SNOW DYNAMICS AND SOIL MOISTURE
IN COMMERCIALLY THINNED LODGEPOLE PINE SILVOPASTURES
ON THE OKANAGAN PLATEAU OF SOUTHERN BRITISH COLUMBIA
By
Kyle Gillich
Thompson Rivers University, 2023
A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE
DEGREE OF
MASTERS OF SCIENCE in ENVIRONMENTAL SCIENCE
in the Faculty of Science

Thesis examining committee:
Dr. Lauchlan Fraser (PhD), Professor and Thesis Supervisor, Department of Natural
Resource Sciences, Thompson Rivers University.
Dr. Tom Pypker (PhD), Professor and Thesis Committee Member, Department of
Natural Resource Sciences, Thompson Rivers University
Dr. Brian Wallace (PhD), Range Soil Ecologist and Thesis Committee Member,
Ministry of Forests
Dr. David Hill (PhD), Professor and Thesis Committee Member, Department of
Natural Resource Sciences and Department of Geography, Thompson Rivers University

© Kyle Gillich, 2023
i

ABSTRACT
Forest harvesting systems may be strategically designed to manipulate snow
accumulation and soil moisture, not only to improve forage and timber productivity, but
also to improve flood, wildfire and climate change mitigation. The objectives of this
study were to investigate differences in snow accumulation, snow ablation, and soil
moisture across various widths of commercial strip-thinning in silvopasture systems.
Silvopastures were established near Kelowna, British Columbia, Canada. Stands of 45year-old Pinus contorta (lodgepole pine) were commercially thinned in the summer of
2018. Strip-harvest treatments were linear and of various widths: 10 m, 15 m, and 20 m.
Treatments were allocated in a two-factor randomized block design; strip width and
orientation. Adjacent forested areas and a clearcut were also examined as controls.
Strips were harvested with either east-west, or north-south orientations. Snow water
equivalent was manually sampled during winters of 2019-2020. From May 2019 to
October 2020, volumetric water content was continuously sampled at two depths (0-10
cm and 25 cm below the soil surface) and soil temperature was continuously measured
at 5 cm depth. Snow accumulation increased with strip-width, and 20 m strips retained
a mean peak accumulation of SWE which was 11.4 % greater than the clearcut
(P=0.001). Snow accumulation and ablation-rates were directly associated with canopy
cover and estimated solar radiation. The widest 20 m strips maintained greater soil
moisture and soil temperatures throughout the growing season, compared to the
narrower 10 m strips (P<0.001). However, 10 m strips did retain more soil moisture
during drier months with limited precipitation (P<0.001), and strips orientated east-west
retained the most soil moisture. Soil temperature was greater in both 10 m and 20 m
strips compared to forested areas. This resulted in greater cumulative growing degree
days (GDD) above the critical threshold of 5 °C in strips relative to non-harvested areas.
Overall, strip-thinning and orientation of strips directly influenced snow accumulation,
ablation dynamics, soil moisture, and temperature.

Keywords: agroforestry, silvopasture, strip, thin, snow, soil moisture
ii

ACKNOWLEDGEMENTS

Supervisor:
Lauchlan Fraser, Ph.D., NSERC Industrial Research Chair in Ecosystem Reclamation, TRU

Committee:
Tom Pypker, Ph.D., Professor, TRU
David Hill, Ph.D., Professor, TRU
Brian Wallace, Ph.D., Range Soil Ecologist, BC MFLRNORD

Research partners:
Lisa Zabek, Ph.D., P.Ag., Agroforestry, BC Ministry of Agriculture
Harold Hetherington, P.Ag., Range Agrologist, BC MFLRNORD
Kevin Derow, RPF, Stewardship Forester, BC MFLRNORD
Grant Glessing, RPF, Woodlands Manager, Tolko Industries Inc.
Kevin Boon, General Manager, BC Cattlemen's Association
Beef Cattle Research Council

Special thanks to:
Colleagues and the Fraser Lab at TRU
Trevor Blenner-Hassett, Ph.D., Forestec Forestry Consulting
John Church, Ph.D., Associate Professor, TRU
Rita Winkler, Ph.D., RPF, BC MoFLRNOR

iii

TABLE OF CONTENTS

Abstract ...........................................................................................................................ii
Acknowledgements ........................................................................................................iii
Table of Contents ...........................................................................................................iv
List of Tables ...................................................................................................................vi
List of Figures ..................................................................................................................x
List of Acronyms ........................................................................................................... xiii
1.0 Chapter 1 – General Introduction ........................................................................ 1
1.1 Silvopasture .......................................................................................................... 1
1.2 Range & Livestock ................................................................................................ 6
1.3 Timber .................................................................................................................. 7
1.4 Research Questions ............................................................................................ 10
1.5 Literature Cited .................................................................................................. 11
2.0 Chapter 2 - Influence of commercial strip-thinning, of various widths, on snow
accumulation and timing of snow disappearance in lodgepole pine silvopastures on the
Okanagan Plateau of southern British Columbia .............................................................. 19
2.1 Introduction........................................................................................................ 19
2.2 Materials & Methods ......................................................................................... 22
Site Description .................................................................................................... 22
Silvopasture & Commercial Strip-thinning Treatments ....................................... 22
Climate ................................................................................................................. 23
Snow ..................................................................................................................... 24
Canopy Cover & Solar Radiation .......................................................................... 25
Data Analysis ........................................................................................................ 27
2.3 Results ................................................................................................................ 28
Site Characteristics and Climate........................................................................... 28
Snow Accumulation.............................................................................................. 29
Snow Ablation Rates ............................................................................................ 31
Snow Disappearance ............................................................................................ 32
Solar Radiation ..................................................................................................... 33
2.4 Discussion ........................................................................................................... 36
Snow Accumulation.............................................................................................. 36
Snow Ablation Rates ............................................................................................ 39
Management Implications ................................................................................... 41
2.5 Conclusion .......................................................................................................... 42
2.6 Literature Cited .................................................................................................. 44
3.0 Chapter 3 – Influence of commercial strip-thinning, of various widths, on soil
moisture and temperature in lodgepole pine Silvopastures on the Okanagan Plateau of
southern British Columbia ................................................................................................ 49
3.1 Introduction........................................................................................................ 49
3.2 Materials & Methods ......................................................................................... 52
iv

Site Description .................................................................................................... 52
Silvopasture & Commercial Strip-thinning Treatments ....................................... 53
Climate ................................................................................................................. 53
Physical Soil Characteristics ................................................................................. 53
Soil Organic Matter .............................................................................................. 54
Soil Bulk Density ................................................................................................... 54
Soil Moisture & Temperature .............................................................................. 56
Data Analysis ........................................................................................................ 57
3.3 Results ................................................................................................................ 58
Climate ................................................................................................................. 58
Soil Classification .................................................................................................. 58
Soil Organic Matter .............................................................................................. 60
Bulk Density .......................................................................................................... 61
Soil Moisture ........................................................................................................ 61
Soil Temperature .................................................................................................. 64
3.4 Discussion ........................................................................................................... 65
Implications .......................................................................................................... 68
3.5 Conclusion .......................................................................................................... 70
3.6 Literature Cited .................................................................................................. 71
4.0 Chapter 4 – Management Implications & Future Research............................... 78
4.1 Literature Cited .................................................................................................. 82
5.0 Appendix A ......................................................................................................... 87
6.0 Appendix B ......................................................................................................... 89
7.0 Appendix C ......................................................................................................... 90
8.0 Appendix D ......................................................................................................... 94

v

LIST OF TABLES
Table 1. A summary of proposed criteria for selection of successful agroforestry
systems on Crown land in British Columbia, Canada. ........................................................ 2
Table 2. Summary of climatic variables observed during a period between peak snow
accumulation and total snow disappearance (March 1 – May 15) in 2019/2020 at the
Goudie Agroforestry Pilot Project. Snow water equivalent (SWE); snow water density
(SWD); cumulative degree days greater than 0°C (CDD >0°C). ........................................ 29
Table 4. The number of days between peak snow accumulation and complete snow
disappearance in commercially thinned strips of varying widths (10, 15, 20m) in the
Goudie Agroforestry Pilot Project during snowmelt of 2019 and 2020. Snow was
measured in the center of the harvested strips. Means were compared with a GamesHowell post-hoc test. NS= north-south, EW=east-west. .................................................. 32
Table 5. The number of days between peak snow accumulation and complete snow
disappearance in leave-strips of commercially thinned stands of varying widths (20, 30,
40m) in the Goudie Agroforestry Pilot Project during snowmelt of 2019 and 2020. Snow
was measured in the center of the leave-strips. Means were compared with a GamesHowell post-hoc test. NS= north-south, EW=east-west ................................................... 33
Table 6. Results of Games-Howell pairwise comparison test used to determine effects
of treatments on estimated transmitted direct radiation (MJ/m2/d) in harvested strips of
commercially thinned stands of various widths (10, 15, 20m). Strip-width of harvested
strips and strip-orientation were fixed independent variables. NS = north-south, EW =
east-west ......................................................................................................................... 107
Table 7. Means of percent sand, silt, and clay compositions of soil samples taken at
depths of 5 and 25 cm, from holes immediately adjacent to soil moisture monitoring
stations (N=30). ................................................................................................................. 59
Table 8. Results of Dunn-Bonferroni pairwise comparison tests of clay composition in
soils of three treatments (non-harvested controls, 10 m, and 20 m harvested strips), at
two depths (5 and 25 cm), sampled one-year post-harvest (2019). ................................ 60
Table 9. Results of Dunn-Bonferroni pairwise comparison tests of soil organic matter
(SOM) in soils of three treatments (non-harvested controls, 10 m, and 20 m harvested
strips), at two depths (5 and 25 cm), sampled one-year post-harvest (2019). ................ 61

vi

Table B. 1. Summary of mean maximum daily temperatures and monthly
precipitation sums by month and year observed at the Goudie Agroforestry Pilot Project
during 2019-2020. ............................................................................................................. 89
Table E. 1. Summary of coarse fragment content (CFC) for each treatment. CFC was
calculated as the percent volume of dried coarse material (sieved >2 mm, and assumed
volume corrected assuming density of 2.65 Mg/m3) of the total volume of the dried
mineral sample. Summary includes standard deviation (SD), standard error (SE), 95 %
confidence interval, and sample size (n). ......................................................................... 94
Table E. 2. Summary of dead forest floor litter depth by treatment. Summary includes
standard deviation (SD), standard error (SE), 95 % confidence interval (CI), and sample
size (n). .............................................................................................................................. 94
Table E. 3. Summary of dead forest floor litter volume by treatment. Summary
includes standard deviation (SD), standard error (SE), 95 % confidence interval (CI), and
sample size (n). ................................................................................................................. 95
Table E. 4. Summary of dead forest floor litter mass by treatment. Summary includes
standard deviation (SD), standard error (SE), 95 % confidence interval (CI), and sample
size (n). .............................................................................................................................. 95
Table E. 5. Summary of the depth of dead forest floor fermented and humic organic soil
layers (FH) by treatment. Summary includes standard deviation (SD), standard error (SE),
95 % confidence interval (CI), and sample size (n). .......................................................... 96
Table E. 6. Summary of the volume of dead forest floor fermented and humic organic
soil layers (FH) by treatment. Summary includes standard deviation (SD), standard error
(SE), 95 % confidence interval (CI), and sample size (n). .................................................. 96
Table E. 7. Summary of the mass of dead forest floor fermented and humic organic
soil layers (FH) by treatment. Summary includes standard deviation (SD), standard error
(SE), 95 % confidence interval (CI), and sample size (n). .................................................. 97
Table F. 1. Summary of snow sampling point terrain, maximum snow water
equivalent (Max. SWE), and mean date of disappearance for 2019 and 2020, by transect
(n=10). Snow was sampled between March 3 – May 8. Reserve strips were twice as wide
as harvested strips. The strips were orientated North-South (N-S) or East-West (E-W).
Means include 95% confidence interval. .......................................................................... 98
Table F. 2. Snow ablation rates for 2019, summarized by transect (n=10). Snow was
sampled between March 3 – May 5. Ablation was calculated by the difference in snow
water equivalent (SWE) from the previous sample period divided by the number of days
between sampling (cm/day). Average ablation rate was taken as an average of all of the
vii

mean rates by sampling period. Reserve strips were twice as wide as harvested strips.
The strips were orientated North-South (N-S) or East-West (E-W). Means include 95%
confidence interval. .......................................................................................................... 99
Table F. 3. Snow ablation rates for 2020, summarized by transect (n=10). Snow was
sampled between March 7 – May 8. Ablation was calculated by the difference in snow
water equivalent (SWE) from the previous sample period divided by the number of days
between sampling (cm/day). Average ablation rate was taken as an average of all of the
mean rates by sampling period. Reserve strips were twice as wide as harvested openstrips. Strips were orientated North-South (N-S) or East-West (E-W). Means include 95%
confidence interval. ........................................................................................................ 100
Table F. 4. Summary of means and 95 % confidence intervals for terrain, canopy
cover, estimated transmitted direct radiation, peak snow water equivalent (SWE), date
of snow disappearance, and average ablation rate for harvested strips of various widths
(10-20 m) and of two orientations (North-South and East-West). Non-harvested and
clearcut controls were also included. Snow was sampled between March 3 – May 5,
2019 and 2020. ............................................................................................................... 101
Table F. 5. Summary of means and 95 % confidence intervals for terrain, canopy
cover, estimated transmitted direct radiation, peak snow water equivalent (SWE), date
of snow disappearance, and average ablation rate for reserve strips of various widths
(10-20 m) and of two orientations (North-South and East-West). Reserve strips were
twice the width of adjacent harvested strips. Non-harvested and clearcut controls were
also included. Snow was sampled between March 3 – May 5, 2019 and 2020. ............ 102
Table F. 6. Results of a Tukey post-hoc test, summarizing the mean number of days
between snow disappearance observed in all treatments in 2019. .............................. 103
Table F. 7. Results of a Tukey post-hoc test, summarizing the mean number of days
between snow disappearance observed in all treatments in 2020. .............................. 104
Table F. 8. Results of a Tukey post-hoc test, summarizing the mean number of days
between snow disappearance observed in 2019 between all reserve-strips orientated
east-west (E-W), all reserve-strips orientated east-west (N-S), all strip-harvests
orientated north-south (N-S), all strip-harvests orientated east-west (E-W), as well as
clearcut and non-harvested controls.............................................................................. 105
Table F. 9. Results of a Tukey post-hoc test, summarizing the mean number of days
between snow disappearance observed in 2020 between all reserve-strips orientated
east-west (E-W), all reserve-strips orientated east-west (N-S), all strip-harvests
orientated north-south (N-S), all strip-harvests orientated east-west (E-W), as well as
clearcut and non-harvested controls.............................................................................. 106

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Table F. 10. Results of a Games-Howell pairwise comparison test used to determine
effects of treatments on estimated transmitted direct radiation (MJ/m 2/d) in harvested
strips of commercially thinned stands of various widths (10, 15, 20m). Strip-width of
harvested strips and strip-orientation were fixed independent variables. NS = northsouth, EW = east-west .................................................................................................... 107

ix

LIST OF FIGURES
Figure 1. Map of research sites: Block A (strips orientated north-south), Block B
(strips orientated east-west), and a clearcut, which are part the Goudie Agroforestry
Pilot Project, were commercially thinned in summer 2018. ............................................ 22
Figure 2. Simplified diagram of three commercial strip-thinning treatments of varying
harvest widths: 10, 15, and 20 m. Leave-strips (reserves) were twice as wide as adjacent
harvest openings. Actual treatments did not alternate as depicted in the diagram;
several strips of the same width were cut in treatment units as seen in Figure 1. ......... 23
Figure 3. Average wind speed and direction for three climate stations owned and
operated by the BC Government, of nearest proximity to the Goudie Agroforestry Pilot
Project. Wind speed and direction were averaged during the period of snow cover
between Nov. 1 – May 1. .................................................................................................. 29
Figure 4. Snow water equivalent (SWE) in commercially strip-thinned treatments of
various widths (10, 15, and 20 m) expressed as canopy openness (%) at time of peak
snow accumulation. Non-harvested areas were included as controls. SWE was measured
during the years 2019 and 2020. Mixed effects linear model was used with fixed effects:
canopy openness & strip-orientation, sample date was a random effect. Snow water
equivalent (SWE) increased with canopy openness (P<0.001, R2=0.17, n=160). ............. 30
Figure 5. Peak snow accumulation, measured as snow water equivalent (SWE), in
commercially strip-thinned treatments of various widths (10, 15, and 20 m) measured
on a single day of peak snow accumulation. The dates of maximum snow accumulation
for majority of treatments were April 26, 2019, and April 24, 2020. ............................... 31
Figure 6. Nonlinear regression models of estimated transmitted direct radiation at
snow measurement locations in the center of harvested strips of commercially thinned
stands of various widths (10, 15, 20m), plotted by distance from forest edge as a
percentage of the average height of surrounding trees (x̄ = 18.7 m), and by strip
orientation (east-west vs north-south). North-south Y = -8.6 e-0.014 X + 9.5, R2=0.97,
P<0.001. East-west Y = -9.7 e-0.0065 X + 11, R2=0.97, P<0.001. ........................................... 34
Figure 7. Estimated transmitted direct radiation in harvested strips of commercially
thinned stands of various widths (10, 15, 20m), plotted by treatment and blocks of
opposing strip-orientations (north-south vs east-west). Different letters represent
P<0.05 on Games-Howell pairwise comparison tests (n=20). .......................................... 35
Figure 8. Visualization of the estimated path of the sun, between Nov. 1 – May 1, in
10 m wide strips, of opposing geographical orientations (north-south vs east-west)
harvested in commercial strip-thinning operations in the Goudie Agroforestry Pilot
Project. Path of the sun was visualized with Gap Light Analyzer (GLA) software with
hemi-spherical photos. ..................................................................................................... 40
x

