U.S. Department of Commerce, National Marine Fisheries Service, Auke Bay Laboratory, Auke Bay, AK 99821; STEVEN 

T. ELLlOTT and JOHN R. EDGlNGTON are fisheries biologists, 

Alaska Department of Fish and Game, Douglas, AK 99824 and 

Petersburg, AK 99833, respectively; DAVlD R. GlBBONS is a 

regional fisheries biologist, USDA Forest Service, Alaska Region, 

Juneau, AK 99802; WlLLlAM R. MEEHAN is a fisheries biologist, 

USDA Forest Service, Forestry Sciences Laboratory, Juneau, AK 

99802; ROBERT S. BENDA was a wildlife specialist, Tongass 

National Forest, and is currently at Prince William Sound Community College, Valdez, AK 99686; ROBERT R. EVERlTT is a system 

analyst, ESSA Ltd., Vancouver, BC V6Z 2H2; and JOHN R. 

HAMlLTON is a fisheries biologist and LEWlS E. QUElROLO 

is a regional economist, U.S. Department of Commerce, National 

Marine Fisheries Service, Juneau, AK 99802.

46

5

lntroduction
47

Figure 24—lnformation flow and system linkages for the fisheries submodel.
48

Figure 25—Basic model structure and driving parameters for coho salmon in the fisheries submodel.
49

Figure 26—Basic model structure and driving parameters for pink and chum salmon in the fisheries submodel.

Egg deposition in each stream reach is driven by escapement. The number of adult spawners assigned to a selected stream reach is related to available gravel area. In addition, for

pink and chum salmon only, summer flows and summer temperatures affect stream 

assignment. Summer flow depends on the precipitation regime in the watershed and the

evapotranspiration rate of streamside vegetation. Flow variance is strongly associated with

overstory removals and the impacts of this on snowmelt conditions and evaporation during

the spring and early summer runoff.

Summer water temperatures can also affect egg deposition. Summer temperatures are

influenced by air temperature, base water temperature, and streamside vegetation height.

The production of fry from deposited eggs and their survival are directly related to the general morphological condition of the stream and to streamwater temperature, volume flow,

bedload shift, base survival rates, and changes in percentage of fine sediments. Bedload

shift results in destruction of eggs. lncreases in percentage of fine sediment, resulting from

debris torrents, road building, road use, and clearcutting, directly impact egg-to-fry survival.
The development of the coho fry is primarily controlled by summer water temperature, 

which is influenced by air temperature, solar angle, streamside vegetation, and streamflows. Summer flows and streamside vegetation are directly related to the management

treatment of the overstory.

Survival of rearing fish in a stream depends primarily on winter water temperature and

amount of protected area. The amount of protected area is directly related to bedload shift,

side channel area, main channel pool area, and rubble area in the streams. The percentage of area of a stream in pools is directly related to debris density and debris dynamics.

Debris density and its dynamics are influenced primarily by clearcutting and overstory 

manipulation.

The structure of the fisheries submodel includes simulation of primary natural processes:

spawning, egg-to-fry survival, freshwater rearing of coho, migration of smolt, ocean survival, and fish harvest.

After all the fish harvest has occurred, a spawning stock of each species remains. The desired spawning stock in each reach of the stream for each species is calculated as follows:

S = 2(GA/GPP), 

where 

S = number of spawners required to fully utilize the available gravel under long-term average flow conditions, 

GA = gravel area available under average flow conditions, and 

GPP = gravel required per spawning pair (table 6).

Table 6—Gravel area required per 

spawning pair, by salmon species

Species Area

m2

Pink 1.0 

Chum 1.7 

Coho 11.1

Source: (1979) Reiser and Bjornn.

This calculation assumes that the sex ratio is 1:1. The available spawners each year are

allocated to each reach of the watershed in proportion to available gravel area. Reaches

with more gravel area will have more spawners. If the summer flow in the current year falls

below the long-ten average, the desired number of pink and chum spawners is reduced 

proportionately, thereby reflecting reduced available gravel area. The number of spawners

required to seed available gravel for the current year is calculated: 

K = S ✶ LSQ/LALSQ , if LSQ < LALSQ , and

K = S , if LSQ > LALSQ ;

50

Model Stages

Spawning
where 

K = number of spawners required to seed available gravel in the current year, 

LSQ = low summer flow, and 

LALSQ = the long-term average low summer flow.

In some watersheds, pink salmon spawning stocks can differ dramatically in odd and even

years; a year of great abundance may a~ternate with a year of low abundance. The model

accounts for this through the addition of a density-dependent mortality rate (fig. 27): the

mortality rate is low at extremely low stock size, increases rapidly to a maximum at an 

intermediate stock size, and decreases rapidly as stock size increases (fig. 27). The 

density-dependent relation acts to keep a stock of low abundance at a low level and to

maintain a stock of high abundance at a high level. Escapement is always at least 1 percent of desired escapement to account for strays from other streams. This allows a 

decimated population to be reestablished.

The phenomenon of different run sizes for pink salmon in odd and even years is often

observed, but no satisfactory explanation yet accounts for it. Perhaps high exploitation 

rates keep one stock trapped at a level of low abundance, or the stock in odd and even

years could be genetically different. In the model, if the watershed is such that the oddeven phenomenon is present, the relation depicted in figure 27 is included as an additional

mortality factor.

The number of eggs deposited (fig. 28) is determined after the spawners are allocated to

each stream reach. In its simplest form, egg deposition is calculated as follows:

E = F ✶ S/2 , a s < K , and 

E = F ✶ K/2 , if S > K ;

where 

E = number of eggs deposited, 

F = fecundity, 

S = number of returning spawners, and 

K = desired number of spawners required to seed available gravel in current year.

Egg deposition can be reduced by high stream temperatures in summer. Figure 29 shows

the relation used to portray effects of summer temperature on egg deposition for pink and

chum salmon. Below a daily mean temperature of 15.6 °C, no eggs are lost; whereas 

above 20.8 °C, all eggs are lost. This relation does not apply to coho salmon because 

coho generally spawn in fall.

Several factors influence egg-to-fry survival including concentration of fine sediment in the

spawning gravel, mean fall temperature, bedload shift during fall storms, and winter low

flows. Other factors influencing survival during this life stage are assumed to be unaffected

by logging practices and are included as a constant background survival rate. This relation

is given as:

51

Survival From Egg to

Fry
52

Figure 27—Density-dependent mortality rate as a proportion of spawning stock.

Figure 28—Egg deposition as a function of spawning stock.
Figure 29—Relation between egg deposition and high stream temperature in summer for pink and chum 

salmon.

FRY = E ✶ FS ✶ P1 ✶ P2 ✶ P3 ✶ P4 ,

where 

FRY = number of fry surviving to emergence, 

E = number of eggs deposited, 

FS = background survival rate, 

P1 = fine sediment factor, 

P2 = fall temperature factor, 

P3 = bedload shift factor, and 

P4 = winter low-flow factor.

Each of these factors can assume a value between 0 and 1.0, depending on physical conditions. Background survival rate is assumed to be 0.20 for pink, chum, and coho salmon.

The fine sediment factor (P1) is calculated from the concentration of sediments < 3.0 mm

(fig. 30). Currently, the model defines fines as those particles < 3 mm in size and assumes 

a ratio of 60:40 of fine to coarse particles in sediment inputs.

Cold water temperatures during spawning in October and November (P2) are presumed to

have a deleterious effect on early egg development and survival. The model currently

assumes that if average stream temperature in October and November drops below 2 °C,

egg-to-fry survival is reduced by 50 percent. The model uses this relation for all three

salmon species.

53
54

Freshwater Rearing

of Coho

Figure 30— Egg-to-fry survival as a function of percentage of fines.

During large storms, shifts d gravel used for spawning cause mortality because of physical damage to eggs and alvins and because of downstream transport to unsuitable sites.

In the model, the proportion of eggs lost because of shifting gravel is assumed to be 

directly related to the proportion of the bedload shifted by the peak fall storms (provided by

hydrology submodel) as given below:

P3 = 1 - bedload shift.

Abnormally low streamflows in winter are expected to reduce egg-to-fry survival because

of exposure and desiccation of eggs and alvins. The winter flow factor affecting egg survival is calculated as:

P4 = LWQ/ LALWQ ,

where 

P4 = winter flow factor affecting egg-to-fry survival, 

LWQ = low winter flow, and 

LALWQ = long-term average low winter flow.

After emergence, pink and chum salmon fry migrate seaward. The first ocean-age yearclass of pink and chum salmon, thus, becomes the number of fry calculated as described

above, summed over all stream reaches. Coho rear at least 1 year in fresh water.

Coho are produced in several reaches in a watershed, depending on area of spawning

gravel, and then are allocated throughout the watershed by rearing potential. Thus, an 

area having no coho spawning habitat may rear many coho depending on the rearing
habitat. Freshwater rearing is divided into two periods, summer and fall-winter. In summer,

rearing potential sets the upper limit of number of fry; in fall-winter, mortality, primarily from

freshest, reduces fry numbers to some degree depending on severity of fall and winter

storms and available protected area.

Rearing habitat consists of mainstream pools and side channel areas. Mainstream pool 

area for each reach is calculated as:

AM = TA ✶ PP ,

where 

AM = mainstream pool area (m2), 

TA = total surface area (m2) of the reach, and 

PP = proportion of total surface area in pools.

The proportion of total stream area in pools depends directly on the amount of large debris

present (fig. 31). Side channel area is assumed to be constant.

The total rearing potential for a reach is given by:

RP = (AM + ASC) ✶ FPM ,

where 

RP = rearing potential (number of fish), 

AM = mainstream pool area, 

ASC = side channel area, and 

FPM = rearing potential per pool area (number fish/m2).

The FPM depends on streamside shading; more open reaches allow greater rearing 

potential per unit area because of greater food production (fig. 32).

Although reduced shading may increase stream productivity and thus increase rearing

capacity, it may also cause dangerously high water temperatures in summer. If the mean

daily water temperature in any reach exceeds 25°C, 100 percent mortality occurs.

Mortality in fall-winter depends on how much protected area is available, such as side channels, pools, undercut banks, and rubble area. The total protected area is calculated as:

PA = C1 ✶ ASC + C2 ✶ AM + C3 ✶ UB + C4 ✶ RA , 

where 

PA = protected area, 

ASC = side channel area, 

AM = area of pools, 

UB = area of undercut banks, 

RA = rubble area, and 

C1, C2, C3, C4 = protective value of a unit of habitat (the values for C1 [side channel], C2 

[pools], C3 [undercut banks], and C4 [rubble] are, respectively, 1.0, 1.0, 1.0, and 0.25).

