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thesis entitled

Small Mammal, Reptile, Amphibian, and Bat Species

Associations With Habitat Type Classes and
Successional Stages in West Central Idaho

presented by

Brian E. Knapp

has been accepted towards fulfillment
of the requirements for

Master of Science degree in Fish. & Wildl.

 

 

   

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SMALL MAMMAL, REPTILE, AMPHIBIAN, AND BAT SPECIES ASSOCIATIONS
WITH HABITAT TYPE CLASSES AND SUCCESSIONAL STAGES IN WEST
CENTRAL IDAHO
By

Brian E. Knapp

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

MASTER OF SCIENCE

Department of Fisheries and Wildlife

1997

ABSTRACT
SMALL MAMMAL, REPTILE, AMPHIBIAN, AND BAT SPECIES ASSOCIATIONS
WITH HABITAT TYPE CLASSES AND SUCCESSIONAL STAGES IN WEST
CENTRAL IDAHO
By
Brian E. Knapp

This study was directed at identifying species associations of small mammal,
herptile, and bat Species with 3 habitat type classes (cool-moist grand fir, dry grand fir,
and dry Douglas-fir), and 2 successional stages (medium and small tree of the dry
Douglas-fir habitat type class) that were part of an ecosystem diversity matrix (EDM).
This was done in support of an ecosystem management project, conducted in west central
Idaho.

The red-backed vole (Clethrionomys gapperi), masked shrew (Sorex cinereus),
dwarf shrew (Sorex nanus), northern pocket gopher (Thomomys talpoides), and deer
mouse (Peromyscus maniculatus) were associated with the cool-moist grand fir habitat
type class. The deer mouse and red-backed vole were associated with the medium tree,
and the long-toed salamander (Ambystoma macrodactylum) with the small tree
successional stage of the dry Douglas-fir habitat type class. The relationship of site and
vegetation characteristics with individual species were also identified. It was concluded

that there is support for the EDM’s capability to represent the diversity of species and

their needs on the planning landscape.

ACKNOWLEDGMENTS

This thesis was not only the product of my own efforts. I would like to
acknowledge and express my appreciation to those organizations and people without
whom this thesis would never have been possible. I would especially like to thank my
major professor, Dr. Jonathan Haufler, for the opportunity to do this project, and for all
his time, guidance, and support. I would also like to thank the members of my
committee, Dr. Rique Campa, Dr. Carl Ramm, and Dr. Donald Straney, for their time,
insights, and assistance in producing this thesis.

Boise Cascade Corporation provided the means which made this project possible.
I would like to thank several employees of BC who provided invaluable assistance in the
completion of the project. I would especially like to thank Brad Holt, Carolyn Mehl, and
Steve Warren for all their time and help. I would also like to thank the foresters and
secretaries at the Cascade Office for providing much assistance, and for making Cascade a
great place to work. I would also like to thank Michigan State University for providing
me with a place to work and the opportunity for an education.

The interns that worked for me, Chris Hoving and Grant Goltz worked long hard
days for extended periods without days off, and without complaint. Their effort was
instrumental in Obtaining the data for this study, and their company was greatly

appreciated. I would also like to thank my fellow graduate students for thought provoking

discussions, their support, and for making MSU a nice place to have spent several years.
I would especially like to thank my office mates, Christine Hanaburgh and Delia Raymer
for their friendship, support, and insights. I would also especially like to thank my
hunting partners, Peter Fritzell and Bob Holsman. Life in graduate school would have
been hell without the opportunity to occasionally spend some time afield in the company
of great friends. Finally, and above all other people, I would like to thank my parents,
without whose support, guidance, and teaching, none of my accomplishments in life

would be possible.

iv

TABLE OF CONTENTS

LIST OF TABLES ....................................................................................... vii

LIST OF FIGURES ..................................................................................... ix
INTRODUCTION ......................................................................................... l
OBJECTIVES ................................................................................................ 5
STUDY AREA .............................................................................................. 6

METHODS .................................................................................................... 11

Experimental Design .......................................................................... ll

Trapping ............................................................................................. 12

Bat Detection ......................................................................................... l4

Vegetation Sampling ............................................................................. 14

Data Analysis ......................................................................................... 18

Small Mammal, and Herptile .................................................... 18

Bat .......................................................................................... l9

Vegetation .............................................................................. 20

RESULTS ........................................................................................................... 22

Small Mammals ..................................................................................... 22

Habitat Type Classes ................................................................. 22

Vegetative Growth Stages ......................................................... 35

Herptiles .................................................................................................. 39

Habitat Type Classes ................................................................. 39

Vegetative Growth Stages ......................................................... 42

Bats ......................................................................................................... 42

Vegetation ........................................................................................... 45

Habitat Type Classes, and Vegetative growth stages ............. 45

Mammal Species, and Habitat Characteristics ....................... 47

Herptile Species and Habitat Characteristics ......................... 51

DISCUSSION ................................................................................................. 54

Analysis .............................................................................................. 54

Habitat Type Classes, and Vegetative Growth Stages ........................ 56

Mammal Species ................................................................................. 56

Herptiles .............................................................................................. 62

Bats ...................................................................................................... 64

Ecosystem Diversity Matrix ................................................................ 64

CONCLUSIONS ............................................................................................ 67

LITERATURE CITED ................................................................................... 69
APPENDIX A ................................................................................................. 74
APPENDIX B ................................................................................................. 88
Relative Abundance Group Delineation ............................................. 88
Results
Small Mammals and Habitat Variables .................................. 97
Herptiles and Habitat Variables .............................................. 99
Discussion ........................................................................................... 100
Small Mammals and Habitat Variables ................................... 100
Herptiles and Habitat Variables ............................................... 105

vi

LIST OF TABLES

Table l. Stands used in Valley County Idaho in 1995 and 1996, listed by stand ID, habitat
type class, and vegetative growth stage (medium tree, or small tree). Mean basal
area (standard error) of large trees (> 6in) (in square meters/ha), large tree density
(standard error) (in trees/ha), and percent canopy closure (standard error) are also
given for individual stands ..................................................................................... 14

Table 2. Means (standard errors) and test results of vegetation and site variables by
habitat type classes, in Valley County Idaho, in 1995 and 1996 ........................... 58

Table 3. Means (standard errors) and test results of vegetation and site variables by
VGS’S, in Valley County, Idaho, in 1995 and 1996 .............................................. 60

Table 4. Results of tests for significant differences in habitat variables between relative
abundance groups of the yellow-pine chipmunk, deer mouse, red-backed vole,
dusky shrew, and masked shrew, in Valley County, Idaho, in 1995 and 1996 ..... 63

Table 5. Results of tests for significant differences in habitat variables between relative
abundance groups of the vagrant shrew, golden-mantled ground squirrel, pocket
gopher, meadow vole, and dwarf shrew in Valley County, Idaho, in 1995 and
1996 ........................................................................................................................ 65

Table 6. Results Of tests for Significant differences in habitat variables between relative
abundance groups of the long-toed salamander and western toad, in Valley
County, Idaho, in 1995 and 1996 ........................................................................... 67

Appendix A Table 1. Species encountered, and number caught in each field season ....... 94

Appendix A Table 2. Number of yellow-pine chipmunks, Eutamias amoenus, captured,
organized by habitat type class, successional stage, and stand ID ......................... 95

Appendix A Table 3. Number of deer mice, Peromyscus maniculatus, captured, organized
by habitat type class, successional stage, and stand ID ......................................... 96

vii

Appendix A Table 4. Number of northern red-backed voles, Clethrionamys gapperi,
captured, organized by habitat type class, successional stage, and stand
ID ........................................................................................................................... 97

Appendix A Table 5. Number of dusky shrews, Sorex monticolus, captured, organized by
habitat type class, successional stage, and stand ID .............................................. 98

Appendix A Table 6. Number of masked shrews, Sorex cinereus, captured, organized by
habitat type class, successional stage, and stand ID .............................................. 99

Appendix A Table 7. Number of vagrant shrews, Sorex vagrans, captured, organized by
habitat type class, successional stage, and stand ID ............................................ 100

Appendix A Table 8. Number of dwarf shrews, Sorex nanus, captured, organized by
habitat type class, successional stage, and stand ID ............................................ 101

Appendix A Table 9. Number of golden-mantled ground squirrels, Spermophilus
lateralis, captured, organized by habitat type class, successional stage, and stand
ID ......................................................................................................................... 102

Appendix A Table 10. Number of northern pocket gophers, Thomomys talpoides,
captured, organized by habitat type class, successional stage, and stand
ID ......................................................................................................................... 103

Appendix A Table 11. Number of meadow voles, Microtus pennsylvanicus, captured,
organized by habitat type class, successional stage, and stand ID ....................... 104

Appendix A Table 12. Number of long-toed salamanders, Ambystoma macrodactylum,
captured, organized by habitat type class, successional stage, and stand
ID ......................................................................................................................... 105

Appendix A Table 13. Number of western toads, Bufo boreas, captured, organized by
habitat type class, successional stage, and stand ID ............................................ 106

Appendix A Table 14. Results of tests for differences in small mammal species relative
abundance between dry Douglas fir vegetative growth stages by trapping
session .................................................................................................................. 107

Appendix A Table 15. Results of tests for differences in herptile species relative

abundance between dry Douglas fir vegetative growth stages by trapping
session .................................................................................................................. 107

viii

LIST OF FIGURES

Figure 1. Schematic of trap setup within stands on study area in Valley County, Idaho in
1995 and 1996. In the Figure, circles represent pitfall traps, squares are funnel
traps, heavy lines are drifi fences, and X’s are Sherman traps. Sherman traps are
spaced 10m apart, within and between rows. Note that these dimensions are not
depicted accurately ................................................................................................. 18

Figure 2. Schematic of vegetation plot structure used to quantify vegetation attributes in
Valley County, Idaho during 1995 and 1996. The plot consists of a 1/50th acre
(0.0081ha) circular plot nested within a l/5th acre (0.081ha) circular plot, with
three 50ft. (15.24m) transects, at right angles, around the perimeter ..................... 21

Figure 3. Yellow-pine chipmunk mean relative abundance in habitat type classes, expressed
as mean total capture Of individuals by trap session, in Valley County Idaho in 1995
and 1996. No significant differences (P < 0.10) were found by the Kruskal-Wallis
test (Ott 1993). Trapping sessions (Trap 1-4) were each 5 days in length. CM grand
refers to the cool moist grand fir habitat type class, dry grand to the dry grand fir
habitat type class, and dry Doug to the dry Douglas-fir habitat type class ............... 32

Figure 4. Deer mouse mean relative abundance, expressed as mean total capture Of
individuals in habitat type classes, by trap session, in Valley County Idaho in 1995
and 1996. Trapping sessions (Trap 1-4) were each 5 days in length. CM grand
refers to the cool moist grand fir habitat type class, dry grand to the dry grand fir
habitat type class, and dry Doug to the dry Douglas-fir habitat type class. Trap
sessions with letters above the columns differed significantly (P < 0.10) by the
Kruskal-Wallis test (Miller 1985, Ott 1993, Toothaker 1991). Dissimilar letters on
columns within trapping sessions indicate significant differences (P < 0.10) (joint
ranks multiple comparison test) (Miller 1985, Toothaker 1991) ............................... 34

i)

Figure 5. Red-backed vole mean relative abundance, expressed as mean total capture of
individuals in habitat type classes, by trap session, in Valley County Idaho in 1995
and 1996. Trapping sessions (Trap 1-4) were each 5 days in length. CM grand
refers to the cool moist grand fir habitat type class, dry grand to the dry grand fir
habitat type class, and dry Doug to the dry Douglas-fir habitat type class. Trap
sessions with letters above the columns differed significantly (P < 0.10) by the
Kruskal-Wallis test (Miller 1985, Ott 1993, Toothaker 1991). Dissimilar letters on
columns within trapping sessions indicate significant differences (P < 0.10) (joint
ranks multiple comparison test) (Miller 1985, Toothaker 1991) ............................... 35

Figure 6. Dusky shrew mean relative abundance in habitat type classes, expressed as mean
total capture of individuals by trap session, in Valley County Idaho in 1995 and
1996. No significant differences (P < 0.10) were found by the Kruskal-Wallis test
(Ott 1993). Trapping sessions (Trap 1-4) were each 5 days in length. CM grand
refers to the cool moist grand fir habitat type class, dry grand to the dry grand fir
habitat type class, and dry Doug to the dry Douglas-fir habitat type class ............... 37

Figure 7. Masked shrew mean relative abundance, expressed as mean total capture of
individuals in habitat type classes, by trap session, in Valley County Idaho in 1995
and 1996. Trapping sessions (Trap 1-4) were each 5 days in length. CM grand
refers to the cool moist grand fir habitat type class, dry grand to the dry grand fir
habitat type class, and dry Doug to the dry Douglas-fir habitat type class. Trap
sessions with letters above the columns differed significantly (P < 0.10) by the
Kruskal-Wallis test (Miller 1985, Ott 1993, Toothaker 1991). Dissimilar letters on
columns within trapping sessions indicate significant differences (P < 0.10) (joint
ranks multiple comparison test) (Miller 1985, Toothaker 1991) ............................... 39

Figure 8. Vagrant shrew mean relative abundance in habitat type classes, expressed as mean
total capture of individuals by trap session, in Valley County Idaho in 1995 and
1996. No significant differences (P < O. 10) were found by the Kruskal-Wallis test
(Ott 1993). Trapping sessions (Trap 1-4) were each 5 days in length. CM grand
refers to the cool moist grand fir habitat type class, dry grand to the dry grand fir
habitat type class, and dry Doug to the dry Douglas-fir habitat type class ............... 41

Figure 9. Dwarf shrew mean relative abundance, expressed as mean total capture of
individuals in habitat type classes, by trap session, in Valley County Idaho in 1995
and 1996. Trapping sessions (Trap 1-4) were each 5 days in length. CM grand
refers to the cool moist grand fir habitat type class, dry grand to the dry grand fir
habitat type class, and dry Doug to the dry Douglas-fir habitat type class. Trap
sessions with letters above the columns differed significantly (P < 0.10) by the
Kruskal-Wallis test (Miller 1985, Ott 1993, Toothaker 1991). Dissimilar letters on
columns within trapping sessions indicate significant differences (P < 0.10) (joint
ranks multiple comparison test) (Miller 1985, Toothaker 1991) ............................... 43

Figure 10. Golden-mantled ground squirrel mean relative abundance in habitat type classes,
expressed as mean total capture of individuals by trap session, in Valley County
Idaho in 1995 and 1996. No significant differences (P < 0.10) were found by the
Kruskal-Wallis test (Ott 1993). Trapping sessions (Trap 1-4) were each 5 days in
length. CM grand refers to the cool moist grand fir habitat type class, dry grand to
the dry grand fir habitat type class, and dry Doug to the dry Douglas-fir habitat type
class ............................................................................................................................. 45

Figure l 1. Pocket gopher mean relative abundance, expressed as mean total capture of
individuals in habitat type classes, by trap session, in Valley County Idaho in 1995
and 1996. Trapping sessions (Trap 1-4) were each 5 days in length. CM grand
refers to the cool moist grand fir habitat type class, dry grand to the dry grand fir
habitat type class, and dry Doug to the dry Douglas-fir habitat type class. Trap
sessions with letters above the columns differed significantly (P < 0.10) by the
Kruskal-Wallis test (Miller 1985, Ott 1993, Toothaker 1991). Dissimilar letters on
columns within trapping sessions indicate significant differences (P < 0.10) (joint
ranks multiple comparison test) (Miller 1985, Toothaker 1991) ............................... 46

Figure 12. Meadow vole mean relative abundance in habitat type classes, expressed as mean
total capture of individuals by trap session, in Valley County Idaho in 1995 and
1996. No significant differences (P < 0.10) were found by the Kruskal-Wallis test
(Ott 1993). Trapping sessions (Trap 1-4) were each 5 days in length. CM grand
refers to the cool moist grand fir habitat type class, dry grand to the dry grand fir
habitat type class, and dry Doug to the dry Douglas-fir habitat type class ............... 48

Figure 13. Deer mouse mean relative abundance, expressed as mean total capture of
individuals in dry Douglas-fir vegetative growth stages (V GS), by trap session, in
Valley County Idaho in 1995 and 1996. Trapping sessions (Trap 1-4) were each 5
days in length. Trap sessions with letters above the columns differed significantly
(P < 0.10) by the Kruskal-Wallis test (Miller 1985, Ott 1993, Toothaker 1991).
Dissimilar letters on columns within trapping sessions indicate significant
differences (P < 0.10) (joint ranks multiple comparison test) (Miller 1985,
Toothaker 1991) .......................................................................................................... 49

Figure 14. Red-backed vole mean relative abundance, expressed as mean total capture of
individuals in dry Douglas-fir vegetative growth stages (V GS), by trap session, in
Valley County Idaho in 1995 and 1996. Trapping sessions (Trap 1-4) were each 5
days in length. Trap sessions with letters above the columns differed significantly
(P < 0.10) by the Kruskal-Wallis test (Miller 1985, Ott 1993, Toothaker 1991).
Dissimilar letters on columns within trapping sessions indicate significant
differences (P < 0.10) (joint ranks multiple comparison test) (Miller 1985,
Toothaker 1991) .......................................................................................................... 51

Figure 15. Long-toed salamander mean relative abundance in habitat type classes, expressed
as mean total capture of individuals by trap session, in Valley County Idaho in 1995
and 1996. No significant differences (P < 0.10) were found by the Kruskal-Wallis
test (Ott 1993). Trapping sessions (Trap 1-4) were each 5 days in length. CM grand
refers to the cool moist grand fir habitat type class, dry grand to the dry grand fir
habitat type class, and dry Doug to the dry Douglas-fir habitat type class ............... 53

Figure 16. Western toad mean relative abundance in habitat type classes, expressed as mean
total capture of individuals by trap session, in Valley County Idaho in 1995 and
1996. No significant differences (P < 0.10) were found by the Kruskal-Wallis test
(Ott 1993). Trapping sessions (Trap 1-4) were each 5 days in length. CM grand
refers to the cool moist grand fir habitat type class, dry grand to the dry grand fir
habitat type class, and dry Doug to the dry Douglas-fir habitat type class ............... 54

Figure 17. Long-toed salamander mean relative abundance, expressed as mean total capture
of individuals in dry Douglas-fir vegetative growth stages (V GS), by trap session, in
Valley County Idaho in 1995 and 1996. Trapping sessions (Trap 1-4) were each 5
days in length. Trap sessions with letters above the columns differed significantly
(P < 0.10) by the Kruskal-Wallis test (Miller 1985, Ott 1993, Toothaker 1991).
Dissimilar letters on columns within trapping sessions indicate significant
differences (P < 0.10) (joint ranks multiple comparison test) (Miller 1985,
Toothaker 1991) .......................................................................................................... 56

Appendix B Figure 1. Deer mouse relative abundance groups, based on the mean of
relative abundance in each stand during the 4 trapping sessions in Valley County
Idaho during 1995, and 1996. Arbitrary stand identification numbers were
assigned in ascending order of mean relative abundance .................................... 109

Appendix B Figure 2. Northern Pocket Gopher relative abundance groups, based on the
mean of relative abundance in each stand during the 4 trapping sessions in Valley
County Idaho during 1995, and 1996. Arbitrary stand identification numbers
were assigned in ascending order of mean relative abundance ............................ 11 1

Appendix B Figure 3. Masked shrew relative abundance groups, based on the mean of
relative abundance in each stand during the 4 trapping sessions in Valley County
Idaho during 1995, and 1996. Arbitrary stand identification numbers were
assigned in ascending order of mean relative abundance .................................... 112

Appendix B Figure 4. Red—backed vole relative abundance groups, based on the mean of
relative abundance in each stand during the 4 trapping sessions in Valley County
Idaho during 1995, and 1996. Arbitrary stand identification numbers were
assigned in ascending order of mean relative abundance .................................... 113

xii

Appendix B Figure 5. Yellow-pine chipmunk relative abundance groups, based on the
mean of relative abundance in each stand during the 4 trapping sessions in Valley
County Idaho during 1995, and 1996. Arbitrary stand identification numbers
were assigned in ascending order of mean relative abundance ............................ 114

xiii

INTRODUCTION

The concept of ecosystem-based management lst appeared in the literature in the
early 1930’s and 40’s, in reports produced by committees of the Ecological Society of
America (Shelford 1933), reports on fauna of the national parks (Wright and Thompson
1935), and in the writings of other early ecologists (Grumbine 1994). Leopold (1949)
also presented ecosystem-based ideas in “Sand County Almanac”. However, Grumbine
(1994) stated that the focus of our current attention on ecosystem management is
generally credited to Frank and John Craighead, who wrote (Craighead 1979) of
Yellowstone National Park being inadequate in size because the park did not contain the
complete ecosystem necessary to support the system’s top predator, the grizzly bear
(Ursus arctos) (Grumbine 1994).

