AN ABSTRACT OF THE DISSERTATION OF
Kenneth J. Ruzicka Jr. for the degree of Doctor of Philosophy Name in Forest Science
presented on December 10, 2014.
Title: Context Matters: A Multi-scale Assessment of Riparian Tree Response to
Upslope Density Reduction.
Abstract approved:
______________________________________________________
Klaus J Puettmann
Deanna H. Olson
Forest management is rapidly undergoing a transformation from a discipline
based on efficient commodity production to one for multiple uses, especially on
federally managed land in the United States. This new management paradigm has
challenged silviculturists to develop and adapt forest management techniques that can
deal with increased demands. Using the Density Management and Riparian Buffer
Study of western Oregon, USA I highlight the importance of context in forest
management in three interconnected studies. When viewed together, these studies
show the implications of understanding both regional-scale climate and plant-plant
interactions for forest stand management.
In the first study I examined the Density Management and Riparian Buffer
Study of western Oregon in a social context, specifically of the Northwest Forest
Plan. I briefly discuss the development of the Northwest Forest Plan and how it
changed the way forest policy was developed in the region. The Density Management
and Riparian Buffer Study came to fruition within this new management framework
and resulted in a proof-of-concept for adaptive management approaches. The Density
Management Study serves as a model for integrated knowledge discovery and
adaptive management within the context of the federal forest plan. The future of the
study appears to be similarly interconnected with interagency plans for federal lands
management.
In the second study, I examined the potential of using upslope density
management to influence growth and drought tolerance of trees in untreated
downslope riparian forests. Trees responded to an apparent edge effect up to 15 m
downslope of thinned areas but not downslope of clearcut gaps. Additionally, in a
retrospective analysis of tree growth and seasonal growth allocation patterns
(represented by ratio of early to latewood) and climate after treatment over a 12-year
period, trees in our study area did not appear to be water limited and did not show a
strong correlation with regional drought metrics. My study demonstrates that
managers can affect riparian forests with upland treatments to a limited spatial extent.
In the third study, I investigated potential cross-scale interactions that explain
the variability found in the growth responses of the second study. Using the same
trees but with the addition of water-use efficiency data, I investigate the impacts of
thinning thorough progressively finer spatial scales. Trees at the wettest site were
growing faster and responded more strongly to density reduction than trees at the
driest site. Additionally, trees at the driest site responded with increased water-use
efficiency while trees in treated stands at the wettest site showed no change in wateruse efficiency. I hypothesized that water is not the primary limiting factor for growth
at the wettest site, while water was a more-limiting factor at the driest site. When
examining finer spatial scales I found that trees downslope of treated stands
responded to an apparent edge effect that was primarily driven by changes in local
density that were only found near the buffer edge. The results of this study
demonstrate that forest management treatments, such as density reduction, although
applied widely, may have different results. Forest ecosystems are a complex
collection of interacting factors and changing a single factor will result in a cascade of
different reactions based on local conditions and existing plant ecophysiology. The
potential for these cross-scale interactions should be taken into account when
planning forest management actions.
©Copyright by Kenneth J. Ruzicka Jr.
December 10, 2014
All Rights Reserved
Context matters: A Multi-scale Assessment of Riparian Tree Response to Upslope
Density Reduction
by
Kenneth J. Ruzicka Jr.
A DISSERTATION
submitted to
Oregon State University
in partial fulfillment of
the requirements for the
degree of
Doctor of Philosophy
Presented December 10, 2014
Commencement June 2015
Doctor of Philosophy dissertation of Kenneth J. Ruzicka Jr. presented on December
10, 2014
APPROVED:
Co-Major Professor, representing Forest Science
Co-Major Professor, representing Forest Science
Head of the Department of Forest Ecosystems and Society
Dean of the Graduate School
I understand that my dissertation will become part of the permanent collection of
Oregon State University libraries. My signature below authorizes release of my
dissertation to any reader upon request.
