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THESIS 10381 5019

This is to certify that the

thesis entitled

PRE - SETTLEMENT BEAVER POPULATION
DENSITY IN THE UPPER GREAT LAKES REGION

presented by
THOMAS MOORE ALCOZE

has been accepted towards fulfillment
of the requirements for

Ph.D. Zoology

 

 

degree in

 

 

Major professor

Date June 25, 1981

 

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PRE-SETTLEMENT BEAVER POPULATION DENSITY IN THE
UPPER GREAT LAKES REGION

BY

Thomas Moore Alcoze

A DISSERTATION

Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of

DOCTOR OF PHILOSOPHY

Department of Zoology

1981

ABSTRACT

PRE-SETTLEMENT BEAVER POPULATION DENSITY IN THE
UPPER GREAT LAKES REGION

BY

Thomas Moore Alcoze

The objective of this study was to develop a
reliable method to quantitatively assess the population

density of beaver (Castor canadensis Kuhl) in the Upper

 

Great Lakes Region during the pre-settlement period
prior to the fur trade, ca. 1600. The methodology
developed in this study involved the use of regression
analysis techniques to demonstrate the relationship
between beaver lodge density and specific habitat
characteristics. This correlation was extended to
reconstructed vegetation associations during the historic
period to estimate the historic population density of
beaver during the period.

Historic beaver population density in the Upper
Great Lakes Region was estimated based on the
contemporary correlation between beaver abundance and
habitat associations combined with historic vegetation
reconstructions. Contemporary beaver populations were

surveyed to collect precise lodge density and habitat

data. Beaver lodge density counts were obtained from
active traplines where the dominant tree species
abundance was known. These data were correlated using
bivariate analysis to characterize the relationship
between lodge density and habitat associations. The
predictive value of this correlation was established
based on a step-wise multiple regression program for
each of 23 tree species examined. Reconstructions of
historic vegetation associations for the Upper Great
Lakes Region were then used to describe pre-settlement
forest conditions in the Great Lakes watershed. The
vegetation reconstructions were entered into the
multiple regression program to arrive at the estimated
beaver lodge density. The actual population density
estimate was calculated on the basis of the number of
individuals known to occur in active lodges in the
Great Lakes Region.

It was determined that approximately two million
beaver represents a reasonably accurate assessment of
beaver density in the drainage area of the Great Lakes

during the pre-European settlement period, ca. 1600.

ACKNOWLEDGEMENTS

I would like to express my gratitude and
appreciation to Dr. Rollin Baker, chairman of my
graduate committee and to the other members of my
committee, Dr. Charles Cleland, Dr. Alan Holman, and
Dr. Jack Bain. The advice and criticism which they
provided in the development and completion of this
research was invaluable. Acknowledgements are also
extended to Dr. Roger Pitblado and Mr. Wade Blake of
Laurentian University for their assistance in the
programming and statistical analysis of the data. The
cooperation and effort exerted by Ms. Verna Brunet in
the typing of the manuscript deserves special
recognition for without her excellent assistance the
preparation of the dissertation would have been
difficult. The patience and support given to me for
the completion of this work by my wife Joan and members
of the Heartland community were important in providing
the encouragement and determination to finish this
study. Recognition must also be given to the faculty
and staff of the Native Studies Department of the

University of Sudbury for their cooperation.

ii

TABLE OF CONTENTS

Introduction 1
Methods ~ 4
Results 18
Discussion 37
Conclusion 49
Bibliography 53
Appendix A 57
Appendix B 106
Appendix C 107

iii

Table

Table

Table

Table

Table

Table

Table

Table

Table

Table

Table

Table

Table

Table

10.

Al.

A2.

A4.

LIST OF TABLES

North Bay District Traplines Descriptive
Data

Vegetation Species Abundance and Area
Values

North Bay Trapline Harvest and Stream
Abundance

Statistically Significant Associations
for Independent and Dependent Variables
Measured by the Bivariate Analysis

Statistical Significance between
Independent and Dependent Variables
Measured by Step-wise Regression Analysis

Regression Variables Used to Predict an
Estimate of Lodge Density as a Function
of Combined Vegetation Species

Predicted and Observed Lodge Density
Values

Regression Variables Used to Estimate
Lodge Density as Function of Dominant-
Deciduous Vegetation Species

Regression Variables Used to Estimate
Lodge Density as a Function of Dominant-
Coniferous Vegetation Species

Regression Variables Used to Estimate
Lodge Density as a Function of Mixed
Coniferous-Deciduous Vegetation Species
Vegetation Data for Trapline #1
Vegetation Data for Trapline #2
Vegetation Data for Trapline #3

Vegetation Data for Trapline #4

iv

20

22

23

25

27

28

30

32

33

34

57

58

6O

Table
Table
Table
Table
Table
Table
Table
Table
Table
Table
Table
Table
Table
Table
Table
Table
Table
Table
Table
Table
Table
Table
Table
Table
Table

Table

A5.

A6.

A7.

A8.

A9.

A10.

A11.

A12.

A13.

A14.

A15.

A16.

A17.

A18.

A19.

A20.

A21.

A22.

A23.

A24.

A25.

A26.

A27.

A28.

A29.

A30.

Vegetation
Vegetation
Vegetation
Vegetation
Vegetation
Vegetation
Vegetation
Vegetation
Vegetation
Vegetation
Vegetation
Vegetation
Vegetation
Vegetation
Vegetation
Vegetation
Vegetation
Vegetation
Vegetation
Vegetation
Vegetation
Vegetation
Vegetation
Vegetation
Vegetation

Vegetation

Data

Data

Data

Data

Data

Data

Data

Data

Data

Data

Data

Data

Data

Data

Data

Data

Data

Data

Data

Data

Data

Data

for
for
for
for
for
for
for
for
for
for
for
for
for
for
for
for
for
for
for

for

Trapline
Trapline
Trapline
Trapline
Trapline
Trapline
Trapline
Trapline
Trapline
Trapline
Trapline
Trapline
Trapline
Trapline
Trapline
Trapline
Trapline
Trapline
Trapline
Trapline
Trapline
Trapline
Trapline
Trapline
Trapline

Trapline

#30

1‘1;_,

61

62

63

64

65

66

67

68

69

7O

71

72

73

75

76

77

78

79

80

81

82

83

84

85

86

Table
Table
Table
Table
Table
Table
Table
Table
Table
Table
Table
Table
Table
Table
Table
Table
Table
Table
Table
Table

Table

A31.

A32.

A33.

A34.

A35.

A36.

A37.

A38.

A39.

A40.

A41.

A42.

A43.

A44.

A45.

A46.

A47.

A48.

A49.

Bl.

C1.

Vegetation
Vegetation
Vegetation
Vegetation
Vegetation
Vegetation
Vegetation
Vegetation
Vegetation
Vegetation
Vegetation
Vegetation
Vegetation
Vegetation
Vegetation
Vegetation
Vegetation
Vegetation

Vegetation

vi

Data

Data

Data

Data

Data

Data

Data

Data

Data

Data

Data

Data

Data

Data

Data

Data

Data

Data

Data

for
for
for
for
for
for
for
for
for
for

for

for
for

for

Beaver Density Data

Trapline
Trapline
Trapline
Trapline
Trapline
Trapline
Trapline
Trapline
Trapline
Trapline
Trapline
Trapline
Trapline
Trapline
Trapline
Trapline
Trapline
Trapline

Trapline

Vegetation Stand Data

#31
#32

#33

87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106

107

Fig.

1

LIST OF FIGURES

North Bay District Traplines by Number

vii

19

INTRODUCTION

The great quantity and quality of furbearing
animals in the Upper Great Lakes Regionlee been
suggested as one of the primary factors in the
exploration and settlement of North America (Schorger,
1965; Longly and Moyle, 1963). The states of Michigan,
Minnesota, Wisconsin and the Canadian provinces of
Ontario and Quebec have often been discussed with
reference to the trade in beaver furs and the general
observation that beaver were numerous and probably
occurred in all waters of the Great Lakes watershed
(Winterhalder, 1980; Heidenreich and Ray, 1976; Innis,
1927). Studies of the fur trade have not dealt with the
actual abundance of beaver in the Upper Great Lakes
Region. The competitive interactions between rival fur
companies and Native people for the fur resources of the
Upper Great Lakes Region have previously relied on infer-
ence and extrapolation to estimate the availability of
beaver and other fur bearers in this region (Martin,
1978; Ray, 1975; McManus, 1972).

Early explorers and traders often aluded to the

high abundance of beaver in the Great Lakes (Biggar,

1923). Historic records for the amount of furs
collected during short periods of time generally reflect
the high population levels which must have been present
in the region (Schorger, 1965; Innis, 1927). One of the
first explorers to travel the "Great Northwest" was
Pierre Radisson. During one of his expeditions to the
Upper Great Lakes Region between 1665 and 1670 he
obtained some 60 canoes filled with peltries which
equaled approximately 10,000 pelts (Johnson, 1971). Other
trade accounts also attest to high beaver availability.
For example the upper Mississippi district produced more
than 100,000 good beaver skins during the 1734-1735
season alone (Hocquart, 1906).

To date, there has not been an attempt to quantify
the abundance of beaver prior to the fur trade in North
America. The availability of this important resource
needs further study to better understand the events
which led to near extinction of the species as a result
of the fur trade.

The objective of this study was to develop a
reliable method to quantitatively assess beaver
population density in the Upper Great Lakes Region
during the pre-settlement period prior to the fur trade,
ca. 1600. To determine such an estimate, it was
first necessary to assess contemporary beaver

colonies within the Upper Great Lakes Region,

and to acquire precise habitat data for each area where
colonies were surveyed. These data were used to examine
the statistical correlation between beaver lodge

density and specific habitat characteristics which
influence beaver site selection. The relationship
between beaver lodge density and contemporary vegetation
associations was combined with pre-settlement forest
reconstructions to estimate beaver lodge density in the
Upper Great Lakes Region prior to the fur trade and
European settlement. This calculated value was used

to estimate beaver population density based on the
average number of individuals known to occur in active

lodges in the Upper Great Lakes Region.

METHODS

Contemporary beaver colonies in the Upper Great
Lakes Region were examined to acquire precise data
on lodge density and obtain accurate descriptions of the
habitats selected by beaver for occupancy. The Ontario
Ministry of Natural Resources cooperated in this effort
by providing access to trapping records, habitat data,
and other information concerning beaver populations in
the trapping district of North Bay, Ontario. This area,
located within the drainage basin of Lake Huron, was
selected for study due to the availability of data and
because it represented the general ecological conditions
characteristic of the Upper Great Lakes Region. All
information was recorded in English units of measure.
This system of measurement was used in the study to
maintain continuity with the original data.

Beaver lodge density was derived from records
provided by the Ministry of Natural Resources. The
number of beaver lodges observed within the boundaries
of surveyed traplines were taken from aerial census
records. Standardized aerial survey techniques were

employed by Ontario Ministry of Natural Resources

personnel to census the traplines. The reliability of
these aerial census methods has been established by
other researchers for the study of animal populations
distributed over large areas (Evans, Troyer and Lensink,
1966; Hay, 1955; Swank and Glover, 1948).

