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Identifier
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Thesis_MES_2020_ReynoldsC
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Title
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Evaluation of upland hardwood patches using three taxa in Douglas-fir production forests
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Date
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2020
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Evergreen Subject
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Environmental Studies
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Creator
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Reynolds, Claudine
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extracted text
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EVALUATION OF UPLAND HARDWOOD PATCHES USING THREE TAXA IN
DOUGLAS-FIR PRODUCTION FORESTS
by
Claudine R. Reynolds
A Thesis
Submitted in partial fulfillment
of the requirements for the degree
Master of Environmental Studies
The Evergreen State College
December 2020
©2020 by Claudine R. Reynolds. All rights reserved.
This Thesis for the Master of Environmental Studies Degree
by
Claudine R. Reynolds
has been approved for
The Evergreen State College
by
___________________
John Withey, Ph.D.
Director & Faculty, Master of Environmental Studies Program
December 11, 2020
Date
ABSTRACT
Evaluation of upland hardwood patches using three taxa in Douglas-fir production forests
Claudine R. Reynolds
Forests are high-functioning and ecologically productive in both natural and managed
settings. They provide ecosystem functions and societal benefits including water
filtration, carbon storage, and habitat for fish and wildlife. More than 10 million acres of
forestland are managed for forest products and ecosystem services in Washington State.
Managers of forestlands have the opportunity, through intentional conservation and
silvicultural practices, to manage for forest resiliency and biological diversity while
maintaining alignment with business and societal objectives. Managed forests form a
mosaic of habitat types across the landscape, many of which are conifer dominated with
hardwood patches scattered throughout. For this study, I examined the use of conifer- and
hardwood-dominated habitat types by three forest taxa (ground beetles, amphibians, and
songbirds). Small, upland hardwood patches within the managed conifer matrix were
high functioning, with utilization of both habitat types by all taxa. Of the 45 species that
were included in the analysis, 14 (31%) were unique to one habitat type or the other, with
four species unique to conifer-dominated habitats and ten species unique to hardwooddominated habitats. The mean species richness of ground beetles and birds was similar in
both conifer- and hardwood-dominated plots, while the mean species richness of the
herpetofauna community was greater in hardwood-dominated plots. Forest structure and
composition components were also evaluated. Across all surveys, significantly more
plant species occurred in hardwood-dominated plots than in conifer-dominated plots. The
results of this study suggest that upland hardwood patches within the managed forest
setting provide conservation value for many species.
Table of Contents
Chapter 1. Introduction ....................................................................................................... 1
Chapter 2. Literature Review .............................................................................................. 7
Ecological Framework .................................................................................................... 7
Natural History ................................................................................................................ 9
Indigenous Land Management ...................................................................................... 11
Contemporary Forest Management and Regulatory Framework .................................. 12
Measuring Forest Diversity ........................................................................................... 14
Landscape Scale Biodiversity Metrics ...................................................................... 15
Local Scale Biodiversity Metrics .............................................................................. 16
Habitat Indicators ...................................................................................................... 17
Wildlife Indicators .................................................................................................... 18
‘Representatives’ of Biodiversity .................................................................................. 19
Specific Taxa as Indicators ........................................................................................... 20
Ground Beetles.......................................................................................................... 21
Amphibians ............................................................................................................... 22
Songbirds .................................................................................................................. 23
Conclusion..................................................................................................................... 24
Chapter 3. Methods ........................................................................................................... 27
Study Area ..................................................................................................................... 27
Monitoring Design ........................................................................................................ 28
Forest Structure and Composition ................................................................................. 29
Ground Beetle Surveys.................................................................................................. 31
Amphibian Surveys ....................................................................................................... 32
Songbird Surveys .......................................................................................................... 33
Data Analysis ................................................................................................................ 33
Chapter 4. Results ............................................................................................................. 37
Forest Structure and Composition ................................................................................. 37
Species Richness and Abundance ................................................................................. 41
Ground Beetles .............................................................................................................. 43
iv
Amphibians and Reptiles .............................................................................................. 46
Terrestrial Salamanders ............................................................................................ 49
Woody Debris and Soil Temperature ....................................................................... 52
Songbirds ....................................................................................................................... 54
Chapter 5. Discussion ....................................................................................................... 59
Forest Structure and Composition ................................................................................. 60
Species Richness and Abundance ................................................................................. 61
Ground Beetles .............................................................................................................. 62
Amphibians and Reptiles .............................................................................................. 62
Terrestrial Salamanders ................................................................................................. 63
Songbirds ....................................................................................................................... 64
Conclusions ................................................................................................................... 64
References ......................................................................................................................... 66
Appendices ........................................................................................................................ 78
Appendix 1: Table of species associations per forest habitat type. ............................... 78
Appendix 2: Species occurrences by forest habitat type and location. ......................... 81
Appendix 3: Correlation matrix – habitat patch structure and composition ................. 83
Appendix 4: Correlation matrix – forest stand physical characteristics........................ 85
Appendix 5. Songbird species richness and abundance for birds detected within and
adjacent to survey plots during point-count surveys by forest habitat type (excluding
birds observed flying overhead). ................................................................................... 87
v
List of Figures
Figure 1. The study was conducted within the temperate forests of western Washington,
specifically the westside lowland conifer-hardwood and montane mixed-conifer
forest habitat types. ................................................................................................... 28
Figure 2. The paired study design was comprised of both conifer- and hardwooddominated forest habitat types (diagram not to scale). The two forest habitat plots
were located at least 250 meters apart from each other and at least 95 meters away
from a habitat boundary, as indicated by a change in forest type or seral age. ........ 30
Figure 3. Woody debris volume (≥ 3 cm diameter by ≥ 10 cm length) by forest habitat
type was similar (t(4) = 0.35, p = 0.74). ..................................................................... 39
Figure 4. Tree basal area by forest habitat type was similar (conifer basal area >
hardwood basal area, t(4) = 2.06, p = 0.11). ............................................................... 40
Figure 5. Plant richness (including forbs, shrubs, and trees) by forest habitat type was
significantly different (hardwood richness > conifer richness, t(4) = -4.63, p = 0.01).
................................................................................................................................... 40
Figure 6. Nonmetric multidimensional scaling ordination performed across all animal
taxa in conifer- (CON) and hardwood- (HW) dominated sites were not significantly
different from each other when grouped by habitat type (conifer vs. hardwood;
ANOSIM R = -0.08, p = 0.69) but were different when grouped by location
(ANOSIM R = 0.50, p = 0.02). 4-letter species codes shown in Appendix 1. stress <
0.13............................................................................................................................ 42
Figure 7. When all animals were combined, the observed c-score (2.46) was statistically
higher (p = 0.03) than the mean simulated c-score (2.40), although this represents a
very small absolute difference (0.06)........................................................................ 43
Figure 8. Carabid beetle species richness by forest habitat type was virtually equal (t(4) =
0.00, p > 0.99). ......................................................................................................... 45
Figure 9. Carabid beetle relative abundance by forest habitat type was similar (t(4) = 1.40,
p = 0.24). ................................................................................................................... 45
Figure 10. Nonmetric multidimensional scaling ordination performed using carabid
beetles in conifer- (CON) and hardwood- (HW) dominated sites show a minimal
pattern of dissimilarity by habitat type (ANOSIM R = 0.009, p = 0.44). 4-letter
species codes shown in Appendix 1. stress < 0.08 ................................................... 46
Figure 11. Herpetofauna species richness by forest habitat type was significantly different
(hardwood richness > conifer richness, t(4) = -2.59, p = 0.06). ................................. 48
Figure 12. Herpetofauna relative abundance by forest habitat type was similar (t(4) = 0.42,
p = 0.70). ................................................................................................................... 48
Figure 13. Nonmetric multidimensional scaling ordination performed on herpetofauna in
conifer- (CON) and hardwood- (HW) dominated sites show a minimal pattern of
dissimilarity by habitat type (ANOSIM R = 0.14, p = 0.22). 4-letter species codes
shown in Appendix 1. stress < 0.09 .......................................................................... 49
vi
Figure 14. Ensatina (ENES) and Western redback salamander (PLVE) snout to vent
length (SVL) measurements in conifer- and hardwood-dominated forest habitat
types were similar (ENES: fixed effect of habitat type, F1, 37 = 0.68, p = 0.42; PLVE:
fixed effect of habitat type, F1, 35 = 0.51, p = 0.48). .................................................. 51
Figure 15. Ensatina and Western redback salamander cover types in conifer- and
hardwood-dominated forest habitat types. Three Ensatina salamanders were detected
on the surface of the ground (Top). CWD = coarse woody debris. .......................... 51
Figure 16. The diameter (cm) of woody debris pieces utilized by amphibians in coniferand hardwood- dominated forest habitat types was similar when the single large
piece in a hardwood plot (120 cm) was removed (fixed effect of habitat type, F1, 32 =
0.24, p = 0.64). .......................................................................................................... 53
Figure 17. Soil temperatures (C°) measured at the sites of herpteofauna detections were
significantly different (hardwood temperatures > conifer temperatures, fixed effect
of habitat type, F1, 96.6 = 3.75, p = 0.06). ................................................................... 54
Figure 18. Songbird species richness (inside habitat patches) by forest habitat type was
similar (t(4) = -1.17, p = 0.31). See Appendix 5 for a bird richness plot for all birds
that were detected. .................................................................................................... 57
Figure 19. Songbird species abundance (inside habitat patches) by forest habitat type was
similar (t(4) = -0.72, p = 0.51). See Appendix 5 for a bird abundance plot for all birds
that were detected. .................................................................................................... 57
Figure 20. Nonmetric multidimensional scaling ordination performed using birds detected
inside conifer- (CON) and hardwood- (HW) dominated sites show a minimal pattern
of dissimilarity by habitat type (conifer vs. hardwood; ANOSIM R = -0.12, p = 0.71.
4-letter species codes shown in Appendix 1. See Appendix 5 for an NMDS plot for
all birds that were detected. stress < 0.13. ................................................................ 58
Figure 21. Songbird species richness by forest habitat type was virtually equal (t(4) = 0.0,
p > 0.99). ................................................................................................................... 87
Figure 22. Songbird species abundance by forest habitat type was similar (t(4) = -0.09, p =
0.94). ......................................................................................................................... 87
Figure 23. Nonmetric multidimensional scaling ordination performed on all birds
detected in conifer- (CON) and hardwood- (HW) dominated sites show a minimal
pattern of dissimilarity when grouped by habitat type (ANOSIM R = -0.15, p =
0.89), but were significantly different when grouped by location (ANOSIM R =
0.34, p = 0.07). 4-letter species codes shown in Appendix 1. stress < 0.10 ............. 88
vii
List of Tables
Table 1. Summary of physical characteristics at each forest habitat type where: CON =
conifer and HW = hardwood. Source HW = rational for why the hardwood patch
exists, Dist. Water = the distance from plot center to the nearest source of water, and
Dist. Forest = the distance from plot center to the nearest change in forest habitat.
*Root rot, **Depressional feature. ........................................................................... 27
Table 2. Summary of forest structure and composition attributes at each forest habitat
plot. CON = conifer and HW = hardwood................................................................ 39
Table 3. Summary of species richness by taxa and plant richness detected in each forest
habitat plot. CON = conifer and HW = hardwood. ................................................... 42
Table 4. Summary of pitfall trapping effort and carabid beetle captures by forest habitat
type. CON = conifer and HW = hardwood. .............................................................. 44
Table 5. Summary of amphibian survey effort and herpetofauna detections by forest
habitat type. CON = conifer and HW = hardwood. .................................................. 47
Table 6. Summary of Ensatina and Western redback salamander detections by forest
habitat type. CON = conifer and HW = hardwood. .................................................. 51
Table 7. Summary of bird detections by forest habitat type. Individuals were tallied based
on if they were inside or outside the 20- by 20-meter plot. The “inside plots”
category includes birds that were detected utilizing habitat inside the survey plots,
while the ‘all detections’ category includes birds that were detected inside and
around the survey plots. Birds that were observed flying overhead were not
included. CON = conifer and HW = hardwood. ....................................................... 56
viii
Acknowledgements
I am grateful to Port Blakely for their support of this research. From Port Blakely, I thank
Court Stanley and Mike Warjone for their encouragement, Leif Hansen for his technical
expertise, and Lauren Magalska for reviewing my proposal. To my colleagues that helped
with field data collection, I thank Dakota Vogel of West Fork Environmental and Amber
Mount for their expertise and contribution to the quality of this research. Many experts
provided advise throughout the project, I thank Dr. Jake Verschuyl and Dr. Diana
Lafferty for providing thoughtful feedback on my study design; Dr. Jessica Homyack,
Aimee McIntyre, and Lisa Hallock for providing amphibian survey methods and analysis
expertise; and Dr. Sean Sultaire and Dr. David Muehleisen for providing beetle survey
methods and identification expertise. Zack Hovis deserves special thanks, he loaned me
materials to conduct the beetle surveys. To my thesis reader, Dr. John Withey, I am
appreciative of the technical support and expertise you provided through all the phases of
this research. To my MES colleagues, I am grateful for the thoughtful reviews you
provided on various drafts of this writing. I could not have made it this far without the
support of my family. To my mom, dad, sisters, brother, incredible children, and loving
partner, thank you so much for your love, patience, and encouragement over the last three
years.
ix
Chapter 1. Introduction
Pacific Northwest (PNW) forests provide ecosystem functions that filter and
produce healthy air, regulate, filter and store water, prevent soil erosion and cycle
nutrients, store carbon, and provide a diversity of wildlife habitats, while also providing
aesthetic, medicinal, scientific, recreational, spiritual, and economic benefits to society
(Carey et al., 1999; Carey, 2003; Gustafsson et al, 2012). More than 10 million acres of
forestland are managed for forest products and ecosystem services in Washington State.