55
56

Figure 31—Proportion of area in pools as a function of volume of large debris per unit area of

reach.

Figure 32—The effect of shading on density of coho fry per square meter of pool area.
Current evidence points to large organic debris as a major component of rearing habitat 

for fry in summer and juveniles in winter. In the model, the area of large pools is directly

related to large organic debris (fig. 31). The presence of debris, independent of its role in

pool formation, is believed also to be an important determinant of habitat. The model does

not include this contribution to rearing habitat.

lntense fall storms reduce the protected area actually available (fig. 33). Protected area is

reduced if stormflows are sufficient to cause bedload to shift; a 75-percent reduction in 

protected area results when a 100-percent shift in bedload occurs.

Winter survival of rearing fish is calculated from the protected area (fig. 34). If there is no

protected area, only 10 percent of the rearing fish survive per year. If 30 percent or more of

the stream area is protected, 25 percent of the fish survive each year.

Survival of second-year coho is 1.6 times the survival of first-year coho. Cold water temperatures in January and February further reduce overwinter survival (fig. 35).

After a year in fresh water, some of the coho migrate to the sea. The percentage of 1-yearold fish that migrate depends on temperature the previous summer, which regulates 

growth. A high summer temperature leads to a larger percentage migrating after 1 year 

(fig. 36). All remaining coho migrate to the sea after 2 years.

Once fish migrate to the sea, they are subject to natural mortality, marine harvest, and

freshwater harvest. Annual survival rates in the ocean without harvest are assumed to be

0.02 (pinks), 0.20 (chums), and 0.10 (coho). Coho and pink salmon spawn after 1 year at

sea, and chum salmon spawn at 2, 3, and 4 years.

Fish harvest may occur in the ocean and in fresh water. A mixed-stock fishery occurs in 

the ocean and can consist of both commercial and sport harvest. A terminal fishery occurs

in fresh water or in the ocean near the mouth of the river and consists of commercial, 

sport, and personal-use harvest.

The mixed-stock fishery is not localized near the mouth of the river. Thus, it is not selective

on stocks from a given watershed. A fixed commercial harvest rate is specified by the 

user. For coho, a fixed sport harvest rate is also input by the user. All remaining fish return

to the watershed of origin, where they are available for escapement and terminal harvest 

by commercial, sport, and personal-use fisheries.

Two methods are available for allocating fish in the terminal fishery. In the first method, the

potential personal use is assumed to be 1 percent of the desired escapement unless a logging camp is present, which doubles the potential harvest to 2 percent. The potential sport

harvest is then calculated. If the sum of the two potential harvests exceeds the available

fish, all fish are allocated to the two fisheries in proportion to the potential harvest in each,

and escapement is zero. If the sum of the two potential harvests does not exceed the 

available fish, both potential harvests are met, and the remaining fish are escapement. 

This allocation usually leads to a decimated population at some point in a simulation.

57

Migration of Smolt

Ocean Survival

Fish Harvest
58

Figure 33—Effect of fall storms on protected area for wintering coho.

Figure 34—Winter survival of rearing coho as a function of proportion of area protected.
59

Figure 35—Effect of low winter water temperatures on overwinter survival.

Figure 36—Percentage of coho that become smolt after 1 year in fresh water as a function of average

stream temperature the previous summer.
ln the second method, fish are allocated by priority and are harvested only if surplus fish

are expected. Personal use has first priority and occurs if the expected number of returning fish exceeds desired escapement. The personal-use harvest is 1 percent of desired

escapement unless a logging camp is present, which doubles the harvest to 2 percent. 

This harvest occurs even if it reduces escapement below desired escapement. Sport harvest has second priority, but will occur only if the potential sport harvest does nat reduce

escapement below desired escapement. Whether or not the sport harvest operates, fish in

excess of desired escapement are allocated to the commercial fishery.

The only sport-fishery species modeled in SAMM is coho salmon. The freshwater harvest

depends on the fishing effort, abundance of fish, and access to sites within the watershed.

Fishing effort is based on past fishing success and presence and size of nearby human

operations.

The model considers two human populations, one nonlocal and one local. Total fishing

effort is the sum of the effort of these two populations. The nonlocal population contributes

a Potential number of annual fishing days, which is input by the user. This potential can be

changed over time by a constant number of fishing days per year to account for growth in

demand for sport fishing.

The potential number of nonlocal fishing days is achieved only if success in the previous

year (measured as catch per unit effort) exceeds some minimum level below which 

demand will be reduced (fig. 37). Nonlocal effort each year is given by:

ENL = CF ✶ PD ,

where 

ENL = total nonlocal effort, 

CF = factor from figure 37, and 

PD = nonlocal potential fishing days (user input).

The local effort is calculated from the following:

EL = (EMP ✶ DF) + DFL ,

where 

EL = total local effort, 

EMP = total employment in watershed (person years), 

DF = days fished per person year of employment, and 

DFL = user-specified fishing days by the rest of the local population.

Access for sport anglers is markedly affected by road construction. lncreasing access by

anglers will increase vulnerability of fish to being caught. This process is modeled by:

60
61

Figure 37—Freshwater sport fishing effort by nonlocal populations as a function of success in

the previous year.

Figure 38—Relation between relative vulnerability of fish in fresh water and access by anglers.
VR = 0.5 + 0.5 ✶ [1 - e (-roads ✶ p)] ,

where 

VR = relative vulnerability, 

e = base of natural logarithms, 

roads = total miles of roads, and 

p = catchability parameter that determines the Vulnerability of fish to access by anglers

(fig. 38).

Finally, the total freshwater harvest is given by:

HF = (ENL + EL) ✶ F ✶ VR ✶ VA ,

where 

HF = freshwater harvest, 

ENL = total nonlocal effort, 

EL = total local effort, 

F = fish available for freshwater sport harvest, 

VR = relative vulnerability, and 

VA = absolute vulnerability (user input).

The absolute vulnerability is the percentage of the entire spawning stock that will be

caught in one angler-day of effort under full access conditions.

Many of the relations in the fisheries submodel are based on experience in Alaska and the

Pacific Northwest. Other relations rely solely on the professional judgment of participants

in model development. The important model relations and assumptions are summarized in

appendix 4. The relations describing the spawning, egg-to-fry survival, and ocean survival

processes are well documented. Processes regulating overwinter survival of coho and

effect of summer temperature on age of migrating smolt are not well documented.

The relations in the model that describe spawning, egg-to-fry survival, ocean survival, and

harvest are documented with existing data. The relations describing many of the processes for species that rear in streams are presently neither well documented nor understood. Processes regulating success of rearing juvenile salmonoids are subjects of current

research. New information on rearing processes will have to be incorporated in the model

as the data become available.

Users of the model should bear in mind that many of the relations have been derived from

work done in the Pacific Northwest in addition to southeast Alaska. Geographical differences will probably not change the shape of the relations but could conceivably shift the

threshold values and saturation levels. The relation between coho rearing potential and

canopy density, in particular, is known to differ between watersheds, depending on whether

or not nutrients, such as nitrogen or phosphorus, limit primary aquatic production.

The behavior of the fisheries submodel, to a large degree, depends on inputs from other

submodels, especially the hydrology and soils submodel. Most of the major effects of logging are, at least in conceptual form, represented in the whole model but need to be 

thoroughly examined and reviewed to assure that inputs to the fisheries submodel are

62

Model

Documentation
appropriate. This is especially true for sediment and debris dynamics in the hydrology and

soils submodel and effective canopy closure in the timber submodel. These are probably 

the most important inputs to the fisheries submodel and are critical to its correct operation.

Changes after logging depend on the timber harvest practice used: clearcutting to the

streambank, stream cleaning after harvest, salvage of timber from the stream channel, or

use of buffer strips (Swanson and Lienkaemper 1978, Toews and Moore 1982). Management actions, such as use of buffer strips, substantially alter debris dynamics. Because

debris dynamics is, in reality, directly linked to alternative management’ actions, such 

management actions and their consequences for debris movement, timber costs, and 

revenues may be built into future versions of the model. Comparisons of costs and benefits of different management alternatives could then be done.

63
The Deer Submodel

Matthew D. Kirchhoff,Thomas A. Hanley, John W, Schoen,

Martin L. Prather, Rodney W. Flynn. Nicholas C. Sonntag, Peter J.

McNamee, Paul B. Alaback, and Dale L. Weyermann1

The deer submodel simulates changes in the population of Sitka black-tailed deer (Odocoileus hemionus sitkensis) resulting from changes in the vegetative characteristics of their

habitat, varying snowfall, and hunting mortality. Habitat is defined in terms of overstory 

characteristics (canopy cover, tree height, basal area, stems per acre, and timber volume),

understory characteristics (abundance and quality of fort~s, shrubs, and lichens), and prevailing depths of snow and slash. Most habitat characteristics are passed to the deer 

submodel from the hydrology and timber submodels (fig. 39). The deer population is composed of a number of hypothetical deer herds, each assumed to have access to one or

more spatial units (home range) within a predator-free watershed. The flow of submodel 

calculations is shown in figure 40.

The deer submodel operates on bioenergetic relations and has three major components:

deer energy intake, deer energy cost, and population dynamics. Deer receive all their 

energy from forbs, shrubs, and lichens, which vary seasonally in both quantity and quality.

The maximum biomass of forage occurring on a site varies as a function of site productivity and overstory canopy cover. Snowfall buries a certain proportion of each forage class

in winter, thereby making it unavailable to deer. Slash in young clearcuts reduces forage

1MATTHEW D. KIRCHHOFF, JOHN W. SCHOEN, and RODNEY

W. FLYNN are wildlife biologists, Alaska Department of Fish and 

Game, Douglas, AK 99824; THOMAS A. HANLEY is a wildlife biologist and PAUL B. ALABACK is a research ecologist, USDA Forest 

Service, Forestry Sciences Laboratory, Juneau, AK 99802; MARTIN L. PRATHER was a wildlife biologist, USDA Forest Service, 

Tongass National Forest, Ketchikan, AK 99901, and is currently with 

USDA Forest Service, Northern Region, Missoula, MT 59807; 

NlCHOLAS C. SONNTAG and PETER J. JCNAMEE are system 

ecologists, ESSA Ltd., Vancouver, BC Y6Z 2H2 and Toronto, ON 

M4Y 1N3, respectively; and DALE L. WEYERMANN is a programmer analyst, USDA Forest Service, Forestry Sciences Laboratory, 

Portland, OR 97208-3890.