The ever increasing human population has created demands for natural resources
and space, and has reduced available habitat for many species. The limitations of these
resources, the state of flux in the human perception of our relationship with the
environment, and the intensification of both endangered species and biodiversity issues
have created a environmental situation, and sociO-political climate, that advocates the
application of ecosystem management concepts (Roloff and Haufler 1993, Grumbine
1994). Some authors (Franklin 1993, Rohlf 1991) have argued that the current single

species management approach, and the Endangered Species Act (BSA), especially for

smaller little known or unknown species, are incapable of protecting the biodiversity of
ecosystems. This is not intended to mean that the ESA or single species management
should be eliminated, but rather that ecosystem management should be used to improve
on the protection of biodiversity (Franklin 1993). The increasing intensity of the
endangered species and biodiversity issues led President Clinton, in 1993, to order the
USPS and BLM to adopt an ecosystem-based management approach (Grumbine 1994,
Thomas and Dombeck 1996). Many agencies and private organizations are now trying to
use ecosystem management approaches (Haufler et a1. 1996).

Ecosystem management is characterized by several components. It is an
ecologically-based land management approach. Ecosystem management attempts to
maintain the diversity, structure, and functions of ecosystems across a given area over
time. At the same time, ecosystem management attempts to provide for the achievement
of multiple use objectives, including ecological, economic, and social (Grumbine 1994,
Haufler et a1. 1996). The maintenance of proper ecosystem structure and functions is
dependent upon maintaining diversity at the ecosystem, community, species, and genetic
levels (Mefi‘e and Carroll 1994). Landscapes are comprised of hierarchies of successively
smaller ecosystems, groups of smaller ecosystems composing larger ecosystems, which
are distributed as a dynamically shifting mosaic across that landscape. The dynamic
nature of these ecosystems is due to the progress of succession, and setbacks in
succession, caused by both human and naturally induced disturbances (Haufler 1990).
One of the goals of ecosystem management is to understand the distribution of

ecosystems across a landscape, and their role in maintaining the biodiversity (Haufler

pers. comm.) Given this understanding, it is believed that by producing the ecosystem
conditions of natural disturbances, ecosystem managers can provide “adequate” quantities
and appropriate patch sizes and distributions of ecosystems across the landscape, thus
maintaining the diversity, structure, and functions of the planning landscape (Haufler
1994)

A tool which can be used to gain an understanding of the distribution of
ecosystems across a landscape, and their role in contributing to the larger landscape, is
the ecosystem diversity matrix (EDM). The EDM is comprised of ecological land units
(ELU’s), which are defined by a combination of their habitat type and successional stage
(Haufler 1994). Daubenmire (1968) defined a habitat type as the land capable of
producing an ecologically equivalent plant community at climax (Steele et a1. 1981). The
potential community at climax gives an indication of a site’s potential productivity, and
its ecological functions at climax. Different habitat types have their own successional
pathways (Anderson 1985). Each stage of succession along a given pathway has different
plant and animal community compositions, and distinct ecological functions. By
combining habitat types and successional stages, ELU’s can be used to represent
ecologically distinct components of a landscape ecosystem. Using ELU’s as a basis for
analysis, the ecosystem diversity matrix provides a tool to track quantities and
distributions of ecosystems, and their components on the landscape. The EDM is
basically a matrix with habitat type classes represented in columns and successional
stages in rows. Each cell in the matrix contains the acreage of the planning landscape

that is in each habitat type and successional stage combination (ELU) (Haufler 1994).

Wildlife species have been shown to respond to changes in succession (Anderson
1985, Hunter 1990, Yahner 1995). Huff et al. (1993) found stand age to be the best
indicator of bat activity. In addition, Krusic et al. (1996) found within stand bat activity
to be greatest in overmature hardwood stands (>119 yrs.) and within regenerating stands
(0-9 yrs) of both hardwoods and conifers of the White Mountain National Forest, New
Hampshire and Maine. Krusic et al. (1996) also found stand age to be the best
determinant of bat activity within stands. Succession influences the activity of highly
mobile species within stands, and the composition of less mobile species inhabiting
stands. A greater diversity of invertebrates, with very different life histories, have been
found in old growth Douglas-fir (Pseudotsuga menzr'esii) forest canopies than in the
canopies of nearby 2nd-growth stands (Franklin 1993, Schowalter 1989). Thomas and
Verner (1986) identified successional stage as important in determining the availability of
conditions and resources required by many species. For example, tree diameter, snag
diameter, and the size of downed woody materials is dependent upon the size of the trees
in the initial stand, which is often dependent upon successional stage.

Various species of wildlife have shown different responses to general habitat
characteristics, which may be important in determining their presence or abundance
(Brown 1967, DeGraaf et al. 1991, Perkins 1976, Williams and Braun 1983). Patterson et
al. (1990) found that small mammal species differed significantly in the use of 12 of their
24 habitat variables, in ways that were generally consistent with the species morphology,
diet, and behavior. DeGraaf et a1. (1991) concluded that small mammals are tied into the

moisture levels of a given site. Higher degrees of patchiness and vegetation density at the

ground herb and shrub layers were also shown to be related to the richness of the rodent
fauna in forest ecosystems (DeGraaf et a1. 1991). In addition, Tummlison et al. (1990)
found that at a coarse scale, substrate size was an important indicator of salamander
presence. Krusic et al. (1996) found differences in bat activity between forest types in
White Mountain National Forest, New Hampshire and Maine. However, wildlife
responses to habitat types, as defined by Daubenmire (1968), and ELU’S have not been
investigated.

Since the EDM is a matrix of habitat type class and successional stage
combinations, it provides an excellent tool to investigate the factors influencing wildlife
species distributions across the landscape. By identifying species associations with
different ELU’s, the EDM can be used to gain further understanding of the distribution of
wildlife species across the landscape (Haufler pers. com.). A species is considered to
be associated with an ELU if it is relatively more abundant on that ELU than on other
ELU’s, as compared to a population that is distributed randomly among ELU’s. In
addition, the identification of species associations with ELU’s will aid in the
determination of what is adequate representation of ELU’S across the planning landscape.
The use of the EDM and knowledge of species associations with ELU’s, in a geographic
information system (GIS) will further allow resource managers to predict the results of
both natural and human induced disturbances across and within the landscape on faunal
communities. Knowledge of habitat associations can also provide managers with
information about how best to manage ecological land units to enhance the habitat for

species of interest. According to Roloff and Haufler (1993), these capabilities become

increasingly important to resource managers as habitat fragmentation, endangered
species, and biodiversity issues become more prominent.

Small mammals, reptiles, and amphibians are important members of ecosystems.
Through their behaviors and dietary habits, they have a large impact on the plant, animal,
and insect communities of ecosystems. They provide a food source for many of the
predator species within ecosystems. Through their dietary habits they influence insect
populations and impact the regeneration and density of plant species. Small mammals
and herptiles create macropores in the soil, and aid in keeping soil aerated. Due to their
relative immobility, and inability to acquire their life requisites from outside a given area,
small mammals and herptiles make excellent indicators of habitat quality and ecosystem
health (Szaro 1988). Bats, though more mobile, also have major impacts on insect, and
therefore plant and animal communities (Wackenhut and McGraw 1996). As important
members of ecosystems, it is necessary to consider small mammals, reptiles, amphibians,
and bats in management decisions. The ecosystem management approach, by
considering species associations with ELU’s, and providing for the full range Of habitat
requirements of all species, could prove useful in accounting for small mammals, reptiles,

amphibians, and bats when managing landscapes.

OBJECTIVES

This investigation was initiated in June of 1994. The study has several objectives
relevant to ecosystem management. The primary objective of the study was to identify
species associations with selected habitat type classes, and secondarily with selected
successional stages within one habitat type class. The 2nd objective of the study was to
document the relative abundance of individual species in the Idaho planning landscape.
The 3rd objective was to identify site and stand characteristics, such as vegetation
variables, that may influence individual species’ relative abundances, and that may aid in

predicting the animal species composition at different sites.

STUDY AREA

The study area was situated in the Donnelly and Cascade area of Valley County,
Idaho, (T15-17N; R2E), on the southern lobe of the Idaho Batholith. The area is primarily
drained, to the west and then south, by the North fork of the Fayette River, and by the Gold
Fork River. Most of the area is underlain by granitic rock of the Idaho Batholith. Soils
from the granitic parent material are usually moderately coarse to coarser in texture
throughout their profiles. Most soils are deep except on extremely steep slopes, ridges, and
headlands. The area has a primarily mountainous relief ranging from 1,520m-2,740m
(5,000—9,000ft) (Steele et al. 1981).

Average annual precipitation is 81.5cm (31.2in), varying widely with the season.
July and August precipitation averages about 1.3cm (0.5in), with a wet season occurring
between November and March. Winter snows comprise 55-60% Of the annual
precipitation. Slopes below 1370m (4500ft) are usually bare in winter. Average annual air
temperatures vary between 8°C and —6°C (46°F and -22°F), with a mean around 2°C (36°F)
(Steele et al. 1981).

Boise Cascade lands in the Donnelly and Cascade area contain 3 primary habitat
type classes, the cool-moist grand fir, dry grand fir, and dry Douglas-fir habitat type classes.

Each of the 3 predominant habitat type classes includes several habitat types that are very

sirnilar in structure and ecological fimctions. The similar habitat types, which comprise the
habitat type classes, are listed in the descriptions of the habitat type classes that follow in
this paper. The 3 habitat type classes listed above are arranged in order from most to least
productive (Steele et al. 1981). Within each habitat type class, there are two predominant
age-based successional stages on Boise Cascade lands. These are being defined as small
tree (preponderance of trees 12.7-30.5cm, 5.0—12.0in dbh), and medium tree (preponderance
of trees 30.5-50.8cm, 12.0-20.0in dbh) successional stages.

The cool moist grand fir habitat type class usually occurs at elevations between
1,160 and 1,950m (3,800-6,400ft). It is usually on the mid to lower slopes, with a northerly
aspect. It tends to occur on clay loam to sandy loam soils, from granitic or occasionally
basalt or andesite parent materials, with an average pH of around 6.1. The early seral
dominants are ponderosa pine (Pinus ponderosa) on warmer sites, and western larch (Larix
occidentalis) and lodgepole pine (Pinus contorta) on cooler sites. Douglas-fir (Pseudotsuga
menziesii) dominates later seral stages, and grand fir (A bies grandis) dominates sites of this
group at climax. The shrub layers on cool moist grand fir sites are usually composed of
huckleberry (Vaccinium Sp.) with ninebark (Physocarpus malvaceus) well represented on
warmer dryer sites, and rocky mountain maple (A cer glabrum) well represented on cooler
moist sites. On sites where ninebark is present, snowberry (Symphoricarpos albus) and
white spirea (Spirea betulifolia) may be present, with a layer of pine grass (Calamagrostis
rubescens). Pine grass rarely forms a dense covering on sites where rocky mountain maple
is common, but some of the shrubs commonly found with ninebark may be present. Cool

moist grand fir sites tend to be highly productive (Steele et al. 1981). Included in the cool-

10

moist grand fir habitat type class, and very similar in structure and ecological functions, are
the Abies grandis / Acer glabrum, Abies grandis /Xerophy11um tenax, Abies grandis /

C optis occidentalis, Abies grandis / vaccinium globulare, Abies grandis / Clintonia
uni/10rd, Abies grandis / vaccinium caespitosum, and Abies grandis / Linnaea borealis
habitat types.

The dry grand fir habitat type class usually occurs at elevations between 1,310 and
l950m (4,300-6,400ft). These sites are usually found on dryer gentle benches, upper
slopes, and ridges. They tend to occur on clay loam to sandy loam soils, from primarily
granitic parent material, with an average pH of 6.0. However, they may also be found on
soils formed fi'om rholite, andesite, and quartz diorite parent rock. The species that
dominate seral stages are ponderosa pine and Douglas-fir, with grand fir dominating at
climax. The undergrowth usually contains a thin cover of Spirea, with Thalictrum
occidentale (western meadowrue) as the dominant forb. Also common on undisturbed sites
are pine grass and elk sedge (Carex geyeri). Disturbed sites often have shiny leaf ceanothus
(Ceanothus velutinus), bitter cherry (Prunus emarginata), schoulers willow (Salix
scouleriana), and service berry (A melanchier alm'folia) as common components.
Snowberry may also dominate the understory on some sites. Heartleaf arnica (Arm'ca
cordifolia) and prince’s pine (Chimaphila umbellata) can dominate the understory layer in
the lower light conditions of a dense overstory. In more open canopy conditions of the seral
stages, pine grass can form a thick sod. Dry grand fir sites tend to be very productive

(Steele et al. 1981). Included in the dry grand fir habitat type class, and very similar in

11

structure and ecological functions are the Abies grandis / Spirea betulifolis, and Abies
grandis / Calamagrostis rubescens habitat types.

The dry Douglas-fir habitat type class ranges in elevation from 980 to 2,160m
(3,200-7,100fl). It is mostly found on soils with granitic and basalt parent materials, with
an average pH of 6.4, but may also come from andesite or Precambrian metasedirnents.
Dry Douglas-fir sites usually occupy warm dry southerly aspects, on the lower to mid
slopes. Overstory species vary between ponderosa pine at lower warmer elevations, and
mixed Douglas-fir and ponderosa pine on higher moister sites. The understory is usually
dominated by common snowberry, but is often mixed with spirea, Rosa species, and
ninebark. Pine grass and elk sedge often form a layer below the shrubs. Quaking aspen
(Populus tremuloides) may be present in the seral stages of this habitat type class. Sites of
the dry Douglas-fir habitat type class tend to be moderately to highly productive (Steele et
al. 1981). Included in the dry Douglas-fir habitat type class, and very similar in structure
and ecological functions, are the Pseudotsuga menziesii / Symphoricarpos albus, Pinus
ponderosa / Symphoricarpus albus, Pinus ponderosa / Physocarpus malvaceus,
Pseudotsuga menziesii / Physocarpus malvaceus, Pseudotsuga menziesii / Calamagrostis
rubescens, Pseudotsuga menziesii / Carex geyeri, and Pseudotsuga menziesii / Spiraea
betulifolia habitat types.

Fire has been shown to be a common part of the ecosystems of this area, and it is
important to recognize its influence on stand structure and species composition. Fire scars
are common on many samples of trees taken in central Idaho, indicating that 1 or more

ground fires would be expected in the life of any stand (Steele et al. 1981). The dry grand

12

fir and dry Douglas-fir habitat type classes are believed to have had frequent light
understory burns, with fires occurring every 3-30 years. Frequent understory burns kept
stand conditions Open and park like with the majority of trees being large and of fire
adapted species (ponderosa pine, western larch). Less fire resistant species (grand fir,
Douglas-fir) were uncommon in these fire conditions (Arno and Peterson 1983). It is also
believed that historically the cool moist grand fir habitat type class was subjected to a range
of frequent (3-30 yr.) understory burns on the dryer sites, to infrequent (70-120 yr.) stand
destroying fires on wetter sites (Arno 1980). With infrequent stand destroying fires, forests
would be comprised of species of both fire adapted and non-adapted trees. These stands
would have had dense understory layers forming fire ladders into the upper canopy, very
similar to the conditions that fire suppression has created over much of the landscape Of
today (Arno and Peterson 1983).

Since European settlers arrived in the area, grazing has had a Significant impact on
the plant communities of central Idaho. Grazing is now managed in this area, and will
likely continue to be an influence in the region (Steele et al. 1981). Logging has
significantly influenced the area’s ecosystems as well, and will continue to be a major
influence (Steele et a1. 1981). The study area is primarily used for timber production.

Historically, all stands included in the study were managed with selective timber
harvesting, with approximately 15 years between re-entry. As a result of the selective
harvesting, these stands possessed multiple age-classes of trees, but were assigned to l Of 2
vegetative growth stages (V GS’s) (Table 1). If the preponderance of trees were between

12.7cm and 30.5cm dbh (5.0-12.0in), they were considered small tree stands. If the

l3

preponderance of trees were between 30.5cm and 50.8cm dbh (12.0-20.0in), they were
assigned to the medium tree group of stands. Mean basal area of large trees (2 6in) in
individual stands varied between 9.0 and 29.8m2/ha (4O.0~132.9ft2/acre) (Table 1). Stands
were chosen based on habitat type, VGS, and stand size or width. Stands were only used if
they were large enough to contain the trapping setup with no trap within 50m of boarder of
the stand or road, to avoid the influence of the conditions within adjoining stands and
roads. Tree density varied among stands as well (1 l6-435trees/ha, 48-180trees/acre).
Percent canopy closure ranged from 20% to 52% among stands. Understory density,

height, and composition (shrubs, grass, forbs, etc.) varied among stands as well.

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METHODS

Experimental Design

A total of 19 stands were sampled to identify species associations with different
habitat type classes and successional stages. Stand 9 (Table 1) had to be replaced when
the specific phases included in each habitat type class were changed, shifting stand 9 to a
different habitat type class. Six replicates in each habitat type class were selected. After
reclassification of successional stages, varying numbers of replicates (1-5) remained in
each Of 2 successional stages, within habitat type classes (Table 1).

Two 1 month long small mammal and herptile trapping sessions were conducted
in each of the 18 stands used each year: from mid-July to mid-August 1995, October
1995, June 1996, and July 1996. Since only 6 stands could be trapped at a time, due to
logistical constraints, 3 trapping periods, lasting 1 weak, were conducted during each 1
month trapping session. To avoid temporal effects between ELU’s, l replicate of each
ELU was trapped in during each trapping period. Trapping periods lasted 7 days,
including 2 set-up days and 5 trap nights. A trap night was defined as a 24 hour period in
which all the traps in a given stand were set. This trapping design gave 5 trap nights in
each stand per trapping session, 10 trap nights in each stand per field season, 20 trap
nights in each stand total, and a total of 360 trap nights during the study in all stands

combined.

15

16

During the summer of 1995 Anabat V bat detectors (Titley Electronics, Balina
N.S.W., Australia) were left set up in each stand on 2 separate nights between mid July
and the end of September. During the 1996 field season, 2 or 3 bat detector nights were
obtained in each stand between mid-June and mid-August. Bat detectors were moved
between stands daily until all stands had 1 detector night. The cycle was then repeated, to
avoid temporal effects. This allowed a total of 4 or 5 bat detector nights in each stand.

F our vegetation sampling plots were located in each of the 19 stands. This gave a
total of 24 plots per habitat type class, and 76 plots total. In the dry Douglas-fir habitat
type class, three small tree and three medium tree stands were compared, with 12
vegetation sampling plots per ELU. In addition, 36 moosehom readings (Cook et al.
1995) and 72 sets of profile board horizontal cover estimates (Hays et al. 1981, Higgins et
al. 1994) were made in each trapping site.

Trapping

The trapping design was intended to catch as many species of small mammals,
reptiles, and amphibians concurrently as possible. Individuals were marked with the toe
clip method to identify individuals which had been captured previously. The design
consisted of 2 drift fence triads, placed 150m (492ft) apart within the given stand. Triads
consisted of 3 drift fences running out from the center at 120° fi'om each other (Fig. 1).
Each arm of the triad was made of 7.5m (25ft) of aluminum valley flashing, buried 21cm
(8in) deep in the soil, and having 47cm (16in) extending above the surface. A pitfall trap
was placed at the end of each triad and at the intersection of the 3 aluminum drift fence

arms (Jones 1981). Pitfalls were made Of 2 number 10 coffee cans, taped end to end, with

17

the bottom removed from the top can. Holes were punched in the bottom of the lower can
to allow for the drainage of rain water. Funnel traps (C. Peterson, Idaho State University,
Pers. Comm.) were placed on both sides of the drift fence arms, approximately in the
middle. Funnel traps were 21cm (8in) diameter tubes of 3.5mm (1/8in) metal screen, 76cm
(30in) long, with a cone of the same material in each end.

The lst triad was randomly located within the stand using a number grid over a map
of the stand, and randomly generating numbers until an intersection within the stand was
located. The 2nd triad was located by generating a random bearing from the lst triad and
placing it 150m (492ft) out on that bearing. Three rows of 10 Sherman traps (HB Sherman
Co., Tallahassee, Florida) (10cm X 9cm X 30.5cm, 4in X 3.5in X 12in) were placed along
the bearing between the 2 triads (Fig. 1). Both traps and rows of traps were spaced 10m
(32.7ft) apart, forming a 10 x 3 grid. Pitfall traps were used to catch amphibians and small
mammals. Funnel traps were intended to catch snakes, which are not susceptible to pitfall
traps (Bury and Corn 1987). Sherman naps were used to catch species of small mammals
that are not susceptible to pitfalls, such as chipmunks and deer mice.

Small mammals were identified according to Burt and Grossenheider (1980), and
Whitaker et al. (1980). Herpetiles were identified according to Behler (1979). Shrew
species overlapped extensively in color, body measurements, and other physically
distinguishing characteristics. It was therefore necessary to identify shrews to species using
dental characteristics, and skull measurements given in Junge and Hoffinann (1981).