Forestland managers have the opportunity, through intentional forest management
activities, to manage for forest resilience and ecological function while also maintaining
alignment with business and societal objectives.
Forest management has intensified on privately managed forestlands in order to
maintain older habitats on publicly owned forestlands and to meet the growing demand
for high-quality building materials. Overtime, the PNW managed forest landscape has
shifted from naturally diverse second-growth forests to planted Douglas-fir dominated
third-growth forests. As a result, the diversity of plants and habitat features in managed
forests has declined in some areas and shifted spatially in others. The simplification of
forest habitat structure has led to concerns about the impacts of forest management on
forest resilience and biological diversity. Evaluation of the biological function of specific
habitats will improve understanding of high functioning habitats and provide data to
inform function-based conservation practices across the managed landscape.
Conservation of forest ecosystem functions, specifically biological diversity, has
been identified as a global concern (Chandra & Idrisova, 2011). Healthy ecosystems
1
contain diverse species that provide functional roles important to a systems stability and
productivity, many of which are not well understood. Although forestland managers may
desire to ensure optimal diversity across their ownership, it is difficult to know what
strategies to employ to ensure the effectiveness of specific activities (Simberloff, 1999).
Research that identifies high-functioning forest habitats can be used to guide successful
conservation strategies.
Understanding the relative contribution of conifer- and hardwood-dominated
forest habitats to species biodiversity has significance for forest managers. West of the
Cascade mountains in Washington and Oregon, forests managed for forest products are
primarily reforested with Douglas-fir (Pseudotsuga menziesii) tree species. Although it is
known that these forests provide habitat important to an array of species, the relative
contributions and required abundances of different elements of forest structure and
composition are not well understood. Sustainable forest management is governed by state
rules and regulations however, applying function-based protections requires site-specific
planning to ensure important forest structural features are maintained at levels that
support healthy ecosystem function.
Planted Douglas-fir dominated third-growth forests are regularly interspersed with
patches of shrubs and hardwood vegetation. At the time of reforestation, Douglas-fir trees
are planted densely and as they grow, they stretch out, blocking sunlight to the forest
floor. Sunlight is a limited resource within closed-canopy, conifer dominated forests.
Sunlight provides the energy to the subcanopy and forest floor that makes photosynthesis
possible. Before sunlight becomes blocked and when sunlight is again made available, a
variety of plant species will occur. Many plant species are adapted to thrive in the open
2
forest and understory environments when the right conditions exist. The vegetation
response to either access or denial of sunlight occur quickly and frequently in the
managed forest setting and create a shifting mosaic of habitat types.
Douglas-fir trees grow better in some soils than others. They do not prefer
habitats where the soil is seasonally inundated or occupied by pervasive root-rotting
fungus. When the conifer trees die in these areas, canopy gaps are created. Gaps are also
created in areas where wind causes trees to topple. The creation of canopy gaps restores
sunlight access to the forest floor, creating opportunities for understory species to grow.
Many hardwood species are quick to colonize areas where the soil has been disturbed.
The result is a conifer-dominated forested landscape with patches of hardwood forest
scattered throughout. The physical structure of hardwood vegetation (deciduous leaves
instead of evergreen needles) allows sunlight to infiltrate through the canopy, supporting
the development of understory vegetation.
Canopy density is an important driver for regulating forest floor light, moisture
content, and temperature regimes (Gray et al., 2002; Muscolo et al., 2014). The timing of
gap formation, variation in gap size, and differences in microsites within gaps contribute
to the diversity of species within forests. Resources vary within and among canopy gaps
and also by location and forest type (Gray et al., 2002). Hardwood patches in the PNW
are often dominated by red alder (Alnus rubra) or bigleaf maple (Acer macrophyllum)
and a variety of other forb and shrub species. They are utilized by canopy epiphytes and
invertebrates within the conifer matrix and can serve as an important source of nitrogen
in the nitrogen-limited forest ecosystem typical of the region (Kennedy and Spies, 2005).
3
Habitat patch size and species richness are associated. Larger patches can have
greater resources to support the habitat needs of more kinds of species, even if they
utilize similar resources (Andren, 1994). In smaller habitat patches, limited resources
may more successfully support species that do not compete for the same habitat elements.
Habitats become fragmented when there is a loss of cover type across the landscape.
Isolated and fragmented habitats can provide critical habitat for specialized species and
play key roles in maintaining biodiversity (Andren, 1994). For taxon that have very small
home ranges, the hardwood patches within Douglas-fir production forests could perform
similar ecosystem functions. For this study, small sized, upland hardwood patches were
selected in order to assess canopy gaps at a scale that exists naturally in the managed
forest setting.
A comparison of historic and contemporary hardwood patch dynamics in
managed forests of the Oregon Coast Range indicate that hardwood patches have
declined in size, number, and total area (Kennedy & Spies, 2005), with numerous
implications for conservation. The study of gaps has contributed to our understanding of
small-scale disturbance and their importance in maintaining habitat heterogeneity in
managed forests (Coates & Burton, 1997). An evaluation of the biological function of
small hardwood patches will improve understanding of the shifting patch dynamics and
habitat utilization within managed forests.
Mature mixed conifer and hardwood stands provide habitat for a diversity of
wildlife species in forested environments, where both coniferous and non-coniferous
vegetation species make important contributions to forest biodiversity. Out of more than
430 species of forest-dependent wildlife on the west side of the Cascades, more than 200
4
species breed or rear young in hardwood-dominated forests (Allbriten & Bottorff, 2004)
and 78 species have been associated with non-coniferous vegetation for food resources
(Hagar, 2007). Upland hardwood stands provide habitat for cavity-nesting and upper
canopy dwelling birds, food resources and nesting cavities for mammals, amphibians, and
reptiles, and forage habitat for deer and other browsers (Allbriten & Bottorff, 2004).
However, the biological function of small, upland hardwood patches within the context of
Douglas-fir production forests is poorly documented.
Conservation of biological diversity requires an understanding of how habitat
features, and species compositions vary within ecosystems. Different species select for
and utilize different habitat features. These differences are related to evolutionary
adaptations and preferences for foraging, breeding, and nesting habitats. Examination of
specific taxa within specific forest types can be used to describe the relative contribution
of those forest types to biodiversity and the ecosystem. Consideration of multiple taxa
will provide a comprehensive evaluation of relative contributions of different forest types
and features to biodiversity. For this study, ground beetles, amphibians and forest
songbirds were evaluated within conifer- and hardwood-dominated forest habitats to
examine the relative contribution of each habitat to biodiversity.
The study was conducted in the lowlands of western Washington. Five sites were
selected based on their forest management history and the prevalence of upland
hardwood patches. At each site, a 20- by- 20 meter paired-plot design was established,
one plot in hardwood-dominated forest, and a paired plot in the adjacent coniferdominated matrix. Three pitfall traps were created at each plot to survey for ground
beetles, establishing a total of 30 traps. Amphibian surveys were conducted across 100%
5
of the plot area, three surveys were conducted at each site. Point count surveys for forest
songbirds were conducted from each plot center and occurred during three occasions
from late spring to early summer. Forest structure and composition data was collected at
each site to describe the association of the dominant tree canopy type on various forest
attributes and habitat conditions.
An assessment of the ground beetle, amphibian, and songbird communities,
alongside the biotic and abiotic features within the conifer- and hardwood- dominated
forest habitat types will provide for an improved understanding of the associations that
exist between forest structural and compositional features and biological responses to
forest management. Results may be used to better understand forest management
contributions to biodiversity conservation, provide evidence for the conservation benefit
of upland hardwood patches, and provide support to forestland managers who wish to
quantify the effect of their conservation efforts. Additionally, the results of research like
this could support the implementation of function-based forest management practices.
6
Chapter 2. Literature Review
Ecological Framework
Ecosystem diversity is the variation in ecosystems found in a region (or the whole
planet) and includes variation in both terrestrial and aquatic ecosystems (Lapin, 1995).
Ecosystem diversity considers the variation in the physical environment and the
biological community. The physical environment of a forest is composed of elements
such as mature forests, young forest, wetlands, streams, and meadows. Ecosystem
diversity is the largest scale of biodiversity, and within each ecosystem, there is a great
deal of both species and genetic diversity.
Forest biological diversity underpins the ecosystem’s production, resilience, and
stability (Thompson, 2011). Healthy ecosystem function relies in part on the health and
function of each of the species within it (Luck et al., 2003). Forest biological diversity
results from evolutionary processes that occur over thousands to millions of years which,
in themselves, are driven by ecological forces such as climate, fire, competition, and
disturbance (Carey and Curtis, 1996; Drever et al., 2006). The diversity exists at the
ecosystem, landscape, species, populations, and genetics levels and complex interactions
occur within and amongst these levels. In biologically diverse forests, the complexity
allows organisms to adapt to continually changing environmental conditions and to
maintain ecosystem functions. Within forest ecosystems, the maintenance of ecological
processes is dependent upon the maintenance of their physical and biological diversity.
Ecosystems are stable when mechanisms are in place that help them return to their
original state after a disturbance has occurred (Connell & Sousa, 1983). Disturbances that
7
occur too quickly or over too large an area can pose a threat to overall forest health and
resiliency (Folke et al., 2004).
Loss of forest biological diversity can be the result of compounding or individual
events. Historically, human demands on natural systems have resulted in modification
and simplification of biological systems. The conversion of forests to alternate land uses,
unsustainable forest management, introduction of invasive plant and animal species,
infrastructure development, catastrophic forest fires, and climate change all can
negatively impact forest biodiversity (Braunisch et al., 2014). These influences can
decrease the resilience of forest ecosystems and make it more difficult for them to cope
with changing environmental conditions. Some ecosystems experience tipping points in
which significant environmental changes result in the inability to return to previous
conditions.
Superimposed on the many anthropogenic impacts on forest ecosystems is global
climate change. Climate has a major influence on rates of photosynthesis and respiration
(Thompson, 2011), and on other forest processes, acting through temperature, radiation,
and moisture regimes over medium and long time periods. Climate and weather
conditions also directly influence shorter-term processes in forests, such as frequency of
storms and wildfires, herbivory, and species migration (Gundersen et al., 2000). As the
global climate changes, forest ecosystems will change because species’ physiological
tolerances may be exceeded and the rates of biophysical forest processes will be altered
(Litten et al., 2010; Weed et al., 2013). Maintaining forest biodiversity is a key element to
maintaining forest resiliency.
8
In forests managed for timber products, the heterogeneity of natural forests is
minimized in order to produce consistent and high-quality building products (Drever et
al., 2006). However, management of forests for forest products and biodiversity do not
have to be mutually exclusive (Aubin et al., 2008; Bunnell & Dunsworth, 2010; Carey &
Curtis, 1996; Lindenmayer et al., 2000 & 2012; Rapp, 2002). Understanding how
biodiversity supports local forest resilience and resistance will provide important
information that can be used to improve forest management.
Natural History
Present patterns of diversity represent the culmination of ecological,
climatological, and geological processes spanning over several time scales. The PNW has
been shaped by millions of years of glacial advances and retreats. Each episode shaped
the landscape, creating ravines that formed the structure for many of our waterways and
depositing deep beds of gravel in their wakes. The form and structure of plate tectonics in
the PNW have worked over millions of years to create the volcanic ridge of the Cascade
Mountains. The array of active volcanoes shaped the environment in ways that fostered
resilient and adaptive ecosystems. As the mountains rose, they also functioned to shape
weather patterns, creating a wet temperate environment that is conducive to a productive
and diverse growing environment on their west side (Franklyn & Dyrness, 1973).
Fire has also been a dominant force in PNW evolution. The fire return interval
based on forest age-class data shows that the Olympic Peninsula in Washington may have
experienced fire once in several centuries, with the sporadic nature of the fires
contributing to catastrophic events (Agee, 1993). The structure of the Siskiyou mountains
of southern Oregon and norther California, however, suggest that they may have
9
experienced low-severity fire every few decades, creating fire-dependent ecosystems
(Martin, 1997). The forest response to these fire regimes created notable differences in
structure and diversity. In the PNW, the catastrophic nature of historic fire regimes
created stand renewing conditions, while the more frequent fires in the south were less
catastrophic, leaving behind surviving trees while creating openings for new vegetation
and creating multi-layered forests.
Forests have shaped the region’s history for more than two million years;
however, the remnants of the oldest forests are relatively young, having emerged in the
past few thousand years following the retreat of the ice sheets of the last ice age. The
ecosystems of the PNW are so productive and diverse that they contain more biomass
than natural forests of equivalent size in tropical forests. In lower elevations of western
Washington, Douglas-fir (Pseudotsuga menziesii), western hemlock (Tsuga
heterophylla), western red cedar (Thuja plicata), and grand fir (Abies grandis) are
common, while Pacific silver fir (Abies amabilis), mountain hemlock (Tsuga
mertensiana), lodge pole pine (Pinus contorta) and subalpine fir (Abies lasiocarpa) exist
in higher elevations (Chappell et al., 2001). Canopy structure varies from single to multistoried and tree size varies from small to large. Large snags and downed trees vary from
uncommon to abundant based on past land practices and naturally occurring events.
