HomeMy WebLinkAboutAPA527SUSITNA HYDROELECTRIC PROJECT
TERRESTRIAL ENVIRONMENTAL WORKSHOP
AND PRELIMINARY SIMULATION MODEL
ENVIRONMENTAL AND SOCIAL
S YSTEMS ANALYSTS LTD.
SUSITNA HYDROELECTRIC PROJECT
TERRESTRIAL ENVIRONMENTAL WORKSHOP
AND PRELIMINARY SIMULATION MODEL
by
Robert R. Everitt
Nicholas C. Sonntag
ESSA Environmental and Social Systems Analysts Ltd.
Vancouver, B.C., Canada
Gregory T. Auble
James E. Roelle
U.S. Fish and Wildlife Service
Fort Collins, Colorado
Wi 11 i am Gazey
LGL Ecological Research Associates
Bryan, Texas
for
LGL Alaska
Anchorage and Fairbanks, Alaska
ARLIS
IK
l Li-;tS
.,)'~
A:l3
'r\CJ"Sl7
October 22, 1982 AlaskR Resources
Library & Infonnatwn Servtces
Anchorage, Alaska
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ACKNOWLEDGEMENTS
We would like to thank the over forty particip~nts
at the workshop who devoted much time and considerable
energy to the process of building the model. In particular,
we thank Joe Truett of LGL and Ann Rappoport of the USFWS
for notes they kept on the conceptual and information needs
that arose during the workshop. Steve Fancy chased down
numerous pieces of information, without which the report
would be incomplete. Robin Sener helped make numerous
connections and offered considerable encouragement during
the course of the writing.
Once again Jean Zdenek worked magic in typing and
correcting this and earlier drafts of this report under
impossible time constraints.
ARLIS
Alaska Resources
Library & InformatiOn Servtces
Anchorage, Alaska
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TABLE OF CONTENTS
1. 0 INTRODUCTION. . . . . . . . . . . . . . .
1.1 Objectives .........•....
1.2 Relationship to Mitigation Planning •.
1.3 Simulation Modelling Workshops •.
2.0
3.0
1.3.1 Workshop Activities .
1.3.2 Beyond the Workshop ..
BOUNDING •.
2 .1 Actions. .
2.2 Indicators •.
2.3 Spatial Considerations
2.4 Temporal Considerations ..
2.5 Submodel Definition •..
2.6 Looking Outward ..•...
SUBMODEL DESCRIPTIONS . . . . . . . . .
3.1 Physical Processes/Development/Recreation.
3.1.1 Physical Processes ..... .
3.1.1.1 Reservoir Elevations .. .
3.1.1.2 Changes in Stage ... .
3.1.1.3 Side Channel and Slough
Habitat for Beaver .. .
3.1.1.4 Scouring ....... .
3.1.1.5 Water Surface.Area in the
Downstream Floodplain . .
3 . 1.1. 6 Snow . . . . . . . . . . .
3 .1. 2 Hydroelectr,ic Development Activities. .
3.1.3 Other Land Use Activities .....
3.1.4 Disturbance to Wildlife •..
3.1.5 Access. . . . ...
3. 2 Vegetation . . . . . . . . . . . . . .
3.2.1 Structure . . . . . . . . ..
3.2.2 Classification System • . . •.•
3.2.3 Development Activities.
3.2.4 Riparian Succession
3.2.5 Wildlife Habitat.
3.3 Furbearers and Birds .
3.3.1 Beaver ...... .
3.3.2
3.3.1.1 Beaver Carrying Capacity .
3.3.1.2 Intrinsic Growth Rate ..
3.3.1.3 Mortality .....
3.3.1.4 Initiation of Main Channel
Population ....
Birds .
3.3.2.1
3.3.2.2
3.3.2.3
Passerine Birds.
Trumpeter Swan
Golden Eagle . .
1
2
3
3
4
6
7
7
9
9
13
15
15
19
19
20
20
23
23
24
24
26
26
26
30
30
33
33
33
36
38
38
43
43
46
48
49
53
53
54
54
57
~.'l.'hl:.
'"''""
f~
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/IIJ~b ...
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!1~
4.0
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TABLE OF CONTENTS (cont•d)
Page
3.4 Moose . . . . . . . . . . . . . . . . . 57
3.4.1 Structure. . . . . . . . . . . . 58
3.4.2 Winter Carrying Capacity 60
3.4.3 Population Dynamics. . . . . . 61
3.4.3.1 Reproduction. . . . . . . 62
3.4.3.2 Summer. . . . . . . . . . . 62
3.4.3.3 Harvest . . . . . . . . 64
3.4.3.4 Overwinter Mortality. . . . . . 66
3.5 Bears . . . . . . . . . . . . . . . 68
3.5.1 Structure. . 68
3.5.2 Reproduction . . . . . . 72
3.5.3 Mortality. . . . . . . 73
3.5.4 Dispersal. . . 75
3.6 Model Results . . . . . . . . . . . . 75
3.6.1 Physical Processes/Development/
Recreation. . . . . . . 77
3.6.2 Vegetation . . . . . . . . 85.
3.6.3 Fur bearers and Birds . . . . . 91
3.6.4 Moose. . . . . . 95
3.6.5 Bears. . 95
PRODUCTS . . . . . . 101
4.1
4.2
4.3
Conceptual Model ...•.. . 101
103
. . 103
Summary of Conceptual and Information Needs
Model
4.3.1
4.3.2
4.3.3
4.3.4
4.3.5
Refinements . . . . . . . .
Physical Processes/Development/
Recreation. • . • . . ...
4.3.1.1 Recreation ..... .
4.3.1.2 Land Use •....•.
4.3.1.3 Physical Processes ..
Vegetation . . . . . . . . . . . • .
4.3.2.1 Spatial Resolution ..
4.3.2.2 Resolution of Development
. . 107
107
. 107
107
110
110
Activities . . . . . . . . 110
4.3.2.3 Wildlife Food . . . . . . . 111
4.3.2.4 Riparian Succession ....•. 111
4.3.2.4 Dynamics of Upland Vegetation . 112
Furbearers/Birds . . . . 113
4.3.3.1 Beaver Model. . ~ 113
4.3.3.2 Passerine Birds ..•..... 115
Moose. . • . • . . . . . . . 115
4.3.4.1 Winter Carrying Capacity .... 115
4.3.4.2 Reproduction. . . . 116
4.3.4.3 Summer Mortality. . 116
4.3.4.4 Predation ........... 116
4.3.4.5 Harvest ............ 117
4.3.4.6 Winter Mortality. . 118
Bear . . • . . . . . . . . 119
4.3.5.1 Spring Food .......... 119
4.3.5.2 Mortality . . .... 120
4.3.5.3 Dispersal . . . . 120
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5.0 FUTURE WORK .
6.0 REFERENCES ..
TABLE OF CONTENTS (cont•d)
7.0 LIST OF PARTICIPANTS ..
Page
. . . 121
. . 123
. 124
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2.1
2.2
2.3
2.4
2.5
LIST OF TABLES
Actions Identified at Workshop .....•
Indicators Identified at Workshop . • . .
Fourteen Vegetation Types Associated with the Spatial
Areas. . . . . . . . . . . . . . .
Submodel Components Decided on by Workshop
Participants . . . . . • .
Looking Outward Matrix ......... .
Page
8
10
14
16
17
3.1 Hydroelectric Development Project Actions . 27
3.2 Estimates of Current Land Use and Recreational Use in
Georgraphic Area Considered in the Mqdel . . • . . • 31
3.3 Disturbance Associated with Construction Workers and
Vehicle Traffic. . . . . . . • . . . . . . 32
3.4 Initial conditions for vegetation types estimated at
workshop . . . . . . . . . . . . . . . . . . . . 3 5
3.5 Estimates of average values for potentially
available browse standing crop and annual berry
production in each land class. . . . . • . . 41
3.6 Passerine bird density and number of species
associated with different vegetation types . . . 55
3.7 Proportion of females reaching maturity by age ••.. 69
3.8 Scenarios Used in the Simulations • . 78
4.1 Information Needs 104
(~' LIST OF FIGURES
Page
... -"""" 2.la Upper Susitna Basin showing the Devil Canyon and
Watana impoundments . . . . . . . . . . . . 11
2.lb Lower Susitna Basin showing Devil Canyon to
-~~ Talkeetna riparian zone designated for the model. 12
3.1 Gold Creek flows for preproject, case A and case D . 21
3.2 Watana Reservoir elevations throughout the year. 22
3.3 Stage -discharge rating curve for Gold Creek
Station . . . . . . . . . . . . . . . . . . . . . . 22
3.4 Water surface area in the downstream floodplain. 25
/~ 3.5 Calculation sequence for the vegetation submodel 34
3.6 Successional sequence in the Talkeetna to Devil
Canyon riparian zone. . . . . . . . . . . . 39
f"~ 3.7 Time dynamics of a population based on the logistic
growth model. . . . . . . . . . . . . . . . . . . . 45
3.8 Percent survival of beaver colonies on main channel. so
3.9 Maximum beaver trapping mortality. . . 52
3.10 Trapper access factor. . . . . . 52
3.11 The relative value of species in any given
vegetation type . . . . . . . . . . . . . . . . . . . 56 ,-3.12 Relative value of bird density in any given
vegetation type . . . . . . . . . . . . . . . 56
3.13 Calculation sequence for the moose submodel. . . . 59
3.14 Relationship between moose density and ovulation
'~ rate. . 63 . . . . . . . . . . . . . . . . . . . .
3.15 Relationship between moose calf density and bear
predation rate. . . . . . . . . . . . . . . . . 63
,... 3.16 Relationship between snow depth and half-saturation
constant for bear predation function. . . . . 65
3.17 Relationship between snow depth and proportion of
winter range accessible to moose. . . . . . . . 65
3.18 Relationship between forage availability and moose
winter survival rate. . . . . . . . . 67
3.19 Life structure of brown bear . . . . . . . 70
3.20 Life structure of black bear . . . . . . . 71
3.21 Reproduction relationships as a function of the
previous year's food. . . . . . . . . . 74
3.22 Mortality of cubs and yearlings. . . . . 76 ,-3.23 Maximum annual change in stage a"t Gold Creek Station 79
3.24 Amount of reservoir clearing per year. . . . . . . . 81
3.25 Construction personnel on site at any one time . 82 -3.26 Recreational use days in the Upper Susitna Basin 83
3.27 Potential overwintering habitat for beaver in
sloughs and side channels . . . . . . . . . . . 84
3.28 Changes in areas of selected vegetation types in
Watana impoundment area . . . . . . . 86
3.29 Change in areas of selected vegetation types in
Devil Canyon impoundment area . . . . 87
LIST OF FIGURES (cont'd)
3.30 Changes in areas of selected vegetation types in
the Upper Susi tna Basin . . . . . . . . . . . . 88
3.31 Area of deciduous and mixed forest in the downstream
riparian zone . . . . . . . . . . . . . . . . . . . 89
3.32 Areas of tall shrub and low mixed shrub in the
downstream riparian zone. . . . . . . . . . . . . . 90
3.33 Areas of water and pioneer species in the downstream
riparian zone . . . . . . . . . . . . . . . . . . . 92
3.34 Beaver colonies utilizing sloughs and side channels
and their carrying capacity in the downstream
riparian zone . . . . . . . . . . • . . • . . • . . 93
3.35 Main channel beaver colonies and carrying capacity . 9~
3. 36 Habitat units for passerines in the Upper Susi tna
3.37
3.38
3.39
3.40
Bas in . . . . . . . . . . . . • . . . .
Fall post-harvest moose population . • .
Moose lost to bear predation and through
Brown bear density . . . . . • . • .
Black bear density ..•..
hunting
4.1 Conceptual model of major components and linkages
included in the model of the terrestrial environment
96
97
98
99
100
in the Susitna Basin. . . . . . . . . • . . . . . . 102
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1.0 INTRODUCTION
The technical feasibility, economic viability, and
environmental impacts of a hydroelectric development project in
the Susitna River Basin are being studied by Acres American, Inc.
on behalf of the Alaska Power Authority. As part of these
studies, Acres American recently contracted LGL Alaska
Research Associates, Inc. to coordinate the terrestrial
environmental studies being performed by the Alaska Department
of Fish and Game and, as subcontractors to LGL, several
University of Alaska research groups. LGL is responsible for
further quantifying the potential impacts of the project on
terrestrial wildlife and vegetation, and for developing a
plan to mitigate adverse impacts on the terrestrial
environment. The impact assessment and mitigation plan will
be included as part of a license application to the Federal
Energy RegulatoryCommission (FERC) scheduled for the first
quarter of 1983.
The quantification of impacts, mitigation planning,
and design of future research, is being organized using a
computer simulation modelling approach. Through a series of
workshops attended by researchers, resource managers, and
policy-makers, a computer model is being developed and refined
for use in the quantification of impacts on terrestrial
wildlife and vegetation, and for evaluating different mitigation
measures such as habitat enhancement and the designation of
replacement lands to be managed by wildlife habitat. This
report describes the preliminary model developed at the first
workshop held August 23-27, 1982 in Anchorage.
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1.1 Objectives
The ultimate purpose of the workshops and simulation
modelling is to develop a framework that can be used as a
basis for assessing impacts of and evaluating mitigation
options for the effect of the Susitna Hydroelectric Project
on the terrestrial environment in the Susitna Basin.
a)
b)
c)
The specific objectives for achieving this purpose are to:
develop an understanding of the biophysical
processes of the Susitna Basin with respect to
wildlife and vegetation;
develop this understanding by integrating information
on big game, furbearers, small mammals, birds, and
plant ecology into a computer simulation model;
refine the model during a series of technical meetings;
d) update the model as new information becomes available
from field studies; and
e) use the model as a framework and guide to assess
terrestrial impacts of the Susitna Hydroelectric
Project and to evaluate ways of mitigating impacts.
The workshops play a major role in attainment of
these objectives. They provide a systematic approach to
organizing information and people. As such, they are a
major tool for consensus building and interdisciplinary
coordination.
1,.,.._1
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1.2 Relationship to Mitigation Planning
Many aspects of mitigation planning will be accomplished
outside of the simulation modelling workshop process. Many
mitigation measures, such as controlling dust along roads,
leaving clumps of trees along the reservoir margin for eagle
nesting, minimizing aircraft disturbance, locating recreation
facilities away from critical wildlife areas, and deciding
upon environmentally sound access road design criteria can
easily be developed without a quantitative model. Most of
these measures to be incorporated into engineering design and
construction planning have been developed or will be developed
prior to the submittal of the FERC application.
However, certain mitigation measures, such as habitat
enhancement or compensation lands for habitat lost, may
require several years of analysis and discussion. The primary
purpose of the simulation modelling workshop process is to
incorporate these more complex issues into the mitigation
planning. Recognizing that ~hese issues will not be
resolved prior to the license application, the workshop
process allows for an adaptive approach to planning. It
provides a framework for increased communication, and a
mechanism for designing and utilizing the results of future
research and monitoring studies.
1.3 Simulation Modelling Workshops
There has been an enormous increase in public concern
over environmental impacts of development projects in the past
two decades. One consequence of this concern has been the
use of detailed environmental impact assessments as an integral
part of major resource development activities. These impact
assessments are always multidisciplinary, but, in most cases,
little effort is made to develop a coordinated, interdisciplinary
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approach. Consequently, vital information required to make
predictions of impacts encompassing more than one discipline
is often overlooked or not collected.
Over the past ten years a group of environmental
scientists and systems analysts at the University of British
Columbia and the International Institute for Applied Systems
Analysis (IIASA) in Austria have developed a methodology to
deal explicitly with interdisciplinary ecological problems
(Holling, 1978). The core of the methodology is a five day
workshop involving a team of four or five experienced simulation
modellers and a group of fifteen to twenty specialists. The
· focus of the workshop is the construction of a quantitative
simulation model of the system under study. The development
of the simulation model forces specialists to view their area
of interest in the context of the whole system. This promotes
an interdisciplinary understanding of the system, and allows
ecological and environmental knowledge to be integrated with
economic and social concerns at the beginning, rather than
at the end, of an impact assessment.
Simulation models require unambiguous information.
In the workshop setting specialists are forced to be explicit
about their assumptions. This objectivity exposes critical
conceptual uncertainties about the behavior of the system,
and identifies research needs.
1.3.1 Workshop Activities
The first step in the workshop is to clearly define
and bound the problem. Bounding makes the modelling problem
more explicit, thereby making it easier to decompose the
system into manageable components or subsystems. In bounding,
development actions (alternate controls available to management
or development strategies) and indicators (those measures used
by management in evaluating system performance in response to
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various combinations of actions) are generated. The model
embodies the biophysical rules required to transform the
actions into indicator time streams. Bounding also involves
defining the spatial extent and resolution required to
adequately represent the system, and by specifying the
temporal extent or time horizon and an appropriate time
step.
The final bounding exercise of the workshop is called
"looking outward". It focuses attention on the subsystems
defined by the actions and indicators and those variables
required by each subsystem from the other subsystems. In
looking outward, the standard question of analysis is recast.