64

6

lntroduction
65

Figure 39—lnformation flow and system linkages for the deer model.
66

Figure 40—Basic structure and driving parameters for the deer submodel.

availability year-round. The rate at which forage grows back each summer is a function of

intrinsic growth rates and the degree of use by deer in the preceding winter. Lichens, 

which are available only in old-growth stands Q250 years-old), are supplied at a fixed level

year-round.

Forage intake rates vary as a function of forage abundance, preference for a particular forage class, searching rate, and handling time. The amount of energy obtained per unit of

forage eaten is based on the gross energy of the forage and its digestibility factors, which

vary seasonally and by forage class. The net energy available for growth and reproduction

is equal to total digestible energy less the amount needed for nutrient metabolism.

Snow is an important variable in this model; it reduces the availability of forage and

increases the energetic cost of locomotion. The ability of the canopy to intercept snow

varies as a function of canopy cover (represented by tree height and basal area) in young

stands and as a function of basal area per stem in old-growth stands. Large-diameter
old-growth trees intercept snow most effectively. Other energetic costs to deer are related

to active metabolic rates, which vary by age class of the deer and by season.

Reproduction and mortality vary by age class (table 7) and are a function of net energy

balance at the end of the year. This net energy balance is the difference between energy

intake and cost in each season, modified by the deficit or surplus carried over from the

season before. Hunting mortality is additive at the end of summer and varies as a function

of deer density, degree of hunter access, and hunter days of effort.

Within each home range, deer are allocated each season to spatial units by net energy

availability. This allocation takes place at the beginning of summer (1 June through 31

October) and once during winter (1 November through 31 May).

Deer harvest is associated with hunting by local populations, residents of logging camps,

and nonlocal hunters. Nonlocal hunter success in one year affects the effort in the following year by this source. Success by local and logging camp hunters does not affect the

hunting effort in the following year.

Table 7— Definition of deer 

operational groups

Age class Operational group

Years

0-1 Fawns 

1-2 Adults 

2-3 Adults 

3-4 Adults

4-5 Adults 

5-6 Adults 

>6 Adults

The deer submodel characterizes several natural processes important to the life cycle of

Sitka black-tailed deer: understory relations, energy availability, effects of slash, effects of

snow cover, feeding, energy balance, reproduction, natural survival, and hunting mortality.

Deer are linked to forest management actions through changes in overstory, which alter

understory production and forage quality. Snow and slash accumulations increase with 

logging or thinning of the overstory and influence how much of the forage produced is

available to deer.

Factors controlling understory forage production in a forest environment are complex. Light

and soil conditions are important, and other factors such as stand age and disturbance history can strongly influence understory development. In this model, understory dynamics for

all stands, young and old, are represented as a function of tree canopy cover and SI class.

The deer submodel keeps track of the edible plant biomass in each of three board plant

groups: woody shrubs, herb, layer vegetation (fort~s), and arboreal lichens.

After clearcutting in southeast Alaska, understory biomass increases greatly in response to

increased light. Understory biomass remains high as tree seedlings become established

and the overstory canopy begins to develop. By 20-30 years after clearcutting, the canopy

67

Model Stages

Understory Relations 
of most second-growth stands has closed over, effectively shading out the understory vegetation below. The hypothesized relation between canopy cover and understory production

is shown in figure 41.

In addition to light, understory growth is determined by the soil conditions of a particular

site. Deep, well-drained soils capable of sustaining rapid tree growth are also capable of

supporting high levels of understory biomass. This potential, however, is seldom realized in

forested sites because the amount of light reaching the forest floor (as indicated by canopy

cover) is normally a limiting factor. The potential maximum biomass for shrubs and forbs in

the absence of any canopy cover (for example, after clearcutting) is shown in figures 42

and 43, respectively. This potential, as modified by the effect of canopy cover (fig. 41), is

used to calculate total shrub and forb biomass for all combinations of SI class and stand

age class on spatial units in the model.

Lichens, which have received little study in southeast Alaska, are assumed to fall to the 

forest floor at a constant rate of 25 (kg/ha)/year in all old-growth regardless of SI class.

Lichens are not present in clearcuts and young-growth stands (Rochelle 1980).

In general, herb, layer vegetation comprises forage of the highest nutritional quality. The

evergreen forbs in this group (for example, Coptis asplenifolia Salisb., Rubus pedatus J.E.

Smith, Cornus canadensis L., Pyrola uniflora L., and Tiarella trifoliata L.) are high in digestible protein and energy, but because of their low-lying growth form are largely unavailable

under moderate and deep snow conditions (Hanley 1984).

Woody shrubs (for example, Vaccinium spp., Oplopanax horridum (Smith) Miq., and 

Rubus spectabilis Pursh) grow taller, but lose their leaves in winter, leaving relatively indigestible woody stems.

68

Figure 41—Proportion of potential carrying capacity achieved as a function of canopy

cover.
69

Figure 42—Maximum shrub carrying capacity as a function of site index class.

Figure 43—Maximum forb carrying capacity as a function of site index class.
The energy available to deer for maintenance, growth, and reproduction is dependent on

the types and quantities of forage ingested. All three forage classes (forbs, shrubs, and

lichens) are assumed to have a gross energy equivalent of 4500 kcal/kg in both summer

and winter.

The proportion of digestible energy available to deer differs by forage class and season. In

late fall and early winter, plants grown under open light conditions in recent clearcuts are

less digestible than plants grown under the shade of nearby closed canopy sites.

Variation in forage quality between open and forested sites is incorporated in the model via

a 3-percent reduction in winter digestibility of forbs and shrubs on all sites having crown

closures of less than 40 percent. Seasonal dry matter digestibility factors for forbs, shrubs,

and lichens under canopies 240 percent are given in table 8.

Not all the digestible energy in plant material is available to the deer for maintenance and

growth. About 15 percent of digestible energy is (lost to urine production and about 30 percent to heat of nutrient metabolism. The balance, termed true net energy, available from

each plant type in winter and summer seasons is computed as follows:

TNE = GE ✶ DMD ✶ MEC ✶ NEC ,

where 

TNE = true net energy, 

GE = gross energy 

DMD = dry matter digestibility (variable), 

MEC = metabolizable energy coefficient (0.85), and 

NEC = net energy coefficient (0.70).

The true net energy available from each forage type, summer and winter, is given in table

9. The following tabulation of preference indices for each of the three forage types gives

subjective factors based on observations of tame deer foraging in the wild and on studies

of food habits.

Forage class Preference factor

Forbs 0.6

Shrubs .2

Lichens .2

The model maintains a record of the biomass associated with each SI class and age class

in each spatial unit. The total amount of shrubs available at the end of each season is

described by:

Shrubs, end of season - shrubs, beginning of season + growth 

- shrubs eaten - mortality.

70

Energy Availability
A logistic model was chosen to represent shrub growth in year t:

Bt = r ✶ Bt ✶ (1 - Bt/Kt) ,

where 

t = current simulation year, 

Bt = shrub biomass in year t, 

r = intrinsic growth rate, and 

Kt = carrying capacity in year t.

Growth of both forbs and shrubs is reduced as a result of heavy grazing in the previous

year by deer (fig. 44). The amount of summer shrub biomass available to deer is equivalent to the annual growth of the current year, or 13 percent of the total shrub biomass in a

steady state, old-growth condition. Available summer shrub biomass is reduced by 40 percent in winter to reflect the biomass lost when the leaves are dropped. No shrub growth

takes place in winter.

Unlike shrubs, forbs do not carry over from year to year. Only about 50 percent of the forbs

available at the end of summer live into winter, and all forbs die during winter. Forbs grow

back instantly at the beginning of each summer (intrinsic growth of 1.0), except when more

than 50 percent of the biomass was consumed during the previous year by deer foraging

(fig. 44).

The depth of slash resulting from various logging and thinning regimes is passed to the

deer submodel from the timber submodel. Although deer suffer increased energy costs

when they move through and over slash, they typically walk around slash in normal foraging activities. The primary effect of slash is to reduce the total amount of available shrubs

and forbs in recently logged or thinned areas. The initial reduction in forage availability due

to slash is given in figure 45. Forage availability increases over time as the slash decays.

Snow is an important factor influencing habitat selection by deer in winter. Snow impedes

movement, increases energy costs, and reduces forage availability. An average snow 

depth in each stand is passed to the deer submodel from the hydrology submodel, where

the amount of snow intercepted by the canopy increases with increasing canopy cover 

and mean basal area per stem. The hypothesized effects of increasing snow depth on forb

and shrub availability in the stand are shown in figure 46.

71

Effects of Slash

Effects of Snow Cover
72

Figure 44—Effects of deer foraging on understory growth rate.

Figure 45—Effect of logging slash on forage availability.
73

Figure 46—Effect of average snow depth on forb and shrub availability.

Figure 47—Added daily energetic cost to fawns and adults resulting from snow accumulation.
Energy costs increase with increasing snow depth (Parker and others 1984). When snow 

is <2 inches deep, deer experience negligible added costs. The added daily cost of moving

through snow is shown in figure 47.

Feeding is represented in a way that accounts for the effects of competition on forage 

abundance. The formulation used, taken from predation research (Charnov 1973, Holling

1959), calculates expected intake of each forage type by deer group (fawns and adults) as

a function of deer density, forage abundance, search rates, handling times, and foragespecific preference factors:

Ei,j = (Aj ✶ Dj ✶ Pi ✶ Wi

)/[1 + (Aj ✶ X)] ,

where 

Ei,j = the potential kilograms of forage group i consumed by deer group i in a season, 

Aj = the rate of effective search by deer group j,

Dj = number of deer in deer group j, 

Pi = the relative preference for forage type i, and 

Wi = kilograms of plant group i available to deer across all age classes and spatial units in

that population’s home range, and

3 

X = Σ hi,j ✶ Wi ✶ Pi ,

i=1

where 

hi,j = handling time, or the fraction of a season required to digest and process a unit biomass of forage type i by deer group j.

The potential forage consumed is next modified by the total forage available to ensure that

actual intake never exceeds the amount available. The following formulation is used to

compute actual forage intake:

EATi = Wi ✶ [1 - ez(Ti/wi)] ,

where 

EATi = the actual kilograms of plant group i ingested by all deer in the particular herd, 

Wi = kilograms of plant group i available to deer across all age classes and spatial units in

deer population’s home range, 

e = base of natural logarithms, and 

Ti = total deer herd requirements for plant group i, which also equals j = Ei,j where j = 

number of operational groups in deer populations.