Figure 9. Diagram of approximate seasonal changes in the angle of the sun and its
interaction with terrain, slope, and aspect in the northern hemisphere......................... 40
Figure 10. Map of research sites: Block A, Block B, and a clearcut, which are part the
Goudie Agroforestry Pilot Project were commercially thinned in summer 2018. ........... 52
Figure 11. Sandy loam soil texture determined for locations of all 30 soil moisture
sensors on site (all blocks & treatments combined). Left (blue) was sampled at 5 cm
depth, right (red) was sampled at 25 cm depth (n=30). ................................................... 58
Figure 12. Clay composition in soils of three treatments (non-harvested controls, 10
m, and 20 m harvested strips), at two depths (5 and 25 cm), sampled one-year postharvest (2019). Different letters represent P<0.05 on Dunn-Bonferroni pairwise
comparison tests, within each series of depth (n=10). .................................................... 59
Figure 13. Soil organic matter (SOM) in soils of three treatments (non-harvested
controls, 10 m, and 20 m harvested strips), at two depths (5 and 25 cm), sampled oneyear post-harvest (n=25). Different letters represent P<0.01 on Dunn-Bonferroni
pairwise comparison tests, within each series of depth (n=5). ........................................ 60
Figure 14. Surface soil (0-10 cm depth) volumetric water content continuously
measured in the center of 10 and 20 m harvested strips and non-harvested control from
June 1, 2019, to September 1, 2020. VWC units are a fraction of water volume measured
in m3/m3. Data includes observations from both north-south and east-west orientated
strips of the Goudie Agroforestry Pilot Project (n=10). Shaded ribbons are 95% CI. ....... 62
Figure 15. Soil volumetric water content (25 cm depth) continuously measured in the
center of 10 and 20 m harvested strips and non-harvested control from March 1, 2020,
to September 1, 2020. Data includes observations from both north-south and east-west
orientated strips of the Goudie Agroforestry Pilot Project (n=10). VWC units are a
fraction of water volume measured in m3/m3. Shaded ribbons are 95% CI. ................... 64
Figure A.1. Soil pit displaying the sandy loam texture of brunosolic gray luvisolic soil.
Bt horizon has higher clay content than Ae. Btg horizon from water table within <50cm
of surface. The water table was at a depth of 42cm. ....................................................... 87
Figure C. 1. Photo facing east in 20 m leave-strip, between 10 m wide harvested
strips. Block B, strips orientated east-west. Date taken: March 29, 2019. ...................... 90
Figure C. 2. Photo taken facing south. Photographer was standing in 15 m wide
harvested strip, between 30 m leave-strips. Snow is still visible on north half of 30 m
leave-strip. Block B, strips orientated east-west. Date taken: March 29, 2019. .............. 90

xi

Figure C. 3. Photos of snow pack observed simultaneously on May 1, 2019 at
12:00pm on permanently installed trail cameras. Delayed snow disappearance in eastwest orientated strips, compared to north-south orientated strips. Snow has
disappeared in forested areas and leave-strips................................................................ 91
Figure C. 4. Photo of east-west orientated (Block B) 10 m harvested trips, 20 m leavestrips, road, and clearcut with flat slope. Delayed snow disappearance was observed in
10m-wide harvested strips, relative to the road, clearcut, and forested stands. Photo
taken: March 25, 2019. ..................................................................................................... 92
Figure D. 1. Locations of all treatments, soil moisture stations, snow transects, and
permanent cameras. Reserve strips are twice the width of adjacent harvested strips.
Snow samples were at 20 m intervals along 180 m transects parallel to strip direction.
All samples were located in the center of the strips (harvest or reserve). ...................... 93

xii

LIST OF ACRONYMS

AAC: Annual allowable cut
AUM: Animal unit month
BEC: Biogeoclimatic (ie. Biogeoclimatic Zone)
FC: Field capacity
GDD: Growing degree days
IFM: Intensive forest management
LLWR: Least-limiting water range
MS: Montane Spruce (ie. Montane Spruce Biogeoclimatic Zone)
PWP: Permanent wilting point
SOM: Soil organic matter
SWE: Snow water equivalent
TSA: Timber supply area
UWR: Ungulated winter range
VWC: Volumetric water content

xiii

1.0 CHAPTER 1 – GENERAL INTRODUCTION

1.1

SILVOPASTURE

The first assumption in the discussion of strategic forest and range planning is that
sustainability is a universal objective. The concept of sustainable forest practices can be
defined from three perspective pillars: environmental, social, and economical
(Hansmann et al. 2012). Sustainable resource management involves the improvement of
environmental stewardship, in combination with long-term promotion of socioeconomic values (Mann et al. 2018). Maintenance of economies, cultures, and quality of
life constitute the socio-economic pillars (Purvis et al. 2019). However, the long-term
availability of resources for future generations, such as timber and rangeland, depend
on all three conceptual pillars of sustainability. Only with the promotion of healthy and
resilient ecosystems, can we maintain the longevity of our land and resources. Beyond
the resources for immediate sale, are the resources and ecological processes on which
life and productivity depend on, including: climate, habitat, soil, water, sunlight, and
nutrients. Forest management strategies are becoming increasingly complex as the
number of land-uses continue to increase and management objectives evolve (Fox
2000). In order to remain effective, successful forest practices and policies are required
to adapt more quickly than the rate of climate change and must also adapt more quickly
than the decline of available resources (Hannah et al. 2015). Integrated resource
management strategies, such as silvopastures, have the potential to satisfy some of
these objectives.
Mixed approaches, incorporating both fine and coarse-scale strategies, can help
satisfy the wide range of integrated objectives involved in sustainable forest
management. Agroforestry is one form of integrated resource management that has the
potential to help satisfy multiple levels of objectives, from site-level to watershed scales
(Anderson et al. 2008; Udawatta et al. 2011). Silvopasture, and other intensive forms of

1

forest management, may help optimize the use of resources, such as water, sunlight,
and soil nutrients (Rodriguez-Rigueiro et al. 2021).
Silvopasture is an agroforestry system designed to integrate timber, forage, and
livestock production on the same land base (Bhardwaj et al. 2018). There are several
forms of silvopasture used in temperate climates (Jose et al. 2019). Selections of
suitable systems are primarily dependent on climate, vegetation communities, crop
choice, natural disturbance regimes, and management objectives. In British Columbia
(B.C.), Canada, silvopasture systems on provincial tenure should be selected for benefits
which meet several criteria (Table 1).
Table 1. A summary of proposed criteria for selection of successful agroforestry systems
on Crown land in British Columbia, Canada.
Criteria

References

Enhance environmental stewardship with
sustainable resource management.

(Jose 2009; Elevitch et al. 2018)

Satisfy, or exceed, objectives of the Forest Act
and Forest and Range Practices Act.

(Forest Act 1996; FRPA 2004)

Optimize value(s) of land use, such as forage
availability and merchantable timber volume.

(Sharrow et al. 2009)

Contribute to carbon sequestration and climate
change mitigation.

(Kim et al. 2016)

Promote resiliency to climate change and
ecological disturbances

(Nitschke and Innes 2008)

Align with ecosystem restoration and wildfire
risk reduction objectives

(Rodriguez-Rigueiro et al. 2021)

Demand for alternative forest harvesting methods is at an all-time high in Canada
and is increasing globally (Fox 2000; Puettmann et al. 2015; Halofsky et al. 2018).
Availability of first-growth timber supply is expected to decline before reliance will shift
entirely onto replanted stands (BC Government 2019). Extensive harvesting, failed
2

silviculture systems, climate change, insect damage, fungal pathogens, and wildfires
have severely reduced timber supply in the interior of B.C. (Kurz et al. 2008; Alfaro et al.
2015; BC Government 2018, 2019). The amount of timber being hauled out of B.C
forests has been declining since 2006, as beetle-killed salvage logging has declined.
Most timber supply areas (TSA) are currently experiencing major decreases in annual
allowable cut (AAC). Forecasts have projected declining trends in AAC, well into the
future (BC Government 2019).
In B.C., the forest industry is highly dependent on public resources and unceded
territory, which come with inherent costs of environmental stewardship. A shift to more
intensive forest management may be one solution to a number of challenges arising in
the industry. Intensive forest management strategies, such as thinning, pruning and
fertilization, are designed to increase timber productivity while simultaneously satisfying
additional land-use objectives, such as water and soil conservation (Fox 2000; Insley et
al. 2002; Sing et al. 2018; Augustynczik et al. 2019). Economists are advocating for the
development of more sustainable, investment-based industries (Insley et al. 2002; BCBC
2009; Baumgartner 2019; Sotirov et al. 2019). There has been an increasing number of
incentives for intensive forest management, as timber supplies are diminished and the
cost of lumber has reached unprecedented prices.
Forestry and cattle ranching have been two major industries both utilizing forested
Crown land in B.C. for over a century. Cattle ranching has been a critical industry in B.C.
since 1846 (McLean 1972). B.C.’s timber industry contributes approximately 19.6 % of
the provincial GDP, or $12.94 billion (BC Council of Forest Industries 2016). Whereas,
B.C.’s beef cattle industry contributes roughly 0.25 % of the provincial GDP, or up to
$600 million annually (BC Cattlemen Association 2021). Strategic intensive forest
management strategies have been shown to integrate these two industries on the same
land base, increasing productivity and yield of both resources (Lindgren et al. 2017).
There is a global push for optimization of multiple values on the land base and
diversification of economies (Jose et al. 2019). Integrated land uses can promote

3

cooperative stewardship and provide alternative strategies for managing ecological
disturbances and effects of climate change (Mosquera-Losada et al. 2018).
Silvopasture systems have been globally recognized as a successful strategy to
increase productivity and yield of trees, crops, and livestock (Verma et al. 2017). Some
systems have been observed to increase timber quality, while simultaneously
benefitting the beef cattle industry, sustaining additional economies, and providing
interim cash-flow between timber harvests (Klopfenstein et al. 1997). Silvopastures also
have the potential to be more environmentally ethical and sustainable, therefore more
socially acceptable than conventional practices, such as: high-grading, large clearcuts, or
slash-and-burn agriculture (Torralba et al. 2016; Jose et al. 2019).
Numerous environmental benefits have been observed in silvopasture systems
compared to conventional practices alone (Pent 2020). Forests are globally recognized
as successful carbon-sinks, estimated to sequester about 30 % of global annual carbon
emissions (Hoberg et al. 2016). The majority of carbon sequestered by trees is stored
above ground; whereas, the majority of carbon in grasslands is stored in roots
belowground (Nilsson and Schopfhauser 1995; Soussana et al. 2006). Strategic
agroforestry systems, with progressive management, have the potential to sequester
more carbon both above and belowground and improve soil carbon processes (Sharrow
and Ismail 2004; Nair et al. 2009; Pent 2020). The integration of silviculture and
agriculture has the potential to provide synergistic benefits, which neither practice can
provide alone.
Successful agroforestry systems are designed to optimize the use of environmental
resources, such as: water, nutrients, and solar radiation (Yunusa et al. 1995). The
optimal use of resources on any given site results in less nutrient loss from the land and
more efficient use of available water (Boyer and Neel; Michel et al. 2007; Pent 2020).
Tree cover naturally provides ecosystems some resiliency to climatic extremes, while
also providing shelter and shade for livestock (Orefice et al. 2017). Silvopastures can also
be designed to promote biodiversity, which indirectly improves ecological resiliency and
4

resistance to climatic, pathogenic anomalies, or insect infestations (Lawrence et al.
1992; Jose et al. 2019). Seeding of multiple agronomic species, in combination with
optimal grazing strategies, has been observed to improve the accumulation of soil
carbon by increasing understory biomass, increasing biodiversity, and increasing root
production (Frank et al. 1995). Strategic range management and rotational grazing
plans, can improve forage productivity, improve plant regrowth after grazing, and
improve soil organic carbon (Sanderman et al. 2015). Therefore, silvopastures have the
potential to facilitate improved soil development, compared to conventional forest
harvesting methods (Alemu et al. 2019). When coupled with greater productivity and
economic yield, the environmental sustainability of silvopasture systems makes these
practices more socially acceptable (Pent 2020).
Provincial Forest Planning and Practices Regulations hold licensees accountable for
the conservation of site productivity and long-term sustainability (FRPA 2004).
Silviculture practices have evolved over time to increase site productivity, which have
involved trials and implementation of slash burning, thinning, pruning, fertilization, and
pre-planting site preparation (BC Government 2003). Pre-commercial thinning involves
the removal of young non-merchantable timber. Whereas, commercial thinning
removes merchantable timber that has value associated with it and can therefore be
sold to recuperate costs of the thinning treatment. Both types of thinning have been
shown to increase overstory and understory productivity with accelerated succession in
forest regeneration (Sullivan and Sullivan 2016). Increased herbaceous cover has been
observed up to 25 years post-thinning, increasing the availability of natural forage for
both cattle and wildlife (Sullivan and Sullivan 2016; Lindgren et al. 2017). Commercial
thinning of mid-rotation trees with interim grass-seeding and livestock grazing has been
trialed in this study as an agroforestry pilot project. Silvopastures have the potential to
achieve multiple land-use objectives, while progressing the norms of sustainable forest
management.

5

1.2

RANGE & LIVESTOCK

Rangeland includes all non-cultivated land that produces natural forage for wildlife
and livestock (Campbell and Bawtree 1998). Forested range is the largest range type in
B.C., which accounts for 90 % of all rangeland in the province (BC Government 1996).
The remaining 10 % is divided between grasslands, meadows, wetlands, riparian zones,
and alpine areas. Forage productivity across all rangeland is primarily determined by
climates, soils, and vegetation communities.
In B.C., the Montane Spruce (MS) biogeoclimatic zones range from 1000 to 1700 m in
elevation (Hope at al. 1991). These areas receive mean annual precipitation of 380 to
900 mm (Hope et al. 1991). Depending on the timing of snow disappearance, green-up
and elevational gradients, these zones provide critical summer range for livestock from
June to September (Wikeem et al. 1993). Calamagrostis rubescens (Pinegrass) often
dominates the understory, which is a less desirable forage for ungulates and livestock
(McLean 1967; Willms et al. 1980; Wikeem et al. 1993; Powell et al. 1994). Pinegrass is a
source of nutrients and crude protein for cattle in early summer, but quickly loses
nutritive value becoming unpalatable by mid-late summer (McLean 1967; Willms et al.
1980). Harvested forests can be seeded with agronomic mixes of higher nutritive
content, including species such as, orchard grass (Dactylis glomerata L.), white clover
(Trifolium hybridum), and wheatgrasses (Wikeem et al. 1993). In harvested openings,
agronomic seeding has been overserved to produce 70 % more forage than unseeded
areas (Wikeem et al. 1993). This not only contributes to increased animal unit months
(AUM), the provincial standard in quantifying livestock forage, but increased yield of
palatable grasses also helps maintain abundance and continuity of essential wildlife
habitats (Nitschke and Innes 2008).
Alternative timber harvesting methods integrated with range management, as
silvopasture, may provide opportunities of redesigning landscapes and vegetation to
help achieve provincial objectives of biodiversity, carbon sequestration, productivity,
and water management. Modification of tree cover in strips of different widths and
6

orientations is expected to influence the snowpack, snow disappearance, timing of
vegetative growth, and available moisture on the site. Such integrated management
strategies have the potential to optimize productivity, by optimizing efficient use of
resources, such as water, sunlight, and soil nutrients.

1.3

TIMBER

Lodgepole pine (Pinus contorta) has been relied on as a primary species for
replanting harvested sites since the 1960s (Wu et al. 2005). However, the province of
B.C. has experienced four massive outbreaks of mountain pine beetle (Dendroctonus
ponderosae) in the last century (Taylor and Carroll 2003). Planting of lodgepole
seedlings was substantially increased in the 1990’s, as there was a provincial shift in
silviculture practices that encouraged the reliance on planting of lodgepole pine due to
the species hardiness and quick growth (Johnstone and van Thienen 2011). Nearly one
billion lodgepole pine seedlings were planted in B.C., between 1999 to 2008 (Johnstone
and van Thienen 2011).
Mountain pine beetle epidemics have been partially attributed to warming winter
temperatures and the lack of extreme cold, which can naturally limit bark beetle
populations. Mountain pine beetle prefers to attack mature trees, typically over the age
of 40 years old (Amman et al. 1988; Amman and Logan 1998). Stressed trees, due to
drought or nutrient deficiencies, are more susceptible to attack (Waring and Pitman
1983). There have been substantial efforts to harvest mid-rotation pine stands as they
reach that age of susceptibility or recent beetle attack; this is known as one type of
salvage logging that occurs within the province (FBP 2020).
Salvage logging with regards to bark beetles is often socially accepted; however,
there has been a trend of licensees harvesting younger trees across the province
regardless of species (Burton 2010). Younger stands are being harvested more
frequently due to reduced timber supply and increased demand. Between 2007 and
2014, 24 % of all harvested timber did not satisfy minimum harvest criteria within the
7

coastal timber supply area (FPB 2018). Minimum harvest criteria (MHC) outline
conditions that must be satisfied prior to timber being harvested (FPB 2018). These
criteria recommend minimum age, volume, or diameter in each timber supply area
(TSA). These criteria prevent the harvest of young stands below culmination age, or
below the age of maximum sustainable yield. Mid-rotation harvests are occurring more
frequently as large diameter timber becomes less accessible and smaller diameter
timber increases in value. Practices of harvesting young timber require alternative
management strategies to maintain sustainable long-term timber supply (FPB 2018).
Existing stands are also continually threatened by environmental and ecological
disturbances, such as climate change, insects, and wildfires.
Climate change has been observed to be a major driver of fungal pathogen and insect
outbreaks (Carroll et al. 2003; Whitehead et al. 2004). In B.C., lodgepole pine forests are
experiencing the greatest environmental threats to their range of distribution relative to
other conifer species (Mathys et al. 2018). Lodgepole pine is widely distributed with a
broad fundamental niche, but increasing temperatures are providing favourable
conditions for more competitive species, insects, diseases, and wildfires (Mathys et al.
2018; Stevens-Rumann et al. 2018). Besides bark beetle infestations, lodgepole pine
stands are also susceptible to Western gull rust (Endocronartium harknessii), terminal
weevil (Pissodes terminalis), and needle cast (Cyclaneusma), which can severely reduce
merchantable timber volumes (Wu et al. 2005). Pre-commercial thinning and pruning of
young lodgepole pine (<20 years old) has been shown to limit outbreaks of the fungal
pathogen Dothistroma needle blight (Dothistroma septosporum) (Whitehead et al.
2004). Commercial thinning has repeatedly been shown to be effective at limiting the
spread of these outbreaks, while promoting resistance and resiliency of the stand
through changes in microclimate (Whitehead et al. 2004).
Lodgepole pine planted on drier sites are most susceptible to drought stress, disease,
and mortality (FBP 2020). A report conducted by the Forest Practices Board (FBP)
between 2007-2017, found that 60 % of all cutblocks harvested in the Interior Douglasfir biogeoclimatic zone (IDF), were in “poor and marginal condition” (FBP 2020). The IDF
8

zone has been projected to increase in size by 91 % by the end of the century, as
historically wetter regions are anticipated to become drier (FBP 2020). Effects of climate
change have been observable within single silviculture rotation periods (80-100 years),
or in other words within the lifetime of a harvestable tree. Proactive forest management
strategies are anticipating these rapid changes in climate (Aitken et al. 2008; Halofsky et
al. 2018; Mathys et al. 2018; Sáenz-Romero et al. 2020).
Forest thinning is one strategy being implemented to improve ecological resiliency to
climate change on drier sites (Wang et al. 2019). Thinning of lodgepole pine stands
reduces competition for resources, such as light, nutrients, and water (Wang et al.
2019). Thinning also increases the width and length of crowns, as the trees take
advantage of available light (Dahms 1971). The increase in radiation to the understory
allows seedlings to grow faster in warmer soils (Balisky and Burton 1995). However,
spacing lodgepole pine too widely can increase branch growth, thus reducing the quality
of merchantable timber (Johnstone and van Thienen 2011). The excessive planting of
lodgepole pine, in combination with poor stand health and management, have resulted
in many cases of non-merchantable regenerated stands. Thus, strategic thinning and
interim-silvopasture may be one solution in mitigating the provinces lodgepole pine
situation, while simultaneously helping to achieve other objectives.
In the past, the primary objective of forest management has been the optimization of
merchantable timber (Bell et al. 2008). The large area of public forests in Canada led to
the development of large management units and an ‘extensive’ forest management
approach. This strategy focuses on the basic requirements of stand regeneration, with
focus on species composition and age-class. Large-scale, extensive management also
facilitates additional landscape-level objectives pertaining to wildfires and insect
mitigation (Bell et al. 2008).
On the other end of the spectrum, ‘intensive’ forest management (IFM) involves
increased investment in silvicultural intensity at the site-level (Bell et al. 2008). IFM aims
to improve stand yields, wood quality, and timber value, all while quickening the times
9

for successful rotations. Globally, IFM has been popular in countries where forestry
operations occur more often on private land with smaller management areas. Intensive
silvicultural practices include: planning, site preparation, thinning, pruning, fertilization,
species selection, and any variation of these interventions which produce faster and
greater yield. On public lands, intensive forest management can adhere to higher-level
plans and strategies facilitating an approach of integrated resource management. In
best practice, all plans and operations on individual sites strive to satisfy all overlaying
management objectives. Some examples include: watershed conservation, livestock
forage, wildlife habitat, recreational value, and fire mitigation.