Instead of.asking "what can you provide to the other subsystems
from subsystem X?", the question "what do you need to know
about all other subsystems in order to predict how subsystem X
will behave?" is asked. This question demands a more dynamic
view and forces one to describe a particular subsystem in the
context of the entire system. The looking outward exercise
generates, for each subsystem, a list of "inputs" it needs
from the other subsystems and a list of "outputs" it must
provide to the other subsystems.
The second step of the workshop is submodel construction.
The workshop and each subgroup develops submodels for one of
the subsystems. One workshop facilitator works within each
subgroup and acts as the submodel programmer. The submodel
must be able to generate the output variables required by
oher submodels and the appropriate indicator variables
identified earlier.
The final step of the workshop is to put each of the
submodels into the computer and link them into the system
model. The system model is run under a variety of development
scenarios to explore the consequences of various actions and
hypotheses about system structure. The principal objective
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of this exercise in an initial workshop is to point out model
deficiences and identify areas requiring better understanding
and information.
1.3.2 Beyond the Workshop
The first workshop can be followed by a period of
independent work on identified research needs by collaborating
individuals which will lead to a second workshop and possibly
subsequent ones in a phased sequence. Early in the sequence,
workshops concentrate on technical issues, but later, they
focus more and more on communication to policy advisors and
the affected constituencies. The emphasis on communication
enables an effective and logical move to implementation,
either in a pilot project or a full-scale program.
Throughout the workshop sequence, the simulation model
is an expression and synthesis of new information and the
changing mental models of scientists, managers and policy
makers. The involvement and interaction of these groups
means that learning becomes as much a product as does problem
solving.
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2. BOUNDING
All systems are hierarchial in nature; each is
comprised of smaller parts, and is, in turn, embedded in,
or part of larger systems. The most critical decisions
that are made in planning research and analysis are the
choice of components to be explicitly addressed. The same
is true for modelling.
Within simulation modelling workshops, these choices
are made during an exercise called bounding. Bounding
forces the participants in the workshop to define lists of
actions and indicators and places those in an appropriate
spatial and temporal framework. Once this is accomplished,
an exercise called "looking outward" defines the key
interrelationships between components of the system under
scrutiny.
2.1 Actions
Actions, in the context of modelling, are normally
thought of as human intervention into the environment. With
reg:ard to thl~ Froposed developments on the Susitna, four major
categories of actions (Table 2.1) were identified during the
workshop. The first relates to the construction and
operation of reservoirs; the second relates to recreational
development, use, and control; the third relates to
development other than hydroelectric; and the fourth
corresponds to mitigation options.
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Table 2.1: Actions Identified at Workshop
I. Reservoirs
a. Construction
• roads
• borrow pits
• transmission lines
• camp sites
• village sites
• temporary diversions
• river bed mining
• reservoir clearing
• soil dispo,sal
• air strip construction
• aircraft use
• staging areas
b. Operation
• operating rule curves
II. Recreation/Access
III.
IV.
General
• reservoir recreational development (access and
facilities)
• recreational use (back packing, hunting, fishing)
• increased traffic on existing roads/railroads
• timber harvest
• changes in land use patterns (mining, oil and
gas development)
• increased population in surrounding communities
Mitigation
• habitat enhancement
• controlled burn
• replacement lands
• vegetation crushing
• flow regulation for fish and wildlife
• fire protection
• control of access
• hunting/fishing regulation
• scheduling of construction activities
• siting of roads
• reclamation/revegetation
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2.2 Indicators
Indicators are those quantities which are used to
evaluate the performance or health of a system in response
to the defined actions. The set of indicators (Table 2.2)
identified by participants in the workshop are primarily
related to wildlife populations and wildlife habitat measures,
although instream flows and indicators of recreational use
are included.
The predicted changes in indicators are used to help
determine the impacts of the actions over time, and in turn,
evaluate the quantity, quality, and timing of mitigative
actions.
2.3 Spatial Considerations
Defining the spatial extent and resolution of any
research or analysis is a critical step. It determines the
level of detail and places geographical limits on what is to
be considered. Simulation models require an unambiguous
definition of the spatial extent and resolution.
The spatial extent of the model was guided by
estimated home ranges of brown bear and moose. An area
corresponding to all of a horne range was included. With this
criterion, the Upper Susitna Basin, extended to include the
Prairie Creek-Stephan Lakes region, was chosen as the area
for assessing impacts upstream of the Devil Canyon Darn site.
Within this upstream area, the Watana and Devil Canyon
impoundments are considered separately and the remaining
land is designated as a third spatial unit (Figure 2.1).
Downstream, (Devil Canyon Dam site to Cook Inlet) an area
corresponding to moose home range was defined using estimates
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Table 2.2: Indicators Identified at Workshop
Hydrology
• instrearn flows
Vegetation
• acres of selected vegetation types
Wildlife
• populations of: moose
black bear
brown bear
sheep
wolves
raptors
caribou
wolverine
small mammals
birds
• carrying capacity for the above populations
• numbers of animals harvested by hunters
• hunter success
• habitat quality
Recreation
• number of user days
• non-consumptive uses of wildlife
Figure 2.la:
l
0 10 l 20
Kilo ers
20
30
-. ~
' J
Upper Susitna Basin showing the Devil Canyon and Watana impoundments
(shaded area) .
1
I
,_. ,_.
0
0
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10 20
Kilo meters
20
30
I
1
COOK INLET
Figure 2.lb: Lower Susitna Basin showing Devil Canyon to
Talkeetna riparian zone (shaded area)
designated for the model.
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fr0f'1 r'bdafferi (1982). 11 1oose home range probably occurs in a
bimd 60 km wide; 30 km on each side of the Susi tna. rThe Imdel
simulates this ba,">"ld as far down.stream as Talkeetna. The Susi tna
floodplain is considered separately within the dovmstream area.
Areas downstream of Talkeetna were not included because the
present and future hydrologic regime there, and its influence on
vegetation dynamics, was considered too COtT'plex to construct an
adequate predictive Imdel.
TI1erefore, there are 5 spatial areas in the model:
a) the Watana impoundment;
b) the Devil Ca..l'lyon impoundment;
c) the remainder of the Susitna Basin upstream of Gold Creek;
d) the floodplain from Devil Canyon Dam to Talkeetna; and
e) the remaining land in a 60 km strip fromDevil Canyon
Dan1 to Talkeetna.
Within each of the spatial areas, fourteen vegetation
types (Table 2.3) were defined.
2.4 Terrporal Considerations
TI1e choice of the te:q:)Qral resolution or ti~ step for
the model is always problematic because of widely different
time scales of important processes. lVIany biological
processes depend on water levels at critical times throughout
the year requiring monthly, and scxnetimes daily, water level
estimates. However, wildlife and waterfowl populations do
not cha..l'lge substantially from one day to the next making
daily population estimates urillecessary. These considerations,
caribined with the necessity of representing much slower
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Table 2.3: Fourteen Vegetation Types Associated with the
Spatial Areas
Conifer forest
• woodland
• open
Deciduous and Mixed Forest
Tundra
Tall shrub -alder
Medium shrub
Low shrub
• birch
• willow
• mixed
Unvegetated
• water
• rock/snow/ice
Disturbed
• temporary
• permanent
Pioneer
-15 ...
successional processes, led to a mixed temporal structure.
Average and peak flows are available monthly from hydrology.
All other submodels have a one year time step but may
implicitly include seasonal dynamics when needed. A time
horizon of 50 -80 years was chosen (to capture the
successional effects) .
2.5 Submodel Definition
The breakdown of the system into component subsystems
is reflected in the breakdown of the simulation model into
the submodels:
a) physical processes/development/recreation;
b) vegetation;
c) furbearers/birds; and
d) large mammals.
The major components of each submodel (Table 2.4) were
decided upon through discussion by workshop participants.
2.6 Looking Outward
The purpose of "looking outward" is to define the
pieces of information that a particular subsystem requires
from all other subsystems to predict its dynamic behavior.
This is a qualitatively different question than the
traditional one which generates lists of factors which affect
a particular component of a system. The product of "looking
outward" is an interaction matrix, with columns specifying
what information a subsystem requires from each of the other
subsystems (Table 2.5). The diagonals are blank because they
represent the internal dynamics of each subsystem.
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Table 2.4: Submodel Components Decided on by Workshop
Participants
1. Physical Processes/Development/Recreation:
2.
• flows
• stages
• ice processes
• reservoir elevations
• aquatic furbearer habitat
• hydroelectric development scenarios
• other development scenarios
• recreational use
• recreational development
Vegetation:
• areal extent of vegetation types
• browse production
• berry production
ecological succession
• vegetation alienation
3. Furbearers/Birds:
• beavers
• golden eagles
• passerine birds
4. Large Mammals:
• moose
• moose habitat
• bears
• bear habitat
)
PHYSICAL
PROCESSES/
DEVELOPf-.1ENT/
RECREATION
VEGETATION
FURBEARERS/
BIRDS
LARGE
MAMMALS
PHYSICAL PROCESSES/
DEVELOPMENT/RECREATION
Table 2,5; Looking Outward Matrix
VEGETATIO~
- 3 day peak flows
-location & areas (ha) of
development activities
-surface area exposed in
floodplain (ha)
-areas (ha) of intensive
beaver use by vegetation
type
-consumption (kg/ha) of
forage species by season
& type
FURBEARERS/BIRDS
-date of break-up/freeze-up
(lakes, ponds, streams)
-da';te of first snow cover
-minimum open water in
river (km)
-length of slough~side
channels with >,5 ~ice
free water
-reservoir elevations (ft)
-human disturbance
-areas of vegetation types
(ha)
-productivity (kg/ha) of:
Paper Birch
Balsam Poplar
Birch shrubs
Black Spruce
1-'lhi te Spruce
Willow shrub
Aspen
LARGE MAMMALS
-date of ice break-up
(edge)
-date of 'ice free'
conditions
-amount of ice shelving
(March 15-June 15)
-snow depths (elevation)
in 150 m intervals,
monthly
-trips/day on access
roads (seasonally)
-trains/day (Nov-March)
-recreational use days
-production of berries
(kg/ha)
-hectares of berries
suitable for bear food
-areas of vegetation types
(ha)
-standing crop (kg/ha) &
areas of:
Paper Birch
Lowbush Cranberry
Balsam Poplar
Nillow Shrub
Aspen
-
-
·-
-18 -
Each piece of information listed in the matrix
represents a specific hypothesis about system behavior. For
example, the furbearers/birds submodel requires information
on the length of sloughs and side channels that maintain at
least .5 m of ice-free water throughout the winter from the
physical processes/development submodel. The underlying
hypothesis is that this represents potential overwintering
habitat for beavers.
-19 -
3.0 SUBMODEL DESCRIPTIONS
The four submodels, hydrology/development/recreation,
vegetation, furbearers/birds, and large mammals, were then
constructed in subgroup meetings of the participants using
the model framework developed during bounding. This section
describes the models conceptualized during subgroup meetings
and during the computer programming phase of the workshop.
These models are the first interdisciplinary
representation of the biophysical processes of the Susitna
Basin. In some cases, the relationships described are based
on good scientific evidence~ in other cases, they 3re simply
crude hypotheses or educated guesses. These models require
considerable critique and refinement before a reasonable
representation of important terrestrial processes is achieved.
3.1 Physical Processes/Development/Recreation
The Susitna hydroelectric development will impact the
terrestrial environment directly through disturbance and
vegetation loss on lands needed for project facilities, and
indirectly through alteration of the hydrologic and ice
regimes of the Susitna River. Another possible and perhaps
major impact on the terrestrial environment will occur
through increased recreational opportunities that may result
from increased access and the development of recreational
facilities at or near the reservoir. Also, while development
associated directly with the hydroelectric project may have
a substantial impact and is the primary focus of this project,
it is important to place this development in the context of
development activities that are indirectly related to the
project, such as mining, oil and gas exploration and
production, and new recreational facilities.
-
-20 -
3.1.1 Physical Processes
Almost all the physical processes considered in the
model are related to the flow regime or climate or the
interaction of both factors. Currently, the model simulates
the flow regime at three stations {Gold Creek, Sunshine, and
Susitna) for three different cases:
a) preproject flows;
b) Case A, which corresponds to optimum power generation;
and
c) Case D, which corresponds to the best development for
meeting instream flow targets.
The flows are based on historical preproject flow data and
estimates provided by Acres American L~d. (pers. comm.) for
·past project flows under different operating conditions.
Thirty years of data for each case are used and repeated.
Figure 3.1 is a comparison among the three cases using the
data used for simulation year 12. Average monthly flow is
usually a poor indicator of the stress on an ecosystem and,
in many cases, extreme flows (minima and maxima) are more
important. The model makes daily and 3 day minimum and
maximum flow estimates using data supplied by R & M
Consultants (pers. comm.).
3.1.1.1 Reservoir Elevations
The operation of the dams causes the reservoirs to
vary throughout the year as seen for the simulation year 12
in Figure 3.2. The model provides the reservoir elevations
for Watana Reservoir based on monthly estimates provided by
Acres American.
PREPROJECT FLOWS -21 -
,~ 36
30 , ...
u
24
(a) 0 z
<t
(/) 18
:::::.
F' ... 0
:J: 12 1-
6
~-
0
OCT DEC. FEB. APR. JUNE AUG.
,_. TIME
CASE A
0"""' 36
30
'""
.., ....
u 24
0
(b) z
<t
(/) 18
:::::.
0
:J: 12 1-
('"~
0
OCT. DEC. FEB. APR. JUNE AUG.
f-i=>
TIME
~~ CASE D
36
i~ 30 .., -u
0 24
(c) z
1""'•
<(
(/) 18 :::::.
0
:J: 12 1-
6
0 ,-OCT. DEC. FEB. APR. JUNE AUG.
TIME
~~
Figure 3.1: Gold Creek Flows for preproject (a) ' case A (b) '
and case D (c) .
~
-
'""'
~
·-
2190
2170
,_ 2150
UJ
UJ
11..
2130
2110
OCT.
-22 -
DEC. FEB. APR. JUNE AUG.
TIME
Figure 3.2: Watana Reservoir elevations throughout the year.
15
-12 -u
QJ --
UJ
(!) 9 <t ,_
(I)
z -6
UJ
(!) z
<t
J: u 3
0 10 20 30 40 50
DISCHARGE (1000 cfs)
Figure 3.3: Stage -discharge rating curve for Gold Creek
Station based on U.S.G.S. discharge data
gathered since October 1, 1967.
-23 -
3.1.1.2 Changes in Stage
The calculation o£ stage is based on stage-discharge
rating curves like the one shown for Gold Creek (Figure 3. 3) .
An estimate of stage variability for beaver dynamics is
calculated as the difference of the stage in the maximum
month, usually August, and the stage in the minimum month,
usually March.
3.1.1.3 Side Channel and Slough Habitat for Beaver
Side channels and sloughs that retain greater than
.5 m in depth of unfrozen water throughout the winter provide
potential overwintering habitat for beaver. In the major
area of concern, downstream of Devil Canyon Dam to Talkeetna,
the amount of this habitat is directly related to water level
(stage) and ice thickness. The stage depends on flow {Section
3.1.1.2), and the ice thickness depends on flow and the
severity of the winter. In the model, the effect of the
severity of winter was simulated as a random process that
increased or decreased the amount of habitat from a mean
value. The mean value was estimated visually from maps and
reflects the fact that only 70% of the length of sloughs
that are deep enough overall is suitable habitat due to the
gradual decrease in depth at the end of sloughs. The
relationship is expressed in the following equation:
Shoreline _ Mean Shoreline * Winter Severity
Habitat -Habitat Factor
where shoreline habitat is defined as slough and side channels
with greater than .5 m of ice-free water. The winter severity
factor was constrained to take a value between .5 and 2.0,
which limits the maximum effect to a doubling or halving of
available habitat.
-24 -
Currently, the model does not estimate flow effects
on overwintering habitat. This is a major deficiency because
of the year to year variation in flow and because of vast
differences between flows throughout the winter that would
occur with and without the project.
3.1.1.4 Scouring
The dynamics of ice scouring are imperfectly understood,
but participants felt that scouring would be less prevalent
after the project because of reduced flows during spring
break-up.
At present, the model simulates ice scouring as a
random process. The probability of significant ice scouring
is .95 before the project and .05 after the project. A
random number drawn from a uniform distribution determines
whether scour occurs.
3.1.1.5 Water Surface Area in the Downstream Floodplain
(Devil Canyon to Susitna-Chulitna Confluence)
Total area of water surface between Devil Canyon and
Susitna-Chulitna confluence was estimated at various flow
levels using the U.S. corps of Engineers HEC-2 runs (dated
February 2, 1982), (R & M Consultants, pers. comm.). Figures
were computed by using the average width of adjacent cross
sections and multiplying by the length between them. The
steep slope around a flow of 20,000 cfs shown in Figure 3.4
exists due to the addition of sloughs to the flow regime of
that level.
Knowledge of the water surface area and an estimate
of the total area in the floodplain allows the vegetation
'~
-
(" ..