Kenneth J. Ruzicka Jr., Author
ACKNOWLEDGEMENTS
Over the course of my time at Oregon State University I’ve come to
appreciate that no scientist is an island. This project would not have been possible
without the army of field and lab assistants, lab mates and mentors. I thank Paul
Puettmann and Fritz Kreatcher for help with field work and sample preparation.
Banner Schaefer, Casey Schaefer, Emelia Blunt helped with sample preparation. I
would also like to thank Kent Rodecap, Bill Rugh and Warren Evans from the ISIRF
lab at the EPA. You made the nine months of extraction possible and even enjoyable.
Heather Peterson, Regina Kurapova and Jennifer McKay from the COAS stable
isotope lab were invaluable for sample unloading, weighing and isotope analysis.
Thank you to my lab mates that have graduated or are still around from the
Puettmann group. Our sharing of information, R code and ideas was a great help
throughout my time here. Also, Danielle Marias commented on and improved
chapters 1 and 5. My funding came from a variety of sources as well. I was primarily
funded by a grant to the Forest Service from the American Recovery and Restoration
Act, with the addition of funds from the US Forest Service Pacific Northwest
Research Station. The Bureau of Land Management maintains the study sites and I
was allowed invaluable lab space and equipment from the US Environmental
Protection Agency Western Ecology Division.
I would also like to thank my committee. I didn’t meet with you as often as I
would have liked, but this project would not have been possible without you. Thank
you Paul Anderson for your comments on the second chapter, help with organizing
my thoughts on the fourth and for our talks about science and silviculture in general.
Marv Pyles was an excellent graduate representative and an active member of the
committee with insightful questions during my preliminary exams. Thank you Renée
Brooks for your expertise with everything isotope and for sponsoring my work in
several EPA labs. Dede Olson was a fantastic co-advisor, co-author and mentor.
Thank you for everything. Thank you Klaus Puettmann for your patience, wisdom
and vision. You helped Eleni and I acclimate to a new area, pushed me when I
needed it but also let me figure things out for myself. I feel lucky to have learned
from the best.
Finally, and most importantly, Thank you Eleni, my beautiful, amazing and
patient wife. It takes a special person to support their significant other while he quits
his job, sells the house and then moves across the country to start a new career. I’m
sorry for my long hours, late nights, preoccupation with work and sometimes vacant,
overwhelmed stares. You carried me through it all and I am eternally grateful.
CONTRIBUTION OF AUTHORS
Klaus J Puettmann assisted with study design, data analysis and the writing of
Chapters 2, 3 and 4. Deanna H. Olson assisted with the study design and the writing of
Chapters 2 and 3. J. Renée Brooks assisted with study design, data analysis and the
writing of Chapter 4. Paul D. Anderson assisted with the writing of Chapter 2. Ariel
Muldoon assisted with the data analysis of Chapters 3 and 4. A manuscript version of
Chapter 2 was published in a US. Forest Service General Technical Report in August