Species identifications were verified against museum vouchers at the University of Idaho.

18

 

 

 

 

 

XXXXXXXXXX
XXXXXXXXXX
XXXXXXXXXX

 

 

 

 

 

0

Figure 1. Schematic of trap setup within stands on study area in Valley County, Idaho in
1995 and 1996. In the Figure, circles represent pitfall traps, squares are funnel traps,
heavy lines are drift fences, and X’s are Sherman traps. Sherman traps are spaced 10m
apart, within and between rows. Note that these dimensions are not depicted accurately.

19

Bat Detection

Anabat 5 bat detectors, with delay switches and tape recorders, were used to detect
and record the calls of echolocating bats. The bat detectors, which turn on and off at a set
light level, were placed in stands in the evening and retrieved in the morning. Sensitivity
levels of the bat detectors were always set to 4.5. Bat calls recorded by bat detectors in the
field were played into a computer interface module. The Anabat 5 software allow the
computer to function as a spectrum analyzer, which produced a graphic image of the
echolocation calls. This graphic image was used to identify the species of bat which had
produced the call, by making comparisons with call shapes, frequency ranges, and
frequency rates of change given in the literature (F enton et a1. 1983, Thomas and West
1989, Corben 1994). Echolocation call descriptions and measures were taken from Fenton
and Bell (1981), Fenton et a1. (1983), Thomas et al. (1987), and Thomas and West (1989).
Vegetation Sampling

TO allow greater understanding of species distributions across landscape, and to
assist managers in enhancing the habitat for species of concern, it is important to understand
why each species is associated with a given ELU. In addition, understanding important
habitat features for a species can aid in the development of models with greater predictive
value of species presence and relative abundance. We therefore needed to identify the
specific habitat features that contribute to a species’ association with an ELU. Since this
project dealt with a wide diversity of species, with diverse life histories, it was important to
collect data on a relatively comprehensive assortment Of habitat variables in the vegetation

sampling design.

20

The vegetation sampling design (Fig. 2) consisted of a 0.0081ha (1/50th acre) fixed
circular plot, nested within a 0.081ha (1/5th acre) fixed circular plot, which in turn had 3
15.24m (50ft) point intercept transects around its exterior. All information from vegetation
plots was recorded using CMT data loggers (CMT Corvallis MicroTechnology Inc. 413
SW. Jefferson Ave. Corvallis Or. 97333 ).

Within the 0.0081ha (1/50th acre) plot, information about the height and number of
all small (<15.28cm) (< 6.0in. dbh) trees was collected. Height of small trees was measured
using a logger’s measuring tape. Where small trees were too tall for measurement using the
loggers tape, heights were measured using a clinometer, and trigonometric hypsometry,
from a distance of 18.3m (60ft) parallel with the tree on the slope (Hays et al. 1981).
Numbers of small trees by height class were recorded to the nearest 30cm (1ft).

Within the 0.081ha (“501 acre) fixed circular plot, relevant information about snags,
trees, and stumps was collected. Relevant information about snags included dbh, and
height. The dbh of snags was measured using a dbh tape, or dbh side of a loggers tape
(Hays et al. 1981). Snag heights were measured using a clinometer from 18.3m (60ft) at a
point parallel on the slope. Where it was not possible to measure height from a point
parallel on the slope, distances were adjusted for slope (Hays et al. 1981).

Large tree information included species, dbh, height, and height to lst live branch.
Large trees were identified to species using vegetative characteristics, such as cones, crown
shape, needles, and bark. Diameter at breast height was measured using a dbh tape, or the
dbh side of a loggers tape. Height, and height to the lst live branch Of large trees, was

measured using a clinometer from 18.3m (601i) across slope, or from a distance adjusted for

21

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

A
Transect #3 I/SoyAch Plot I [57) fl. T130500! #I
—— [End Transect ]
[Walk to Next Transect]
4 j Y

 

[Transect #2 I

 

Figure 2. Schematic of vegetation plot structure used to quantify vegetation attributes in
Valley County, Idaho during 1995 and 1996. The plot consists of a 1/50th acre
(0.0081ha) circular plot nested within a 1/5th acre (0.081ha) circular plot, with three 50ft.
(15.24m) transects, at right angles, around the perimeter.

22

slope (Hays et al. 1981). Information collected about stumps included diameter and
density. Stump height was measured using a loggers tape (Hays et al. 1981). Stumps
within the 0.081ha (l/5th acre) plot were counted to estimate density.

The 3 15.24m (50ft) line transects around the fixed circular plots were used to
collect information with a point intercept method (Higgins et al. 1994). The transect was
used to collect information on the presence and cover of forest litter, herbaceous vegetation,
living woody vegetation, and number and diameter of coarse woody debris pieces at ground
surface, in the lst meter above the ground, and above Im from the ground. Transects were
run north to south, east to west, and south to north, along 3 sides outside of the large fixed
circular plot. Pieces of coarse woody debris > 15.24cm (6in) at their largest end were also
counted and measured for length and for diameter at the largest end. Length of coarse
woody debris pieces was measured using a logger’s tape. Diameter at the largest end of
coarse woody debris was measured using a logger’s tape or dbh tape (Hays et al. 1981).

Coarse woody debris density in logs/ha was estimated using the formula

I
X = 105 o 21L 0 22—;— where L equals line transect length, and 1’ equals log length

n i
(deVries 1986). A pole was placed every 60.96cm (2fi) along the transect, and the identity
of materials contacting it in each of the 6 strata was recorded (Higgins et al. 1994).
Information on canopy closure was also collected at the start of each transect, and at plot
center. Canopy closure measurements were made with a moosehom circular densiometer
(Cook et al. 1995). Using the moosehom, the number of the 25 line intersections with

vegetation over them at all heights, and at heights greater than 4.9m (16ft) were counted.

23

The bubble level in the moosehom was used to assure that the instrument was held
vertically.

Site information was also recorded at each plot. These data included universal
transverse mercator system (UTM) coordinates (Koeln et al. 1994, Samuel and Fuller
1994), slope, aspect, elevation, and plot number. Universal transverse mercator system
coordinates were determined using a CMT global positioning system (GPS) based on the
North American Datum from 1983 (NAB-83) (Koeln et al. 1994, Samuel and Fuller 1994).
Latitude and longitude were projected onto a state plane projection. Slope was measured
using a clinometer. Aspect was measured using a compass. Mean aspect was calculated as

32}, if S > 0, C > 0,

0: $644! if C<0, where f6=arctan(S/C),
an]: if §<o, E>o,

><z

I

C— : l2f, cos¢,., S = 12f, sin¢,., f. is the fiequency ofthe observation, n is the
n n

sample size, and ¢, is the aspect of individual plots measured in radians (Mardia I972).
Elevations were estimated using a map.

Horizontal and vertical cover measurements were also collected at actual trap
locations. At each point a moosehom reading of vertical cover, and 2 sets of profile board
estimates of horizontal cover were taken. Horizontal cover estimates were made using a 1m
(3.27fi), 35cm (14.5in) wide profile board, divided vertically into 25cm (9.8in) sections.
The profile board was held 1m (3.2711) (Nudds 1977) from the observer, who estimated the
percent of each section of the board that was visually obscured by vegetation. The observer

viewed the profile board from approximately the same height as the center of the profile

24

board section for which visual obscurity was being estimated (Higgins et al. 1994, Hays et
al. 1981). To avoid directional biases, profile board estimates were made on opposite sides
of the point, in a direction perpendicular to the transect line at Sherman trap points. At each
of the 3 triad points, estimates were made in opposite directions perpendicular to the line
going directly away fiom center, midway between each set of triad arms. Points on these
lines were 2m (6.6ft) from the triad center. Ten transect line points were located 10m
(32.7ft) apart on each of the 3 Sherman trap lines.
Data Analysis

Small Mammal, and Herptile

Capture data for each species was tested for differences in means among habitat
type classes, and between small tree and medium tree vegetation grth stages
(V GS’s)within the dry Douglas-fir habitat type class. The capture data were used in the
relative abundance format of captures per trapping event, which is in essence animals
captured per 5 trap nights. Data were lefi in this format because all stands had the same
number of trap nights, and to avoid problems with defining the efficiency of different trap
types, which were used simultaneously in the trapping design. All statistical tests were
conducted at the 90% (or=0.10) confidence level. SAS/STAT Program version 6.0 (SAS
Institute Inc., Cary, NC) was used to run all statistical tests.

Because of the high temporal variability among trapping sessions due to seasonal
differences, comparisons of capture rates among different habitat type classes and VGS’s
were made within each trapping session individually, and across all trapping sessions.

Temporal effects of weather changes, caused by having 3 separate 7—day trapping periods

25

within each trapping session may also have existed in the data. Trapping was therefore
done in 2 replicates of each habitat type class, and 1 replicate of each VGS within the dry
Douglas-fir habitat type class, in each of the 3 week long trapping periods per 1 month trap
session. To minimize the influence of these temporal effects on the analysis, and because
the data had missing values, it was necessary to use the general linear model (GLM) to test
for differences in capture rates among habitat type classes and between VGS’s (Ott 1993).
The GLM was used because it provides an equivalent test to ANOVA when data have
missing values. The temporal effects were minimized by including them in both the within
group variability, and between group variability compared in the GLM. Because of the
small sample sizes, and small catches of some species, a non-parametric test was used for
comparisons of capture rates among habitat type classes, and between VGS’s as well. The
non-parametric alternative used was the Kruskal-Wallis non-parametric analysis of
variance. This was done by ranking the data, and then using the GLM on the ranks (Ott
1993, SAS Institute Inc. 1989). This test will be referred to as the Kruskal-Wallis test in
this paper. The GLM was used by itself on individual trapping sessions as a means to
compare with the results of the Kruskal-Wallis test, used in the same way, to identify any
major inconsistencies and points of concern to investigate. The joint ranks multiple
comparison test (multiple comparison test) was used to identify which habitat type classes
were different from others when the Kruskal-Wallis test showed a significant difference
(Miller 1985, Siegel and Castellan 1988, Toothaker 1991). The joint ranks multiple
comparison test is intended to be used on the ranks of data, unlike Tukey’s or Duncan’s

tests, which are not recommended for use on ranks of data. The GLM for repeated measures

26

was done using the multivariate analysis of variance (MAN OVA) option to test for
differences in habitat type classes across all 4 trapping sessions. The MAN OVA option did
not use any replicates (stands) with missing values among the trapping sessions, and ran the
GLM for repeated measures on the remaining replicates (stands) (SAS Institute Inc. 1989).
For this reason, only 17 stands were used with the GLM for repeated measures.

Bat

Bat data could not be used in a relative abundance format for several reasons. lst,
it was not possible to identify individual bats on the tape recordings of echolocation calls.
One bat could therefore be counted as several individuals, or only as a single individual.
secondly, there were questions as to the ability of the equipment and technique to
separate out several species of bats. The similarity of these calls in conjunction with the
geographic variability of a species calls, and the inability to calibrate using the calls of
known individuals of various species from the study area, created the potential for some
individuals to be misidentified (D. Genter, Montana Natural Heratige Program, pers.
comm., Thomas et al. 1987, Thomas and West 1989). In addition, some tapes became
full before a full bat detector night had been completed. To avoid these problems, the
data were represented as the percentage of detector nights an individual species was
detected in a given stand. This relative presence format eliminates the problems listed
above, by making it inconsequential if an individual is counted more than once, and
minimizes the potential for an individual misidentification to influence the outcome of

tests. Bat data were tested using GLM, the Kruskal-Wallis test, MANOVA (Ott 1993,

27

SAS Institute Inc. 1989), and the multiple comparison test (Miller 1985, Siegel and
Castellan 1988, Toothaker 1991) in the same way as previously described.

Vegetation

To identify habitat variables that were important to individual animal species, and
characterized habitat type classes and VGS’s, tests were run to identify differences in
habitat variables among habitat type classes,.and between VGS’s within the dry Douglas-
fir habitat type class. The primary test used was the Kruskal-Wallis test (Ott 1993, SAS
Institute Inc. 1989). If the Kruskal-Wallis test showed a significant difference among
habitat type classes (Ott 1993, SAS Institute Inc. 1989), the joint ranks multiple
comparison test was used to identify which of them had significantly different values
(Miller 1985, Siegel and Castellan 1988, Toothaker 1991). If a species was relatively more
abundant in a habitat type class or VGS, and a habitat variable was also relatively more or
less abundant in the same habitat type class or VGS, the variable was presumed to
potentially be influencing the relative abundance of that species. To lend support to this
presumption, differences between relative abundance groups of individual species for
habitat variables were also investigated. It should be noted however, that the failure of
the relative abundance group tests to corroborate a relationship does not necessarily refute
a relationship identified by habitat type class and VGS tests. The data proved
inappropriate for the use of multivariate techniques because of a lack of multivariate
normality, the small sample size, non-linear relationships, and an inability to linearize
those relationships. Since the data proved inappropriate for use with multivariate

techniques, no attempt was made to identify interacting variables. Interaction between

28

variables could mask relationships that are apparent at the habitat type class or ELU level,
and yet not apparent at the individual species by variable level.

Stands were grouped based on the mean relative abundance of individual species
across the 4 trapping sessions. This was done for each of 10 small mammal species, and
2 amphibian species. Habitat variables were then tested for differences between the
groups (C.W. Ramm, Michigan State University, pers. comm.). To identify natural
groups, stands were sorted by relative abundance of an individual animal species, and
then numbered in that order from 1 to 19. The data were then plotted with the assigned
number on the X axis, and relative abundance on the Y axis. Large breaks or jumps in
relative abundance were then identified occularly as natural cut off points. Stands were
allowed be broken into a maximum of 4 relative abundance groups, with no less than 3
stands in a group. Stands located together between natural breaks in the data were then
assigned to the same group. For a complete description of the methods used for grouping
stands, with examples, see Appendix B.

Differences in habitat variables between relative abundance groups for individual
species were tested for in several ways. The primary test used was the Kruskal-Wallis
test, which was done by using GLM on the ranks of the data (Ott 1993, SAS Institute Inc.
1989). The general linear model (GLM) was used by itself as a parametric alternative for
comparison, to identify any major inconsistencies which required investigation. The
multiple comparison test was used to separate groups which showed significantly
different values for a given variable in the Kruskal-Wallis and/or GLM tests (Miller 1985,

Siegel and Castellan 1988, Toothaker 1991).

29

The combining of stands into relative abundance groups, and testing for
differences in vegetation and site characteristics among the relative abundance groups,
was limited in effectiveness. Small sample sizes, large numbers of variables, and high
variability of conditions, both within and between stands of the same habitat type class or
VGS, made the identification of important variables for individual species difficult, and
made interpretation of results questionable. For this reason only habitat variables that can
be supported biologically, and through literature will be discussed in the main body of
this thesis. A complete discussion of grouping techniques, and the less supportable

results and discussion of the vegetation analysis, are provided in Appendix B.

RESULTS

Small Mammals

A total of 17 small mammal species were captured during the 2 years of trapping.
Of these, the yellow-pine chipmunk (Eutamias amoenus), deer mouse (Peromyscus
maniculatus), northern red-backed vole (Clethrionomys gapperi), dusky shrew (Sorex
monticolus), masked shrew (Sorex cinereus), meadow vole (Microtus pennsylvanicus),
vagrant shrew (Sorex vagrans), dwarf shrew (Sorex nanus), northern pocket gopher
(Momomys talpoides), and golden-mantled ground squirrel (Spermophilus lateralis) were
captured in sufficient numbers to conduct statistical analysis. A minimum of 5 individuals
had to be captured during a trapping session to be considered to have sufficient numbers for
analysis, so a significant difference could potentially be detected. In addition, 4 western
jumping mice (Zapus princeps) were captured during the 1995 field season, and 2 were
captured in 1996 (Appendix A Table 1). Four long-tailed weasels (Mustelafrenata) were
caught in 1995. Three Colombian ground squirrels (Spermophilus columbianus) were
caught in 1995, and 4 were captured during the 1996 trapping sessions. One red squirrel
(Tamiasciurus hudsonicus) was caught during the 1995 field season, and 2 were captured in
1996. One bushy-tailed wood rat (Neotoma cinerea) was caught in 1996, as was I northern
flying squirrel (Glaucomys sabrinus), and 2 water shrews (Sorex palustris) (Appendix A

Table 1).

30

31

By Habitat Type Classes

The most commonly captured species of small mammal was the yellow-pine
chipmunk, with 562 total individuals captured during all 4 trapping sessions (Appendix A
Table 2). During trap session 1 (mid July to mid Aug. 1995), a total of 178 individuals
were captured, 88 individuals during trap session 2 (October 1995), 134 individuals during
trap session 3 (June 1996), and 162 individuals during trap session 4 (July 1996). Specific
capture data for the yellow-pine chipmunk can be found in Appendix A Table 2. The
Kruskal-Wallis test showed no significant differences in relative abundance among habitat
type classes during any of the 4 trapping sessions (Fig. 3) (P = 0.83, 0.62, 0.28, and 0.55 for
trapping sessions 1 through 4 respectively). The GLM, used for individual trap sessions,
showed general agreement with the results of the Kmskal-Wallis test. The GLM for
repeated measures showed no significant difference in capture rates between habitat type
classes across all trapping sessions (P = 0.35). These results indicate that yellow-pine
chipmunk abundance did not differ significantly among the 3 habitat type classes
investigated.

The deer mouse was the 2nd most commonly captured species, with 436 individuals
captured during all 4 trapping sessions combined (Appendix A Table 3). Seventy-one
individual deer mice were captured during the lst trapping session, 88 individuals captured
during the 2nd trapping session, 92 during the 3rd, and 185 during the 4th (Appendix A

Table 3). No significant difference in capture rates was detected with the Kruskal-Wallis

32

Yellow-pine Chipmunk Relative Abundance in
Habitat Type Class

    
   

14
8
r: 127
3 10
C
3 8
< 6
3 CM Grand
0:
r: 4 . //
3 2 L, Dry Grand
2 0 ' * / Hab. Class
' " x Dry Doug
Trap 1 Trap 2 Trap 3 Trap 4 /

Trapping Session

Figure 3. Yellow-pine chipmunk mean relative abundance in habitat type classes,
expressed as mean total capture of individuals by trap session, in Valley County Idaho in
1995 and 1996. No significant differences (P < 0.10) were found by the Kruskal-Wallis test
(Ott I993). Trapping sessions (Trap 1-4) were each 5 days in length. CM grand refers to
the cool moist grand fir habitat type class, dry grand to the dry grand fir habitat type class,
and dry Doug to the dry Douglas-fir habitat type class.

33

test among habitat type classes during trapping sessions 1 and 2 (Fig. 4) (P = 0.66, and 0.62
respectively). A significant difference in capture rates among habitat type classes was
detected during the 3rd trapping session (P = 0.03). The Joint ranks multiple comparison
test showed the mean capture rate of deer mice in the cool-moist grand fir habitat type class
to be significantly different from that in the dry grand fir, but not the dry Douglas-fir habitat
type class. No significant difference was detected between deer mouse capture rates in the
dry Douglas-fir and dry grand fir habitat type classes with the multiple comparison test (Fig.
4). No significant difference in capture rates between habitat type classes was detected by
the Kruskal-Wallis test during trap session 4 (P = 0.18). The GLM applied to individual
trapping sessions produced the same results. The GLM for repeated measures showed no
significant difference among capture rates in different habitat type classes during all
trapping sessions combined (P = 0.40). These results indicate that deer mouse relative
abundance showed no consistent significant differences among the 3 habitat type classes.
The 3rd most commonly captured mammal was the northern red-backed vole, with
a total of 157 individuals captured during all 4 trapping sessions combined (Appendix A
Table 4). Forty-three red-backed voles were captured during the lst trapping session, 55
during the 2nd trapping session, 27 during the 3rd trapping session, and 32 during the 4th
trapping session (Appendix A Table 4). The Kruskal-Wallis test showed significant
differences in capture rates between habitat type classes during the 2nd and 3rd trapping
sessions (Fig. 5) (P = 0.001 , and 0.03 respectively). The multiple comparison test showed a

significant difference in

34

Deer Mouse Relative Abundance in Habitat Type

 

  
 
 
  
 

 

 
 

 

     

Classes

14
o 12
8
g 10 "
g ,
.n 8' ”
< .
7; 6" /
n: 4 ,/ CM Grand
: , 1
3 / / Dry Grand
5 2, _,,/ Hab. Class

0.,, ,, _ _ , j / Dry Doug
"" 77/ ,7 77‘,

Trapping Session

Figure 4. Deer mouse mean relative abundance, expressed as mean total capture of
individuals in habitat type classes, by trap session, in Valley County Idaho in 1995 and
1996. Trapping sessions (Trap 1-4) were each 5 days in length. CM grand refers to the
cool moist grand fir habitat type class, dry grand to the dry grand fir habitat type class, and
dry Doug to the dry Douglas-fir habitat type class. Trap sessions with letters above the
columns differed significantly (P < 0.10) by the Kruskal-Wallis test (Miller 1985, Ott 1993,
Toothaker 1991). Dissimilar letters on columns within trapping sessions indicate
significant differences (P < 0.10) (joint ranks multiple comparison test) (Miller 1985,
Toothaker 1991).