From the above equation, T,NV, is the instantaneous removal rate of forage type i from

deer feeding. The forage eaten (EAT) is then allocated bad< to seasonal intakes per deer

for each deer group in each herd. The forage consumed by each deer is assumed to be

directly proportional to the forage requirements for the group relative to the requirements

for the total herd. For example, if adult deer require 63 percent of the total forage requirements, they receive 63 percent of the total forage consumed.

74

Feeding
Both fawns and adults are assumed to forage an average of 11 hours per day throughout

the year. We assume they effectively search a 6-foot-wide strip and travel at 0.3 km/hour

while foraging. This translates into an effective search of about 1.00 ha per day. The rates 

at which deer process ingested food are controlled by intake rates, rumen size, and passage rates. lntake rates generally are lower for woody browse than for green succulent 

vegetation, lower for fawns than for adults, and lower in winter than in summer. Handling

times, which are the reciprocal of intake rates, are reported for fawns and adults, for summer and winter, and for each of the three forage classes (table 10).

Table l0—Summer and winter values for handling time by forage

class

Summer Winter 

Forage class Fawn Adult Fawn Adult

Days per kilogram

Forbs 0.4 0.3 0.45 0.4

Shrubs .6 .5 .8 .6 

Lichens .4 .3 .5 .4

The net energy surplus or deficit available to deer is equal to energy intake (EAT1, minus

energy cost (ECOST), plus any energy surplus (ESURP) from the previous season. 

Energy intake is the amount of forage consumed, multiplied by the true net energy available

from each forage type. Daily energy cost (ECOST) is computed as follows:

ECOSTij = BMRij ✶ ARFACij + snowcost , 

where 

BMR = 70.0 1 (BWij 0.75); 

ECOSTij - daily energy requirement of deer group i in season j; 

BWij ~ body weight of deer group i in season j; 

ARFACij = activity factor applied to basal metabolic rate to estimate active metabolic rate 

of deer group i in season j; normally varies from 1.25 to 2.0 (Moen 1973); and 

snowcost = added energy cost associated with locomotion in snow.

Active metabolic rates used to calculate energy costs are given in table 11. Seasonal 

energy costs are equal to the daily energy costs multiplied by the number of days in each

season.

Deer are allocated to individual stands in the watershed by the net energy available in 

each stand. The net energy available equals energy available from the understory less the

energetic costs of locomotion through snow in a particular stand.

To calculate energy surplus for an animal, we assumed there are 9.5 kcal/g fat and 5.7

kcal/g protein in each gram of reserves, and that body reserves are 70 percent fat and 30

percent protein. The mean energy value of body reserves, therefore, is:

8.4 kcal/g ✶ ([0.7 ✶ 9.5] + [0.3 ✶ 5.71]).

Finally, we assumed that, on average, reserves of about 30 percent of the weight of adult

deer and 15 percent of the weight of fawns can be expended before starvation.

75

Energy Balance
Deer reproduction and mortality rates are structured by using the energy balance

approach. Forage ingested is converted into equivalent energy, and, after accounting for

energy costs due to snow, the net energy pool remaining determines reproduction and natural mortality. Hunting mortality is represented explicitly.

Table 11—Average body weight and dally metabolic requirements In summer and

winter for adult and fawn deer

76

Figure 48—Effect of winter weight loss on deer reproduction.
Reproduction (R) is a function of the energy balance at the end of winter (fig. 48). Maximum fecundities, averaged over yearling and adult age classes are assumed to be 1.5

fawns per doe. Actual fawn production is equal to reproduction (from fig. 48) multiplied by

1.5 (the maximum).

Survival of fawns and adults is, like reproduction, a function of net energy balance at the

end of each season (fig. 48). When R is equal to 1, maximum survival of fawns (75 percent) and adults (90 percent) is realized. As weight loss increases, the value of R 

decreases, and the percentage of survival declines. Because their fat reserves are proportionately smaller, fawns suffer a greater mortality per unit of weight loss than do adults.

The hunter population consists of three classes of hunters: local residents, loggers, and

hunters from outside the immediate area. Local residents include individuals from nearby

communities who ordinarily travel by skiff to the area for a day hunt. Loggers include those

actively involved in logging who would not otherwise use the area. Outside hunters are

those from communities beyond the immediate locale who travel to the area by aircraft,

large vessel, or ferry.

The model requires user input for the maximum number of local hunters using the area 

and the number of days per hunter. The population of woods workers is computed in the

timber submodel as a function of the volume of timber logged and miles of road built. The

proportion of loggers that hunt and the number of days per season that each logger hunts

are specified by the user. Maximum hunter days for nonlocal hunters is also specified by

the user.

Loggers and local residents are assumed to exhibit no effort-response; that is, the number

of hunter days spent by those hunters is independent of their success rate in the previous

year. For the nonlocal hunter population, the hunting effort varies depending on success in

the previous year (fig. 49).

The number of deer killed per unit of effort varies with deer density, from no deer harvested per hunter day at densities below 10 deer/mis, to a maximum of 0.5 deer per hunter

day at densities in excess of 70 deer/rr\i2. Deer killed by hunters are removed at the end of

each summer in direct proportion to the population in each age class within each herd.

In addition to the legal harvest of deer, a significant number are taken by poaching. The

number of deer poached is expressed as a user-specified proportion of the legal harvest.

The number of deer this represents is added to the legal harvest to arrive at total deer 

mortality.

77

Reproduction

Natural Survival

Hunting Mortality
A summary of the sources for the deer submodel is given in appendix 5. Estimates of

understory production, availability, quality, and digestibility as a function of season, SI

class, and canopy cover are based on research conducted in southeast Alaska and on the

professional judgment of the authors. Energy intake rates and energetic costs associated

with basal metabolism, activity level, and snow depth are based primarily on work conducted outside Alaska. As shown by the frequent mention of “professional judgment” in 

the appendix, many of the parameters and relations used in the deer submodel either 

were unknown or were published in a form not directly transferable to the deer submodel.

In those instances, specific relations were developed by extrapolating from existing data

and by using professional judgment.

78

Model

Documentation

Figure 49—Annual hunter effort as a function of hunter success rate in the previous year.
Using the Model SAMM: Implications

for Management

Lawrence D. Garrett, Roger D. Fight,Dale L. Weyermann, and 

Joseph R. Mehrkens1

This paper documents the development of the prototype model SAMM, which projects 

multiresource impacts of management actions in southeast Alaska forests. The concepts

represented in the model, although not new, represent a significant extension of available

knowledge of Alaska multiresource interaction. Managers and analysts are cautioned, 

however, that formal validation of these concepts is not complete.

The model represents an integration of the knowledge of scientists and specialists involved

in research and management of natural resources in southeast Alaska. Development of

many of the algorithms has evolved through interaction of these specialists in conjunction

with analysis of available data.

The prototype model as currently developed is appropriate for evaluating relative changes

in and integration of resources at the watershed level as a result of management action.

The manager is cautioned that the model has not been tested against observed data for

accuracy of predictions. Submodel structure and outputs and overall model linkage and 

performance have been subjected to intense review by a panel of experts. It is the opinion

of this interdisciplinary panel, who drafted this report, that submodel structure, linkages, 

and outputs represent a state-of-the-art understanding of multiresource integration in 

southeast Alaska. Until tested against an observed data set, the model should be used 

only to characterize relative change in resource outputs as a result of management action.

1LAWRENCE D. GARRETT is a forest economist, Northern Arizona 

University, Flagstaff, AZ 86001; ROGER D. FlGHT is a principal 

economist and DALE L. WEYERMANN is a programmer analyst,

USDA Forest Service, Forestry Sciences Laboratory, Portland, OR 

97208-3890; and JOSEPH R. MEHRKENS was a regional economist, USDA Forest Service, Alaska Region, Juneau, AK 99802, and 

is currently with The Wilderness Society, Juneau, AK 99802.

79

7

lntroduction
To simulate the effects of management actions on various natural resources, the component models are integrated in a cascade fashion; that is, tree stocking affects herbage and

water yield, which in turn affect sedimentation and wildlife habitat. There is currently little

feedback in the system. Feedback mechanisms can be included as sufficient knowledge is

developed on interacting effects.

Like all analytic models and systems, SAMM has limitations. Analysts and managers

should understand these limitations, especially as they relate to the potential applicability

and transportability of the model.

Analytical models like SAMM represent abstractions of the real biological world. They are

imperfect and at best represent a simplistic description of extremely complex biological,

ecological, and socioeconomic processes (Goulding 1979, Taylor 1979, Valentine 1978).

Development of models such as SAMM must be constrained to conform to managers’

requirements for brevity and simplicity; that is, the model is designed to capture the real

world in the fewest variables possible and with a limited amount of complexity in the variable interactions.

The analyst is almost always limited by available data in structuring a biologically reasonable system that also affords predictive capability. Most established guidelines recommend

limiting the variables in the system to a small subset providing the predictive efficiency

appropriate to the intended use. The inclusion of all variables affecting the natural process

is normally undesirable because of restricted data availability, marginal increases in predictive efficiency, and increased cost with increased data requirements and system complexity.

Any application of SAMM will be constrained by how similar or dissimilar the biological

communities to be evaluated are to the community or communities defining it. Even in 

similar biological communities in southeast Alaska, considerable variation can occur in biological growth, physiographic characteristics, and climatic conditions. The analyst therefore

should have trained professionals evaluate the effectiveness of the model in any new or

questionable area. If unacceptable predictions are found, the model or submodels, or both,

must be recalibrated or reformulated with data sets representing the new area.

Most of the resource submodels in SAMM were developed from resource data from southeast Alaska or from information provided by scientists and specialists working there. 

Where possible, information transported from other regions was modified to fit conditions 

in the southeast. When data were not available, model outputs were compared to the literature and evaluated by professionals working in the specific resource areas.

To build a simulation model, data are generally needed for three different procedures:

model development, calibration, and testing or validation. Data for these procedures are

severely lacking for southeast Alaska, which caused reliance on panels of experts, made

up of resource scientists and specialists, for development of model relations. This is one

weakness affecting potential use of the system by managers. These first approximations

are not necessarily in error; however, without observed data bases for testing, validation of

predictive accuracy is impossible.