2013. A manuscript version of Chapter 3 was published in Forest Science in 2014.
TABLE OF CONTENTS
Page
Chapter 1. Introduction ...................................................................................................1
1.1 References ............................................................................................................6
Chapter 2: The Intertwining Paths of the Density Management and Riparian Buffer
Study and the Northwest Forest Plan ..............................................................................9
2.1 Abstract ..............................................................................................................10
2.2 Introduction ........................................................................................................11
2.3 The Northwest Forest Plan: Abbreviated Synopsis ...........................................12
2.4 The Density Management and Riparian Buffer Study as an adaptive learning
platform ....................................................................................................................15
2.5 Application of the DMS .....................................................................................22
2.6 Conclusion .........................................................................................................29
2.7 References ..........................................................................................................31
Chapter 3: Management of riparian buffers: Upslope thinning with downslope
impacts ..........................................................................................................................39
3.1 Abstract ..............................................................................................................40
3.2 Introduction ........................................................................................................41
3.3 Methods ..............................................................................................................45
3.4 Results ................................................................................................................54
3.5 Discussion ..........................................................................................................55
3.6 Conclusion .........................................................................................................61
3.7 References ..........................................................................................................63
TABLE OF CONTENTS (Continued)
Page
Chapter 4: Cross-scale interactions and the importance of spatial context in land
management .................................................................................................................87
4.1 Abstract ..............................................................................................................88
4.2 Introduction ........................................................................................................89
4.3.Methods ..............................................................................................................92
4.4 Results ..............................................................................................................102
4.5 Discussion ........................................................................................................106
4.6 Conclusion .......................................................................................................112
4.7 References ........................................................................................................115
Chapter 5: Conclusion.................................................................................................143
5.1 Chapter Summaries ..........................................................................................143
5.2 Emergant themes ..............................................................................................146
5.3 Management Implications ................................................................................146
5.4 Final Thoughts .................................................................................................148
5.4 References ........................................................................................................150
LIST OF FIGURES
Figure
Page
3.1 Study sites from the Density Management and Riparian Buffer Study in
Western Oregon, …………………………………………………………………….81
3.2. Basal area increment (BAI) for trees growing in different treated areas in our
western Oregon study………………………………………………………………...82
3.3. Boxplots of the difference in tree growth between pre- and post-treatment, of
trees growing in riparian areas downslope of the treatments (thin and gap) and in
the riparian area of unthinned controls in our western Oregon study ………………..83
3.4. Tree growth difference between pre- and post-treatment and distance
downslope of the treatment edge for trees growing in riparian areas downslope
of the treatments in our western Oregon study ………………………………………84
3.5. Growth difference and percent slope for trees growing in riparian areas
downslope of the treatments in our western Oregon study …………………………..85
3.6. Comparison of regression lines of growth and growing season drought
between post- and pretreatment in our western Oregon study ……………………….86
4.1. Study sites from the Density Management and Riparian Buffer Study in
western Oregon, USA …..……………………………………………………….......134
4.2. Site difference by year in entire ring, earlywood and latewood growth for trees
growing in the unthinned stand of each site ……...………………………………….135
4.3. Site difference by year in the proportion of earlywood and the WUEi of trees
growing in the unthinned stands of each site …………………....…………………...136
4.4. Difference in stand level growth for trees within our sites ………………….…..137
4.5. Difference in stand level WUEi for trees within sites …..………………………138
4.6. Differences in post-pre latewood growth for trees downslope of the thinned
and gap treatments…………………………………………..………………………..139
4.7. Differences in local neighborhood density (number of trees within 11.5m) for
trees that responded to upslope density reductions ………………….…...………..…140
4.8. Difference in post-pre difference in normalized WUEi at each site ………….....141
LIST OF FIGURES (Continued)
Figure
Page
4.9. Conceptual model for cross scale interactions and hierarchal analysis for
our study ……………….……………………………………………….…………142
LIST OF TABLES
Table
Page
3.1
Study site description including physical characteristics and treatment
details…………………………………………………..………………………..... 78
3.2
Effect sizes and 95 percent confidence intervals for treatments and
topographic covariates on the difference in growth for trees growing in riparian
areas ……………………………………………………………………………… 79
3.3
Significant trends for comparison of multiple regression lines using
yearly growth and growing season Palmer Drought Severity Index………………78
4.1
Physical habitat characteristics…………..………………………………..127
4.2
Site differences in growth and water use efficiency as indicated by
Type III Repeated measures ANOVA …………………………………………… 128
4.3
Mixed model Type III Repeated measures the difference in growth for
stands within sites …..…………………………………………………….……… 129
4.4
Mixed model Type III Repeated measures contrasts for the difference in
WUEi for stands within sites ……………………..……………………………… 130
4.5
Type I ANOVA table to investigate latewood growth response of trees
downslope of thinned and gap stands for trees within the 15 m edge ………..……131
4.6
Type I ANOVA table to investigate latewood growth response of trees
downslope of thinned and gap stands……………………………..…………..……132
4.7
Type I ANOVA table to investigate latewood WUEi response of trees
downslope of thinned and gap stands for trees within the 15 m edge ………..……133
1
Chapter 1
Introduction
Forest management is rapidly undergoing a transformation from a discipline
based on efficient commodity production to one for multiple uses including
ecosystem services like water, biodiversity and recreation. Typically, management is
based on silvicultural principles developed and implemented at the stand level with
uncertain ability to scale up or down to other levels (Puettmann and Tappeiner 2014).