The boundaries of all traplines to be surveyed were
located on topographic maps provided by the Canadian
Department of Energy, Mines and Resources prior to aerial
surveys. Small fixed wing aircraft were used to fly
over each trapline until all active beaver lodges had
been observed and recorded on the map representing the
trapline. Lodges were counted and assumed to be active
at the time of the survey if the presence of food caches
in the immediate vicinity of the lodge could be
positively confirmed by the observers. Each active lodge
located on the topographic map was counted as one
colony. Lodge counts were thus compiled for each trap-
line surveyed. The number of lodges observed within each
trapline was then compared with the area in square miles
for the trapline established by the Ministry of Natural
Resources. This comparison provided the necessary
information to calculate the lodge density for each
trapline by dividing the number of observed lodges by
the total number of square miles represented by each

trapline.

The reliability of the aerial census data was
maximized by conducting all surveys during October and
November of 1972, prior to the onset of the winter
trapping season. There are a number of reasons why
beaver lodge counts are most reliable during this period
of time. Food caches, which are a major factor in the
classification of lodges as active, are more readily
observable at this time due to the greater visibility
afforded by the loss of deciduous foliage in areas
adjacent to the lodges (Novak, 1977; Brandt, 1938). In
addition to lodge visibility, the age structure of the
beaver population appears to be most stable in the fall
of the year. Dispersing juveniles and other individuals
isolated due to the previous trapping seasons are most
likely to have found companions and begun to reestablish
abandoned lodges. Existing pairs and females with young
have also been shown to begin maintainance of existing
lodges at this time (Brandt, 1947; Warren, 1932).

To correlate beaver lodge density with ecological -
conditions, it was necessary to determine which specific
habitat characteristics may be associated with site
selection. The suitability of sites for beaver occupancy
depends upon a broad spectrum of ecological parameters,

however, the principal environmental factors which

determine the suitability of habitats for beaver
occupancy have been shown to be associated with
appropriate vegetation, compatible stream conditions,
and topographic relief (Gill, 1972; Arner, 1964; Retzer,
1955). The data required to accurately describe the
habitat characteristics of each of the surveyed
traplines was collected from forestry inventory studies
obtained from the Department of Forestry Branch of the
Ministry of Natural Resources.

Forest inventory maps available from the Department
of Forestry Branch were used to accurately describe the
vegetation associations for each of the surveyed
traplines. Based on the 1972 forest inventory, Forest
Stand maps were compiled for all townships within the
North Bay trapping district. It was therefore possible
to locate and outline the specific boundaries for all
traplines examined. Each Forest Stand map provided
detailed information concerning the dominant woody
vegetation of the trapline. The maps contained a
description of the dominant vegetation species occurring
in the township. Each individual stand was labelled by
number and contained the species composition of the
stand, represented by a percentage of the area of the
stand in acres, mean height of tree species in feet, and

the average estimated age of the overall stand.

A vegetation profile was constructed to represent
the plant associations of each trapline by using the
Forest Inventory maps and a standard sampling technique
appropriate for the forest stand mosaic available from
the inventory data. Using the line transect sampling
technique outlined by Oosting (1956) a pair of transects
were drawn on the inventory map(s) within the boundaries
of the trapline. The transects were distributed over
the trapline area to include as large an area as possible,
and thus adequately describe the vegetation associations.
Each stand encountered along a transect was tabulated
separately. The number of the stand, species composition,
age and total area were then recorded on the data forms.

When the required information was recorded for each
stand sampled along the transects, the data were combined
to describe the plant associations of each trapline. The
species composition, represented by a percentage was
converted to area units to reflect the total acreage of
each species observed from all stands sampled within
the trapline. The combined acreage of all stands
sampled from a trapline was divided into the total
acreage representing each species to obtain a profile
of the species composition of each trapline represented

as a percent of the total acreage sampled. The mean

age of stands sampled from the trapline was calculated
by dividing the total age of each stand by the number
of stands sampled.

Stream abundance was also determined for each
trapline and assumed to be an important factor in the
selection of suitable sites for occupancy by beavers.
Each trapline was located on a l:50,000 scale
topographic map which clearly illustrated the location
of streams and other waterways. The number of streams
within each of the trapline boundaries was determined
by a method of stratified random sampling (Snedecor and
Cochran, 1967). Four quadrats, of one square mile each,
were randomly distributed throughout each trapline.

The total number of stream miles present in each
quadrat was measured and recorded. The average number
of stream miles per trapline was obtained by dividing
the total number of stream miles sampled by the

number of quadrats. Using this method, the average
stream density in miles of stream per square mile and
the total number of stream miles occurring in each
trapline were obtained.

Harvest data represented by the number of beaver
taken from each trapline were also obtained.fnanfinhfizy
of Natural Resources records. These data were based on

the actual trapping results for the 1972-1973 trapping

10

season. The total number of individuals removed from
each trapline was divided by the total number of square
miles of the trapline to determine the mean harvest
from each trapping area. No information was available
concerning the intensity of trapping effort expended

by individual trappers.

The statistical correlation between beaver lodge
density and specific habitat characteristics was
examined using four statistical analysis techniques.
The first statistical operation was a bivariate
correlation, that provided a summary statement about
the overall relationship between the habitat
characteristics and lodge density. The second operation
was a general multiple regression which could be used
to predict lodge density as a function of the total
set of habitat characteristics. The next analysis
conducted was the step-wise multiple regression. This
statistical technique allowed for the prediction of
lodge density based on the independent contribution of
each habitat variable to lodge density. The above
statistics predicted beaver lodge density as a function
of the habitat characteristics encountered in each of
the traplines. The chi-square test of dispersion was
used to examine the difference between the predicted
value calculated for lodge density and the observed

trapline densities.

11

The initial bivariate correlation analysis
provided a summary of the relationship between habitat
characteristics, which were the independent variables,
and lodge density, the dependent variable. In this
regression analysis, predicted values for the dependent
variable were obtained using the following linear
function:

Y' = A + BX
where Y' is the estimated value of the dependent
variable Y, B is a constant, multiplied by all values
of X, and A is an additive constant (Klecka, Nie and
Hull, 1975).

The bivariate regression program involved the
selection of A and B in a manner which insured that the
sum of squares for the residuals, the difference
between the actual and estimated values of Y for each
case, was smaller than any possible alternative values.
With the results of this analysis it was possible to:
l) arrive at a measure of the degree of association
between the variables by the use of the correlation
coefficient (r), 2) quantify the variation explained
by (r) with the use of the coefficient of determination
(r2), and 3) obtain an estimate of the statistical
significance of the associations between the dependent

and independent variables.

12

A general multiple regression analysis was
performed on the data to further clarify the relation—
ship between the dependent variable, lodge density, and
the set of independent variables represented by the
habitat characteristics. This program provided a method
to evaluate the contribution of the habitat
characteristics to the variability observed in lodge
density. This method is an extension of the bivariate
analysis in that the total set of habitat character-
istics could be used to provide an estimate of the
lodge density. The general form of this function was
as follows:

I—
Y —A+Ble+BZX2+....+Ban

where Y' represented the estimated value of lodge
density, A the Y intercept, and B the regression
coefficients for the values of X, which were the habitat
characteristics. The results of this analysis yielded
the following information; 1) a multiple regression
coefficient, which described the degree of association
between the independent and dependent variables, 2) a
multivariate coefficient of determination which provided
an explanation for the amount of variation in the depen—
dent variable, explained by the independent variables,
and 3) the level of confidence which indicated how
significant the relationship was between the variables

(Klecka, Nie, and Hull, 1975).

13

A stepwise multiple regression analysis was also
conducted on the data to determine the combinations of
independent variables which accounted for the greatest
amount of explained variation in lodge density.

This program entered the independent variables only if
they met certain statistical criteria. The independent
variables were selected according to the levels of
significance in a cummulative series, while the order of
inclusion was determined by the respective contribution
of each variable to the explained variance (Klecka, Nie,
and Hull, 1975). The resulting statistics were the same
as those obtained from the general multiple regression
program, except that each independent variable could

be examined separately. This statistical analysis
provided sufficient information to arrive at a
quantitative description of the relationship between
lodge density and environmental parameters. Using these
data, the regression equation was used to predict lodge
density as a function of habitat characteristics.

To evaluate the accuracy of the regression analysis
as a predictor of lodge density, the datavrne subjected
to the Chi-square test for dispersion to determine if

the predicted value for lodge density actually

represented the observed lodge densities of the traplines.

This test is designed to examine whether or not the

predicted frequency of a sample accurately depicts

14

the observed frequency of the sample. The general form

of the equation is:

X2 =Z <f-F)2/F
whereX2 represents the ratios between the sum of the
squared deviation between f, the observed sample
frequency and F the predicted sample frequency divided
by the predicted sample frequency F (Snedecor and
Ochran, 1967).

To estimate the number of beaver expected to occur
in the Upper Great Lakes Region, based on lodge density,
it was necessary to determine the average number of
individuals known to occur within a single lodge. Recent
ecological studies of the population dynamics of beaver
in the Upper Great Lakes Region were used to arrive at
an estimate of the number of beaver which could be
expected to occur within the drainage basin of the Great
Lakes (Novak, 1977; Brandt, 1947).

The abundance of beaver in the Upper Great Lakes
Region during the pre-settlement period prior to 1600
was estimated. The relationship demonstrated between
beaver populations and habitat characteristics for
contemporary beaver colonies were combined with a
reconstruction of the vegetation associations for this
region developed by Veatch (1959). This reconstruction

was based on the classification and distribution of soil

15

types in the state of Michigan. The vegetation types
described by Veatch (1959) were classified into three
categories which would represent the major plant
communities of the Great Lakes Region. These were
dominant deciduous, dominant coniferous, and mixed
coniferous-deciduous. It was assumed that the
relationship between contemporary beaver populations
and habitat characteristics was relatively constant
through time. The regression equation involved in the
prediction of beaver lodge density based on contemporary
vegetation profiles was used to obtain an estimate of
lodge density as a function of the differential species
composition of the three historic vegetation
associations identified.

The determination of an estimate for the
pre-settlement population density of beaver in the Upper
Great Lakes Region was based on the number of lodges
predicted to occur within the historic vegetation
classifications identified for the region, the total
number of square miles of forested area within the
drainage area of the Great Lakes and the average number
of beaver known to occur within a single lodge in this
area of the anhmflfs range based on recent population

estimates.

16

The methodology developed for the determination
of this estimate of beaver lodge density during the
early historic period represents a unique approach
to the study of past faunal associations. To
clarify the process involved in estimation of beaver
population density the following summary is presented.

It was first necessary to survey contemporary
beaver populations and collect data on the density
of lodges observed in an area where the variability
of habitat conditions could be measured quantitatively.
Precise data concerning vegetation abundance by
dominant tree species and stream availability was
obtained from 49 traplines. The specific habitat
characteristics for each individual trapline were
examined using bivariate analysis to determine the
correlation between ecological conditions and beaver
lodge density. The correlation obtained in this
manner was tested using multiple regression statistics
to establish the predictive value of the relationship
between lodge density and habitat characteristics.

The estimation of historic beaver lodge density
required the use of reconstructed vegetation associa-
tions of the Great Lakes Region. This data was
abailable in a form comparable to the contemporary

environmental data used to establish the initial

17

correlation. The multiple regression statistics were
used to predict an estimate of beaver lodge density
based on the reconstructed vegetation associations.
The estimate of beaver lodge density was used as an
index for beaver population density by multiplying
the average number of individuals known to occur in

lodges by the total number of lodges estimated.

RESULTS

Beaver census data based on 82 separate traplines
were cokated from the North Bay Trapping District.
After a detailed examination of these records, 49 of
the traplines were selected for this study which had
been surveyed using standardized techniques. The
location of each trapline within the trapping district
is presented in Figure l. The results of the aerial
census of these traplines indicated that a total of
2,874 active beaver lodges occurred in an area of more
than 2,205 square miles. The average trapline was
determined to be 45 square miles in area and contained
1.52 active lodges per square mile. The variance for
this density value was 0.91. Each trapline is
represented separately in Table l.