80

Model Applicability
Managers should be cautious in applying SAMM, or any biological Model, to areas other

than the one the model was developed for. The transportability of SAMM relates directly to

how parameters and data used to develop the model differ across the southeast Alaska

region. The degree to which SAMM, or any other process model, is transportable depends

on three major factors:

1. How similar are natural and physical processes in the area of application to those in the

area of development; that is, would a different model structure be more accurate? For

example, does an impervious soil layer cause most precipitation to run off as surface

flow rather than percolate through the soils and enter streams as subsurface and surface flow?

2. How accurately do variables describe natural processes in the new area vs. the area

where the model was developed; that is, are the same variables appropriate and adequate? WiII relations among model variables and model structure be the same in the

new area? More simply, are deer and overstory the conflicting resources? Or is it 

understory and deer? Are the variables describing the resource interaction the same in

both areas?

3. How different are data describing a variable, both in diversity and variability? Do tree

form and soils vary radically from site to site? Does precipitation occur as frequent light

rain or infrequent severe storms, or as both but in different seasons?

The more that processes, variables, and data from one area are similar to those from

another area, the more likely a given model can be transported and adequately calibrated

to the new area (Goodall 1972, Pilgrim 1975). In many cases, the more generalized the

model and the more closely it represents natural processes, the greater the success. This

is not always true but is a general guide.

Analysts should evaluate existing submodels for use in any new area under consideration

before making any changes. This requires testing to determine the predictive capability in

the new area. If a particular submodel does not have predictive efficiency but the natural

processes seem properly defined by existing variables, the model should be recalibrated 

to data sets representing the area and resource.

If the accuracy of predictions after calibration is unacceptable, the existing submodel should be

replaced with a resource model with proven predictive capability for the specific area. If 

one does not exist, it should be developed from data or knowledge adequately representing the resource and area.

We do rot mean to imply that statistical tests are the only accepted criterion for model

assessment. Statistical significance, when it can be determined, should be used to assess

model usefulness. Statistical tests normally represent limited variable and data ranges; 

final evaluations of the utility of a model to the forest manager must incorporate a wider

range of knowledge. Knowledge gained from professional experience and information 

taken from the work environment are also important in determining model credibility

(Goodall 1972, Goulding 1979, Pilgrim 1975).

Potential users of SAMM must be aware of the above limitations and understand how the

limitations will constrain the future applicability and transportability of this model.

81

Model

Transportability
The SAMM model needs additional evaluation and testing to determine its predictive 

capability and usefulness to managers. Management models like SAMM are created for

specific purposes, and the adequacy or validity of a model can be evaluated only in terms

of that purpose. To evaluate a model means to develop an acceptable level of confidence

that predictive evaluations conducted and inferences drawn from the model are acceptable

and applicable. Thus, the concept of validation is one of degree; it is not an either-or

notion. The degree of model validation considered acceptable for SAMM depends entirely

on management confidence in available evaluations and inferences drawn from the model.

No magic formula exists for assessing such a concept. For management models, this

remains a deliberate management decision.

Extensive sets of data for multiresource analysis and testing are not available for southeast Alaska. When independent data sets are not available, a model cannot be fully

evaluated in a rigorous statistical sense. Successful application of the following procedures

nonetheless will increase confidence in a model by both managers and analysts:

1. Qualitatively examine the model structure. In general, the variables and their linkages in

ecosystems are similar; there are physical and natural theories supporting basic structures and their differences. The model must conform to these theories, which can be

readily confirmed through the literature.

2. Compare specific output with that from previous studies. Even if no other model is

directly comparable with the model being tested, it is often possible to identify specific

structures and outputs from other studies that can be compared to related structures

and outputs generated by the model. Such comparisons may be subjective. Agreement

among a majority of such comparisons is a positive indication of validity.

3. Prepare a sensitivity analysis of model variables. Sensitivity analysis is an examination of

the response of a model to systematic incremental changes in variables. The magnitude,

direction, and form of the response are analyzed both qualitatively and quantitatively.

Sensitivity analysis can demonstrate how sensitive the results or outputs of the model 

may be to the variable values used. In general, both theory and previous research have

characterized variables the outputs should be most sensitive to. If the response by the

model is highly sensitive to certain variables differing from theory or previous research,

additional effort should be directed at model structure.

Sensitivity analysis by varying the input values over a wide range can yield information 

on the impact of decisions if the external environment changes. If slight changes in 

inputs lead to significantly different outputs, then caution in implementation is indicated,

and viable alternatives, or built-in controls, should be developed.

Sensitivity analysis can provide valuable clues for possible modifications to the model. 

The analysis can indicate what parts of the model can be simplified, for example, by

changing from a Monte Carlo routine to the use of a mean value or by dropping a whole

subsystem from the model. The analysis can also indicate where it might be fruitful to

model relations in more detail.

82

Model Verification

and Validation

Evaluating

Predictive

Capability
4. Establish confidence intervals for population parameters. Confidence intervals can often

be established around the estimates of population parameters. Most statistical texts 

provide suitable formulas if a normal distribution can be assumed. This is not as restrictive as might appear because most large samples are approximately normally distributed, and in any case, the characteristics of most simulation models cause the centrallimit theorem to hold for their outputs.

5. Have a peer review of model structure and outputs. Review by peers of the model’s formulation, composition, responding, and output help to build confidence in the validity of

the model. The value of this process may be limited, however. As noted previously, all

assumptions, interpolations, extrapolations, and approximations should be explicit. Even

then, due to model complexity or perhaps even to lack of time to trace variables through

the model, there is no guarantee that reviewers will achieve any more than a very basic

understanding of what the model is doing. This process therefore may give a misleading impression of the confidence of peers in the ability of the model to achieve the 

intended objectives.

This model, like any model of a complex biological-physical-economic system, is necessarily incomplete. It is a highly simplified representation of a real system and contains 

conceptual biases and incorrect data. It does, however, represent a synthesis of the knowledge of a diverse group of scientists and managers who worked with-the system on a 

regular basis. The assumptions and simplifications applied in building the model were a 

first attempt at an integrated system model capable of exploring the implications of a wide

range of management options in southeast Alaska.

The accepted form of model evaluation involves extensive validation. If the model passes

various tests including both statistical and professional judgment, it is classified as “valid.” 

In truth, no biophysical model can be totally valid because a is, by necessity, simple in 

comparison to the real world. In any case, the more tests a model survives, the greater the

confidence that can be placed in its predictions. Model outputs cannot be accepted as

absolutes; they should be used as guidelines to suggest ideas and issues requiring action.

The major contribution a model can make is to efficiently display the relative probable

changes occurring in several resources as a result of management action.

No matter how much effort goes into testing a model, one cannot, a priori, identify the 

limits of predictive power or robustness. invariably, a real-world process not included in the

model will eventually cause a divergence between model and observation. But management decisions have to be made in a world of uncertainty, and models have been proven

useful in helping managers deal with this uncertainty.

Evaluating the model as outlined above is partially intended to de-emphasize the quantitative nature of the model output and concentrate more on the qualitative aspects. Managers

are not only interested in the absolute numbers of resources over the next 150 years but

also in how the resources respond qualitatively, given various imposed management 

actions or natural perturbations. In other words, SAMM is also intended:

1. To develop an understanding of how robust the forest system is when stressed.

83

A Final Note on

Model Evaluation
2. To determine if there are management actions completely inappropriate over time and,

more importantly, to identify those actions that can partially or completely mitigate an

adverse impact.

3. To come to some agreement on the quantity, quality, and kinds of information needed to

both improve our understanding of the system and help us monitor its health.

The southeast Alaska multiresource model is comprised of many algorithms that define how

each submodel is to operate and how the four submodels are linked. The algorithms are in a

computer program written in ANSl FORTRAN 77. A graphic interface for SAMM is accomplished with commercial software designed for IBM PC/ATs2. The complete model can run 

on a microcomputer, minicomputer or mainframe computer of any design, if appropriate

reprogramming is accomplished and compatible graphics software is linked to the model.

Currently, SAMM is programmed for IBM PC/ATs or full compatibles. Access to the current

model is through the individual designated as the leader of the SAMM technical committee

under the interagency memorandum of understanding (see footnote 2, chapter 1). The current leader of the technical committee is Donald J. DeMars, Forestry Sciences Laboratory,

Juneau, AK 99802. Model development was managed by the Project Leader for Economics

of Forest Land Management, Forestry Sciences Laboratory, Portland, OR 97208-3890.

A users guide is in preparation for the software. Operation of the software without a users

guide requires training by a specialist skilled in computer operation and knowledgeable of

the SAMM structure.

Outputs derived from the model are dependent on three primary factors: 

1. Required inputs to the model that describe the physical character of a watershed to be

evaluated.

2. Required inputs to the model that describe the current state of the resources being 

modeled: timber, water, fish, and deer.

3. Required management actions to be performed, such as harvest, thinning, stream 

cleaning, fish harvest, or deer harvest.

Given the specified inputs, the model will project changes over time in the state of the resources given interactions of management, natural occurrence, and the resources 

themselves.

The following examples provide simulated resource outputs from a southeast Alaska 

watershed covering several thousand acres. The watershed is dominated at the start by

unmanaged old-growth hemlock and spruce. Twenty-five percent of the area is clearcut in

year 20. The area is then naturally reforested. Management activities included in the simulation are clearcutting, stream cleaning, natural reforestation, and road building. The area 

is assumed to have SI classes from 70 to 130 represented and to cover a normal spectrum of physiographic conditions.

2Use of a trade name does not imply endorsement or approval of 

any product by the USDA Forest Service to the exclusion of others 

that may be suitable.

84

Status of SAMM

and Example of

Outputs

Status

Example of Outputs
Figures 50-56 are actual outputs from the model. They illustrate effects of real parameters

operating in the model, including maximum snow depth, timber growth, timber harvesting,

deer density, road building and use, proportion of fine sediments, large organic debris, and

fish spawning. Parameters illustrated occur in the four major submodels: timber, deer,

hydrology, and fisheries.

Timber and snow—Clearcut logging, followed by regeneration and eventual crown 

closure of the new stand, directly impacts several resources in the system. Figure 50 

relates recovery of canopy cover in stands clearcut in year 20. Yearly maximum snow 

depth over the 100-year cycle is given in figure 51. Peaks are noted in years 27, 36, 40, 

47, 62, and 77. Excessive snow depths are detrimental to deer, especially when the 

depths exceed 20 inches in recently harvested stands.