Mid to lower forest canopies vary in structure and density. They are comprised of
a variety of native species depending on sunlight penetration, elevation, and precipitation
(McIntosh et al., 2009). Deciduous broadleaf shrubs are the most common understory
dominants (Chappell et al., 2001). Primary understory coverage in middle-aged forests
include vine maple (Acer circinatum), red huckleberry (Vaccinium parvifolium), dwarf
10
Oregon grape (Mahonia nervosa), oceanspray (Holodiscus discolor), and sword fern
(Polystichum munitum). In younger, open forests, trailing blackberry (Rubus ursinus),
snowberry (Symphoricarpos albus), and bracken fern (Pteridium aquilinum) are more
common in the understory (McIntosh et al., 2009). Other understory vegetation often
includes species such as hazelnut (Corylus cornuta), salal (Gaultheria shallon), dwarf
rose (Rosa gymnocarpa), thimbleberry (Rubus parviflorus), salmon berry (Rubus
spectabilis), elderberry (Sambucus spp.), and honeysuckle (Lonicera ciliosa).
Indigenous Land Management
Indigenous people influenced the shape and structure of their landscape through
active management (Lepofsky & Lertzman, 2008). In the PNW, fire was utilized as a tool
for manipulating and maintaining natural resources (Williams, 2002). It was used in low
levels but often frequently to maintain a forested, meadow mosaic and prairies that
encouraged the growth of food crops and augmented the amount of open land used by
game species. The cumulative effects of thousands of years of burning altered the
function and vitality of the habitat at a landscape level in ways that were mutually
beneficial for plants, animals, and humans in the region. The careful application of fire
also reduced the fuel load that could be burned by naturally occurring wildfires.
Tribal communities also managed the forests for wood to make harpoons, baskets,
and mats. Western red cedar was especially important for the construction of homes,
canoes, and totem poles (Williams, 2002). It also provided the raw material to make
clothing and intricately carved masks. Plant resources were harvested from the forest that
provided food, medicine, and material for mechanical and spiritual purposes (Berg,
2007).
11
Contemporary Forest Management and Regulatory Framework
Pacific Northwest forests changed rapidly following the westward advance of
European descendants. The first significant investor in the region’s timber resources, The
Hudson’s Bay Company in the mid-1800s, introduced drastically different practices of
forest management. At first, there were no rules or regulations to dictate how the forests
and its resources should be managed and over time it was realized that the health and
integrity of the forests and streams were declining. As a result, Federal and State rules
and regulations evolved to require protection of sensitive fish and wildlife species, their
habitats, and forest ecosystem resources like water.
In 1973, the federal Endangered Species Act (ESA) was enacted with the intent of
protecting and recovering imperiled plant and animal species and the ecosystems upon
which they depend. It is administered by the U.S. Fish and Wildlife Service (USFWS)
and the National Ocean and Atmospheric Administration Fisheries (NOAA Fisheries).
Under the ESA, species may be listed as threatened or endangered. Once a species is
listed, harassment or harm towards that species or its habitat is prohibited. As of 2020,
1,634 species were listed, several of which are known to reside within western
Washington forests and forest streams (USFWS, 2020).
In 1994, the federal Northwest Forest Plan was adopted to establish an ecosystem
and watershed-based management plan for federal lands in western Oregon, western
Washington, and part of northern California. It is a series of policies and guidelines
designed to govern long-term management of late-successional forest habitat in response
to declines in Northern spotted owl (Strix occidentalis) populations. A multi-disciplinary
team composed of tribes, federal agencies, scientists, and others worked together to
12
develop the plan. The plan identified five major goals: 1) never forget the human and
economic dimensions of the issue, 2) protect the long-term health of forests, wildlife, and
waterways, 3) focus on scientifically sound, ecologically credible, and legally responsible
strategies and implementation, 4) produce a predictable and sustainable level of timber
sales and nontimber resources, and 5) ensure that the federal agencies work together
(U.S. Forest Service et al., n.d.). This legislation caused a prominent shift in the
distribution and intensity of forest management from public to private lands.
In 1999, the Washington Forest and Fish Report (FFR) was produced by a multistakeholder group composed of tribes, forest landowners, federal and state governments,
counties, environmental groups, and others. The FFR was developed for non-federal
landowners, in response to the federal listing of several species of Pacific salmon as well
as the continued listing of surface waters under the federal Clean Water Act 303(d) list.
To address these issues the FFR outlined protections for water quality and aquatic
wildlife. The plan identified four key goals: 1) provide compliance with the federal
Endangered Species Act for aquatic and riparian dependent species, 2) restore and
maintain riparian habitat to maintain a harvestable supply of fish, 3) meet the
requirements of the Clean Water Act for water quality, and 4) keep the forest products
industry economically viable (Washington Department of Natural Resources et al., 1999).
That same year, the Salmon Recovery Act of 1999, also known as the Forest and Fish
Law was enacted. It directed the adoption of the Forest and Fish Report goals and
protective strategies into State Forest Practice Rules. The Forest Practice Rules are
governed by the state’s Forest Practices Board.
13
In 2006, the Washington Department of Natural Resources (WDNR), in
collaboration with USFWS and NOAA Fisheries, completed the statewide Forest
Practices Habitat Conservation Plan. This legislation endorsed the Forest and Fish Law
and ensured that landowners who conducted their forest practices activities in compliance
with the Forest Practices Act and rules, were also adhering to the requirements for
aquatic species under the federal Endangered Species Act (WDNR, 2005).
Despite the regulatory framework that exists to manage and conserve the regions
forests and forest waters, questions linger regarding the suitability of protective measures.
Diverse interpretations of the science and disparate values contribute to an ongoing
debate about how to approach conservation policy for managed lands. Some land
managers are concerned about the reduced economic value that comes with setting land
aside, while some members of the public are concerned that not enough is being done to
conserve species and shared resources. Successfully integrating science and societal
values will be necessary to develop effective environmental policy (Wilhere & Quinn,
2018).
Measuring Forest Diversity
Resilient forests are high-functioning and ecologically productive in both natural
and managed settings, however the ecological and biological responses to management
are not well understood. Biodiversity indicators are needed to measure and monitor
changes and trends in aquatic and terrestrial habitats (Brown & Pollock, 2019).
Conservation of biological diversity requires an understanding of how habitat features,
and species compositions vary within ecosystems. Identifying landscape level forest
cover and vegetation patterns can provide landowners a reference condition, from which
14
they can develop landscape level biodiversity targets. Identifying habitat -species
relationships within forested ecosystem at the local scale can help determine which forest
features contribute to high-functioning wildlife habitat (Brown and Pollock, 2019).
Combining landscape and local scale assessment methods can provide forestland
managers tools to evaluate current conditions, compare them to regional and local
biodiversity targets, implement plans, and measure change.
Landscape Scale Biodiversity Metrics
A landscape is an area of land with groups of vegetation communities or
ecosystems forming an ecological ‘unit’ with distinguishable structure, function,
geomorphology, and disturbance regimes (Gaines et al., 1999). Landscape diversity is the
amount and percentage of different ecosystems within the landscape and to some degree
also represents the interactions and/or disturbance regimes within it. Landscape features
such as forest cover by size and percentage, wetland or aquatic habitat by size and
percentage, habitat connectivity and ratio between interior and edges all describe
elements of the landscape that affect species composition, viability, and distribution.
Assessments of the level of diversity that exists within areas of interest and an
evaluation of how it compares with historic levels are important landscape scale analyses.
Gaining an understanding of the trends in landscape composition, features, and specific
habitats help shape landscape diversity targets. Developing a baseline is challenging but
critical to efforts that aim to recognize patterns and changes in species populations,
compositions, and distributions over time.
15
Biological indicators can be monitored for change to answer and validate specific
species-habitat relationships and to measure the status of biodiversity from landscape to
local scales (Noss, 1990). A multi-taxon framework is likely best suited to understand
biological responses to management activity (Lelli et al., 2019) and could be applied at
landscape and local scales. By assessing the patterns of many species in an area, a
stronger correlation to the importance of certain habitat features can be drawn. However,
consideration of the three attributes of biodiversity (composition, structure, and function)
at the landscape, population, species, and genetic levels will help define more specific
areas of biological significance (Noss, 1990).
Local Scale Biodiversity Metrics
Monitoring biodiversity at the local scale is important when trying to identify
patterns and trends in ecosystem function and integrity. Important assessments at this
scale include how management activities or natural disturbances affect species diversity
and richness or rarity in localized areas, how species function in their ecosystems, and
how well their associated habitats are perpetuated across the landscape. Forestland
managers may be interested in the results of these types of assessments to better
understand the impacts of forest management activities (positive or negative) and use
results to guide best management practices.
Assessing the spectrum of species is complex and many researchers have
attempted to group them into categories that are easier to assess. Common groupings are
based on habitat preference, behavioral similarities, or by the functional role they perform
in the ecosystem. For assessments based on ecosystem function, the relative importance
of each guild (or kind of specie) is considered based on the relative importance of its role
16
in the ecosystem (e.g., decomposer, seed disperser). However, this type of research can
be flawed in its underlying assumption, if it assumes that each specie provides only one
specific function with which its relative importance is solely based.
Many forest biodiversity indicators have been used to assess (or indicate) levels of
conservation success and/or need, however evaluation and critical assessment of the
scientific rigor at the individual indicator scale (species or habitat feature) as well as at
the ecosystem scale is lacking (Gao et al., 2015). Additionally, potential gaps and
overlaps exist that need evaluation. A review of 142 (European) published studies which
included 83 indictor groups were analyzed (Gao et al., 2015). Of 412 indicators identified
in the studies, 6 indicators were supported by strong evidence and scientific rigor.
Species richness indicators were best represented when correlating deadwood volume
with wood-living fungi and saproxylic beetle richness; when evaluating deadwood
diversity with saproxylic beetle richness; and when relating canopy tree age with
epiphytic lichen richness (Brin et al., 2009; Gao et al., 2015).
Habitat Indicators
High-functioning, diverse forested landscapes are ecologically complex and can
be described as having vertical and horizontal heterogeneity which includes vegetation
size and species mix, live and dead standing trees of various species, heights, and
diameters, dead trees and limbs on the forest floor, and a healthy and diverse developed
understory (Carey & Curtis, 1996, Lindenmayer et al., 2000 & 2012). Multiple
dimensions within the forest landscape creates micro-habitats, each with its own light,
temperature, and moisture conditions, which allows for a variety of flora and fauna to
17
thrive. The structural complexity of any habitat can represent the diversity of wildlife
habitat available.
Wildlife utilize a variety of habitat components. All forest types provide habitat to
some species. Developing diverse and complex stand structures is key to providing for a
diversity of species and processes and can be achieved at the stand and landscape levels
in managed forests (Zobrist & Hinckley, 2005).
Wildlife Indicators
Suitable wildlife habitat provides connected feeding, roosting, breeding, nesting,
and refuge habitat for a wide spectrum of native terrestrial and aquatic species. When
habitats become highly fragmented, species that are habitat specialists (e.g., spotted owls)
are vulnerable to becoming restricted to their isolated patch of habitat, limiting foraging
and the potential for genetic mixing (Newmark et al., 2017). Species that are generalists
(e.g., barred owls) have adapted to a wide variety of habitats and disturbance regimes,
and therefore are often more resilient to disturbance imposed by human activity.
Each species requires a unique suite of habitat elements (i.e., snags, live trees,
woody debris) which provides for its ability to thrive and perpetuate, therefore, a ‘one
size fits all’ approach to biodiversity monitoring is ineffective at evaluating accurate
baseline diversity metrics (Dale & Beyeler, 2001). The relative sensitivity of wildlife
species to their environments can help to determine which species to focus on for
measuring and monitoring biodiversity, whereas a suite of taxa may be most beneficial to
understand broader ecosystem diversity (Dale & Beyeler, 2001). Indicators are most
18
effective when they represent key attributes of the ecological functions of interest
(Juutinen & Mönkkönen, 2004).
‘Representatives’ of Biodiversity
Habitat effectiveness is often evaluated by monitoring wildlife in one of three
categories. Monitoring for the presence, absence, or relative well-being of ‘indicator’
species, ‘keystone’ species, or ‘umbrella’ species in a given environment is often utilized
to represent larger groups of species and the overall health of the ecosystem. Emphasis on
indicator, keystone, and umbrella species has illuminated the role of specific species
within ecosystems. For example, indicator species are sensitive to foreign disturbance. By
monitoring the condition of them, scientists can make correlations to the well-being of
other species in the same habitat (e.g., Pacific salmon). They are chosen to assess how
specific ecosystems are doing and to assess the effect of change (Carignan & Villard,
2001; National Geographic, n.d.).
Keystone species provide unique functions in their habitats, whereby few, if any
other species in that habitat provide the same function (e.g., gray wolf). These species
have notable influence on local food webs and without them, the local ecosystem is
radically different (Mills & Doak, 1993; Society, 2017). Umbrella species are similar to
keystone species except they are more likely to utilize large tracts of land (e.g., grizzly
bear). They have a high degree of influence on local and disbursed food webs and
therefore their value can cover broad geographic range (Roberge & Angelstam, 2004;
Society, 2017).
19
Focus on specific species as representatives of entire ecosystems does not likely
accurately represent all potential species and their functional relationships in the
ecosystem (Simberloff, 1999; Dale & Beyeler, 2001), while focus on stand-level species
richness or sensitive species habitat areas may not capture the full spectrum of
biodiversity status and responses (Lelli et al., 2019). Management designed to protect one
species are unlikely to be successful if conservation of the full range of species and their
functions is desired. An functional approach which includes grouping species by ‘kinds’
rather than by abundance, could lead to a better understanding of land management
activities that alter habitats and species assemblages, as long as species are not valued on
a single functional trait. By evaluating the effects of forestry on a variety of species (that
each provides a different function in the ecosystem), assessment of how the habitat has
been altered and its effect on biodiversity may possibly be more easily understood.