Vegetation profiles were constructed for each
trapline based on forest stand maps available from
the Ministry of Natural Resources. The dominant
vegetation types encountered along sample transects
established on forest stand maps were tabulated for
all traplines. Total acreage, represented by each

species and the acreage of all stands within a trapline,

18

20
Table 1. North Bay District Traplines Descriptive Data

Trapline No. Size(Sq. Mi.) Total Lodges Lodge Density

01 32.4 53 1.6
02 39.2 50 1.3
03 35.6 73 2.1
04 26.4 57 2.2
05 31.2 52 1.7
06 42.4 39 0.9
07 76.8 18 0.2
08 72.8 60 0.8
09 84.4 70 0.8
10 45.6 46 1.0
11 61.6 44 0.7
12 94.8 41 0.4
13 47.2 145 3.1
14 25.6 91 3.6
15 41.2 68 1.7
16 20.0 17 0.8
17 12.4 13 1.1
18 71.2 48 0.7
19 42.0 33 0.8
20 33.6 16 0.5
21 58.0 44 0.8
22 85.6 40 0.5
23 34.0 39 1.2
24 14.8 08 0.5
25 45.2 77 1.7
26 73.6 116 1.6
27 40.0 78 2.0
28 40.0 70 1.8
29 56.0 66 1.2
30 132.8 280 2.1
31 30.4 24 0.8
32 40.8 138 3.4
33 94.4 49 0.5
34 32.8 21 0.6
35 32.8 17 0.5
36 53.6 42 0.8
37 26.4 12 0.5
38 22.4 77 3.4
39 16.4 26 1.6
40 19.2 20 1.0
41 12.0 24 2.0
42 14.0 56 4.0
43 14.4 44 3.1
44 40.0 83 2.1
45 36.4 96 2.6
46 28.0 58 2.1
47 38.0 62 2.2
48 14.8 22 1.5
49 11.6 28 2.4

21

were used to arrive at percent abundance values for the
23 vegetation species observed in the 49 traplines
sampled (see Table 2). The average number of stands
sampled within each trapline was 16.1, while the average
acreage per stand was 180.0. The total acreage sampled
for the traplines ranged from 6342.6 to 1412.0. The
mean acreage sampled per trapline was 2865.3.

Harvest intensity, expressed as the total number of
beaver taken from each trapline, was also obtained from
the Ministry of Natural Resources records. During the
1972-1973 trapping season, a total of 3343 beaver were
harvested from the 49 traplines. The mean harvest was
determined to be 1.75 beaver per square mile with a
variance of 1.78. Total and mean harvest values for all
traplines are presented in Table 3.

The number of stream miles was determined for each
trapline as an index of the availability of suitable
sites for beaver lodges. The average number of stream
miles observed within the quadrats sampled from each
trapline were used to calculate mean stream density for
each trapline (see Table 3). The mean stream density as
measured by stream miles was found to be 2.19 linear
miles of stream per square mile with a variance of .09.

A summary of the statistically significant variables

from the bivariate analysis is presented in Table 4. When

22

Table 2. Vegetation Species Abundance and Area Values

Vegetation
SpeCies
Hard Maple
White Birch'
Poplar
Balsam Fir
White Pine
Black Spruce
Yellow Birch
White Spruce
Jack Pine
Soft Maple
Alder
Hemlock
Red Pine
Cedar
Mixed Hardwood
American Beech
Red Oak
Ash
Basswood
Elm
Ironwood
Black Cherry

Larch

Mean
Abundance

16.10
15.17
13.09
6.68
5.89
5.42
5.12
3.57
3.06
2.66
2.74
2.36
1.66
1.54
1.51
1.03
.74
.36
.32
.29
.26
.19
.09

Total
Acreage

27,169.4
26,527.6
22,002.1

8,935.2
8,857.2
7.485.4
9,008.4
5,325.1
4,349.1
3,306.0
3,239.8
3,254.4
2,576.8
2,172.4
1,884.0
1,090.4
1,095.0

403.8

430.6

450.4

314.5

276.4

104.0

Mean

Acreage

5554.5

541.4
449.0
182.4
180.8
152.8
183.8
108.7
88.8
67.5
66.1
66.4
52.6
44.3
38.4
22.4
22.3
8.2
8.8
9.2
6.4
5.6
2.1

Table 3.

Trapline No.

01
02
03
04
05
06
07
08
09
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49

Total Harvest Mean Harvest

30
123
36
98
72
46
32
26
38
58
13
45
98
203
50
16
11
107
29
23
46
115
27
25
59
336
68
77
123
363
21
63
164
23
23
75
50
71
26
39
24
63
43
94
49
52
21
32
17

23

0.93
3.14
1.01
3.71
2.34
1.08
0.42
.36
.45
.27
.21
.47
.08
.93
.21
.78
.89
.50

l-‘MOI-4HNNANNHWl—‘OOOHHONNP—‘l—‘hl—‘l—‘OHOOP—‘I—‘OOHQNOOi-‘OO
O
N

Stream

|'--'l\)NI—‘NNNNNNNNNNNNNNNNNNl—‘NNNHNNHNNNNNNHHNNNNNNHNNNN
O O .0 O O .0 O O O O O O O. O O O. O O O. O O C O O I. O O I. O O. O O C O

O O O O
04>wal—‘NMNbbbl—‘WHI—‘Ql—‘OKDMHQHLDmmi—‘fl\OD-‘(DNWI—‘OCDKDUTUJOHHHUIDMMN

North Bay Trapline Harvest and Stream Abundance

Density

24

lodge density was regressed with each of the independent
variables, represented by vegetation species, mean age
of stand, harvest and stream density, it was found that
the most statistically significant associations were
between vegetation species and lodge density. Seven
significant independent variables, representing dominant
vegetation types, were found to have a positive
association with lodge density. These were Poplar
(Populus sp.), Elm (Ulmus, sp.), Oak (Quercus sp.), Ash
(Fraxinus sp.), Balsam Fir (Abieg sp.), Beech (Fagus sp.),
and mixed hardwoods. Three independent variables were
found to exhibit low to moderate inverse associations

with lodge density, White Birch (Betula papyrifera),

 

Yellow Birch (Betula lutea), and Cedar (Thuja occiden-

 

 

talig). Balsam Fir and White Cedar both showed low
associations with beaver lodge density. The coefficient
of determination, presented in Table 4, represents the
amount of variation accounted for by each of the
selected variables.

A multivariate regression analysis was undertaken
to regress the dependent and independent variables.
Twenty-four independent variables, representing
vegetation species and stand age, were regressed with the
dependent variable, lodge density. The multiple

regression coefficient (b) was found to have a value of

25

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26

.08737 which indicates a high positive association
between the independent and dependent variables.
Furthermore, the value of the multiple regression
coefficient of determination (b2) was .65185, which
indicated that more than 65% of the variation in lodge
density was explained by the variation in vegetation
species. This result was found to be significant at
the 90% confidence level (F-l.87) and demonstrated a
high degree of accuracy for the analysis.

A third statistical analysis was conducted on the
data. This program, a step-wise multiple regression, was
used to clearly define which of the vegetation species
had the greatest influence on lodge density. A summary
of the results obtained from the 24 steps involved in
this program is presented in Table 5. It can be observed
from this table that a majority of the variables in the
analysis were found to be significant at the 99%
confidence level. The final stages of the analysis were
found to be significant at the 90% confidence level.

The results of the step—wise regression analysis
were used to obtain an estimate of beaver lodge density
based on the vegetation analysis of the North Bay
District traplines. A summary of the data involved in
this determination is presented in Table 6. A predicted

value of 1.64 lodges per square mile was compared to the

27

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Table 6. Regression Variables Used to Predict an Estimate
of Lodge Density as a Function of Combined
Vegetation Species

Variable Regression Abundance Product

Value (bn) Percentage (bnxn)

Hard Maple -.0904 16.105 -1.4558

White Birch -.1164 15.170 -1.7657

Poplar —.0942 13.091 —1.2331

Elm .5112 0.296 .1513

Ash .0216 0.362 .0078

Soft Maple -.1685 2.666 -.4492

Alder -.0414 2.740 -.ll34

Red Oak -.0680 0.744 -.0505

Yellow Birch -.2549 5.124 1.3056

Basswood -.2696 0.328 -.0884

Black Cherry .0449 0.191 .0084

Balsam - 0806 6.681 —.5384

White Spruce -.1l76 3.566 -.4193

Black Spruce -.1023 5.423 -.5547

White Pine -.0838 5.890 -.4935

Red Pine -.l730 1.661 -.2873

Jack Pine -.0917 3.060 -.2806

Cedar -.1178 1.540 -.1814

Hemlock -.0599 2.367 -.1417

American Beech -.0864 1.030 -.0889

Larch .1960 0.097 .0190

Mixed Hardwood -.1045 1.514 -.1582

Ironwood .4065 0.266 .1081

Constant (A) = 10.959

28

Lodge Density (Y)

= 1.64 lodges/square mile

29

observed mean lodge density of 1.52 lodges per square
mile for the traplines. The percent deviation
represented by these values was 8%.

The Chi-square distribution was calculated to
compare the observed beaver lodge density with the
predicted value obtained from the multiple regression
equation. The Chi-square value obtained from this
calculation, based on all 49 traplines, was 31.03 with
48 degrees of freedom. The percentage point distribution
of the Chi-square values indicated that the predicted
lodge density estimate represented the actual beaver
lodge density at the 99% level of accuracy (Fisher and
Yates, 1970). The observed and predicted values of
beaver lodge density are presented in Table 7.

The regression equation developed to estimate beaver
lodge density for the vegetation associations observed in
the North Bay Trapping District was used to predict
beaver lodge density for the pre-settlement period. The
pre-settlement forest conditions which were involved in
this determination were derived from the study of
reconstructed forest types in the state of Michigan
(Veatch, 1959). Based on the results of this pre-
settlement vegetation study, three distinct categories
were classified to represent the major vegetation types

of the Upper Great Lakes Region. These communities were

30

Table 7. Predicted and Observed Lodge Density Values
Trapline No. Observed Density Predicted Density

1 1.6 1.67

2 1.3 1.76

3 2.1 1.99

4 2.2 2.08

5 1.7 1.64

6 0.9 1.06
7 0.2 .80

8 0.8 0.91

9 0.8 4.01
10 1.0 1.59
11 0.7 0.95
12 0.4 0.72
13 3.1 2.73
14 3.6 2.43
15 1.7 1.06
16 0.8 0.43
17 1.1 1.70
18 0.7 1.55
19 0.8 0.66
20 0.5 1.01
21 0.8 0.96
22 0.5 0.02
23 1.2 1.46
24 0.5 1.83
25 1.7 6.58
26 1.6 1.42
27 2.0 2.14
28 1.8 1.48
29 1.2 0.58
30 2.1 1.74
31 0.8 1.88
32 3.4 2.73
33 0.5 0.54
34 0.6 0.69
35 0.5 1.02
36 0.8 1.24
37 0.5 0.76
38 3.4 1.42
39 1.6 1.04
40 1.0 0.80
41 2.0 2.15
42 4.0 3.49
43 3.1 3.29
44 2.1 2.20
45 2.6 2.44
46 2.1 1.79
47 2.2 2.45
48 1.5 1.13
49 2.4 2.39

 

31

described as: l) dominant deciduous for those
vegetation associations where the dominant vegetation
species were broadleaved trees, 2) dominant coniferous
for those vegetation associations where the dominant
vegetation species were evergreen, and 3) mixed
coniferous-deciduous for those vegetation associations
where evergreen and broadleaved species were equally
represented.