Matching the scale of interventions with the ecosystem goods and services like
commodity production or clean water needed for human activities will be a challenge
to land managers (Cumming et al. 2013). Down-scaling in ecology can lead to a
failure to realize the larger process we are studying and where they fit into scales
above and below (Wiens 1989). The mangement of ecological systems depends on
the scale in question and the demands humans make upon them. For example, local
scale processes are normally driven by organism interactions such as competition or
facilitation, while regional patterns like plant distribution and productivity are
controlled by regional processes like the prevailing climate (Wiens 1989).
Identifying a desired ecosystem service such as clean water, and then matching it to a
scale manageable by humans, such as riparian forests, takes into account matching
management scale to a controllable process such as density for plant-plant
interactions.
Water is a basic human need and one of the most important ecosystem
services that forests provide and managers must take into account. Riparian forests
near streams are vitally important for a host of ecosystem services related to water
2
quality, but also habitat and biodiversity (Gregory et al. 1991). Streams were often
used as transportation corridors in early forest management practices. Additionally,
riparian forests were actively removed to facilitate efficient commodity movement to
markets (Richardson et al. 2012) Whether purposeful or incidental, degradation of
watersheds have led to a sharp decline in the productivity of aquatic habitats (Gregory
1997). Forestry practitioners and policy makers responded with the widespread
adoption of voluntary and in some cases mandated fixed-width riparian buffers, or
undisturbed vegetation along a stream in the United States (Gregory 1997; Lee et al.
2004; McBroom et al. 2013; Richardson et al. 2012) and globally (Langer et al. 2008;
McDermott et al. 2012), although buffers had been used in forestry practices in
Europe since the 1700s (Porter 1887 reference in (Lee et al. 2004). Protection of
forested headwater riparian areas is important at the landscape scale because many of
these habitats are in areas of mixed management for production and conservation
(Herbert et al. 2010). Headwater streams also prevent the majority of the inorganic
nitrogen from fertilizer application or atmospheric deposition collected in a watershed
from being exported to larger streams and rivers (Peterson et al. 2001). Despite the
ease of implementation and enforcement of fixed-width riparian buffers, researchers
have recognized that a universal approach to headwater riparian protection may not
be appropriate in all circumstances (Gregory 1997; Lee et al. 2004; Olson et al. 2007;
Richardson and Danehy 2007; Richardson et al. 2012; Young 2000). Headwater
riparian areas are a prominent portion of the forested landscape and their protection,
to ensure clean water and other aquatic ecosystem services, was a developing
paradigm under expanded forest management practices (Gomi et al. 2002).
3
The Density Management and Riparian Buffer Study of western Oregon, USA
(DMS) was initiated in response to changing attitudes about forest management and
riparian protection. It was set up at regional and operational scales to test thinning
prescriptions to efficiently develop old-growth structures in young Douglas-fir
plantations (Anderson and Poage 2014; Cissel et al. 2006). It also includes provisions
to test buffer widths for the protection of aquatic habitat. I utilize components of the
DMS study sites to test several important questions: First, how, and at what scales
does upland forest management impact the growth and physiological response of
trees growing in riparian areas. Second, what are the implications of riparian forest
management for managers hoping to manage for multiple ecosystem services?
The primary theme of this dissertation is that context matters. It flows along a
path that narrows the scale of investigation. First, I place the DMS into a larger
socio-political context. Then I show how using the study in context can be used to not
only inform regional management options, but also examine small scale plant-plant
interactions. Next, I investigate a subset of sites and stands from a portion of the
overall DMS to investigate changes in riparian areas over western Oregon. Finally, I
explore variability identified at that broad landscape scale though the investigation of
processes operating at smaller scales.