. ~
'
-25 -
3000
2500
0 .c:
< 2000 LIJ a: <
1.1.1
(.) 1500 < lJ.. a:
::>
(/)
a: 1000
1.1.1
1-
~
500
0 10 20 30 40 50
DISCHARGE (1000 cfs)
Figure 3.4: Water surface area in the downstream floodplain
(Devil Canyon to Susitna-Chulitna confluence)
as a function of discharge measured at Gold Creek
Station .
-26 -
submodel to estimate the total surface area exposed in the
floodplain.
3 . 1. 1. 6 Snow
Snowfall is simply generated stochastically because
there was insufficient conceptual understanding of snow
dynamics. This is a major model deficiency because snow
levels can seriously affect utililation of moose winter
range.
3.1. 2 Hydroelectric Development Activities
The timing, location, and areas affected by project
activities considered by the model are listed in Table 3.1.
At the appropriate time and location, the model alters the
vegetation classification for the area associated with the
site for the activity to the 11 disturbed" category (c.f. Table
2.3). The site may be permanently disturbed or may be
reclaimed or revegetated at a .later date.
3.1.3 Other Land Use Activities
There are a number of current and potential uses for
the land with the geographic area being considered by the
model. These include agriculture, forestry, recreation,
settlement, coal development, mining development, oil and
gas development, and transportation. There appears to be
little potential for agriculture, coal development, and
oil and gas development although lease sales have been
proposed. Forestry and settlement may increase in the
downstream portion of the Susitna. Perhaps the greatest
potential is for increased mineral development and recreational
opportunities.
]--'> .--·
Table 3,1: Hydroelectric Development Project Actions
ACTION
1. TRA~SMISSION CORRIDORS (clearing)
• Watana to Devil Canyon
• Devil Canyon to Intertie
2. CAMPS
• ~~atana
• Devil Canyon
3. VILLAGES
• Watana (permanent)
• Watana (temporary)
• Devil Canyon (no permanent
buildings)
AREA AFFECTED
41 mi x 400' = 1988 acres
= 804 hectares
11 mix 700' = 933 acres
= 378 hectares
75 acres = 30 hectares
70 acres = 28 hectares
Reclamation starts
(No permanent structures)
45 acres = 18 hectares
15 acres = 6 hectares
Reclamation starts
(No permanent structures)
31 acres
35 acres
13 hectares
14 hectares
120 acres 49 hectares
TIME
1989-1990
1989-1990
1985-1994
1986-1995
1994
1994-2002
1995-2002
2002
1987-
1988-
24 hectares 1995-2002
LOCATION
Watana to Devil Canyon
Devil Canyon to Chulitna
Pass/Indian River
Between Tsusena & Deadman
Creeks
South of Susitna River on
plateau opposite Portage
Creek
Between Watana Camp site
and Tsusena Creek,
surrounding small lake
Adjacent to and south of
permanent buildings
South of Susitna River
on plateau opposite
Portage Creek
ACTION
4. RESERVOIR CLEARING
• Watana
• Devil Canyon
5. STAGING AREAS
• Access Plan #13 (north)
Access Plan #16 (south)
· Access Plan #17 (Denali)
6. CONTRACTOR WORK AREAS
· Watana
Devil Canyon
(including hatching plant)
Table 3.1 (cont'd)
AREA AFFECTED
1214 hectares
3642 hectares
3642 hectares
4047 hectares
607 hectares
729 hectares
607 hectares
61 hectares
61 hectares
6I hectares
61 hectares
61 hectares
77 hectares
146 hectares
77 hectares
61 hectares
61 hectares
61 hectares
12 hectares
TIME
1989
1990
1991
1992
1999
2000
2001
1985-2002
1985-2002
1985-2002
1985-2002
1994-2002
1985-1995
1986-1995
1987-1995
1994-2002
1995-2002
1996-2002
1997-2002
LOCATION
~Tatana impoundment
Watana impoundment
Watana impoundment
Watana impoundment
Devil Canyon impoundment
Devil Canyon impoundment
Devil Canyon impoundment
Hurricane
Hurricane
Gold Creek
Cantwell
Gold Creek
Between ~va tan a
Camp and
Dam Site
Between Devil
Canyon Camp
and
dam site
N
OJ
l
ACTION
7. CONTAINMENT STRUCTURES
• Watana
• Devil Canyon
8. AIRSTRIPS
l·latana
Devil Canyon
9, ACCESS ROADS (clearing)
• II 13 (north)
• II 16 (south)
• U 7 (Denali)
l
Table 3,1 (cont'd)
AREA AFFECTED
20 hectares
32 hectares
36 hectares
26 hectares
3 hectares
10 hectares
4 hectares
1 hectare
5 hectares
13 hectares
2 hectares
47 hectares
9 hectares
59 mix 60' width
= 174 hectares
69 mi x 60' width
= 203 hectares
40 mix 60' width
= 118 hectares
55 mi x 60' width
162 hectares
429 acres
502 acres
TIME
1985-
1986-
1987-
1988-
1989-
1990-
1991-
1996-
1997-
1998-
1999-
1985-
1994-
Construction: 1985
Intensive use: 1985-1995
Intensive use: 1994-2002
Construction: 1985
Intensive use: 1985-1995
Intensive use: 1994-2002
291 acres Construction: 1985
Intensive use: 1985-2002
400 acres* Construction: 1991-1993*
Intensive use: 1994-2002
]
LOCATION
\.Jatana Dam
site
including
floodplain
Devil Canyon
Dam site
including
floodplain
Adjacent to '\'latana Camp
Adjacent to Devil
Canyon Camp
Hurricane to ~va tana
Hurricane to Watana
Hurricane to Devil Canyon
Hurricane & Gold Creek
to Watana
Hurricane & Gold Creek
to Devil Canyon
Denali Hwy to l.Jatana
Denali Hwy to l.Jatana
l·Tatana to Gold Creek*
Natana to Gold Creek
-30 -
Currently, the model only considers additional lands
needed for settlement, mining development, and recreational
development. Present use of the area is low, although
substantial growth is expected if the Susitna project goes
ahead. Estimates of current use are given in Table 3.2,
are unsubstantiated, and must be revised when better estimates
appear.
3.1.4 Disturbance to Wildlife
Associated with project activities and other land use
activities is disturbance to wildlife as a result of the
presence of humans. The model keeps track of three major
classes of disturbance:
a) disturbance from recreational usei
b) disturbance due to the influx of construction
workers; and
c) disturbance from vehicle and aircraft movements.
The disturbance from construction workers and vehicle traffic
is provided in Table 3.3. Recreational disturbance is based on
the use information in Table 2.2 and a small annual growth rate.
3 .1. 5 Access
The model allows for a choice of access route {Table
3.1). The choice of the access route will affect the amount
and leve~l of vegetation impacted and may impact critical
wildlife areas. Another aspect is whether public access to
the project area via the new access road is desirable. The
model allows for completely open access or to restrict access
in some manner.
-
-31 -
Table 3.2: Estimates uf Current Land Use and Recreational
Use in Geographic Area Considered in the Model
Mining (hectares)
Recrea·tion (user days)
Settlement (hectares)
Upper Susitna
Basin
10,000
13,000
2,021
Downstream
(Devil Canyon-Talkeetna
14,000
6,064
.-
-32 -
Table 3.3: Disturbance Associated with Construction Workers and
Vehicle Traffic
DISTURBANCE
Construction workers
Vehicle traf.ffc
Big Game Harvests
Diversion Structures
-Blasting -
LOCATION
Watana Camp &
Construction Area
Devil Canyon Camp
& Construction
Area
To Watana
TIME
1983
84
85
86
87
88
89
90
91
92
93
94
95
1994
95
96
97
98
99
2000
01
02
1985-1995
To Devil Canyon 1994-2002
Gold Creek to 1994-2002
Devil Canyon
Game Management Present
Unit #13
Watana Dam site 1985-1987
Devil Canyon Dam 1995-1996
site
MAGNITUDE
180
192
690
780
1,140
1,500
1,680
2,070
1,920
1,500
780
360
48
60
240
480
750
990
1,020
900
540
48
workers on site
at one time
workers on site
at one time
53 trucks per week
each direction
92 trucks per week
each direction
4 trains per week
each direction {if
Denali Route is
chosen)
Caribou -750/year
Moose -750/year
Brown Bear -100/yea:
Black Bear -60/year
Unknown
Unknown
-33 -
3 . 2 Veg,eta tion
The vegetation submodel is a set of rules for simulating
vegetation and land use processes in response to direct Susitna
development activities and indirect changes of the hydrologic
regime in the downstream floodplain. The model is based on a
land classification system in which areas in each land class are
updated annually in response to human activities and processes
of natural vegetation change. The Looking Outward Matrix
(Table 2.5) identifies the processes simulated by the vegetation
submodel in terms of information required by other submodels.
The information consists of area of various land classes for
each spatial unit, berry production in each land class, the
standing stock of potential browse for moose in each land class,
and a measure of the proportion of both ma~n channel and sloughs
or side channels with associated vegetation preferred by beaver.
The only actions for which the vegetation submodel is directly
responsible are controlled burning and vegetation crushing.
3.2.1 Structure
The sequence of calculations for the vegetation submodel
is outlined in Figure 3.5. Given current knowledge of
vegetation dynamics in the area, constant conditions, or no
net change, in the absence of development activities were
assumed. Areas in the various land classes do not change in
the model in the absence of development.
3.2.2 Classification System
The classification system was developed from work
described in the Plant Ecology Phase I Final Report (McKendrick
et al., 1982). The classification system in the model
distinguishes 14 classes of land, primarily defined on the
basis of vegetation type, in each spatial unit (see Section 2.3).
Initial conditions (Table 3.4) were estimated for all spatial
units, except the one representing moose range in the area
downstream from Devil Canyon. The impoundment areas
LAND DEMANDS
FOR VEGETATION
MANIPULATION
ACTION
-34 -
LAND DEMANDS FOR
MAKE DIRECT RESERVOIRS, FACILITIES,
--~[:> TRANSFERS AMONG LAND ~ BORROW PITS,
CLASSES TO MEET TRANSMISSION CORRIDORS,
DEMANDS AND ROADS FROM
~ DEVELOPMENT SUBMODEL
CALCULATE REVEGETATION
TRANSFERS ON
DEVELOPED LAND
~
WATANA NO-----------OPERATING?~-------YES
CALCULATE RIPARIAN
SUCCESSION TRANSFERS
~
CALCULATE BRmvSE AND
BERRY PRODUCTION IN
EACH 1 CLASS
CACULATE PROPORTION
OF RIPARIAN CHANNELS
WITH ASSOCIATED BEAVER-
PREFERRED ~EGETATION
CALCULATE TOTALS
FOR UPPER BASIN
Figure 3.5: Calculation sequence for the vegetation submodel.
J ] l
'l."d.iJ.l.e J.""t,; .L1J..LL.LU....L \...UJ.J.U...L\....LUiJ......J .J-_U.L VI.,....:'::J'-''-U.t-...LVi1 l.-jj:-Jt....:......} L,;JL..J...lUUl..-~U U.l..-WU.1..J)......J11UJ:..I• L.l...L.L VU....LU'-oJ U.L\... .1.!! !H.....:\....l.-c..t-Ltb,.
REST RIPARIAN ZONE
WATANA DEVIL CANYON OF UPPER TALKEETNA TO
LAND CLASS IMPOUNDMENT AREA IMPOUNDMENT AREA SUSITNA BASIN DEVIL CANYON
Coniferous Forest-
woodland and closed 4275 153 183963 0
Coniferous Forest-
open 3633 633 114607 0
Deciduous and Mixed Forest 2911 1516 36218 3500
Tundra 84 11 394590 0
Tall Shrub 537 3 128495 300
Medium Shrub 44 5 3306 0
Low Birch Shrub 400 44 29750 0
Low Willow Shrub 66 14 10565 0
w
Low Mixed Shrub 673 4 470784 400 U1
Unvegetated-water 2060 813 36967 600
Unvegetated-rock, snow, ice 60 15 203478 0
Disturbed-temporary 0 0 0 0
Disturbed-permanent 1 1 1 0
Pioneer 1 1 1 200
-36 -
estimated are slightly larger than the areas that would be
cleared if the development proceeds. In addition to the
spatial units described above, total areas in the upper
Susitna Basin were calculated as the sum of the two
impoundment areas and the rest of the upper Susitna unit.
The land classification was expanded. A medium shrub
class was defined in order to calculate bird indicator
variables. Two disturbed classes were defined to represent
land disturbed by construction of permanent facilities or
by temporary activities which would be followed by artificial
or natural revegetation. A pioneer class was added to
represent the initial stages of herbaceous vegetation in
riparian areas and following temporary human disturbance.
3.2.3 Development Activities
The vegetation submodel responds to demands for land
associated with reservoir development, road construction,
transmission corridor construction, borrow pits, and
construction of permanent facilities. These demands, calculated
each year by the development submodel, result in transfers of
land among various land classes within the respective spatial
units. Generally, the development land demands in a given
spatial unit are met from the various land classes in the
spatial unit according to their relative proportions in that
unit. However, land demands for roads are specified as
proportions of various classes associated with specific routes.
Clearing for reservoirs is simulated by subtracting
the appropriate proportions of the reservoir land demand
from the respective land classes and adding the total to the
inundated land class.
The development demand for facilities is met by
transferring land to the permanently disturbed class.
r-
1
-37 -
Access road construction is simulated by taking land
from various land classes according to development submodel
demand and route-specific land class proportions. Land for
roads is added to the low mixed shrub class under the
assumption that the biggest areal change is in the associated
right-of-way.
The demand for transmission corridors is met by
initially transferring land to the low mixed shrub class.
This land is then subject to succession to the medium shrub
class at an annual proportional rate of 20%.
Borrow pits are developed by transferring land to the
temporarily disturbed class. User specified fractions of the
borrow pit land are then subject to either inundation or
revegetation. Inundated borrow pits are transferred to the
water class, while revegetation of borrow pits consists of
an initial transfer to the pioneer land class followed by a
transition to low mixed shrub at a proportional rate of 10%
per year.
Finally, the action of vegetation manipulation
(controlled burning and crushing) transfers land from the
deciduous and mixed forest class to the low mixed shrub
class. This land is then subject to succession to the medium
mixed shrub class (at a rate of 20% of the low mixed shrub
class per year) , followed by transfer to the deciduous and
mixed forest class (at a rate of 7% of the medium shrub
class per year) . The area of land transferred by vegetation
manipulation is provided as an action to the model as a
whole, rather than as a value calculated by the development
submodel. This action is intended to roughly simulate
controlled burning and vegetation crushing which were
discussed as possible mitigation measures designed to increase
-38 -
wildlife habitat value. The land is transferred only from
the deciduous and mixed forest land class. It was felt
that this would be the preferred land for vegetation
manipulation because of relative increase in habitat value
resulting from converting this land class to earlier
successional stages.
3.2.4 Riparian Succession
Under current hydrologic conditions, vegetation
succession and disturbance in the riparian zone are assumed
to be in equilibrium (i.e. no net change from the current
land class composition). In the model, operation of the
Watana Dam triggers two changes in the riparian zone from
Talkeetna to Devil Canyon. First, initiation of the new
hydrologic regime triggers a transfer of land from the water
class to the pioneer class. Second, a process of net
successional change is initiated because of stabilized flow
patterns and lessened ice scouring causing a drastic
reduction in disturbance intensity. This successional
sequence is represented in Figure 3.6. The annual transfers
among land classes ( Figure 3. 6) were estimated from a
consideration of the observed ages of individual trees and
shrubs within the various vegetation types. O~eration of
the Devil Canyon Dam has no additional effect because it
was assumed that additional reductions in the intensity of
disturbance would be small.
3.2.5 Wildlife Habitat
The wildlife submodels required a measure of browse,
a measure of berry production, and an index of the
suitability of vegetation along channels in the riparian
zone (for beaver) as measures of habitat.
-39 -
LOW MIXED TALL
PIONEER 20% SHRUB 20% SHRUB -t>--t> 200 ha 400 ha 300 ha
4
1150 ha 7%
I \7
UNVEGETATED DECIDUOUS
WATER AND
600 ha MIXED FOREST
3500 ha
Figure 3.6: Successional sequence in the Talkeetna to
Devil Canyon Riparian Zone. Numbers within
each compartment are the estimated initial
conditions. Numbers on the solid arrows
represent the annual percentage transfer
under post-Watana dam conditions. The
dashed arrow represents a single addition
of land to the sequence in the year Watana
operations commence.
-
-40 -
An estimate of potential browse (kg dry weight/ha)
is obtained for each land class by multiplying the relative
cover of the primary browse species in each of the land
classes by the quantity (kg/ha) of browse associated with
each species (Table 3.5). Random variation (standard
deviation of 10%) is applied to these estimates to yield
annual values. Annual berry production (kg dry weight/ha)
is calculated in a similar fashion by applying the same
random annual variation to an average production estimate
{Table 3.5) based on production of berry species and their
relative cover in the various land classes.