35

Red-backed Vole Relative Abundance in Habitat Type
Classes

 

    
 

  

CM Grand

, /,
~- .4 / Dry Grand
/ Hab. Class

/
’ Dry Doug

Mean Rel. Abundance
w

   

     
   

Trap1 V ' ,«
Trap 2 Trap 3

Trap 4

Trapping Session

Figure 5. Red-backed vole mean relative abundance, expressed as mean total capture of
individuals in habitat type classes, by trap session, in Valley County Idaho in 1995 and
1996. Trapping sessions (Trap 1-4) were each 5 days in length. CM grand refers to the
cool moist grand fir habitat type class, dry grand to the dry grand fir habitat type class, and
dry Doug to the dry Douglas-fir habitat type class. Trap sessions with letters above the
columns differed significantly (P < 0.10) by the Kruskal-Wallis test (Miller 1985, Ott 1993,
Toothaker 1991). Dissimilar letters on columns within trapping sessions indicate
significant differences (P < 0.10) (joint ranks multiple comparison test) (Miller 1985,
Toothaker 1991).

36

capture rates between the cool-moist grand fir habitat type class and the dry Douglas-fir
habitat type class, but not for the dry grand fir habitat type class during trap session 2. No
difference in capture rates was detected among the 3 habitat type classes by the multiple
comparison test during trap session 3. Trap sessions 1 and 4 showed no significant
differences between habitat type classes using the Kruskal-Wallis test (Fig. 5) (P = 0.17,
and 0.22 respectively). The results of the GLM used on individual trapping sessions agree
with this assessment. The GLM for repeated measures showed a significant difference in
capture rates between habitat type classes for all trapping sessions as a whole (P = 0.01).
Though not statistically significant in all cases, it is interesting to note that the red-backed
vole had higher mean relative abundance’s in the cool moist grand fir habitat type class
during all 4 trapping sessions. These results indicate that northern red-backed voles were
more abundant in the cool-moist grand fir habitat type class.

The dusky shrew was the 4th most commonly captured mammal, with 133 total
individuals captured during all 4 trapping sessions (Appendix A Table 5). Twenty- seven
individual dusky shrews were captured during the lst trapping session, 22 in the 2nd, 32 in
the 3rd, and 52 in the 4th trapping session (Appendix A Table 5). The Kruskal-Wallis test
showed no significant differences in capture rates between habitat type classes during any
of the 4 trapping sessions (Fig. 6) (P = 0.21 , 0.21, 0.16, and 0.56 for trap sessions 1 through
4 respectively). The results of the GLM for individual trap sessions were in general
agreement with these results. It should be noted, however, that mean dusky shrew capture

rates, while not significant, were highest in the cool-moist grand fir habitat type class during

37

Dusky Shrew Relative Abundance in Habitat Type
Classes

  
 
 
 
  

 

/ 7 CM Grand

Mean Rel Abundance

re '7’
“i / Dry Grand Hab. Class

4

 
 
 
 
   

 

 

,, ,x ,, 77 / / Dry Doug
Trap1 " ’ " r ,
Trap 2 Trap 3 Trap 4

Trapping Session

Figure 6. Dusky shrew mean relative abundance in habitat type classes, expressed as mean
total capture of individuals by trap session, in Valley County Idaho in 1995 and 1996. No
significant differences (P < 0.10) were found by the Kruskal-Wallis test (Ott I993).
Trapping sessions (Trap l-4) were each 5 days in length. CM grand refers to the cool moist

grand fir habitat type class, dry grand to the dry grand fir habitat type class, and dry Doug to
the dry Douglas-fir habitat type class.

38

all 4 trapping sessions. The GLM for repeated measures detected no significant
differences in mean capture rates between habitat type classes across all trapping sessions (P
= 0.11). These results indicate that the dusky shrew showed no significant difference in
relative abundance between habitat type classes, though larger sample sizes would likely
reveal significant differences.

The masked shrew was also a commonly captured species, with 87 total individuals
captured during all 4 trapping sessions combined (Appendix A Table 6). During the lst
trapping session, 26 individuals were captured, 12 during the 2nd trapping session, 29
during the 3rd, and 20 during the 4th trapping session. More detailed information about
masked shrew captures can be found in Appendix A Table 6. The Kruskal-Wallis test
detected a significant difference in relative abundance among habitat type classes in both
trap sessions 1 and 2 (Fig. 7) (P = 0.02, and 0.04 respectively). No difference in capture
rates was detected between habitat type classes during either the 3rd or 4th trapping sessions
(Fig. 7) (P = 0.16, and 0.12 respectively). The GLM for individual trap sessions showed the
same number of trap sessions with significant differences, but switched the significance to
the lst and 4th trap sessions. However, as a whole, the two tests still seem in general
agreement.

The multiple comparison test showed no significant difference in the capture rates between
the cool-moist grand fir and dry grand fir habitat type classes, or between the dry grand fir
and dry Douglas-fir habitat type classes during the lst trapping session. The multiple
comparison test did, however, show a significant difference between the capture rates in

cool-moist grand fir habitat type class, and dry Douglas-fir habitat type class during the lst

39

Masked Shrew Relative Abundance in Habitat Type
Classes

 

   

s 3,
5
1, 2.5
C
3 2
<
7; 1.5 ,
n: 7 , ' .. : CM Grand
5 1 ' e‘ ’ , , 7 7 /
t” ,4 ' ., I D Grand
0.5 . W
E if / ' Hab. Class
0. i / Dry Doug
Trap1
Trap2 /
Trap3 Trap4
Trapping Session

Figure 7. Masked shrew mean relative abundance, expressed as mean total capture of
individuals in habitat type classes, by trap session, in Valley County Idaho in 1995 and
1996. Trapping sessions (Trap 1-4) were each 5 days in length. CM grand refers to the
cool moist grand fir habitat type class, dry grand to the dry grand fir habitat type class, and
dry Doug to the dry Douglas-fir habitat type class. Trap sessions with letters above the
columns differed significantly (P < 0.10) by the Kruskal-Wallis test (Miller 1985, Ott 1993,
Toothaker 1991). Dissimilar letters on columns within trapping sessions indicate
significant differences (P < 0.10) (joint ranks multiple comparison test) (Miller 1985,
Toothaker 199 1 ).

40

trapping session. The multiple comparison test did not detect a significant difference
among habitat type classes during the 2nd trapping session (Fig. 7). It should also be noted
that, while not statistically significant, the means of capture rate ranks were ordered from
highest to lowest as cool-moist grand fir, dry grand fir, and dry Douglas-fir in all trap
sessions. The GLM for repeated measures detected a significant difference between the
capture rates of masked shrews in different habitat type classes across all trapping sessions
(P = 0.02). These results indicate that the masked shrew was more abundant in the grand fir
habitat type classes, if not significantly so in the cool-moist grand fir habitat type class.
Larger sample sizes may have produced significant differences between the grand fir habitat
type classes.

A total of 30 vagrant shrews were captured during all 4 trapping sessions combined
(Appendix A Table 7). Five vagrant shrews were captured during the lst trapping session,
4 during the 2nd trapping session, 12 during the 3rd, and 9 during the 4th trapping session.
Specific capture data are presented in Appendix A Table 7. No significant differences in
capture rates were detected by the Kruskal-Wallis test during any of the 4 trapping sessions
(Fig. 8) (P = 0.40, 0.19, 0.44, and 0.18 respectively). The results of the GLM for individual
trapping sessions were in general agreement with the Kruskal-Wallis test. No significant
difference in capture rates was shown by the GLM for repeated measures across all trapping
sessions (P = 0.48). These results indicate that vagrant shrew relative abundance was not
significantly different among these habitat type classes.

A total of 14 dwarf shrews were captured during all trapping sessions combined
(Appendix A Table 8). Six dwarf shrews were captured during the lst trapping session, 1

during the 2nd trapping session, 2 during the 3rd, and 5 during the 4th trapping session. See

41

Vagrant Shrew Relative Abundance in Habitat Type
Classes

0.8 *

  
   

0.6

 

0.4 7 / CM Grand

Mean Rel. Abundance

-.. , . / D Grand
./ ., / ry Hab. Class

Trap 2 Trap 3 Trap 4 L

0.2 , '

 

 
   

Trap 1

Trapping Session

Figure 8. Vagrant shrew mean relative abundance in habitat type classes, expressed as
mean total capture of individuals by trap session, in Valley County Idaho in 1995 and 1996.
No significant differences (P < 0.10) were found by the Kruskal-Wallis test (Ott 1993).
Trapping sessions (Trap 1-4) were each 5 days in length. CM grand refers to the cool moist
grand fir habitat type class, dry grand to the dry grand fir habitat type class, and dry Doug to
the dry Douglas-fir habitat type class.

42

Appendix A Table 8 for specific capture information on the dwarf shrew. The Kruskal-
Wallis test showed a significant difference in the capture rates between habitat type classes
for the 1st trapping session (P = 0.07) (Fig. 9). However, the multiple comparison test
detected no significant differences between habitat type classes for trapping session 1. The
Kruskal-Wallis test showed no significant difference in capture rates between habitat type
classes during trap sessions 2 through 4 (Fig. 9) (P = 0.39, 0.12, and 0.11 respectively). The
GLM for individual trap sessions is in general agreement with these results. Though not
statistically significant, the order of rank means, from highest to lowest, shows the cool-
moist grand fir to be highest in all but the 2nd trapping session, which only had 1 animal
captured. The GLM for repeated measures showed a significant difference in capture rates
between habitat type classes across all trapping sessions (P = 0.05). Since a significant
difference was detected in the individual trap session with the largest number of captures,
and was very nearly significant in the two trap sessions with the 2nd and 3rd largest number
of captures, and a significant difference was detected across all trapping sessions combined,
these results give some support for the dwarf shrew being more abundant in the cool-moist
grand fir habitat type class.

A total of 33 individual golden-mantled ground squirrels were caught during all 4
trap sessions combined (Appendix A Table 9). Six golden-mantled ground squirrels were
captured during the lst trapping session, 0 during the 2nd, 10 during the 3rd, and 17
individuals during the 4th trapping session. See Appendix A Table 9 for specific capture

information on the golden-mantled ground squirrel. No significant differences were

43

Dwarf Shrew Relative Abundance in Habitat Type
Classes

 

 

 

 

 

 

 

    

O

0

2

ll

1:

C

3

a

< ._

3? CM Grand
n:

5 Dry Grand

g Hab. Class

 

 

47g...

Trap 3 Trap 4
Trapping Session

Tmpt

Trap 2

Figure 9. Dwarf shrew mean relative abundance, expressed as mean total capture of
individuals in habitat type classes, by trap session, in Valley County Idaho in 1995 and
1996. Trapping sessions (Trap 1-4) were each 5 days in length. CM grand refers to the
cool moist grand fir habitat type class, dry grand to the dry grand fir habitat type class, and
dry Doug to the dry Douglas-fir habitat type class. Trap sessions with letters above the
columns differed significantly (P < 0.10) by the Kruskal-Wallis test (Miller 1985, Ott 1993,
Toothaker 1991). Dissimilar letters on columns within trapping sessions indicate
significant differences (P < 0.10) (joint ranks multiple comparison test) (Miller 1985,
Toothaker 1991).

detected in capture rates among habitat type classes during the lst trapping session (P =
0.31) (Fig. 10). Due to the lack of captures during the 2nd trapping session, no calculations
were possible. No significant difference in capture rates between habitat type classes was
detected during trap sessions 3 and 4 (Fig. 10) (P = 0.32, and 0.17 respectively). The results
from the GLM for the individual trapping sessions are in general agreement with the results
of the Kruskal-Wallis test. The GLM for repeated measures showed no significant
difference in capture rates among habitat type classes across all trapping sessions (P = 0.29).
These results show that the golden-mantled ground squirrel did not differ in relative
abundance among the 3 habitat type classes.

A total of 25 northern pocket gophers were captured during all 4 trapping sessions
1 combined (Appendix A Table 10). Seven northern pocket gophers were captured during the
lst trapping session, 0 during the 2nd, 8 during the 3rd, and 10 during the 4th trapping
session. For more detailed information about northern pocket gopher captures, see
Appendix A Table 10. No differences in capture rates among habitat type classes during
the lst trapping session were found to be significant using the Kruskal-Wallis test (P =
0.44) (Fig. l 1). No calculations could be made for the 2nd trapping session, due to a lack
of captures. A significant difference in the capture rates among habitat type classes was
detected for both trapping sessions 3 and 4 (Fig. 1 1) (P = 0.02, and 0.05 respectively). The
multiple comparison test was unable to identify significant differences among the habitat
type classes during either the 2nd or 3rd trapping session. The GLM for individual trap
sessions gave similar results. The GLM for repeated measures showed a significant
difference in the capture rates among habitat type classes across all trapping sessions (P =

0.01). These results indicate that northern pocket gopher relative abundance was

45

Golden-mantled Ground Squirrel Relative Abundance in
Habitat Type Classes

 

   
    

 

Q9
8
a 1.2
'5
C
3 .
.0
‘5 CM Grand
32 0.6 1 ~ ,
C l.
3 0.4 / Dry Grand Hab. Class
5 0.2 71
0 / Dry Doug

Trapping Session

Figure 10. Golden-mantled ground squirrel mean relative abundance in habitat type classes,
expressed as mean total capture of individuals by trap session, in Valley County Idaho in
1995 and 1996. No significant differences (P < 0.10) were found by the Kruskal-Wallis test
(Ott 1993). Trapping sessions (Trap 1-4) were each 5 days in length. CM grand refers to
the cool moist grand fir habitat type class, dry grand to the dry grand fir habitat type class,
and dry Doug to the dry Douglas-fir habitat type class.

46

Northern Pocket Gopher Relative Abundance in Habitat
Type Classes

 

 

    

 

 

 

 

CM Grand

Mean Rel. Abundance

Hab. Class

 

Trap 1

 

Trap 2

Trapping Session

Figure 11. Pocket gopher mean relative abundance, expressed as mean total capture of
individuals in habitat type classes, by trap session, in Valley County Idaho in 1995 and
1996. Trapping sessions (Trap 1-4) were each 5 days in length. CM grand refers to the
cool moist grand fir habitat type class, dry grand to the dry grand fir habitat type class, and
dry Doug to the dry Douglas-fir habitat type class. Trap sessions with letters above the
columns differed significantly (P < 0.10) by the Kruskal-Wallis test (Miller 1985, Ott 1993,
Toothaker 1991). Dissimilar letters on columns within trapping sessions indicate
significant differences (P < 0.10) (joint ranks multiple comparison test) (Miller 1985,
Toothaker 1991).

47

significantly different between at least some of the habitat type classes, with the cool moist
grand fir habitat type class exhibiting the highest relative capture rates.

A total of 20 meadow voles were caught during all trapping sessions combined
(Appendix A Table 11). Nine meadow voles were captured during the lst trapping session,
6 during the 2nd, 0 during the 3rd, and 5 during the 4th trapping session. For more detailed
information on meadow vole captures see Appendix A Table 11. The Kruskal-Wallis test
detected no significant differences in meadow vole capture rates among habitat type classes
during trapping sessions 1, 2, and 4 (Fig. 12) (P = 0.89, 0.57, and 0.32 respectively). A lack
of captures made it impossible to run the Kruskal-Wallis test on the data from trap session
3. The results from the GLM used for individual trap sessions were in general agreement
with the results from the Kruskal-Wallis test. The GLM for repeated measures found no
significant difference in capture rates among habitat type classes across all trapping sessions
combined (P = 0.59). These results indicate that meadow vole relative abundance was not
different among these 3 habitat type classes.

Vegetative Growth Stages

Only 2 small mammal species showed significant differences between the dry
Douglas-fir medium and small tree BLU’s (Vegetative Growth Stages, here afler referred to
as VGS’s), only 1 of which was consistently different. Many small mammal species
showed no significant differences between VGS’s, or were not caught in numbers sufficient
to make analysis meaningful. The probabilities of a greater P (P values) for these species
can be found in Appendix A Table 14. The Kruskal-Wallis test detected significant
differences in deer mouse relative abundances between the 2 dry Douglas-fir VGS’s during

all 4 trapping sessions (Fig. 13) (P = 0.02, 0.01, 0.02, and 0.02 respectively).

48

Meadow Vole Relative Abundance in Habitat Type
Classes

    
 

CM Grand

Mean Rel. Abundance
O
A

Dry Grand
Hab. Class

Trapping Session

Figure 12. Meadow vole mean relative abundance in habitat type classes, expressed as
mean total capture of individuals by trap session, in Valley County Idaho in 1995 and 1996.
No significant differences (P < 0.10) were found by the Kruskal-Wallis test (Ott 1993).
Trapping sessions (Trap 1-4) were each 5 days in length. CM grand refers to the cool moist
grand fir habitat type class, dry grand to the dry grand fir habitat type class, and dry Doug to
the dry Douglas-fir habitat type class.

49

Deer Mouse Relative Abundance in Vegetative Growth
Stages

16.00
14.00
12.00 , "
10.00
8.00
6.00
4.00 1
2.00.
0.00

    

Mean Rel. Abundance

,/ Medium Tree

    
 

/ Small Tree VGS

Trap 1

Trapping Session

Figure 13. Deer mouse mean relative abundance, expressed as mean total capture of
individuals in dry Douglas-fir vegetative growth stages (V GS), by trap session, in Valley
County Idaho in 1995 and 1996. Trapping sessions (Trap 1-4) were each 5 days in length.
Trap sessions with letters above the columns differed significantly (P < 0.10) by the
Kruskal-Wallis test (Miller 1985, Ott 1993, Toothaker 1991). Dissimilar letters on columns
within trapping sessions indicate significant differences (P < 0.10) (joint ranks multiple
comparison test) (Miller 1985, Toothaker 1991).

50

Deer mouse relative abundance was higher in the medium tree VGS during all 4 trapping
sessions. The only other small mammal to show a significant difference in relative
abundance between VGS’s was the red-backed vole, which showed a significant difference
only during trapping session 2 (P = 0.001) (Fig. 14).
Herptiles

Six species of reptiles and amphibians were captured (Appendix A Table 1). Of
these, only the long-toed salamander (Ambystoma macrodacozlum), and the western toad
(Bufo boreas) were captured in sufficient numbers for statistical analysis. A minimum of 5
individuals had to be captured during a trapping session to be considered to have sufficient
numbers for analysis, so a significant difference could potentially be detected. In addition 5
pacific tree fi'ogs (Pseudacris regilla) were captured in both 1995 and 1996. Two striped
chorus frogs (Pseudacris triseriata) were captured in 1995. One common garter snake
(Thamnophis sirtalis) was caught in 1995, and 2 were captured in 1996. One rubber boa
(Charina bottae) was captured in 1996. These capture results do not necessarily indicate
the abundance of individual species, or the richness of herptile species on the landscape as a
whole. Trapping was conducted only in the interior of forested stands and not in riparian
areas, which is where herptile species are more abundant and diverse in the dry
interrnountain northwest. Had trapping been conducted in riparian areas, more individuals
and species may have been encountered.

Habitat Type Classes

A total of 33 individual long-toed salamanders were captured during all 4 trapping

sessions combined (Appendix A Table 12). One long-toed salamander was captured during

Red-backed Vole Relative Abundance in Vegetative
Growth Stages

3.50

3.00
2.50
2.00

1.50 .

 

1.00

Mean Rel. Abundance

Medium Tree

  
   

0.50

 

VGS

0.00 Small Tree

Trap 1

Trap 2 Trap 3

Trap 4
Trapping Session

Figure 14. Red-backed vole mean relative abundance, expressed as mean total capture of
individuals in dry Douglas-fir vegetative grth stages (V GS), by trap session, in Valley
County Idaho in 1995 and 1996. Trapping sessions (Trap 1—4) were each 5 days in length.
Trap sessions with letters above the columns differed significantly (P < 0.10) by the
Kruskal-Wallis test (Miller 1985, Ott 1993, Toothaker 1991). Dissimilar letters on columns
within trapping sessions indicate significant differences (P < 0.10) (joint ranks multiple
comparison test) (Miller 1985, Toothaker 1991).

 

52

the lst trapping session, 18 during the 2nd trapping session, 9 during the 3rd, and 5 during
the 4th trapping session. For specific capture data for the long-toed salamander see
Appendix A Table 12. The Kruskal-Wallis test showed no significant differences in capture
rates between habitat type classes during any of trap sessions 1 through 4 (Fig. 15) (P =
0.29, 0.11, 0.29, and 0.61 respectively). The GLM applied to individual trapping sessions
gave similar results. The GLM for repeated measures showed no significant difference in
capture rates among habitat type classes (P = 0.20). These results show that the long-toed
salamander was not more abundant in any of the 3 habitat type classes.