Correlation coefficients and mean abundance values
were obtained from the multiple regression analysis and
trapline data of the North Bay District to calculate

beaver lodge density based on the species composition of

pre-settlement Upper Great Lakes forests. Using the
prediction equation, it was determined that beaver lodge
density in dominant deciduous vegetation associations was
1.45 lodges per square mile. The data for this
association were collected in Table 8. Dominant coniferous
vegetation association data are summarized in Table 9.
The prediction equation for this association was used to
arrive at an estimate of .84 beaver lodges per square
mile for the density of lodges in coniferous vegetation
associations. Mixed coniferous-deciduous vegetation
associations were estimated to support 1.95 beaver lodges
per square mile. The data used for this category of

vegetation are summarized in Table 10.

32

Table 8. Regression Variables Used to Estimate Lodge
Density as a Function of Dominant-Deciduous
Vegetation Species.

Variable Regression Abundance Product

Value (bn) Value(xn) (bnxn)
White Birch -.1164 9.53 -1.1092
Hard Maple -.0904 34.32 -3.1025
Poplar - -.0942 4.56 -0.4295
Yellow Birch -.2549 11.87 -3.0256
Ash .0216 2.48 0.0535
White Pine -.0838 4.46 -0.3737
Hemlock .0599 3.34 0.2000
American Beech -.0864 5.49 -0.4743
Elm .5112 3.48 1.7789
Balsam -.0806 3.02 -0.2434
Spruce White -.1176 2.62 -0.3081
Red Oak -.0680 3.29 —0.2237
Cedar -.ll78 1.90 -0.2238
Soft Maple -.1685 5.62 -0.9469
Basswood -.2696 4.02 —1.0837
Constant (A) = 10.959

Lodge Density (Y) = 1.45 lodges/square mile

33

Table 9. Regression Variables Used to Estimate Lodge
Density as a Function of Dominant-Coniferous
Vegetation Species.

Variable Regression Abundance Product
Value (bn) Value(xn) (bnxn)
White Pine —.1164 13.50 -1.5714
Black Spruce -.1023 12.16 -1.2439
White Spruce -.1l76 6.40 -0.7526
Balsam -.0806 8.51 -0.6859
Jack Pine -.0917 6.78 -0.6217
Hemlock -.0599 7.25 0.4342
Cedar -.1178 6.45 -0.7598
Larch .1960 4.51 0.6879
Red Pine -.1730 9.07 -1.5691
Poplar -.0942 1.55 -0.1460
Alder -.0414 3.81 -0.1577
Mixed Hardwood -.1045 3.48 -0.3636
White Birchr -.1164 3.09 -0.3596
Basswood -.2696 2.45 -0.6605
Elm -.5112 2.78 -1.4211
Ash .0216 3.41 0.0736
Soft Maple -.1685 2.56 -0.4313
Yellow Birch —.2549 2.24 -0.5709

Constant (A) = 10.959

Lodge Density (Y) =

0.84 lodges/square mile

Table 10.

34

Regression Variables Used to Estimate Lodge

Density as a Function of Mixed Coniferous-
Deciduous Vegetation Species.

Variable

Hard Maple
Soft Maple
Poplar

Mixed Hardwood
White Pine
Black Spruce
Jack Pine
Hemlock

Red Pine
Cedar

Balsam

Elm

Larch
Basswood

Ash

White Birch
Yellow Birch
Alder

Constant (A) = 10.

Lodge Density (y)

Regression
Value (bn

.0904
.1685
.0942
.1045
.0838
.1023
.0917

.0599

.1730
.1178
.0806

.5112
.1960

.2696

.0216

959

.1164
.2549
.0414

Abundance
Value (xn

9.67
4.48
4.45
3.90
8.85

10.95

3.76
2.97
4.89
8.02

10.13

2.84
1.90
5.02
2.61
4.35
6.92
4.29

1.95 lodges/square mile

Product

(bnxn)

-0.8741
-0.7548
-0.4191
-0.4075
-0.7416
-1.1201
-0.3447

0.1779
-0.8459
-0.9447
-0.8164

1.4518

0.3724
-1.3533

0.0563
-0.5063
-1.7639
-0.1776

35

To apply the results of the above calculations
to historic beaver lodge density it was necessary to
determine the extent of forest cover for the Great Lakes
drainage basin. The Upper Peninsula of Michigan was
used to estimate the area represented by each of the
forest classifications identified in this study. The
total area of the Upper Peninsula of Michigan was found
to be 16,500 square miles; however, the forested regions
of this area are represented by 15,851 square miles when
coastal and other unforested regions are eliminated
(Winters, 1976; Veatch, 1959). Based upon Veatch's
(1959) vegetation reconstruction, the ration of the area
represented by each of the vegetation classifications
examined in this study was determined. It was found
that dominant deciduous vegetation represented 6379
square miles, dominant coniferous vegetation 6960 square
miles, and mixed coniferous-deciduous 2512 square miles,
in the Upper Peninsula of Michigan.

The total drainage area for the Upper Great Lakes
Region was determined to be 288,770 square miles with
approximately 12,000 square miles of coastal and
unforested area (Hilborn and Fawcett, 1972). The total
forested area of the region when these areas were taken
into consideration was 276,770 square miles. The area

represented by the identified vegetation classifications

36

in the Upper Great Lakes Region based on the ratio
established for the upper peninsula are as follows:
dominant deciduous 111,382 square miles, dominant
coniferous 121,527 square miles, and mixed coniferous-
deciduous 43,861 square miles. Using the values for
beaver lodge density arrived at through the previous
analysis, the total number of beaver lodges which could
have occurred in these vegetation asSociations during
the pre-settlement period were: 1) dominant deciduous:
161,504 lodges 2) dominant coniferous: 102,083 lodges
3) mixed coniferous-deciduous: 185,528 lodges or an
estimated total equal to 349,115 lodges for the Great
Lakes drainage basin.

Novak (1977) concluded that an estimate of 5.42
individuals per colony accurately represented the number
of beaver present in lodges in north central Ontario.
Using the expected value for beaver lodge density in the
Upper Great Lakes Region and Novak's (1977) density of
beaver per lodge, it was possible to calculate an
estimate for the number of beaver which were present in
the drainage areas of the Upper Great Lakes Region. The
estimate was determined to be 1,892,203 beaver in the

region during the pre-settlement period, ca. 1600.

DISCUSSION

The purpose of this study was to develop a reliable
method to estimate pre-settlement beaver population
density. To accomplish this objective, it was necessary
to isolate a set of ecological factors which could be
incorporated into an index of beaver density for
contemporary and historic environmental conditions.
Numerous studies have demonstrated the ecological
relationship between beaver colonies and environmental
parameters such as stream flow, valley width and
gradient, soil conditions and vegetation associations
(Gill, 1972; Novakowski, 1967; Retzer, 1955; Brandt,
1947). However, to date there has not been an attempt
to correlate beaver population density with specific
environmental conditions so as to predict beaver
population density based on these conditions.

Essentially the problem was to study the
relationship between an extinct animal and the physical
and biotic environments which influenced the population
dynamics of the species. For this assessment it was
_imperative that the ecological conditions prevalent

before the extirpation of the species be established.

to
\J

38

This information was obtained from habitat
reconstructions which were based on contemporary
ecological associations and other data relevant to

the biological requirements of the beaver in the Upper
Great Lakes Region.

Reconstruction of the paleoecology of ancient forms
by reference to the ecology of recent representatives
has been shown to be an accurate method of reconstructing
the historic environment conditions with which extinct
fauna and flora were associated. This has been
especially true in studies of recent and ancient taxa
not widely separated in time (Laporte, 1977; McAlester,
1968).

Ecological reconstructions of postglacial
environmental conditions have been attempted using a
variety of techniques (McAlester, 1968). Vegetation
associations have been successfully reconstructed for the
Upper Great Lakes Region using pollen analysis of bogs
and other sedimentary sources and studies of Bryophyte
(Diatome) distribution in Michigan (Farrand and Eschman,
1974; Dillon, 1956). Potter (1947) analyzed the pollen
content of postglacial bogs in the Southern Great Lakes
Region and found a sequence of dominant tree genera

which examined include Picea, Abies, Pinus, Betula,

 

 

 

Quercus, Tsuga, Carya, and Fagus. Specific forest cover

 

39

types have been reconstructed for the State of Michigan
based on soil mapping and subsequent correlations with
the known ecological relationships between plant species
and soil conditions (Veatch, 1959). This study
established that correlations could be obtained between
soil type and vegetational units, and demonstrated the
utility of such information in the establishment of
historic ecological relationships.

The ecological requirements of wildlife species are
usually associated with biotic rather than abiotic
factors and ecological reconstruction of faunal
associations present complex problems which cannot
always be resolved due to the large set of unknown
factors influencing the population (Laporte, 1977).
However, the ecological requirements of beaver represent
an important exception to this general rule in that the
presence of beaver colonies has been shown to be
correlated with specific ecological conditions because of
the dietary selectivity of the species and the physical
limitations of beaver dam and lodge site construction
(Hay, 1958; Retzer, 1955; Ives, 1942; Brandt, 1938).

The ecological requirements for continuous beaver
occupancy, has been considered to be more restricted than
for many other wildlife species (Gill, 1972; Smith, 1950).

The specific habitat characteristics which have been

40

shown to limit the presence of beaver colonies can be
segregated into two distinct categories: 1) vegetation
suitability for dietary and construction purposes and
2) geomorphology of terrain and stream availability.
Vegetation associations required for successful
beaver occupancy were found to be an essential factor
association with the presence or absence of beaver
colonies throughout the animal's range. The presence of
specific vegetation species were found to be essential
to meet the nutritional requirements of the animal and
provide suitable construction materials for lodges and
dams (Novakowski, 1967; Hall, 1960). Beaver food
utilization studies have consistently demonstrated that
various species of the genus Populus (Cottonwood, Aspen,
Poplar, etc.) constitute an extensively utilized dietary
component whenever it is available in the environment
(Novakowski, 1967; Hammond, 1943). Other woody
vegetation found by Nixon and Ely (1969) to be of major
importance in the beaver diet were Common Alder (Algus

serrulata), Buttonbush (Cephalanthus occidentalis), Red

 

 

Elm (Ulmus rubra), Maple (Acer rubrum, A; saccharinum),

 

 

 

and Ash (Fraxinus americana, F; pennsylvanica). Hall

 

 

(1960) found that Willow (Salix spp.) and Quaking Aspen

 

(Populus tremuloides) constituted important forage species

 

for beaver while some evidence for the use of coniferous

species was also indicated.

41

Herbaceous vegetation has been shown to be
important in the summer and fall diet (Northcott, 1972;
Nixon and Ely, 1969; Aldous, 1938). Nixon and Ely
(1969) found that herbaceous species in the beaver diet

include Water Lillies (Nuphar variegatum, N; micro-

 

phyllum), Queen-of—the-Meadow (Filipendula ulmaria), and

 

grasses (Gramineae). Under normal summer conditions,

 

beaver feed on grasses, forbs, and aquatic plants
whenever possible and consume woody plants during this
season only when the former are unavailable (Brenner,
1967; Rutherford, 1964; Brandt, 1938). However, woody
plants constitute the bulk of the winter diet and were
considered to be an important limiting factor throughout
the animal's distribution in northern latitudes
(Novakowski, 1967).