Deer—When extreme snow depths coincide with timber harvest, considerable impacts 

can occur in deer populations. This results from reduced browse availability due to snow

and slash and increased energy cost for the deer from moving through deep snow. Figure

52 demonstrates the combined effects of snow and timber harvest on deer density. The 

harvest activity in year 20, combined with heavy snows, severely impact populations for 20

years after year 20. As the timber stands regrow, deer populations are maintained but at

lower levels than could be maintained in old-growth stands.

Hydrology and fisheries—figures 53-56 illustrate the impact of road building (year 15) 

and timber harvest (year 20) on fisheries. Creation of large debris during logging operations actually can have a positive effect on returning adults (fig. 53). Large debris is 

important in creation of pool, protective, and rearing areas.

Fine sediments are also created from logging and road building activities (fig. 54). Logging

causes a gradual increase in fines entering streams and then a decrease over time until 

levels build up again. Road construction and use creates a much greater amount of fines

entering streams. This is from the settling of the bed of a new road and the abrasive action

of tires on road surfaces. The road building and logging scenario has a sharp peak for 

proportion of fine sediments in the year of logging, which is due to road use. This peak is

from a 1-year input of fines, so its effect is primarily focused on one year-class of fish. This

explains the jagged appearance of the dotted line in figure 55, because one year-class is

dramatically affected by the sediments while the other year-class is at sea.

Fine sediments wash out of a system faster than coarse sediments, resulting in a period

when the proportion of fines in the system is below that for a steady-state system. As the

coarse materials wash out, the proportion of fines return to steady-state conditions. This is

an advantageous situation for pink salmon and results in slightly higher long-term fish 

numbers after logging. The 40-year cycle in fish populations is due to the long-term cycling

of precipitation simulated in the hydrology model.

The dynamics of coho salmon fisheries are more complex. Coho are affected by the 

proportion of fine sediments but to a lesser degree than are pink and chum salmon. Coho

dynamics are also driven by:

1. Available spawning gravel area.

2. Large debris added or removed during logging.

3. Proportion of streambank that is cut.

85
86

Figure 50—Percentage of canopy cover for five site index classes.

Figure 51—Maximum annual snow depth at sea level in the south part of southeast Alaska.
87

Figure 52—Deer densities in a watershed under average snow conditions, with and without logging.

Figure 53—Change in number of coho salmon spawners due to rearing pools being formed by large organic

debris.
88

Figure 54—Creation of fine sediments in streams as a result of logging, road construction, and road use.

Figure 55—lmpact of logging, road construction, and road use on pink salmon spawners.
89

Figure 56—lmpact of logging, road construction, and road use on coho salmon spawners.

Depending on the amount of roads built and used, the impact of roads may be an 

increase in coho spawners for 10-20 years, after which the population declines significantly

below that under old-growth timber conditions (fig. 56).
Metric and English Equivalents

When you know: Multiply by: To find:

Length and Distance

lnches 2.54 Centimeters

Centimeters 0.393 lnches

Feet 0.304 Meters Meters 

3.280 Feet

Yards 0.914 Meters

Meters 1.093 Yards

Miles 1.609 Kilometers

Kilometers 0.621 Miles

Volume

Quarts 0.946 Liters

Liters 1.056 Quarts

Cubic feet 0.028 Cubic meters

Cubic meters 35.314 Cubic feet

Gallons 3.785 Liters

Liters 0.264 Gallons

Gallons 0.0037 Cubic meters

Cubic meters 264.17 Gallons

90
When you know: Multiply by: To find:

Acre-Feet 1,233.48 Cubic meters

Cubic meters 0.000811 Acre-feet

Area

Square feet 0.092 Square meters

Square meters 10.764 Square yards

Square yards 0.836 Square meters

Square meters 1.196 Square feet

Acres 0.404 Hectares 

Hectares 2.471 Acres

Weight

Pounds 0.453 Kilograms

Kilograms 2.204 Pounds

Short tons 0.907 Metric tonnes

Metric tonnes 1.102 Short tons

Speed

Feet/second 0.304 Meters/second

Meters/second 3.281 Feet/second

Miles/hour 1.609 Kilometers/hour

Kilometers/hour 0.621 Miles/hour

Volume Per Unit of Time

Cubic feet/second 0.0283 Cubic meters/second

Cubic meters/second 35.315 Cubic feet/second

Temperature

Fahrenheit (°F) (°F-32)(5/9) Celsius

Celsius (°C) (°C)(9/5)+32 Fahrenheit

91
Literature Cited

Alaback, P.B. 1982. Dynamics of understory biomass in Sitka spruce, western hemlock 

forests of southeast Alaska. Ecology. 63: 1932-1948.

Bjornn, T.C. 1971. Trout and salmon movements in two ldaho streams as related to temperature, food, stream flow, cover, and population density. Transactions of the American

Fisheries Society. 100: 423-438.

Bliss, L.C. 1962. Caloric and lipid content in alpine tundra plants. Ecology. 43: 753-754.

Bradley, R.T.; Christie, J.M.; Johnston, D.R. 1966. Forest management tables. Forestry 

Commission Booklet 16. London, England: Forestry Commission. 218 p.

Brown, G.W. 1970. Predicting the effect of clearcutting on stream temperature. Journal of 

Soil and Water Conservation. 25: 11-13.

Bustard, D.R.; Narver, D.W. 1975a. Aspects of the winter ecology of juvenile coho salmon

(Oncorhynchus kisutch) and steelhead trout (Salmo gairdneri). Journal of the Fisheries

Research Board of Canada. 32: 667-680.

Bustard, D.R.; Narver, D.W. 1975b. Preferences of juvenile coho salmon (Oncorhynchus

kisutch) and cutthroat trout (Salmo clarki) relative to simulated alteration of winter habitat. Journal of the Fisheries Research Board of Canada. 32: 681-687.

Chambers, C.J.; Foltz, B.W. 1979. The tariff system: revisions and additions. DNR Note 

27. [Location of publisher unknown]: Washington Department of Natural Resources. 8 p.

Charnov, E.L. 1973. Optimal foraging: some theoretical explorations. Seattle: University of 

Washington. 95 p. Ph.D dissertation.

Collins, W.B.; Urness, P.J.; Austin, D.D. 1978. Elk diets and activities on different 

lodgepole pine habitat segments. Journal of Wildlife Management. 42: 799-810.

Curtis, Robert O.; Clendenen, Gary W.; DeMars, Donald J. 1981. A new stand simulator 

for coast Douglas-fir: DFSlM user’s guide. Gen. Tech. Rep. PNW-128, Portland, OR:

USDA Forest Service, Pacific Northwest Forest and Range Experiment Station. 79 p.

92
deCalesta, D.S. 1975. Starving and refeeding mule deer. Journal of Wildlife Management. 

38:815-822.

Dong, J.; Brannon, E.L. 1980. Effects of temperature on development rate and mortality 

of incubating coho salmon. Seattle: University of Washington; final Report to USDA Forest Service; [Contract number unknown]. 51 p. [Availability unknown].

Edwards, P.N.; and Christie, J.M. 1981. Yield models for forest management. Forestry 

Commission Bookley 48. London, England: Forestry Commission. 32 p.

Environmental Protection Agency. 1980. An approach to water resources evaluation of 

non-point silvicultural sources: a procedural handbook. EPA-600/8-80-012. [Location of

publisher unknown]. 85 p.

Farr, Wilbur. A. 1984. Site index and height growth curves for unmanaged even-aged 

stands of western hemlock and Sitka spruce in southeast Alaska. Res. Pap. PNW-326.

Portland, OR: USDA Forest Service, Pacific Northwest Forest and Range Experiment

Station. 26 p.

Fetter, C.W., Jr. 1980. Applied hydrogeology. Columbus, OH: Charles E. Merrill Publications. 4 p.

Goodall, David W.1972. Building and testing ecosystem models. In: Jeffers, J.N.R., ed. 

Mathematical models in ecology. Oxford, England: Blackwell Scientific Publications: 

173-194.

Goulding, C.J. 1979. Validation of growth models used in forestry management. New Zealand Journal of Forestry. 24: 108-124.

Graybill, J.P. 1974. Effects of discharge in the Cedar River on sockeye salmon spawning 

area. Seattle: University of Washington. 60 p. M.S. thesis.

Hanley, Thomas A. 1984. Relationships between Sitka black-tailed deer and their habitat. 

Gen. Tech. Rep. PNW-168. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Forest and Range Experiment Station. 21 p.

Hanley, Thomas A.; McKendrick, Jay D. 1983. Seasonal changes in chemical composition and nutritive value of native forages in a spruce-hemlock forest, southeast Alaska.

Res. Pap. PNW-312. Portland, OR: U.S. Department of Agriculture, Forest Service,

Pacific Northwest Forest and Range Experiment Station. 41 p.

Helmick, D.E.; Anderson, R.L. 1982. Transportation planning road construction cost esti

mating guide. Washington, DC: U.S. Department of Agriculture, Forest Service. [Pages

unknown].

Holling, C.S. 1959. Some characteristics of simple types of predation and parasitism. 

Canadian Entomology. 91:385-398.

Holling, C.S., ed. 1978. Adaptive environmental assessment and management. Chichester, England: John Wiley and Sons. [Pages unknown].

93
Johnson, L.J.; Larsen, D.N. 1986. Reproductive potential of Sitka black-tailed deer in 

southeast Alaska. Federal Aid in Wildlife Restoration, Project W-22-4, Job 2.8R. Juneau,

AK: Alaska Department of Fish and Game. 11 p.

Kirchoff, M.D.; Schoen, J.W. 1987. Forest cover and snow implications for deer habitat in 

southeast Alaska. Journal of Wildlife Management. 51: 28-33.

Mattfield, F.G. 1974. The energetics of foraging by white-tailed deer: a perspective on winter concentration. [Location unknown]: NY State University. 306 p. Ph.D. dissertation.

Mehrkens, J.R. 1983. Timber supply and demand. Rep. 3. Juneau, AK: U.S. Department 

of Agriculture, Forest Service, Tongass National Forest. 49 p. Report required by Alaska

National lnterest Lands Conservation Act. Section 706(a). Alaska.

Merriam, H.R. 1966. Deer report. Vol. 7. Projects W-6-R-6 and W-15-R-1 Jobs 1-4; 

Juneau, AK: Alaska Department of Fish and Game. 22 p.