Thirty-three individual western toads were captured during all 4 trapping sessions
combined (Appendix A Table 13). No western toads were captured during the lst trapping
session. One western toad was captured during the 2nd trapping session, 26 during the 3rd,
and 6 during the 4th trapping session. For more detailed capture information on the western
toad, see Appendix A Table 13. Since no individuals were captured during the lst trapping
session, no statistical comparisons were possible. No significant difference in capture rates
was detected between habitat type classes for trap sessions 2 through 4 (Fig. 16) (P = 0.39,
0.12, and 0.76 respectively). The GLM applied to individual trap sessions was in general
agreement with these results. The GLM for repeated measures showed no significant
differences in capture rates among habitat type classes (P = 0.21). These results indicate
that the western toad was not significantly more abundant in any of the 3 habitat type

classes.

53

Long-toed Salamander Relative Abundance in Habitat
Type Classes

 

1.87 7 ‘ l
1.6 " '
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0.8 a J
0.6. I
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Mean Rel. Abundance

“£3.22; .4.

. y/‘TCM Grand
.. Dry Grand Hab.Ciass
Dry Doug

 

   

Trap 1 Trap 2 Trap 3

Trapping Session

Figure 15. Long-toed salamander mean relative abundance in habitat type classes,
expressed as mean total capture of individuals by trap session, in Valley County Idaho in
1995 and 1996. No significant differences (P < 0.10) were found by the Kruskal-Wallis test
(Ott 1993). Trapping sessions (Trap 1-4) were each 5 days in length. CM grand refers to
the cool moist grand fir habitat type class, dry grand to the dry grand fir habitat type class,
and dry Doug to the dry Douglas-fir habitat type class.

54

Western Toad Relative Abundance in Habitat Type
Classes

 

 

 

 

 

Mean Rel. Abundance

 

 

Trapping Session

Figure 16. Western toad mean relative abundance in habitat type classes, expressed as
mean total capture of individuals by trap session, in Valley County Idaho in 1995 and 1996.
No significant differences (P < 0.10) were found by the Kruskal-Wallis test (Ott 1993).
Trapping sessions (Trap 1-4) were each 5 days in length. CM grand refers to the cool moist
grand fir habitat type class, dry grand to the dry grand fir habitat type class, and dry Doug to
the dry Douglas-fir habitat type class.

55

Vegetative Growth Stages

The long-toed salamander was the only herptile to show a significant difference
between the 2 dry Douglas-fir VGS’s, based on the Kruskal-Wallis test (P = 0.05 for trap
session 2) (Fig. 17). Long-toed salamander relative abundance was consistently higher in
the small tree VGS through all 3 trap sessions in which captures were made, though not
statistically significant in all. Due to the lack of significant results between VGS’s in other
species, whether due to lack of captures or lack of a relationship, the VGS’s will not be
expanded on firrther here. For probabilities of a greater F see Appendix A Table 15.
Bats

Bat detectors were used to detect and identify 6 species, or species groups, of bats
(Appendix A Table 1). These species consisted of the big brown bat (Eptesicusfirscus),
long-cared myotis (Myotis evotis), hoary bat (Lasiurus cinereus), silver-haired bat
(Lasionycteris noctivagans), myotis species group (Myotis sp.), and Townsend’s big-
eared bat (Plecotus townsendiz). The bat detection equipment can not differentiate among
species of the genus Myotis, which were therefore grouped. The myotis species group
was the most evenly distributed among the VGS’s and habitat type classes. It is
interesting to note that the Townsend’s big-eared bat was detected, as this species was a
C2 candidate for the endangered species list in this part of their range prior to this
category being dropped by the US Fish and Wildlife Service. Only 1 significant
difference was detected by the Kruskal-Wallis test for all tests of bat species relative
presence between habitat type classes, and the dry Douglas-fir VGS’s. This one

significant difference was well within the number of spurious results expected, given

Long-toad Salamander Relative Abundance in
Vegetative Growth Stages

 

3.00

2.507 ,

2.007 *

1.50 7'

1.007'

 

   
   

 

Mean Rel. Abundance

0-50 ' Medium Tree

 

 

VGS

0.00 . ; 1 Small Tree

/

Trap 1 Trap 2

Trap 3 Trap 4

Trapping Session

Figure 17. Long-toed salamander mean relative abundance, expressed as mean total capture
of individuals in dry Douglas-fir vegetative growth stages (V GS), by trap session, in Valley
County Idaho in 1995 and 1996. Trapping sessions (Trap 1-4) were each 5 days in length.
Trap sessions with letters above the columns differed significantly (P < 0.10) by the
Kruskal-Wallis test (Miller 1985, Ott 1993, Toothaker 1991). Dissimilar letters on columns
within trapping sessions indicate significant differences (P < 0.10) (joint ranks multiple
comparison test) (Miller 1985, Toothaker 1991).

57

=0.10 and the number of tests run. Therefore no further analysis was conducted on the
bat data.
Vegetation

Habitat Type Classes, and Vegetative Growth Stages
Analyses were conducted for differences in vegetative and site characteristics

between habitat type classes, as well as the 2 dry Douglas-fir VGS’s. Five significant
differences were detected in habitat variables between habitat type classes (Table 2). The
Kruskal-Wallis test showed a significant difference
in large tree diameter (P = 0.07) among the habitat type classes, with the largest mean
being in the dry grand fir habitat type class. The joint ranking multiple comparison test,
however, was not sensitive enough to detect any differences between habitat type classes.
A significant difference in snag density (P = 0.01) was detected among habitat type
classes. The multiple comparison test detected a difference between the cool moist grand
fir habitat type class and both the dry grand fir, and dry Douglas-fir habitat type classes.
No difference was detected between the dry grand fir and dry Douglas-fir habitat type
classes by the multiple comparison test. A significant difference in stump diameter (P =
0.08) was also detected by the Kruskal-Wallis test. The multiple comparison test showed
a significant difference between the cool moist grand fir and dry Douglas-fir habitat type
classes, but not between either of these and the dry grand fir habitat type class. An
elevational difference was also detected between the habitat type classes (P = 0.0003) by
the Kruskal-Wallis test. The multiple comparison test detected this elevational difference

only between the cool moist grand fir and dry Douglas-fir habitat type classes.

58

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59

Differences between the dry grand fir habitat type class, and both the dry Douglas-fir and
cool moist grand fir habitat type classes were not detected by the multiple comparison
test. A significant difference in the percent slope (P = 0.09) was also detected among
habitat type classes by the Kruskal-Wallis test. The multiple comparison test only
detected the difference in percent slope between the cool moist grand fir and dry
Douglas-fir habitat type classes, but not between the dry grand fir habitat type class and
the other 2 habitat type classes.

The Kruskal-Wallis test also found 5 habitat variables that differed significantly
between the dry Douglas-fir small and medium tree VGS’s (Table 3). Both coarse woody
debris length and diameter were significantly different (P = 0.02) between dry Douglas-
fir VGS’s, with the small tree VGS having the larger mean values in both cases, possibly
due to more recent cutting. Aspect was also found to be different between VGS’s by the
Kruskal-Wallis test (P = 0.02), possibly due to chance. The medium tree VGS showed
the more northerly mean aspects. The Kruskal-Wallis test also detected a difference in
the percentage of the ground covered by forest litter (P= 0.02) between the dry Douglas-
fir VGS’s, possibly due to time since harvest. The medium tree VGS had the higher
percent litter coverage. The Kruskal-Wallis test detected a difference between the 2
VGS’s in the percent vertical coverage of woody vegetation in the lst meter above the
ground (P = 0.06). Again, the higher mean vertical coverage was present in the medium

tree VGS.

60

Table 3. Means (standard errors) and test results of vegetation and site variables by
VGS’s, in Valley County, Idaho, in 1995 and 1996.

 

 

 

Dry Douglas Fir ELU's

Variable Small Tree Med. Tree Prob. > F

Coarse Woody Debris Length (m) 7.48(1.44) 5.12(0.3l) 0.02
Coarse Woody Debris Diameter (cm) 36.86(4.13) 24.64(2.63) 0.02
Coarse Woody Debris Density (#lha) 398.77( 18.68) 634.97(232.55) 0.57
Large Tree dbh (cm) 29.76(2.06) 32.33(1.96) 0.33
Large Tree Bole Height (m) 3.87(0.74) 5.19(0.4S) 0.33
Large Tree Density (#lha) 183.19(6.26) 245.97(99.51) 0.57
Small Tree Height (m) 1.46(0.23) l.62(0.18) 0.57
Small Tree Density (#lha) 802.75(324.31) 1060.04(601.16) 0.85
Snag dbh (cm) 22.92(2.85) 16.90(2.8) 0.33
Snag Density (#lha) 30.88(12.35) 21.6l(7.77) 0.85
Stump Diameter (cm) 40.76(6.56) 25.96(9.25) 0.33
Stump Density (#lha) 227.45(94.48) 337.57(145.39) 0.57
Elevation (m) 1572(13) 1567(16) 0.85
Aspect (Degrees) 150.21(29.42) 292.96(l 7.76) 0.02
Slope (%) 15.75(l .98) 21.50(5.97) 0.57
Canopy Closure (%) 37.01(5.l9) 46.24(2.73) 0.14
Horizontal Cover First 1/4m (%) 38.11(3.60) 33.20(6.09) 0.33
Horizontal Cover Second 1/4m (%) 12.38(1.81) 15.54(5.61) 0.85
Horizontal Cover Third l/4m (%) 5.86(1.75) 6.75(3.45) 0.85
Horizontal Cover Fourth 1/4m (%) 5.52(l.81) 3.73(2.04) 0.57
Ground Cover Herb. Veg. (%) 32.56(5.90) 35.11(2.75) 0.85
Ground Cover Woody Veg. (%) 2.0(0.58) 2220.16) 1.00
Ground Cover Litter (%) 93.0(3.27) 99.67(0.19) 0.02
Cover < 1m Herb. Veg. (%) 62.33(8.96) 64.89(8.96) 0.85
Cover < 1m Woody Veg. (%) 14.44(4.64) 33.44(8.20) 0.06
Cover < 1m Litter (%) 43.22(9.85) 47.89(4.62) 0.85
Cover > 1m Herb. Veg. (%) 0.11(0.11) 0.0(0.0) 0.37
Cover > 1m Woody Veg. (%) 20.0(4.8l) 27.67(2.14) 0.33
Cover >1m Forest Litter (%) 0.89(0.73) 0.0(0.0) 0.13

 

Probability of a greater P is from the Kruskal-Wallis test, significant differences
(P < 0.10) bolded (Ott 1993).

 

61

Mammal Species, and Habitat Characteristics

Mammal species were compared with habitat variables that differed significantly
among the relative abundance groups of each species. As mentioned earlier, only species
relationships with habitat variables that are well supported biologically or in the literature
will be presented in this section. Variables that do not represent reported relationships
are presented in Appendix B.

Having broken stands into groups based on breaks in the relative abundance of
each species, the Kruskal-Wallis test was used to identify significant differences (P <
0.10) in individual habitat variables among the relative abundance groups. Since the
Kruskal-Wallis test works on the ranks of the data, extreme values should not have had a
great influence on the results. Significant differences in habitat variables among the
relative abundance groups indicated a potential relationship between the variable and the
species whose relative abundance was used to form those groups. The number of
significant differences, among the 29 variables, ranged from 2 to 7 for different species.
For 3 species, the number of groups the data were broken into was changed from 4 to 2,
and fi'om 4 to 3, with changes in detected relationships not changing appreciably in 2 of
the 3 species. This indicates that group membership should not have had a major
influence on the relationships that were detected. Relative abundance groups were
numbered from lowest relative abundances (group 1) to highest relative abundances
(groups 2, 3, or 4).When the results of the joint ranks multiple comparison test (multiple
comparison test) are listed, they will always be given with the group with the highest

mean value for that variable lst, and the next group with a mean value that is

62

significantly different 2nd. Any group listed as not being significantly different from the
group with the highest mean will have a mean between the 2 significantly different
groups.

For the red-backed vole, the Kruskal-Wallis test detected significant differences
(P < 0.10) between its 2 relative abundance groups in 7 habitat variables (see Table 4), 4
of which are well supported. With only 2 red-backed vole relative abundance groups, it
was not necessary to use the multiple comparison test. A difference was detected in
coarse woody debris diameter between relative abundance groups (P = 0.09), with higher
relative abundance corresponding to greater diameter. A difference between groups was
also detected in large tree dbh (P = 0.05), with group 1 having the higher mean value.
The density of small trees also differed significantly between relative abundance groups
(P = 0.08), with group 1 having higher densities of small trees. Stump diameters were
also significantly different between the 2 relative abundance groups (P = 0.03), group 2
having the higher mean values.

The Kruskal-Wallis test detected significant differences (P < 0.10) in 4 habitat
variables among the 3 relative abundance groups of the dusky shrew (Table 4), all 4 of
which are well supported. All 4 significant differences were in the horizontal cover
measurements taken at l/4m increments above the ground. The probabilities of a greater
F were 0.00, 0.02, 0.04, and 0.08 for the lst through 4th l/4m increments, respectively.
The multiple comparison test found a significant difference among relative abundance
groups 3 and 1 for all of the lst 3 l/4m increments, but no groups were found different

from group 2. In the 4th 1/4m increment, the multiple comparison test was too

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64

conservative to detect a significant difference in horizontal cover between any of the 3
dusky shrew relative abundance groups.

Four habitat variables were found to have significantly different mean values
among the 3 relative abundance groups of the masked shrew (Table 4), 1 of which is well
supported. The Kruskal-Wallis test detected a significant difference in mean large tree
diameter (P = 0.03) between the masked shrew relative abundance groups. The multiple
comparison test detected a significant difference between groups 1 and 3. Neither group
differed significantly from group 2.

The vagrant shrew showed significant differences in 7 habitat variables, between
its 2 relative abundance groups (Table 5), 2 of which are well supported. Due to the
existence of only 2 relative abundance groups for the vagrant shrew, the use of the
multiple comparison test was not necessary to separate out significantly different groups.
The Kruskal-Wallis test detected a significant difference between the vagrant shrew
relative abundance groups in large tree dbh (P = 0.06), with group 1 having the higher
mean tree diameter. Large tree bole height was also found to differ significantly between
the 2 groups (P = 0.03), with group 1 having the higher mean bole height.

The Kruskal-Wallis test detected significant differences in 4 habitat variables
between the 2 relative abundance groups of the golden-mantled ground squirrel (Table 5).
Two of these relationships are well supported. The percent vertical cover of forest litter
was significantly greater in the lst relative abundance group for both the lst meter above

the ground, and above 1m (P = 0.07, and 0.04 respectively).

65

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66

Four habitat variables were found to differ significantly between the 2 relative
abundance groups of the meadow vole (Table 5), 2 of which are well supported. Large
tree bole height was significantly higher in the meadow vole’s relative abundance group 1
(P = 0.02). In addition, the Kurskal-Wallis test showed percent canopy closure to be
significantly higher (P = 0.01) in the lst relative abundance group for the meadow vole.

Herptile Species and Habitat Characteristics

Among the 3 relative abundance groups of the western toad, the Kruskal-Wallis
test detected 5 habitat variables which differed significantly (Table 6), l of which is well
supported. Stump density was significantly different among relative abundance groups
for the western toad (P = 0.03). The multiple comparison test found relative abundance
group 2 to have significantly higher stump densities than group 1, but not than group 3.

Six habitat variables showed significant differences between the 2 relative
abundance groups of the long-toed salamander (Table 6), all of which are well supported.
Mean large tree density was greater in relative abundance group 1 of the long-toed
salamander (P = 0.03), according to the results of the Kruskal-Wallis test. Snag densities
were also greater in relative abundance group 1 of the long-toed salamander (P = 0.04).
The Kruskal-Wallis test also detected a significant difference in the elevation of stands
between the relative abundance groups (P = 0.07), with group 1 having higher mean
elevations. The percent vertical cover of woody vegetation in both the lst meter above
the ground, and above 1m, was higher in the 1st relative abundance group of the long-
toed salamander (P = 0.06, and 0.0001 respectively). Relative abundance group 1 also

had a higher percent vertical cover of forest litter in the lst meter above the ground (P =

0.02).

67

Table 6. Results of tests for significant differences in habitat variables between relative
abundance groups of the long-toed salamander and western toad, in Valley County,
Idaho, in 1995 and 1996.

 

 

 

Species
Variable Long-toed Salamander Western Toad
Coarse Woody Debris Diameter (cm) Not Sig.
Large Tree Bole Height (m) Not Sig.
Large Tree Density (#/ha) Sig. Not
Small Tree Height (m) Not Sig.
Snag Density (#lha) Sig. Not
Stump Density (#/ha) Not Sig.
Elevation (m) Sig. Not
Cover < 1m Woody Veg. (%) Sig. Not
Cover < 1m Litter (%) Sig. Not
Cover > 1m Woody Veg. (%) Sig. Sig.

 

"Sig." represents a significant difference (P < 0.10) in the Kruskal-Wallis test; "Not"
indicates no difference detected (Ott 1993)

DISCUSSION

Analysis

When considering the results of the analysis, there are several important points to
keep in mind. First, the sample size for this study was relatively small, with only 19
stands sampled and only 18 used in the analysis of any 1 trapping session. Each habitat
type class included just 6 to 7 stands. The 2 dry Douglas-fir vegetative growth stages
used in the analysis included only 3 stands. At smaller sample sizes, the probability of a
Type 2 error (failing to reject Ho when a true difference existed) is relatively high, and
results in a lower power of the test (ability of the test to identify significant differences).
Due to the small sample size, and its effect on the probability of a Type 2 error, some true
relationships may not have shown up as strong, or at all in the analysis. There may be
more relationships between species and ecological units, or habitat variables than the
analysis was able to detect. Because the probability of a Type 2 error and the probability
of a Type 1 error (rejecting Ho when no true difference existed) share an inverse
relationship, a somewhat liberal or was chosen (a = 0.10) to partially mitigate the effects
of the small sample size and increase the power of the test.

Choosing a somewhat liberal or created the 2nd important consideration when

considering the results of the analysis. With the large number of species, habitat type

68

69

classes, ELU’s, and habitat variables, a large number of tests were required to analyze the
data. Since there were a large number of tests done at the 90% level of confidence, the
number of type 1 errors that may have been committed was somewhat high. For the 12
species tested for differences in relative abundance among habitat type classes, 12 tests
were run per trapping session, so 1 Type 1 error could have resulted in each trapping
session at a = 0.10. One Type 1 error could also have occurred in each trapping session
for the tests between the dry Douglas-fir VGS’s. Eight significant differences were found
in the species by habitat type class tests, and 6 in the species by dry Douglas-fir VGS’s,
for all 4 trapping sessions. Twenty-nine habitat variables were tested for differences
among the relative abundance groups of each species. Of these 29 tests per species, up to
3 Type 1 errors could have occurred. However, just because a certain number of Type 1
errors could occur does not mean that they did occur. Only species relationships with
habitat variables that are well supported biologically and in the literature will be
discussed in this section (see Appendix B for others). And, there are ways to help
identify some of the spurious results. Since the analysis was done to identify habitat
associations and the habitat characteristics which could help to explain them, and major
management decisions would not be based on the results of this analysis, it was decided
that setting a at 0.10 gave the best balance between the problems with the Type 1 and
Type 2 errors.

It is important to recognize that where a significant difference was detected by the
tests, there is some support for a relationship. Where significant results for a relationship

are detected more than 1 time, or in multiple ways, further support is provided for that

70

relationship. For example, if a species shows a significantly different relative abundance
between habitat type classes during more than 1 trapping session, and has a mean capture
rate that is consistently higher in one habitat type class, there would be more support for a
relationship than if a significant difference was only detected once. If another test, such
as the GLM for repeated measures, finds a significant difference in relative abundance for
a species that also showed significant differences in individual trapping sessions, the
relationship receives firrther support.

When considering a species association with a specific habitat type class, or
vegetative growth stage, it could be helpful to keep in mind the specific attributes that
characterize that class or stage. If a species shows a relationship with a habitat variable
that also characterizes a habitat type class, or VGS that the species is also associated with,
it could be considered as added support for the species association with that ecological
unit, as well as for the influence the variable has on the species. It could help to explain
why the species is associated with the particular habitat type class, or VGS.