1.4

RESEARCH QUESTIONS

Chapters 2 and 3 describe my research. The overall purpose of this research is to
investigate hydrological effects of various widths of commercially strip-thinned
silvopastures (ie. 10, 15, 20 m strips). The major question is whether or not there is a
relationship between the widths of treatments and the water balance throughout all
seasons of the year. The interaction of slope, aspect, and geographical orientation of the
strips is also of interest.
In Chapter 2 I investigate responses to strip-thinning treatments in the following
variables:
1) Snow accumulation.
2) Timing of peak snow pack.
3) Rates of snow disappearance / ablation.
In Chapter 3 I investigate responses to strip-thinning treatment in the following
variables:
4) Volumetric soil water content.
5) Soil temperature.
6) Canopy cover
7) Estimated solar radiation
For my final chapter, Chapter 4, I address the management implications of my research.

10

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doi:10.1007/BF00711571.

18

2.0 CHAPTER 2 - INFLUENCE OF COMMERCIAL STRIP-THINNING, OF VARIOUS WIDTHS,
ON SNOW ACCUMULATION AND TIMING OF SNOW DISAPPEARANCE IN
LODGEPOLE PINE SILVOPASTURES ON THE OKANAGAN PLATEAU OF SOUTHERN
BRITISH COLUMBIA

2.1

INTRODUCTION

In snow-dominated watersheds, snow accumulation and dynamics of disappearance
are critical to ecological water budgets, human water supplies, and flood mitigation
strategies (Winkler et al. 2005; Merritt et al. 2006; Harma et al. 2012). Water volumes
contributed by snowpack, snowmelt timing, and water storage are influenced by
disturbances, such as timber harvesting, that alter canopy cover and netevapotranspiration (Pike et al. 2008; Brekke et al. 2009; Islam et al. 2019; Winkler et al.
2021). Snow processes are vulnerable to rapidly changing climatic variables, such as
temperatures and precipitation, and further interactions occur at the site-specific level
with slope, terrain, solar radiation, and altered canopy cover (Winkler et al. 2014;
DeBeer and Pomeroy 2017; Dickerson-Lange et al. 2017). Alternative timber harvesting
strategies, such as commercial thinning, may be strategically designed to help conserve
ecological values and freshwater systems.
Leaders in water management and hydrological research mostly agree there are
knowledge-gaps in the understanding of many hydrological processes at the watershed
scale (Lapp et al. 2015; Scherer et al. 2018). Topics of greatest concern in the drought
and flood-prone Thompson-Okanagan region of British Columbia, Canada include: total
water budgets, effects of forest harvesting, climate change, snow monitoring, and
volumes and timing of peak stream flows (Lapp et al. 2015). Most approaches to
progressive watershed governance share the fundamental objectives of sustainability,
conservation, and ultimately gaining control of above and below-ground water flows to
satisfy all other objectives (Pike et al. 2010; Brandes and O’Riordan 2014; Lapp et al.
2015; Scherer et al. 2018). We do have the knowledge and ability to manipulate forest

19

structure in attempts to control hydrological processes, while paying particular
attention to the terrain and aspect on which that forest structure is altered.
Snow water equivalent (SWE) is commonly used to discuss the size of the snowpack.
SWE represents the depth of liquid water that would drain from a completely melted
snowpack. Snow ablation, or disappearance, is often the result of multiple unique
processes, which include: melt, sublimation, evaporation, and wind drift (Winkler et al.
2005, DeWalle and Rango 2008). Snow melt is defined as liquid water drained from the
snowpack, as snow changes from solid phase to liquid (DeWalle and Rango 2008). The
temperature of the snowpack is influenced by ground heat flux, longwave/shortwave
radiation fluxes, or fluxes due to additional precipitation such as rain. Latent heat is
absorbed during energy fluxes and is released as water is drained from the snowpack.
Sublimation involves the direct phase change of solid to gas, with sufficient latent heat
flux (DeWalle and Rango 2008). Evaporation may occur when the snowpack
approximates 0°C, when water is present, and the atmospheric vapour pressure is less
than the saturation vapour pressure of the melting snow surface (DeWalle and Rango
2008). Additionally, snow may disappear from a permanent sampling location due to
wind transport.
Snow accumulation, ablation-rate, and timing of disappearance are critical processes
that we can gain greater understanding and control over in order to improve forest
management (Jost et al. 2007). At the landscape-level, peak snow water equivalent
(SWE) and ablation-rates have high spatial variability due to dependence on
environmental factors, such as latitude, elevation, and climate. At the site-level, the
snowpack is further influenced by slope, aspect, terrain, and tree density or canopy
cover (Winkler et al. 2005; Jost et al. 2007; DeBeer and Pomeroy 2017; Dickerson-Lange
et al. 2017; Winkler et al. 2017).
Larger canopy openings have been consistently correlated with greater snow
accumulation and faster rates of snow disappearance (D’Eon 2004; Winkler et al. 2005;
Woods et al. 2006; Veatch et al. 2009). This is explained by increased forest cover
20

resulting in increased interception loss and decreased solar radiation exchanged with
the surface of the snowpack (Faria et al. 2000; Pomeroy et al. 2001; Pomeroy et al.
2002). Therefore, wider gaps in the canopy allow more throughfall of snow and more
net-radiation to be absorbed by the snowpack.
Open clearcuts have been observed to have 37-75 % greater peak SWE, and 2.4 times
greater rates of disappearance, compared to mature forests (Winkler et al. 2005). Wider
strip-harvest treatments are expected to accumulate more snow; however, edge effects
have been observed to a have a limited influence within a distance approximate to the
height of surrounding trees (Spittlehouse et al. 2004). This edge effect has been
observed to interact with the aspect and orientation of the forest edge. In the northern
hemisphere, ablations are shaded to their north or exposed to sunlight to their south.
Sunlight can also penetrate further into the sides south-facing forest edges, providing
solar radiation deeper into the stand (Spittlehouse et al. 2004).
Snow ablation in mature forests is believed to be nearly 100 % influenced by net solar
radiation; whereas, radiation in clearcuts only accounts for about 75 % of the
disappearance (Spittlehouse et al. 2004). This suggests that larger openings are exposed
to greater losses attributed to evaporation and sublimation (Winkler et al. 2005; Woods
et al. 2006). In combination with greater net-radiation reaching the snow surface, wider
openings experience faster rates of disappearance.
Objectives of this study were to investigate how different strip-thinning widths
influenced total snow accumulation, snow disappearance rates, and time to total snow
disappearance, in both harvested and reserve strips. Interactions between various stripwidths and strip-aspect were also investigated in this study. For all of these observations
within the strip-thinning treatments, it is important to acknowledge the edge-effects
associated with observing snowpacks along a forest edge (within a tree length of
surrounding trees).

21

2.2

MATERIALS & METHODS

Site Description
The Goudie Agroforestry Pilot Project (GAPP) was an operational-scale silvopasture
experiment designed to integrate commercial strip-thinning of timber with interim
forage and range for cattle. The blocks were located 20 km east of Kelowna, BC
(49.941°, -119.243°), with a mean elevation of 1375 m (Figure 1). These sites were
within the Okanagan Dry Mild Montane Spruce Biogeoclimatic-variant (BGC)(MSdm1)
with site series ranging from 03-06 (Lloyd et al. 1990; Curran et al. 2000). Previously
logged in 1977, the stands were replanted with 100 % Pinus contorta (lodgepole pine).
The stands were 45 years old at the time of harvest, between March and July 2018.

Figure 1. Map of research sites: Block A (strips orientated north-south), Block B (strips
orientated east-west), and a clearcut, which are part the Goudie Agroforestry Pilot
Project, were commercially thinned in summer 2018.

Silvopasture & Commercial Strip-thinning Treatments
The randomized block design included two blocks, which each received four
treatments: commercial strip-thinning of widths 10 m, 15m, and 20 m, and a nonharvested control. Between the harvested strips, the width of non-harvested leavestrips are approximately twice the width of adjacent harvested openings. Therefore,
22

each treatment contained two nested parts: harvested strips and leave-strips of
proportional widths (i.e., 10 m harvested strip = 20 m reserve strip, 20 m harvested strip
= 40 m reserve strip) (Figure 2). An adjacent clearcut was also used as a control, which
had dimensions of 100 m x 200 m (2 ha). All blocks and controls received treatments
during the same year, so stands were the same age.
Additional factors included ground slope and aspect. Block A had strips harvested in
north-south orientations; whereas, strips in Block B were in an east-west orientation.
Slopes varied between 5 – 12 %.

Figure 2. Simplified diagram of three commercial strip-thinning treatments of varying
harvest widths: 10, 15, and 20 m. Leave-strips (reserves) were twice as wide as adjacent
harvest openings. Actual treatments did not alternate as depicted in the diagram;
several strips of the same width were cut in treatment units as seen in Figure 1.
A map of snow sampling locations can be found in Appendix D (Figure D.1.).
Climate
A climate station was installed in the clearcut. A tipping-bucket rain gauge (HOBO
RG3-M) was mounted 1.5 m above ground, which continuously measured and recorded
rainfall with a datalogger (HOBO UA-003-64). An air temperature sensor (HOBO S-TMBM002) was also mounted 1.5 m above ground with a solar radiation shield (HOBO MRSA) and continuously recorded every 30 min with a datalogger (HOBO H21-USB).

23

Three nearest weather stations, owned and operated by the Government of B.C.,
were used to determine average windspeed and direction on the plateau: Kettle 2
(1341 m), Beaverdell (807 m), and Idabell 3 (1300 m).

Snow
Snow depth and snow water equivalent (SWE) were measured for two consecutive
years (2019-2020). Measurements began at peak snow accumulation, immediately
before anticipated melt. Spot forecasts (SpotWx 2019) and two regional automated
snow measurement stations were monitored for accurate timing of peak snowpack
(Mission Creek & Penticton Creek). Snowpacks were measured on two or three-week
intervals and the frequency of measurements increased to every 6-8 days as complete
disappearance approached.
Sampling followed a systematic stratified design. Each treatment, in each block,
received 10 samples. In total, there were 160 sampling locations. Strips in the center of
each treatment area were selected for observation, in order to minimize edge effects.
Strip selection was also intended to overlap concurrent research on the site (Kega 2021).
Sample locations were in 20 m intervals, along 180 m linear transects, which ran directly
down the center of each selected strip, parallel to the strip orientation. Intervals of 20 m
were selected to ensure samples were independent of each other; point separations of
greater than 15 m have not been spatially correlated (Winkler and Moore 2006).
Transects had a buffer of 25 m from roads or treatment boundaries. The start of each
transect began 25 m from the forest edge adjacent to the nearest road. If strips were
shorter than 230 m, transects were split into two strips.
A metric Federal snow sampling tube, with calibrated spring scale, was used to
measure snow depth and SWE (Winkler et al. 2015). Each snow sampling location
received an ID number, while bamboo stakes with flagging tape were placed as
permanent markers. All sequential snow samples occurred within 1 m of the same
markers over the two years (Winkler et al. 2015). Measurements with the Federal
24

sampling tube are believed to have maximum potential errors of 7-12% (Winkler et al.
2005). Sample locations were not allowed within 1 m of the base of trees or shrubs of
any size.
Peak snow accumulation was filtered by finding the maximum value of SWE at each
unique sample location for each year. Between sampling dates, ablation rate was
calculated by dividing the observed change in SWE by the number of days between
samples. Average daily ablation rates were calculated by peak (maximum) SWE, divided
by the number of days between peak SWE and snow disappearance.
At the sampling location, snow disappearance was determined when greater than
50 % of the snow had disappeared within a 1 m radius circle around the bamboo marker
(Winkler et al. 2005). If snow disappeared between or after sampling dates, dates of
disappearance were estimated based on mean ablation rates between the final two
sampling dates.

Canopy Cover & Solar Radiation
Hemispherical canopy photos were taken at each snow sampling location. A fullframe digital camera (Canon 5D Mark III) was used with a 180° hemispherical lens
(Meike 6-11mm F/3.5). A flash-mount bubble level was used to level the camera on a
tripod, 0.5 m above ground, facing directly up in the zenith direction. The top of the
camera was orientated north (0°), sighted with an aiming stick and compass. Photos
were captured on overcast and smokey days, because diffused light optimizes the postprocessing of images. GPS waypoints of each sampling location were averaged for 2
minutes with a Garmin eTrex 20.
Gap Light Analyzer software (GLA v2.0) was used to estimate canopy cover and solar
radiation from the photos (Frazer et al. 1999). The software calculates canopy openness
as the proportion of white pixels (sky) to black pixels (non-sky) in the images. Images
were pre-processed with a blue light filter to optimize contrast and thresholding. Images
25

were registered with initial cursor points at geographical north (0°). Users must
determine a “threshold” for each individual image, which is the sensitivity of each pixel
to accurately be classified as either a sky (white) or non-sky (black) pixel. Thresholding
was completed manually, with pixel values ranging between 100-140 where all tree
branches and foliage were in focus and light-gaps matched original photos. Latitude,
longitude, elevation, slope, and aspect of each sampling location were entered into the
site configuration settings. Sun tracking was configured for snow season (Nov 1 – Apr
30), with 16 azimuth regions, and 4 zenith regions, with a solar time step of 2 minutes.
Solar radiation was modelled with a solar constant of 1367 W/m2, cloudiness index of
0.5 kt, spectral fraction of 0.5, and beam fraction of 0.5. The universal overcast sky
model (UOC) was used for sky-region brightness, with a clear-sky transmission
coefficient of 0.65. The parameters mentioned above are recommended as default
settings for GLA software by the software creators, as they are an average from peerreviewed studies with many sampled locations in North America (Frazer et al. 1999).

Tree Height and Density
Airborne light detection and range (LiDAR) was used to estimate tree height and
density. A remotely piloted aircraft system (RPAS) included a DJI Matrice-300 RTK, with
DJI RTK ground station, and DJI Zenmuse L1 LiDAR payload. Raw point clouds were
archived as LASer (.las) files with DJI Terra software.

LASer(.las) files were analyzed with R (version 4.1.2 2021; RStudio 2021.09.1+372, 2021)
and the “lidR” package (version 4.0.2)(Roussel et al. 2022). Ground points were
classified with the cloth simulation filtering (csf) algorithm. Z (height) coordinates were
normalized with the triangulated irregular network (tin) algorithm. Individual trees were
selected with the “locate_trees()” function based on a local maximum filter (lmf) with
diameter of 3 m. LASer(.las) files were clipped to circular 5.64 m radius plots either
directly centered on, or directly adjacent to, snow sampling locations.
26

Data Analysis
Data were analyzed in R (version 4.1.2 2021; RStudio 2021.09.1+372, 2021) using a
value α=0.05 as the threshold for statistical significance. Data distributions were
assessed graphically with plotted residuals and Shapiro-Wilk’s test for assumptions of
normality and equal variances. Levene’s test was used for assessment of homogeneity
of variances.
The snow measurement data met parametric assumptions and included both random
and fixed variables, so a mixed-effects general linear model was used for analysis.
“lme4” and “lmerTest” packages were used in R, which depend on Satterthwaite’s
method of approximation for p-values (Bates et al. 2015, Kuznetsova et al. 2017). Fixed
effects in the models included treatment (strip-width) and block (strip-orientation).
Canopy openness (100 - canopy cover %) was used as a continuous independent
variable representing the treatment, as harvested strip-width directly influenced the
canopy openness. Strip-width was used in some analyses as a categorical variable, but
canopy openness was found to better capture the uniqueness of each sample location
while accounting for structure of the forest edge (i.e. beetle attack, natural canopy gaps,
stem density, and variability in tree heights). Geographical strip-orientations were
differentiated by block, which was used as a fixed effect due to the effect of striporientation on the dependent variable. Sample date was used as a random effect,
because there was year-to-year variability in the intercept, as well as interannual
variability in the slope of the association between SWE and the fixed effects. Time and
interannual climatic variability were not variables of interest, so random intercepts were
used for each sample date. Sample points were spaced at 20 m, so samples were
assumed to be independent of each other (Winkler and Moore 2006). Post-hoc TukeyHSD tests were used for pairwise comparisons.
Estimated solar radiation also met parametric assumptions. A non-linear regression
model was also used to compare transmitted direct radiation between blocks, plotted

27

by distance from forest edge as a percentage of adjacent trees. BasicTrendline package
was used for the non-linear regression model (Mei et al. 2018).
Canopy cover data met parametric assumptions, so ANOVA with post-hoc Tukey-HSD
tests were used. ANOVA was also used to compare some effects within a single year,
such as with days to complete snow disappearance. Games-Howell post-hoc tests were
used for some comparisons, due to its leniency of non-equal variances.

2.3

RESULTS

Site Characteristics and Climate
Elevations of snow sampling locations ranged from 1349-1395 m and slope ranged
from 1-10°. The clearcut was flattest with mean slope of 1.3°, non-harvest areas had a
mean of 3.9°, and slope across treatments were statistically similar with means of 5-7°
(P=0.8). North-south orientated strips (Block A) had a mean elevation 20 m greater than
east-west strips (Block B) (P<0.001).
Tree heights ranged from 6.8 to 23.4 m (x̄ = 18.7 m).Canopy cover was similar within
treatments, between blocks (P>0.60). One exception was the 15 m strips having 3 %
greater canopy cover in strips orientated east-west compared to strips orientated northsouth, 50.6 and 47.5 % respectively (P=0.02). Mean canopy cover of reserve-strips
between blocks was similar (80-81 %).
During the spring melt (April 1 – May 15), air temperatures in the clearcut were
similar between both years, with similar number of days and nights above 0 °C (Table 2.
There were 17 more cumulative degree days, greater than 0 °C, in 2019 compared to
2020. There was more rain in 2020, relative to 2019. In 2020, there was
59.6 mm more total precipitation than in 2019. Also in 2020, 52.2 mm of precipitation
fell in the first week of May alone, which occurred with rapid simultaneous snow
disappearance across all treatments, primarily due to rapid melt. Wind predominantly
blew north to south, or south to north, between Nov. 1 and May 1 (Figure 3. Average
28

wind speed and direction for three climate stations owned and operated by the BC
Government, of nearest proximity to the Goudie Agroforestry Pilot Project. Wind speed
and direction were averaged during the period of snow cover between Nov. 1 – May 1.
Table 2. Summary of climatic variables observed during a period between peak snow
accumulation and total snow disappearance (March 1 – May 15) in 2019/2020 at the
Goudie Agroforestry Pilot Project. Snow water equivalent (SWE); snow water density
(SWD); cumulative degree days greater than 0°C (CDD >0°C).

Year
2019
2020

Max.
Mean
SWE
(cm)
13.1
22.6

Max.
Mean
Snow
Depth
(cm)
62.1
74.6

Max.
SWD
(%)
30.6
36.0

# of days
above 0
°C
45
43

# of
nights
above
0 °C
7
7

CDD
(>0°C)
236
219

Total
precip
(mm)
27.4
87.0

Avg. Max.
Temp. (°C)
10.3
9.4

Avg.
Min.
Temp
. (°C)
-2.8
-4.1

Figure 3. Average wind speed and direction for three climate stations owned and
operated by the BC Government, of nearest proximity to the Goudie Agroforestry Pilot
Project. Wind speed and direction were averaged during the period of snow cover
between Nov. 1 – May 1.