Mifflin, R.; Lysons, H.A. 1978. Skyline yarding cost estimating guide. Portland, OR: U.S. 

Department of Agriculture, Forest Service, Pacific Northwest Region. [Pages unknown].

Moen, A.N. 1973. Wildlife ecology—an analytical approach. San Francisco: W.H. Freeman and Co. 458 p.

Murphy, M.L.; Hawkins, C.P.; Anderson, N.H. 1981. Effects of canopy and modification 

and accumulated sediment on stream communities. Trans. American Fisheries Society.

110:469-478.

Murphy, M.L.; Heifetz, J.; Johnson, S.W. [and others]. 1986. Effects of clear cut logging 

with and without buffer strips on survival of salmonoids in Alaska streams. Canadian

Journal of Fisheries and Aquatic Sciences. 34(8): 1521-1533.

Murphy, M.L.; Thedinga, J.F.; Koski, K.V.; Garrett, G.B. 1984. Seasonal changes in 

habitat utilization by juvenile salmonoids. In: Fish and wildlife relationships in old-growth

forests: Proceedings of the American lnstitute of Fisheries Research Biologists Symposium; 1982 April 12-15; Juneau, AK. [Location of publisher unknown]: American lnstitute

of Fisheries Biologists: 89-98.

National Oceanic and Atmospheric Administration. 1982. Local climatological data: 

annual summary with comparative data. Annette, AK. [Pages unknown].

Parker, K.L.; Robbins, C.T.; Hanley, T.A. 1984. Energy expenditures for locomotion by 

mule deer and elk. Journal of Wildlife Management. 48: 474-488.

Patric, J.H.; Black, P.E. 1968. Potential evapotranspiration and climate in Alaska by 

Thornthwaite’s classification. Res. Pap. PNW-71. Portland, OR: U.S. Department of

Agriculture, Forest Service, Pacific Northwest Forest and Range Experiment Station. 

28 p.

Paustian, S.J. 1987. Monitoring nonpoint source discharge of sediment from timber harvesting activities in two southeast Alaska watersheds. In: Hunsinger, R.G., tech.

94
chairman. Water quarry in the great land; Alaska’s challenge: Proceedings, American 

Water Resources Association 5th annual Alaska State conference; 1987 October 21-23;

Homer, AK. Rep. IWR-109. Fairbanks, AK: Water Research Center, lnstitute of Northern

Engineering, University of Alaska: 153.

Pilgrim, D.H. 1975. Model evaluation, testing and parameter estimation in hydrology. In: 

Prediction in catchment hydrology: Proceedings of a national symposium on hydrology;

1975 November 25-27; Canberra, Australia. Canberra, Australia: Australia Academy of

Science: 305-333.

Reid, L.M.; Dunne, T.1984. Sediment production from forest road surfaces. Water 

Resources Research. 20: 1753-1761.

Reid, L.: Dunne, T.; Cederholm, C.J. 1981. Application of sediment budget studies to the 

evaluation of logging road impact. New Zealand Journal of Hydrology. 20: 49-62.

Reiser, D.W.; Bjornn, T.C. 1979. Habitat requirements of anadromous salmonoids. Gen. 

Tech. Rep. PNW-96. Portland, OR: U.S.Department of Agriculture, Forest Service, 

Pacific Northwest Forest and Range Experiment Station. 54 p. (Meehan, W.R. ed. Influence of forest and rangeland management on anadromous fish habitat in western North

America. pt. 1).

Robbins, C.T. 1983. Wildlife feeding and nutrition. New York: Academic Press. 343 p.

Rochelle, J.A. 1980. Mature forests, litterfall and patterns of forage quality as factors in the 

nutrition of black-tailed deer on northern Vancouver Island, British Columbia. Vancouver,

BC: University of British Columbia. 295 p. Ph.D. dissertation.

Schoen, J.W.; Kirchhoff, M.D. 1983. Food habits of Sitka black-tailed deer in southeastern Alaska. Juneau, AK: Alaska Department of Fish and Game; final report; Federal

Aid in Wildlife Restoration project W-22-1 and W-21-2. 14 p.

Schoen, J.W.; Kirchhoff, M.D. 1984. Seasonal distribution and habitat use by Sitka black

tailed deer in southeast Alaska. Juneau, AK: Alaska Department of Fish and Game;

progress report; Federal Aid in Wildlife Restoration, Vol. 5, Project W-22-2, job 2.6R. 

25 p.

Seegrist, D.W.; Gard, R. 1972. Effects of floods on trout in Sagehen Creek, California. 

Transactions of the American Fisheries Society 3: 478-482.

Smithsonian Miscellaneous Collections. 1966. Smithsonian meteorological tables. 6th 

ed. [Location unknown]: [pages unknown]. Vol. 114.

Stage, Albert R. 1973. Prognosis model for stand development. Res. Pap. INT-137. 

Ogden, UT: U.S. Department of Agriculture, Forest Service, lntermountain Forest and

Range Experiment Station. 32 p.

Swanson, F.J.; Lienkaemper, G.W. 1978. Physical consequences of large organic debris in

Pacific Northwest streams. Gen. Tech. Rep. PNW-69. Portland, OR: U.S. Department of

Agriculture, Forest Service, Pacific Northwest Forest and Range Experiment Station. 12 p.

95
Swanston, D.N.; Swanson, F.J. 1976. Timber harvesting, mass erosion, and steepland 

forest geomorphology in the Pacific Northwest. In: Coates, D.R., Geomorphology and

engineering. Stroudsburg, PA: Dowden, Hutchinson, and Ross, Inc.: 199-221.

Taber, R.D.; Dasmann, R.F. 1976. The dynamics of three natural populations of the deer 

Odocolileus hemionus columbianus. Ecology. 38: 233-246.

Tagart, J.V. 1976. The survival from egg deposition to emergence of coho salmon in the 

Clearwater River: Jefferson County, Washington. Seattle: University of Washington. 101

p. M.S. thesis.

Taylor, M.A.D. 1979. Evaluating the performance of a simulation model. Transportation 

Research. Oxford, NY: Pergamon; Part A: 13A: 159-173.

Taylor, R.F. 1934. Yield of second-growth western hemlock-Sitka spruce stands in southeastern Alaska. Tech. Bull. 412. [Location of publisher unknown]: U.S. Department of

Agriculture. 27 p.

Toews, D.A.A.; Moore, M.K. 1982. The effects of streamside logging on large organic 

debris in Carnation Creek. Land Management Rep. 2. Victoria, BC: British Columbia

Ministry of Forestry. [Pages unknown].

Torbit, S.C.; Carpenter, L.H.; Swoft, D.M.; Alldredge, A.W. 1985. Differential loss of fat 

and protein by mule deer during winter. Journal of Wildlife Management. 48: 474-488.

U.S. Department of Agriculture, Forest Service. 1977. Southeast Alaska area guide, 

Tongass National Forest. Juneau, AK. [Pages unknown]. Part A.

U.S. Department of Agriculture, Forest Service. 1979. Tongass land management plan; 

Tongass National Forest. Juneau, AK. [Pages unknown].

U.S. Department of Commerce, Weather Bureau. 1978. Probable maximum precipitation and rainfall frequency data for Alaska for areas to 400 square miles, durations to 24

hours, and return periods from 1 to 100 years. Tech. Pap. 47. [Location of publisher

unknown]. [Pages unknown].

Valentine, W.D. 1978. Analysis of ecosystem models. Logan, UT: Utah State University. 

165 p. Ph.D. thesis. [Also available as Microfilm 791.7990, University Microfilms

International, Ann Arbor, Michigan].

Van Es, A.J.H. 1977. The energetics of fat deposition during growth. Nutrition and Metabolism. Basel, NY: S. Karger; 21; 88-104.

Wallmo, O.E.; Schoen, J.W. 1980. Response of deer to secondary forest succession in 

southeast Alaska. Forest Science. 26: 448-462.

Wickett, W.P. 1958. Review of certain environmental factors affecting the production of 

pink and chum salmon. Federal Research Board of Canada. 15: 1103-1126.

96
Appendix 1: Economics of Forest

Management

97
98
Appendix 2: Documentation of

the Timber Submodel

Process and data Sources and comments

Basal area table Taylor (1934)

Basal area growth rate Professional judgment; part of set of relations to simulate growth responses to 

thinning

Relation between maximum rate of basal Professional judgment; part of set of rela

area growth and stand age tions to simulate growth responses to 

thinning

Table of stem density Taylor (1934)

Stem mortality as a function of the number Developed; part of set of relations to simuof stems in existing stand and in a normal late growth responses to thinning

stand of the same site and age and as a 

percentage of maximum basal area

Overstory height growth Farr (1984)

Relation between mean stand height of Developed from permanent sample plot

trees 9.0 inches and larger and overstory data

height

Number of trees 9.0 inches and larger Developed from Taylor (1934, table 18)

Mean diameter of trees 9.0 inches and Developed from Taylor (1934, tables 5

99
Process and data Sources and comments

Timber volume Chambers and Foltz (1979)

Development of canopy cover in stands Professional judgment; canopy cover

less than 20 years old develops very rapidly in young stands

Development of canopy cover in stands Professional judgment; presently a function

more than 20 years old of basal area; canopy cover is not regularly

measured in stand inventories

Relation between canopy cover, stand type, Professional judgment; has little influence

and slope on understory forage and deer dynamics

Slash levels from various timber manage- Professional judgment

ment activities

Slash depth decay rates Professional judgment

Old-growth wood values Timber Management, USDA Forest Service, Alaska Region; regional averages are 

presently used; can adjust values to suit

watershed-specific conditions

Revenue multipliers for second growth Mehrkens (1983)

Road construction costs Helmick and Anderson (1982); figures used

are regional averages; can adjust values to

suit watershed-specific conditions

Road maintenance costs Helmick and Anderson (1982); figures used 

are regional averages; can adjust values to 

suit watershed-specific conditions

Logging costs Alaska Region timber appraisal handbook 

(FSH 2409.22); Mifflin and Lysons (1978); 

figures used are regional averages adjusted 

by time and site-specific assumptions; can 

adjust values to suit watershed-specific 

conditions; these costs include falling and 

bucking, yarding, loading, depreciation, and 

overhead; costs for the 0-8-mbf class have 

been extrapolated from costs in higher volume classes

100
Process and data Sources and comments

Hauling costs Professional judgment; Helmick and 

Anderson (1982); costs include road hauling costs only to the boundary of the 

watershed; values estimated from Alaska 

Region transportation planning guide

Young-stand management costs Mehrkens (1983)