The geomorphology of terrain and stream availability
have also been found to influence the site suitability
for beaver dam construction and lodge occupancy. Retzer
(1955) studied the physical environmental effects of
beavers in the Colorado Rockies and determined that
beaver occupancy was dependent upon valley grade, valley
width and bedrock geology. It was concluded that
valleys with less than six percent grade were most
suitable for successful beaver occupancy. Streams which

had a greater than 11% grade were considered questionable

42

to unsuitable for permanent occupancy by beaver (Retzer,
1955). Other ecological studies have not shown stream
gradient to be of major importance for the establishment
of beaver colonies in northern latitudes and mountainous
areas (Hay, 1958; Smith, 1950). Beaver dam construction
appeared to have no correlation with either stream
gradient or stream flow according to Smith (1950). All
new and rebuilt dams observed in Smith's study were
constructed on streams with less than 4.0% slope. Based
on the data collected by Hay (1958), it was evident that
no significant relationship existed between stream
gradient, width of floodplain and beaver colony density.
Hay concluded that further study of the role of physical
environmental features on beaver populations has no
direct utility other than the possible effect on the
type and amount of food available.

Beaver populations have been shown to demonstrate a
positive association with the type of vegetation
available in the environment. In northern habitats, such
as in the Upper Great Lakes Region where topographic
relief is not extreme, they may represent the most
important limiting factor for beaver site selection
(Gill, 1972; Smith, 1950; Brandt, 1947). A statistical
correlation between beaver lodge density and specific

habitat characteristics which influence beaver site

43

selection and occupancy was completed in the present
study based on vegetation profiles developed from
North Bay, Ontario,traplines records.

The initial bivariate regression analysis of the
trapline data resulted in a significant correlation
between lodge density and ten dominant vegetation types.
A majority of these species demonstrated a high positive
correlation with lodge density and were also found to be
associated with beaver diet. The remaining species
demonstrated a significant negative correlation with
lodge density. Two of these were hardwood species,
White and Yellow Birch, which are generally associated
with beaver habitat but occur predominantly on upland
sites and are therefore not directly important to beaver
as resources for food or construction materials. The
harvest of beaver was found to have a statistically
significant association with lodge density. This appears
to be an indication of a direct and positive relationship
between lodge density and trapping success; as lodge
density increased, there was a corresponding increase in
trapping success. 'flmme data however, did not involve an
expression of the effort associated with a given harvest
statistic and was therefore not a measure of actual

harvest intensity.

The bivariate analysis provided a preliminary

description of the association between each of the

44

independent variables and the dependent variable, lodge
density. The coefficient of determination represented
the amount of variation accounted for by each of the
selected variables considered independently. The
explained variation expressed by this statistic ranged

from 9.30% for White Ash (Fraxinus americana) to 4.13%

 

for American Elm (Ulmus americana). A large degree of

 

 

variation remained unexplained when only the bivariate
analysis technique was used.

A second analysis was used to further clarify the
variation in lodge density and the dominant vegetation
species. The multivariate regression analysis was
chosen to regress lodge density with habitat
characteristics. This technique provided a method
to quantify the contribution of the set of independent
variables to the dependent variable. The effect of
the combined set of vegetation species on lodge
density was found to be significant at the 90% level
of confidence indicating a high degree of accuracy. It
was considered that the remaining variation not accounted
for with this analysis could possibly be further
clarified if all of the ecological determinants of
lodge density could be introduced into the analysis.
The data required for such an analysis however, were

not available for this study.

45

A third statistical approach was undertaken to
determine which combinations of vegetation species have
the greatest influence on the dependent variable. The
step-wise multiple regression analysis was employed for
this determination because it was designed specifically
to estimate the contribution of each independent
variable toward explaining the variation of the dependent
variable. The application of this regression technique
provided a hierarchical list of vegetation combinations
which have varying degrees of association with lodge
density. The program illustrated that many of the
negatively associated independent variables encountered
during the bivariate analysis were of importance, and
could be used to more clearly explain the variation in
lodge density. A number of inverse relationships were
found to be important for this purpose. For example,
Yellow and White Birch were found to have a negative
association with lodge density in the bivariate analysis;
however, both species were ranked in the first five
variables selected for analysis by the step-wise program.
This ranking was directly related to the contribution
of the variable to the explained variance and thus
indicated the usefulness of these variables in accounting
for the variation in the dependent variable. Independent

variables positively associated with lodge density

46

according to the bivariate analysis was a major
component of the variables selected in the first ten
steps of the step-wise program. Ash, Mixed Hardwood,
American Elm, Red Oak and American Beech were found to
contribute most significantly to an explanation for
the variation of the dependent variable.

The value of the step-wise analysis was that the
influence of any vegetation species on lodge density was
identified in the hierarchical list of vegetation
combinations generated from step 1 to step 24. Thus
each vegetation species was examined independently to
determine the influence it exerted on lodge density.
The ability to isolate each vegetation species became
increasingly important when lodge density was predicted
as a function of specific vegetation associations.

The strongest association and greatest amount of
explained variation, as measured by the correlation
coefficient and coefficient of determination, were
obtained from the step-wise multiple regression program.
The data derived from this regression analysis were
used to predict lodge density by substituting into the
appropriate regression equation. The mean abundance
values and corresponding regression coefficients
for the 23 independent variables representing

vegetation species were regressed to determine a

47

predicted value for lodge density using these vegetation
parameters (see Table 6). When the predicted and
observed lodge density values were compared using the
Chi-square test for dispersion it was concluded that the
predicted value for lodge density was sufficiently close
to the actual value observed to demonstrate the
accuracy of the prediction techniques and extend the
analysis.

The determination of an accurate method to
estimate beaver population density has been the object
of numerous investigations (Novak, 1977; Aleksiuk, 1968;
Hay, 1955; Brandt, 1947). As early as 1868, Morgan
arrived at an estimate of beaver density in the Lake
Superior Region which indicated 7.0 beaver per colony.
More recent studies of beaver population densities in
the Great Lakes Region have shown this value to be an
overestimate. Brandt (1947) using lodge counts in the
state of Michigan determined the average colony size to
be 5.1 beaver per lodge. Beaver population density for
Ontario beaver colonies was examined by Novak (1977).
The average beaver per colony based on the results of
his investigation was shown to be 5.4 individuals per
lodge when factors such as age and year class ratios
were considered.

The use of beaver lodges as an index of beaver

population density can not be regarded as a reliable

48

estimate of population density without giving
consideration to the wide variety of variables
concerning colony size and the distribution of adult
beaver within the territory of a colony (Smith, 1950;
Warren, 1932; Aleksiuk, 1968). In the Upper Great Lakes
Region, many colonies have individuals which do not
utilize lodges and live in bank burrows exclusively
(Brandt, 1938). Other animals utilize more than one
lodge and often occupy bank burrows as well (Hay, 1958).
Brandt (1947) conducted an extensive examination of
beaver colonies in Michigan and concluded that a
reasonable method for estimating beaver population
density would utilize an October or November census of
all lbdges which could be positively associated with
beaver activity. Brandt stated that if census were
taken during the fall season, estimates would be
substantially correct. As stated earlier, Novak (1977)
developed a method to determine the number of individuals
per colony using lodge counts in the Upper Great Lakes
Region. This estimate may be considered high, under
some conditions. However, an inflated estimate would

be offset by individuals living in bank dens and omitted

in the lodge census.

CONCLUSION

The results of this investigation have
demonstrated a reliable method to quantitatively assess

the population density of beaver (Castor canadensis

 

Kuhl) in the Upper Great Lakes Region during the
pre-settlement period prior to the fur trade, ca. 1600.
The methodology developed in this study involved the
use of regression analysis techniques to demonstrate
the relationship between beaver lodge density and
specific habitat characteristics. This correlation was
extended to reconstructed vegetation associations
during the historic period to estimate the historic
population density of beaver during this period.

Historic beaver population density in the Upper
Great Lakes Region was estimated based on the contem—
porary correlation between beaver abundance and
habitat associations combined with historic vegetation
reconstructions. Contemporary beaver populations were
surveyed to collect precise lodge density and habitat
data. Beaver lodge density counts were obtained from
active traplines where the dominant tree species

abundance was known. These data were correlated using

49

50

bivariate analysis to characterize the relationship
between lodge density and habitat associations. The
predictive value of this correlation was established
based on a step-wise multiple regression program for
each of 23 tree species examined. Reconstructions

of historic vegetation associations for the Upper
Great Lakes Region were than used to describe pre-
settlement forest conditions in the Great Lakes
watershed. The vegetation reconstructions were entered
into the multiple regression program to arrive at the
estimated beaver lodge density. The actual population
density estimate was calculated on the basis of the
number of individuals known to occur in active lodges
in the Great Lakes Region.

It was determined that approximately two million
beaver represented a reasonably accurate assessment of
beaver density in the drainage area of the Great Lakes
during the pre-European settlement period, ca. 1600.

The statistical analysis used to correlate beaver
lodge density with vegetation species indicated that
deciduous trees such as Ash (Fraxinus sp.), Poplar
(Populus sp.), Oak (Quercus sp.), Beech (Fagus sp.),
Elm (Ulmus sp.), and other mixed hardwoods were
important indicators for predicting lodge density.

Coniferous trees, for example, Balsam (Abies balsamea)

 

51

and Cedar (Thuja occidentalis), were also correlated

 

with suitable sites for beaver occupancy, but to a
lesser extent than broadleaved trees. Other ecological
factors relating to beaver habitat characteristics

such as stream availability and topographic relief did
not exhibit high correlations with lodge density due

to the relative homogeneity of these conditions in the
Upper Great Lakes Region.

Based on the results of this study, it can be
shown that some of the important ecological relationships
between wildlife species and their environmental
conditions can be examined systematically in situations
where the species in question are no longer living.

This method of study has potential utility for examining
the biological relationships among species in the past

by combining present knowledge of ecological interactions
with reconstructions of historic and pre—historic

habitat conditions.

The utility of examining beaver population density
in an historic context has important implications for
historians, sociologists and anthropologists concerned
with the dynamics of the fur trade in the Upper Great
Lakes Region and other areas of North America where the
fur trade was an important factor in the exploration
and settlement of the continent. Previous studies of

the fur trade and interactions between fur companies

52

and Native people have been limited to data based on
fur company returns and harvest statistics. Fur

trade interactions and competition were dependent in
many cases on the abundance and distribution of beaver;
however, at the present time there exists a paucity

of data relating to this important factor in the
development and expansion of the trade throughout the
beaver's range. This study has shown that it is possible
to systematically examine the population density of
extinct beaver populations and provide a technique to
further develop our understanding of the complexities

of the early history and development of North America.

BIBLIOGRAPHY

BIBLIOGRAPHY

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Jour. Wildl. Mgt. 2(4): 216-223.

Aleksiuk, 1968. Scent-mound communication,
Territoriality and population regulation in beaver
(Castor canadensis Kuhl). Jour. Mammal. 49(4):
759-762.

 

Arner, H. 1964. Research and a practical approach
needed in management of beaver and beaver habitat
in the southeastern United States. Trans. N. Am.
Wildl. and Natural. Resources Confer. 29: 150-158.

Bigger, H.P. ed. 1923. The works of Samuel de Champlain
(6 vols.). Univ. Toronto Press.

Brandt, G.W. 1938. A study of beaver colonies in
Michigan. Jour. Mammal. 19(2): 139-162.

Brandt, G.W. 1947. Michigan beaver management. Game
Div. Mich. Dept. Cons., Lansing. 56 p.