The suitability of channel vegetation in the riparian
zone for beaver was difficult to calculate given the available
information and the spatial scale of the model. The furbearer/
bird submodel requires the proportion of both main channel and
sloughs/side channels, with certain substrate conditions,
which have willow or balsam poplar in close proximity to the
channel. · While it was not possible to make distinctions
between main and sloughs/side channels or substrate conditions,
an examination of aerial photographs indicated approximately
25% of the channels in the riparian spatial unit (Talkeetna
to Devil Canyon) currently have willow or balsam poplar
vegetation in close proximity to the banks. Initially, it
was assumed that this proportion will change in relation to
the fraction of the riparian zone in the low mixed shrub land
class.
A more reasonable, although still crude, assumption
based on cover has since been incorporated. Cover values for
willow and balsam poplar in each of the land classes in the
riparian zone as estimated from data in McKendrick et al.
(1982) are combined to yield a total cover value for the
vegetation preferred by beaver for each land class. These
cover values are then averaged across the various land
classes, weighting each value by the relative area in that
land class:
,_
,~
P~'Wll.
Fl'.,
-41 -
Table 3.5: Estimates of average values for potentially
available browse standing crop and annual berry
production in each land class. Average values
are modified in the model by a random variation.
POTENTIALLY
AVAILABLE BROWSE BERRY PRODUCTION
LAND CLASS (kg dry weight/ha) (kg dry weight/ha)
Coniferous Forest-
woodland and closed 570 60
Coniferous Forest-
open 570 20
Deciduous and Mixed Forest 329 70
Tundra 120 2
Tall Shrub 0 0
Medium Shrub 2395 15
Low Birch Shrub 1975 20
Low Willow Shrub 600 0
Low Mixed Shrub 1410 20
Unvegetated-water 0 0
Unvegetated-rock, snow, ice 0 0
Disturbed-temporary 0 0
Disturbed-permanent 0 0
Pioneer 0 0
~:
. ~
where,
-42 -
TBC = total cover value (percent) of beaver
preferred species:
( 6)
BCt = cover value (percent) of species preferred
by beaver in each land class;
HAt = area of each land class (hectares) :
THA = total non-water area in riparian zone
(hectares); and
t = land class type (1 through 14) .
TBC increases if vegetation changes increase the
proportions of riparian area in land classes with high cover
values for willow and balsam poplar and decreases if
vegetation changes result in proportionally more areas with
low cover values for willow and balsam poplar. Encouragingly,
the value of TBC calculated from the initial areas in each
land class is within 0.5% of the independently estimated 25%
of channel currently having willow or balsam poplar in close
proximity. Since a value of 0 for TBC would also imply that
0 percent of the channels had willow or balsam poplar in
close proximity, TBC was assumed to be a reasonable, direct
indicator of the percent of channels in the riparian zone
which had associated vegetation characteristics suitable for
beaver.
-·
-
-
-43 -
3.3 Furbearers and Birds
The Susitna hydroelectric development will impact
furbearers and birds primarily through habitat changes,
although increased access may cause increase trapping
intensity on furbearers. Habitat changes will result from
habitat losses due to impoundments and to alteration of the
downstream hydrologic and ice regimes.
Participants decided early in the development of the
furbearer/bird submodel to concentrate on the population
dynamics of one furbearer, the beaver, and to utilize a
habitat approach for birds.
3.3.1 Beaver
The major sources of impact on beaver were
hypothesized to be:
1) a change in the amount of appropriate habitat
for food and denning sites; and
2) an increase in beaver trapping intensity due
to improved access to the region.
A simple beaver population model was built to
simulate the effects of these two sources of impact. A
simple but rigorous approach, neglecting some detailed
biology (i.e. ingestion rates, growth rates, fat content,
fecundity, etc.), is appropriate given the current state
-44 -
of knowledge. A more detailed representation of beaver
may be needed when more data and understanding are available.
The model chosen is commonly used in biology -the
logistic growth model with an additional mortality term:
where,
dB = dt
B rB(l --) - M K
B = number of beaver colonies;
r = intrinsic growth rate (yr-1 );
K = carrying capacity (number of beaver colonies);
and
M = mortality term.
The group chose the number of beaver colonies (also
called dens or lodges) as the measure of population because
the number of beaver in a colony is extremely variable. The
population time trajectory is easily predicted (Figure 3.7)
if the carrying capacity, intrinsic growth rate, and
mortality are constant over time. However, the trajectory
is more complex if the parameters change with time. The
remainder of this section describes how the subgroup chose
to represent the variation of these parameters as a function
of the information available from the other subsystems.
·-
-
I
-45 -
--------------
TIMEt
Figure 3.7: Time dynamics of a population based on the
logistic growth model. A population that starts
above its carrying capacity (K) will decline to
its carrying capacity. A population that starts
below its carrying capacity will increase towards
its carrying capacity.
''''""
-46 -
3.3.1.1 Beaver Carrying Capacity
In the context of this model, carrying capacity is
the maximum number of beaver colonies that can be supported
within each spatial unit. To determine this number, it is
necessary to first define good beaver habitat and second,
to estimate the maximum number of colonies that can
successfully use that habitat.
Beaver habitat was defined as kilometers of shoreline
satisfying the following conditions:
a) willow and balsam poplar are the dominant vegetation
·-adjacent to the shoreline which has a bank composed
primarily of silt {from the vegetation submodel); and
'~
b) the water adjacent to the bank is sufficiently deep
that there is at least .5 m of unfrozen water below
the maximum ice cover (from the physical processes/
development/recreation submodel) .
The willow and balsam poplar vegetation is required by
beaver both as a source of food as well as lodge construction
material. Only vegetation in the riparian zone on either
side of the river is of interest because beaver rarely
travel more than 100 m from their lodge location. The silty
bank is hypothesized to be an indicator of suitable slope for
den construction and lack of ice scouring.
The severe annual ice scour under the present flow
and 1ce regimes prohibits development of suitable habitat
along the main channel, and beaver habitat is only associated
with the proper vegetation in sloughs and side channels.
However, severe ice scour will likely be a rare event after
impoundment. This will probably result in more willow and
-
-
-47 -
balsam poplar stands along the main channel which, given the
predicted stabilization of water levels between Devil Canyon
and Talkeetna, could result in beaver establishing colonies
on or near the main channel.
To capture this effect, the length of potential main
channel shoreline that does not freeze to within .5 m of
the bottom is assumed to be double the length of the stream
reach in each spatial unit. This is probably an underestimate
because it ignores small bays and secondary channels currently
exposed to ice scouring. It does, however, provide an
indicator of positive habitat changes along the main channel.
A proportion £actor for willow and balsam poplar along the
main channel provided by the vegetation submodel is used to
convert shoreline length to appropriate habitat.
Ice-free water is a critical condition to the
definition of habitat. Because a beaver den entrance is
below the water line, ice-free water is the route by which
the beaver leave their den in the winter to feed. The
hypothesis is that the beaver will not survive the winter
if there is less than .5 m of ice-free water.
To arrive at an actual carrying capacity for beaver
colonies, it was assumed that the maximum colony density
is 1 colony/2 km of habitat. Therefore, the total carrying
capacity for beaver in each spatial unit is:
where,
K = ((S * V) + (2 * S * V ))/2 s s m m
K = carrying capacity;
S = km of suitable sloughs and side channels; s
·-
-
-48 -
Vs = proportion of willow and balsam poplar with
silty banks associated with Ss;
S = km of suitable main channel; and m
Vm = proportion of willow and balsam poplar
associated with Sm.
3.3.1.2 Intrinsic Growth Rate (r)
The intrinsic growth rate is the maximum rate at
which the population can fncrease. It assumes ideal
conditions (i.e. plentiful resources, no competition for
habitat, etc.). This growth rate is only realized in the
logistic model when the population is very much smaller
than the carrying capacity (i.e. when B is much less than K in
the logistic equation, page 44). The intrinsic growth rate (r)
can be estimated as the exponential growth rate in the equation:
where,
Nt = number beaver colonies after t years;
N0 = number initial beaver colonies; and
r = exponential growth rate.
Participants hypothesized onP. beaver colony would spawn
a second colony in a minimum of two years if there was a
great deal of appropriate habitat and no other beaver
colonies competing for space. Therefore, a doubling of
colony size in 2 years means:
-
-49 -
N2 No * r*2 2N 0 = e =
and r = ln2
-2-
~ • 3
The intrinsic growth rate was assumed constant for
this model.
3.3.1.3 Mortality
Water Levels
Beaver colonies are vulnerable to changes in water
level within the year. Increases in water level on the
order of a few meters can result in the flooding of a den
(in summer) or the freezing of a food cache (in winter).
Similarly, a drop in water level will expose the colony to
increased predation or, even more likely, severe winter
temperatures if the water level falls below the den entrance.
This is likely not a problem in the sloughs and side channels
but is definitely a major factor (along with ice scouring)
currehtly preventing establishment of beaver colonies along
the main channel. Since decreased fluctuations in water
level are predicted after impoundment, the simulated beaver
colonies which may have established themselves in available
habitat along the main channel are subjected to a mortality
factor from water level changes (Figure 3.8). Total mortality
of main channel colonies is possible with sufficiently
extreme water level fluctuations.
Predation
After some discussion, the subgroup felt that
predation on beaver probably is insignificant. Beaver is
-
;'!!4'J>roo:
."'~
p:~
-50 -
...J
~
> a::
:;:)
(I)
~ 0
0 ~--------------------~------------------~ 0 2
MAXIMUM CHANGE IN WATER LEVEL ( m)
Figure 3.8: Percent survival of beaver colonies on main
channel as a function of maximum change in water
level from summer to winter.
-
-51 -
a minor food item for both wolves and bear. Therefore,
predation is not presently included in the model.
Trapping
Trapping is certainly one of the major potential
sources of beaver mortality. Beaver are especially
vulnerable to trapping during the winter when traps can
be set over the beaver's access hole in the ice. The rapid
decline of beaver populations in the lower 48 states when
beaver trapping was a viable occupation is evidence of high
vulnerability to trapping. Three factors were hypothesized
to influence trapping effort:
1) beaver pelt prices;
2) knowledge about the location of beaver colonies; and
3) the number of other trappers in the area.
Price is certainly a key factor. Participants
suggested that the beaver population in the Susitna Basin
would probably be decimated within one year if beaver
pelts were suddenly worth 5 to 10 times their current price
(given the trappers knew where to go) .
A maximum trapping mortality is calculated (Figure
3.9) using a price factor between 0 and 1. The price
factor is model input and can be changed to explore the
effect of a sudden price shift. This maximum mortality is
modified by an access factor (Figure 3.10) expressed as a
function of the number of people using the spatial area
(i.e. construction workers plus public). For any given
population, the access factor will change as a function of
-
~
ilbw.."""
, ....
I~
-52 -
MAX. T
>-!:::
...J
<(
1-
0::
0
::::E
0::
1.1.1 a.. a..
<(
0::
1-
::::E
::::>
::::E
X
<(
::::E 0.1
o~------------------------------~----------0
PRICE FACTOR
Figure 3.9: Maximum beaver trapping mortality as a function
of a user specified price factor.
Price Factor = I
0::
0
1-u
<(
Lt..
II)
II)
1.1.1
0 u
<(
NUMBER OF PEOPLE
Figure 3.10: Trapper access factor as a function of the
number of people using the area.
--
-53 -
the user-specified price factor. The assumption is that access
becomes less important as the relative price for beaver increases.
Therefore, if the price factor reaches 1, then the beaver will
experience the maximum trapping mortality (i.e. maxT). At
present, maxT is equal to .9 and maxA is equal to 1. To limit
access, an identified mitigation possibility, the user must
specify a lower value for maxA.
3.3.1.4 Initiation of Main Channel Population
Since the water level changes are large before impoundment,
the main channel population invariably suffers total mortality
each year. However, the model does assume that a certain fraction
(i.e. 10%} of the surviving beaver (in the side channels) will
attempt to colonize under utilized habitat along the main channel
in the spring.
The number of these migrants that succeed in establishing
main channel colonies is reduced in direct proportion to the
difference between the carrying capacity and the spring population
along the main channel. Therefore, if the main channel population
is zero (which it is prior to impoundment) then all of the migrants
will establish a colony and their survival will depend on the
simulated changes in water level and the degree of ice scouring
during the following winter.
3.3.2 Birds
Participants identified the golden eagle, yellow-rumped
warbler, tree sparrow, fox sparrow, and the trumpeter swan as
key bird species for discussion. However, after considerable
discussion, participants concluded that the limited state of
knowledge about these birds precluded a species by species
description of how they might be impacted by the project. Also,
many critical survival processes for these species are controlled
by events and conditions external to the model because they are
migratory. Therefore, impacts were simulated as changes in
habitat.
~-
-
-54 -
3.3.2.1 Passerine Birds
The approach used for this group was the Habitat
Evaluation Procedure (HEP). The number of species and bird
density were identified as important to establishing the value
of any particular habitat. Average magnitudes for these two
criteria were specified for each vegetation type (Table 3.6)
using data from field studies in 1980 and 1981 in the upper basin.
A per hectare suitability index is calculated for each
vegetation type by taking the sum of 1/3 of the species number
value from Figure 3.11 and 2/3 of the bird density value from
Figure 3.12.
The relative weights for each criterion selected by the
subgroup indicate that bird density is somewhat more important
than number of species.
A total number of habitat units is then calculated
within each spatial unit:
where,
Habitat __ E. TU. * Area. Units ~ ~ ~
= suitability index for a given hectare of
habitat i (from Figures 3.11, 3.12); and
Area. = area of habitat i in spatial unit.
~
This representation assumes the birds, on average, will
use land of any given vegetation type in exactly the same way
each year. Although this is probably not a reasonable assumption,
there is not enough information to take the model much further at
this time.
3.3.2.2 Trumpeter Swan
Trumpeter swans are very sensitive to human disturbance.
"~"'~
......
~il'l,
!"""'
4<-
+~'"
-
.-
-
-55 -
Table 3.6: Passerine bird density and number of species
associated with different vegetation types.
DENSITY SPECIES
VEGETATION TYPE #/10 ha #/10 ha
Coniferous Forest
O:pen 15.7 8
Woodland 34.3 17
Deciduous and Mixed Forest 43.9 22
Tundra 3.9 7
Tall Shrub 12.5 10
Medium Shrub 39. 6
Low Shrub
Birch 10.6 6
Willow (10.6)
Mixed (10.6)
-
~.,.,
-
'~"
-
,-
-
-
-56 -
1.1.1
::l
..J
~
>-1-
(I)
z
11.1
0
NUMBER OF SPECIES I 10 ha
Figure 3.11: The relative value of species in any giren
vegetation type.
UJ
::l
..J
~
>-1-
(I)
z
1.1.1 c
0
0 75
DENSITY (NUMBER I 10 ha)
Figure 3.12: Relative value of bird density in any given
vegetation type.
-'
;~
~
-57 -
Although there are only a few breeding pairs in the area, it is
known that Stephan Lake is a favored staging area during the
spring and fall migraiton. Participants felt that the construction
and use of roads and the transmission line would cause the major
impacts. It was concluded that because potential impacts are
known and predictable, the concern involved proper siting of
roads and transmission lines to ensure minimum interference with
nesting/staging areas. This was not included in the model.
3.3.2.3 Golden Eagle
The major impact of the Susitna project on the golden
eagle will probably be the destruction of their traditional
cliff nesting sites due to inundation.
Most of the good eagle nesting sites that may be affected
have been found in the Watana impoundment area. Representation
of this imapct in the model is done by comparing the elevation of
each active site to the maximum elevation of the reservoir. If
the nest elevation is less than the maximum reservoir level, then
the nest site is counted as flooded. No attempt was made to
determine just which sites had an active nest in any given year,
nor what effect an inundated nest might have on the young.
Instead, this indicator shows the potential reduction in existing
eagle nest carrying capacity as a consequence of impoundment.
3.4 Moose
Discussions in the moose subgroup focused on alternative
approaches to constructing a generalized population dynamics
model that could later be refined to examine questions concerning
the probable impacts of the Susitna hydroelectric development and
the effectiveness of various mitigation measures. Subgroup
participants stated clearly that having a model running at the
end of the workshop was not their principal goal. Rather, they
chose to concentrate on the development of a conceptual frame-
work suitable for later refinement.
!~
-
-58 -
Neverless, it seemed desirable to have some form of
moose model operating at the workshop simply for the purposes
of demonstration. The remainder of this section, therefore,
describes an attempt on the part of the workshop programmer
to illustrate some of the kinds of relationships that might
eventually be incorporated in the model. The specifics of
the relationships should in no way be attributed to any of the
workshop participants. Hopefully, however, the example does
capture in a crude way some of the processes that were discussed
and will serve as a stimulus for further thought.
3.4.1 Structure
Development of the moose submodel was guided by the need
to produce indicators for evaluating both the impacts of Susitna
hydroelectric development on moose and the potential effectiveness
of various mitigation measures. The bounding exercise (Table 2.2)
identified three general types of indicators:
1) measures of numbers of animals (total population
size, harvest, numbers of animals dispersing out
of the Susitna Basin);
2) indices or measures.of habitat quality; and
3) indices or measures of habitat carrying capacity.