Specific Taxa as Indicators
Several forest wildlife taxa have been identified as effective indicators of forest
health and environmental change. The ground beetle, amphibian, and avian communities
represent groups of wildlife that are reasonable to monitor and are often abundant,
diverse, and sensitive to environmental change. They also utilize different resources in
the forest, from the forest floor to the canopy, providing a unique perspective of the
resource utilization by taxa. Evaluation of the species composition and abundance at each
of the conifer and hardwood forest habitat types will aid understanding of the relative
contribution of each habitat to biodiversity in managed forests.
20
Ground Beetles
Arthropods represent 65-70% of species in forests (Langor & Spence, 2006) and
perform ecological functions such as pollinating, cycling nutrients, dispersing seeds, and
controlling invertebrate populations. Carabid beetles are arthropods that make up a wide
variety of ground-dwelling beetles with more than 2,000 known species in North
America (Langor & Spence, 2006). They are among the best studied taxa regarding the
effects of forest management on forest biota (Niemelä et al., 2006). They have been
identified as good bioindicators of ecosystem disturbance in forested landscapes because
they are diverse, abundant, sensitive to environmental change, and reasonably easy to
monitor (Pearce & Venier, 2006). Some studies indicate they poorly represent the species
abundance and richness of other taxa (Koivula, 2011), while other studies indicate they
are well-suited as representatives (Pearce & Venier, 2006). However, many agree they
are most effective as ecological indicators when evaluated in tandem with other
environmentally sensitive taxa (Koivula, 2011; Pearce & Venier, 2006; Rainio &
Niemelä, 2003).
The diversity of specialized habitat associations known to exist within the ground
beetle community make them especially useful in evaluating changes in habitat
conditions (Niemelä et al., 1996). In the forested environment, many species have
demonstrated a sensitivity to changes in canopy cover (e.g., clearcut harvest), with
observed shifts from forest habitat species to open habitat species once the canopy is
removed (Pearce & Venier, 2006; Niemelä et al., 2006). Additionally, the creation of
canopy gaps within the forested environment has been shown to increase the species
richness in members of the ground beetle family (Perry et al., 2018). Small-scale forest
21
disturbances are linked to greater vegetation diversity, influencing microhabitats and leaf
litter composition that are known to be favored by members of the Carabidae family
(Koivula et al, 1999). The use of ground beetles as bioindicators has led to greater
understanding about the importance of heterogenous habitats in the forested environment.
Amphibians
Amphibians have been widely studied in ecological research to increase
understanding of the changing environment, pollution thresholds, and climate change
(Hopkins, 2007). Amphibians are critical components of both aquatic and terrestrial
communities. They occupy diverse trophic niches and often serve as abundant prey
sources for wildlife. In some environments, certain species compose the most abundant
vertebrate in the population, forming important trophic roles up and down the food web.
Their environmental sensitivity, trophic importance, and detectability make them well
suited as bioindicators.
Amphibians have complex life cycles, often requiring both aquatic and terrestrial
habitats. Environmental variables important to amphibians include suitable breeding,
feeding, and resting habitats, which maintain suitable moisture and temperature regimes.
The composition and density of the forest canopy is an important driver for controlling
light availability to the forest floor, which in turn controls the composition and vigor of
the understory vegetation community. In the forested environment, canopy density also
regulates forest floor moisture content and temperature regimes. However very few
studies describe the composition of the forest canopy or correlate canopy species
composition with amphibian abundance and diversity (Bennett et al., 1980; Gomez &
Anthony, 1996).
22
Amongst the amphibian community, the plethodontid salamanders are terrestrial
salamanders that live in forested environments across North America. Their sensitivity to
environmental change, along with their longevity, small territory size, site fidelity and
tendency to occur in high densities have made them presumed indicators of biodiversity
and ecosystem integrity in forested environments (Welsh & Lind, 1991). The
plethodontid species known to occur in east coast forested habitats are known to thrive in
hardwood forests whereas west coast plethodontid species occur primarily in conifer
forests. Studies indicate their abundance and density are correlated with microhabitat
variables such as moisture content, leaf litter depth, understory vegetation, woody debris,
and temperature (Pough et al., 1987; Homyack & Kroll, 2014). Investigations have
identified species-habitat relationships with forest edges, forest canopy removal,
historical disturbance regimes, and various silvicultural practices.
Songbirds
The distribution and composition of birds across forested landscapes has been
well documented. Songbird communities are a species-rich component of many forests.
They facilitate important forest ecosystem processes such as nutrient cycling and transfer,
seed dispersal, and maintaining balance in invertebrate communities. Abundances and
populations of some species have been linked to changes in habitat quality including
changes in nesting and foraging habitat. While many species are forest generalists, some
are specialized, utilizing specific elements of forest structure and composition, and
making them effective bioindicators of forest conditions (Gregory et al., 2003). These
qualities make them an excellent taxonomic group for evaluating the difference between
forest habitat types.
23
The managed forest environment is subject to shifting habitat conditions and
disturbance. The landscape is primarily conifer dominated, with hardwood vegetation
scattered throughout. Hardwood tree and shrub species have been identified as important
contributors to avian habitat, either through structural cover and nesting habitat or as a
source of prey (Ellis & Betts, 2011). Early seral hardwood cover provides critical nesting
and foraging habitat for many neotropical migrants (Ellis & Betts, 2011). Some studies
indicate that a decline in early seral habitats may be linked to the decline in populations
of several avian species (Keller et al., 2003). Habitat relationships exist throughout
natural and managed forests that benefit different species at different times (Hansen et al.,
1995). However, the contribution that dispersed hardwood dominated patches, located
within the managed forest matrix, make to avian richness and abundance has not been
well evaluated.
Conclusion
Forests perform ecosystem functions critical to the health of our planet. Healthy
ecosystem function relies in part on the health and function of each of the species within
it. Determining scientifically rigorous methods to measure forest biodiversity is important
for forest managers who strive to incorporate biodiversity goals into long-term forest
management plans. Forestland managers have the opportunity, through intentional forest
management activities, to optimize performance of ecological functions while also
maintaining alignment with business and societal objectives. Although it is known that
conservation of forest biodiversity is important, it is difficult to quantify baseline
conditions and measure the effectiveness of efforts to maintain or increase it.
24
Conservation of biological diversity requires an understanding of how habitat
features within ecosystems function to create the diversity of life. Ensuring diversity of
habitat or structural features may serve as a suitable proxy for biological diversity
however, increased evidence is necessary to understand the relationships and therefore
ensure management efforts are appropriately prioritized and successfully meeting
biodiversity goals.
Biological response to forest management is complex and mechanisms that shape
responses in diversity are variable. Biological indicators can be monitored for change to
answer and validate specific species-habitat relationships and to measure the status of
biodiversity from landscape to local scales. Clearly articulated objectives are necessary to
develop well-designed and effective research strategies. A multi-pronged approach is
useful to accurately assess elements of biodiversity at landscape and local scales.
However, focus on keystone, umbrella, or indicator species alone does not accurately
represent the full spectrum of species within an ecosystem. Hundreds of studies have
been conducted to evaluate and validate the use of additional biological indicators to
assess ecosystem health (Gao et al., 2015).
Quantification of change in biodiversity, by species or ecosystem richness, can be
determined once a reference condition has been established. Repeating methods overtime,
providing for the same variables, will allow assessment of what has changed, although it
may not allow for understanding why. If conducted in a methodical fashion, the
examination of three taxa can be used to measure the relative contribution of forest
habitats to the conservation of biodiversity. Ground beetles, amphibians, and forest
25
songbirds have characteristics that make them useful biological indicators in forested
habitats.
26
Chapter 3. Methods
Study Area
The study was conducted within the temperate forests of western Washington,
specifically the westside lowland conifer-hardwood and montane mixed-conifer forest
habitat types (Chappell, 2001, Figure 1). The topography in the study area is generally
mild, with elevations ranging from 128 to 280 meters (Table 1). The climate is mild and
typically comprised of wet winters and dry summers. Precipitation occurs most often as
rainfall, with 81-114 centimeters in a typical year (Western Regional Climate Center,
2020). Dominant forest trees are Douglas-fir (Pseudotsuga menziesii), western hemlock
(Tsuga heterophylla), western red cedar (Thuja plicata), red alder (Alnus rubra) and big
leaf maple (Acer macrophyllum). The understory is often composed of a wide variety of
woody shrubs and herbaceous forbs. Natural and anthropogenic influences on the
forested landscape have created a mosaic of forest ages and habitat conditions. Private
and publicly held Douglas-fir managed forests are intermixed with rural and agricultural
communities.
Table 1. Summary of physical characteristics at each forest habitat type where: CON = conifer and HW =
hardwood. Source HW = rational for why the hardwood patch exists, Dist. Water = the distance from plot
center to the nearest source of water, and Dist. Forest = the distance from plot center to the nearest change
in forest habitat. *Root rot, **Depressional feature.
Elevation
Location
Age
(m)
Aspect
Dist. Water
Dist. Forest
(m)
(m)
Source
CON
HW
CON
HW
HW
CON
HW
CON
HW
Brooklyn
43
145
160
N
W
RR*
75
95
230
175
Lake Creek
43
280
280
E
E
Dep**
115
115
215
95
Langworthy
30
135
135
NE
N
RR*
120
130
170
120
Redfield
39
140
130
W
SW
RR*
120
120
175
145
Skookum
31
175
200
SW
SW
Dep **
70
85
115
160
27
Monitoring Design
Five paired plots were established in third-growth forests between the ages of 30
and 43 years old (Table 1). Paired plot locations were selected based on the prevalence of
deciduous patches within third-growth conifer dominated forest matrices. Plots were
randomly selected from a list of locations that had been assessed for stand characteristics.
Stands were considered if they were dominated by Douglas-fir and had average stocking
densities. Stands were disqualified if management activities were being conducted within
close proximity.
Washington
Oregon
Study locations
Figure 1. The study was conducted within the temperate forests of western Washington, specifically the
westside lowland conifer-hardwood and montane mixed-conifer forest habitat types.
28
Hardwood dominated patches are the focal points of the study and thus
determined the location of the first plot. Each hardwood plot was evaluated to determine
the primary functions influencing the hardwood vegetation community and to ensure the
presence of water was not persistent (Table 1). Plots were centered within the hardwood
patch, with the second plot located within the adjacent conifer forest, located at least 250
meters away. Each plot was 20- by 20-meters in size (0.04 ha), measured from the plot
center and oriented in cardinal directions (Figure 2). Hardwood patches were irregularly
shaped, however, they all fit approximately within the 20- m by 20-meter plot.
From the hardwood patch, the location of the conifer dominated patch was
determined using randomly generated compass bearings, but the end location had to fall
at least 250 meters away and meet the conditions identified above. Both plots at a site
were established within a forest of the same age and management history. All plots were
between 95 and 230 meters away from a habitat boundary as indicated by a change in
forest type or seral stage and were at least 70 meters away from aquatic features, such as
streams or wetlands.
Forest Structure and Composition
Forest structure and composition variables related to leaf litter depth, woody
debris volume, vegetation cover, vegetation composition, canopy cover, and basal area
were evaluated across all plots. An assessment of soil moisture regimes was conducted
during winter 2020, all sites were considered upland terrestrial habitats based on a lack of
hydric soils, hydrology, and hydric vegetation.
29
Conifer-dominated
plot
Hardwood-dominated
plot
Hardwood patch
Figure 2. The paired study design was comprised of both conifer- and hardwood- dominated forest habitat
types (diagram not to scale). The two forest habitat plots were located at least 250 meters apart from each
other and at least 95 meters away from a habitat boundary, as indicated by a change in forest type or seral
age.
A grid-based design was established, with the plot center located at the center of
the grid and oriented to the north. The 20- by 20- meter plot was composed of 5-meter
transect lines established longitudinally and latitudinally within it, creating a 5- by 5meter grid with 16 subplots and establishing intervals 5 meters apart along each transect
line, creating 25 intervals.
Leaf litter was recorded as the depth of the O soil horizon (cm). This was
measured at three of the 25 intervals and reported as an average depth per site. Eight of
the 16 subplots were randomly selected. Within those eight subplots, ground cover
measurements (mosses, forbs, shrubs,) were estimated based on ocular assessments.
30
Overstory canopy cover was estimated based on the average of four measurements taken
with a spherical densitometer at the center of each of the randomly determined subplots.
Cover and canopy measurements were averaged across the subplots and reported as an
average per site.
Woody debris volume was measured in each of the eight randomly determined
subplots. Portions of pieces that spanned outside the boundary of the plot were not
measured. The minimum diameter of the pieces measured was 3 cm, slightly smaller than
the smallest size that an amphibian had been detected in association with (4 cm). Each
piece of woody debris ≥ 3 cm wide and ≥ 10 cm long within the randomly determined
subplot was measured and assigned to one of five decay classes as modified from Maser
(1979) and identified to species if possible. Pieces were also assessed for if they had
fallen naturally or had been cut, as all sites had been previously pre-commercial thinned.
Woody debris was reported as the total volume of all pieces within the subplots, per site.
Vegetation composition was determined for the entire 20- by 20-meter plot using
simple presence/absence. Mosses, grasses, and vetches were lumped by division or genus
and not identified to species, therefore each of those categories counted as 1 if its type
was represented. All trees 10 cm in diameter or larger were measured to determine a total
basal area by species for each site.