Habitat Type Classes, and Vegetative Growth Stages

The study areas in the cool moist grand fir habitat type class were characterized
by steeper slopes, and higher elevations than the dry Douglas-fir habitat type class, but
not steeper than the dry grand fir stands (Table 2). Moisture and temperature regimes are
strongly influenced by elevation in the inland northwest and grand fir is a wetter area tree
species than Douglas-fir. At these higher elevations, mountain slopes may be steeper
than the lower elevation slopes, where Douglas-fir is more dominant. Snag densities

were larger on cool moist grand fir sites than on either dry Douglas-fir, or dry grand fir

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sites. Stump diameters were also greater on cool moist and dry grand fir sites than on
dry Douglas-fir sites. More available moisture may allow grand fir site trees to increase
in size faster, and therefore be larger when they are cut. Large tree dbh was greater on
dry grand fir, and dry Douglas-fir sites, than on cool moist grand fir sites. This may be
due to the fact that only 1 of the 7 cool moist grand fir sites was a medium tree stand,
while the 6 stands of the dry grand fir and dry Douglas-fir habitat type classes had 2 and
3 medium tree stands respectively. No difference was found between the mean dbh of
dry grand fir and dry Douglas-fir stands, nor between the small and medium tree VGS’s
within the dry Douglas-fir.

Dry Douglas-fir small tree stands tended to have larger pieces of coarse woody
debris of greater length and diameter (Table 3). This could be due to the debris left when
the larger trees in these stands were cut or died. Medium tree stands tended to have
higher ground cover of forest litter, and higher percent cover of woody vegetation in the
lst meter above the ground. The higher vertical cover of woody vegetation could be due
to moister conditions under the canopy of larger trees, or shrubs may take longer to
reestablish on sites after disturbance, but I have no data to support this hypothesis. The
higher ground cover of litter is probably the result of the larger trees and higher densities
of brush. Medium tree dry Douglas-fir stands were found to be on significantly more
northerly aspects. No explanation can be offered for this.

Mammal Species
The red-backed vole shows some of the strongest support for an association with a

specific habitat type class. Red-backed vole relative abundance was significantly

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different among habitat type classes in 2 of the 4 trapping sessions (Fig. 5). The multiple
comparison test found red-backed vole relative abundance to be highest in the cool moist
grand fir habitat type class during 1 of those 2 trapping sessions. The multiple
comparison test didn’t detect a difference among habitat type classes during the other
trapping session, however this test can be too conservative or insensitive to detect
differences where true differences are identified by the Kruskal-Wallis test (Miller 1985).
The GLM for repeated measures showed a significant difference in red-backed vole
abundance across all trapping sessions as a whole as well. Though not statistically
significant, mean relative abundance was highest in the cool moist grand fir habitat type
during all 4 trapping sessions, while the other 2 habitat type classes switched back and
forth in the other 2 positions. Moris (1996) identified the red-backed vole as a mesic
specialist. This fits in well with the red-backed vole being associated with the moister
grand fir habitat type class. Walters (1991), and Wywialowski and Smith (1988) also
identified the red-back vole as preferring moister conditions. Several habitat variables
appear to be involved in the association that the red-backed vole shows with the cool
moist grand fir habitat type class. The cool moist grand fir habitat types tended to have
larger stump diameters, and smaller large tree diameters (Table 2). These same 2
relationships were found to be significant in the same way with red-backed vole relative
abundance groups (Table 4). Hays and Cross (1987), and Belk et al. (1988) identified
preference of red backed voles for areas with larger pieces of coarse woody debris.
Wywialowski and Smith (1988) found a preference for stands with smaller, less dense

trees. The red-backed vole also had consistently higher mean relative abundances in the

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dry Douglas-fir small tree VGS during all 4 trapping sessions, though only significantly
different during the 3rd (Fig. 14). Coarse woody debris diameter was significantly higher
in the small tree VGS (Table 3), and in the stands of the higher red-backed vole relative
abundance group (Table 4). Coarse woody debris length was also significantly higher in
the small tree VGS. Perhaps coarse woody debris volume would be a better measure to
use in future investigations. Again, Hays and Cross (1987), and Belk et al. (1988)
identified preference of red backed voles for areas with larger downed logs. This makes
sense, because red-backed voles nest under large pieces of decaying woody debris (Burt
and Grossenheider 1980).

Several other factors may be influencing red-backed vole relative abundance.
Elevation was greater in the cool moist grand fir habitat type class (Table 2). Elevation
has a significant influence on both the temperature and moisture regimes in the
intermountian northwest, and red-backed voles have been shown to prefer cool moist
forests (Moris 1996, Walters 1991). In the species relative abundance group analysis,
small tree density was negatively related to red-backed vole relative abundance (T able 4).
This negative relationship may be due to the competition that occurs between small trees
and other green vegetation, the red-backed vole’s primary food source.

There is also some fairly good support for masked shrew being associated with
some habitat type classes. A significant difference in masked shrew relative abundance
was detected by the Kruskal-Wallis test among habitat type classes in 2 of the 4 trapping
sessions. The multiple comparison test showed the cool moist grand fir habitat type class

to have higher relative abundances of masked shrews than the dry Douglas-fir, but not

74

dry grand fir habitat type classes. The multiple comparison test found no differences
among habitat type classes during the other trapping session, which showed a significant
difference by the Kruskal-Wallis test (Fig. 7). This may be due to the multiple
comparison test being too conservative, or too insensitive, to separate out the habitat type
classes (Miller 1985). The GLM for repeated measures found masked shrew relative
abundance to be significantly different between habitat type classes as well. Though not
statistically significant, it is interesting to note that masked shrew mean relative
abundance was highest in the cool moist grand fir habitat type class, and 2nd highest in
the dry grand fir habitat type class, during all 4 trapping sessions. MacCracken et al.
(1985) found the masked shrew to prefer wetter sites, with greater canopy cover for
moisture retention. Wrigley et al. (1979) found that the masked shrew preferred wetter
conditions as well. The results of both these studies support the association between the
masked shrew and the wetter grand fir habitat type classes, if not the cool moist grand fir
habitat type class. Only 1 potentially important habitat variable for the masked shrew
was detected in both the habitat type class by vegetation, and species relative abundance
by vegetation analysis. Mean large tree dbh was significantly different among habitat
type classes (Tables 2 and 4). Again, the multiple comparison test was incapable of
separating out which habitat type classes were different. The mean large tree dbh was
smaller in the cool moist grand fir habitat type class however, and the masked shrew
relative abundance group with the largest mean values had the smallest mean large tree
dbh’s. Large trees with smaller diameters would have smaller canopies which, at lower

densities, could allow for more shrub cover, providing good moisture and food

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availability for the masked shrew (MacCracken et a1 1985). Masked shrews only
occurred in the dry Douglas-fir habitat type class in one of the 4 trapping sessions. No
significant difference in masked shrew relative abundance was detected between the
VGS’s during that trapping session.

One habitat variable that shows a potential relationship with masked shrew
abundance, through the association with the grand fir habitat type classes is stump
diameter (Table 2). Masked shrews are known to nest in stumps (Burt and
Grosssenheider 1980). Larger stumps could provide more nesting area, with greater
protection from predators. This source of decaying woody material may also afford
hunting areas with larger densities of bugs, the masked shrews primary food source
(Whitaker et al. 1980), with greater protection from other predators.

The northern pocket gopher also showed significant differences in relative
abundance among habitat type classes during 2 of 3 trapping sessions where the species
was captured (Fig. 11). Pocket gopher relative abundances were higher in cool moist
grand fir stands during both trapping sessions where the Kruskal-Wallis test detected a
significant difference, though the multiple comparison test was too conservative to
separate the habitat type classes in them (Miller 1985). The relative abundance of pocket
gophers was higher in the cool moist grand fir habitat type class during the trapping
session where no significant difference was detected as well. Also, the GLM for repeated
measures detected a significant difference in pocket gopher relative abundance between
habitat type classes. Some authors have debated whether or not pocket gophers prefer

digging in moister soils (Scrivner and Smith 1981). Soils would tend to be moister in the

76

cool moist grand fir habitat type class. Stump diameter was significantly larger in the
cool moist and dry grand fir classes as well as in the higher relative abundance group
(Tables 2 and 5). Scrivner and Smith (1981) found pocket gophers to be more abundant
in stands with higher densities of smaller shrubs. They hypothesized that the areas with
smaller shrubs provided higher densities of palatable roots (Scrivner and Smith 1981).
Perhaps the cutting of the large trees released the understory species and smaller trees
allowing the increase of palatable roots and tubers, the pocket gophers primary food
source.

Dwarf shrew relative abundance was significantly different among habitat type
classes during the lst trapping session, though the multiple comparison test was too
conservative or insensitive to distinguish between the habitat type classes (Fig. 9) (Miller
1985). Though not significant, dwarf shrew relative abundance showed its highest mean
values in the cool moist grand fir habitat type class during 3 trapping sessions, but was
lowest in the cool moist grand fir habitat type class during the other (Fig. 9). The GLM
for repeated measures showed a significant difference in dwarf shrew relative abundance
among habitat type classes as well. MacCracken et al. (1985) found that dwarf shrews
preferred moister conditions. Their finding is in keeping with the potential association of
dwarf shrews with the cool moist grand fir habitat class. However, Hoffman and Owen
(1980) depict the dwarf shrew as being able to take advantage of a wide variety of
conditions, including doing well on drier sites, and found the species to be common in
rocky areas. If the requirements of dwarf shrews are similar to those of other long-tailed

shrews, as Whitaker et al. (1980) presumed, the dwarf shrew may nest in stumps and

77

logs, and feed primarily on insects. These nesting areas and food sources would be more
abundant in the larger stumps of the cool moist grand fir habitat type class. Dwarf
shrews, as most shrews, could be a wet area species, which also fits in with conditions of
the cool moist grand fir habitat type class. However, support for an association between
dwarf shrews and the cool moist grand fir habitat type class is pretty weak, and
contradictions are present in the literature (Hoffman and Owen 1980). Larger sample
sizes, and higher capture rates would be needed in future investigations to sort this out.
There is some support for an association between the deer mouse and habitat type
classes, though this support is weak. Deer mouse density was significantly different
between habitat type classes during 1 of the 4 trapping sessions (Fig. 4). The GLM for
repeated measures, however, showed no significant difference between habitat type
classes. While not significant, it is interesting to note that mean deer mouse relative
abundance was highest in the cool moist grand fir habitat type class, and 2nd highest in
the dry Douglas-fir habitat type class, during all 4 trapping sessions. Deer mice were
caught in large numbers, but apparently larger sample sizes will be needed to clarify this
relationship. Much stronger evidence exists for an association between the deer mouse
and the dry Douglas-fir medium tree VGS. Deer mouse relative abundance was
significantly higher in the dry Douglas-fir medium tree VGS during all 4 trapping
sessions (Fig. 13). The GLM for repeated measures also detected a significant difference
in deer mouse relative abundance between VGS’s. No habitat variables were
significantly related to deer mouse relative abundance in more than one way, possibly

owing to the deer mouse’s “generalist” nature (Walters 1991).

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The yellow-pine chipmunk was not associated with any of the habitat types or
VGS’s investigated (F ig. 3 and Appendix A Table 14). The yellow-pine chipmunk is
known to be a generalist species (Moris 1996). Larger sample sizes in future
investigations may help to identify any associations which were not detected due to small
sample size and high variability.

The dusky shrew was not associated with any of the habitat type classes or VGS’s
tested (Fig. 6 and Appendix A Table 14). This makes sense, because the dusky shrew
uses a relatively broad range of habitats, from marshes to dry hillsides (Burt and
Grossenheider 1980). Dusky shrew relative abundance was significantly higher with
greater percent horizontal cover in all 4 of the 1/4m increments up to 1m above the
ground (Table 4). Belk et al. (1990), Smith and Belk (1996), and Terry (1981) all found
the dusky shrew to be most abundant in areas with dense understories and lots of coarse
woody debris. These 2 habitat characteristics would both increase the horizontal cover on
sites. Being small and susceptible to predation in the open, it makes sense that dusky
shrews would prefer areas with greater visual obscurity. The higher densities of
vegetation which produces the greater horizontal cover would also provide larger
densities of insects, the dusky shrews primary prey. Since the dusky shrew showed this
relationship with horizontal cover in all 4 strata, there is fairly strong support for this
relationship. However, it may merely mean that the 4 strata should have been combined
into 1 measurement. Unfortunately, the 4 strata were not strongly correlated enough to

combine.

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The vagrant shrew showed no association with any of the habitat type classes, or
VGS’s tested (Fig. 8, and Appendix A Table 14). Vagrant shrews are known to prefer
cool moist conditions (Whitaker et a1. 1980), and sample sizes may have been too small
to see a difference between habitat type classes. Several habitat variables were found to
differ significantly between vagrant shrew relative abundance groups (Table 5). Large
tree dbh, and bole height were inversely related to vagrant shrew relative abundance.
Terry (1981) found vagrant shrew abundance to be higher in more open patchy stands.
Higher densities of herbaceous vegetation would provide hiding cover, and higher insect
densities for the vagrant shrew to feed on. Perhaps vagrant shrews prefer the denser
understories of smaller tree stands. Vagrant shrews were also more commonly captured
in areas with a higher herbaceous cover over Im from the ground.

The golden-mantled ground squirrel showed no association with any of the habitat
type classes, or VGS’s tested (Fig. 10, and Appendix A Table 14). The golden-mantled
ground squirrel was found to be more abundant in areas with lower vertical cover of
forest litter up to and above 1m (Table 5). This makes sense when considering that the
species is known to prefer more open forests. Bartels and Thompson (1993) reported
that this species prefers open pine forest with light brush, high densities of stumps and
logs, and rocky areas.

Meadow vole relative abundance was not associated with any of the habitat type
classes, or VGS’s tested (Fig. 12, and Appendix A Table 14). Several habitat variables
which could influence meadow vole abundance were identified by the Kruskal-Wallis test

(Table 5). Meadow vole relative abundance was significantly lower in stands with higher

80

large tree bole heights, and greater canopy closures. This makes sense, because meadow
voles are known to prefer forests with more open understories (Burt and Grossenheider
1980). Reich (1981) found meadow vole abundance to be higher in areas with higher
cover of herbaceous vegetation.
Herptiles

The long-toed salamander showed no association with habitat type classes (Fig.
15). There is some support however for the long-toed salamander being more abundant
in the dry Douglas-fir small tree VGS. The Kruskal-Wallis test detected a significant
difference in long-toed salamander relative abundance between the 2 VGS’s during 1 of
the 3 trapping sessions in which salamanders were caught (Fig. 17, and Appendix A
Table 15). The GLM for repeated measures also detected a significant difference in
salamander relative abundance between the VGS’s. Though not significant, it is
interesting to note that mean long-toed salamander relative abundance was highest in the
small tree VGS during all 3 trapping sessions in which they were caught. This support
for an association between long-toed salamanders and the small tree dry Douglas-fir VGS
is somewhat limited, and thus unclear. Perhaps obtaining larger sample sizes in future
investigations could clarify this potential relationship. The percent vertical cover of
woody vegetation in the lst meter above the ground was significantly lower in the small
tree VGS (Table 3), and was also lower for both the lst meter above the ground and
above 1m in the higher long-toed salamander relative abundance groups (Table 6). Also
the percent litter cover at ground level was lower in the small tree VGS, and the percent

litter cover in the lst meter above the ground was lower in the higher relative abundance

81

group. Having these 2 variables in common between the preferred VGS, and the higher
relative abundance group, provides further support for an association between long-toed
salamanders and the dry Douglas-fir small tree VGS, and seem to indicate that the long-
toed salamander prefers more open forest conditions.

Long-toed salamanders may be associated with other habitat variables that were
not identified by more than one testing format. In dry Douglas-fir small tree stands,
coarse woody debris tended to be longer and larger in diameter (Table 3). Larger volume
pieces of decaying coarse woody debris would maintain cool moist conditions in their
interiors better than smaller pieces. Long-toed salamanders are known to spend much of
their time in decaying logs and stumps, especially during the drier periods of the year
(Behler 1979, Nussbaum et al. 1983). Aspect was also found to be significantly different
between VGS’s, with small tree stands found on more southerly facing sites. Also
relative abundance was higher at lower elevations (Table 6). These warmer south-facing
low elevation sites may have afforded longer periods of proper temperature conditions for
long-toed salamanders to feed and move about the landscape. Howard and Wallace
(1985) found that long-toed salamanders at lower elevations produced more eggs and
metamorphosed at earlier ages. This could account for there being more salamanders
captured at lower elevation sites. Snag and large tree density were inversely related to
salamander abundance. This could be related to a preference for more open forest stands,
though I could find nothing in the literature to support such a preference.

The western toad was not associated with either habitat type classes or VGS’s

(Fig. 16, and Appendix A Table 15), though several habitat characteristics that may be

82

important to western toads were identified (Table 6). Stump densities were greater in the
higher relative abundance groups. Decaying stumps hold moisture during dry periods
and may provide good micro climate conditions for western toads (Nussbaum et al.
1983). Decaying stumps also leave macropores into the soil when their roots rot away,
these macro-pores are often used by herptiles to access the better microconditions below
the surface (Jones 1986).
Bats

No associations of bat species with habitat type classes or VGS’s were identified
in the analysis. Potential reasons for the lack of associations between bat species and
habitat type classes could have been the limited range of habitat type classes that were
investigated. Krusic et a1. (1996) found differences in bat activity between forest types in
White Mountain National Forest, New Hampshire. But Krusic et al. (1996) were testing
between spruce-fir, and northern hardwood forests. Perhaps if habitat type classes that
were more distinctly different in nature were investigated in the future some associations
could be found. Huff et al. (1993) found stand age to be the best indicator of bat activity.
In addition, Krusic et al. (1996) found that within stand bat activity was greatest in
overrnature stands (> 119 yrs.), and in regenerating stands (0-9 yrs.). The lack of bat
species associations with VGS’s may simply have been due to the use of too fine of a
temporal scale for bats.
Ecosystem Diversity Matrix

One of the purposes for trying to identify species associations with different

habitat type classes, and vegetative growth stages was to determine if the ecosystem

83

diversity matrix, which these classifications came from, is capable of representing the
diversity of species and the habitat components that they are dependent upon on the
landscape. If the EDM is capable of representing this diversity through its ELU’s,
ecosystem management approaches based on the use of this tool could be a viable option
for planning forest management activities. The big question is, can the EDM represent
the diversity of all indigenous species and their requirements on the planning landscape.
By identifying species associations with habitat type classes, and VGS’s, more support is
given to the ability of the EDM to represent this diversity.

Results of this study indicate that there is some potential for the EDM to represent
the diversity of species on the landscape, based on species associations with the
ecological units of the EDM. Several potential species associations with both habitat
type classes, and VGS’s were identified. Three of the associations of species found with
habitat type classes have fairly good support. The red-backed vole, masked shrew, and
northern pocket gopher had relative abundances that were significantly different between
habitat type classes in 2 of the 4 trapping sessions, and the GLM for repeated measures
used for all trapping sessions together lent further support for this. Some habitat
variables that differed significantly among habitat type classes also varied significantly
with the relative abundance of these species, and could potentially explain those
associations. Fairly strong support also exists for the deer mouse’s association with the
dry Douglas-fir medium tree VGS. This species showed significant differences in
relative abundance between the 2 VGS’s in all 4 trapping sessions, and with the GLM for

repeated measures used for all trapping sessions together lent further support. The dwarf

84

shrew and deer mouse showed potential associations with habitat type classes, but with
less strong support. The red-backed vole and long-toed salamander also showed potential
associations with one of the dry Douglas-fir VGS’s, but with less strong support.

These species associations lend further support to the usefulness of the EDM for
representing the diversity of species and their habitat requirements on the landscape,
when a few other factors are considered. First, trapping of species, and testing for
differences in relative abundances between habitat type classes was only done for 3 of the
11 habitat type classes represented in the matrix. These 3 habitat type classes were also
located beside each other on the matrix, indicating that they were more similar to each
other than many other habitat type classes of the matrix. Had the study investigated
species associations in more widely separated, or more habitat type classes in the EDM,
more associations between species and habitat type classes could potentially have been
found. Statistical tests for differences in small mammal and herptile relative abundance
were only done for 2 of the 14 VGS’s of the matrix, and only in one habitat type class.
Had more and/or more widely separated VGS’s in additional habitat type classes (ELU’s)
been investigated, it is very likely that more species associations would have been found.

Much more research involving many more species and cells of the EDM will be
necessary to prove that the EDM can represent the full indigenous range of biodiversity
on the planning landscape. Only the most common and easily captured members of the
small mammal, and herptile communities were investigated. While these species may
give a hint as to the usefirlness of the EDM for representing the diversity and habitat
requirements of small mammal and herptile species, small mammals and herptiles only

comprise a very small portion of the biota that ecosystem management strives to

85

represent. However, it is my opinion that the results of this study in conjunction with its
previously mentioned limitations does give some support to the usefulness the EDM as a
viable tool for representing the diversity of indigenous species, and their habitat

requirements, in ecosystem management.

CONCLUSIONS

The results of this research indicate that several species were associated with
different habitat type classes. The red-backed vole, masked shrew, and northern pocket
gopher showed relatively strong support for an association with a habitat type class, and
the dwarf shrew and deer mouse showed less strong support for an association with a
habitat type class. These associations indicate that, at least for the species investigated,
the habitat type classes of the EDM are properly chosen to represent the diversity of
species and their needs on the landscape.