Brenner, J. 1967. Spatial and energy requirements of
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Dillon, L.S. 1956. Wisconsin climate and life zones in
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Evans, C.D., W.A. Troyer and C.J. Lensink. 1966. Aerial
census of Moose by quadrat sampling units. Jour.
Wildl. Mgt. 30(4): 767-766.

Farrand, W.R. and D.F. Eschman. 1974. Postglacial
environmental studies. Mich. Acad. 7(1): 31-56.

Fisher, R.A. and F. Yates. 1970. Statistical tables
for biological, agricultural and medical research.
Hafner Publ. Co. 347 pp.

Gill. 1972. The evolution of a discrete beaver habitat
in the Mackenzie river delta, northwest territories.
Canad. Field-Natur. 86(3): 233-239.

54

Hall, G. 1960. Willow and aspen in the ecology of
beaver on Sagehen creek, California. Ecology.
41(3): 484-494.

Hammond, C. 1943. Beaver on the Lower Souris Refuge.
Jour, Wildl. Mgt. 7(3): 316-321.

Hay, K.G. 1955. Development of a beaver census method
applicable to mountain terrain in Colorado. Unpubl.
M.Sc. Thesis. Colow Agr. and Mech. Col. Fort
Collins. 115 pp.

Hay, G. 1958. Beaver census methods in the Rocky
Mountain region. Jour. Wildl. Mgt. 22(4): 395-401.

Heidenreich, C.E. and A.J. Ray. 1976. The early fur
trades: A study in cultural interaction. McClelland
and Stewart Ltd. 93 pp.

Hilborn, G. and H. Fawcett. 1972. Geographical Atlas
of the Great Lakes and Canada. Clark Publ. Ltd.
382 p.

Hocquart, G. 1906. Letter to the comptroller general
dated Quebec, Oct. 26, 1735. Wis. Hist. Colls.,
“17:230.

Innis, H.A. 1927. The fur trade in Canada. Univ.
Toronto Press. 172 p.

Ives, R.L. 1942. The beaver-meadow complex. Jour.
Geomorphology. 5(3): 191-203.

Johnson, I.A. 1971. The Michigan fur trade. Black
letter press. Grand Rapids. 201 pp.

Klecka, W.R., N.H. Nie and C.H. Hull. 1975. Statistical
package for the social sciences. McGraw-Hill,
N.Y. 278 p.

Laporte, L.F. 1977. Paleoenvironments and Paleoecology.
Am. Sci. 65: 720-728.

Longley, W.H. and J.B. Moyle. 1963. The beaver in
Minnesota. Minn. Dept. of Cons. Pittman-Robertson
Project W-ll-R Tech. Bull. No. 6. 87 p.

Martin, 1978. Keepers of the game: Indian-animal
relationships and the fur trade. Univ. Calif.
Press Berkeley. 226 p.

55

McAlester, A.L. 1968. The history of life. Prentice-
Hall. 329 p.

McManus, J.C. 1972. An economic analysis if Indian
behavior in the North American fur trade. Jour.
Econ. Hist. 32: 36-53.

Morgan, L.H. 1868. The American beaver and his world.
J.B. Lippincott and Co. Philadelphia. 330 pp.

Nixon, M. and R. Ely. 1969. Foods eaten by a beaver
colony in south east Ohio. Ohio Jour. Sci.
69(5): 313-319.

Northcott, T.H. 1972. Water lilies as beaver food.
Oikos 23(3): 408-409. (Copenhagen).

Novak, M. 1977. Determining the average size and
composition of beaver families. Jour. Wildl. Mgt.
41(4): 751—754.

Novakowski, N.S. 1967. The winter bioenergetics of a
beaver population in northern latitudes. Can.
Jour. Zool. 45: 1107-1118.

Oosting, H.J. 1956. The study of plant communities.
Freeman and Company San Francisco. 440 p.

Potter, L.D. 1947. Pollen analysis of postglacial
bogs in Ohio. Ecology. 28: 396-411.

Ray, A.J. 1975. Some conservation schemes of the
Hudson's Bay Company, 1821-1850: An examination of
the problems of resource management in the fur
trade. Jour. Hist. Geog. 1(1): 49-68.

Retzer, J.L. 1955. Physical environment effects on
beavers in the Colorado rockies. Western Assoc.
Game and Fish Commission-Procedings 35: 279-287.

Rutherford, W.H. 1964. The beaver in Colorado, its
biology, ecology, management and economics. Tech.
Publ. 17, Colorado Game, Fish and Parks Dept. 49 pp.

Schorger, A.W. 1965. The beaver in early Wisconsin.
Wis. Acad. Sci. 54: 147-179.

Smith, E. 1950. Effects of water run-off and gradient
on beaver in mountain streams. Unpubl. M.Sc. Thesis
Univ. of Michigan. 34 pp.

 

56

Snedcor, G.W. and W.G. Cochran. 1967. Statistical
methods. Iowa State Univ. Press. 593 p.

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by airplane. Jour. Wildl. Mgt. 12(2): 214.

Veatch, J.O. 1959. Reconstruction of forest cover
based on soil maps. Mich. Quart. Bull. 10(3): 1-11.

Warren, E.R. 1932. The abandonment and reoccupation of
pond sites by beavers. Jour. of Mammal 13(3):
343-346.

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Cree-Ojibwa hunting and trapping practices. Am.
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manuscript. 11 p.

APPENDICES

APPENDIX A

57

Table A1. Vegetation Data for Trapline #1
Percentage Total

Species Abundance Acreage
White Birch 32.18 1171.0
Balsam Fir 15.80 575.0
White Pine 13.37 484.0
Poplar 8.62 313.8
White Spruce 8.26 300.8
Hard Maple 6.32 230.4
Black Spruce 6.26 228.0
White Cedar 3.08 112.2
Red Pine 2.83 103.2
Jack Pine 1.64 60.0
Soft Maple 1.29 47.0
Elm 0.23 8.4

Ash 0.12 4.2

 

 

 

58

Table A2. Vegetation Data for Trapline #2
Percentage Total
Species Abundance Acreage
White Birch 25.14 544.0
Poplar 20.75 659.0
White Pine 9.57 250.8
Black Spruce 8.23 215.6
Hard Maple 7.41 189.0
Balsam Fir 7.02 184.8
Soft Maple 6.63 174.0
White Spruce 5.53 145.8
Red Oak 5.53 145.2
Alder 3.62 95.0
Jack Pine 0.68 16.0

Red Pine 0.06 1.8

59

Table A3. Vegetation Data for Trapline #3
Percentage Total
Species Abundance Acreage
Jack Pine 40.67 1719.0
White Birch 21.89 921.0
Black Spruce 15.64 658.3
Poplar 8.58 361.0
White Pine 5.81 246.6
Mixed Hardwood 2.26 95.0
Balsam Fir 2.05 86.2
Alder 1.21 51.0
Red Pine 0.85 35.6

Red Oak 0.77 32.2

 

60

Table A4. Vegetation Data for Trapline #4
Percentage Total
Species Abundance Acreage
White Birch 35.80 988.0
Poplar 29.04 801.4
Black Spruce 14.16 390.8
Jack Pine 7.66 211.4
Red Pine 3.35 92.8
White Pine 2.49 68.6
Alder 2.36 65.0
Balsam Fir 2.29 63.2
Hard Maple 1.95 54.0
White Spruce 0.72 19.8

Larch 0.18 5.0

61

Table A5. Vegetation Data for Trapline #5
Percentage Total
Species Abundance Acreage
White Birch 25.38 706.4
White Pine 23.92 665.7
White Cedar 16.67 463.8
Balsam Fir 16.39 456.3
Black Spruce 5.17 143.8
White Spruce 4.07 113.3
Red Pine 2.85 79.3
Soft Maple 2.07 57.6
Yellow Birch 2.00 55.7
Poplar 0.76 21.1

Alder 0.72 20.0

 

62

Table A6. Vegetation Data for Trapline #6
Percentage Total
Species Abundance Acreage
White Birch 48.40 1789.4
Poplar 19.43 718.2
Hard Maple 7.41 274.0
Balsam Fir 5.32 196.6
Soft Maple 4.51 166.4
Black Spruce 4.25 157.2
White Pine 3.77 139.6
White Spruce 3.72 137.6
White Cedar 1.56 57.8
Red Oak 0.82 30.2
Alder 0.68 ' 25.0

Ash 0.14 4.8

 

63

Table A7. Vegetation Data for Trapline #7
Percentage Total
Species Abundance Acreage
Poplar 28.13 458.0
White Birch 27.70 451.0
Jack Pine 16.50 268.6
White Spruce 7.29 118.8
Yellow Birch 5.46 88.6
White Pine 5.43 88.4
Hard Maple 3.26 53.2
Balsam Fir 2.90 47.2
Black Spruce 1.71 27.8

Red Pine 1.62 26.4

Table A8.

Species
White Birch
Poplar

White Pine
White Spruce
Jack Pine
Red Pine
Balsam Fir
Soft Maple

Hard Maple

64

23.

9.

Percentage
Abundance

54.50

86

15

.01
.15
.07
.61
.48

.17

Vegetation Data for Trapline #8

Total
Acreage

844.8
369.8
141.8
62.2
48.8
47.6

25.0

Table A9.

Species

Jack Pine
Poplar

White Birch
Red Pine
Black Spruce
White Pine
Soft Maple

Hard Maple

White Spruce

65

Vegetation Data for Trapline #9

Percentage Total
Abundance Acreage
33.69 855.0
30.76 780.8
22.34 566.9
4.09 103.8
3.38 85.8
1.98 50.4
1.78 45.3
1.48 37.2
0.50 12.8

66

Table A10. Vegetation Data for Trapline #10
Percentage Total
Species Abundance Acreage
Black Spruce 26.65 827.9
Balsam Fir 24.15 750.4
White Pine 15.56 483.6
Red Pine 12.19 378.8
White Spruce 7.94 246.8
White Birch 7.73 240.2
White Cedar 2.56 79.4
Alder 1.13 35.0
Mixed Hardwood 0.97 30.0
Soft Maple 0.49 15.2
Poplar 0.44 13.7

Ash 0.19 6.0

67

Table A11. Vegetation Data for Trapline #11
Percentage Total
Species Abundance Acreage
White Birch 37.56 871.1
Poplar 27.83 645.6
Jack Pine 7.33 170.0
Red Pine 5.23 121.2
White Spruce 4.50 104.4
Black Spruce 3.58 83.0
Soft Maple 3.12 72.4
Hard Maple 2.90 67.2
Balsam Fir 2.41 56.0
White Pine 2.17 50.4
Alder 2.16 50.0
Yellow Birch 0.81 18.8

White Cedar 0.41 9.4

68

Table A12. Vegetation Data for Trapline #12
Percentage Total
Species Abundance Acreage
White Birch 31.29 811.2
Poplar 24.17 627.0
Hard Maple 17.33 449.6
White Cedar 7.50 194.6
Yellow Birch 6.92 179.6
White Spruce 5.02 130.2
Black Spruce 3.30 85.8
Balsam Fir 2.55 66.2
White Pine 0.99 25.6

Soft Maple 0.93 24.2

69

Table A13. Vegetation Data for Trapline #13
Percentage Total
Species Abundance Acreage
Poplar 29.28 717.1
Jack Pine 16.46 403.1
White Birch 15.89 389.2
Black Spruce 12.23 299.6
Balsam Fir 9.65 236.3
Alder 6.45 158.0
White Spruce 4.59 112.3
White Pine 2.85 69.8
Larch 0.85 21.6
Red Oak 0.78 19.0
White Cedar 0.74 18.2

Red Pine 0.20 4.8

Table A14. Vegetation Data for Trapline #14
Percentage Total
Species Abundance Acreage
Poplar 26.00 496.0
Balsam Fir 15.97 304.8
White Birch 13.16 251.0
Alder 13.10 250.0
White Spruce 12.69 242.2
Black Spruce 10.50 200.4
Jack Pine 2.80 53.4
Mixed Hardwood 2.63 50.0
White Pine 1.99 38.0
Soft Maple 0.70 13.4
Red Pine 0.46 8.8

70

Table A15.