Ground Beetle Surveys
Ground beetle diversity and abundance was measured according to existing
protocols (Hoekman et al., 2014). Thirty un-baited pitfall traps were deployed across the
ten sites, three each in every conifer and hardwood plot. Traps were deployed near the
31
center of each plot and were equipped with cover boards positioned approximately 1.0
cm above them, so as to avoid being flooded by rainwater and to prevent inadvertent
captures of amphibians or other taxa. Traps were deployed at the beginning of June and
collected by mid- July. Traps were checked frequently throughout the duration of
deployment to reduce specimen loss due to degeneration or predation within the trap. All
specimens that were captured were retained for later species identification. Each plot was
deployed between 35 to 42 nights, with equal nights for both plots at any given site.
Traps were deployed for a combined total of 1158 nights.
Amphibian Surveys
Amphibian surveys were conducted in the spring when they were most likely to
be surface active. A crew of two people conducted area-constrained, ‘light-touch’
searches consistent with existing protocols (Corn & Bury, 1990; Wilson, 2016). Surveys
were conducted across 100% of the plot area, including all 16 plots generated by the grid
design. Three separate surveys were conducted, with a minimum of four days between
surveys. Search time per survey was not restricted and varied based on the level of effort
required to search all potential habitat structures consistently.
Observers searched under all cover objects (woody debris, bark, and rocks),
vegetation (mosses, forbs, and ferns), and probed all crevices. Objects were returned to
their original position, and woody features were surveyed only when it could be
accomplished without causing habitat destruction. Captured amphibians were identified
to species, measured (snout to vent and total length, mm) and photographed. Reptiles that
were detected during surveys were identified to species and photographed where
possible, but measurement of length was not collected. All captured amphibians and
32
reptiles were retained in conditions emulating the habitat they were found in until surveys
were completed for all plots within a site. Once the survey was completed and
measurements had been obtained, they were released at the location of capture.
Songbird Surveys
Auditory and visual surveys for forest songbirds were conducted in late spring
when they were most likely to be breeding (late- May to early- July). Point-count surveys
were conducted consistent with protocols identified by Ralph et al (1995). Surveys were
conducted from each plot center and included all birds that could be heard or seen from
the plot center. Three separate surveys were conducted, with a minimum of four days
between surveys. All birds that were detected by sight or sound during 30-minute survey
durations were recorded. Surveys were performed between sunrise and 10:00am, during
calm weather conditions. All detected birds were allocated to a category of either ‘in’ the
plot or ‘out’ of the plot depending on where the bird was active at the time it was
observed.
Data Analysis
All individuals that were detected during surveys were identified and recorded
(Appendix 1), however, only specific individuals and species were included in the
analyses. For the beetle analysis, only species detected from the Carabidae family were
included. For the amphibian analysis, an additional taxonomic group was detected while
conducting surveys and was included (reptile). For the rest of this thesis, where
amphibians and reptiles were combined in the analysis, they are collectively referred to as
herpetofauna. For the bird analysis, only individuals that were observed utilizing forest
33
habitats were included (individuals observed flying overhead were excluded). To aid
visualization of species occurrences across locations and habitat types, a checkerboard
plot was generated (Appendix 2).
A relative abundance index was generated for carabid beetles and herpetofauna to
account for varying survey efforts. For beetles, because the duration of trap deployment
varied from site to site, I divided the total number of detections at each site by the number
of trap nights for that site. The number was then multiplied by 100 for easier visual
interpretation. For herpetofauna, because the duration of the survey varied from visit to
visit and from site to site, I divided the total number of detections at each site by the
combined duration of survey time in hours at each site. Bird surveys were all conducted
for 30-minutes per visit, and each site was visited three times, so abundance totals were
not adjusted by effort. The relative abundance index results were used to develop a
correlation matrix, and to calculate descriptive statistics and paired t-tests using Microsoft
Excel (2009).
The correlation matrix was generated to measure the strength and direction of the
relationship between communities (overall and by taxa) and habitat patch forest structure
and composition variables (Appendix 3) as well as with forest stand scale physical
characteristics (Appendix 4). Descriptive statistics and paired t-tests were calculated to
evaluate the difference between richness and abundance means for all faunal categories
and forest structure attributes at conifer and hardwood plots. Using the R statistical
computing platform (R Core Team, 2020), c-scores were created using the package
‘EcoSimR’ (Gotelli et al., 2015) to evaluate species co-occurrence across all possible
species pairs. Co-occurrence evaluations were also conducted using the package
34
‘cooccur’ (Griffith et al., 2016) to evaluate whether certain species were either more or
less likely to occur in the same site compared to random chance alone.
I normalized species abundances to determine how common or rare a species was
compared to other species that were detected across all taxa. To do this, I divided the
number of detected individuals at a site by the maximum number of individuals detected
across all sites. Once the abundances were normalized, I used the Bray-Curtis distance
index (Bray & Curtis, 1957) to quantify taxonomic dissimilarity for each taxon. I then
used nonmetric multidimensional scaling (NMDS) in the package ‘vegan’ (Oksanen,
2019) to characterize species associations with each of the conifer and hardwood plots
and to visualize associations with forest structure and composition variables related to
leaf litter, woody debris volume, vegetation cover, and basal area. NMDS was performed
for all species combined as well as at the level of the taxonomic group (ground beetles,
herpetofauna, birds). Additionally, using the package ‘lme4’ (Bates et al, 2014), I ran a
mixed-effects model, with the habitat types treated as the fixed effect and the locations
treated as the random effect. For this analysis, the 8 randomly selected subplots (see
above) in a given plot were the individual sample units. To compare communities of all
hardwood vs. all conifer plots, as well as communities by location, I performed a posthoc analysis of similarity test (ANOSIM) in the package ‘vegan’.
With five paired plots, the analysis of the data was primarily exploratory and not
focused solely on statistically significant relationships. Multiple approaches were used to
determine if patterns or trends emerged that could be useful in future studies. An alpha of
0.10 was chosen to increase the power of the individual statistical tests (with a small
35
sample size), and the p-values shown in individual scatter plots were not corrected for
multiple comparisons.
36
Chapter 4. Results
Forest Structure and Composition
Leaf litter depth was similar between conifer- and hardwood-dominated plots
(while controlling for the random effect of location (p = 0.01), fixed effect of habitat
type: F1, 34 = 1.80, p = 0.19; Table 2). Percent of moss ground cover was also similar
(while controlling for the random effect of location (p > 0.99), the fixed effect of habitat
type: F1, 78 = 2.73, p = 0.10; Table 2). However, significantly more forb and shrub ground
cover occurred at hardwood-dominated plots as compared to conifer-dominated plots
(while controlling for the random effect of location forb: (p < 0.001) fixed effect of
habitat type: F1, 74 = 48.15, p < 0.001; shrub: (p = 0.73) fixed effect of habitat type: F1, 74
= 32.95, p < 0.001; Table 2) and forb and shrub cover were positively correlated with
plant richness (forb: Pearson’s r = 0.74, p = 0.02; shrub: Pearson’s r = 0.62, p = 0.05).
Woody debris volume in conifer- and hardwood-dominated plots was similar (t(4)
= 0.35, p = 0.74; Table 2, Figure 3). At the plot scale, an average of 9.8 cubic meters of
woody debris was measured at conifer plots (range = 6.2-16.3, SD = 4.0) and an average
of 9.1 cubic meters was measured at hardwood plots (range = 2.7-14.8, SD = 4.6; Table
2). All conifer plots had woody debris pieces that were sourced from cut pieces (from
prior pre-commercial thin management). Of the woody debris pieces measured in conifer
plots, an average of 2.5 cubic meters of woody debris were sourced from cut pieces
(range = 1.1-3.6, SD = 1.0), representing 9 to 58 percent. In the hardwood plots, three out
of the five plots had woody debris pieces that were sourced from cut pieces. Of those
37
three plots, an average of 1.4 cubic meters were sourced from cut pieces (range = 0-1.7,
SD = 0.2), representing 8 to 50 percent.
Tree basal area in conifer- and hardwood-dominated plots was similar (t(4) = 2.06,
p = 0.11; Figure 4). At the individual plot scale, an average of 2.5 square meters of basal
area was measured at conifer plots (range = 1.8-4.3, SD = 1.0) and an average of 1.0
square meters was measured at hardwood plots (range = 1.2-1.6, SD = 0.6; Table 2).
Sixty plant species were identified across all surveys with 42 species observed in
conifer-dominated plots and 54 species observed in hardwood-dominated plots
(Appendix 1). More plant species were found in hardwood-dominated plots than coniferdominated plots (t(4) = -4.63, p = 0.01; Figure 5). Of the species observed in each habitat
type, five were unique to conifer-dominated plots and 13 were unique to hardwooddominated plots (Appendix 1). At the individual plot scale, an average of 16.6 species
were observed at conifer plots (range = 8-21, SD = 5.4) and an average of 24.8 species
were observed at hardwood plots (range = 22-29, SD = 2.6; Table 3).
Two Washington noxious weeds were identified during surveys. Robert geranium
(Geranium robertianum), a Class B noxious weed, was observed in one plot each of both
conifer and hardwood sites, while English holly (Ilex aquifolium), a species on the State
monitor list, was observed at one hardwood site (Appendix 1).
38
Table 2. Summary of forest structure and composition attributes at each forest habitat plot. CON = conifer
and HW = hardwood.
Leaf Litter
Woody Debris
Basal Area
(cm)
(m3)
(m2)
Location
Brooklyn
Lake Creek
Langworthy
Redfield
Skookum
CON
HW
CON
HW
CON
HW
3.0
5.9
3.3
2.0
5.3
2.6
4.4
3.0
2.5
3.6
11.1
16.3
6.2
7.5
8.1
7.5
12.2
2.7
14.8
8.4
4.3
2.3
2.0
1.8
1.9
0.0
0.9
1.0
1.6
1.3
Location
Moss Cover
(%)
CON
HW
Forb Cover
(%)
CON
HW
Shrub Cover
(%)
CON HW
Brooklyn
Lake Creek
Langworthy
Redfield
Skookum
29.5
1.6
68.1
18.1
54.4
11.4
3.7
44.4
5.1
48.1
1.6
5.5
7.0
2.3
45.6
69.1
68.8
16.3
61.3
13.1
27.1
68.8
74.4
12.5
76.3
32.4
62.5
50.0
68.1
17.1
Canopy Cover
(%)
CON
HW
100
100
100
100
100
100
100
99.4
98.9
100
Figure 3. Woody debris volume (≥ 3 cm diameter by ≥ 10 cm length) by forest habitat type was similar
(t(4) = 0.35, p = 0.74).
39
Figure 4. Tree basal area by forest habitat type was similar (conifer basal area > hardwood basal area,
t(4) = 2.06, p = 0.11).
Figure 5. Plant richness (including forbs, shrubs, and trees) by forest habitat type was significantly
different (hardwood richness > conifer richness, t(4) = -4.63, p = 0.01).
40
Species Richness and Abundance
When individuals and species across all animal taxa were tallied, 388 individuals
representing 45 species were observed (Appendix 1). The detection of herpetofauna and
birds were not independent, as the same individuals may have been detected during more
than one survey. Of the 45 species that were detected, 33 occurred in conifer-dominated
plots and 41 occurred in hardwood-dominated plots (Appendix 2). Of those species, four
were uniquely associated with conifer-dominated plots and ten were uniquely associated
with hardwood-dominated plots.
When all animal taxa were combined, the mean species richness was similar when
comparing conifer- to hardwood-dominated sites (t4 = -1.11, p = 0.33; Table 3). When
NMDS was performed across all taxonomic groups, communities were not significantly
different from each other when grouped by habitat type (ANOSIM R = -0.08, p = 0.69),
but were different when grouped by location (ANOSIM R = 0.50, p = 0.02; Figure 6).
Pairs of sites tended to be closer to each other in the NMDS ordination plot than to other
sites of the same habitat type.
For all taxa combined, there was significantly more species-pair segregation (p =
0.03; Figure 7) than expected by chance alone, using ‘EcoSimR’ to evaluate species cooccurrence. The observed c-score (2.46) is statistically higher than the mean simulated cscore (2.40), although this represents a very small absolute difference (0.06), so the
biological meaning of that difference is unclear. For each taxon analyzed individually,
ground beetles (p = 0.48) and birds (p = 0.36) were not significantly aggregated or
segregated, but herpetofauna were (p = 0.08), compared to randomized occurrences. In
41
the case of herpetofauna, the observed c-score (0.86) was higher than the mean simulated
c-score (0.72) by 0.14, which is also a small absolute difference.
Table 3. Summary of species richness by taxa and plant richness detected in each forest habitat plot. CON
= conifer and HW = hardwood.
Location
Brooklyn
Lake Creek
Langworthy
Redfield
Skookum
Ground Beetles
CON
HW
0
3
5
2
6
2
2
5
3
3
Herpetofauna
CON HW
3
2
1
2
2
3
6
2
4
7
Birds
CON
HW
12
17
8
9
11
12
11
9
12
10
Plants
CON
HW
18
15
21
8
21
25
24
29
22
24
Figure 6. Nonmetric multidimensional scaling ordination performed across all animal taxa in conifer(CON) and hardwood- (HW) dominated sites were not significantly different from each other when
grouped by habitat type (conifer vs. hardwood; ANOSIM R = -0.08, p = 0.69) but were different when
grouped by location (ANOSIM R = 0.50, p = 0.02). 4-letter species codes shown in Appendix 1. stress <
0.13
42
Figure 7. When all animals were combined, the observed c-score (2.46) was statistically higher (p = 0.03)
than the mean simulated c-score (2.40), although this represents a very small absolute difference (0.06).