Chapter 2 provides an introduction to the Density Management and Riparian
Buffer Study and places it into the context of federal forest management policy and
research. I examine the factors that led to the creation of the DMS, its
implementation and the impacts it has had on forest management in the Pacific
Northwest. This chapter also sets the stage for future chapters by discussing the
4
adoption of ecosystem scale management (Thomas 2008). By placing the DMS in the
context of ecosystem management, I show how the study design and goals allow for
the comparison of management techniques at multiple scales.
Chapter 3 was published in Forest Science as part of a series of papers
selected from the 2013 North American Forest Ecology Workshop. Using three DMS
sites, I examined the potential of using upslope thinning to manage tree growth in
riparian buffers. It revealed an expected result, that residual trees in thinned stands
increased growth after treatment, but also two unexpected results. The first was that
trees downslope of thinning also saw an increase in growth but trees downslope of
gaps, the more intense disturbance, did not increase in growth after treatment. The
second unexpected result wass that tree growth, averaged across treatments, was not
correlated to regional measures of drought and water availability. The results of the
third chapter inspired the more detailed research questions addressed in chapter 4.
Specifically, chapter 4 identifies potential mechanisms to explain the apparent
response discrepancy between trees in riparian areas downslope of gaps and thinning.
In chapter 4 I break down the scales of investigation, from larger-scale
attributes averaged across sites for western Oregon, to finer-grained spatial scales at
the site, stand, hillslope, and local neighborhood levels. I used stable isotope analysis
to model water use efficiency to investigate the physiological response of the trees to
thinning, and to provide clues about what resources were or were not critical to trees
and influenced growth responses. Cross-scale interactions between site, stand, and
neighborhood level processes indicated that climate and site quality influence stand-
5
level responses to thinning while local tree density controls the variability in
individual tree response.
Chapter 5 provides a synthesis of the knowledge gained from chapters 2-4 and
sets them into a framework usable for forest managers. Primarily, I identify ways that
my research offers insights to the scales impacted by potential management
operations. Finally, by presenting potential processes that impact tree growth at
multiple scales I suggest that managers need to match the scale of desired outcomes
with the scale of potential silvicultural techniques.
6
References
Anderson, P. D., and N. J. Poage. 2014. The Density Management and Riparian
Buffer Study: A large-scale silviculture experiment informing riparian
management in the Pacific Northwest, USA. Forest Ecology and Management
316:90-99.
Cissel, J., P. Anderson, D. Olson, K. Puettmann, S. Berryman, S. Chan, and C.
Thompson. 2006. BLM density management and riparian buffer study:
Establishment report and study plan. US Department of the Interior, US
Geological Survey.
Cumming, G., P. Olsson, F. S. Chapin, III, and C. S. Holling. 2013. Resilience,
experimentation, and scale mismatches in social-ecological landscapes.
Landscape Ecology 28:1139-1150.
Gregory, S. V. 1997. Riparian management in the 21st century. Creating a forestry for
the 21st century. Island Press, Washington, DC, USA:69-85.
Gregory, S. V., F. J. Swanson, W. A. McKee, and K. W. Cummins. 1991. An
Ecosystem Perspective of Riparian Zones. BioScience 41:540-551.
Herbert, M. E., P. B. McIntyre, P. J. Doran, J. D. Allan, and R. Abell. 2010.
Terrestrial Reserve Networks Do Not Adequately Represent Aquatic
Ecosystems
Las Redes de Reservas Terrestres no Representan a los Ecosistemas Acuáticos
Adecuadamente. Conservation Biology 24:1002-1011.
7
Langer, E. R., G. A. Steward, and M. O. Kimberley. 2008. Vegetation structure,
composition and effect of pine plantation harvesting on riparian buffers in
New Zealand. Forest Ecology and Management 256:949-957.