Snow Accumulation

29

The mean snowpack was 75 % greater in 2020 than 2019 (Table 2). Greater snow
accumulation brought less variability between harvest treatments in 2020. Peak snow
accumulation in 2020 had 73 % greater mean snow water equivalent (SWE), 20 %
greater depth, and 18 % greater density than in 2019 (averaged across all treatments).
Snow water equivalent (SWE) increased with canopy openness (P<0.001, R2=0.74)
(Figure 4). There was an interaction with strip-orientation, where greater increase in
SWE per unit increase in strip-width was found in north-south orientated strips
(P<0.001). At peak accumulation, the narrowest strips (10 m) increased snow
accumulation relative to non-harvested controls by 54 % (P<0.001). The widest strips
(20 m) accumulated 12 % more SWE than 10 m strips (P<0.001). In both years, 20 m
strips retained more snow than the clearcut. The 20 m strips had a peak accumulation of
SWE which was 11.4 % greater than the clearcut, with both years combined (P=0.001)
(Figure 5).

Figure 4. Snow water equivalent (SWE) in commercially strip-thinned treatments of
various widths (10, 15, and 20 m) expressed as canopy openness (%) at time of peak
snow accumulation. Non-harvested areas were included as controls. SWE was measured
during the years 2019 and 2020. Mixed effects linear model was used with fixed effects:
canopy openness & strip-orientation, sample date was a random effect. Snow water
equivalent (SWE) increased with canopy openness (P<0.001, R2=0.57, n=160).

30

Figure 5. Peak snow accumulation, measured as snow water equivalent (SWE), in
commercially strip-thinned treatments of various widths (10, 15, and 20 m) measured on
a single day of peak snow accumulation. The dates of maximum snow accumulation for
majority of treatments were April 26, 2019, and April 24, 2020.

Snow Ablation Rates
Differences in ablation rates between thinning treatments were only observed in
2019. In 2019, east-west strips had a faster mean ablation rate than north-south strips,
with a difference of 0.4 mm/day (12 %) (P=0.028, n=30). The greatest differences in
ablation rates were observed between March 8-29, 2019, when north-south strips lost –
-15 % of the mean snowpack while east-west strips gained +13 %. During this period,
10 m east-west strips gained 4.5 cm SWE, which contributed to 27 % of their total peak
accumulation. 15 m east-west strips maintained zero change during this time; whereas,
20 m east-west strips lost 4.6 cm, or 22 % of their peak accumulation. Snow
disappearance in 2020 was almost simultaneous with rain and warming events, while
ablation rates were consistent across thinning treatments.

31

Snow Disappearance
Patterns of snow disappearance were inconsistent between both years. In 2019,
harvested strip-width had no effect on the number of days for snow to disappear in the
center of the strips. However, in 2020 snow in harvested east-west strips completely
disappeared in a mean of 5.3 days before the north-south strips (P<0.001). In 2020 there
was an interaction between treatment and strip-orientation. Snow in harvested 20 m
east-west strips disappeared at least 5 days before any other treatment, and 9 days
before strips of equal width orientated north-south (P<0.001) (
Table 3).
Table 3. The number of days between peak snow accumulation and complete snow
disappearance in commercially thinned strips of varying widths (10, 15, 20m) in the
Goudie Agroforestry Pilot Project during snowmelt of 2019 and 2020. Snow was
measured in the center of the harvested strips. Means were compared with a Games-

10 m
15 m
20 m
Mean

NS
52.6
52
53.3
52.6

2019
EW
54.4
54.5
52.2
53.7

P-value
0.33
0.13
0.55
0.287

10 m
15 m
20 m
Mean

NS
68.8
69.7
69.3
69.3

2020
EW
65.9
65.8
60.1
63.9

P-value
0.175
0.042
<0.001
<0.001

Howell post-hoc test. NS= north-south, EW=east-west.

Further treatment effects on snow disappearance were found in the reserve-strips,
where trees remained between the harvested strips. In both years, snow disappeared in
reserve strips orientated east-west before any other treatment. In 2019, reserve-strips
orientated east-west disappeared 10.2 days before reserve-strips orientated northsouth (P<0.001). In 2020, a similar effect was observed with a difference of 4.7 days
(P<0.001) (Table 4).

32

Table 4. The number of days between peak snow accumulation and complete snow
disappearance in leave-strips of commercially thinned stands of varying widths (20, 30,
40m) in the Goudie Agroforestry Pilot Project during snowmelt of 2019 and 2020. Snow
was measured in the center of the leave-strips. Means were compared with a GamesHowell post-hoc test. NS= north-south, EW=east-west

20 m
30 m
40 m
Mean

NS
41.1
43.5
47.1
43.9

2019
EW
29.1
38.4
33.6
33.7

P-value
0.024
0.034
<0.001
<0.001

20 m
30 m
40 m
Mean

NS
60
65
64.5
63.2

2020
EW
57.6
60.3
57.8
58.6

P-value
0.088
0.034
0.002
<0.001

Solar Radiation
The effect of harvested strip-width on estimated transmitted direct radiation was
dependent on the height of surrounding trees and distance from the forest edge.
Overall, direct radiation increased with the distance from forest edge in proportion to
the surrounding tree heights (P<0.001) (Figure 6). There was an interaction, as northsouth orientated strips had greater increases of estimated direct radiation with
increasing strip-width, compared to east-west orientated strips (P<0.001).

33

Trans. Direct Radiation (MJ/m2/d)

Distance from trees as percentage of avg. tree height (%)
Figure 6. Nonlinear regression models of estimated transmitted direct radiation at snow
measurement locations in the center of harvested strips of commercially thinned stands
of various widths (10, 15, 20m), plotted by distance from forest edge as a percentage of
the average height of surrounding trees (x̄ = 18.7 m), and by strip orientation (east-west
vs north-south). North-south Y = -8.6 e-0.014 X + 9.5, R2=0.97, P<0.001. East-west Y = -9.7 e0.0065 X + 11, R2=0.97, P<0.001.

Differences in transmitted direct radiation in harvested strips, were only observed
where distance from forest-edge was equal to, or less than, the height of surrounding
trees. The 10 m strips orientated north-south were estimated to receive 1.1 MJ/m2/day
more direct radiation than 10 m strips orientated east-west (P<0.001) (Figure 7).
The difference between 15 m strips of opposing orientation, was 2.2 MJ/m 2/day
(P<0.001). There was no difference in direct radiation between 20 m strips of opposing
orientations (P=0.47). Overall, all harvested strips orientated north-south received 1.3
MJ/m2/day (36 %) more direct radiation than east-west strips (P<0.001).

34

Figure 7. Estimated transmitted direct radiation in harvested strips of commercially
thinned stands of various widths (10, 15, 20m), plotted by treatment and blocks of
opposing strip-orientations (north-south vs east-west). Different letters represent P<0.05
on Games-Howell pairwise comparison tests (n=20).

Transmitted diffuse radiation in the center of 40 m leave-strips (between 20 m
harvested strips) was estimated to be 43 % greater than non-harvested areas (P<0.001)
and 33 % greater than 20 m leave-strips (P<0.001). Whereas, a difference in directly
transmitted radiation between 20 and 40 m leave-strips was not observed (P=0.99). The
20 m leave-strips had mean canopy cover of 83.1 %, while 40 m leave-strips had canopy
cover of 77.6%, a difference of 5.5% (P<0.001).

Snow results are summarized in Appendix E (Tables F.1 – F.10)

35

2.4

DISCUSSION

The various widths of harvested strips in the commercial stand-thinning treatments
influenced snow accumulation, ablation rates, and time to total snow disappearance.
These effects were further influenced by the geographical orientation of the strips
(north-south vs east-west). The designs of the alternative harvesting strategies affected
snow dynamics in the harvested strips, as well as the snow dynamics of the reservestrips between the harvested strips. The fundamentals of these snow processes have
been explained in detail in many previous studies and this experiment put the theories
to test, with multiple harvest treatments and orientations, side-by-side within a 100 ha
area. Strip-width increments of 5 m (i.e., 10, 15, 20 m strips), in combination with nonharvested areas and a clearcut, provided a fine-scale experiment with a spectrum of
canopy-gaps ranging from less-than to greater-than the average height of surrounding
trees. This is the first study in B.C. that has examined the snow dynamics in
commercially thinned, mid-rotation stands with strips 10-20 m wide. The broad
implications of this study are that the chosen strip-widths of commercial thinning
operations do influence the volume of snow water equivalent (SWE) at any given time,
and also influence the ablation rates, and timing of total snow disappearance. This study
provides evidence to suggest that harvesting strategies can be more deeply assessed
prior to implementation, in order to determine the effects of the geographical
orientation, slope, and aspect of resulting canopy-gaps to control the snowpack in
accordance with a broad range of land management objectives.

Snow Accumulation
It is important to highlight the spatial and temporal variability inherent in all
repeated-measures snow studies, especially as the climatic variables change year-toyear (Winkler et al. 2005). Most notably was the 73 % difference in peak SWE observed
between 2019 and 2020. This study benefitted from capturing such annual variability
within a two-year sampling window. It is important to acknowledge that variability, or
36

difference between treatment effects, decreases as snow accumulation increases
(Winkler and Moore 2006).
Peak snow accumulation increased with harvested strip-width, which is consistent
with previous studies. It has been well established, that canopy-openness has been
positively correlated with snow accumulation due to reduced snowfall canopyinterception loss (D’Eon 2004; Winkler et al. 2005; Woods et al. 2006; Veatch et al.
2009). However, it was surprising to see 8-17 % greater accumulation in the center of
20 m strips than in the clearcut of much larger open area. Snow was sampled in the
center of the strips, so the center of the widest 20 m strips is only 10 m from forest
edge. This 10 m distance is equivalent to 53 % of the adjacent tree heights (x̄ = 18.7 m),
which is well within the range of influence of forest-edge (Spittlehouse et al. 2004).
Although the clearcut was a much larger opening size, this is likely explained by the
forest edge providing more shelter in the strips. Although wind was not measured in this
study, 20 m strips are likely sheltered by forest edge from wind, and processes of
sublimation and evaporation, effects of which may be greater in the clearcut
(Spittlehouse et al. 2004; Winkler et al. 2005). All snow samples were taken on linear
transects down the center of open- or reserve-strips. All snow sampling locations in
harvested treatments had a distance from forest edge that was equivalent to 31 – 123 %
of the height of surrounding trees. All snow sampling locations were under influence of
edge effect, within one tree length from surrounding trees (Spittlehouse et al. 2004).
This edge effect has been observed to interact with the orientation of the forest
edge. Previous studies suggest that greatest snow accumulation occurs in gap-widths
3-8x the average tree-height and which are orientated parallel to the wind (Hoover
1969; Gary 1974). However, in this study, there was no observable influence of striporientation on snow accumulation (P=0.61). This is inconsistent with the understanding
of interception, sublimation, and redistribution processes occurring at micro-scales
(Pomeroy et al. 1998). North-south strips were parallel to the predominant wind
direction. North-south orientated strips also had greater increases of estimated direct
radiation with increasing strip-width, compared to east-west orientated strips (P<0.001).
37

Increased solar energy inputs increase probability of sublimation, evaporation. With
differences in wind direction and solar radiation between strip orientations it was
expected to observe some influence on snow accumulation.
The 40 m leave-strips (between 20 m harvested strips) accumulated 20 % more SWE
than 20 m leave strips (between 10 m harvested strips). Wind and sublimination have a
greater effect on more exposed, or less sheltered, snowpacks, which may be the case
with leave-strips surrounded by wider openings (Pomeroy et al. 2009; Molotch et al.
2004; Molotch et al. 2007; Reba et al. 2012; Rasouli et al. 2014). Narrower reserve-strips
had greater canopy-openness, but lesser SWE accumulation. Solar radiation is also a
critical factor in snowpack dynamics, as both shortwave and longwave radiation
influence the snow energy balance (Stoy et al. 2018; Dombrovsky et al. 2019). Forest
structures influence radiation by favouring conditions for either long or shortwave
radiation (Essery et al. 2008). Gaps in the forest canopy allow direct transmittance of
solar radiation and increased shortwave radiation (Essery et al. 2008). Canopy cover
increases longwave radiation to the ground or understory, while decreasing the
influence of shortwave radiation (Essery et al. 2008). Tree canopies and stems can retain
and emit more thermal energy than the surrounding air (Pomeroy et al. 2009).
Longwave radiation emittance from tree canopies and stems will have a greater
influence on snow dynamics during periods of lower air temperatures (Pomeroy et al.
2009). Variations of forest structure with different thinning widths, combined with
terrain and strip orientation, results in varying levels of long and shortwave radiation.
Since the center of harvested strips and leave-strips were observed, sampling
locations were of varying distances from the forest edge. For example, the center of a
20 m leave-strip is only 10 m from the open gap in the canopy, which sunlight
penetrates through. Whereas, the center of a 40 m leave-strip is 20 m from the canopy
gap, which is beyond a tree length from the forest edge. It is most likely that snow
disappeared earlier in narrower leave-strips, due to increased shortwave radiation with
more frequent canopy gaps. The canopy cover associated with the 20 m leave-strips also

38

provides shelter for increased longwave radiation below the canopy (Pomeroy et al.
2009).

Snow Ablation Rates
The greatest differences in ablation rates were observed between March 8-29, 2019,
when north-south strips lost 15 % of the mean snowpack while east-west strips gained
13 %. During this period, 10 m east-west strips gained 4.5 cm SWE, which contributed to
27 % of their peak accumulation; meanwhile, ablation occurred in all other treatments.
These findings were consistent with the estimated transmitted direct radiation, as
harvested north-south strips had a mean daily direct radiation of 1.3 MJ/m2/day (36 %)
greater than east-west strips (P<0.001). When the path of the sun is overlaid on the
forest structure, it helps to visualize the effect of strip orientation on direct solar
radiation (Figure 8). The suns angle gets steeper towards the earth’s surface during
summer, which increases solar insolation and transmitted radiation (Figure 9). This
evidence suggests that the resulting forest structure from the thinning treatments
influences the effect of long and shortwave solar radiation and, when combined with
wind speed and air temperature, likely contributes to heat exchange between the snow
surface and atmosphere (Spittlehouse et al. 2004).

39

Figure 8. Visualization of the estimated path of the sun, between Nov. 1 – May 1, in 10 m
wide strips, of opposing geographical orientations (north-south vs east-west) harvested
in commercial strip-thinning operations in the Goudie Agroforestry Pilot Project. Path of
the sun was visualized with Gap Light Analyzer (GLA) software with hemi-spherical
photos.

Figure 9. Diagram of approximate seasonal changes in the angle of the sun and its
interaction with terrain, slope, and aspect in the northern hemisphere.

40

Snow disappeared sooner in leave-strips and non-harvested areas, relative to
harvested areas, due to there being less SWE with increasing canopy closure. Highest
ablation rates tend to occur in areas with shallowest snowpacks. Snow tends to
disappear quickest near the base of trees. Previous studies have attributed the quicker
ablation rates to increased longwave radiation emitted from tree stems (Faria et al.
2000; Musselman et al. 2008; Pomeroy et al. 2009). Sunlight can penetrate further into
south-facing forest edges, providing solar radiation deeper into the stand (Spittlehouse
et al. 2004). At these latitudes, forest edges are shaded to their north or exposed to
sunlight to their south. Sunlight can also penetrate further into south-facing forest
edges, providing solar radiation deeper into the stand (Spittlehouse et al. 2004).

Management Implications
With the ultimate goal of sustainable resource management in mind, these results
highlight the importance of strategic structural design of forest stands to help achieve
objectives. These results suggest that fine-scale differences, such as differences of 5 m
in strip-widths and the orientation of these strips, directly influence resources on which
timber and forage productivity depend on.
Manipulation of snow accumulation is important both at the watershed-scale and
site level, as it determines the amount of water available for reservoir storage, spring
runoff, and biological availability for timber and forage productivity (Winkler et al. 2005;
Brekke et al. 2009; Hu et al. 2010). These results also remind us that the timing of total
snow disappearance depends on the amount of snow allowed to accumulate in that
particular area.
Harvested strips that are slightly wider than the height of surrounding trees will
accumulate more snow water equivalent than thinner strips or clearcuts with larger
openings. If lesser snow depth and faster snow disappearance are required for critical
ungulate winter ranges, then narrower strips orientated east-west could be considered

41

for less snow accumulation and faster disappearance rates within reserve strips (Safford
2004).
Since the effects of the forest-edge are the primary causes of these observed snow
dynamics, it is recommended to use site index or inventory data to make estimates of
tree heights. With estimates of average tree heights it is possible to determine
appropriate strip-widths to achieve management objectives. It is important to consider
the height of the trees for the duration of their expected life, which is why site index is
an appropriate variable to consider.
The timing of snow disappearance defines the length of growing season; thus, the
timing also defines the limits on biodiversity as the length of growing season combined
with moisture availability determine the organisms that the ecosystem can support.
Snow insulates vegetation, microbes, biological activity, and chemical reactions in the
soil from extremely low temperatures (Pulliainen et al. 2017; Krinner et al. 2018).
Snowpack is critical to the carbon balance, both as frost protection and in the timing of
disappearance and soil moisture availability that initiates growth of vegetation. The
timing of snow disappearance and soil temperature that initiates growth of vegetation,
is therefore analogous to carbon sequestration (Pulliainen et al. 2017). Reduced annual
snowpack, anticipated with climate change, will have widespread effects on all
processes dependent on snow accumulation (Islam et al. 2019).

2.5

CONCLUSION

Commercial strip-thinning and agronomic seeding for operational-scale silvopasture
is an alternative harvesting strategy that can influence snow dynamics in both the
harvested and reserve strips. These effects were further influenced by the geographical
orientation, or aspect, of the strips (north-south vs east-west).
Responses in the snowpack to strip-width were largely due to the proportion of the
canopy-gap to the height of surrounding trees. Wider strips accumulated more snow
42

water equivalent (SWE). The 20 m strips even accumulated more snow than the
clearcut, likely due to the reserve strips providing shelter from wind and sublimation;
whereas, the clearcut is more exposed. Harvested strips that are slightly wider than the
height of surrounding trees will accumulate more snow water equivalent than thinner
strips or clearcuts with larger openings.
Snow processes have highly variable temporal and spatial patterns, but under certain
conditions east-west orientated strips can increase snow accumulation and delay snow
disappearance by several days due to shade provided by the southern forest-edge.
Orientation of the strips also influenced ablation in the leave-strips, as east-west leavestrips disappeared 5-10 days before any other treatments.

43

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Woods SW, Ahl R, Sappington J, McCaughey W. 2006. Snow accumulation in thinned
lodgepole pine stands, Montana, USA. For Ecol Manage. 235(1–3):202–211.
doi:10.1016/j.foreco.2006.08.013.