Ground Beetles
A total of 99 individual beetles were captured, representing 12 species and nine
genera (Amara, Cychrus, Necrophilus, Nicrophorous, Omus, Promecognathus,
Pterostichus, Scaphinotus, and Zacotus). Of the nine genera, seven represented the
Carabidae family, one represented the Agyrtidae family (Necrophilius hydrophiloides),
and one was from the Silphidae family (Nicrophorous defodiens) (Appendix 1). Members
of the Carabidae family comprised 80% of total captures and were the focus of analysis.
When all conifer and hardwood plots were combined, nine carabid species were detected
in conifer plots and nine carabid species were detected in hardwood plots. Of the species
observed, one was uniquely associated with conifer-dominated plots and one was
uniquely associated with hardwood-dominated plots (Appendix 2).
43
Trapping effort and carabid beetle captures by forest habitat were similar (Table
4). The mean species richness of ground beetles captured at conifer and hardwood
dominated forest habitats was virtually equal (t(4) = 0.00, p > 0.99; Figure 8). At the
individual plot scale, an average of 3.2 species were detected at conifer plots (range = 06, SD = 2.4) and an average of 3.2 species were represented at hardwood plots (range =
2-5, SD = 1.3; Table 4).
Zero to 14 individual carabid beetles were captured in conifer-dominated plots
and two to 12 beetles were captured in hardwood-dominated plots (Table 4). Carabid
beetles were captured at all sites except one, the Brooklyn conifer site. The mean relative
abundance of captured ground beetles for each forest habitat type was similar (t(4) = 1.40,
p = 0.24). The relative abundance of beetles averaged 7.5 in conifer plots (range = 0-12.6,
SD = 4.9) and 5.7 in hardwood plots (range = 1.8-9.5, SD = 3.7; Figure 9). NMDS
ordination performed on carabid beetles in conifer- and hardwood- dominated sites show
a minimal pattern of dissimilarity by habitat type (ANOSIM R = 0.009, p = 0.44; Figure
10).
Table 4. Summary of pitfall trapping effort and carabid beetle captures by forest habitat type. CON =
conifer and HW = hardwood.
Location
Brooklyn
Lake Creek
Langworthy
Redfield
Skookum
Trap Nights
111
105
126
111
126
Species
CON
HW
0
3
5
2
6
2
2
5
4
3
Individuals
CON
HW
0
6
13
14
11
2
2
8
10
12
Relative Abundance
CON
HW
0
5.7
10.3
12.6
8.7
1.8
1.9
6.3
9
9.5
44
Figure 8. Carabid beetle species richness by forest habitat type was virtually equal (t(4) = 0.00, p >
0.99).
Figure 9. Carabid beetle relative abundance by forest habitat type was similar (t(4) = 1.40, p =
0.24).
45
Figure 10. Nonmetric multidimensional scaling ordination performed using carabid beetles in conifer(CON) and hardwood- (HW) dominated sites show a minimal pattern of dissimilarity by habitat type
(ANOSIM R = 0.009, p = 0.44). 4-letter species codes shown in Appendix 1. stress < 0.08
Amphibians and Reptiles
A total of 102 amphibians and three reptiles were captured across all surveys. The
detection of all individuals was not independent, as the same individuals may have been
detected during more than one survey. Of the 105 individuals that were captured, eight
species representing seven genera were detected (Plethodon, Ensatina, Rana,
Ambystoma, Taricha, Pseudacris, and Thamnophis) (Appendix 1). When all conifer and
hardwood plots were combined, three species were detected in conifer plots and eight
species were detected in hardwood plots. All species that were detected within the conifer
plots were also detected within the hardwood plots, but not the other way around
(Appendix 2).
46
Survey effort by forest habitat type was similar (Table 5). The mean species
richness of hardwood dominated plots was greater than in conifer dominated plots (t(4) = 2.59, p = 0.06; Table 5, Figure 11). At the individual plot scale, an average of 2.0 species
were detected in conifer plots (range = 1-3, SD = 0.7) and an average of 4.4 species were
detected in hardwood plots (range = 2-7, SD = 2.0; Table 5).
Four to 15 individual herpetofauna were captured in conifer-dominated plots and
three to 26 individuals were captured in hardwood-dominated plots (Table 5). The mean
relative abundance of herpetofauna was similar in both hardwood- and conifer-dominated
plots (t(4) = 0.42, p = 0.70; Figure 12). The relative abundance of herpetofauna averaged
2.9 in conifer-dominated plots (range = 1.0-5.0, SD = 1.5) and 2.4 in hardwooddominated plots (range = 0.8-5.1, SD = 1.8). NMDS ordination performed on
herpetofauna in conifer- and hardwood- dominated sites show a minimal pattern of
dissimilarity by habitat type (ANOSIM R = 0.14, p = 0.22; Figure 13).
Table 5. Summary of amphibian survey effort and herpetofauna detections by forest habitat type. CON =
conifer and HW = hardwood.
Location
Brooklyn
Lake Creek
Langworthy
Redfield
Skookum
Total Survey Hours
CON
HW
3.0
3.4
3.9
2.8
3.9
3.9
4.8
4.0
4.9
5.1
Species
CON
HW
3
2
1
2
2
3
6
2
4
7
Individuals
CON
HW
15
9
4
10
9
5
17
3
7
26
Relative Abundance
CON
HW
5.0
2.6
1.0
3.6
2.3
1.3
3.5
0.8
1.4
5.1
47
Figure 11. Herpetofauna species richness by forest habitat type was significantly different
(hardwood richness > conifer richness, t(4) = -2.59, p = 0.06).
Figure 12. Herpetofauna relative abundance by forest habitat type was similar (t(4) = 0.42, p =
0.70).
48
Figure 13. Nonmetric multidimensional scaling ordination performed on herpetofauna in conifer- (CON)
and hardwood- (HW) dominated sites show a minimal pattern of dissimilarity by habitat type (ANOSIM R
= 0.14, p = 0.22). 4-letter species codes shown in Appendix 1. stress < 0.09
Terrestrial Salamanders
Of the 105 individual amphibians and reptiles that were captured across all
surveys, Ensatina and Western redback salamanders comprised 73% of the total. This
provided the basis to evaluate more specific associations. The mean relative abundance of
both taxa was similar across habitat types (t(5) = 1.90, p = 0.12, t(8) = -0.78, p = 0.45,
respectively). For Ensatina salamanders, the relative abundance averaged 1.5 in coniferdominated plots (range = 0.3-2.9, SD = 1.0) and 0.7 in hardwood-dominated plots (range
= 0.3-1.0, SD = 0.3; Table 6). For Western redback salamanders, the relative abundance
averaged 1.2 in conifer-dominated plots (range = 0.0-2.1, SD = 0.9) and 0.7 in hardwooddominated plots (range = 0.2-2.4, SD = 0.9; Table 6).
49
The snout to vent length (SVL) measurement of Ensatina salamanders was similar
by habitat type (while controlling for the random effect of location (p > 0.99), fixed effect
of habitat type: F1, 37 = 0.68, p = 0.42; Figure 14). The average SVL was 43.8 mm in
conifer-dominated plots (range = 18.4-59, SD = 9.7) and 40.8 mm in hardwooddominated plots (range = 18.8-54, SD = 12.9). The snout to vent length measurement of
Western redback salamanders was also similar by habitat type (while controlling for the
random effect of location (p > 0.99), fixed effect of habitat type: F1, 35 = 0.51, p = 0.48;
Figure 14). The average SVL was 40.8 mm in conifer dominated plots (range = 18-50.6,
SD = 8.9) and 43.1 mm in hardwood dominated plots (range = 16.2-58.9, SD = 11.1).
Ensatina and Western redback salamanders were detected in association with
various forest floor habitat features (Figure 15). 96% were detected under a forest floor
feature (boulder, woody debris, moss, fern fronds, needles, or bark), while 4% (three
Ensatina’s) were detected roaming on the surface of the forest floor. Across all surveys,
59% of Ensatina’s were detected in association with woody debris cover (n = 23), while
32% of Western redbacks were detected in association with woody debris cover (n = 12)
and 42% were detected in association with sword fern frond cover (Polystichum
munitum) (n = 16). In conifer dominated plots, 71% of Ensatina salamanders were
detected under woody debris (n = 17), while in hardwood dominated plots, 40% of
individuals were detected under woody debris (n = 6). For Western redback salamanders,
47% of individuals in conifer dominated plots were detected under woody debris (n = 9)
and 31% were detected under fern fronds (n = 6), while in hardwood plots, 17% were
detected under woody debris (n = 3) and 56% were detected under fern fronds (n = 10).
50
Table 6. Summary of Ensatina and Western redback salamander detections by forest habitat type. CON =
conifer and HW = hardwood.
Location
Individuals
Ensatina
Western Redback
CON
HW
CON
HW
Relative Abundance
Ensatina
Western Redback
CON
HW
CON
HW
Brooklyn
Lake Creek
Langworthy
6
5
4
3
5
1
6
4
0
1
2
2
2.0
1.5
1.0
0.8
1.0
0.3
2.0
1.2
0.0
0.3
0.4
0.5
Redfield
8
3
2
1
2.9
0.6
0.7
0.2
Skookum
1
3
8
12
0.3
0.6
2.1
2.4
Western Redback
Ensatina
Figure 14. Ensatina (ENES) and Western redback salamander (PLVE) snout to vent length (SVL)
measurements in conifer- and hardwood-dominated forest habitat types were similar (ENES: fixed effect of
habitat type, F1, 37 = 0.68, p = 0.42; PLVE: fixed effect of habitat type, F1, 35 = 0.51, p = 0.48).
20
12
Ensatina
15
Western Redback
10
8
6
10
4
5
2
0
0
Boulder CWD Fronds Moss Needles Top
Conifer
Hardwood
Bark
CWD
Conifer
Fronds
Moss
Needles
Hardwood
Figure 15. Ensatina and Western redback salamander cover types in conifer- and hardwood-dominated
forest habitat types. Three Ensatina salamanders were detected on the surface of the ground (Top). CWD =
coarse woody debris.
51
Woody Debris and Soil Temperature
Woody debris volume is a function of woody debris size. To understand the
utilization and selection by salamanders of individual pieces, volume and size were
evaluated separately. Woody debris piece sizes that salamanders utilized for cover were
significantly different between habitat types when all pieces were considered (while
controlling for the random effect of location (p > 0.99), fixed effect of habitat type: F1, 33
= 3.08, p = 0.09; Figure 16), however this result was driven by the presence of a single
large-sized wood piece. Without that outlier value, piece size used by salamanders were
similar between habitat types (while controlling for the random effect of location (p >
0.99), fixed effect of habitat type: F1, 32 = 0.24, p = 0.63). The mean diameter of woody
debris pieces that salamanders utilized in conifer dominated plots was 12.8 cm (range =
4.5-50.6, SD = 9.8) and the mean diameter of pieces utilized in hardwood dominated
plots was 26.7 cm (range = 4.0-120.0, SD =37.6). Of the nine woody debris pieces that
salamanders were detected under in hardwood dominated plots, one piece was an outlier
at 120 cm. When that piece was excluded from the analysis, the mean piece size in
hardwood dominated plots was 15.0 cm.
Soil temperatures where herpetofauna were detected were slightly warmer in
hardwood dominated habitat types than in conifer dominated habitats (while controlling
for the random effect of location (p = 0.10), fixed effect of habitat type: F1, 96.6 = 3.75, p =
0.06; Figure 16). With this p-value there is a difference between the mean temperatures
across habitat types while taking into the random effects of location. The mean
temperature of the soil in conifer plots where herpetofauna were detected was 10.5 C°
52
(range = 8.7-13.6, SD = 1.1) and the mean temperature of the soil in hardwood plots
where herpetofauna were detected was 11.1 C° (range = 8.8-14.6, SD = 1.5).
Figure 16. The diameter (cm) of woody debris pieces utilized by amphibians in conifer- and hardwooddominated forest habitat types was similar when the single large piece in a hardwood plot (120 cm) was
removed (fixed effect of habitat type, F1, 32 = 0.24, p = 0.64).
53
Figure 17. Soil temperatures (C°) measured at the sites of herpteofauna detections were significantly
different (hardwood temperatures > conifer temperatures, fixed effect of habitat type, F1, 96.6 = 3.75, p =
0.06).
Songbirds
A total of 205 individual birds were seen or heard during surveys, representing 25
species. Of the 205 birds that were detected, 38 were detected inside the 20- by 20- m
plots, while 167 were detected adjacent to, but outside the plots. When all conifer and
hardwood plots were combined, 21 species were detected in or around conifer plots and
22 species were detected in or around hardwood plots (Appendix 1). Of all the birds that
were detected, three species were uniquely associated with conifer plots and four species
were uniquely associated with hardwood plots (Appendix 2). For the following analysis,
birds that were detected inside the plots (‘in’) were evaluated separately from the
combination of all birds detected in and around the plots (‘all’).
The mean bird species richness for detections that occurred in plots were similar
by habitat type (t(4) = -1.17, p = 0.31; Figure 18). For birds that were detected in the plots,
54
an average of 2.6 species were detected in conifer plots (range = 2-4, SD = 0.9) and an
average of 4.2 species were detected in hardwood plots (range = 1-6, SD =2.2; Table 7).
The mean bird species richness for all birds that were detected was virtually equal by
habitat type (t(4) = 0.00, p > 0.99; Appendix 5, Figure 21). For all birds that were
detected, an average of 11.8 species were detected in association with both conifer and
hardwood plots (range = 8-16, SD = 3.0 and range = 10-13, SD = 1.3, respectively; Table
7).