Species
White Birch
Black Spruce
Hard Maple
Balsam Fir
Soft Maple
Poplar

Alder

White Pine
Yellow Birch
White Cedar

White Spruce

71

Vegetation Data for Trapline #15

Percentage
Abundance

37.21

32

8

.86
.31
.23
.81
.81
.92
.62
.58
.23

.42

Total
Acreage

968.2
854.8
216.2
162.2
125.2
99.2
50.0
42.2
41.2
31.4

11.0

 

72

Table A16. Vegetation Data for Trapline #16
Percentage Total
Species Abundance Acreage
Hard Maple 39.58 1086.8
White Birch 17.07 468.6
Yellow Birch 12.06 331.0
Black Spruce 8.77 240.0
Poplar 7.93 217.8
Mixed Hardwood 5.46 150.0
White Cedar 2.95 81.0
White Spruce 2.60 71.4
White Pine 1.32 36.2
Soft Maple 0.98 26.8
Balsam Fir 0.89 24.4

Hemlock 0.39 10.8

73

Table A17. Vegetation Data for Trapline #17
Percentage Total
Species Abundance Acreage
White Birch 26.62 656.0
Hard Maple 19.27 474.8
Black Spruce 16.60 409.1
White Spruce 8.67 213.5
Poplar 7.13 175.8
Alder 5.07 125.0
Yellow Birch 4.71 116.0
Mixed Hardwood 4.22 104.0
Soft Maple 2.40 59.2
Hemlock 2.06 50.8
Balsam Fir 1.59 39.0
White Spruce 1.18 29.2

White Cedar 0.48 12.0

74

Table A18. Vegetation Data for Trapline #18
Percentage Total
Species Abundance Acreage
White Birch 37.19 1380.2
Poplar 26.40 930.0
Hard Maple 16.61 584.8
Yellow Birch 4.29 151.2
White Spruce 3.94 138.8
Balsam Fir 1.98 69.8
Ironwood 1.30 45.8
Hemlock 1.25 42.8
Black Spruce 1.11 39.2
White Cedar 1.08 38.2
Soft Maple 0.98 34.6
Mixed Hardwood 0.71 25.0
Red Oak 0.68 24.0

White Pine 0.48 16.8

75

Table A19. Vegetation Data for Trapline #19

Percentage Total
Species Abundance Acreage
Hard Maple 39.59 982.6
Soft Maple 16.15 400.4
White Birch 16.08 ' 399.2
Black Spruce 11.27 279.8
Alder 5.03 125.0
Yellow Birch 4.31 107.0
Poplar 4.16 103.2
White Spruce 1.94 48.2

Jack Pine 1.47 36.6

76

Table A20. Vegetation Data for Trapline #20
Percentage Total
Species Abundance Acreage
Hard Maple 53.09 1291.8
Yellow Birch 17.93 436.2
Alder 8.22 200.0
Poplar 7.21 175.4
White Birch 4.19 102.0
Black Spruce 2.33 56.8
White Cedar 1.66 40.4
Red Oak 1.18 28.4
Ironwood 1.17 28.4
Hemlock 1.17 28.4
White Spruce 0.83 20.2
Soft Maple 0.51 12.4

Balsam Fir 0.51 12.4

77

Table A21. Vegetation Data for Trapline #21

Species

Hard Maple
Yellow Birch
Black Spruce
Balsam Fir
Hemlock
White Cedar
White Birch
Alder

Larch

Mixed Hardwood
White Spruce
Red Oak

Black Cherry

Percentage
Abundance

43.

20.

94

12

.89
.61
.80
.53
.32
.96
.96
.97
.50
.73

.67

Total
Acreage

891.2
408.0
160.0
113.8
97.4
71.6
67.4
60.0
60.0
40.0
30.4
14.8

13.6

78

Table A22. Vegetation Data for Trapline #22
Percentage Total
Species Abundance Acreage
Hard Maple 29.66 784.2
Yellow Birch 23.68 626.0
White Cedar 10.98 290.4
Black Spruce 7.74 204.6
Hemlock 6.32 167.0
White Pine 5.08 134.4
Balsam Fir 4.93 130.4
White Spruce 3.94 104.2
White Birch 3.51 92.8
Soft Maple 1.47 38.8
Ironwood 1.36 36.0
Mixed Hardwood 0.76 20.0
American Beech 0.57 15.2

79

Table A23. Vegetation Data for Trapline #23
Percentage Total
Species Abundance Acreage
Poplar 37.45 669.8
Balsam Fir 25.65 458.8
Alder 15.10 270.0
White Birch 14.63 261.6
Soft Maple 2.35 42.0
Mixed Hardwood 1.40 25.0
Black Spruce 1.08 19.4
White Spruce 0.86 15.4
Ash 0.74 13.2

White Cedar 0.74 13.2

80

Table A24. Vegetation Data for Trapline #24
Percentage Total
Species Abundance Acreage
Poplar 37.67 815.2
White Birch 23.97 518.8
Alder 13.86 300.0
Balsam Fir 10.99 237.8
Soft Maple 5.16 111.6
White Spruce 2.85 61.6
Black Spruce 1.30 28.2
White Cedar 1.30 28.2
Yellow Birch 1.01 21.8
Hard Maple 1.01 21.8
Red Pine 0.54 11.6

Ash 0.34 7.4

81

Table A25. Vegetation Data for Trapline #25
Percentage Total
Species Abundance Acreage
Hard Maple 48.92 1465.4
Yellow Birch 20.60 617.2
Hemlock 8.55 256.2
Red Oak 4.93 147.8
Black Cherry 4.56 135.8
Poplar 2.60 78.0
Mixed Hardwood 2.50 75.0
Balsam Fir 2.14 64.0
Ironwood 1.38 41.2
White Birch 1.20 36.0
Elm 0.94 28.2
White Spruce 0.93 28.0

White Cedar 0.75 21.6

Table A26. Vegetation Data for Trapline #26
Percentage Total
Species Abundance Acreage
White Birch 24.73 792.2
Hard Maple 18.88 604.6
Balsam 17.37 556.4
Poplar 13.22 423.4
Alder 9.37 300.0
Mixed Hardwood 5.59 150.0
Soft Maple 5.01 160.6
Basswood 3.18 102.0
Hemlock 0.87 28.0
Red Pine 0.79 25.2
White Spruce 0.74 23.6
White Pine 0.25 8.0

82

83

Table A27. Vegetation Data for Trapline #27
Percentage Total
Species Abundance Acreage
Hard Maple 26.90 1029.6
Yellow Birch 18.71 716.0
Poplar 9.87 377.8
Balsam Fir 8.63 330.6
Soft Maple 7.90 302.4
Basswood 7.20 275.2
Elm 7.20 275.2
White Cedar 4.70 180.0
Hemlock 4.01 153.4
White Birch 1.87 71.6
White Spruce 1.00 38.2
Black Spruce 0.90 34.2
Red Oak 0.89 34.0

Ash 0.22 8.6

 

 

84

Table A28. Vegetation Data for Trapline #28
Percentage Total
Species Abundance Acreage
Hard Maple 42.85 1245.2
Poplar 22.64 644.4
Yellow Birch 14.45 410.4
White Birch 5.33 160.4
Black Cherry 3.82 117.0
White Spruce 3.08 101.8
Balsam 2.47 84.4
Hemlock 2.02 71.6
Elm 1.37 39.0
Black Spruce 0.88 25.0
Red Oak 0.68 19.4

Ash 0.61 17.6

85

Table A29. Vegetation Data for Trapline #29
Percentage Total
Species Abundance Acreage
Hard Maple 36.72 1286.2
Yellow Birch 19.19 671.9
White Birch 10.73 376.6
Hemlock 6.46 225.4
Alder 5.00 175.0
Black Spruce 4.28 149.8
White Cedar 3.38 118.4
Balsam Fir 2.78 97.6
White Spruce 2.68 93.8
Poplar 2.29 80.2
Soft Maple 2.17 76.0
White Pine 2.08 72.8
Red Oak 1.00 35.0
Ironwood 0.95 33.3

Black Cherry 0.29 10.0

86

Table A30. Vegetation Data for Trapline #30
Percentage Total
Species Abundance Acreage
Poplar 27.71 555.0
White Birch 20.27 406.4
White Pine 12.98 260.0
Jack Pine 10.03 200.6
White Spruce 7.12 142.6
Red Pine 4.39 88.0
Black Spruce 4.19 84.0
Balsam Fir 4.11 82.4
Alder 3.25 65.0
Soft Maple 2.60 52.0
White Cedar 1.91 38.2
Ash 1.41 28.8

Table A31.

Species

Poplar

Mixed Hardwood

White Pine
White Birch
Balsam Fir
Hard Maple
Black Spruce
White Spruce
Soft Maple
Alder

Yellow Birch
Hemlock

Red Pine

Jack Pine

87

20.
14.

12.

9

Percentage
Abundance

61
97

09

.48

.57

.16

.39

.92

.19

.49

.59

.86

.84

.84

Vegetation Data for Trapline #31

Total
Acreage

413.0
300.0
242.2
190.0
171.8
163.6
128.4
118.6
104.0
70.0
51.6
17.2
16.8

16.8

 

88

Table A32. Vegetation Data for Trapline #32
Percentage Total
Species Abundance Acreage
Poplar 49.17 1916.3
White Pine 21.44 847.4
Balsam Fir ‘ 6.81 265.4
White Spruce 6.05 235.9
Mixed Hardwood 4.35 185.0
Alder 3.21 125.0
White Birch 2.64 102.8
Jack Pine 1.57 61.4
Soft Maple 1.20 49.0
Ash 1.01 39.6
Red Oak 0.92 36.0
Black Spruce 0.51 20.0

White Cedar 0.34 13.2

 

89

Table A33. Vegetation Data for Trapline #33
Percentage Total
Species Abundance Acreage
Hard Maple 39.06 1301.6
White Birch 13.93 464.4
Yellow Birch 13.45 448.2
Balsam Fir 11.51 383.6
Poplar 9.66 322.0
Hemlock 4.09 136.2
White Spruce 3.43 114.2
White Pine 3.41 113.8
Red Pine 1.46 48.6

90

Table A34. Vegetation Data for Trapline #34
Percentage Total
Species Abundance Acreage
Hard Maple 35.76 1369.4
White Birch 22.46 860.0
Yellow Birch 10.05 385.0
Poplar 9.81 375.6
White Spruce 7.25 277.8
Hemlock 6.98 267.4
White Pine 2.68 102.2
White Cedar 2.03 77.6
Red Pine 0.96 36.6
Black Spruce 0.76 29.2
Mixed Hardwood 0.65 25.0

Red Oak 0.61 23.4

91

Table A35. Vegetation Data for Trapline #35
Percentage Total
Species Abundance Acreage
Hard Maple 28.86 1366.0
White Birch 16.94 801.8
White Pine 12.88 609.4
Yellow Birch 10.15 504.2
White Spruce 9.04 453.8
Black Spruce 5.06 239.6
Hemlock 4.03 190.8
Red Pine 3.36 159.0
Poplar 2.95 139.8
Red Oak 2.53 119.6
Alder 1.06 . 50.0
Balsam Fir 1.06 50.0
White Cedar 1.05 49.8