Lee, P., C. Smyth, and S. Boutin. 2004. Quantitative review of riparian buffer width
guidelines from Canada and the United States. Journal of Environmental
Management 70:165-180.
McBroom, M. W., J. Louch, R. S. Beasley, M. Chang, and G. G. Ice. 2013. Runoff of
Silvicultural Herbicides Applied Using Best Management Practices. Forest
Science 59:197-210.
McDermott, C., B. Cashore, and P. Kanowski. 2012. Global environmental forest
policies: an international comparison. Routledge.
Olson, D. H., P. D. Anderson, C. A. Frissell, H. H. Welsh Jr, and D. F. Bradford.
2007. Biodiversity management approaches for stream–riparian areas:
Perspectives for Pacific Northwest headwater forests, microclimates, and
amphibians. Forest Ecology and Management 246:81-107.
Peterson, B. J., W. M. Wollheim, P. J. Mulholland, J. R. Webster, J. L. Meyer, J. L.
Tank, E. Martí, W. B. Bowden, H. M. Valett, A. E. Hershey, W. H.
McDowell, W. K. Dodds, S. K. Hamilton, S. Gregory, and D. D. Morrall.
2001. Control of Nitrogen Export from Watersheds by Headwater Streams.
Science 292:86-90.
Puettmann, K. J., and J. C. Tappeiner. 2014. Multi-scale assessments highlight
silvicultural opportunities to increase species diversity and spatial variability
in forests. Forestry.
8
Richardson, J. S., and R. J. Danehy. 2007. A Synthesis of the Ecology of Headwater
Streams and their Riparian Zones in Temperate Forests. Forest Science
53:131-147.
Richardson, J. S., R. J. Naiman, and P. A. Bisson. 2012. How did fixed-width buffers
become standard practice for protecting freshwaters and their riparian areas
from forest harvest practices? Freshwater Science 31:232-238.
Wiens, J. A. 1989. Spatial Scaling in Ecology. Functional Ecology 3:385-397.
Young, K. A. 2000. Riparian Zone Management in the Pacific Northwest: Who's
Cutting What? Environmental Management 26:131-144.
9
Chapter 2:
The Intertwining Paths of the Density Management and Riparian Buffer Study
and the Northwest Forest Plan
Kenneth J. Ruzicka, Jr., Deanna H. Olson, and Klaus J. Puettmann
Kenneth J. Ruzicka Jr. is a doctoral student, Department of Forest Ecosystems and
Society, Oregon State University, Corvallis, OR 97331
Deanna H. Olson is a research ecologist, Forestry Sciences Laboratory, 3200 SW
Jefferson Way, Corvallis, OR 97331
Klaus J. Puettmann is Edmund Hayes Professor in Silviculture Alternatives,
Department of Forest Ecosystems and Society, Oregon State University, Corvallis,
OR 97331
In: Anderson, Paul D.; Ronnenberg, Kathryn L. eds. Density Management for the 21st
century: west side story. General Technical Report PNW-GTR-880. Portland, OR:
U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station.
Pages 10-21.
10
Abstract
Initiated simultaneously, the Density Management and Riparian Buffer Study of
western Oregon and the Northwest Forest Plan have had intertwining paths related to
federal forest management and policy changes in the Pacific Northwest over the last
15 to 20 years. We briefly discuss the development of the Northwest Forest Plan and
how it changed the way forest policy was developed in the region. The concurrent
conceptualization and implementation of the Density Management and Riparian
Buffer Study within this new management framework resulted in a proof-of-concept
for adaptive management approaches outlined in the Plan, especially relative to
riparian and upland restoration practices. The Density Management Study serves as a
model for integrated knowledge discovery and adaptive management within the
context of the federal forest plan. The future of the study appears to be similarly
interconnected with the interagency plan for federal lands management.