Several associations of species with VGS’s were identified. There was strong
support for an association between the deer mouse and the medium tree dry Douglas-fir
VGS, and less strong support for an association between both the red-backed vole and
long-toed salamander with the small tree dry Douglas-fir VGS. These associations
indicate that the vegetative growth stages of the EDM were properly chosen to represent
the diversity and habitat needs of the diversity of species across the landscape.

Some site and vegetation characteristics were identified as important to different
species. Some of these relationships were useful in explaining the reasons for species
associations with different habitat type classes and VGS’s, others were not. Due to the
high variability of site and vegetation characteristics within stands, and small sample

sizes, much of this vegetation analysis is not strongly supported by our biological

86

87

knowledge of these species. The high variability of conditions within stands may have
made it more difficult to identify important site and vegetation characteristics than if the
stands had been more uniform. More research with larger sample sizes will be necessary
to sort these relationships out.

More research on a wider variety of species will be needed to strengthen the
support, provided by this research, for the EDM’s usefulness as an ecosystem
management tool. Future research will also need to include more cells of the EDM.
However, given the species associations with both habitat type classes and VGS’s
identified in this research, there is support for the usefulness of the EDM as an ecosystem
management tool for representing the diversity of species and their habitat needs across

the landscape.

LITERATURE CITED

LITERATURE CITED

Anderson, S. H. 1985. Managing our wildlife resources. 2nd ed. Prentice Hall Inc.,
Englewood Cliffs, NJ. 492pp.

Arno, S. F. 1980. Forest fire history in the northern rockies. J. For. 78:460-465.
, and T. D. Peterson. 1983. Variation in estimates of fire intervals: a closer look at
fire history on the Bitterroot National Forest. USDA For. Serv. Res. Pap. INT-301.
Interrnountain For. Range Exp. Stn., Ogden UT. 1-8pp.

Bartels, M. A., and D. P. Thompson. 1993. Spermophilus lateralis. Mammal. Species. No.
440 1-8pp.

Behler, J. S. 1979. The Audubon Society field guide to North American reptiles and
amphibians. Alfred A. Knopf Inc. Press, New York, NY 743pp.

Belk, M. C., H. D. Smith, and J. Lawson. 1988. Use and partitioning of montane habitat by
small mammals. J. Mammal. 69:689-695.

, C. L. Pritchett, and H. D. Smith. 1990. Patterns of microhabitat use by Sorex
monticolus in summer. Great Basin Nat. 71 :387-3 89.

Brown, L. N. 1967. Ecological distribution of six species of shrews and comparison of
sampling methods in the central Rocky Mountains. J. Mammal. 48:617-623.

Burt, W. H., and R. P. Grossenheider 1980. A field guide to the mammals of North
America north of Mexico. Houghton Mifflin Co. Boston, MA. 289pp.

Bury, R. B., and P. S. Corn. 1987. Evaluation of pitfall trapping in northwestern forests:
trap arrays with drift fences. J. Wildl. Manage. 51:112-119.

Cook, J. G., T. W. Stutzrnan, C. W. Bowers, K. A. Brenner, and L. L. Irwin. 1995.
Spherical densiometers produce biased estimates of forest canopy cover. Wildl. Soc.
Bull. 23:711-717.

Corben, C. 1994. Anabat5 software. Titley Electronics, Balina, N.S.W., Australia. 28pp.

88

89

Craighead, F. 1979. Track of the grizzly. Sierra Club Books, San Francisco, CA. 210pp.

Daubenmire, R. 1968. Plant communities: a textbook of plant synecology. Harper and
Row, New York, NY. 300pp.

DeGraaf, R. M., D. P. Snyder, and B. J. Hill. 1991. Small mammal habitat associations in
poletimber and sawtimber stands of four forest cover types. For. Ecol. and Manage.
46:227-243.

de Vries, P. G. 1986. Sampling theory for forest inventory. Springer-Verlag Publ. New
York, NY. 450pp.

Fenton, M. B., and G. P. Bell. 1981. Recognition of species of insectivorous bats by their
echolocation calls. J. Mammal. 62:233-243.

, H. G. Merriam, and G. L. Holroyd. 1983. Bats of Kootenay, Glacier, and Mount
Revelstoke national parks in Canada: identification by echolocation calls,
distribution, and biology. Can. J. 2001. 61:2502-2508.

Franklin, J. F. 1993. Preserving biodiversity: species, ecosystems, or landscapes? Ecol.
Appl. 32202-205.

Grumbine, R. E. 1994. What is ecosystem management? Conserv. Biol. 8:27-38.

Haufler, J. B. 1990. Static management of forest ecosystems. Pages 123-130 in J. M.
Sweeney, ed. Management of dynamic ecosystems. North Cent. Sect, The Wildl.
Soc., West Lafayette, Ind.

. 1994. An ecological framework for planning for forest health. J. Sustainable For.
2:307—3 16.

, C. A. Mehl, and G. J. Roloff. 1996. Using a coarse-filter approach with species
assessment for ecosystem management. Wildl. Soc. Bull. 24:200-208.

Hays, J. P., and S. P. Cross. 1987. Characteristics of logs used by the red-backed vole
(Clethrionomys californicus) and deer mice (Peromyscus maniculatus). Can. Field-
Nat. 101:543-546.

Hays, R. L., C. Summers, and W. Seitz. 1981. Estimating wildlife habitat variables.
U.S.D.I. Fish and Wildl. Serv. FWS/OBS-81/47. lllpp.

90

Higgins, K. F., J. S. Oldemeyer, K. J. Jenikins, G. K. Clambey, and R. F. Harlow. 1994.
Vegetation sampling and measurement. Pages 567-591 in T. A. Bookhout, ed.
Research and management techniques for wildlife and habitats. Fifth ed. The
Wildlife Society, Bethesda, Md.

Hoffrnann, R S., and J. G. Owen. 1980. Sorex teneIIus and Sorex nanus. Mammal.
Species. No. 131 l-4pp.

Howard, J. H., and R. L. Wallace. 1985. Life history characteristics of populations of the
long-toed salamander (A mbystoma macrodactylum) for different altitudes. Am.
Midl. Nat. 113:361-373.

Huff, M. H., J. F. Lehmkuhl, and J. A. Young. 1993. Linking bat activity with landscape-
level attributes using GIS. Northwest Sci. 67: 131 (abstract only).

Hunter, M. S. JR. 1990. Wildlife forests and forestry: principles of managing forests for
biological diversity. Prentice-Hall, Inc., Englewood Cliffs, NJ. 370pp.

Jones, K. B. 1981. Effects of grazing on lizard abundance and diversity in western
Arizona. Southwest. Naturl. 26: 107-1 15.

. 1986. Amphibians and reptiles. Pages 267-290. in Cooperrider, A. Y., R. J. Boyd,
and H. R. Stuart, ed. 1986. Inventory and monitoring of wildlife habitat. US. Dept.
Inter., Bur. Land Manage. Service Center. Denver, Co. Xviii, 85 8pp.

Junge, J. A., and R. S. Hoffrnann. 1981. An annotated key to the long-tailed shrews

(Genus Sorex) of the United States and Canada, with notes on middle American
Sorex. Occas. Pap. Mus. Nat. Hist. University of Kansas. 94:1-48.

Koeln, G. T., L. M. Cowardin, and L. L. Strong. 1994. Geographic information systems.
Pages 540-566_in T. A. Bookhout, ed. Research and management techniques for
wildlife and habitats. Fifih ed. The Wildlife Society, Bethesda, Md.

Krusic, R A., M. Yamasaki, C. D. Neefus, and P. J. Pekins. 1996. Bat Habitat use in
White Mountain National Forest. J. Wildl. Manage. 60:625-631.

Leopold, A. 1949. A sand county almanac. Oxford University Press, New York. 295pp.

MacCracken, J. G., D. W. Uresk, and R. M. Hansen. 1985. Habitat used by shrews in
southeastern Montana Northwest Sci. 59:24-27.

Mardia, K. V. 1972. Statistics of directional data. Academic Press. New York, NY. 200pp.

Meffe, G. K., and C. R. Carroll. 1994. Principles of conservation biology. Sinauer
Associates Inc., Sunderland, MA. 600pp.

91

Miller, R. G. Jr. 1985. Simultaneous statistical inference. 2nd ed. Springer-Verlag, Inc.
New York, NY. 265pp.

Moris, D. W. 1996. Coexistence of specialist and generalist rodents via habitat selection.
Ecology 77:2352-2364.

Nudds, T. D. 1977. Quantifying the vegetative structure of wildlife cover. Wildl. Soc.
Bull. 52113-117.

Nussbaum, R. A., E. D. Brodie, and R. M. Storm. 1983. Amphibians & reptiles of the
Pacific Northwest. University of Idaho Press, Moscow, ID. 332pp.

Ott, R. L. 1993. An introduction to statistical methods and data analysis. 4th ed. Duxbury
Press, Belmont, Ca. 1051pp.

Patterson, B. D., P. L. Meserve, and B. K. Lang. 1990. Quantitative habitat associations of
small mammals along an elevational transect in temperate rainforests of Chile. J.
Mammal. 71:620-630.

Perkins, M. 1976. A survey of bat populations and their habitat preferences in southern
Oregon. Unpublished Student Originated Studies Project Report. University of
Portland, Portland, OR. 89pp.

Reich, L. M. 1981. Microtus pennsylvanicus. Mammal. Species. No. 159 l-8pp.

Rohlf, D. J. 1991. Six biological reasons why the Endangered Species Act doesn’t work -
and what to do about it. Conserv. Biol. 52273-282.

Roloff, G. J ., and J. B. Haufler. 1993. Forest planning in Michigan using a GIS approach.
Proc. Internat. Union of Game Biol. 21:364-369.

Samuel, M. D., and M. R. Fuller. 1994. Wildlife radiotelemetry. Pages 370-418 in T. A.
Bookhout, ed. Research and management techniques for wildlife and habitats. Fifth
ed. The Wildlife Society, Bethesda, Md.

SAS Institute Inc. 1989. SAS/STAT User’s Guide, Release 6.0 Edition, Volumes 1 and 2.
Cary, NC: SAS Institute Inc., 1028pp.

Schowalter, T. D. 1989. Canopy arthropod community structure and herbivory in old-
growth and regenerating forests in western Oregon. Can. J. For. Res. 19:318-322.

Scrivner, J. H., and H. D. Smith. 1981. Pocket gophers (Thomamys talpoides) in
successional stages of spruce fir forest in Idaho. Great Basin Nat. 41:362-367.

92

Siegel, S., and N. J. Castellan Jr. 1988. Nonparametric statistics for the behavioral
sciences. 2nd ed. McGraw-Hill Book Inc. New York, NY. 399pp.

Smith, M. E., and M. C. Belk. 1996. Sorex monticolus. Mamm. Species. No. 528 1-5pp.

Sharples, F. E. 1983. Habitat use by sympatric species of Eutamias. J. Mammal. 64:572-
579.

Shelford, V. E. 1933. Ecological Society of America: a nature sanctuary plan unanimously
adopted by the society, December 28, 1932. Ecol. 14:240-245.

Steele, R., R. D. Pfister, R. A. Ryker, and J. A. Kittams. 1981. Forest habitat types of
central Idaho. USDA For. Serv. Gen. Tech. Rep. INT-144, Interrnt. For. and Range
Exp. Stn., Ogden, UT. 138pp.

Sutton, D. A. 1992. Tamias amoenus. Mammal. Species. No. 390 1-8pp.

Szaro, R. C. 1988 The management of amphibians, reptiles, and small mammals in North
America: Historical perspective and objectives. Pages 1-3 in Szaro, R.C., B.E.
Severson, and DR Patton, ed. Management of amphibians, reptiles, and small
mammals in North America. USDA For. Serv. Gen. Tech. Rept. RM—166.

Terry, C. J. 1981. Habitat differentiation among three species of Sorex, and Neurotrichus
gibsi in Washington, Am. Midl. Nat. 106:119-125.

Thomas, D. W., G. P. Bell, and M. B. Fenton. 1987. Variation in echolocation call
frequencies recorded from North American vespertillonid bats: a cautionary note. J.
Mammal. 68:842-847.

, and S. D. West. 1989. Sampling methods for bats. Gen. Tech. Rep. PNW-GTR-
243. Portland, OR: USDA, For. Serv., Pac. Northwest Res. Stn. 20pp.

Thomas, J. W., and J. Verner. 1986. Forests. Pages 73-91. in Cooperrider, A. Y., R. J.
Boyd, and H. R. Stuart, ed. 1986. Inventory and monitoring of wildlife habitat.
US. Dept. Inter., Bur. Land Manage. Service Center. Denver, Co. Xviii, 858pp.

, and M. Dombeck. 1996. Ecosystem management in the interior Columbia river
basin. Wildl. Soc. Bull. 24:180-186.

Toothaker, L. E. 1991. Multiple comparisons for researchers. SAGE Publications, Inc.
Newbury Park, CA. 168pp.

Tummlison, R., G. R. Cline, and P. Zwank. 1990. Surface Habitat associations of the
Oklahoma salamander (Ewycea tynerensis). Herpetol. 44: 169-175.

93

Wackenhut, M., and M. McGraw. 1996. Idaho’s bats: description, habitats, &
conservation. Nongame Leaflet #1 1. Idaho Fish and Game, Boise, ID. 24pp.

Walters, B. B. 1991. Small mammals in a subalpine old growth forest and clearcuts.
Northwest Sci. 65:27-31.

Whitaker, J. O., R. Ehnan, and C. Nehring. 1980. The Audubon Society field guide to
North American mammals. Alfred A. Knopf Inc. Press, New York, NY. 745pp.

Williams, D. F., and S. B. Braun. 1983. Comparison of pitfall and conventional traps for
sampling small mammal populations. J. Wildl. Manage. 47:841-845.

Wright, G. M., and B. Thompson. 1935. Fauna of the national parks of the US. USDA
Dep. of Inter., Washington, DC. 310pp.

Wrigley, R. E., J. E. Dubois, and H. W. R. Copland. 1979. Habitat abundance, and
distribution of six species of shrews in Manitoba. J. Mammal. 60:505-520.

Wywialowski, A. P., and G. W. Smith. 1988. Selection of microhabitat by the red-backed
vole, Clethrinomys gapperi. Great Basin Nat. 48:216-223.

Yahner, R. H. 1995 Eastern deciduous forest: ecology and wildlife conservation.
University of Minnesota Press, Minneapolis, MN. 220pp.

APPENDIX A

Appendix A Table 1. Species encountered, and number caught in each field season.

 

Common Name Scientific Name *1995 *1996
SmallenmaLSpsgies
Yellow-pine Chipmunk Eutamias amoenus 266 296
Deer Mouse Peromyscus maniculatus 159 277
Northern Red-backed Vole Clethrionomys gapperi 98 59
Dusky Shrew Sorex monticolus 49 84
Masked Shrew Sorex cinereus 38 49
Meadow Vole Microtus pennsylvanicus 15 5
Vagrant Shrew Sorex vagrans 9 21
Dwarf Shrew Sorex nanus 7 7
Northern Pocket Gopher Thomomys talpoides 7 18
Golden-mantled Ground Squirre Spermophilus Iateralis 6 27
Western Jumping Mouse Zapus princeps 4 2
Long-tailed Weasel Mustelafrenata 4 0
Columbian Ground Squirrel Spermophilus columbianus 3 4
Red Squirrel T amiasciurus hudsonicus 1 2
Bushy-tailed Wood Rat Neotoma cinerea 0 1
Northern Flying Squirrel Glaucomys sabrinus 0 1
Water Shrew Sorex palustris 0 2
Hammer:
Long-toed Salamander Ambystoma macrodaclylum l9 14
Pacific Tree Frog Pseudacris regilla 5 5
Striped Chorus Frog Pseudacris triseriata 2 0
Western Toad Bufo boreas l 32
Common Garter Snake T hamnophr's sirtalis 1 2
Rubber Boa Charina bottae 0 1
B315
Big Brown Bat Eptesicusfitscus 220 554
Long-cared Myotis Myotis evotis 34 83
Myotis Species Group Myotis sp. 469 820
Hoary Bat Lasiurus cinereus 283 80
Silver-haired Bat Lasionycteris noctivagans 30 103
Townsend's Big-cared Bat Plecotus townsendii 30 53

 

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Appendix A Table 14. Results of tests for differences in small mammal species relati
abundance between dry Douglas-fir vegetative growth stages by trapping session.

 

 

 

Probability of > F
Species Trap 1 Trap 2 Trap 3 Trap 4
Yellow-pine Chipmunk 0.71 0.14 1.00 0.57
Deer Mouse 0.02 0.01 0.02 0.02
Red-backed Vole 0.12 0.00 0.83 1.00
Dusky Shrew 0.12 0.41 0.85 0.44
Masked Shrew . . . 0.37
Vagrant Shrew 0.13 . 0.12
Golden-mantled Ground Squir 0.83 . 0.83 0.84
Northern Pocket Gopher 0.37 . 0.37
Meadow Vole 0.12 0.37
Dwarf Shrew 0.37 0.37

 

(.) indicates no captures, so no test was run.
All probabilities of > F from Kruskal-Wallis Test (Ott 1993).

Appendix A Table 15. Results of tests for differences in herptile species relative
abundance between dry Douglas-fir vegetative growth stages by trapping session.

 

 

 

Probability of > F
Species Trap 1 Trap 2 Trap 3 Trap 4
Long-toed Salamander . 0.05 0.12 0.37
Western Toad . 0.37 0.37 0.37

 

(.) indicates no captures, so no test was run.
All probabilities of > F from Kruskal-Wallis Test (Oct 1993).

APPENDIX B

APPENDIX B
Relative Abundance Group Delineation
To aid in identifying habitat variables that played a role in the presence and
abundance of individual species, mean capture rates for the 4 trapping sessions within
stands were broken into relative abundance groups, based on natural breaks in the data.
This was done by sorting the stands in ascending order of mean relative abundances, of
all trapping sessions, for one individual species. Stands were then numbered 1-19 based
on lowest to highest mean relative abundance. The data for the individual species were
then plotted with mean relative abundance on the Y axis and the assigned number on the
X axis. Natural gaps in the data were chosen as dividing points between the relative
abundance groups. The stands were divided into a minimum of 2, to at most 4 relative
abundance groups. Relative abundance groups were required to have a minimum of 3
stands, to make them useful for analysis. Though natural breaks in the data were
important to use, if possible, groups were kept to a relatively similar range of relative
abundance, based on the total range of mean relative abundance for that species (i.e. 0-2,
2-4, 4-6). Given the opportunity, group sizes were kept as similar as possible as well.
Extremely difl‘erent group sizes can cause the joint ranks multiple comparison test to be
unable to distinguish between groups when the Kruskal-Wallis test detected an overall
difference. The mean relative abundance data for some species lent itself to this way of
grouping stands. However, other species data did not conform as well to being divided
into groups in this manner. To clarify this, several examples will be given.
The deer mouse is a good example of the data breaking nicely into 3 relative

abundance groups, with relatively even ranges, and sizes (Appendix B Fig. 1). Deer

108

109

Relative Abundance Groups of the Deer Mouse

 

 

 

 

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Appendix B Figure 1. Deer mouse relative abundance groups, based on the mean of
relative abundance in each stand during the 4 trapping sessions in Valley County Idaho
during 1995, and 1996. Arbitrary stand identification numbers were assigned in
ascending order of mean relative abundance.

“0

mouse mean relative abundance in these groups was separated by gaps in mean relative
abundance of approximately 1.5-1.75, ranged from 0-4, 4-8, and 8-12, and contained 7, 8,
and 4 stands respectively. Some less commonly captured species such as the pocket
gopher (Appendix B Fig. 2) had a very small range of mean relative abundance (18 of 19
stands having a mean relative abundance of 0-0.75) , and could only be broken into 2
groups (presence, and absence). Another good example is the masked shrew (Appendix
B Fig. 3), which was broken into 3 relative abundance groups with mean relative
abundances of 0, 0-2 and 2-5. While the break between the 2nd and 3rd group could have
been made 1 stand higher on the scale, at a bigger gap, the gap used to separate groups
was the largest gap encountered thus far and a compromise with group size was made.
Some species did not show large natural breaks in mean relative abundance at
which to separate the groups. The red-backed vole is a good example of this (Appendix
B Fig. 4). For the red-backed vole, the stands were ultimately broken into 2 groups at the
largest available gap which gave an acceptable compromise between relative abundance
ranges and group sizes. Another good example of a species that was not well suited to
being broken into groups was the yellow-pine chipmunk (Appendix B Fig. 5).
Relatively large gaps in relative abundance were only present at the 2 extremes of the
graph. This would only have given 2 small groups, with one relatively large group in
between, covering a much wider range of relative abundance than the other 2 groups. To
reach a reasonable compromise between the ranges of relative abundance within groups,
and group sizes, the largest gap available, as close to the center of the larger middle group
as possible, was used to make another break in the data. This gave a total of 4 relative

abundance groups for the Yellow-pine chipmunk.

lll

MativeAbmdmceGrupsofflteNomemPocketGopher

 

1.8

1.6 ..