48

3.0 CHAPTER 3 – INFLUENCE OF COMMERCIAL STRIP-THINNING, OF VARIOUS
WIDTHS, ON SOIL MOISTURE AND TEMPERATURE IN LODGEPOLE PINE
SILVOPASTURES ON THE OKANAGAN PLATEAU OF SOUTHERN BRITISH COLUMBIA

3.1

INTRODUCTION

Soil moisture and soil temperature are two of the most critical ecological factors
influencing the outcomes of forest and range management. Aspects of soil moisture,
including biological availability and storage capacity, link fundamental hydrological
processes, from precipitation to groundwater and runoff, to the ecological functions
that determine vegetative productivity, diversity, and carbon sequestration (Kupfer and
Cairns 1996; Rodriguez-Iturbe et al. 1999; Koster et al. 2004; Hirschi et al. 2010).
Whereas soil temperature directly influences the timing and length of growing seasons,
overall biomass production, and processes relating to carbon sequestration, such as soil
respiration (Peng and Dang 2003; Almagro et al. 2009; Bürli et al. 2021). Alternative
forest harvesting systems may be strategically designed to optimize soil moisture
availability and water utilization, not only to improve forage and timber productivity,
but also to improve flood, wildfire and climate change mitigation
In forests and rangeland, water balance is crucial in determining vegetative
productivity and biodiversity. Beyond the broad spatial and temporal patterns of climate
and precipitation, soil moisture content at the site-level is dependent on many factors
from parent materials of the soil, physical properties and composition of soils, to terrain
and topography (Koster 2004). Soil texture, structure, porosity, bulk density, and carbon
content all control the distribution of water throughout the vadose zone (Vereecken et
al. 1989; Lin et al. 2006). Spatial patterns of soil moisture are further influenced by
vegetative cover, leaf-area-index, solar radiation, air temperature, and the volume of
water transferred to the atmosphere via evapotranspiration (Teuling and Troch 2005).
The research site used for this thesis was mapped in the Montane Spruce
biogeoclimatic zone (Lloyd at al. 1990). These zones typically have wetter soils;
therefore, soil moisture may not be a limiting resource to the productivity of lodgepole
49

pine within these zones. In previous studies, east of the Rocky Mountains in similar
climate to the montane spruce zones of BC, various soil moisture and nutrient regimes
were observed to have little to no influence on productivity of established lodgepole
pine stands (Wang et al. 2004). With abundant moisture availability, differences in site
productivity may rather be due to differences in temperature and length of growing
season associated with elevation (Wang et al. 2004). However, in more temperate and
drier climates, soil water content is often a limiting resource for forest productivity and
carbon storage (Chimner et al. 2010; Pretzcsh et al. 2012). Soil moisture can also be
limiting to land management objectives, as there is often competition between
understory vegetation and tree seedlings, as is the case with conifers and tall grasses in
drier climates (Wallace et al. 2021).
It is generally understood that timber harvesting immediately increases water
availability in the soils and ground due to increased precipitation throughfall and
decreased transpiration resulting from the removal of trees (Gebhardt et al. 2014;
Korres et al. 2015). Timber harvesting has also been observed to influence
transmittance of solar radiation to the ground and understory, as well as increased
wind, which together often cause an increase in evaporative losses of ground moisture
(Simonin et al. 2007; Traff et al. 2015). Controlling the amount of water yield and runoff
caused by timber harvesting is a primary concern for land managers (Zhao et al. 2021).
Removal of any amount of trees is expected to increase water yield; however, stand
thinning has been observed to lessen the severity of runoff relative to clearcutting (Zhao
et al. 2021). Flood mitigation, reservoir volumes, and water quality are top priorities in a
community watershed; however, forest planners are also striving for optimal water
usage and efficiency (Guerrieri et al. 2019; Arroyo-Rodríguez et al. 2020). In regards to
achieving goals of sustainable resource extraction, optimal carbon sequestration, timber
and forage productivity and biodiversity, on-site water usage should be optimized to the
best of our ability (Arroyo-Rodríguez et al. 2020). Water efficiency is particularly
important in drier or drought-prone ecosystems. Forests stands should be designed to

50

be resilient to a range of weather anticipated with climate change, while balancing the
water budget to account for objectives at the site-level and also downstream.
Previous studies suggest that soil temperature may be more growth-limiting for Pinus
contorta (lodgepole pine) than soil moisture in montane forests of higher elevation (Wang
et al. 2004). Soil temperature is primarily influenced by transmittance of solar radiation
to the ground (Onwuka et al. 2016). Timber harvesting and removal of debris is generally
expected to increase soil temperatures due to increased transmittance of radiation
(Zabowski et al. 2000).
Previous studies have determined that a minimum soil temperature of 5-6 °C is
required for most herbaceous crop and grass seeds to germinate (Dubetz et al. 1962;
Singh and Dhaliwal 1972). Germination success of perennial forage species tends to
increase with increasing soil temperature; however, optimal soil temperature for
germination and growth have been observed to be between 18-26 °C (Dubetz et al.
1962; Singh and Dhaliwal 1972; Morrow and Power 1979). Wheatgrasses have been
observed to require 82-98 growing degree days (GDD) to produce their first leaf during
the initial growth phase, and between 135-372 additional GDD for the first leaf to
develop in the second regrowth phase (Frank 1991). Optimal soil temperatures for root
and shoot growth of lodgepole pine seedlings has been observed to be around 20°C
(Lopushinsky and Max 1990). Therefore, it is expected that stand thinning may increase
the length of growing seasons for timber and forage in these montane ecosystems, by
indirectly increasing soil temperatures and cumulative growing degree days.
Combinations of soil temperature and moisture are also pertinent to the
conservation and diversity of microbes, forest fungi, and processes of soil respiration
(Wilhelm et al. 2017). Larger forest openings have lower carbon to nitrogen (C:N) ratios,
greater moisture availability, and warmer soil temperatures which aid in the production
of organic nitrogen (Parsons et al. 1994). Increased growing degree days (GDD) have
been positively correlated with faster wood decomposition (Finér et al. 2016). There
was also found to be a positive interaction with nitrogen content, resulting in faster
51

decomposition of organic materials which have greater nitrogen content and exposed to
higher temperatures (Finér et al. 2016). Therefore, balances of soil moisture and
temperature not only contribute to carbon sequestration in above-ground productivity,
but also below-ground processes of soil respiration and carbon storage.

3.2

MATERIALS & METHODS

Site Description
The Goudie Agroforestry Pilot Project (GAPP) was an operational-scale silvopasture
experiment designed to integrate commercial strip-thinning of timber with interim
forage and range for cattle. The blocks were located 20 km east of Kelowna, British
Columbia (B.C.), Canada (49.941°, -119.243°), with a mean elevation of 1375 m
(Figure 10). These sites were within the Okanagan Dry Mild Montane Spruce
Biogeoclimatic-variant (BGC)(MSdm1) with site series ranging from 03-06 (Lloyd et al.
1990; Curran et al. 2000). Previously logged in 1977, the stands were replanted with 100
% Pinus contorta (lodgepole pine). The stands were 45 years old at the time of harvest,
between March and July 2018.

Figure 10. Map of research sites: Block A, Block B, and a clearcut, which are part the
Goudie Agroforestry Pilot Project were commercially thinned in summer 2018.
52

Silvopasture & Commercial Strip-thinning Treatments
The randomized block design included two blocks, which each received four
treatments: commercial strip-thinning of widths 10 m, 15 m, and 20 m, and a nonharvested control. The blocking factor was strip orientation. Block A had strips in northsouth orientations; whereas, strips in Bock B were in an east-west orientation (Figure
10). The 5 m difference between strip-widths was anticipated to have a limited effect
size on all dependent variables, so 15 m strips were not observed in this particular
study, allowing resources and analysis to focus on differences between the narrowest
and widest strips (10 - 20 m). All blocks and controls received treatments during the
same year, so stands were the same age.

Climate
A climate station was installed in the clearcut. A tipping-bucket rain gauge (HOBO
RG3-M) was mounted 1.5 m above ground, which continuously measured and recorded
rainfall with a datalogger (HOBO UA-003-64). An air temperature sensor (HOBO S-TMBM002) was also mounted 1.5 m above ground with a solar radiation shield (HOBO MRSA) and continuously recorded every 30 min with a datalogger (HOBO H21-USB).

Physical Soil Characteristics
Sixteen soil pits were dug in order to classify the soils and determine soil moisture
sampling methods (Lloyd et al. 1990; SCWG 1998). An additional 30 pits were dug with
establishment of soil moisture sampling stations, with one pit immediately adjacent to
each station. For soil texture and soil organic carbon analysis, samples were collected at
two depths (5 cm and 25 cm). Soil texture analysis was completed at the Analytical
Laboratory of the BC Government, in Victoria BC, and was reported as percent
composition of sand, silt, and clay.

53

Soil Organic Matter
Soil organic matter (SOM) content was measured using the loss on ignition (LOI)
method. Approximately 500 mL of mineral soil was sampled from two depths, 5 cm and
25 cm, from each soil pit. Five sub-samples of 1.5 g were analyzed from each sample. To
dry the sub-samples, they were placed into aluminum tin foil pans and heated at 105°C
for 24 hours using a YAMATO forced convection constant temperature drying oven
(DKN818, Yamato Scientific Co. Ltd). The soils were then weighed on an analytical scale
and masses were recorded before placing the dried samples into the muffle furnace.
The Barnstead Thermolyne 62700 furnace was used to ignite the soils at 500 °C for
5 hours. Samples remained in the desiccator for at least 30 minutes until room
temperature was reached. Samples were weighed and recorded again one final time.
The soil organic matter was calculated using the following equation (Wang et al. 2012):
Equation (1):
SOM𝐿𝑂𝐼 =

(Mass @ 105°C) − (Mass @ 500°C)
𝑀𝑎𝑠𝑠 @ 105°𝐶

Soil Bulk Density
Soil bulk density was measured and analyzed using the excavation method, due to
the high coarse fragment content and rockiness of the soil (Gatea et al. 2018). Mineral
soils were sampled as per Canada’s National Forest Inventory Ground Sampling
Guidelines (NFI 2008).
To maintain randomness in sampling, a two dimensional transect system was used in
randomly selected strips of each treatment in each block. This system involved a 90 m
transect placed parallel with the strip, and a second perpendicular transect running
across the width of the strip. Random numbers were paired for each dimension. Field
crews walked up the transect according to the random length drawn, and walked into
the strip according to the random width drawn. The lengthwise transect was placed at a
1 m buffer from the forest edge, beginning with a buffer of 25 m from any roads,
running parallel to the forest edge. This 90 m transect was binned into quarters: 0-22.5
54

m, 22.5-45 m, 45-67.5 m, 67.5-90 m. Two random numbers were drawn for each
quartered bin to ensure sampling locations were uniformly distributed lengthwise down
each strip. Two strips of each treatment were selected, 8 samples were drawn from
each strip, resulting in 16 samples per treatment per block including non-harvested
control areas. A total of 128 samples were used for the analysis of litter and bulk
density.
Live green vegetation was clipped above the sample area. Forest floor was sampled
with a 20 x 20 cm template above the hole from which mineral soil was sampled.
Mineral and litter were separated individually; fermented and humic layers were
included together in the same sample. Average depth of organic layers was recorded.
Mineral soil was sampled in a cylindrical hole 10 cm in diameter and 15 cm deep. All
material from the hole was included in the sample, including rocks and roots.
Mineral, fermented and humic soil samples were placed in a drying oven at 105°C,
and litter samples were dried at 70 °C, for 24 hours or until a constant weight was
achieved. Dried mineral samples were weighed before fine fractions were sieved to 2
mm. Fine fraction bulk density was calculated following the volume correction of coarse
fragment content (>2 mm), assuming a particle density of 2.65 Mg/m3 (Blaisdell et al.
2003; Maynard and Curran 2006). Bulk density was calculated using the following
equation (Krzic et al. 2010):
Equation (2):
𝐵𝑢𝑙𝑘 𝐷𝑒𝑛𝑠𝑖𝑡𝑦 (𝑀𝑔/𝑚3 ) =

Mass of dried fine material
Volume of dried fine material

Coarse fragment content was calculated with the following equation:
Equation (3):
𝐶𝑜𝑎𝑟𝑠𝑒 𝐹𝑟𝑎𝑔𝑚𝑒𝑛𝑡 𝐶𝑜𝑛𝑡𝑒𝑛𝑡 (%) =

Volume of dried coarse material
𝑥 100
Total volume of mineral soil sample

55

Soil Moisture & Temperature
Volumetric water content (VWC) was sampled in 10 m and 20 m harvested strips, as
well as non-harvested controls, in blocks A and B. From May 2019 to October 2020,
volumetric water content was continuously sampled every 30 minutes at two depths (010 cm and 25 cm below the soil surface). Soil temperature was also continuously
measured at 5 cm depth. Each sampling station consisted of HOBO® Micro Stations
(H21-USB) with Decagon® soil moisture sensors (S-SMD-M005) at both depths and
HOBO® 12-bit temperature sensors (S-TMB-M006).
Sampling locations followed a stratified systematic approach. Strips in the center of
each treatment were purposefully selected to reduce edge effects of other treatments,
and also to overlap with snow measurements and other concurrent research on the site
(Kega 2021). Stratification criteria included: >25 m from roads and >25 m from different
treatments as a buffer for edge effects, centered within the strip between forest edges,
mid-slope position, similar slope, similar aspect, no mottling within 35 cm of soil surface,
and low enough proportions of coarse fragment material to allow manual digging and
placement of sensors. This approach was also systematic, as stations were intended to
be placed 50 m apart within the same strip, or on the same slope-position in adjacent
strips. However, there was a limitation with the sensors, as they must be placed in a
substrate free of rocks and gravel. If rocks were encountered 50 m from the last station,
an attempt was made every meter from this point (down strip center) until a rock-free
area was found. This approach resulted in full and even coverage of the targeted strata
to be sampled. Due to differences in soil texture and SOM between treatments, VWC
measurements were not calibrated to ensure standard objective observations.

56

Data Analysis
Data distributions were assessed graphically with plotted residuals and ShapiroWilk’s test for assumptions of normality. Levene’s test was used to confirm
homogeneity of variances. Soil texture data did not have normally distributed residuals,
so Kruskall-Wallis ANOVA and Dunn-Bonferroni pairwise comparison tests were used.
Soil organic content was measured in grams (g), this weight was converted to a
percentage of the dry-soil weight of the sample. This data was log-transformed to satisfy
the parametric assumption of normality. ANOVA and Tukey post-hoc were used for
statistical analysis of soil organic content.
Differences in volumetric water content (VWC) between treatments were analyzed
with a linear mixed-effects model. Random variables were sampling date and sample
station identification number. Fixed variables included harvested strip-width
(treatment) and strip-orientation (block). The correlational structure was grouped by
random variables: time and station identification number. A Tukey post-hoc was used
for pairwise comparison.
Growing degree days equal to or greater than 5 °C were calculated by subtracting
5 °C from the average of daily maximum and minimum temperatures measured at each
station. A threshold of 5 °C was selected, because previous studies have determined
that a minimum soil temperature of 5 °C is required for most herbaceous crop and grass
seeds to germinate (Dubetz et al. 1962; Singh and Dhaliwal 1972). Values were excluded
if daily average was less than 5 °C. Cumulative degree days were calculated by summing
the accumulation of growing degree days from a specified start date. Differences in
growing degree days were analyzed with a linear mixed-effects model. Random
variables were sampling date and sample station identification number. Fixed variables
included harvested strip-width (treatment) and strip-orientation (block). The
correlational structure was grouped by random variables: time and station identification
number. A Tukey post-hoc was used for pairwise comparison.

57

3.3

RESULTS

Climate
Mean monthly temperatures were similar for both years, 2019 and 2020. Maximum
temperatures were higher in summer 2020, reaching 32.6 °C; whereas, the maximum
recorded temperature in 2019 was 29.4 °C. During the snow-free period (April 1 –
September 1) there was more precipitation in 2020 than 2019, with 348 and 217.6 mm
respectively. Summer 2020 had higher daytime temperatures and less precipitation
(Appendix Figure B.1).
Soil Classification
Soils were determined to be of sandy loam texture, belonging to the order of luvisolic
soils, as clay content increased with depth. Presence of mottling, within <50 cm of the
surface showed a gleyed soil type which was imperfectly drained. These soils may be
classified as gleyed gray-brown brunosolic-luvisols. Photos of two soil pits are displayed
in Appendix Figures A.1-A.2. Soil texture composition of sand, silt, and clay-sized
particles were consistent between 5 cm and 25 cm depths (P > 0.4) (Figure 11, Table 5).

Figure 11. Sandy loam soil texture determined for locations of all 30 soil moisture
sensors on site (all blocks & treatments combined). Left (blue) was sampled at 5 cm
depth, right (red) was sampled at 25 cm depth (n=30).

58

Table 5. Means of percent sand, silt, and clay compositions of soil samples taken at
depths of 5 and 25 cm, from holes immediately adjacent to soil moisture monitoring
stations (N=30).
Depth

Sand (%)

Silt (%)

Clay (%)

5 cm

45.7

48.5

5.9

25 cm

47.0

47.4

5.6

Composition of sand and silt were consistent across all treatments, at both depths;
however, clay composition tended to increase with harvested strip-width. At both
depths, clay composition was approximately 2 % greater in 20 m harvested strips
compared to non-harvested controls (Figure 12, Table 8). The block factor did not have
significant influence on this effect (P=0.13) and there were no interactions between
blocks and treatments (P=0.64).

B
B

A

Y

X

X

Figure 12. Clay composition in soils of three treatments (non-harvested controls, 10 m,
and 20 m harvested strips), at two depths (5 and 25 cm), sampled one-year post-harvest
(2019). Different letters represent P<0.05 on Dunn-Bonferroni pairwise comparison tests,
within each series of depth (n=10).

59

Table 6. Results of Dunn-Bonferroni pairwise comparison tests of clay composition in
soils of three treatments (non-harvested controls, 10 m, and 20 m harvested strips), at
two depths (5 and 25 cm), sampled one-year post-harvest (2019).
Depth

Treatment Comparison

Z-Stat

P-Value

5 cm
5 cm
5 cm
25 cm
25 cm
25 cm

10 m – Non-harvested
20 m – Non-harvested
10 m – 20 m
10 m – Non-harvested
20 m – Non-harvested
10 m – 20 m

2.07
3.09
-1.02
2.36
3.84
-1.48

0.058
0.006
0.307
0.027
< 0.001
0.138

Soil Organic Matter
On the soil surface, at 5 cm depth, percent composition of soil organic matter (SOM)
increased with harvested strip-width (Figure 13, Table 9). Mean SOM was 12.7 % in 10
m strips and 20.0 % in 20 m strips (P<0.001). There was an effect of the block factor
(P=0.003), as mean SOM in Block B was 4.3 % greater than Block A.
Mean mass of above-ground litter samples also increased with strip-width: 44.5 g in
10 m strips, 48.7 g in 15 m strips, and 61.2 g in 20 m strips. The 20 m strips had a mean
litter mass 16.7 g greater (+38 %) than 10 m strips (P=0.027).

Figure 13. Soil organic matter (SOM) in soils of three treatments (non-harvested
controls, 10 m, and 20 m harvested strips), at two depths (5 and 25 cm), sampled oneyear post-harvest (n=25). Different letters represent P<0.01 on Dunn-Bonferroni pairwise
comparison tests, within each series of depth (n=5).
60

Table 7. Results of Dunn-Bonferroni pairwise comparison tests of soil organic matter
(SOM) in soils of three treatments (non-harvested controls, 10 m, and 20 m harvested
strips), at two depths (5 and 25 cm), sampled one-year post-harvest (2019).

Depth

Treatment Comparison

Z-Stat

P-Value

5 cm
5 cm
5 cm
25 cm
25 cm
25 cm

10 m – Non-harvested
20 m – Non-harvested
10 m – 20 m
10 m – Non-harvested
20 m – Non-harvested
10 m – 20 m

5.12
8.54
-3.37
2.14
2.19
-0.047

< 0.001
< 0.001
< 0.001
0.048
0.086
0.96

Bulk Density
There was a trend for bulk density to increase with harvested strip width. Mean soil
bulk densities were 0.87 Mg/m3 in 10 m strips, 1.04 Mg/m3 in the 20 m strips, and 0.72
Mg/m3 in the non-harvested areas. The 20 m strips had 20 % greater mean density than
10 m strips (P=0.057). The 20 m strips had 44 % greater density than non-harvested
areas (P<0.001). Results of organic layer observations and coarse fragment content
were inconclusive, but included in Table D.1.