Employment rates Mehrkens (l983)

101
Appendix 3: Documentation of the

Hydrology Submodel

Process and data Sources and comments

Natural inorganic sediment inputs Swanston and Swanson (1976)

Loadings of inorganic sediments from mass Swanston and Swanson (1976)

movements in clearcuts

Loadings of inorganic sediments from mass Swanston and Swanson (1976)

movements from roads

Loadings of inorganic sediments from sur- Reid and others (1981k Reid and Dunne 

face erosion from roads (1984) Paustian (1987); Paustian and 

others (1987); Paustian (personal communication1); quantities and rates are based 

on extrapolations from published data 

and preliminary analysis of ongoing monitoring information

Fractions of fine and coarse inorganic sedi- Professional judgment; the assumption that

ments in loadings from all sources fractions in all types of input are the same 

means that relative loadings are similar in 

logged and unlogged situations

Loadings of organic debris Professional judgment; extrapolations made

from published data; Swanson and 

Leinkaemper (1978)

Fractions of small and large organic debris Professional judgment; the assumption that

in loadings fractions remain constant means that rela

tive loadings are similar in logged and 

unlogged situations

1 Personal communication, S.J. Paustian, hydrologist, USDA Forest Service, Chatham Area, Tongass 

102
Process and data Sources and comments

Equilibrium levels and movement rates of Can be estimated by a hydrologist from

inorganic sediments and organic debris knowledge of stream gradients and water

shed characteristics

Probability of debris torrents and stream Professional judgment 

types that have potential for debris torrents

Dynamics of undercut banks Preliminary data from Murphy and others 

(1984, 1986) coupled with professional 

experience and judgment; initial values and 

dynamic processes need to be supplemented and verified over a larger sample

area

Size distributions of peak fall storms Size distribution developed from maximum 

precipitation and frequency data (U.S. 

Department of Commerce, Weather bureau); must be calibrated for each general 

region of southeast Alaska

Relation between velocity of water in peak Professional judgment

fall storm and degree of bedload shift

Maximum bedload shift for each stream Professional judgment; must be estimated

reach for each drainage

Mean monthly precipitation Monthly multipliers must be used; U.S.

Department of Commerce (1978)

Long-term cyclical variation in precipitation Professional judgment; developed from

long-term record

Short-term variation in precipitation Developed from long-term record and professional judgment

Mean monthly temperature National Oceanic and Atmospheric 

Administration (1982)

Short-term variation in temperature Professional judgment; developed from 

long-term record

Effect of elevation on temperature Professional judgment

103
Process and data Sources and comments

Rate of snowmelt Professional judgment; traditional snowmelt 

models are too complex for purposes of this

model

Evapotranspiration over the year Extrapolated from Patric and Black (1968)

Change in maximum evapotranspiration Professional judgment; shape of the curve

from logging reflects the general changes in evapotranspiration in recent clearcuts

Soil water capacity Soil resource inventory field manual, USDA

Forest Service

lnfiltration rate Professional judgment

Base flow rate Can be estimated by using general concepts outlined in Fetter (1980) 

Equation for changes in stream tempera- Brown (1970)

ture

Equation for potential rate of heat absorp- Environmental Protection Agency (1980)

tion against stream discharge 

Monthly solar angle Smithsonian Miscellaneous Collections 

(1966); the date 15 should be used for 

each month

Effect of tree height and canopy cover on Professional judgment

snow interception rates

104
Appendix 4: Documentation of the

Fisheries Submodel

Process and data Sources and comments

Relation between available spawning area Graybill (1974) and Wickett (1958) support

and summer low flow the conceptual relation but do not provide 

the specific relation used in the model

Estimates of gravel requirements Reiser and Bjornn (1979k data provided 

cover a broader area at a coarser resolution than is included in the model and were 

adjusted accordingly

Fecundities Alaska Department of Fish and Game

(unpublished data)1

Relation between egg deposition and high Lethal temperatures from Reiser and

summer temperature Bjornn (1979)

Fall temperature effect on egg-to-fry Dong and Brannon (1980) extremely low

survival temperatures (less than 2°C) cause high 

mortalities

Winter flow sedimentation effect on egg-to- Tagart (1976) and Reiser and Bjornn

fry survival (1979) large percentages of fine particles 

(<3.35 mm) cause high mortalities

Bedload shift effects on egg-to-fry survival Wickett (1958) and Seegrist and Gard 

(1972) support the conceptual relation; it 

was assumed that the proportion of eggs 

lost would be proportional to the percentage 

of the bedload overturned

1Elliott, Steven. Unpublished data. On file with: Alaska Department of Fish and Game, Douglas, AK 99824.

105
Process and data Sources and comments

lmpact of volume of large debris on rearing Murphy and others (1984k Murphy 

habitat (unpublished data2)

lmpacts of streamside canopy on rearing Murphy and others (1981) show inverse 

habitat relation between canopy closure and rearing density of juvenile salmonoids; summer 

mortality

lmpacts of high temperature on rearing Reiser and Bjornn (1979)

habitat 

Protected areas along side channels Bustard and Narver (1975a)

Protected areas in pools Murphy and others (1984)

Protected areas along undercut banks Bustard and Narver (1975a, 1975b)

Protected areas of rubble Bustard and Narver (1975a), Bjornn (1971)

lmpacts of protected area on overwinter Professional judgment

survival 

lmpact of degree-days in winter on overwin- Professional judgment

ter survival 

lmpacts of temperature on migration of Professional judgment based on docusmolt mented geographical variation that seems 

to be caused by different temperatures

Ocean survival Alaska Department of Fish and Game, (see

footnote 1)

Harvest rates Alaska Department of Fish and Game 

Sport Fish Division; estimates for sitespecific cases can be derived from regional 

estimates (see footnote 1)

Harvest allocation to fisheries 1982 allocation estimates are used

2 Murphy, Michael. Unpublished data on impact of large debris on rearing habitat. On file with: National 

Marine Fisheries Service Northwest and Alaska Center, Auke Bay, AK 99821.

106
Appendix 5: Documentation of the 

Deer Submodel

Process and data Sources and comments

Understory response to forest succession Wallmo and Schoen (1980), Alaback (1982)

Rate of canopy closure after clearcutting Alaback(1982) professional judgment

Understory production as a function of Alaback (1982) professional judgment

crown closure 

Understory production as a function of site Professional judgment

index

Lichen availability as a function of age class Rochelle (1980)

Lichen litterfall rates in old-growth Rochelle (1980); professional judgment 

Nutritional quality of forage classes Hanley and McKendrick (1983)

Gross energy of forage classes Golley (1961), Bliss (1962)

Digestibility by forage class and season Regelin (1979), Hanley and McKendrick 

(1983), Schoen and Kirchhoff (1984k professional judgment

Preference by deer of forage classes From observations of tame deer and food 

habit studies (Schoen and Kirchhoff 1983)

Energy cost of nutrient metabolism Moen (1973), Robbins (1983)

107
108

Process and data Sources and comments

lntrinsic growth rate of forbs and shrubs Professional judgment

Percentage of current annual growth or edi- Alaback (unpublished data1)

ble biomass each season

Effects of deer foraging on understory Merriam (1966) professional judgment

growth rates

Snow interception as a function of canopy Kirchhoff and Schoen (1987); professional 

cover and basal area per stem judgment

Forage availability as a function of snow Hanley (1984); professional judgment

depth

Forage availability as a function of slash Professional judgment

depth 

Energy cost as a function of snow depth Parker and others (1984); professional 

judgment

Consumption rates as a function of forage Patterned after predation research (Holling

abundance, search rates, handling times, 1959, Charnov 1973k specific algorithms

and preference factors developed by N.C. Sonntag2

Search rates Collins and others (1978); professional 

judgment

Handling time as a function of forage class Robbins (1983), Wickstrom and others

and deer group (1984k professional judgment; model outputs especially sensitive to these 

parameters

Basal and active metabolic rates Moen (1973), Mattfield (1974)

Energy content of body reserves Van Es (1977), Torbit and others (1985)

Reproduction and survival as a function of deCalesta (1975); professional judgment 

winter weight loss

1Alaback, Paul. Unpublished data. On file with: USDA Forest Service, Pacific Northwest Forest and Range

Experiment Station, Juneau, AK 99802.

2Sonntag, Nicholas. Unpublished data. On file with: ESSA Ltd., Vancouver, BC V6Z 2H2.
Process and data Sources and comments

Number of fawns produced per doe Johnson and Larsen (1986); professional

judgment

Maximum survival rates of fawns and adults Taber and Dasmann (1976) professional

judgment

Hunter effort as a function of success the Professional judgment

previous year

Harvest rates as a function of deer density Alaska Department of Fish and Game

(unpublished data3); professional judgment

3Kirchhoff, Mathew; Schoen, John. Unpublished data. On file with: Alaska Department of Fish and Game, 

Douglas, AK 99824.

✩U.S. GOVERNMENT PRlNTlNG OFFICE: 1990-791-171/20020 109
Fight, Roger D.; Garrett, Lawrence D.; Weyermann, Dale L, tech. eds. 1990. 

SAMM: a southeast Alaska multiresource model. Gen. Tech. Rep. PNW-GTR255. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific

Northwest Research Station. 109 p.

The adaptive environmental assessment method was used by an interdisciplinary 

team of forest specialists to gain an understanding of resource interactions and 

tradeoffs resulting from forest management activities in southeast Alaska. A forest 

multiresource projection model was developed in the process. The multiresource 

model, “SAMM,” is capable of characterizing and displaying interactions of four 

major resources over a 150-year rotation: timber, wildlife (Sitka black-tailed deer), 

hydrology (streams), and fisheries (anadromous). SAMM is in a prototype stage of 

development; final testing is required before it can be used by managers for quantitative analysis. Sufficient development and evaluation has been done by the team of 

specialists to permit its use in qualitative assessments planning.

Keywords: Multiresource, forest management, systems, models, southeast Alaska, 

simulation.

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.

The U.S. Department of Agriculture is an Equal

Opportunity Employer. Applicants for all Department

programs will be given equal consideration without

regard to age, race, color, sex, religion, or national

origin.

Pacific Northwest Research Station 

319 S.W. Pine St. 

P.O. Box 3890 

Portland, Oregon 97208
U.S. Department of Agriculture 

Pacific Northwest Research Station 

319 S.W. Pine Street 

P.O. Box 3890 

Portland, Oregon 97208

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