The structure of the moose submodel combines a simple
model of winter carrying capacity and a generalized population
dynamics model that can later be refined for the Susitna
project as additional information and understanding become
available. The computational sequence for the model is
illustrated in Figure 3.13.
-
-
-59 -
COMPUTE WINTER LAND CLASS ACREAGES
CARRYING <Jt---AND BROWSE AVAILABILITY
CAPACITY FROM VEGETATION SUBMODEL
~
INCREMENT
AGE CLASSES
~
COMPUTE
CALF CROP
~
REDUCE AGE
. CLASSES DUE TO
SUMMER MORTALITY
~
NUMBER OF GRIZZLY REDUCE CALVES
BEARS FROM ---[::> DUE TO BEAR
BEAR SUBMODEL PREDATION
~
REDUCE AGE
CLASSES DUE TO
HARVEST
~
COMPUTE POPULATION,
SIZE, AGE RATIO,
AND SEX RATIO
~
REDUCE AGE CLASSES LAND CLASS ACREAGES
DUE TO WINTER <j--AND BROWSE AVAILABILITY
MORTALITY FROM VEGETATION SUBMODEL
Figure 3.13: Calculation sequence for the moose submodel.
-
-60 -
3.4.2 Winter Carrying Capacity
The winter carrying capacity for each spatial unit
is calculated as the number of moose-days of browse
available:
where,
u =
u =
A.
J
=
B. = J
L =
14
E A . B . ( 1 -L) /F
j=l J J
moose-days of browse available;
area in land class j (ha} ;
available browse in land class j
weight/ha) ;
proportion of available browse at
summer lost due to leaf fall; and
(kg dry
end of
F = individual moose forage requirement (kg dry
weight/day) •
The vegetation submodel provides the area (Aj) and
amount of browse available at the end of the summer (Bj)
for each land class. Available browse is defined as the
standing crop of plant material of species, size, and
height suitable for moose forage. The amount of browse
available in the winter is the amount available at the end
of the summer reduced by a proportion representing leaf fall.
Division by a daily forage requirement produces the number
of moose-days of winter forage available.
--61 -
, ....
3.4.3 Population Dynamics
The basis of the population dynamics model is a
simple life table model that represents the birth and death
processes for 20 age classes of moose for each sex. The
biological year for the model begins with calving. Animals
surviving from the previous year are first advanced to the
next age class. Calf production is then calculated based
on the number of females of reproductive age in the herd.
The remainder of the year is divided into three periods
for the calculation of various forms of mortality:
a) a summer period representing the time from calving
to the start of the harvest;
b) the harvest period itself; and
c) a winter period representing the time from the
end of harvest to calving the next year.
The number of animals in each population class is reduced
by an age-and sex-specific mortality rate during each
of these periods.
The utility of this model for assessing impacts and
mitigation success is strongly dependent on the extent to
which the reproductive and mortality rates incorporated in
the model can be functionally related to factors influencing
moose dynamics that may change with hydroelectric development.
Much of the discussion in the subgroup focused on which of
these factors might be important and how they might be
quantified for representation in a simulation model. While
a variety of interesting ideas emerged, there was not
sufficient time or information at the workshop to begin to
quantify such relationships.
-
-
-62 -
3.4.3.1 Reproduction
Reproduction is calculated separately for yearlings
(those 2 years old at the time calves are dropped) and
adults (those 3 years or older at the time calves are
dropped) . Each of these groups has a fixed pregnancy rate
(currently set at 0.85 for adults and 0.80 for yearlings)
and a density-dependent ovulation rate per pregnant
female (Figure 3.14). Ovulation rates are presently the
same for both groups of females though the rate in
yearlings should probably be somewhat lower. Pregnancy
rates and ovulation rates are multiplied by the number of
females to arrive at the number of calves born. The calf
sex ratio is assumed to be 50%.
3.4.3.2 Summer
The population classes are first reduced by an age-
specific mortality rate (presently 0.35 for calves, 0.01
for adults) during the summer period.
An additional mortality rate for calves is then
calculated from the number of grizzly bears present
(provided by the bear submodel) and the density of moose
calves:
P = B * ((C * M)/(C +H))
where,
P = number of moose calves killed by bears~
B = number of bears~
I~
-
LLI
..J
<(
:E
LLI
II..
1-
2.0
z 1.0
<( z
(!)
LLI a::
0..
' ~
-63 -
0~--------~--------~--------~--------~ 0 s,ooo 10,000 15,000 20,000
NUMBER OF MOOSE
Figure 3.14: Relationship between moose density and
ovulation rate.
a::
<[
LLI
Ill
a::
LLI
0..
0
LLI
..J
..J
::J::
18
12
~ 6
> ..J
<[ u
M -------------------~---------
I
I
I
I
l
I
I
I
1/H
0 ~--------~~--------~--------~~------~ 0 4,000 s,ooo 12,000 16,000
NUMBER OF MOOSE CALVES
Figure 3.15: Relationship between moose calf density and bear
predation rate with a half-saturation constant (H)
of 4,000.
-64 -
C = number of moose calves;
M = maximwn number of calves that would be killed
by a single bear in one summer; and
H = calf density at which a single bear can kill
half of the maximwn (M) .
Bear predation on calves is asswned to be equally distributed
between males and females. The form of this relationship
(Figure 3.15) asswnes that:
1) an individual bear finds it more difficult to locate
and kill calves as calf density declines; and
2) bear predation saturates at some maximum level.
The half-saturation constant (H) varies in response to the
randomly generated snowfall pattern as shown in Figure 3.16.
This assumes that predation is heavier in years following
heavy snowfall because calves are less healthy and therefore
more vulnerable to bears. Figures 3.15 and 3.16 suggest an
individual bear will find it easier to find and kill calves
at low calf density in years following heavy snowfall.
3.4.3.3 Harvest
Harvest is assumed to be a constant rate (currently
set at 40%) that is applied to a user-specified range of
male age classes (presently males 3 years of age and older) .
The age ratio, sex ratio, and size of the herd are
calculated following the harvest calculation. The age
ratio is obtained by dividing the number of surviving
calves by the number of cows 2 years of age or older and
the sex ratio is obtained by dividing the number of bulls
-
-
-65 -
4,000
2
SNOW DEPTH (m).
Figure 3.16: Relationship between snow depth and half-saturation
constant for bear predation function.
1.0
IJJ
-l m
(/)
(/)
IJJ u u
<t
LIJ
(!)
z
<t a::
a:: 0.5 IJJ ... z
i
II..
0
z
Q ... a::
0 a..
0 a:: a.. 0
Figure 3.17:
0 2
SNOW DEPTH ( ml
Relationship between snow depth and proportion
of winter range accessible to moose.
-66 -
2 years of age or older by the number of cows 2 years of age
or older. These ratios are expressed as calves/100 cows and
bulls/100 cows, respectively. The simulated age ratio, sex
ratio, and population size calculated after the harvest thus
correspond roughly in time to composition counts actually
done in the field.
3.4.3.4 Overwinter Mortality
The final part of the example moose submodel calculates
calf and adult winter mortality rates based on food
availability. The area of winter range potentially
available in any simulation year is first calculated by:
t~reproject
. ~nter range
area
area of J Watana
impoundment * proportion of winter
range accessible
The randomly generated snowfall pattern affects the
proportion of winter range accessible (Figure 3.17). The
total amount of forage available on the winter range is then
calculated using an equation similar to that for winter
carrying capacity (page ) , but assuming that all of the
winter range is in the conifer woodland class. The amount
of food available per moose per day is computed as the
total amount of available forage divided by the total number
of moose present and the average number of days spent on
the winter range. Forage available per individual is used
to calculate calf and adult survival rates (Figure 3.18).
r""""""
r~
.....
-
-
-67 -
1.0
~
........
........
......... ........
Adults\ ,..,..
y .....
ILl ..........
1-I".; <(
Q: I ..J
<( 0.5 I > > I
Q: I ::;)
(/) I
I
I
I
I.
0
1.5 3.0 4.5 6.0
FORAGE AVAILABILITY {kg dry weight/day)
Figure 3.18: Relationship between forage availability and moose
winter survival rate.
r~·
-
-
-68 -
3.5 Bears
The bear submodel relates population response of
black and brown bears to changes in habitat structure and
to more direct human influences (hunting, disturbance from
construction activity, etc.). The model contains two major
simplifications. First, only female bears are considered.
Mature·males are assumed to always be sufficiently numerous
to mate the reproductively active females. Second, hunting
is not included because the kill of bears is heavily biased
towards males due to hunting regulations and the desire of
hunters to take large males as trophy animals.
The structure of the model is a simple life table
that represents the birth and death processes for various
age classes of black and brown bears. The population
dynamics of bears in the study area are assumed to be
controlled by reproduction, mortality, and dispersal.
3.5.1 Structure
The life history structures used for brown and
black bears are portrayed in Figures 3.19 and 3.20
respectively. Mature females are partitioned into groups
based on the presence or absence of offspring (two groups
for black bears (Figure 3.20); three groups for brown bears
(Figure 3.19)). Immature female black bears are partitioned
into four age classes and immature female brown bears are
partitioned into six age classes.
The proportions of females in a given age class
that have reached maturity (Table 3.7) are assumed constant.
For example {in Figure 3.19), a three year old immature
brown bear that survives the year must become either a
-69 -
-
-Table 3.7: Proportion of females reaching maturity by age.
PROPORTION REACHING MATURITY
AGE BLACK BROWN
2 0.5
3 0.75 0.44
4 1.0 0.76
5 0.9
6 1.0
-
-
0
1:'--
l1J
0::
::>
1-
<(
~
:::!:
NO
OFFSPRING
6-
YEAR
5-
YEAR
WITH
CUB
I cusl
4-
YEAR
3-
YEAR
WITH
"' YEARLING
• YEARLING
2-
YEAR
Figure 3.19: Life structure of brown bear~ Each arrow represents a time
step of one year.
l --
.--1
r--.
w a::
:::::>
I-
<{
~
w a::
::1
1-
<t
~
:::!:
NO
OFFSPRING
WITH
CUB
CUB
1-
YEAR
Figure 3.20: Life structure of black bear. Each arrow
represents a time step of one year.
~-
-
-72 -
mature animal with no offspring or a four year old immature
animal. Mature animals without offspring either remain in
that condition or produce cubs.
3.5.2 Reproduction
The proportion of females emerging with cubs and
litter size is a function of the previous summer's food
availability (primarily blueberries). The model uses an
index of summer food availability because little is known
about the levels of berry production (biomass) that
constitute a good or bad year for bears. The index of summer
food (I 5 F) is defined as:
= total berry production in year t
ISF total berry production in 1980
The total berry production for a given year is a sum of the
total berry production in each vegetation type. The vegetation
submodel provides berry production per hectare for each
vegetation type and the area in each vegetation type to
calculate total production. The summer food index is
modified by use of the salmon resource from Prairie Creek.
Twenty five percent of brown bears in the study area are
assumed to use this resource during one third of their
summer feeding periods. It is assumed that future
recreational developments or material sites in the area will
preclude bear use of this resource. Because the level of
disturbance (number of recreational use days per y~ar)
necessary to preclude use could not be determined, it was
arbitrarily assumed that this resource would be lost if
recreational use becomes double the 1980 level. If this
recreational use level is reached, the summer food index
is reduced by 8%.
-~
'""''
-73 -
The proportion of females emerging with cubs as a
function of the index of summer food availability is shown
in Figure 3.21. Fifty percent of the females emerge with
cubs when the food index is 1.0, representing an average
berry crop. The a parameter governs the sensitivity of
pregnancy rate to food availability. When the food index
(in Figure 3.2la} is near 1 -a, the proportion with cubs
is near 0; when it is near 1 + a, the proportion is close
to 1.0. In the current version of the model, a is 0.2 for
black bears and 0.5 for brown bears; black bears are
assumed more sensitive to changes in berry production.
Mean litter size is a linear function of the summer
food index (Figure 3.2lb). The maximum mean litter size
is 2.5 for brown bears and 2.7 for black bears. The number
of cubs is the product of the number of females emerging
with cubs and the mean litter size. It is assumed that 50%
of the cubs are males and 50% are females.
3.5.3 Mortality
Animals two years of age or greater are assumed to
have a constant mortality rate (.OS for brown and .08 for
black bears} .
Mortality of cubs and yearlings is assumed to be a
function of spring food availability. Spring food, which
includes such items as equisetum, moose calves, small
mammals, skunk cabbage, roots, and cottonwood buds, is
more vulnerable to inundation than summer food. Because of
the lack of understanding of the relationship between cub
and yearling mortalities and spring food availability, an
index of spring food availability is used. The index
,_
-74 -
(J) ------------------..,.....-----
IIl
::1 u
:z::
1-
~
(a) (!)
~
(!) a::
LLI
~ 0.5
(J)
UJ
..J
<3: :a
LLI
1.1..
~
z
0
\3
<3: a::
1.1.. 0
1-"' I+"'
INDEX OF FOOD
MAX. ----------------·--------
(b)
LLI
!::::! 2 (J)
a::
LLI
1-
1-
:::::i
1.1
INDEX OF FOOD
Figure 3.21: Reproduction relationships as a function of the
previous year's food: (a) proportion of females
emerging with cubs; (b) mean litter size.
-75 -
(IWF) relates vegetation types utilized by bears (open conifer
forest, medium shrubs, and all low shrub types) to the base
year 1980 and is calculated as:
_ total area of suitable bear habitat in year t
IWF -total area of suitable bear habitat in 1980
In any given year, the total area of suitable habitat is
found by ~umming the vegetation types utilized by bears.
Mortality is linearly related to the spring food index (IWF)
(Figure 3. 22) •
3.5.4 Dispersal
Dispersal to and from the study area by subadult brown
bears is probably common while black bears in the study area
may contribute to bear populations in other areas. Dispersal
is thought to be controlled by the density o£ one year or
older black bears and two years or older brown bears. Therefore,
the base year (1980) was assumed to have no net dispersal.
Dispersal from the study area in subsequent years is directly
proportional to any increase in density; however, only
immature animals {one year or older for black bears and two
years or older for brown bears) disperse. The total density
of bears can exceed the density set in the base year because
mature animals are included in the calculation of dispersal
rates but only applied to immature animals.
3.6 Model Results
During the workshop, the participants constructed a
number of relationships to functionally relate the biophysical
processes operating in the Susitna Basin. Lack of data and
understanding forced an overly simplistic representation o£
many o£ these processes. As a result, great care must be
taken in evaluating the results presented in this section.
,l!i.~
,l!".ai!Jf
....
,,. ..
-76 -
> ~
~
~ a:
0
:::i:
BASE
INDEX OF SPRING FOOD
Figure 3.22: Mortality of cubs and yearlings. Base mortality
for black bears is 0.2 for cubs; 0.2 for yearlings.
Base mortality for brown bears is .15 for c.ubs;
.10 for yearlings.
I~
-
-77 -
We caution against considering the results to be valid
projections of what might happen in the Susitna Basin. In
particular, the moose submodel and the bear submodel results
are examples of how the important processes affecting moose
and bear can be incorporated into a simulation model. They
are not intended to represent the moose and bear populations
of the Susitna Basin.
Three scenarios (sets of actions) to be simulated were
developed at the workshop:
a) a baseline or no project scenario;
b) an optimum power generation scenario with little
mitigation; and
c) a Watana only scenario with a hydrologic regime
based on instream flow targets.
The major differences between scenarios (Table 3.8) relate
to flow regime, number of dams constructed, choice of access
route, and control of access.
The following figures compare indicators for the three
scenarios. It may ultimately be desirable to compare the
quantitative results but, at present, only the qualitative
results should be considered. It is more appropriate to
examine the general temporal differences in the indicators
among the scenarios, rather than to focus on their actual
values.
3.6.1 Physical Processes/Development/Recreation
The maximum annual change in stage measured at Gold
Creek Station (Figure 3.23) is considerably less under the
-
,_
-
~""'"'
-
-78 -
Table 3.8: Scenarios Used in the Simulations
Flow Regime
Access Route
Access Control
Dams Constructed
No Project
preproject
none
no increased
access
none
Full Project
case A
(optimum
power
generation)
plan 17
open access
Watana,
Devil Canyon
Watana Only
case D
(best for
fish)
plan 13
no increased
public access
Watana
-
Figure 3.23:
l'
:.! !::
·' "'!' " II-~. f\ ... _; · .... tt ! r·· ~ ' .. '
t q
-+ ! ,.
I
t
t
I
!. T
!
!. r~~-/'\t ~''-
:.... ~ I ~ .. ;
+ ' i
s +
i
+ !
-t
! ...
i
79
2 l su
1 o~-------------------------------1
:
~
I + !
• l
:u. S!.l
T I 1·t:
Maximum annual
Station. The
change in stage
maximum value on
a) No Project
b) Full Project
c) Watana Only
at Gold
y-axis
Creek
is 10 feet.
-
-80 -
regulated scenarios (Figures 3.23b and 3.23c). The drop that
occurs at simulation year 12 is associated with the commencement
of the operation of the dams. The average change in stage with
dam operation is about twice as high under the hydrologic
regime based on instream flow targets (Figure 3.23c) than it is
under the hydrologic regime that is optimum for power generation
(Figure 3.23b).