Soil Moisture
Maximum soil volumetric water content (VWC), at all depths in all treatments,
peaked simultaneously with total snow disappearance in 2020. Peak VWC was not
observed in 2019, because instruments were not fully installed until May 30.

VWC on Soil Surface
Differences in VWC were most noticeable on the surface (0-10 cm depth). On the soil
surface, during the snow-free season of 2019 (June 1 – October 1), the 20 m strips
maintained the most soil moisture with a mean VWC of 0.330 m3/m3. Whereas, 10 m
61

strips had a mean of 0.298 m3/m3 and non-harvested areas had a mean of 0.259 m3/m3.
The 20 m strips maintained 11 % greater VWC than 10 m strips (P<0.001, R2=0.97), and
20 % greater VWC than non-harvested areas (P<0.001, R2=0.97) (Figure 14). East-west
orientated strips maintained 10 % greater VWC relative to north-south strips, with
means of 0.309 and 0.282 m3/m3 respectively (P<0.001). There was an interaction
between strip-width and strip-orientation, where north-south orientation was
associated with greater difference in VWC between treatments(P<0.001).

Figure 14. Surface soil (0-10 cm depth) volumetric water content continuously measured
in the center of 10 and 20 m harvested strips and non-harvested control from June 1,
2019, to September 1, 2020. VWC units are a fraction of water volume measured in
m3/m3. Data includes observations from both north-south and east-west orientated
strips of the Goudie Agroforestry Pilot Project (n=10). Shaded ribbons are 95% CI.

The soil surface, in harvested strips within treatments, maintained equivalent means
of VWC during both snow-free and snow-covered seasons. Whereas, non-harvested
areas experienced significant reduction of VWC during summer months with minimal
precipitation. On the soil surface (0-10 cm depth), during the winter season (November
1, 2019 to April 1, 2020), the 20 m strips maintained 11 % greater VWC than 10 m strips
62

(P<0.001, R2=0.98), and 20 % greater VWC than non-harvested areas (P<0.001, R2=0.98).
The 20 m strips maintained a mean VWC of 0.330 m3/m3, 10 m strips had a mean of
0.298 m3/m3 and non-harvested areas had a mean of 0.276 m3/m3
Similar trends were seen in 2020. On the soil surface, during the snow-free season of
2020 (May 1 – October 1), the 20 m strips maintained the most soil moisture with a
mean VWC of 0.353 m3/m3. Whereas, 10 m strips had a mean of 0.325 m3/m3 and nonharvested areas had a mean of 0.290 m3/m3. The 20 m strips maintained 9 % greater
VWC than 10 m strips (P<0.001, R2=0.94), and 22 % greater VWC than non-harvested
areas (P<0.001, R2=0.94).

VWC at 25 cm Depth
Strip-width had no effect on soil VWC at 25 cm depth. During the 2019 growing
season (June 1 – Oct 1), 10 m and 20 m strips maintained mean VWC of 0.303 and 0.306
respectively at 25 cm depth (P=0.08, R2=0.96). Both 10 and 20 m strips maintained
greater VWC than non-harvested areas, 20 and 22 % greater respectively (P<0.001,
R2=0.96) (Figure 15). At 25 cm depth, during the winter season (November 1, 2019 to
April 1, 2020), the 20 m strips maintained 1 % greater VWC compared to 10 m strips,
with means of 0.320 and 0.316 m3/m3 respectively (P=0.001, R2=0.97). The nonharvested areas had a mean of 0.267 m3/m3.

63

Figure 15. Soil volumetric water content (25 cm depth) continuously measured in the
center of 10 and 20 m harvested strips and non-harvested control from March 1, 2020,
to September 1, 2020. Data includes observations from both north-south and east-west
orientated strips of the Goudie Agroforestry Pilot Project (n=10). VWC units are a
fraction of water volume measured in m3/m3. Shaded ribbons are 95% CI.
Soil Temperature
During the growing seasons of (June 1 – September 1) of 2019 and 2020, harvested
strips were associated with higher soil temperatures at 5 cm depth compared to nonharvested areas. The soil surface in 20 m strips had a mean daily maximum temperature
5.2 °C (42 %) greater than non-harvested areas, while mean maximum temperature in
10 m strips was 4.3 °C (35 %) greater than non-harvested areas (P < 0.001, R2=0.76).
There was an interaction observed between strip-width and strip-orientation on the
effect of cumulative growing degree days (GDD) above 5 °C during June, July, and
August of 2019 and 2020 (P<0.001). In east-west strips, GDD increased with increased
strip-width (P = 0.004). East-west orientated 20 m strips accumulated a yearly average
of 184 GDD more than 10 m strips (P=.035). Whereas, GDD was not influenced by stripwidth in north-south strips (P=0.64).

64

3.4

DISCUSSION

Biologically available soil water content is often described using the least limiting
water range approach (LLWR), which is bound between field capacity (FC) and
permanent wilting point (PWP) (Seneviratne et al. 2010). LLWR has frequently been
used to assess the limitations or availability of soil water in seedling germination studies
(Blouin et al. 2008). During the period of this study, permanent wilting point (PWP) was
never observed in any treatment. This was determined by the observation of continuous
change in VWC, without ever approaching or achieving a constant PWP. The high nonpermanent water table and mottling in this soil confirm that moisture is abundant on
this site. Soil moisture may not be a limiting resource for vegetation on this particular
site. However, results of this study showed that commercial strip-thinning did have a
noticeable effect on soil moisture availability. The width of strips and their orientation
both influenced VWC near the soil surface throughout the entire year.
Harvested areas maintained greater VWC than non-harvested areas, which is
consistent with previous research (Adams et al. 1991; Gebhardt et al. 2014; Hannah et
al. 2015). The differences in VWC are primarily the result of harvesting reducing overall
stand transpiration and increasing throughfall (Spittlehouse et al. 2004; Famiglietti et al.
2008; Gebhardt et al. 2014). Although transpiration and precipitation throughfall were
not measured in this study, they are major factors to consider with the observations as
described in previous literature.
There were observable differences in the responses of soil moisture at the two
different depths of 0-10 and 25 cm; greater effect size was observed closer to the soil
surface. This was expected, as soil moisture is more responsive to climatic conditions;
such as precipitation, temperature, relative humidity, and wind near the surface and
response to these environmental factors decreases with depth (McMillan and Srinivasan
2015).
It has been well established that finer soil particle size provides greater surface area
for hygroscopic moisture and more micro-pore space for capillary water holding
65

capacity (Susha Lekshmi et al. 2014). This difference in clay composition may have
existed in these sites prior to treatments, as litter density and soil organic matter were
also greater in wider strips. The approximate 2 % difference in clay content observed
between treatments is not expected to have a significant impact on water holding
capacity.
Bulk density has been observed to be effected by heavy machinery, but does trend
back towards pre-harvest levels after one year post-harvest (Botta et al. 2006; Sakai et
al. 2008; Wallace et al. 2021). The observed compaction would not be considered severe
and the resulting bulk densities were well within the range (0.7 – 1.15 g/cm3) previously
observed to have no effect on growth of Pinus contorta (lodgepole pine) seedlings
(Zabowski et al. 2000).
Previous studies have shown how the probability of soil disturbance during
harvesting is proportional to stand density (Sowa and Kulak 2008). A denser stand being
harvested, results in more felling, pushing, pulling, dragging, scraping, lifting, and
transportation of logs, compared to stands with fewer trees. All of these motions of
heavy machinery and movement of pieces contribute to soil disturbance. Bulk density is
the most common soil property used to assess compaction (Nawaz et al. 2013). It is well
understood that increased compaction leads to increased bulk density, which is
consistent with the findings of this study. It is important to acknowledge the effects of
compaction on bulk density and the differences in clay content observed; however,
these are minor factors in the overall effects of these treatments on soil moisture
availability.
Soil organic matter content (SOM) was 7.3 % greater in 20 m strips compared to 10 m
strips. It has been clearly established that greater SOM increases the water holding
capacity of soil (Ise and Moorcroft 2006; Sierra et al. 2015). This trend in SOM was
consistent with increased above-ground litter mass observed in wider strips. The
observed positive correlation between litter mass and strip width contradicts the results
of previous studies, which found a negative correlation between canopy gap size and
66

ground litter (Zhu et al. 2003). Previous studies have found positive correlations
between amounts of litter and stand density (Fish et al. 2006). Although harvest
treatments were allocated randomly, analysis of pre-harvest timber cruising data
suggests that wider strips may have been harvested in areas with greater basal area of
timber. Pre-harvest stand density may have been an uncontrolled variable in this study
which affected litter inputs, which may indirectly influence organic matter content and
water holding capacity of the soil.
Increasing strip width may also increase wind speed and turbulence, which has been
shown to increase ground litter (Bilby and Heffner 2016; Webb et al. 2021). Litter input
has been shown to increase soil organic matter (SOM) and soil moisture in pine
plantations (Yun Wang et al. 2019). Soil texture, SOM, litter inputs, and bulk density may
all influence the soil volumetric water content; however, the majority of influence on
soil moisture is likely caused by timber harvesting, reduced transpiration, and increased
throughfall (Spittlehouse et al. 2004; Famiglietti et al. 2008; Gebhardt et al. 2014).
Soil temperature was greater in both 10 m and 20 m strips compared to forested
areas. This resulted in greater cumulative growing degree days (GDD) above the critical
threshold of 5 °C. Beneath snow cover, soil temperatures were greater in narrower
strips (i.e. 10 m). This observation was likely due to lesser snow-depth in narrower
strips, which reduced the insulating capacity of the snow cover. Previous studies have
observed air temperatures to have greater influence over soil temperatures when
snowpack is reduced (Sanders-DeMott et al. 2018). Although warmer soil temperatures
are beneficial for vegetative growth in the spring, reduced snow packs and the resulting
reduction of insulation can be detrimental during freeze-thaw cycles with extremely low
temperatures, and have the potential to cause root damage or increased herbivory
(Sanders-DeMott et al. 2018).

67

Implications
Soil water content is often a limiting resource for forest productivity and carbon
storage in temperate climates (Chimner et al. 2010; Pretzcsh et al. 2012). In some
ecosystems, soil moisture has been observed to be a limiting resource for productivity
of lodgepole pine (Pretzsch et al. 2012). This research site was mapped in the Montane
Spruce biogeoclimatic zone (Hope at al. 1991). It is important to note that these zones
typically have wetter soils; therefore, soil moisture may not be a limiting resource to the
productivity of lodgepole pine within these zones.
In similar climate to the montane spruce zones of BC, East of the Rocky Mountains,
previous studies have found various soil moisture and nutrient regimes had little to no
influence on productivity of established lodgepole pine stands (Wang et al. 2004). With
abundant moisture availability, differences in site productivity are rather due to
differences in temperature and length of growing season associated with elevation
(Wang et al. 2004). Although it was determined that moisture is abundant and currently
not a limiting resource on the site discussed throughout this thesis, it is important to
consider anomalies of drought years or other responses of potential climate change. For
example, increased soil moisture availability may improve the resiliency of all vegetation
to drought-stress. Frequent narrow strips, that increase stem spacing and decrease
stand density, may be more beneficial for wide-scale tree productivity under drought
conditions. Whereas, wider strips providing more soil moisture availability and more
growing degree days may be more beneficial for growing forage or establishing conifer
seedlings.
Nitrogen availability is often another limiting factor for forest productivity (Hunt et al.
1988). In soils, mineralization is the process involving the decomposition of organic
nitrogen and release of ammonium (NH4+) (Vitousek and Matson 1985). Ammonium
may then be converted to biologically available nitrate (NO3) through the process of
nitrification (Jaffe 2000). In montane pine stands, the amount of naturally occurring
68

ammonium is often limited; therefore, there is slow conversion of ammonium to nitrate
and biologically available nitrate is usually depleted relatively quickly (Hunt et al. 1988;
Jaffe 2000).
Mineralization rates have been observed to be relatively low in lodgepole pine
stands, compared to other forest types (Fahey et al. 1985). Sites with warmer
temperatures, greater moisture availability, and more aerobic conditions associated
with higher coarse fragment content have demonstrated higher rates of mineralization
(Hunt et al. 1988; Parsons et al. 1994; Jaffe 2000). The removal of trees, during the
commercial thinning treatment, reduces the density of roots in the soil. This reduction
of fine-root density has been associated with higher moisture levels, faster N
mineralization and increased nitrification (Parsons et al. 1994). However, these effects
are not realized until large enough root-gaps have been created. Larger gaps have been
observed to have increased mineralization and nitrification rates, thus larger gaps have
more available nitrate and total N to lower the C:N ratio (Parsons et al. 1994). Clearcuts
in montane lodgepole pine forests have been found to increase soil nitrate (NO 3-)
concentrations 10-40x that of undisturbed control stands (Knight et al. 1991).
Therefore, wider harvested strips have the potential to increase rates of mineralization,
due to the increased soil moisture and temperature. This effect may be greater in years
with limited precipitation and warmer temperatures. These findings also suggest that
wider strips may have a greater benefit from fertilization.
The majority of mineralization occurs during snow-free growing seasons (Fahey et al.
1985). In temperate montane forests, nitrogen mineralization has been found to peak in
the spring, immediately after snow disappearance (Parsons et al. 1994). Soil
temperatures can be warmer beneath snow, relative to open areas or clear cuts, as the
snowpack provides insulation (Hunt et al. 1988). If forest floors and soils are not frozen,
then decomposition can have an early start beneath the snow. This justifies further
research into the dynamics between snow-depth and soil temperature.

69

Sites with greater water availability are also more resistant to weather anomalies
associated with climate change, such as droughts or extreme precipitation events
(McLaughlin et al. 2017). Hydrological processes are direct influencers of ecological
succession through natural disturbance regimes. Water volumes are directly linked to
floods, slides, and avalanches, while atmospheric, soil, and foliar moistures are all
related to wildfires. Water inherently shapes our landscapes through these natural
disasters; however, these incidents pose a challenge for Emergency Management BC, BC
Wildfire Service, and numerous industries.

3.5

CONCLUSION

Commercial strip-thinning has direct effects on surface soil moisture and
temperature. The wider 20 m strips maintained greater soil moisture than 10 m strips
throughout the year, even throughout winter. The narrower 10 m strips did retain more
soil moisture during the drier summer months of 2020, when there were below average
rates of precipitation. Strip-orientation also influenced soil moisture during this dry
summer. Under dry conditions, east-west orientated strips maintained greater soil
moisture than north-south. Greater soil temperatures were also observed in wider 20 m
strips, compared to 10 m strips. Soil temperature was greater in both 10 m and 20 m
strips compared to forested areas. This resulted in greater cumulative growing degree
days (GDD) above the critical threshold of 5 °C.

70

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4.0 CHAPTER 4 – MANAGEMENT IMPLICATIONS & FUTURE RESEARCH

Extensive harvesting, insect damage, fungal pathogens, and wildfires have severely
reduced timber supply in the interior of B.C. (Kurz et al. 2008; Alfaro et al. 2015; BC
Government 2018, 2019). Available first-growth timber supplies are expected to
continue to decline before reliance will shift entirely onto replanted stands (BC
Government 2019). Demand for alternative forest harvesting methods is at an all-time
high in Canada and is increasing globally, especially for replanted stands (Fox 2000;
Puettmann et al. 2015; Halofsky et al. 2018). Agroforestry is one form of integrated
resource management that has the potential to help satisfy multiple levels of objectives,
from site-level to watershed scales (Anderson et al. 2008; Udawatta et al. 2011). This
study demonstrated how commercial thinning of timber, combined with silvopastural
alley cropping of agronomic grasses, may help satisfy multiple land objectives.
In Chapter 1, we discussed six primary criteria which silvopastures on provincial
tenure must aim to achieve in British Columbia (B.C.), Canada (Table 1). Sustainable
resource management involves the assurance of future supply and productivity of
natural resources (Gilani and Innes 2020). Priority objectives of the Forest and Range
Practices Act include stewardship of timber, forage, soils, water, wildlife, and
biodiversity (FRPA 2004). Carefully designed silvopastures have the potential to help
with sustainable management of each of these resources and provincial objectives.
Alley cropping was the form of silvopasture used in this study, which provided
increased habitat, and therefore productivity, of herbaceous forage species utilized by
wildlife and livestock. Commercially thinned strips of all widths provided greater forage
productivity than un-thinned stands, and forage productivity increased with strip width
(Kega 2021).
Timber productivity may be increased with strategic thinning designs. Previous
studies have observed thinning of lodgepole pine stands to increase timber productivity
by increasing radial growth and sap flow through reallocation of site resources
78

including: water, sunlight, and soil nutrients (Lagergren et al. 2008; Moreno and Cubera
2008; Moreaux et al. 2011; Skubel et al. 2017; Wang et al. 2019). A review of lodgepole
thinning experiments determined that stand thinning may increase radial growth and
volumes of individual trees, but excessive thinning may reduce the overall basal area
and volume of the entire stand up to 20 years post-thinning (Johnstone and van Thienen
2011). Beneficial effects of thinning have been observed at up to 3 m stem spacing
(Wang et al. 2019), which is less than one-third of the spacing in our lightest thinning
treatment of 10 m stem spacing. This suggests that 10-20 m wide strip-thinning may be
too wide of spacing for optimal timber production, but offers a trade-off of
microclimates suited for understory and herbaceous forage species. This trade-off can
also be viewed as a diversification of economies, which brings its own financial resiliency
that may be studied from an economical perspective. National food security and
sustainable agriculture are important social values that must also be considered. The
effect of strip-width on timber growth is currently being investigated (Spencer
unpublished 2022). The effects of net carbon sequestration could also be investigated
further to include above and below ground carbon storage, as well as C02 flux and soil
emissions.
The differences in soil bulk density between treatments suggest that harvest
operations do bring a recognizable amount of soil disturbance, which is typical with any
form of timber harvesting. Bulk density has been observed to be affected by heavy
machinery one year post-harvest, but does trend back towards pre-harvest levels over
several years (Botta et al. 2006; Sakai et al. 2008; Wallace et al. 2021). Overall, soil
disturbance was relatively low, but future harvesting operations may consider winter
harvesting in wetter areas to minimize disturbance as much as possible.
With regards to water stewardship, we did observe increased snowpacks in wider
strips. The 20 m strips accumulated more snow than the clearcut. There was also
greater soil moisture, to depths of 25 cm, in wider strips throughout the entire year. As
discussed in Chapter Three, increased soil moisture and temperature with increasing
strip-width may accelerate nitrogen mineralization (Hunt et al. 1988; Parsons et al.
79

1994). Drought stress not only limits the amount of biologically available moisture for
vegetation, but also limits microbial activity, soil respiration, nitrogen mineralization,
and nutrient availability (Wilhelm et al. 2017). Adequate soil moisture is necessary for
successful fertilization treatments (Cole et al. 1990; Rose and Ketchum 2011). Further
research may explore the interaction between strip-width and efficacy of fertilization
treatments with the optimal combination of soil moisture and temperature.
It is important to note that the observed effects of strip-thinning widths on
hydrological processes are proportional to the ratio of strip-width to surrounding tree
heights. Therefore, the desired strip-width will depend on the biogeoclimatic zone, site
index, and tree species in question. Furthermore, it is essential to consider the current
height of the stand, growth rates, and potential heights during the period for which
foresters are planning.
Soil moisture can also be limiting to land management objectives, as there is often
competition between understory vegetation and tree seedlings, as is the case with
conifers and tall grasses in drier climates (Wallace et al. 2021). Soil moisture did not
appear to be a limiting resource on this site; however, moisture regimes continue to
change with climate change. Future studies may investigate the effects of strip-thinning
on a broader watershed scale, including: peak stream flows, ground water, and optimal
water usage on site.
Snow-depth and tree cover are critical factors that influence ungulate winter ranges
(UWR) (Safford 2004). Narrower strips would be more beneficial in UWR due to reduced
snow accumulation. An east-west orientation of these narrower strips was also
observed to cause quicker snow disappearance in reserve strips, between harvested
strips. Quicker snow ablation and earlier growth of forage is essential for ungulates,
such as moose and mule deer (Safford 2004). Land managers must consider the widths
of strips and how the aspect and orientation of those strips influences wildlife habitat.
A mixture of various strip-widths and orientations over diverse terrain, results in a
diversity of microenvironments, which in turn results in increased biodiversity (Wei et al.
80

2020). Diversity of microenvironments leads to diverse timings of snow disappearance,
seed germination, and vegetative growth. In this way, an assortment of thinning
treatments on a single site can achieve objectives of biodiversity.
Alternatively, these observations can be used to implement control over more
uniform and desirable rates of ablation and timing of disappearance. More control over
the timing of snow disappearance may be used to influence the timing of forage
availability. We observed significant differences in cumulative growing degree days, in
soil temperature, between the various treatments. Stands can be strategically designed
to increase the amount of growing degree days, which has the potential to facilitate
secondary re-growth phases in herbaceous forage (Frank 1991). Manipulation of forest
structure to maximize solar radiation, soil temperature, and water availability can
ultimately be used to provide increased forage quality and quantity for livestock and
wildlife.
In regards to climate change mitigation, thinning of lodgepole pine has been
suggested to help offset the negative effects of drought by reallocation of moisture
availability (Misson et al. 2003; Simonin et al. 2007; Kohler et al. 2010; RodríguezCalcerrada et al. 2011; Sohn et al. 2013; Elkin et al. 2015; del Río et al. 2017; Ambrose et
al. 2018; Cabon et al. 2018; Park et al. 2018). Sites with greater water availability are
more resistant to weather anomalies associated with climate change, such as droughts
or extreme precipitation events (McLaughlin et al. 2017). Hydrological processes are
direct influencers of ecological succession, through processes which influence
vegetative productivity and through natural disturbance regimes. Water volumes are
directly linked to floods, slides, and avalanches, while atmospheric, soil, and foliar
moistures are all related to wildfires. Water inherently shapes our landscapes through
these natural disasters; however, these incidents pose a challenge for Emergency
Management BC, BC Wildfire Service, and numerous industries.