Two to five individual birds were detected in conifer-dominated plots and one to
six individuals were detected in hardwood-dominated plots (Table 7). Bird abundance for
species that were detected in hardwood- and conifer-dominated plots was similar (t(4) = 0.72, p = 0.51; Figure 19). An average of 3.2 birds were detected in conifer-dominated
plots (range = 2-5, SD = 1.3) and average of 4.4 birds were detected in hardwooddominated plots (range = 1-7, SD = 2.4; Table 7). Bird abundance for all birds detected at
hardwood- and conifer-dominated plots was also similar (t(4) = -0.09, p = 0.94; Appendix
5, Figure 22). The abundance of all birds detected averaged 20.4 in conifer-dominated
plots (range = 14-27, SD = 4.7) and averaged 20.6 in hardwood-dominated plots (range =
18-23, SD = 2.3; Table 7).
Nonmetric multidimensional scaling ordination performed on birds detected in
conifer- and hardwood- dominated sites show a minimal pattern of dissimilarity by
habitat type (ANOSIM R = -0.12, p = 0.79; Figure 20). NMDS performed for all birds
that were detected also were not significantly different from each other when grouped by
habitat type (ANOSIM R = -0.15, p = 0.89), but were significantly different when
55
grouped by location (ANOSIM R = 0.34, p = 0.07; Appendix 5, Figure 23). Pairs of sites
tended to be closer to each other in the NMDS ordination plot than to other sites of the
same habitat type (Appendix 5). Bird species richness for birds detected in conifer- and
hardwood-dominated plots was positively correlated with the percent of shrub cover
(Pearson’s r = 0.69, p = 0.03) and distance to a change in forest habitat (Pearson’s r =
0.59, p = 0.07). However, these p-values were not corrected for multiple comparisons.
Co-occurrence analysis of all taxa pairs in the R package ‘cooccur’ suggest the
Red-breasted nuthatch (Sitta canadensis) and Varied thrush (Ixoreus naevius) were
negatively associated, but there were no other species pairs with positive or negative
associations. These two species occurred both in conifer- and hardwood-dominated sites,
but never overlapped at sites where they were observed (Appendix 2).
Table 7. Summary of bird detections by forest habitat type. Individuals were tallied based on if they were
inside or outside the 20- by 20-meter plot. The “inside plots” category includes birds that were detected
utilizing habitat inside the survey plots, while the ‘all detections’ category includes birds that were detected
inside and around the survey plots. Birds that were observed flying overhead were not included. CON =
conifer and HW = hardwood.
Location
Brooklyn
Lake Creek
Langworthy
Redfield
Skookum
Inside Plots
Species
Abundance
CON
HW
CON
HW
2
6
2
6
3
3
4
3
2
5
3
5
2
6
2
7
4
1
5
1
All Detections
Species
Abundance
CON
HW
CON
HW
13
13
21
23
16
12
27
19
8
10
14
20
10
13
21
23
12
11
19
18
56
Figure 18. Songbird species richness (inside habitat patches) by forest habitat type was similar (t(4)
= -1.17, p = 0.31). See Appendix 5 for a bird richness plot for all birds that were detected.
Figure 19. Songbird species abundance (inside habitat patches) by forest habitat type was similar
(t(4) = -0.72, p = 0.51). See Appendix 5 for a bird abundance plot for all birds that were detected.
57
Figure 20. Nonmetric multidimensional scaling ordination performed using birds detected inside
conifer- (CON) and hardwood- (HW) dominated sites show a minimal pattern of dissimilarity by
habitat type (conifer vs. hardwood; ANOSIM R = -0.12, p = 0.71. 4-letter species codes shown in
Appendix 1. See Appendix 5 for an NMDS plot for all birds that were detected. stress < 0.13.
58
Chapter 5. Discussion
Upland conifer- and hardwood-dominated forest habitat types contributed to
species richness in managed forestlands in this study. Differences by habitat type were
not significant across all taxa, but results indicate a contribution to both structural
diversity and species richness by both habitat types. Overall, plant species and
herpetofauna richness were greater in hardwood-dominated habitats, as compared to
conifer-dominated habitats, and 14 species (31% of the total detected) were observed
uniquely in either conifer- or hardwood-dominated habitat types.
The managed forest landscape is often a mix of habitat types, as indicated by
forest age and composition, creating an interplay between forest interior and edge
habitats. These areas are often highly diverse and productive. Although habitat patches
that are located in the interior of some broader matrices appear (and potentially are)
fragmented, the isolated patches do not function in isolation. They are functioning within
the matrix of forestland that surrounds them and forming edges with adjacent habitats
that create opportunities for additional species. Future research that quantified the habitat
features and connectivity qualities within the conifer-dominated matrix may aid
understanding of species occurrences and distributions in managed forests.
In Washington, forest practice rules require a minimum number of trees to be
retained per acre of harvested land. Trees must be a minimum of 12” in diameter to count
towards this requirement. Focus on the retention of trees provides a regulatory framework
that incentivizes the reforestation of habitats that are not treed such as shrub dominated or
seasonally wetted areas, although they may be providing valuable and limited habitat, and
59
are biologically high functioning. Results of this type of research can help improve forest
practices and encourage conservation efforts that are focused on maintaining highfunctioning habitats across the landscape.
Forest Structure and Composition
Small, upland hardwood patches within the managed conifer matrix were high
functioning, with utilization of both habitat types by all taxa. Forest habitat development
and availability is intrinsically linked to forest structure and composition. Forests that are
comprised of mixed tree species, including hardwoods, provide pathways for solar
penetration into the subcanopy environment (Gray et al., 2002). Sunlight is a limited
resource for plant growth in the understory of densely planted managed forests. The solar
resources available in hardwood gaps provide energy for photosynthesis, encouraging
understory plant development. Results in this study were consistent with the results in
other studies where plant richness and understory cover were found to be significantly
greater in hardwood- dominated habitats, rather than in conifer-dominated habitats
(Figure 5).
Woody debris is known to provide important forage and cover habitat for insects
and amphibians (Rose et al., 2001). Woody debris volume was similar in both coniferand hardwood- dominated forest habitat types, however, the source of woody debris
varied. Pre-commercial thinning (PCT) is a common forest management practice where
densely planted forest stands are thinned to create more space for the remaining trees to
grow (Reukema, 1975). This typically occurs in stands that are between 10 and 15 years
old. Trees that are cut during the thinning process are retained on the forest floor. Areas
that are hardwood dominated do not typically meet PCT management criteria (i.e., they
60
are not overcrowded). In this study, forest floor woody debris that had been sourced from
cut pieces represented 18% of the total volume and 14% of the total pieces, with the
majority of cut pieces observed in conifer-dominated plots.
Species Richness and Abundance
Analysis of species richness when all four animal taxa were combined did not
appear to have meaningful differences by habitat type, however, they did appear to have
meaningful differences by location. When NMDS was performed across all taxonomic
groups and was grouped by location, communities were significantly different from each
other (Figure 6). This indicates that species compositions were more similar when they
were located near to each other, rather than when evaluated by habitat type. Further
analysis of this pattern suggested that the distribution of the bird community was
primarily driving this result. When examining species co-occurrences there was
significantly more species-pair segregation than expected by chance, but the absolute
difference in the observed c-score (0.06 higher than the mean simulated score) made this
of limited practical significance.
Measuring species presence and distribution allows for understanding how
species are distributed across habitat types. Of the 45 species that were included in the
analysis, 14 (31%) were unique to one habitat or the other, with four species unique to
conifer habitats and ten species unique to hardwood habitats (Appendix 2). Although
most of the species were widely distributed across the two habitat types, the presence of
rarer species indicates some potentially unique habitat associations. Typically, these
results would suggest that the widely abundant species are habitat generalists, adapted to
depend on a wider range of habitat resources, while the rarer species may be more
61
specialized and adapted to specific habitat resources. To understand the relative
importance of maintaining upland hardwood habitats across the forested landscape,
additional studies focused on habitat utilization by the species that were detected in
association with one habitat type or the other would be beneficial.
Ground Beetles
Carabid beetle species richness and abundance at each of the conifer- and
hardwood-dominated forest plots during 1158 trap nights was similar (Figures 8 & 9).
Two carabid species (of the ten detected) occurred uniquely at either one site or the other.
Previous research suggests we may have expected to see a difference in the species
richness between the two forest habitat types due to differences in habitat preferences
(Perry et al., 2018). A larger sample size and expanding the trapping effort to span greater
seasonal variation may be valuable considerations during future studies.
Amphibians and Reptiles
The results of this study suggest that upland hardwood-dominated habitats are
high-functioning for the herpetofauna community. Herpetofauna species richness was
significantly greater in hardwood-dominated plots than in conifer-dominated plots
(Figure 11), however, both habitats supported similar numbers of individuals (Figure 12).
Using NMDS, there was significant overlap in conifer and hardwood herpetofauna
communities where three species (of the eight detected) were common across both habitat
types and the remaining five species were unique to hardwood-dominated habitats
(Figure 13). An evaluation of woody debris volume (m3), and the moss, forb, and shrub
cover components did not help explain the variance. Differences in habitat utilization are
62
possibly linked to seasonal moisture regimes and the diversity and abundance of prey
species that exist in the microhabitats dominated by hardwood vegetation. These
components may be useful co-variates to consider during future studies.
Soil temperatures measured at each herpetofauna detection site were significantly
warmer in hardwood-dominated plots than in conifer-dominated plots, however the actual
mean difference (0.6 C°) may be of limited practical significance. While the minimum
temperature was nearly the same between both habitat types, the maximum temperature
varied, with higher temperatures reached in hardwood-dominated plots. The variability in
the maximum temperature is likely a function of the increased solar penetration typical of
conditions in hardwood-dominated forest canopies (Gray et al., 2002). Variability in
forest floor temperatures likely have important implications for species occurrences and
seasonal distributions.
Terrestrial Salamanders
Ensatina and Western redback salamanders composed 73% of the herpetofauna
detections. Abundances of both species were similar across habitat types. Locations of
terrestrial salamander detections suggested potential preferences for habitat cover types.
Although cover type results varied by species, woody debris and sword fern fronds
comprised 69% of total occurrences. The relationship between terrestrial salamander
abundance and woody debris volume has been widely recognized (Kluber et al., 2009;
Aubry, 2000; Mutts & McComb, 2000). However, in addition to woody debris, the
results of this study indicate that sword ferns provide an important source for terrestrial
salamander cover, specifically for Western redback salamanders.
63
Songbirds
Songbird species richness was similar in both conifer- and hardwood-dominated
forest habitat types (Figures 18 and 19). However, out of 25 detected bird species, seven
were uniquely observed in one habitat type or the other, where three species were
identified in association with conifer-dominated habitats and four were identified in
association with hardwood-dominated habitats. NMDS performed for all bird species
were significantly different from each other when grouped by location (Figure 23). This
result indicates that bird communities were more similar by location than they were
across habitat types. Based on previous studies, we may have expected to see a difference
in the species richness between the two forest habitat types (Gregory et al., 2003; Ellis &
Betts, 2011). Bird species richness for birds detected inside survey plots was positively
correlated with the distance to a change in forest habitat type. This result indicates that
habitat isolation and fragmentation may be important factors effecting bird richness and
distribution.
Conclusions
Forestlands across the managed landscape form a mosaic of mixed-aged, coniferdominated habitats, with hardwood-dominated habitats scattered throughout. The species
richness and abundance of ground beetle, herpetofauna, and avian communities were
evaluated to determine their use of conifer- and hardwood- dominated habitat types.
Small, upland hardwood patches within the managed conifer matrix were found to be
high functioning, with utilization of both habitat types by all taxa.
64
Canopy composition was found to be an important driver for regulating forest
floor vegetation structure and temperature regimes. Hardwood patches allowed for
sunlight infiltration resulting in a higher diversity of plants, while conifer forests were
effective at blocking solar infiltration and therefore maintaining cooler soil temperatures.
Each forest habitat type was composed of an arrangement of resources, based on
vegetation composition, cover, and woody debris components. The diversity of resources
available between the two types provided habitat elements beneficial to a diversity
species.
Forest management alters the condition of the vegetation community and woody
debris components, however, PNW native species are adaptable and have evolved in a
disturbance rich environment. Managed forests perform important societal roles and have
unique opportunities to maintain forest resiliency and conserve biodiversity. The results
of this study indicate that upland hardwood and conifer habitats that exist in managed
forests contribute to biodiversity. Maintaining diversity of forest habitats throughout
production forests will ensure the ongoing resilience of native species and forest
ecosystems. Balancing forest production with biodiversity conservation is a critical and
achievable objective.
65
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77
Appendices
Appendix 1: Table of species associations per forest habitat type.