The amount of reservoir clearing in a year (Figure 3.24)
follows the schedules outlined in Table 3.1. The large jump
in reservoir claring in both development scenarios (Figures
3.24b and 3.24c) is associated with the clearing for Watana;
the smaller jump later in time in the optimum power generation
scenario (Figure 3.24b) is associated with clearing for Devil
Canyon.
Influx of construction personnel is associated with
dam construction (Figure 3.25). In the model, this influx
is simulated using the schedule outlined in Table 3.3. The
large peaks are associated with the construction of Watana
(Figures 3.25b and 3.25c); the lesser peak is associated with
the construction of Devil Canyon {Figure 3.25b).
Recreational use of the area is assumed to increase
gradually without the project (Figure 3.26a). There is a
steeper increase for ten years after Watana is completed
under the full project scenario with no restriction on access
(Figure 3.26b). The Watana only scenario with restricted
access (Figure 3.26c) has the same gradual increase in use
as the no project scenario.
Potential overwintering habitat for beaver in sloughs
and side channels (Figure 3.27) is unaffected by the
introduction of the projects. This is because the changes
in the availability of habitat are assumed to be based only
on changes in winter severity and not on the flow regime.
f....,
~·~
,.!~
·'""""
-
:-
~-"lll""~
Figure 3.24: -
-81
~: E $ •: 1) M>l:".: saaa.
~:E :-(a:. r·1Q ::<= s c a~.
1 ~
i
~
I +
l
t
f r •t a)
+
No Project
I
t-
t
i + !
!.1 2 1 3l -~ T 1 :""£
~•: E $' ( 1;. 11>1 )(: :u~~-r:E ~ (a J t1~ ~<= sa a a.
1 l
' I ,,
t 1-:
'
t
5 t
"'
b) Full Project
I t
+ I -! r '•
I n
lJ. 01 31 SD
T I ~11:
!-O:E :" ( .1. :• r1,;e ><= sa~ a.
;:;:~ ~ (a;. 11>n<:= s D a a.
1 ~
+ .~ I t ,-'
' ' I ' ' 1' '
s +
1
I c) 1 Watana Only I +
+ I
t ' t
I
:!.:1. H :! SD
T I rt:
Amount of reservoir clearing (ha) per year.
The maximum value on the y-axis is 5000 ha.
-
Figure 3.25:
. ~ • 1
t +
1
; r
5 + 1
+ ' + i ...
i .,.
i
! ,.
l
I + I + I
t . 5 T
+ ' ...
,. . .
1:!
,:
i ' ' . . -· ' .
' • \
' .. ',
! 1\ ' 1
J
-82 -
31
~ ,.. \/ \
a~------------~-----------------!:. 31 sa
T I r1:
~ T ..
i
+ .'· I I ' + l ' ~ . \
t I ' ' ' I
' 5 + J i ·' ' + . I
I ' : 1 . I
I ' ... J ' ' I I,
i I
.I ..
!.!
T I 1·-t
a) No Project
b) Full Project
c) Watana Only
Construction personnel on site at any one time.
The maximum on the y-axis is 2500 workers.
Figure 3.26:
-83 -
l i
' i
~ ! ........ -.....
r -.... ---
~ --~J------... -~~-----
!-----
. 5 r
I
!"
L
i
I
~ a'-----------------
1 r
~
11
rii"tE
j.
r ---~ ....... r ~------~·· I .~·-
. s: r-~--.-.~-·
r
I
~
r
a~.~----------------------------
~ I
~
I
TIME
t-
1 t--.~-----~~-··-.
~-~~--
5 t
t
:a
__ .. ~,. --...
a) No Project
b) Full Project
c) Watana Only
Recreational use days in the Upper Susitna Basin.
The maximum on the y-axis is 25000 use days.
-
Figure 3.27:
;
l ;~ t/ 4\ ~ I!
., . , ,·
84
' ..
'' ~
~ ~ I t ;'"1 r . 4 ~~' a) No Project
I + ·,
+
+
~'T
l
I
~-
' + L·, ,., '
~I 'I " .
:11 su
? .• + b) Full Project
I
~
~
' ·+
1
~ i
J.
I + I + !
-+;
I;'
s 1
• I +
' +
'
:::.
-,
c) Watana
:u
Potential overwintering habitat for beaver
sloughs and side channels. The maximum on
y-axis is 200 krn.
Only
in
the
-
-85 -
3.6.2 Vegetation
Only a few selected vegetation types are presented.
The major changes in vegetation in the Upper Susitna Basin
are assumed to occur in the impoundment areas. It is
important to remember that perpetuation of present conditions
is assumed without project development (Section 3.2.1). In
the model, the vegetation in the impoundment zone decreases
and the area of water increases as the reservoirs are
cleared and filled. With the project, the vegetation in
the Watana impoundment is cleared and the area inundated,
hence, the coniferous and mixed and deciduous types decline
(Figure 3.28). A similar pattern is observed in the Devil
Canyon impoundment area (Figure 3.29). The model currently
assumes that vegetation in Devil Canyon impoundment will be
unaffected if only Watana is constructed (Figure 3.29c).
Although the changes in vegetation in the impoundment areas
(Figures 3.28 and 3.29) appear dramatic, they actually
represent a small proportion of the total vegetation in the
Upper Susitna Basin. The proportional changes in vegetation
are small when viewing the entire upper basin as a unit
(Figure 3.30).
It is assumed that changes in the downstream riparian
zone will be identical whether both dams or only Watana is
constructed. The area of deciduous and mixed forest increases
with the project (Figure 3.31).
In the model, the tall shrub community first increases
and then decreases as the later successional stages become
dominant and the low mixed shrubs decline after the project
begins operation (Figure 3.32b, c). The mechanisms underlying
these changes are depicted in Figure 3.6 (page 39). It is
assumed that after the project, the low mixed shrub will
succeed rapidly to the tall shrub which in turn succeeds
-
1 ~ I
-86
~ ~ (!. ~ i ) !"-~!~ >~= . S U !i D.
~iS:. ( 1 ~ ~ .t !'"·~:~ ::<: S UIJ ll.
H A o" 1 .· 1 a :• 1111 )<:: S 0 0 U .
1
1--··------------------------------------------· +
!
! 3
~----------------------
; t 10 .;:----------------· I ...
i
1
!
1 r
;.
11
1-lw ( 1, 1 :0 MA ~<;: 1 50 D a. !il'H 1 .. ~ :o r1.:. ;.;: 1 s a a u.
· H A ( 1 • 1 a 1 ~1.:. :>(~ 1 s a a a.
r I rt:
H fl •. 1 .. :1 .• ~·lA :--:r.:: 1 S 1! !U: .
!·Ht(:l..3) r·n~>=.= 1S:!l!IO.
H>H1 .. 1D:• t10:.i~• 1Saaa.
Sll
10
I
1· . ---·-- ------
I
~
I t . s r
~
I
I'
I'
r
1:.--------J I , t. -_: :. -{ ~\
I
1 ,._ -=-=-=--.: -.:-.:-.:---_-------=------2 r
Q
sa
a) No Project
b) Full Project
c) Watana Only
Figure 3.28: Changes in areas of selected vegetation types in
Watana impoundment area: closed coniferous forest
(1) 1 deciduous and mixed forest (3) 1 and water (10).
The maximum value on y-axis is 15,000 ha in
(b) and (c); 5,000 ha in (a).
-
-
·-
Figure 3.29:
-87
:-~ ~ ( 2 .· 1 .' l"lW >=.= a u !l a.
H H J: 2 .. :r .~ r-1,:. x= a !l !l a .
H 1:1 .: a .· i a ) r-1~ ::(: a a a a .
l I
I + 3 ~----------------------+
I +
. 5 L -------------~o
I + ~ ------------------------------------------~-
l 11 31
HA.(:!, !.J 1"1!=t>=.-= a sua.
H ·~ •: a , 3) ~1.:. x~ as ~ u .
"'"' •: ; , 1 a;. r1o< ;,;: as: au. 11
~
J ,.
! r---------1
, 5 J \ l :~
1-----'
I
t .,.
I
r-----
f
sa
-_lQ.
3
[-------------,_ 1 a ~--------·· --·--·------------
31
T I ~':E
HA •: a .. 1 J r-1r=t::-::= a: u a a.
H A 1: ~ ,. -= ) 1'-1::. >~= ~ !l U U •
H.:. .; a .. 1 a :o r111 ;.,;= a a a a .
!. j
t t---------------------~
I
f
I
~ ,. i-------- - - ----10
./.
~
l 1 r--------------------------------------------·
<t u
Tit'£
a) No Project
b) Full Project
c) Watana Only
Change in areas of selected vegetation types in
Devil Canyon impoundment area: closed coniferous
forest (1) , deciduous and mixed forest (3) , and
water (10). The maximum value on the y-axis is
2000 ha in (a) and (c); 2500 in (b) .
-
-~
-
·-
Figure 3.30:
88
·-~~~ ::·:_: .::1 ,;. -.:
~-~-:.::: :-.::::: ~· ,. c::
------------------------------------------~-
c; -j. a) No Project
+
.,-------·--------~ l-.9
1!
. T! ~£
~ ~~ ~ :: "i :: ~~ :~~ ~ : ~ ~ ~
~~:. 'f .· 1 [I ) trtH ::(: ~. ~ S
~ ~ 1 F--------......... ________ -----------------------·
~
b) Full Project
10 ------------;---_ _....;_ - - - - - - ----~------ 3
j..
~~::~:: i::
.H H ( lot , 1. Jl)
... -----------.. 1 ----------------------------------·
c) Watana Only
10
.,_ __ __....:..-----------------3
a----------------------------------
Changes in areas of selected vegetation types in
the Upper Susitna Basin: closed coniferous forest
{1), deciduous and mixed forest (3), and water (10).
The maximum on they-axis is 200,000 ha.
-
-
-
,..... .
ii
l
I
-89 -
t· -----------------··-------------------------·
t
. s t a) No Project
Figure 3.31:
+
t ...
i
11 31 sa
1 t -~-~-~-~-----
i------------------------------·-
s ~
t
1"
I
~
I r
·~ • I
11 su
T I~'£
i -----------·-~-·------
t-------------~--~--~--~ ~
! . s 1
+ ' +
t + I
: 1. sc
b) Full Project
c) Watana Only
Area of deciduous and mixed forest in the
downstream riparian .zone. Maximum on the
y-axis is 5000 ha.
,~,
-
Figure 3.32:
li
f
t
t
-90
H ~71 ·~ s ~ s :r !'-1~ >~= 1 u u a .
~ >H S .. 9 :0 ,.,,:,;>;: t U C D •
.sf 9
~---------------------t·-----------------------------------------~--~
I
t
1+
t +
1::1. 31 .
T I r"E
HA!S.SI M~X: l~DQ.
HA!S.~') ~lAX: lDDU.
. 5 t. _,•"""'·~--.
I t ~. -~-, -----,,.. ,_ .... ' ... ..., r-----------/· .... .... ... ..
t " ----._ 5 I .... .. ........ _
t
l T
l
I +
t +
~A •: S , S ) MA ;,;: 1 D D Q •
,., ):! ( s . 9 ) !'-~~ ::<= l :lll u.
. 5 t ... -·-·-···-. __
..f-- - - - -' .,.·' ....... I /-. •, +---------~ .... ... ... "!.... c;,
I " '•-.t
t "·--. ; -..........
1' ----~9 ! -...,_
11 5U
a) No Project
b) Full Project
c) Watana Only
Areas of tall shrub (5) and low mixed shrub (9)
in the downstream riparian zone. The maximum
value on the y-axis is 1000 ha.
-
,.,. ...
-91 -
more slowly to the mixed and deciduous forest. The difference
in conversion rates gives rise to the initial increase and
eventual decline of the tall shrubs.
The model projects that the surface area of water in
the floodplain will decline with development and pioneer
species will increase immediately after impoundment then
gradually decrease (Figure 3.33). The decrease in surface
area of water is assumed to occur becaus·e of the reduction
in peak flows; the dynamics of the pioneer species are
described in Figure 3.6 (page 39).
3.6.3 Furbearers and Birds
Under the current assumptions in the model, the beaver
colonies and carrying capacity associated with sloughs and
side channels in the downstream riparian zone are similar for
all three scenarios (Figure 3.34). Beaver populations are at
or near their carrying capacity through the 50 year time
horizon in all three scenarios. One possible explanation is
absence of direct linkages between the hydrologic regime and
beaver, and between the vegetation and beaver.
Main channel colonies and their carrying capacities
exhibit a more interesting behavior (Figure 3.35). Without
the project (Figure 3.35a), there are no main channel beavers
although there is ample carrying capacity. Under the project
scenarios (Figure 3.35 b, c), the carrying capacity increases
slightly. Main channel beaver colonies appear after the
project begins operation but are kept at a level well below
their carrying capacity by periodic severe ice scouring events
and years of unusually high stage fluctuations.
The change in the number of habitat units for
passerines is small in relation to the total for the Upper
Figure 3. 33:
-92
11 ~-1 t.' ~ .• 1 ~ _, r-,~~ ::<= 1 t1 e !.! •
~"'t:. -: ~ .. :!. "! :• r.,w :=<= 1 a a a .
... - i
.j.
i + I + / 10 i--------------------------------------------·
~ + I
t r 14 r----------------------1 ·• i
11
l T
t
t +-----------I
M,;",f;:<:
r•m)•.= 1e a a. 1u u a.
I ' st ~ 10
.J. • ___ ... ----------·· ... --------------..
I
t -'' .,.------' ' ~ ",14
I '• a - -..
t i
+ t
H t=t '· S .. l :2 .• ."1~L:{: 1 Q !] iJ. •
:--! H ~: 2 .. 1 ~ ) r1~ ;.:-.: 1. u o £J .
t. ----------1,
. 51 ···----------------··---------J-_9 __ _ .,.
I ..
t------/'' t ",!4
a ---
5U
a) No Project
b) Full Project
c) Watana Only
Areas of water (10) and pioneer species (14) in
the downstream riparian zone. The maximum value
on the y-axis is 1000 ha.
-!
-
Figure 3.34:
l .. I .:.
I + I
I + :r'
1 r
1"
i
'
93
B' r: Q L. 1• 1 .· 5) r-1r:a :·<= 2 S.
8 (.: :..:n•: (!. .. .5) !"'1r:.>·::-2 S.
ll
11
T I 1'1:
r-iA~<= as.
M;j;<: .:!5.
r I t't
a) No Project
S!!
b) Full Project
c) Watana Only
Beaver colonies utilizing sloughs and side channels
(solid line) and their carrying capacity (broken
line) in the downstream riparian zone. The maximum
on the y-axis is 25 colonies.
1-~
Figure 3.35:
l i
.j.
I
-94 -
s ~: •J L. .: 2: . s :. r·!::. :<= a 5 . ~· 1.:. A R '· : -. 5 J 1'1A ::-::: ~ 5 . ·
r----------------------
+
' !
i
5 t
~
i
t
I ...
! r a
1 11 31 sa
T I ~t:
%. T
~ -----------.~------·~-·~--
I + I
~
5 ~
! + I
t
i
1'
a ....._i -"'-~--¥...¥:¥.-..::!...--lLL_
l T
t -----------i-------
1 -------
+ I
t
. 5 t
+
+ I +
! ~
a...._, _ ___..._./\...:llll,.4c_:_-:_=-~--.l..J'\CL..L.
! 1 2 1 sa
a) No Project
b) Full Project
c) Watana Only
Main channel beaver colonies (solid line) and
carrying capacity (broken line). The maximum
on the y-axis is 25 colonies.
-
-95 -
Susitna Basin (Figure 3.36). A slight decrease in the total
number of units can be observed for the project scenarios
(Figure 3.36b, c).
3.6.4 Moose
The projections for moose should be regarded as being
for a hypothetical population in an area similar to the Upper
Susitna Basin. The fall post harvest moose population exhibits
considerable year to year variation (Figure 3.37). There is
a severe winter in year 10 that causes a severe drop in the
population in all scenarios. The population then gradually
recovers in the no project scenario (Figure 3.37a), but, with
the project (Figure 3.37b, c), the population fails to recover
as rapidly and fails to reach as high a level as without the
project. The reason for the lower population appears to be
the loss of home range associated with the clearing and filling
of the impoundments.
The number of animals lost to bear predation (Figure
3.38) is slightly less with the project than without. The
harvest (Figure 3.38) declines proportionally with the
population due to the assumed constant harvest rate.
3.6.5 Bears
The grizzly or brown bear is not affected by the
projects (Figure 3.39). The black bear (Figure 3.40)
declines rapidly after the project in response to loss of
habitat within the impoundment areas.
-I
Figure 3.36:
-96
1 I
l ...:-.... __________ --------------------------------·
I + ! r
. 5 t
-+ !
1'
I + ~
I
~ T
t
:!.1 :a
r--------------------------------------------·
+
5 t
I
t
t
I ...
i
t
I
1i
4
I
1!