81

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86

5.0 APPENDIX A

Figure A.1. Soil pit displaying the sandy loam texture of brunosolic gray luvisolic soil. Bt
horizon has higher clay content than Ae. Btg horizon from water table within <50cm of
surface. The water table was at a depth of 42cm.

87

Figure A.2. Soil pit displaying the sandy loam texture of brunosolic gray luvisolic soils. Bt
horizon has higher clay content than Ae. Btg horizon from non-permanent water table
within <50cm of surface. Water table was at a depth of approximately 40cm.

88

6.0 APPENDIX B

Table B. 1. Summary of mean maximum daily temperatures and monthly precipitation
sums by month and year observed at the Goudie Agroforestry Pilot Project during 20192020.

Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec

2019
Mean Max. Daily
Temp. (°C)
Precip. Sum (mm)
12.1
7.6
20.8
16.0
27
17.6
78.4
19.9
56.8
22.3
34.6
13.9
88.4
5.6
35
2.7
22
-1.0
24

2020
Mean Max. Daily
Precip. Sum
Temp. (°C)
(mm)
-1.9
16.2
0.6
22.4
3.0
18.4
7.7
17.8
13.4
160
15.7
104.8
21.2
53.4
22.3
12
18.7
-

89

7.0 APPENDIX C

Figure C. 1. Photo facing east in 20 m leave-strip, between 10 m wide harvested strips.
Block B, strips orientated east-west. Date taken: March 29, 2019.

Figure C. 2. Photo taken facing south. Photographer was standing in 15 m wide
harvested strip, between 30 m leave-strips. Snow is still visible on north half of 30 m
leave-strip. Block B, strips orientated east-west. Date taken: March 29, 2019.

90

North-South

East-West

10 m
wide

20 m
wide

Control (Forested)

Figure C. 3. Photos of snow pack observed simultaneously on May 1, 2019 at 12:00pm
on permanently installed trail cameras. Delayed snow disappearance in east-west
orientated strips, compared to north-south orientated strips. Snow has disappeared in
forested areas and leave-strips.
91

Figure C. 4. Photo of east-west orientated (Block B) 10 m harvested trips, 20 m leavestrips, road, and clearcut with flat slope. Delayed snow disappearance was observed in
10m-wide harvested strips, relative to the road, clearcut, and forested stands. Photo
taken: March 25, 2019.

92

8.0 APPENDIX D

Figure D. 1. Locations of all treatments, soil moisture stations, snow transects, and
permanent cameras. Reserve strips are twice the width of adjacent harvested strips.
Snow samples were at 20 m intervals along 180 m transects parallel to strip direction. All
samples were located in the center of the strips (harvest or reserve).

93

9.0 APPENDIX E

Table E. 1. Summary of coarse fragment content (CFC) for each treatment. CFC was
calculated as the percent volume of dried coarse material (sieved >2 mm, and assumed
volume corrected assuming density of 2.65 Mg/m3) of the total volume of the dried
mineral sample. Summary includes standard deviation (SD), standard error (SE), 95 %
confidence interval, and sample size (n).
Harvested
Strip-width
10 m
10 m
15 m
15 m
20 m
20 m
Non-Harvested
Non-Harvested

Orientation
North-south
East-west
North-south
East-west
North-south
East-west
North-south
East-west

Mean
CFC (%)
16.2
19.2
19.8
17.1
17.4
25.5
12.7
10.6

SD
3.7
7.8
11.7
10.0
5.5
16.8
3.2
7.4

SE
0.91
1.95
2.92
2.50
1.36
4.20
0.81
1.84

95 % CI
1.95
4.15
6.23
5.33
2.91
8.95
1.72
3.93

n
16
16
16
16
16
16
16
16

Table E. 2. Summary of dead forest floor litter depth by treatment. Summary includes
standard deviation (SD), standard error (SE), 95 % confidence interval (CI), and sample
size (n).
Harvested
Strip-width
10 m
10 m
15 m
15 m
20 m
20 m
Non-Harvested
Non-Harvested

Orientation
North-south
East-west
North-south
East-west
North-south
East-west
North-south
East-west

Mean Litter
Depth (cm)
0.64
1.26
0.89
0.82
1.59
1.41
2.20
1.96

SD
0.42
1.40
0.48
0.65
1.54
2.05
0.87
1.01

SE
0.11
0.35
0.12
0.16
0.38
0.51
0.22
0.25

95 % CI
0.23
0.74
0.26
0.34
0.82
1.09
0.46
0.54

n
16
16
16
16
16
16
16
16

94

Table E. 3. Summary of dead forest floor litter volume by treatment. Summary includes
standard deviation (SD), standard error (SE), 95 % confidence interval (CI), and sample
size (n).
Harvested
Strip-width
10 m
10 m
15 m
15 m
20 m
20 m
Non-Harvested
Non-Harvested

Orientation
North-south
East-west
North-south
East-west
North-south
East-west
North-south
East-west

Mean Litter
Volume (mL)
256.50
502.50
355.69
327.50
635.00
560.56
880.00
785.00

SD
170.60
559.23
192.71
258.50
614.82
818.29
347.79
404.47

SE
42.65
139.81
48.18
64.62
153.70
204.57
86.95
101.12

95 % CI
90.90
297.99
102.69
137.74
327.61
436.04
185.33
215.53

n
16
16
16
16
16
16
16
16

Table E. 4. Summary of dead forest floor litter mass by treatment. Summary includes
standard deviation (SD), standard error (SE), 95 % confidence interval (CI), and sample
size (n).
Harvested
Strip-width
10 m
10 m
15 m
15 m
20 m
20 m
Non-Harvested
Non-Harvested

Orientation
North-south
East-west
North-south
East-west
North-south
East-west
North-south
East-west

Mean Litter
Mass (g)
31.75
57.23
56.03
41.36
67.42
54.98
47.08
47.79

SD
25.24
46.60
34.13
21.31
48.85
90.43
19.83
21.99

SE
6.31
11.65
8.53
5.33
12.21
22.61
4.96
5.50

95 % CI
13.45
24.83
18.19
11.36
26.03
48.19
10.57
11.72

n
16
16
16
16
16
16
16
16

95

Table E. 5. Summary of the depth of dead forest floor fermented and humic organic soil
layers (FH) by treatment. Summary includes standard deviation (SD), standard error (SE),
95 % confidence interval (CI), and sample size (n).
Harvested
Strip-width
10 m
10 m
15 m
15 m
20 m
20 m
Non-Harvested
Non-Harvested

Orientation
North-south
East-west
North-south
East-west
North-south
East-west
North-south
East-west

Mean FH
Depth (cm)
3.11
4.96
4.81
4.55
4.27
6.54
4.63
3.88

SD
2.45
2.64
2.19
3.54
2.78
3.71
3.32
2.44

SE
0.61
0.66
0.55
0.88
0.70
0.93
0.83
0.61

95 % CI
1.31
1.40
1.17
1.89
1.48
1.97
1.77
1.30

n
16
16
16
16
16
16
16
16

Table E. 6. Summary of the volume of dead forest floor fermented and humic organic soil
layers (FH) by treatment. Summary includes standard deviation (SD), standard error (SE),
95 % confidence interval (CI), and sample size (n).
Harvested
Strip-width
10 m
10 m
15 m
15 m
20 m
20 m
Non-Harvested
Non-Harvested

Orientation
North-south
East-west
North-south
East-west
North-south
East-west
North-south
East-west

Mean FH
Volume (mL)
1238.75
1985.00
1915.63
1820.00
1703.75
2612.50
1850.00
1552.50

SD
981.25
1054.65
871.73
1415.27
1112.94
1479.58
1326.69
977.31

SE
245.31
263.66
217.93
353.82
278.23
369.90
331.67
244.33

95 % CI
522.87
561.99
464.51
754.14
593.04
788.41
706.94
520.77

n
16
16
16
16
16
16
16
16

96

Table E. 7. Summary of the mass of dead forest floor fermented and humic organic soil
layers (FH) by treatment. Summary includes standard deviation (SD), standard error (SE),
95 % confidence interval (CI), and sample size (n).

Harvested
Strip-width
10 m
10 m
15 m
15 m
20 m
20 m
Non-Harvested
Non-Harvested

Orientation
North-south
East-west
North-south
East-west
North-south
East-west
North-south
East-west

Mean FH
Mass (g)
292.64
500.69
493.11
477.42
470.78
808.05
269.25
304.38

SD
272.72
302.69
338.23
550.17
477.90
669.59
228.78
345.95

SE
68.18
75.67
84.56
137.54
119.47
167.40
57.20
86.49

95 % CI
145.32
161.29
180.23
293.17
254.65
356.80
121.91
184.34

n
16
16
16
16
16
16
16
16

97

10.0 APPENDIX F
Table F. 1. Summary of snow sampling point terrain, maximum snow water equivalent (Max. SWE), and mean date of disappearance
for 2019 and 2020, by transect (n=10). Snow was sampled between March 3 – May 8. Reserve strips were twice as wide as harvested
strips. The strips were orientated North-South (N-S) or East-West (E-W). Means include 95% confidence interval.

2019
Transect #
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16

Treatment
Harvested Strip
Harvested Strip
Harvested Strip
Harvested Strip
Harvested Strip
Harvested Strip
Reserve Strip
Reserve Strip
Reserve Strip
Reserve Strip
Reserve Strip
Reserve Strip
Non-harvested
Non-harvested
Clearcut
Clearcut

Strip
Width
(m)
10
15
20
10
15
20
20
30
40
20
30
40
-

Strip
Orientation
N-S
N-S
N-S
E-W
E-W
E-W
N-S
N-S
N-S
E-W
E-W
E-W
-

Mean
Aspect (°)
312 ± 1.8
21 ± 0.9
199 ± 2.0
216 ± 1.1
236 ± 1.3
248 ± 4.0
313 ± 2.0
76 ± 2.6
72 ± 2.6
217 ± 1.3
229 ± 1.7
307 ± 1.4
283 ± 2.6
199 ± 4.1
116 ± 0.0
116 ± 0.0

Mean
Slope (°)
6±0
3±0
7.3 ± 0.1
10 ± 0
8±0
7±0
6±0
2.6 ± 0.2
6.6 ± 0.1
10 ± 0
8±0
7±0
6±0
2±0
1.6 ± 0.3
1±0

Mean
Max. SWE
(cm)
16.3 ± 1.7
16.8 ± 0.9
18.8 ± 2.3
16.8 ± 0.9
18.6 ± 1.6
21.3 ± 1.3
9.9 ± 1.7
10.1 ± 2.0
11.5 ± 2
9.1 ± 1.2
12.4 ± 2.7
11.3 ± 1.0
7.4 ± 1.1
10.9 ± 2.3
17.1 ± 1.6
-

Mean date of
disappearance
Apr-30 ± 3.0
Apr-29 ± 2.5
Apr-30 ± 3.0
May-01 ± 2.8
May-01 ± 2.6
Apr-29 ± 2.8
Apr-18 ± 5.2
Apr-21 ± 4.1
Apr-24 ± 5.0
Apr-06 ± 9.4
Apr-15 ± 2.8
Apr-11 ± 5.7
Apri-22 ± 4.8
Apr-23 ± 6.1
Apr-23 ± 5
-

2020
Mean
Max.
SWE (cm)
28.7 ± 1.2
29.9 ± 1.2
31.1 ± 1.6
28.5 ± 1.1
29.4 ± 1.2
30.1 ± 1.7
18.6 ± 3
18.7 ± 2.5
20.3 ± 2.2
17.8 ± 2
17.8 ± 1.6
16.9 ± 1.4
18.5 ± 3.3
21.9 ± 3.9
28 ± 2.5
28.7 ± 1.7

Mean date of
disappearance
May-14 ± 2.2
May-14 ± 1.1
May-14 ± 1.6
May-11 ± 4.0
May-11 ± 3.7
May-05 ± 3.0
May-05 ± 2.7
May-10 ± 3.3
May-10 ± 3.8
May-03 ± 1.0
May-05 ± 3.1
May-03 ± 1.0
May-09 ± 3.4
May-14 ± 2.6
May-05 ± 2.1
May-04 ± 1.5

98

Table F. 2. Snow ablation rates for 2019, summarized by transect (n=10). Snow was sampled between March 3 – May 5. Ablation was
calculated by the difference in snow water equivalent (SWE) from the previous sample period divided by the number of days between
sampling (cm/day). Average ablation rate was taken as an average of all of the mean rates by sampling period. Reserve strips were
twice as wide as harvested strips. The strips were orientated North-South (N-S) or East-West (E-W). Means include 95% confidence
interval.
Transect
#
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15

Treatment
Harvested Strip
Harvested Strip
Harvested Strip
Harvested Strip
Harvested Strip
Harvested Strip
Reserve Strip
Reserve Strip
Reserve Strip
Reserve Strip
Reserve Strip
Reserve Strip
Non-harvested
Non-harvested
Clearcut

Strip
Width
(m)
10
20
15
10
15
20
10
40
30
40
30
20
-

Strip
Orientation
N-S
N-S
N-S
E-W
E-W
E-W
N-S
N-S
N-S
E-W
E-W
E-W
-

Ablation Rate
(cm/day)
2019-Mar-29
-0.05 ± 0.14
-0.24 ± 0.09
-0.14 ± 0.05
-0.23 ± 0.09
-0.02 ± 0.13
0.22 ± 0.18
-0.26 ± 0.12
-0.23 ± 0.1
-0.06 ± 0.16
-0.44 ± 0.06
-0.22 ± 0.21
-0.36 ± 0.08
-0.16 ± 0.08
-0.22 ± 0.22
-0.29 ± 0.07

Ablation Rate
(cm/day)
2019-Apr-12
-0.23 ± 0.09
-0.09 ± 0.1
-0.13 ± 0.1
-0.24 ± 0.11
-0.11 ± 0.16
-0.19 ± 0.06
-0.19 ± 0.09
-0.22 ± 0.19
-0.34 ± 0.25
-0.15 ± 0.09
-0.42 ± 0.3
-0.06 ± 0.08
-0.16 ± 0.07
-0.1 ± 0.33
-0.39 ± 0.25

Ablation Rate
(cm/day)
2019-Apr-26
-0.6 ± 0.14
-0.63 ± 0.11
-0.69 ± 0.09
-0.73 ± 0.12
-0.74 ± 0.18
-0.65 ± 0.05
-0.09 ± 0.1
-0.25 ± 0.12
-0.18 ± 0.08
-0.03 ± 0.06
-0.06 ± 0.05
-0.07 ± 0.08
-0.13 ± 0.07
-0.23 ± 0.14
-0.39 ± 0.16

Ablation Rate
(cm/day)
2019-May-5
-0.3 ± 0.24
-0.46 ± 0.37
-0.26 ± 0.21
-0.33 ± 0.3
-0.6 ± 0.29
-0.53 ± 0.29
-0.04 ± 0.1
-0.1 ± 0.12
-

Ablation Rate
(cm/day)
2019 Average
-0.43 ± 0.05
-0.39 ± 0.04
-0.45 ± 0.09
-0.47 ± 0.06
-0.53 ± 0.06
-0.46 ± 0.06
-0.25 ± 0.06
-0.32 ± 0.2
-0.29 ± 0.05
-0.36 ± 0.08
-0.4 ± 0.3
-0.36 ± 0.05
-0.18 ± 0.03
-0.39 ± 0.11
-0.43 ± 0.06

99

Table F. 3. Snow ablation rates for 2020, summarized by transect (n=10). Snow was sampled between March 7 – May 8. Ablation was
calculated by the difference in snow water equivalent (SWE) from the previous sample period divided by the number of days between
sampling (cm/day). Average ablation rate was taken as an average of all of the mean rates by sampling period. Reserve strips were
twice as wide as harvested open-strips. Strips were orientated North-South (N-S) or East-West (E-W). Means include 95% confidence
interval.