1.4 ~~

1.2 «-

0.8 1»

0.6 ~~

Mean Relative Abundance

99909
0.4”

0.2.» Gram . i .

 

 

 

0 -—+—+—O—+—O—O—H . .
0 5 10 15 20
WMWW

Appendix B Figure 2. Northern Pocket Gopher relative abundance groups, based on the
mean of relative abundance in each stand during the 4 trapping sessions in Valley County
Idaho during 1995, and 1996. Arbitrary stand identification numbers were assigned in
ascending order of mean relative abundance.

112

Relative Abundance Groups of the Masked Shrew

 

4.5 «r

Group 3
3.5 ..

2.5 ~»

Mean Relative Abundance

1-5 -~ Group 2

Group 1 e
0.5 .. e

 

 

 

0 —--O—O—O—H—+—O—¢ . 1
O 5 10 15 20
Assigned Stand Identification Nimbus

Appendix B Figure 3. Masked shrew relative abundance groups, based on the mean of
relative abundance in each stand during the 4 trapping sessions in Valley County Idaho
during 1995, and 1996. Arbitrary stand identification numbers were assigned in
ascending order of mean relative abundance.

113

WWMdflBWWs

 

Mean Relative Abundance
h.
9

00431

 

 

 

 

0 2 4 6 8 10 12 14 16 18 20
WMWW

Appendix B Figure 4. Red-backed vole relative abundance groups, based on the mean of
relative abundance in each stand during the 4 trapping sessions in Valley County Idaho
during 1995, and 1996. Arbitrary stand identification numbers were assigned in
ascending order of mean relative abundance.

114

Relative Abundance Groups of the Yellow-pine

20 Chipmunk

 

18 ..
16 s
14 -_
12 .- Group3 ‘ o o

10 --

Mean Relative Abundance

 

 

 

0 5 10 15 20
Assigned Stand Identification Numbers

Appendix B Figure 5. Yellow-pine chipmunk relative abundance groups, based on the
mean of relative abundance in each stand during the 4 trapping sessions in Valley County
Idaho during 1995, and 1996. Arbitrary stand identification numbers were assigned in
ascending order of mean relative abundance.

115

Having broken stands into groups, based on natural breaks in the relative
abundance of each species, the Kruskal-Wallis test was used to identify significant
differences (P < 0.10) in individual habitat variables among the relative abundance
groups. Since the Kruskal-Wallis test works on the ranks of the data, extreme values
should not have had a great influence on the results. Significant differences in habitat
variables among the relative abundance groups indicated a potential relationship between
the variable and the species whose relative abundance was used to form those groups.
The number of significant differences, among the 29 variables, ranged from 2 to 7 for
different species. For 3 species, the number of groups the data was broken into was
changed from 4 to 2, and from 4 to 3, with changes in detected relationships not changing
appreciably in 2 of the 3 species. This indicates that group membership should not have
had a major influence on the relationships that were detected. Relative abundance groups
were numbered from lowest relative abundances (group 1) to highest relative abundances
(groups 2, 3, or 4). When the results of the joint ranks multiple comparison test (multiple
comparison test) are listed, they will always be given with the group with the highest
mean value for that variable lst, and the next group with a mean value that is
significantly different 2nd. Any group listed as not being significantly different from the
group with the highest mean will have a mean between the 2 significantly different
groups.

Small Mammals and Habitat Variables
The yellow-pine chipmunk had only 2 variables with significant differences

detected among relative abundance groups by the Kruskal-Wallis test (Table 4). A

116

significant difference was detected in the density of snags between 2 of the 4 relative
abundance groups (P = 0.06). The multiple comparison test only detected a significant
difference between relative abundance groups 3 and l. Snag densities in relative
abundance groups 4 and 2 were not significantly different from any of the other groups.
This minimal number of relationships, and the disorder of relative abundance groups may
imply that no true relationship exists. The Kruskal-Wallis test also detected a difference
in the percent vertical cover of forest litter more than 1m from the ground (P = 0.04). The
multiple comparison test was too conservative, or not sensitive enough, to detect any
significant differences among the 4 relative abundance groups at the a=0.10 level.

The Kruskal-Wallis test detected significant differences in 3 habitat variables for
the 3 groups based on the relative abundance of the deer mouse (see Table 4). Coarse
woody debris densities were significantly different among deer mouse relative abundance
groups (P = 0.05). The multiple comparison test detected a significant difference
between the 3rd and 2nd relative abundance groups, but not between the lst relative
abundance group and either of the other 2 groups. The Kruskal-Wallis test also detected
a significant difference in percent canopy closure (P = 0.08) between deer mouse relative
abundance groups. The 3rd relative abundance group had a different canopy closure
from the lst group, but not from the 2nd, according to the results of the multiple
comparison test. The 3rd variable to show a significant difference between deer mouse
relative abundance groups was the percent vertical cover of herbaceous vegetation
greater than 1m above the ground (P= 0.04). The multiple comparison test was too

conservative to detect the difference in this variable.

117

Several habitat variables differed significantly between the relative abundance
groups of the red-backed vole. Percent slope differed significantly between relative
abundance groups of the red-backed vole (P = 0.09), with steeper slopes corresponding to
higher relative abundances. The percent vertical cover of herbaceous foliage in the lst
meter above the ground also differed significantly between red-backed vole relative
abundance groups (P = 0.09), with the lower relative abundance group corresponding to
higher coverages of herbaceous vegetation. The final difference detected between
relative abundance groups by the Kruskal-Wallis test was the percent vertical cover of
herbaceous vegetation above 1m from the ground (P = 0.06), with group 2 having the
higher mean values.

Several habitat variables were found to be significantly different among the
relative abundance groups of the masked shrew. Large tree bole height was found to be
significantly different between the relative abundance groups of the masked shrew by the
Kruskal-Wallis test (P = 0.03). The multiple comparison test found group 1 to have
significantly higher mean bole heights than group 2, but not than group 3. Relative
abundance group 3 was also not found to have different bole heights than group 2. Snag
dbh differed significantly among the relative abundance groups of the masked shrew (P =
0.01). The multiple comparison test showed a significant difference between groups 2
and 1, with group 2 having the higher mean value. No difference was detected between
group 3 and either of the other 2 groups for snag dbh. The Kruskal-Wallis test also
detected a significant difference in the percent vertical cover of herbaceous vegetation

above 1m (P = 0.04) among the relative abundance groups of the masked shrew.

118

However, the multiple comparison test was unable to separate out any significantly
different relative abundance groups for this variable.

The vagrant shrew showed potential relationships with several site and vegetation
variables. Snag density was significantly higher in relative abundance group 2 for the
vagrant shrew (P = 0.06). Aspect was significantly different between the relative
abundance groups as well (P = 0.04), with group 1 having the higher mean value. The
percent vertical cover of woody vegetation in the lst meter above the ground was
significantly higher in relative abundance group 1 (P = 0.09) for the vagrant shrew.
Percent of vertical cover of herbaceous vegetation above the lst meter from the ground
was also found to be significantly different between the relative abundance groups (P =
0.03), with group 2 having the highest mean values. Lastly, the percent of vertical cover
of forest litter above the lst meter fi'om the ground was also significantly higher in the
2nd relative abundance group for the vagrant shrew (P = 0.002), according to the results
of the Kruskal-Wallis test.

Several site and vegetation variables differed significantly between the relative
abundance groups of the golden-mantled ground squirrel. Coarse woody debris diameter
was significantly larger in the 2nd relative abundance group for the golden-mantled
ground squirrel (P = 0.05). The Kruskal-Wallis test also found a significant difference in
aspect between the 2 groups (P = 0.04), with group 2 having a more westerly aspect.

The Kruskal-Wallis test only detected significant differences in 2 habitat variables
between the 2 relative abundance groups of the pocket gopher (Table 5). Small tree

height differed significantly between the 2 groups (P = 0.099). Mean small tree height

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was greatest for relative abundance group 2. Stump diameter was also found to be
significantly larger in relative abundance group 2 for the pocket gopher (P = 0.099).

Several vegetation variables differed significantly between the relative abundance
groups of the meadow vole. The Kruskal-Wallis test showed a significant difference in
coarse woody debris diameter (P = 0.099), with relative abundance group 2 of the
meadow vole having the largest mean diameters. Small tree height was greater in relative
abundance group 2 (P = 0.01) for the meadow vole.

The Kruskal-Wallis test detected significant differences between the 2 relative
abundance groups of the dwarf shrew in 2 habitat variables (Table 5). The lst relative
abundance group of the dwarf shrew had significantly higher large tree densities than the
2nd group (P = 0.07). The Kruskal-Wallis test also detected a significant difference in
the percent ground cover of woody vegetation (P = 0.003) for the relative abundance
groups of the dwarf shrew, with the higher mean coverage in group 1.

Herptiles and Habitat Variables

The western toad had several habitat variables differ significantly among its 3
relative abundance groups. Coarse woody debris diameter differed significantly among
relative abundance groups of the western toad (P = 0.09). However, the multiple
comparison test was too conservative to detect differences among any of the 3 groups for
this variable. The Kruskal-Wallis test showed large tree bole height to be significantly
different among the relative abundance groups (P = 0.01). The multiple comparison test
showed relative abundance group 2 of the western toad to have significantly higher large

tree bole heights than both groups 1 and 3, which did not differ significantly. Small tree

120

height differed significantly among the relative abundance groups, according to the
Kruskal-Wallis test (P = 0.08), but no differences were detected by the multiple
comparison test. The Kruskal-Wallis test also found the percent vertical coverage of
woody vegetation above 1m to differ significantly between the relative groups of the
western toad (P = 0.05). The multiple comparison test identified relative abundance
group 1 as having significantly greater vertical cover of woody vegetation above 1m than
group 2. Relative abundance group 3 did not differ significantly from either of the other
groups in this variable.

Discussion

Small Mammals and Habitat Variables

Twenty-nine habitat variables were tested for differences among the relative
abundance groups of each species. Of these 29 tests per species, up to 3 Type 1 errors
could have occurred. However, just because a certain number of Type 1 errors could
occur does not mean that they did occur. And, there are ways to help identify some of the
spurious results. Habitat variables that differed significantly among the relative
abundance groups of species but were poorly supported biologically and in the literature
are discussed in this section.

Several habitat variables that differed significantly between the relative abundance
groups of the red-backed vole are not well supported. The cool moist grand fir habitat
types, that the red-back vole was associated with, tended to have steeper slopes (Table 2).
The higher relative abundance of red-backed voles on sites with steeper slopes may just

be an artifact of the location of the moister sites. Snag densities and elevation were

121

greater in the cool moist grand fir habitat type class (Table 2). These don’t appear related
to red-backed vole abundance. The small tree VGS that the vole was more abundant in
had more southerly aspects, lower ground cover of litter, and lower coverage of woody
vegetation (Table 3). The lower percent cover of woody vegetation may be a spurious
result, because other authors have found the opposite result (Belk et a1. 1988, Walters
1991, Wywialowski and Smith 1988). These authors all hypothesized that higher shrub
covers in the lst meter above the ground were creating the more preferred mesic
conditions, and higher food abundance for the red-backed vole. These variables may
influence red-backed vole relative abundance, or could be related to the mesic conditions
which the vole was selecting, and should be considered in future investigations. Percent
vertical cover of herbaceous vegetation in the lst meter above the ground was negatively
related to red-backed vole relative abundance, while above 1m it was positively related to
red-backed vole relative abundance (Table 4). Since you can not have higher densities of
herbaceous vegetation above 1m with lower density of herbaceous vegetation below 1m,
one of these results must be spurious, possibly due to Type 1 error, or small sample size.
Since the percent of vegetative cover in the lst meter above the ground was found
positively related to red-backed vole abundance in several studies (Belk 1988, Walters
1991, Wywialowski and Smith 1988), and since green vegetation is its primary food
source, I would consider the negative relationship with percent cover of herbaceous
vegetation in the lst meter above the ground to be the spurious result.

One habitat variable that shows a potential relationships with masked shrew

abundance, through the association with the grand fir habitat type classes, is snag density

122

(Table 2). Higher densities of snags could provide more nesting area, with greater
protection from predators. This source of decaying woody material may also afford
hunting areas with larger densities of bugs, the masked shrews primary food source
(Whitaker et al. 1980), with greater protection from other predators. Snag diameter was
also significantly larger in higher masked shrew relative abundance groups (Table 3). In
the comparison of relative abundance groups with habitat variables, large tree bole height
showed a significant difference between relative abundance groups. However the
multiple comparison test showed the lowest relative abundance group to be significantly
different than the middle relative abundance group, but not than the highest group, which
I can not explain. There could be a relationship, but it could also be Type 1 error. The
percent cover of herbaceous vegetation above 1m was also found to be significantly
different among relative abundance groups. The multiple comparison test was unable to
distinguish between the groups, but the higher mean values were in the 2nd and 3rd
groups respectively. Higher coverages of herbaceous vegetation would provide a moister
microhabitat and higher bug densities, but I can not explain why this relationship would
not have shown up with herbaceous vegetation densities in lower strata as well.

Habitat variables that are potentially important to the northern pocket gopher, due
to their mutual relationship with the different habitat type classes, include large tree dbh,
snag density, elevation, and percent slope (Table 2). The cool moist grand fir habitat type
class had significantly smaller large tree dbh’s than the other 2 habitat type classes. The
high densities of large woody roots in areas with larger trees may make digging more

difficult for pocket gophers, thereby limiting their presence (Scrivner and Smith 1981).

123

This may indicate a more open, earlier successional condition, where plant productivity is
higher, providing a greater abundance of palatable roots and tubers for the pocket gopher
to consume, is preferred by pocket gophers. The moister conditions in the cool grand fir
habitat types, due to the higher elevations, may create conditions more conducive to
pocket g0pher digging and feeding. It has been shown that pocket gophers prefer moister
soil conditions for digging in (Whitaker et al. 1980, Scrivner and Smith 1981). These
moister conditions may provide more productive plant communities which are rich in
palatable roots and tubers as well (Scrivner and Smith 1981). I can provide no
explanation why higher snag densities, and steeper slopes would positively influence
pocket gopher abundance. These variables may just be coincidental with the cool moist
grand fir habitat type class, and not influence pocket gopher abundance at all, and may be
Type 1 errors. Small trees were significantly taller in the higher pocket gopher relative
abundance group (Table 5). I can not explain this relationship either, and it may be a
spurious result.

Two habitat variables were shown to be negatively related to dwarf shrew relative
abundance by the Kruskal-Wallis test (Table 5). Large tree densities were highest in the
lower relative abundance group. High densities of large trees could possibly reduce the
productivity of the understory in stands, thereby reducing the availability of insect prey.
Percent vertical cover of woody vegetation at ground level was highest in the lower
relative abundance group for the dwarf shrew (Table 5). MacCracken et al. (1985) found
dwarf shrews in Montana to be more abundant in areas of high brush and litter cover,

hypothesizing that this created better moisture conditions. I can think of no reason for

124

dwarf shrews to avoid areas of high shrub cover, and would consider it to be a spurious
result. The low density at which dwarf shrews exist caused capture rates to be very low.
This in conjunction with small sample sizes may be masking relationships between the
dwarf shrew and habitat variables. Obtaining larger sample sizes in future investigations
could help to clarify dwarf shrew habitat type class associations and habitat relationships.
Several habitat variables could potentially influence deer mouse relative
abundance based on its association with the dry Douglas-fir medium tree VGS (Table 3).
The medium tree VGS tends to be on more westerly aspects, with higher ground
coverages of litter, and higher percent vertical coverages of woody vegetation in the lst
meter above the ground. More westerly aspects are moister, potentially producing the
higher shrub densities. Belk et al. (1988) found deer mice to prefer areas with higher
shrub densities. Walters (1991) found deer mice to prefer areas with high densities of
forest litter, stumps and logs. The primary food of the deer mouse is seeds and nuts
(Whitaker et al. 1980) which could be more abundant under these conditions. Higher
shrub densities and litter coverages could also provide more hiding cover, for protection
fi'om predators. Deer mouse relative abundance was lower in areas with higher coarse
woody debris lengths and diameters. This contradicts the findings of both Belk et al.
(1988), and Walters (1991). This relationship is also contradicted by the density of
coarse woody debris being highest in the stands composing the deer mouse’s highest
relative abundance group. But, the higher coarse woody debris densities are in the lowest
relative abundance group, which was not shown to be significantly different than that in

the highest relative abundance group. No clear interpretation can be made of this

125

situation, and these may just be a spurious results. Larger sample sizes might allow
future investigators to clarify this. Canopy closure was also significantly greater in the
higher relative abundance groups. It is possible that the higher canopy closures of some
sites raised moisture levels to the point where higher densities of food and cover
providing shrubs could grow, but differences in mean canopy closure don’t appear large
enough to support this. Mean canopy closure was only 37% in the small tree VGS, 46%
in the medium tree VGS, and had a mean of 42% in the dry Douglas-fir habitat type
class, which was the highest of all habitat type classes. The percent vertical cover of
herbaceous vegetation above 1m was highest in the 2nd relative abundance group, and
lowest in the 3rd relative abundance group. I can not explain why this would be and
expect that it is a spurious result.

Two habitat variables that could potentially influence yellow-pine chipmunk
relative abundance were detected by the Kruskal-Wallis test (Table 4). Yellow-pine
chipmunk relative abundance was higher in areas with greater percent cover of litter
above 1m. Forest litter at this level would be composed mostly of dead brush. This
species is known to inhabit open forests with brush. Both Sharples (1983) and Sutton
(1992) found the yellow-pine chipmunk to prefer open forest with brush composing the
understory. Snag density was greater in the higher relative abundance groups for the
yellow-pine chipmunk. The yellow-pine chipmunk is a generalist species (Moris 1996)
and may not have a true relationship with these variables. Both these variables are not

very easily explained, and may have been found significantly different due to type 1

126

error. Larger sample sizes in future investigations may help to sort out the uncertainty
associated with these variables.

Several habitat variables that differed significantly between the relative abundance
groups of the vagrant shrew are difficult to support biologically. Vagrant shrews showed
a negative relationship with percent vertical cover of woody vegetation up to 1m above
the ground. Perhaps this woody vegetation had a negative effect on the density of
herbaceous cover. However, percent cover of forest liter above Im from the ground was
also higher in the 2nd relative abundance group for the vagrant shrew. Forest litter in this
height strata would primarily be composed of dead brush, or litter suspended on brush,
which contradicts the inverse relationship with woody cover in the lst meter above the
ground. One of these findings is apparently incorrect. Larger sample sizes may have
been needed to clarify this. Mean snag density was greater in the higher vagrant shrew
relative abundance group. This is probably a spurious result, but could be because
vagrant shrews nest in decaying woody material (Burt and Grossenheider 1980), and
snags may provide for a larger insect food source as well.

Two habitat variables that differed significantly between the relative abundance
groups of the golden-mantled ground squirrel were coarse woody debris density, and
aspect. Coarse woody debris density was found to be significantly higher in the 2nd
relative abundance group for the golden-mantled ground squirrel. This relationship could
potentially be supported by the fact that golden-mantled ground squirrels usually have
their dens under or near downed logs and stumps, or brush (Burt and Grossenheider

1980). Golden-mantled ground squirrel relative abundance was found to be higher in

127

stands with more northerly aspects. This contradicts what would be expected, because
northerly aspects usually support denser forests. The relationship identified with aspect,
is probably therefore be a spurious result, due to Type 1 error.

The meadow vole showed 2 potential vegetation relationships that are difficult to
support biologically. Meadow vole relative abundance was higher in stands with larger
diameter pieces of coarse woody debris. Small tree height was greater in the higher
meadow vole relative abundance group. No explanation for either of these findings could
be found.

Herptiles and Habitat Variables

Several habitat characteristics that may be important to western toads were
identified (Table 6). Percent cover of woody vegetation above 1m was lowest in relative
abundance groups 2 and 3 respectively. The Kruskal-Wallis test showed a significant
difference in coarse woody debris diameter between relative abundance groups. The
multiple comparison test was too conservative to separate the groups out (Miller 1985),
but mean values appear to be inversely related to relative abundance. The opposite
relationship would be expected, and I have no feasible explanation. This may simply be
one of the expected spurious results. Large tree bole height was highest in relative
abundance group 2, with the lowest mean value being in group 3. I can not explain this
order and this may be another potentially spurious result. Small tree height was greatest
in relative abundance group 3, and 2nd highest in group 2. Again, I can not explain this.

The confused relationship of large tree bole height, and small tree height may indicate

128

these as spurious results due to Type 1 error. Larger sample sizes in future investigations

could possibly aid in sorting these relationships out.

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