T I 1-£
1--------------------------------------------·
1
I .s-+
a) No Project
b) Full Project
t c) Watana Only
t
I
t + a~~-------------------------------:!..1
Habitat units for passerines in the Upper Susitna
Basin. The maximum on they-axis is 400,000 units.
-
Figure 3.37:
97
~-P•:.F· r-1fo~::<:= 1 u !l a a.
a} No Project
n~-----------------------------sc
1 f
t .~
t '' ..
f. / \ .. ~ I t .... I
1\, ' ' I ·I \
5 r
t b) Full Project
t
i
2:!
c) Watana Only
..
i
a~T _________________________ ___
Fall post-harvest moose population. The maximum
value on they-axis is 10,000 animals.
Figure 3.38:
1 ·-1
I
...;; ~ j
1.,.:-t' .. , r, ~
!
!t
•'I
-98
T ~~I L L t'-1A )(: 1 li 1.! ll .
,:·~:E··,·· i'lll)<• 1~DD.
T ~~I~I.. !'n=t:.<.: 1SQL1.
:>~:E . .,.. !"W)<:: 15 a a.
li r' r
I I ' 1. r I ...,r 1 !~
II' • + I \f I ,,
I + I
5 + I + ~
i
t
11 01
T I ~t:
r 'f.-' !. L. t'-1!:. .1<: 1 e uu .
t=·F·.E '( !''A)<;: 15 U D.
A 1' ... r r,. " ' ,.., II. r.t' rl' r L .• ! \ t ~ : ~~ r..,t \ t t ~ \1 '·I "1 ,_ ','
11 2:. ~1
a) No Project
b) Full Project
sa
c) Watana Only
Moose lost to bear predation (broken line) and
through hunting (solid line). Maximum value
on y-axis is 1,600 animals.
Figure 3.39
99
/'". 1 ~
..... ~ •• • ..: ~·-~ t
/\
a) No Project
+
•1
b) Full Project
+
T: t<E
,_
!
:· l., .... • ...
1 .. • • "' ,' 1 ;\ ' I -~ ,. 1\ t-.,, ,. ... r ""'I -'L
1
I \ \'• ,",_ ........... -••• / ·-".... ~·-", ,'' < :." .. ... .. -. ... " .; t.... \.; .. -~ .,. ..-~ .. "
·-!-
c) Watana Only
Brown bear density
value on the y-axis
(animals/ha) . The maximum
is .0003 animals/ha.
Figure 3.40:
11
1 ·' ~ "' ' ... ,.. '-1
~"• ; I, ~ :, } t ,\
1 \' .... ~~ '1 ~
t
I s -+
L
! + .~
I t
!1
T I HE
100
·--
... ,
' '
sa
---... ~-·-...... -......
a) No Project
b) Full Project
c) Watana Only
Black bear density (animals/ha). The maximum
value on the y-axis is .012 animals/ha.
-
-
-101 -
4.0 PRODUCTS
The most highly visible product, the working simulation
model, is given a conceptual treatment in Section 4.1. While
the preliminary model is important, the process of building the
model within the workshop process has generated two additional
and perhaps more valuable products: a synthesis of gaps in
our understanding and data (Section 4.2), and an analysis of
how model refinements can direct efforts into filling these
gaps (Section 4.3).
4.1 Conceptual Model
The looking outward matrix (Table 2.5) provided the
framework for linking the component submodels. The completely
integrated model is a complex set of numerous relationships
within and between submodels. To gain a broad understanding
of the major processes included in the model, the simulation
model has been translated through a process of simplification
and compression into a conceptual model of the terrestrial
environment in the Susitna Basin (Figure 4.1).
In the conceptual model, the major components (boxes)
and the major linkages (arrows) represent the processes and
information transfers considered to be imporant to understanding
the biophysical system in the Susitna Basin. In the diagram
(Figure 4.1), solid lines represent linkages that are inc:uded
in the numerical simulation model; broken lines represent
critical linkages that could not be conceptualized during the
workshop and were not included into the numerical simulation
model.
li
('~"'-'"""'
I
I
I
I
I
I
I
habitat .. ...
I
Waterfowl
Passerlnes
----..,__, _____________ J
Human ....
Recreation
Susltna
Hydroelectric 1----'1--... ~
Project
disturbance
vehlcles,alr-
craft, people
Land for
facilities,
roads,
reservoirs
Flows
r----~lsp~~~~er~dl~-----~
food aears
veoetatlon alteration .... ... Vegetation
Erosion I
Sedlm1111tat1on
r t
_ _j I
I
I
I
I ___ ..J
.. ...
home range ....
... --.-__,r--..
food
predallon
,F
food
home ranQe.., t Moose
snow depth =:-harvest r-F;,;;,;,:;., ... ..._ __ _J
habitat ..
flooding of colonies Flooding
aeaver
Ice
Regime
... L-----1
Ice scouring
flooding of nest sites ..,
Roptors
Ice thlcknen Climatic
Effects
...
1
Hunting
Trapping
Figure 4.1: Conceptual model of major components and linkages included in
the model of the terrestrial environment in the Susitna Basin.
··~
-103 -
The model depicted in Figure 4.1 represents the first
interdisciplinary perspective of the potential impact of the
Susitna Hydroelectric Project on the terrestrial environment
in the Susitna Basin. As such, it provides an overall framework
for assessing deficiencies in our current understanding.
4.2 Summary of Conceptual and Information Needs
Numerous gaps in data and understanding became apparent
during the workshop. Throughout the workshop, notes were made
as these gaps arose during discussion and a formal session was
conducted toward the enq of the workshop to pull together the
many thoughts and ideas on future research.
The information needs discussed at the workshop (Table
4.1) are divided into two categories: conceptual and data.
Conceptual needs are those requiring the development and/or
testing of relationships. Data needs, for the most part, can
be satisfied through data collection and searches of existing
information sources.
4.3 Model Refinements
The more detailed discussion of conceptual and information
needs presented in this section is based on analysis of what
information is required to refine the model. A refined model
implies an increase in understanding, for the model represents
a synthesis of our current understanding. Judging from the
long list of conceptual and data needs presented in Table 4.1,
our current understanding is far from adequate. By critically
examining the components and linkages depicted in Figure 4.1,
this analysis addresses most of the information needs (Table
4.1) and illustrates how refinements to the model can focus
efforts directed towards satisfying them.
I
Physical Processes/
Development/Recreation
Vegetation
I l J
Table 4.1: Information Needs
CONCEPTUAL
relationship of riparian surface
areas to flow in the reach Devil
Canyon to Talkeetna
-relationship of ice scouring to
flow in downstream area
-relationship of stage to flow
in downstream area
ice hazard index for reservoir
(March 15 -June +5)
-model for predicting monthly
snow depth in elevation ranges
-relationship between over-
wintering habitat for beaver
and flow
DATA
. ,
j l
location, size, and structural
characteristics of material
mining sites
-access roads routing and design
-extent and nature of non-project
development expected to impinge
on the area within the next
50-75 years
-estimates of current and projected
recreational use in the area from
both project and non-project sources
-vehicle traffic along roads
-location, timing, and areas of
planned activities
-expected impoundment water levels
(seasonal)
-estimates of mean monthly snow
depths in 200 m elevation ranges
-better understanding of successional -areas of balsam poplar and willow
dynamics of all vegetation types in dominant vegetation types currently
both upland and riparian areas available as riparian habitat for
-relationship of successional
dynamics to changes in flow in the
downstream area
-annual variation in productivity of
forage in selected vegetation types
-seasonal variation in crude protein
content and digestability of forage
species
-the role of fire in upland
succession
beaver
-estimates of current productivities
of forage in selected vegetation types
-estimates of current productivities
of berries
-stream bank characteristics
-length of side channels and sloughs
in the downstream area
-estimates of crude protein content
and disgestability for forage species
in mid-summer and mid-winter
. I
Furbearers/Birds
Moose
l l )
Table 4.1 (cont'd)
CONCEPTUAL
-clear definition of beaver habitat
-rel~tionship of trapping effort to
trapping mortality
relationship between beaver
utilization of vegetation and
succession
-relationship between stage
fluctuation in main channels and
suitability of banks as habitat
relationship between ice scouring
in main channels and suitability
of banks as habitat
-horizontal measure of cliff nesting
habitat available
-relationship of the logistic growth
rate (r) and habitat quality, winter
weather, and interference from other
colonies and man
-colonization of main channel habitat
-measures for comparing loss of
passerine habitat due to the
impoundment
-clear definition of home range
-behavioral reactions of moose to
human disturbance caused by the
project
-a definition of winter carrying
capacity that considers:
1) species composition at browse;
2) protein content of each species;
3) digestability of each species;
4) moose requirements for protein
and digestable energy
-reexamination of the density
dependent reproduction (Figure
3.14)
DATA
-areas of intensive beaver use
by vegetation type
-data on current beaver trapping
mortality
-data on size and composition of
food caches
-proportion of cliff nesting
habitat that will be inundated
at high water in the reservoir
-estimates of summer mortality
by age and sex
-estimates for the parameter
values in the predation
relationships (Figures 3.15 and
3.16)
-estimates of available winter
range
}
Moose (cont•d)
Bear
Spatial
l I l
Table 4.1 (cont•d)
CONCEPTUAL
-inclusion of black bear and wolf
predation on moose calves
)
-inclusion of grizzly bear predation
on older moose
-relationship between harvest rate
and numbers of hunters
-relationship between snow depth
and usable winter range
-habitat classification system
sensitive to quantity of summer
berry production ·
-relationship of bear dispersal and
feeding to disturbances caused by
development activities
-relationship between food
availability and bear survival
-relationship of harvest to
population size and hunting effort
-inclusion of interspecific and
intraspecific bear predation on cubs
-reexamining of the spatial
resolution of the model
- a more detailed representation of
vertical stratification in
vegetation classification systems
- a more detailed representation of
vegetation in areas close to
channels and sloughs
l
DATA
-data on bear utilization of
salmon population in the Prairie
Creek-Stephan Lake area, and also
in downstream sloughs and side
channels
-data on bear diet in the spring
-107 -
4.3.1 Physical Processes/Development/Recreation
4.3.1.1 Recreation
Currently, the model contains little credible
information with respect to recreation. Little or no
information was available on existing or future recreational
use in terms of numbers of use days or amounts of land
needed. Data on current use and credible projections of
future use and need are critical to better understanding
of the impact of recreation on wildlife in the Susitna Basin.
4.3.1.2 Land Use
At present, the model contains only scanty information
about current land use patterns in the study area. Because
of the dynamic nature of land ownership in the area brought
about primarily by the Alaska Native Claims Settlement Act,
it is extremely difficult to make projections about future
land use patterns. However, a credible development scenario
requires that the model make projections about changing land
use patterns with and without the project. This is
inadequately represented in the present model.
4.3.1.3 Physical Processes
Flooding and Ice Scouring -Downstre-am Floodplain
The mechanisms that cause ice scouring are not
clearly understood; t~erefore, it is difficult to develop
a model for this phenomenon. A better understanding of
the changes in frequency and duration of flooding caused by
alteration of the flow regime and changes in the amount and
degree of ice scouring is needed before reasonable predictions
of the potential impacts of the project can be made.
-108 -
overwintering Habitat for Beaver
At present, the suitability of overwintering habitat
for beaver is not directly related to flow regime in the
downstream floodplain. The habitat in side channels and
sloughs is suitable if at least .5 m in depth of unfrozen
water is available throughout the winter. The model
currently assumes that the severity of winter, which
determines the ice thickness, is the only determinant of
the amount of habitat. This is overly simplistic, and it
is likely that the increased winter flows brought about by
the project will have a major effect on the amount of
suitable habitat. A better conceptual understanding of the
relationship between the amount of suitable habitat and the
flow regime must be developed.
Climatic Effects
The importance of climatic effects to understanding
processes that might be affected by the project can not be
overstated. The most important climatic influences are snow
and ice. The interrelationship between the ice regime, flow,
and vegetation has been discussed earlier.
Snow, or rather the amount of snow on the ground,
affects the ability of moose and caribou to utilize winter
range. In the model, the amount of snow on the ground is
stochastically generated and does not provide a realistic
representation of what actually occurs. An alternate approach
is to use a more robust snow model similar to one developed
by McNamee (1982) for simulating the effect of snow in elk
dynamics. Such a model consists of three components:
snowfall, snowmelt, and snow interception. In the simplest
version of the model, snow is assumed to be general in nature,
such that snow depth {not density, crusting, etc.) would be
-109 -
the only influence on ungulate dynamics. The general model
would be:
where,
SN t = SN -MR * SR * f{CC ) + SO * f{CC ) S 1 S 1 t-1 S t S
SN t = snow depth on site s in time step t; s,
MR
SR
= maximum snowmelt;
= snowmelt factor specific to site characteristics
(e.g. elevation);
sot = snowfall; and
CCs = crown closure.
In simple terms, the model suggests that the snow depth in
a given time step is equal to what was there the time step
before less what has melted plus what has fallen through to
the ground. Both snowmelt and snow interception are functions
of stand openness. Work of Harestad and Bunnell (1981)
relates the level of snow interception to snowfall and
canopy closure; the work of Haverly et al. (1978) and Leaf
and Brink (1973) can provide guides for defining snowmelt.
A similar model needs to be developed to better understand
how moose and caribou will adapt to the loss of winter
range as a result of the impoundments.
:tJ"''A,
4.3.2 Vegetation
Each spatial
(e.g. initial areas
-110 -
unit contains a large number of attributes
in various land classes, average annual
be;ry production) . The land classification system, the spatial
scale, and the associated estimates of initial conditions are
structural hypotheses about what is an appropriate representation
of the system. Although they are subject to more precise
quantification based on current and future data, many values
were estimated quickly and roughly at the workshop.
Consequently, they should be considered as very preliminary
estimates.
4.3.2.1 Spatial Resolution
The spatial units and land classification system in
the model are compromises. Clear suggestions for improvement
emerged at the workshop with respect to birds (more detailed
resolution of vertical stratification in the land classification
system) and beaver (more detailed spatial resolution of
vegetation in areas close to channels and sloughs) . The need
for spatial units more appropriate for moose (e.g. winter
range) was also discussed at the workshop. These issues must
be resolved before proceeding to a more precise estimate of
variables within various spatial units and vegetation types.
4.3.2.2 Resolution of Development Activities
Land is removed for development activities from
various land classes based on the relative prop~rtions in
the respective spatial units or, in the case of roads, based
on proportions specific to a given route. The model could
be refined to provide additional activities or to provide a
finer resolution of the land class changes associated with
an activity given its specific location within a spatial unit.
-111 -
An example is the transfer of land in the impoundment spatial
areas to the water class. This transfer is currently based
on the development submodel's calculation of land cleared for
vegetation, rather than on a calculation of the amount of
area actually covered by water.
4.3.2.3 Wildlife Food
Currently, the model simulates the variation in browse
standing crop and berry production as a random process. This
simple representation could be improved by adding mechanisms
that incorporate the effects of consumption of vegetation by
wildlife. This is particularly true in the case of moose
consumption of browse and to some extent, beaver alteration of
habitat in the riparian zone. Further improvements in the
model would result if the productivity of browse and berries
can be functionally related to climatic variables such as
temperature, snowfall, or total precipitation. However,
current understanding of the determinants of productivity in
the area may not be sufficient to fully develop these
relationships.
4.3.2.4 Riparian Succession
The model currently assumes that transitions among
land classes in the riparian zone are in equilibrium before
the Watana Dam. It also assumes that the project will
eliminate disturbance-caused transitions which set vegetation
back to earlier successional stages. This hypothesis is not
completely unreasonable in the Talkeetna to Devil Canyon
riparian zone where postproject flows will be highly regulated
and relatively ice-free. The assumption is clearly not
applicable to riparian areas below Talkeetna where postproject
unregulated flow will be a much higher proportion of total
Susitna flow because of the inflow from major tributaries.
-
-112 -
The representation of riparian succession could be
dramatically improved by including all the transitions (which
would presumably be approximately balanced under current
conditions) . The disturbance-related transitions could then
be functionally related to the hydrologic regime through
variables such as peak flows and ice presence. Hydraulic
simulation models and the supporting channel cross section
data being considered in the instream flow studies of the
aquatic assessment could be very useful in developing such a
representation of the effects of river flow on vegetation
transitions.
4.3.2.5 Dynamics of Upland Vegetation
The current hypothesis is that the areas in various
upland land classes are constant except for changes associated
with specific development activities or vegetation manipulation
actions. While this is a weak assumpt~on, current understanding
·of upland successional processes is not sufficient to suggest
a more dynamic approach.
The most serious drawback of this approach may be an
underestimate of the importance of natural fire in the area
along with its consequent effects on the natural variability
of wildlife habitat. Van Cleve and Viereck (1981) have stated
that:
"The taiga of interior Alaska is dominated by young
stands in various stages of succession -mature stands
of over 200 years in age are rare. Fire is the main
cause of the young ages of the stands -in some areas
fire that kills all of the above ground vegetation
can be expected every 50 -100 years."
If this is the situation in the study area, the natural
fire regime needs to be represented in a 50 year simulation.
-113 -
The long-term habitat value of inundated areas may not be
fairly represented by their current species composition if
fire periodically converts them to earlier successional
stages in the absence of inundation.