Transect
#
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16

Treatment
Harvested Strip
Harvested Strip
Harvested Strip
Harvested Strip
Harvested Strip
Harvested Strip
Reserve Strip
Reserve Strip
Reserve Strip
Reserve Strip
Reserve Strip
Reserve Strip
Non-harvested
Non-harvested
Clearcut
Clearcut

Strip
Width
(m)
10
20
15
20
15
10
10
40
30
40
30
20
-

Strip
Orientation
N-S
N-S
N-S
E-W
E-W
E-W
N-S
N-S
N-S
E-W
E-W
E-W
-

Ablation Rate
(cm/day)
2020-Mar-27
0.11 ± 0.08
0.15 ± 0.11
0.1 ± 0.09
0.11 ± 0.11
0.05 ± 0.07
0.03 ± 0.09
0.04 ± 0.08
0.09 ± 0.24
0.1 ± 0.08
0.12 ± 0.12
0.03 ± 0.13
0.07 ± 0.08
0.05 ± 0.13
-0.02 ± 0.21
0.09 ± 0.08
0.11 ± 0.09

Ablation
Rate
(cm/day)
2020-Apr-10
-0.14 ± 0.11
-0.07 ± 0.18
-0.15 ± 0.27
-0.05 ± 0.14
-0.06 ± 0.12
-0.05 ± 0.13
-0.13 ± 0.15
-0.05 ± 0.39
-0.2 ± 0.13
-0.29 ± 0.16
-0.06 ± 0.22
-0.31 ± 0.12
-0.03 ± 0.3
0.05 ± 0.23
-0.04 ± 0.21
-0.09 ± 0.17

Ablation
Rate
(cm/day)
2020-Apr-24
-0.43 ± 0.13
-0.5 ± 0.14
-0.31 ± 0.17
-0.73 ± 0.23
-0.52 ± 0.12
-0.49 ± 0.14
-0.44 ± 0.1
-0.36 ± 0.41
-0.3 ± 0.14
-0.34 ± 0.11
-0.44 ± 0.15
-0.54 ± 0.08
-0.39 ± 0.25
-0.33 ± 0.13
-0.61 ± 0.15
-0.51 ± 0.18

Ablation Rate
(cm/day)
2020-May-2
-1.26 ± 0.12
-1.23 ± 0.12
-1.26 ± 0.2
-1.94 ± 0.39
-1.6 ± 0.29
-1.57 ± 0.29
-0.88 ± 0.18
-0.86 ± 0.18
-0.89 ± 0.18
-0.9 ± 0.17
-1 ± 0.18
-0.68 ± 0.22
-0.8 ± 0.16
-0.86 ± 0.21
-1.69 ± 0.41
-2.11 ± 0.35

Ablation Rate
(cm/day)
2020-May 8
-0.48 ± 0.19
-0.57 ± 0.3
-0.62 ± 0.23
-0.42 ± 0.37
-0.53 ± 0.34
-0.47 ± 0.38
-0.32 ± 0.29
-0.4 ± 0.2
-0.4 ± 0.24
-0.1 ± 0.12
-0.2 ± 0.15
-0.1 ± 0.16
-0.17 ± 0.28
-0.23 ± 0.36
-0.67 ± 0.37
-0.48 ± 0.32

Ablation Rate
(cm/day)
2020 Average
-0.63 ± 0.11
-0.7 ± 0.09
-0.67 ± 0.07
-0.81 ± 0.17
-0.85 ± 0.15
-1.07 ± 0.23
-0.5 ± 0.07
-0.53 ± 0.06
-0.62 ± 0.09
-0.5 ± 0.1
-0.62 ± 0.13
-0.5 ± 0.06
-0.44 ± 0.07
-0.48 ± 0.07
-0.89 ± 0.11
-1.07 ± 0.18

100

Table F. 4. Summary of means and 95 % confidence intervals for terrain, canopy cover, estimated transmitted direct radiation, peak
snow water equivalent (SWE), date of snow disappearance, and average ablation rate for harvested strips of various widths (10-20
m) and of two orientations (North-South and East-West). Non-harvested and clearcut controls were also included. Snow was sampled
between March 3 – May 5, 2019 and 2020.

Elevation (m)
Slope (°)
Aspect (°)
Canopy cover
(%)
Trans. Direct
Rad. (MJ/m2/d)
2019 - Peak
SWE (cm)
2019 – Date of
Disappearance
2019 – Avg.
Ablation rate
(cm/day)
2020 - Peak
SWE 2020 (cm)
2020 – Date of
Disappearance
2020 – Avg.
Ablation rate
(cm/day)

10 m
1382.8 ± 0.7
6±0
312 ± 1.8

North-South
15 m
1383.6 ± 0.3
3±0
21 ± 0.9

10 m
1357.2 ± 0.7
10 ± 0
216 ± 1.1

East-West
15 m
1367.6 ± 0.9
8±0
236 ± 1.3

20 m
1385.9 ± 0.7
7.3 ± 0.1
199 ± 2.0

61.9 ± 0.6

47.5 ± 0.3

42.6 ± 0.3

59.4 ± 0.4

3.7 ± 0.1

5.3 ± 0.1

5.1 ± 0.1

16.3 ± 1.7

16.8 ± 0.9

Apr-30 ± 3

20 m
1372.8 ± 0.8
7±0
248 ± 4.0

Control NonHarvested
1373 ± 1.8
4 ± 0.3
240 ± 5.9

Control Clearcut
1389.2 ± 0.1
1.4 ± 0.2
116 ± 0.0

50.6 ± 0.3

41.2 ± 0.2

86.1 ± 0.5

13.2 ± 0.2

2.5 ± 0.1

3.1 ± 0.1

4.7 ± 0.1

1.2 ± 0.1

9.3 ± 0.1

18.8 ± 2.3

16.8 ± 0.9

18.6 ± 1.6

21.3 ± 1.3

9.2 ± 1.4

17.1 ± 1.6

Apr-29 ± 2.5

Apr-30 ± 3

May-01 ± 2.8

May-01 ± 2.6

Apr-29 ± 2.8

Apr-22 ± 3.5

Apr-23 ± 4.9

-0.43 ± 0.05

-0.39 ± 0.04

-0.45 ± 0.09

-0.47 ± 0.06

-0.53 ± 0.06

-0.46 ± 0.06

-0.28 ± 0.07

-0.43 ± 0.06

28.7 ± 1.2

29.9 ± 1.2

31.1 ± 1.6

28.5 ± 1.1

29.4 ± 1.2

30.1 ± 1.7

20.2 ± 2.4

28.4 ± 1.4

May-14 ± 2.2

May-15 ± 0.7

May-14 ± 1.6

May-11 ± 4

May-11 ± 3.7

May-05 ± 3

May-11 ± 2.2

May-05 ± 1.2

-0.63 ± 0.11

-0.7 ± 0.09

-0.67 ± 0.07

-0.81 ± 0.17

-0.85 ± 0.15

-1.07 ± 0.23

-0.46 ± 0.05

-0.98 ± 0.1

101

Table F. 5. Summary of means and 95 % confidence intervals for terrain, canopy cover, estimated transmitted direct radiation, peak
snow water equivalent (SWE), date of snow disappearance, and average ablation rate for reserve strips of various widths (10-20 m)
and of two orientations (North-South and East-West). Reserve strips were twice the width of adjacent harvested strips. Nonharvested and clearcut controls were also included. Snow was sampled between March 3 – May 5, 2019 and 2020.

Elevation (m)
Slope (°)
Aspect (°)
Canopy cover
(%)
Trans. Direct
Rad. (MJ/m2/d)
2019 - Peak
SWE (cm)
2019 – Date of
Disappearance
2019 – Avg.
Ablation rate
(cm/day)
2020 - Peak
SWE 2020 (cm)
2020 – Date of
Disappearance
2020 – Avg.
Ablation rate
(cm/day)

20 m
1384.1 ± 0.9
6±0
313 ± 2.0

North-South
30 m
1383.3 ± 0.2
2.6 ± 0.1
76 ± 2.6

20 m
1355.5 ± 0.7
10 ± 0
217 ± 1.3

East-West
30 m
1364.9 ± 0.9
8±0
229 ± 1.7

40 m
1386.9 ± 0.7
6.3 ± 0.2
72 ± 2.6

85 ± 0.5

76.8 ± 0.5

77.2 ± 0.3

81.2 ± 0.4

1.0 ± 0.1

1.5 ± 0.1

1.4 ± 0.1

9.9 ± 1.7

10.1 ± 2

Apr-18 ± 5.2

40 m
1373.6 ± 0.7
7±0
307 ± 1.4

Control NonHarvested
1373 ± 1.8
4 ± 0.3
240 ± 5.9

Control Clearcut
1389.2 ± 0.1
1.4 ± 0.2
116 ± 0.0

84.2 ± 0.3

78.1 ± 0.4

86.1 ± 0.5

13.2 ± 0.2

2.6 ± 0.2

2.2 ± 0.1

2.4 ± 0.1

1.2 ± 0.1

9.3 ± 0.1

11.5 ± 2

9.1 ± 1.2

12.4 ± 2.7

11.3 ± 1

9.2 ± 1.4

17.1 ± 1.6

Apr-21 ± 4.1

Apr-24 ± 5

Apr-06 ± 9.3

Apr-15 ± 2.8

Apr-11 ± 5.7

Apr-22 ± 3.5

Apr-23 ± 4.9

-0.25 ± 0.06

-0.32 ± 0.2

-0.29 ± 0.05

-0.36 ± 0.08

-0.4 ± 0.3

-0.36 ± 0.05

-0.28 ± 0.07

-0.43 ± 0.06

18.6 ± 3

18.7 ± 2.5

20.3 ± 2.2

17.8 ± 2

17.8 ± 1.6

16.9 ± 1.4

20.2 ± 2.4

28.4 ± 1.4

May-05 ± 2.7

May-09 ± 3.4

May-10 ± 3.3

May-03 ± 1

May-05 ± 3.1

May-03 ± 1

May-11 ± 2.2

May-05 ± 1.2

-0.5 ± 0.07

-0.53 ± 0.06

-0.62 ± 0.09

-0.5 ± 0.1

-0.62 ± 0.13

-0.5 ± 0.06

-0.46 ± 0.05

-0.98 ± 0.1

102

Table F. 6. Results of a Tukey post-hoc test, summarizing the mean number of days
between snow disappearance observed in all treatments in 2019.

Treatment #1
20m - Reserve
20m - Reserve
20m - Reserve
40m - Reserve
40m - Reserve
40m - Reserve
30m - Reserve
30m - Reserve
30m - Reserve
20m - Reserve
20m - Reserve
No - Harvest
No - Harvest
No - Harvest
20m - Reserve
20m - Reserve
40m - Reserve
40m - Reserve
40m - Reserve
30m - Reserve
30m - Reserve
No - Harvest
Clearcut
Clearcut
Clearcut
20m - Harvest
20m - Harvest
15m - Harvest

Treatment #2
20m - Harvest
15m - Harvest
10m - Harvest
20m - Harvest
15m - Harvest
10m - Harvest
20m - Harvest
15m - Harvest
10m - Harvest
No - Harvest
Clearcut
20m - Harvest
15m - Harvest
10m - Harvest
40m - Reserve
30m - Reserve
30m - Reserve
No - Harvest
Clearcut
No - Harvest
Clearcut
Clearcut
20m - Harvest
15m - Harvest
10m - Harvest
15m - Harvest
10m - Harvest
10m - Harvest

Mean Number of Days
Treatment #1 Disappeared
Before Treatment #2
17.7
18.2
18.4
12.4
12.9
13.2
11.8
12.3
12.6
10.1
11.4
7.6
8.0
8.3
5.2
5.8
0.6
4.9
6.2
4.3
5.6
1.3
6.3
6.8
7.0
0.5
0.8
0.3

95 %
CI
± 7.1
± 7.1
± 7.1
± 7.1
± 7.1
± 7.1
± 7.1
± 7.1
± 7.1
± 7.1
± 8.7
± 7.1
± 7.1
± 7.1
± 7.1
± 7.1
± 7.1
± 7.1
± 8.7
± 7.1
± 8.7
± 8.7
± 8.7
± 8.7
± 8.7
± 7.1
± 7.1
± 7.1

P-Value
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
0.0006
0.0023
0.0289
0.0148
0.0104
0.3167
0.1911
1.0000
0.4210
0.3746
0.5949
0.5121
0.9998
0.3534
0.2570
0.2157
1.0000
1.0000
1.0000

103

Table F. 7. Results of a Tukey post-hoc test, summarizing the mean number of days
between snow disappearance observed in all treatments in 2020.

Treatment #1
30m - Reserve
40m - Reserve
30m - Reserve
40m - Reserve
30m - Reserve
40m - Reserve
20m - Reserve
Clearcut
Clearcut
Clearcut
No - Harvest
No - Harvest
40m - Reserve
20m - Reserve
20m - Reserve
Clearcut
Clearcut
10m - Harvest
20m - Harvest
20m - Reserve
20m - Harvest
40m - Reserve
20m - Reserve
20m - Reserve
30m - Reserve
Clearcut
20m - Reserve

Treatment #2
20m - Harvest
15m - Harvest
No - Harvest
No - Harvest
15m - Harvest
10m - Harvest
40m - Reserve
15m - Harvest
20m - Harvest
30m - Reserve
10m - Harvest
15m - Harvest
30m - Reserve
10m - Harvest
No - Harvest
No - Harvest
40m - Reserve
15m - Harvest
No - Harvest
Clearcut
15m - Harvest
20m - Harvest
20m - Harvest
15m - Harvest
10m - Harvest
10m - Harvest
30m - Reserve

Mean Number of Days
Treatment #1 Disappeared
Before Treatment #2
2.3
6.2
4.1
4.9
5.4
5.8
2.8
8.2
5.1
2.8
0.9
1.3
0.8
8.5
7.7
6.8
2.0
0.4
1.7
0.8
3.1
3.1
5.9
9.0
4.9
7.7
3.6

95 %
CI
± 4.4
± 4.3
± 4.4
± 4.3
± 4.4
± 4.3
± 4.3
± 4.4
± 4.4
± 4.4
± 4.4
± 4.4
± 4.3
± 4.4
± 4.4
± 4.4
± 4.3
± 4.4
± 4.4
± 4.4
± 4.4
± 4.3
± 4.4
± 4.4
± 4.4
± 4.4
± 4.4

P-Value
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
0.0005
0.0014
0.0016
0.0056
0.0102
0.0144
0.0148
0.0897
0.1882
0.3392
0.3886
0.5011
0.5035
0.5754
0.7372
0.9208
0.9841
0.9983
0.9989
0.9992
1.0000

104

Table F. 8. Results of a Tukey post-hoc test, summarizing the mean number of days between snow disappearance observed in 2019
between all reserve-strips orientated east-west (E-W), all reserve-strips orientated east-west (N-S), all strip-harvests orientated northsouth (N-S), all strip-harvests orientated east-west (E-W), as well as clearcut and non-harvested controls.

Treatment #1
Reserve-strips E-W
Reserve-strips E-W
Reserve-strips E-W
Reserve-strips E-W
Reserve-strips N-S
Reserve-strips E-W
Reserve-strips N-S
Non-Harvested
Non-Harvested
Clearcut
Clearcut
Reserve-strips N-S
Reserve-strips N-S
Strip-Harvests N-S
Non-Harvested

Treatment #2
Strip-Harvests N-S
Strip-Harvests E-W
Non-Harvested
Reserve-strips N-S
Strip-Harvests E-W
Clearcut
Strip-Harvests N-S
Strip-Harvests E-W
Strip-Harvests N-S
Strip-Harvests E-W
Strip-Harvests N-S
Clearcut
Non-Harvested
Strip-Harvests E-W
Clearcut

Mean Number of Days
Treatment #1 Disappeared
Before Treatment #2
18.9
20.0
11.5
10.2
9.8
12.8
8.7
8.5
7.4
7.2
6.1
2.6
1.3
1.1
1.3

95 %
CI
5.0
5.0
5.6
5.0
5.0
7.0
5.0
5.6
5.6
7.0
7.0
7.0
5.6
5.0
7.5

P-Value
< 0.0001
< 0.0001
< 0.0001
< 0.0001
< 0.0001
< 0.0001
< 0.0001
0.0003
0.0024
0.0421
0.1269
0.8941
0.9845
0.9895
0.9960

105

Table F. 9. Results of a Tukey post-hoc test, summarizing the mean number of days between snow disappearance observed in 2020
between all reserve-strips orientated east-west (E-W), all reserve-strips orientated east-west (N-S), all strip-harvests orientated northsouth (N-S), all strip-harvests orientated east-west (E-W), as well as clearcut and non-harvested controls.

Treatment #1
Reserve-strips E-W
Clearcut
Reserve-strips E-W
Reserve-strips N-S
Clearcut
Reserve-strips E-W
Strip-Harvests E-W
Reserve-strips E-W
Clearcut
Clearcut
Reserve-strips N-S
Non-Harvested
Strip-Harvests E-W
Reserve-strips E-W
Reserve-strips N-S

Treatment #2
Strip-Harvests N-S
Strip-Harvests N-S
Non-Harvested
Strip-Harvests N-S
Non-Harvested
Strip-Harvests E-W
Strip-Harvests N-S
Reserve-strips N-S
Strip-Harvests E-W
Reserve-strips N-S
Non-Harvested
Strip-Harvests N-S
Non-Harvested
Clearcut
Strip-Harvests E-W

Mean Number of Days
Treatment #1 Disappeared
Before Treatment #2
10.7
9.7
7.9
6.0
6.9
5.4
5.3
4.7
4.3
3.6
3.2
2.8
2.5
1.0
0.7

95 %
CI
3.0
3.4
3.4
3.0
3.7
3.0
3.0
3.0
3.4
3.4
3.4
3.4
3.4
3.4
3.0

P-Value
< 0.0001
< 0.0001
< 0.0001
< 0.0001
< 0.0001
< 0.0001
< 0.0001
0.0002
0.0042
0.0273
0.0701
0.1650
0.2733
0.9513
0.9842

106

Table F. 10. Results of a Games-Howell pairwise comparison test used to determine
effects of treatments on estimated transmitted direct radiation (MJ/m2/d) in harvested
strips of commercially thinned stands of various widths (10, 15, 20m). Strip-width of
harvested strips and strip-orientation were fixed independent variables. NS = northsouth, EW = east-west

Group 1
Clearcut
Clearcut
Clearcut
Clearcut
Clearcut
Clearcut
Clearcut
Clearcut
NS.10m
NS.10m
NS.10m
NS.10m
NS.10m
NS10m
NS10m
NS15m
NS15m
NS15m
NS15m
NS15m
NS15m
NS20m
NS20m
NS20m
NS20m
NS20m
NS.No-Harvest
NS.No-Harvest
NS.No-Harvest
NS.No-Harvest
EW.10m
EW.10m
EW.10m
EW.15m
EW.15m
EW.20m

Group 2
NS.10m
NS.15m
NS.20m
NS.No-Harvest
EW.10m
EW.15m
EW.20m
EW.No-Harvest
NS.15m
NS.20m
NS.No-Harvest
EW.10m
EW.15m
EW.20m
EW.No-Harvest
NS.20m
NS.No-Harvest
EW.10m
EW.15m
EW.20m
EW.No-Harvest
NS.No-Harvest
EW.10m
EW.15m
EW.20m
EW.No-Harvest
EW.10m
EW.15m
EW.20m
EW.No-Harvest
EW.15m
EW.20m
EW.No-Harvest
EW.20m
EW.No-Harvest
EW.No-Harvest

Estimated difference
in transmitted direct
radiation (MJ/m2/d)
-5.64
-3.99
-4.20
-8.34
-6.75
-6.19
-4.20
-7.88
1.64
1.43
-2.70
-1.11
-0.55
1.43
-2.23
-0.20
-4.34
-2.76
-2.20
-0.21
-3.88
-4.13
-2.55
-1.99
-0.001
-3.67
1.58
2.14
4.13
0.46
0.55
2.54
-1.12
1.99
-1.68
-3.67

95 %
CI
±0.77
±0.47
±0.28
±0.65
±0.98
±0.44
±1.79
±0.36
±0.81
±0.77
±0.89
±1.1
±0.8
±1.82
±0.79
±0.49
±0.72
±1
±0.56
±1.793
±0.52
±0.66
±0.97
±0.45
±1.779
±0.38
±1.05
±0.7
±1.81
±0.67
±0.98
±1.87
±0.98
±1.79
±0.5
±1.78

P-Value
< 0.001
<0.001
< 0.001
< 0.001
< 0.001
< 0.001
< 0.001
< 0.001
< 0.001
< 0.001
< 0.001
0.047
0.31
0.18
< 0.001
0.8
< 0.001
< 0.001
< 0.001
1
< 0.001
< 0.001
< 0.001
< 0.001
1
< 0.001
0.002
< 0.001
< 0.001
0.31
0.528
0.005
0.022
0.026
< 0.001
< 0.001

107