American Beech 1.03 48.8

92

Table A36. Vegetation Data for Trapline #36
Percentage Total
Species Abundance Acreage
White Pine 25.77 929.4
White Birch 20.75 748.4
Poplar 17.07 615.8
Hard Maple 11.32 408.2
Red Pine 6.00 216.5
Yellow Birch 5.84 211.2
White Spruce 3.62 130.6
Black Spruce 3.13 112.8
Alder 2.22 80.0
White Cedar 1.56 56.4
Hemlock 1.03 37.0
American Beech 1.03 37.0

Soft Maple 0.66 23.8

93

Table A37. Vegetation Data for Trapline #37
Percentage Total
Species Abundance Acreage
White Birch 24.39 1256.0
Poplar 16.03 825.8
Black Spruce 14.03 722.6
White Pine 11.49 591.6
Hard Maple 8.23 424.0
Balsam Fir 7.81 299.2
White Spruce 7.41 381.6
Yellow Birch 6.41 329.8
Red Pine 2.82 145.4
Hemlock 2.03 104.6
Alder 0.97 50.0

Ash 0.38 19.4

94

Table A38. Vegetation Data for Trapline #38
Percentage Total
Species Abundance Acreage
White Birch 35.01 1468.4
Poplar 25.43 1063.6
Hard Maple 12.28 512.0
Mixed Hardwood 6.10 255.0
Balsam Fir 5.56 232.4
Jack Pine 5.46 228.4
Soft Maple 3.76 157.2
Yellow Birch 2.76 113.8
Red Pine 1.31 55.0
Red Oak 1.31 54.8
Ironwood 0.76 31.6

White Spruce 0.26 10.8

95

Table A39. Vegetation Data for Trapline #39
Percentage Total
Species Abundance Acreage
Hard Maple 27.65 1754.0
White Birch 21.71 1376.8
Poplar 17.69 1122.2
White Pine 7.62 483.8
Balsam Fir 7.40 482.0
Red Pine 5.44 345.2
Yellow Birch 4.29 277.0
White Spruce 2.30 145.6
Basswood 2.03 128.8
Black Spruce 1.95 123.8
Hemlock 0.49 30.8
American Beech 0.38 24.2
White Cedar 0.38 24.2

Ash 0.38 24.2

96

Table A40. Vegetation Data for Trapline #40
Percentage Total
Species Abundance Acreage
Hard Maple 27.73 1314.4
White Birch 19.79 937.8
Poplar 11.56 547.8
White Spruce 10.18 482.8
Yellow Birch 9.65 457.2
White Pine 8.11 384.4
Hemlock 7.31 346.2
Balsam Fir 1.80 85.4
Red Oak 1.42 67.2
Red Pine 1.42 67.2

Black Spruce 1.03 48.8

97

Table A41. Vegetation Data for Trapline #41
Percentage Total
Species Abundance Acreage
Hard Maple 37.98 518.0
Poplar 16.94 239.2
White Birch 12.76 180.2
Hemlock 10.34 143.2
Soft Maple 9.38 131.6
Balsam Fir 5.58 57.6
Yellow Birch 2.58 32.2
White Spruce 1.33 18.8
Ash 1.33 18.8
American Beech 0.79 11.2

Ironwood 0.79 11.2

Table A42.

Species
White Pine
Poplar

Hard Maple
White Spruce
Red Pine
White Birch
Red Oak
Mixed Hardwood
Soft Maple
Hemlock
Alder

Yellow Birch
Elm

Balsam Fir

Ash

98

Vegetation Data for Trapline #42

Percentage
Abundance

29.89
23.37
9.33

6.99

1.65

Total
Acreage

719.2
562.4
224.6
168.2
138.6
128.2
86.0
70.0
63.4
63.2
50.0
39.6
39.6
32.0

21.0

99

Table A43. Vegetation Data for Trapline #43
Percentage Total
Species Abundance Acreage
Poplar 30.22 441.8
Hard Maple 25.31 370.0
White Birch 13.80 201.8
Mixed Hardwood 10.94 160.0
Hemlock 4.62 67.6
Ironwood 3.93 57.4
Basswood 3.65 53.4
White Pine 2.64 38.6
Balsam Fir 2.18 31.8
Yellow Birch 1.63 23.8
White Spruce 0.79 11.6

Black Spruce 0.29 4.2

 

100

Table A44. Vegetation Data for Trapline #44
Percentage Total
Species Abundance Acreage
Hard Maple 42.99 729.4
Yellow Birch 19.07 323.6
Hemlock 12.66 214.8
Red Oak 5.20 88.2
Ash 4.77 81.0
Balsam Fir 4.02 68.2
Soft Maple 2.40 40.8
American Beech 2.20 37.4
White Cedar 1.95 33.0
Elm 1.65 28.0
Black Spruce 1.30 22.0
White Spruce 1.05 17.8
Larch 0.66 11.0
Ironwood 0.12 2.0

101

Table A45. Vegetation Data for Trapline #45
Percentage Total
Species Abundance Acreage
Hard Maple 40.37 905.2
Balsam Fir 15.68 351.6
Yellow Birch 8.80 197.2
Poplar 8.65 194.0
Soft Maple 5.75 129.0
American Beech 5.04 112.8
Mixed Hardwood 3.79 85.0
Hemlock 3.40 76.2
White Spruce 2.93 65.8
Black Spruce 1.55 34.8
Ash 1.29 29.0
Red Oak 0.99 22.2
Larch 0.78 17.4
Ironwood 0.68 14.6
Elm 0.32 7.2

 

102

Table A46. Vegetation Data for Trapline #46
Percentage Total
Species Abundance Acreage
Hard Maple 35.29 793.4
Hemlock 18.01 393.6
Yellow Birch 13.07 285.8
Soft Maple 11.88 258.6
Balsam Fir 9.79 203.0
American Beech 6.79 137.4
Red Oak 1.67 36.6
White Birch 0.95 20.8
Elm 0.72 15.8
Ironwood 0.59 13.0
Black Spruce 0.48 10.4
White Spruce 0.42 9.2

Poplar 0.34 7.4

103

Table A47. Vegetation Data for Trapline #47
Percentage 'Total
Species Abundance Acreage
Hard Maple 34.84 705.6
Poplar 21.33 432.0
Balsam Fir 13.06 264.4
Soft Maple 10.44 211.4
American Beech 8.53 172.8
Yellow Birch 4.82 97.6
Ash 2.73 55.2
Mixed Hardwood 1.98 40.0
Hemlock 1.83 37.0

Elm 0.44 9.0

104

Table A48. Vegetation Data for Trapline #48
Percentage Total
Species Abundance Acreage
White Birch 26.75 615.6
Alder 19.77 455.0
Poplar 18.75 431.4
Red Pine 9.52 219.0
White Pine 9.04 208.0
Hard Maple 7.43 171.0
Yellow Birch 4.95 114.0
Balsam Fir 2.48 57.0
Soft Maple 1.04 24.0

Red Oak 0.27 6.0

105

Table A49. Vegetation Data for Trapline #49

4 Percentage Total
Species Abundance Acreage
Hard Maple 39.85 816.6
American Beech 24.09 493.6
Balsam Fir 18.75 384.2
Yellow Birch 10.18 208.6
Soft Maple 1.79 36.6
White Spruce 1.30 26.6
Ash 1.22 25.0
Poplar 1.22 25.0
White Cedar 0.92 18.8
Hemlock 0.40 8.2

Alder 0.28 5.8

 

APPENDIX B

 

106

Table B1. Beaver Density Data
Trapline Total Total Density
Number Lodges Square Miles Lodges/Square Mile
1 53 32.4 1.6
2 50 39.2 1.3
3 73 35.6 2.1
4 57 26.4 2.2
5 52 31.2 1.7
6 39 42.4 0.9
7 18 76.8 0.2
8 60 72.8 0.8
9 70 84.4 0.8
10 46 45.6 1.0
11 44 61.6 0.7
12 41 94.8 0.4
13 145 47.2 3.1
14 91 25.6 3.6
15 68 41.2 1.7
16 17 20.0 0.8
17 13 12.4 1.1
18 48 71.2 0.7
19 33 42.0 0.8
20 16 33.6 0.5
21 44 58.0 0.8
22 40 85.6 0.5
23 39 34.0 1.2
24 8 14.8 0.5
25 77 45.2 1.7
26 116 73.6 1.6
27 78 40.0 2.0
28 70 40.0 1.8
29 66 56.0 1.2
30 280 132.8 2.1
31 24 30.4 0.8
32 138 40.8 3.4
33 49 94.4 0.5
34 21 32.8 0.6
35 17 32.8 0.5
36 42 53.6 0.8
37 12 26.4 0.5
38 77 22.4 3.4
39 26 16.4 1.6
40 20 19.2 1.0
41 24 12.0 2.0
42 56 14.0 4.0
43 44 14.4 3.1
44 83 40.0 2.1
45 96 36.4 2.6
46 58 28.0 2.1
47 62 28.0 2.2
48 22 14.8 1.5
49 28 11.6 2.4

 

APPENDIX C

107

Table C1. Vegetation Stand Data
Trapline Number Total Mean Stand
Number of Stands Acreage Acreage Age
1 22 3638.0 163.4 57.2
2 18 2621.0 145.6 55.8
3 22 4210.0 191.4 51.3
4 19 2760.0 145.3 88.5
5 18 2783.0 154.6 83.3
6 25 3697.0 147.8 .53.0
7 14 1628.0 116.3 55.1
8 18 1550.0 86.1 53.0
9 18 2538.0 141.0 50.4
10 19 3107.0 163.5 67.4
11 15 2319.4 154.6 58.4
12 16 2594.0 162.2 76.3
13 15 2449.0 163.3 58.5
14 15 1908.0 127.2 45.1
15 14 2601.6 185.7 62.0
16 17 2745.6 161.5 81.0
17 15 2464.4 164.3 83.3
18 15 3521.8 234.7 58.8
19 14 2482.0 177.3 65.5
20 14 2432 8 173.7 75.4
21 15 2028.2 135.2 83.8
22 23 2644.0 114.9 109.2
23 14 1788.4 127.7 35.0
24 14 2164.0 154.6 36.2
25 14 2995.4 213.9 79.4
26 15 3203.0 213.5 39.8
27 13 3827.4 294.4 52.9
28 14 2839.6 202.8 46.0
29 25 3502.0 140.8 88.6
30 17 2003.0 117.8 52.9
31 11 2004.0 182.1 76.6
32 20 3897.0 194.9 50.4
33 13 3326.6 256.4 71.3
34 13 3829 2 294.6 69.6
35 14 4732.6 338.0 90.6
36 15 3607.1 240.5 78.1
37 15 5150.0 343.3 64.2
38 27 4183.0 154.9 60.1
39 12 6342.6 528.5 70.1
40 14 4739.2 338.5 66.7
41 11 1412.0 128.4 53.6
42 21 2406.0 114.6 75.7
43 19 1462.0 76.9 49.2
44 20 1715.2 84.8 90.7
45 19 2242.0 118.0 84.4
46 12 2186.0 182.1 93.6
47 18 2025.0 112.5 88.0
48 16 2301.0 143.8 36.4
49 13 2049.0 157.6 115.0

   

MCIHJWEIMMITMIEMIIMI#13115