4.3.3 Furbearers/Birds
4.3.3.1 Beaver Model
Given the minimal understanding of beaver physiology
and population parameters, the logistic equation is an
appropriate model for describing the beaver population.
Although structurally simple, its versatility regarding
parameter specification ensures that it is responsive to the
major impacts of the project. As a consequence, the model
dynamics are transparent to the user without losing sensitivity
to the major issues. Therefore, it is recommended that the
logistic structure be maintained until new information dictates
the need for a more detailed approach.
Refinements to the beaver model should concentrate on
specification of the carrying capacity and intrinsic growth rate.
Carrying Capacity
Obviously, the definition of carrying capacity is
critically dependent on how beaver habitat is defined. From
the perspective of the furbearers subgroup, the definition
present in Section 3.3.1.1 was an acceptable compromise given
the relatively coarse spatial representation of the riparian
zone. However, this definition requires information not
easily obtained from the vegetation and hydrology submodels.
Consequently, more effort is required to better establish
how these information needs can be satisfied. This will
require a meeting between the furbearer subgroup and the
vegetation and hydrology groups. The discussion should focus
on defining beaver habitat and its compatibility with the
kinds of information that can realistically be supplied by
the other subsystems.
-
-
-114 -
Related to the discussion of habitat is the carrying
capacity of any given section of habitat. The present
estimate of 1 colony/2 km seems too small especially given
the hypothesis that beaver rarely wander more than 100 meters
from their den site. This may require specification of more
than one kind of habitat with varying levels of beaver
utilization.
Intrinsic Growth Rate
Currently the beaver model assumes the intrinsic
annual population growth rate 'is constant at .33. The
validity of this assumption should be challenged. Growth
rates could be a function of habitat quality, severity of
winter weather, and interference from other colonies or
man. Discussion of these effects and comparison of the
projected population rates of increase to a natural
situation may indicate a need for refinement.
Movement of Beaver Between Side and Main Channels
Currently the model's characterization of cross
fertilization of beaver colonies between the side and main
channels is based very much on fiction. It was structured
following the workshop and purely serves as a mechanism to
ensure main channel habitat is colonized. Just how
reasonable a process that is requires discussion.
Mortality
Currently, the beaver populations are subject to
three sources of mortality: changes in water level, ice
scouring, and trapping. Although all three of these
mortality processes require some refinement, the most
critical one is likely the rate of trapping. As described
-115 -
in the text, trapping is difficult to structure in the model
since the driving forces are the price for beaver pelts
and the attitude of the trappers. Both are unpredictable at
the best of times.
4.3.3.2 Passerine Birds
Using a habitat oriented procedure certainly seems
to be the best way to deal with the migratory passerine
birds, given the model is spatially restricted to the Susitna
Basin. Currently, the model "habitat unit" indicators show
little sensitivity to the impoundment due to the large area
of the region included in the calculation. This region was
chosen somewhat arbitrarily and it may be profitable to
discuss other suitable ways of comparing the loss of habitat
due to impoundments.
4.3.4 Moose
4.3.4.1 Winter Carrying Capacity
The computation of winter carrying capacity assumes
that average browse availability for each land class is an
adequate measure of winter habitat. A better estimate of the
carrying capacity would consider:
1) the species composition of the available browse;
2) the protein content of each species;
3) the digestible energy content of each species; and
4) the daily moose requirement for protein and
digestible energy.
-116 -
4.3.4.2 Reproduction
The reproductive function (Figure 3.14) is a density-
dependent relationship in which population density is a
surrogate for food consumption. The hypothesis is that,
at higher population densities, less food is available per
individual and females are less successful in bringing their
calves to term. Participants indicated that this phenomenon
has never been observed in the Susitna herd, but that it
does occur in other ungulate herds. The density-dependent
reproductive function was incorporated in the example model
largely as a means of preventing unlimited exponential
growth. The density-dependent portion of the curve in
Figure 3.14 is rarely operative with the winter population
sizes (i.e. usually under 8,000 animals) generated from the
parameter set currently being used.
4.3.4.3 Summer Mortality
Summer mortality is.currently hypothesized to be a
constani: fraction of each age and sex class. While this
is probably not the case, there is little understanding of
factors that affect these rates.
4.3.4.4 Predation
~rhere are two principal hypotheses incorporated in
the bear predation portion of the example moose model.
First, the rate of predation by an individual bear is assumed
to be a function of moose calf density as shown in Figure 3.15.
Second, vulnerability of ~cose calves to bear predation is
assumed to be related to snowfall in the previous winter. The
combina·tion of these two assumptions results in a steeper
slope on Figure 3.15 in years of heavy snowfall and thus more
effective predation by bears at lower calf densities.
-
-117 -
The stimulus for this information was a series of
observations indicating lower calf/cow ratios in the Susitna
moose herd in years following heavy snowfall. The
relationship seems to be fairly consistent except in one
year during which there was a bear removal program. In
that year, the fall calf/cow ratio was high despite a hard
previous winter. Biologists hypothesize that these data
indicate a relationship between winter severity and
vulnerability of moose calves to bear predation.
The model formulation probably captures the qualitative
aspects of this hypothesis quite well. However, the parameter
values currently used in Figures 3.15 and 3.16 are merely
guesses and obtaining actual estimates for them may be very
difficult. If reasonable data cannot be obtained, other
formulations for the predation function may prove more useful.
The present model is also deficient in that it:
1) considers predation only by grizzly bears. Black
bears and wolves are also known to prey on moose;
2) considers predation only by the female cohort of
the bear population (the only cohort incorporated
:in the bear submodel); and
3) considers only predation on calves. Grizzly bears
are also known to take older moose.
4.3.4.5 Harvest
The model assumes that male moose between some minimum
and maximum age set by the user are subjected to a harvest
rate that does not vary from year to year. While this is
probably not an accurate assumption, no clear hypotheses
emerged at the workshop concerning how the actual harvest
rate might be related to factors such as level of hunter
-
-'
-
-118 -
activity, moose population size, or weather. For example,
the relationship between number of hunters and harvest rate
should be explored more thoroughly if the hydroelectric
project results in greater hunter activity. The impact of
a larger number of hunters can probably be mitigated through
more stringent permit and harvest quota systems, but such
systems will undoubtedly require more intensive eff9rt by
man.agement agencies ..
4.3.4.6 Winter Mortality
The basic hypothesis articulated at the workshop
concerning winter mortality has two distinct parts. First,
biologists feel that in severe winters a larger proportion
of the moose herd in the Upper Susitna Basin depends on the
area surrounding the proposed hydroelectric project for
winter forage. Second, they believe that more severe winters
restrict the proportion of the area surrounding the proposed
project that is actually usable by moose. If this hypothesis
is true, the proposed project can be expected to impact moose
to the extent that it will destroy or alter winter range.
This may occur through a variety of mechanisms including
direct inundation, facilities construction, frosting of
vegetation, and drifing of snow blown off the surface of
the impoundment.
Unfortunately, the two mild winters so far encountered
in the moose study have not produced a great deal of
information useful in examining this hypothesis. The moose
model is therefore deficient in a number of respects. First,
it assumes that the entire moose herd in the Upper Susitna
Basin winters in the area surrounding the proposed project.
Second, the estimate of the total amount of winter range
available before the project is crude; it is simply the
length of the Watana impoundment (about 50 miles) multiplied
by an average width of 5 miles. Third, the relationship
,~"""-
-119 -
between snow depth and proportion of winter range usable
by moose (Figure 3.17) is arbitrary, as are the relationships
between forage availability and survival (Figure 3.18).
Finally, the assumption that all of the winter range is in
a single land class is clearly erroneous.
Nevertheless, much of the necessary data to test
these hypotheses could probably be obtained from existing
land class and contour maps, a stratified sampling program
for browse production, snow course surveys, and the existing
radio-telemetry program. The existing maps could be used to
determine how much land in each vegetation type exists in
various elevational bands. The browse sampling program
could then provide estimates of forage availability in those
bands. Snow course and radio-telemetry data could be used
to ascertain which elevations are used by moose under what
snow conditions, and thus, how much forage is available.
The final step, relating forage availability to moose
survival, would likely be the most difficult and would
probably have to be based on studies of penned animals.
4.3.5 Bear
'J~here are a number of conceptual and data deficiencies
within the bear model. Many of the functional relationships
need to be reexamined and their parameters reestimated or,
in some cases, completely restructured.
4.3.5.1 Spring Food
The current spring food index does not take account
of the quality, quantity, and desirability of the food
resource associated with different vegetation types. Also,
moose calf predation, a food resource critical to the spring
survival of immature bears, must be explicitly included in
the model.
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-120 -
4.3.5.2 Mortality
Harvest and predation on cubs are two major sources
of mortality not included in the current version of the
model. Relationships needs to be developed to estimate
harvests as a function of population size and hunting effort,
and interspecific and intraspecific predation on cubs by
both brown and black bears need to be included in the model.
4.3.5.3 Dispersal
Currently, dispersal is based on density only and
is not restricted to immature animals. Older animals
probably disperse as well, and a more realistic dispersal
mechanism should be included. Also, the impact of human
disturbance on dispersal and the degree to which human
disturbance acts as impediments to movement to and from
forage areas has been neglected and should be examined.
I"""
-121 -
5.0 FUTURE WORK
Much work is required before the model will be a valuable
aid in mitigation planning. This work has already begun.
Subsequent to the workshop,. a meeting to refine the vegetation
and big game studies to better assess the impacts of habitat
loss on big game was held September 28, 1982 at the Fairbanks
Alaska Department of Fish and Game office. While the meeting
was not directly related to model refinement, the discussion
focused on many aspects of moose habitat utilization that were
considered problem areas during the workshop. Meetings
specifically designed to focus on model refinements have been
tentatively scheduled for the week of November 15 -19. These
technical meetings, to be attended by the participants of the
August workshop, will focus on detailed questions in each of
the submodels. Current planning has one technical meeting for
each of the submodels.
After the technical meetings, work will begin on revising
the existing model by including better data and, where necessary,
restructuring of the functional relationships.
At the workshop tentatively scheduled for late February
or early March, the refined verison of the model will be presented
for critique. That workshop will deal with two other major
questions: a review of research planned in the terrestrial
environmental studies associated with phase II of the Susitna
Hydroelectric Project, and alternative ways of valuing changes
in habitat based on model projections.
Early in November, 1982 the Alaska Department of Fish
and Game staff in Anchorage will begin taking responsibility
for the moose and bear submodels. They will work closely with
the modelling team to refine the model to a state that it
provides a framework for evaluating the impacts of the project.
P'"'"
}~.
-122 -
While the focus of the technical meetings and workshop
will be model refinement, they will also serve as a forum for
discussing issues and information needs related to comprehensive
mitigation planning. This next series of meetings and workshops
are designed to improve our collective understanding and to
clarify the process that will be used to examine the complex
issues of habitat enhancement and compensation lands.
-
-123 -
6.0 REFERENCES
Harestad, A.S. and F.L. Bunnell, 1981. Snow: canopy cover
relationship in coniferous forest. Can. J. For. Res.
Haverly, B.A., R.A. Wolford, K.N. Brooks, 1978. A comparison
of three snowmelt prediction models. 46th Ann. Meeting,
Western Snow Conf., pp. 78-84.
Leaf, C.F. and G.E. Brink, 1973. Computer simulation of
snowmelt within a Colorado subalpine watershed.
u.s. Dept. Agr. For. Ser. Res. Pap. RM-99, 22 pp.
McKendrick, J., W. Collins, D. Helm, J. McMullen and J. Koranda,
1982. Alaska Power Authority, Susitna Hydroelectric
Project, Environmental Studies-Subtask 7.12, Plant
Ecology Studies, Phase I Final Report. University of
Alaska Agricultural Experiment Station. Palmer, Alaska.
McNamee, Peter J., 1982. Description of habitat, deer, and
elk microcomputer models for the integrated wildlife-
intensive forestry research program. Prepared for
Technical Working Group (IWIFR), Province of British
Columbia.
Van Cleve, K. and L.A. Viereck, 1981. Forest succession in
relation to nutrient cycling in the boreal forest of
Alaska. Pages 185-211 in D.C. West, H.H. Shugart and
D.B. Botkin, editors. Forest Succession. Springer-
Verlag, New York.
....
7.0 LIST OF PARTICIPANTS
Attending the Susitna Terrestrial Modelling Workshop
August 23-27, 1982
NAME
Tom Armi nski
Greg Auble
Harren Ba 11 a rd
Keith Bayha
Bruce R. Bedard
Steve Bredthauer
Leonard P. Carin
Ike Ellison
John Ernst
Bob Everitt
Steve Fancy
AFFILIATION
Alaska PovJer Authority
USFWS -Helut
Alaska Dept. of Fish
& Game
USFWS
Alaska Power Authority
R & M Consultants
USFWS
USFl4S -We 1 ut
LGL
ESSA Ltd.
LGL
ADDRESS
344 West 5th Avenue
Anchorage, Alaska 99501
( 907) 277-7641
2625 Redwing Road
Fort Collins, Colorado 80526
(303) 226-9431
P.O. Box 47
Glennallen, Alaska 99588
( 907) 822-3461
1011 East Tudor Road
Anchorage, Alaska 99507
(907) 276-3800
334 West 5th Avenue
Anchorage, Alaska 99501
(907) 277-7641
P.O. Box 6087
5024 Cordova
Anchorage, Alaska 99503
(907) 279-0483
605 West 4th, #~-81
Anchorage, Alaska 99501
( 907) 271,-4575
2625 Redwing Road
Fort Collins, Colorado 80526
(303) 226-9431
1577 11 C11 Street, Suite 305
Anchorage, Alaska 99501
(907) 274-5714
678 West Broadway
Vancouver, B.C.
(604) 872-0691
P.O. Box 80607
Fairbanks, Alaska 99708
(907) 479-6519 .
~-------
NAME
Richard Fleming
Bill Gazey
PhilipS. Gipson
George Gleason
Michael Grubb
John Hayden
Dot Helm
Brina Kessel
Sterling Mi 11 er
Suzanne niller
Ron Modafferi
AFFILIATION
Alaska Power Authority
LGL
Alaska Cooperative Wild-
life Research Unit
Alaska Power Authority
Acres American
Acres American
University of Alaska
Ag. Experiment Station
University of Alaska
Museum
Alaska Dept. of Fish
& Game
Alaska Dept. of Fish
& Game
Alaska Dept. of Fish
& Game
Alaska Power Authority
ADDRESS
334 West 5th Avenue
Anchorage, Alaska 99501
(907) 277-7641
1410 Cavitt Street
Bryan, Texas 77801
(713) 775-2000
University of Alaska
Fairbanks, Alaska 99701
( 907) 47 4-7673
334 West 5th Avenue
Anchorage, Alaska 99501
(907) 277-7641
900 Liberty Bank Building
Buffalo, New York 14202
(716) 853-7525
1577 "CU Street
Anchorage, Alaska 99501
(907) 276-4888
P.O. Box AE
Palmer, Alaska 99645
( 907) 745-3257
P.O. Box 80211
College, Alaska 99708
(907) 474-7359
333 Raspberry Road
. Anchorage, A 1 as ka 99502
(907) 344-0541
333 Rnspberry Road
Anchorage, Alaska 99502
(907) 344-0541
333 Raspberry Road
Anchorage, Alaska 99502
(907) 344-0541
334 West 5th Avenue
Anchorage, Alaska 99501
(907) 277-7641
NA~1E
Carl Neufelder
Ann Rappoport
~Jayne Rege 1 in
Butch Roelle
David G. Roseneau
Karl Schneider
Robin Sener
Nicholas Sonntag
Robert N. Starling
Gary Stackhouse
-Bill Steigers
Nancy Tankersley
AFFILIATION
Bureau of Land
t·1anagement
US HIS
Alaska Dept. of Fish
& Game
IJSFWS -Welut
LGL
Alaska Dept. of Fish
& Game
LGL
ESSA Ltd.
NORTEC
USFWS
University of Alaska
Ag. Experiment Station
Alaska Dept. of Fish
& Game
ADDRESS
4700 East 72nd Avenue
Anchorage, Alaska 99501
( 907) 267-1200
605 West 4th, #G-81
Anchorage, Alaska 99501
(907) 271-4575
1300 College Road
Fairbanks, Alaska 99701
(907) 452-1531
2625 Redwing Road
Ft. Collins, Colorado 80526
(303) 226-9431
P.O. Box 80607
Fairbanks, Alaska 99708
(907) 479-6519
333 Raspberry Road
Anchorage, Alaska 99502
( 907) 344-0541
1577 "C" Street, Suite 305
Anchorage, Alaska 99501
(907) 274-5714
678 West Broadway
Vancouver, B.C.
(604) 872-0691
750 West 2nd Avenue, #100
Anchorage, Alaska 99501
(907) 276-4302
1011 East Tudor Road
Anchorage, Alaska 99507
( 907) 276-3800
P.O. Box AE
Palmer, Alaska 99645
(907) 745-3257
333 Raspberry Road
Anchorage, Alaska 99502
(907) 344-0541
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