Taxa
Scientific Name
Common Name
Beetles
Amara spp
Cychrus tuberculatus
Necrophilus hydrophiloides*
Nicrophorous defodiens**
Omus audouini
Omus degeanii
Promecognathus crassus
Pterostichus algidus
Pterostichus herculaneus
Pterostichus lama
Scaphinotus angusticollis
Zacotus mathewsii
Amara spp
Tuberculate rare snail-eating beetle
Flat brown scavenger beetle
A sexton beetle
Andouin’s night-stalking tiger beetle
Greater night-stalking tiger beetle
Smooth take-caution beetle
No common name
No common name
Giant striated ground beetle
Narrow-collared snail eating beetle
Matthews’ angry gnashing beetle
AMSP
CYTU
NEHY
NIDE
OMAU
OMDE
PRCR
PTAL
PTHE
PTLA
SCAN
ZAMA
Herpetofauna
Ambystoma gracile
Ambystoma macrodactylum
Ensatina eschscholtzii
Plethodon vehiculum
Pseudacris regilla
Rana aurora
Taricha granulosa
Thamnophis elegans
Northwestern salamander
Long toed salamander
Ensatina
Western redback salamander
Pacific tree frog
Red legged frog
Rough-skin newt
Western terrestrial garter snake
AMGR
AMMA
ENES
PLVE
PSRE
RAAU
TAGR
THEL
Birds
Accipiter striatus***
Cardellina pusilla
Cathartes aura***
Catharu ustulatus
Coccothraustes vespertinus
Colaptes auratus
Contopus cooperi
Corvus brachyrhynchos
Corvus corax
Cyanocitta stelleri
Dendragapus fuliginosus
Dryobates pubescens
Dryocopus pileatus
Empidonax difficillis
Ixoreus naevius
Junco hyemalis
Sharp shinned hawk
Wilsons warbler
Turkey vulture
Swainson’s thrush
Evening grosbeak
Northern flicker
Olive sided flycatcher
American crow
Common raven
Steller’s jay
Sooty grouse
Downy woodpecker
Pileated woodpecker
Pacific-slope flycatcher
Varied thrush
Dark eyed junco
SSHA
WIWA
TUVU
SWTH
EVGR
NOFL
OSFL
AMCR
CORA
STJA
SOGR
DOWO
PIWO
PSFL
VATH
DEJU
Specie Code
Forest Habitat Type
Conifer
Hardwood
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
78
Patagioenas fasciata
Perisoreus canadensis
Pheuticus melanocephalus
Pipilo maculatus
Poecile rufescens
Selasphorus rufus
Setophaga igrescens
Sitta canadensis
Spinus pinus***
Spinus tristis***
Troglodytes pacificus
Turdus migratorius
Unknown***
Zenaida macroura
Band tailed pigeon
Canada jay
Black headed grosbeak
Spotted towhee
Chestnut backed chickadee
Rufous hummingbird
Black-throated gray warbler
Red-breasted nuthatch
Pine siskin
American goldfinch
Pacific wren
American robin
Unknown warbler
Mourning dove
Plants
Acer circinatum
Acer macrophyllum
Adenocaulon bicolor
Adiantum pedatum
Alnus rubra
Arunus dioicus
Asarum caudatum
Athyrium filix-femina
Blechnum spicant
Bryophyta
Claytonia perfoliata
Cornus nuttallii
Corylus cornuta
Dicentra formosa
Digitalis purpurea
Disporum hookeri
Galium spp
Gaultheria shallon
Geranium robertianum°
Geum macrophyllum
Goodyera oblongifolia
Holodiscus discolor
Hydrophyllum fendleri
Ilex aquifolium°°
Lapsana communis
Lonicera ciliosa
Mahonia aquifolium
Mahonia nervosa
Maianthemum dilatatum
Marah oreganus
Oemleria cerasiformis
Oxalis oregana
Poaceae gen spp
Vine maple
Big leaf maple
Pathfinder
Maidenhair fern
Red alder
Goat's beard
Wild ginger
Lady fern
Deer fern
Mosses
Miner's lettuce
Pacific dogwood
Beaked hazelnut
Pacific bleeding heart
Foxglove
Hooker's fairybell
Galium spp
Salal
Robert geranium
Large leaved avens
Rattlesnake plantain
Oceanspray
Fendler's waterleaf
English holly
Nipplewort
Western trumpet honeysuckle
Tall Oregon grape
Oregon grape
False lily of the valley
Manroot
Indian plum
Redwood sorrel
Grasses
BTPI
CAJA
BHGR
SPTO
CBCH
RUHU
BTYW
RBNU
PISI
AMGO
PAWR
AMRO
UNK
MODO
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
79
Polypodium glycyrrhiza
Polystichum munitum
Prunus emarginata
Pseudotsuga menziesii
Pteridium aquilinum
Rhamnus purshiana
Ribes spp
Rosa spp
Rubus spectabilis
Rubus ursinus
Rumex spp
Sambucus racemosa
Sambucus spp
Smilacina stellata
Stachys spp
Stellaria media
Thalictrum occidentale
Thelypteris phegopteris
Tolmiea menziesii
Trientalis latifolia
Trillium ovatum
Tsuga heterophylla
Vaccinium parvifolium
Vancouveria hexandra
Vicia spp
Viola glabella
Viola sempervirens
*Agyritadae family
** Silphidae family
*** flyover observation
° noxious weed Class B
°° noxious weed monitor list
Licorice fern
Sword fern
Bitter cherry
Douglas-fir
Bracken fern
Cascara
Gooseberry
Rose
Salmonberry
Trailing blackberry
Dock spp
Red elderberry
Elderberry spp
Star flowered false solomon's seal
Hedge nettle
Chickweed
Western meadowrue
Narrow beech fern
Youth on age
Broad leaved starflower
Western trillium
Western hemlock
Red huckleberry
Inside out flower
Vetch spp
Stream violet
Trailing yellow violet
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
80
HW Skook
HW Red
HW Lang
HW Lake
HW Brook
CON Skook
CON Red
CON Lang
CON Lake
CON Brook
Appendix 2: Species occurrences by forest habitat type and location.
Beetles
Giant striated ground beetle
Greater night-stalking tiger beetle
Tuberculate rare snail-eating beetle
Andouin’s night-stalking tiger beetle
Narrow-collared snail eating beetle
No common name (PTHE)
A sexton beetle**
No common name (PTAL)
Smooth take-caution beetle
Amara spp
Flat brown scavenger beetle*
Matthews’ angry gnashing beetle
Herpetofauna
Ensatina
Western redback salamander
Rough-skin newt
Western terrestrial garter snake
Red legged frog
Pacific tree frog
Northwestern salamander
Long toed salamander
* Agyritadae family
** Silphidae family
81
HW Skook
HW Red
HW Lang
HW Lake
HW Brook
CON Skook
CON Red
CON Lang
CON Lake
CON Brook
Birds
Swainson’s thrush
Pacific-slope flycatcher
American robin
Pacific wren
Evening grosbeak
Band tailed pigeon
Steller’s jay
Black headed grosbeak
Varied thrush
Chestnut backed chickadee
Wilsons warbler
Dark eyed junco
Common raven
Red-breasted nuthatch
Sooty grouse
Pine siskin***
Northern flicker
Mourning dove
Canada jay
American goldfinch***
Turkey vulture***
Spotted towhee
Sharp shinned hawk***
Rufous hummingbird
Pileated woodpecker
Olive sided flycatcher
Downy woodpecker
Black-throated gray warbler
American Crow
*** flyover observation
82
Appendix 3: Correlation matrix – habitat patch structure and composition
Pearson correlation coefficients measuring the relationship between communities (overall
and by taxa group) and habitat patch forest structure and composition variables. Some
communities are subsets of others (i.e., carabids are a subset of beetles, and birds (inside
habitat patches) are a subset of birds (all detections)). Bird observations do not include
flyovers.
Basal Area m2
Shrub % Cover
Forb % Cover
Moss % Cover
Woody Debris m3
Litter Depth cm
Plant Richness
Bird Abundance (In)
Bird Richness (In)
Bird Abundance (All)
Bird Richness (All)
Herp Rel Abundance
Herp Richness
Carabid Rel Abundance
Carabid Richness
Beetle Rel Abundance
Beetle Richness
All Fauna Abundance
All Fauna Richness
1.00
0.03
-0.03
1.00
-0.09
0.49
0.65
1.00
-0.02
-0.20
0.95
0.47
1.00
-0.21
0.27
0.58
0.79
0.57
1.00
0.57
0.66
-0.15
0.08
-0.33
-0.20
1.00
0.55
Herp
Richness
1.00
Carabid Rel
Abundance
Beetle
Richness
Carabid
Richness
All Fauna
Abundance
Beetle Rel
Abundance
All Fauna
Richness
83
-0.60
-0.13
-0.65
-0.16
0.57
1.00
0.16
-0.42
-0.55
-0.35
-0.50
0.14
0.18
1.00
0.09
-0.40
-0.41
-0.34
-0.30
-0.07
-0.01
0.87
1.00
-0.47
0.26
-0.18
0.28
-0.22
-0.19
-0.72
0.28
0.41
1.00
-0.49
0.37
-0.14
0.42
-0.10
-0.29
-0.79
0.30
0.40
0.96
1.00
-0.14
0.48
0.06
0.34
-0.35
0.33
-0.36
-0.13
-0.27
0.45
0.37
1.00
0.10
0.13
-0.27
0.28
-0.14
0.01
0.08
0.48
0.18
-0.16
-0.02
0.02
1.00
0.18
-0.41
-0.45
-0.33
-0.23
0.26
0.27
0.78
0.60
0.01
0.12
-0.33
0.40
1.00
-0.51
0.14
-0.14
0.16
-0.19
0.03
-0.41
-0.20
-0.37
0.41
0.43
0.35
-0.15
-0.02
1.00
0.15
0.50
0.30
0.39
0.00
0.45
-0.02
-0.47
-0.62
-0.10
-0.17
0.74
0.14
-0.53
0.11
1.00
-0.24
0.36
-0.15
0.38
-0.13
0.33
-0.41
0.06
0.02
0.69
0.65
0.62
0.03
0.06
0.50
0.41
1.00
-0.06
-0.41
-0.39
-0.31
-0.11
-0.25
0.47
0.16
0.03
-0.49
-0.40
-0.48
0.12
0.31
-0.33
-0.45
-0.52
1.00
0.66
-0.09
Basal Area
m2
0.22
Shrub %
Cover
0.09
Forb %
Cover
-0.17
Moss %
Cover
0.68
Woody
Debris
m3
0.65
Litter
Depth cm
0.19
Plant
Richness
0.09
Bird
Abundance
(In)
0.03
Bird
Richness
(In)
0.44
Bird
Abundance
(All)
0.71
Bird
Richness
(All)
0.26
Herp Rel
Abundance
84
Appendix 4: Correlation matrix – forest stand physical characteristics
Pearson correlation coefficients measuring the relationship between communities (overall
and by taxa group) and forest stand physical characteristics. Some communities are
subsets of others (i.e., carabids are a subset of beetles, and birds (inside habitat patches)
are a subset of birds (all detections)). Bird observations do not include flyovers.
Distance to Forest Change (m)
Distance to Water (m)
Aspect
Elevation (m)
Age
Plant Richness
Bird Abundance (In)
Bird Richness (In)
Bird Abundance (All)
Bird Richness (All)
Herp Rel Abundance
Herp Richness
Carabid Rel Abundance
Carabid Richness
Beetle Rel Abundance
Beetle Richness
All Fauna Abundance
All Fauna Richness
1.00
1.00
0.49
0.65
1.00
-0.20
0.95
0.47
1.00
-0.21
0.27
0.58
0.79
0.57
1.00
0.57
0.66
-0.15
0.08
-0.33
-0.20
1.00
-0.03
-0.02
1.00
-0.09
Herp
Richness
0.03
Carabid
Richness
0.55
All Fauna
Richness
Beetle
Richness
Carabid
Rel
Abundanc
e
Beetle Rel
Abundanc
e
All Fauna
Abundanc
e
85
1.00
-0.01
0.87
1.00
-0.19
-0.72
0.28
0.41
1.00
-0.29
-0.79
0.30
0.40
0.96
1.00
-0.35
0.33
-0.36
-0.13
-0.27
0.45
0.37
1.00
-0.62
0.14
0.26
0.70
0.68
0.15
0.08
-0.35
1.00
-0.32
0.41
0.31
0.54
0.31
-0.23
-0.21
-0.04
0.40
1.00
0.05
0.42
0.23
0.04
0.08
0.17
0.21
0.20
-0.23
0.05
-0.10
1.00
0.20
0.29
-0.23
-0.53
-0.25
0.07
0.21
0.22
-0.03
-0.01
-0.01
-0.22
1.00
-0.29
-0.60
0.20
-0.17
0.07
0.06
0.59
0.42
0.41
0.41
-0.11
0.12
-0.09
1.00
0.18
-0.07
-0.10
-0.19
-0.47
-0.65
1.00
0.14
-0.22
-0.78
0.13
-0.55
0.57
-0.30
0.34
0.46
-0.13
-0.50
0.42
-0.34
-0.29
-0.40
-0.16
0.28
-0.69
0.19
-0.42
-0.34
0.06
0.11
-0.60
-0.47
-0.35
-0.14
-0.26
-0.42
0.09
-0.18
-0.85
-0.34
0.16
-0.41
0.48
0.46
0.66
0.37
0.29
-0.19
0.09
0.26
-0.08
-0.25
0.03
-0.14
0.15
0.44
Plant
Richness
-0.49
0.72
0.71
Bird
Abundanc
e (In)
0.23
0.26
Bird
Richness
(In)
0.19
Aspect
Bird
Abundanc
e (All)
Age
Distance to
Water (m)
Bird
Richness
(All)
Elevation
(m)
Distance to
Forest
Change
(m)
Herp Rel
Abundanc
e
86
Appendix 5. Songbird species richness and abundance for birds detected
within and adjacent to survey plots during point-count surveys by forest
habitat type (excluding birds observed flying overhead).
Figure 21. Songbird species richness by forest habitat type was virtually equal (t(4) = 0.0, p > 0.99).
Figure 22. Songbird species abundance by forest habitat type was similar (t(4) = -0.09, p = 0.94).
87
Figure 23. Nonmetric multidimensional scaling ordination performed on all birds detected in conifer(CON) and hardwood- (HW) dominated sites show a minimal pattern of dissimilarity when grouped by
habitat type (ANOSIM R = -0.15, p = 0.89), but were significantly different when grouped by location
(ANOSIM R = 0.34, p = 0.07). 4-letter species codes shown in Appendix 1. stress < 0.10
88