Keywords: forest thinning, western Oregon, Bureau of Land Management, Forest
Service
11
Introduction
The Density Management and Riparian Buffer Study (DMS) originated in 1993 as a
long-term, operational-scale experiment to investigate silvicultural techniques
intended to accelerate development of late-successional and old-growth forest
characteristics in western Oregon (Cissel et al. 2006). To accomplish this, the study
tested alternative thinning prescriptions that were not yet tested or established in the
scientific literature at the time of study establishment. The upland thinning design of
DMS was conceived before finalization of the Northwest Forest Plan (hereafter, the
Plan) (USDA and USDI 1994a), but was implemented within the new policy
framework created by the Plan. Although not an initial goal of the study, the
implementation of the DMS identified ways that federal agency land managers in
western Oregon, in particular the U.S. Bureau of Land Management (BLM), could
adapt to work within the new policy framework and more quickly achieve Plan goals,
especially in reserved land allocations.
By examining the DMS study, we explore the intertwining threads of policy changes,
knowledge discovery, and new management questions asked during development and
implementation of the Plan. We briefly discuss the history of the Northwest Forest
Plan and how it changed the way forest policy was developed. We describe how the
DMS arose within the new management framework, and finally discuss how the
DMS has affected federal forest management at district and forest levels relative to
regional management policy. It appears certain that western Oregon forest managers
12
will continue to employ adaptive management to adjust to new challenges. The future
of the DMS appears to be similarly interconnected with the Plan.
The Northwest Forest Plan: Abbreviated Synopsis
The listing of the Northern Spotted Owl (Strix occidentalis caurina) as a threatened
species under the U.S. Endangered Species Act in 1990 (55 FR 26114 26194) was
neither the beginning nor the end of the struggle between old-growth forest
preservation and timber management. Regional scientists had been concerned about
loss of owl habitat since the early 1970s (Forsman 1975; Yaffee 1994). Although the
spotted owl was the poster child for the forest-management conflicts coming to a
head in the early 1990s, numerous scientists had been researching forest
fragmentation effects on many different species, characterizing species-habitat
relationships, and conducting species risk assessments (e.g., Ruggiero et al. 1991;
Thomas et al. 1993). In addition to threatened species concerns, an emerging
paradigm shift toward ecosystem management with multiple resource and restoration
objectives came into conflict with conventional management practices on federal
forests in the Pacific Northwest (Yaffee 1994). Historical timber harvest practices
were geared toward wood commodity production and implemented to “get the cut
out” (Thomas 2008; Williams 2009). Clashes between old-growth forest
preservationists and federal land management agencies climaxed with a series of
lawsuits that prevented timber sale and harvesting activities throughout the range of
the Northern Spotted Owl (Thomas et al. 2006). These lawsuits in part defined a new
13
era for natural resource management in the U.S. where the management of public
land was decided by legal determinations made by judicial courts (Thomas 2008).
In the polarized forest management climate of the early 1990s, President Clinton
assembled the Forest Conference in 1993 to discuss the different social, economic,
and environmental issues concerning old-growth forests. He ordered federal land
management agencies to draft a balanced, long-term policy that would direct the
management of federal forests in the Pacific Northwest in the range of the spotted owl
(USDA and USDI 1994b). President Clinton mandated that the “policy must (FEMAT
1993):
• Never forget the human and the economic dimensions
• Protect long-term health of our forests, our wildlife, and our waterways
• Be scientifically sound, ecologically credible, and legally responsible
• Produce predictable and sustainable levels of timber sales and nontimber
resources
• Make the federal government work together and work for you”
The Forest Ecosystem Management and Assessment Team (FEMAT) which included
representatives from the BLM, U.S. Forest Service, Fish and Wildlife Service,
National Park Service, National Marine Fisheries Service, the National Oceanic and
Atmospheric Administration, and the Environmental Protection Agency was created
to craft this difficult policy. The final report was released in July of 1993 (FEMAT
1993) and was adapted into the Northwest Forest Plan in 1994 (USDA and USDI
1994a; 1994b). Numerous detailed discussions of the Plan are available (e.g., see
USDA and USDI 1994a; Yaffee 1994; Marcot and Thomas 1997; Thomas 2008).
14
The Northwest Forest Plan started a process towards implementing ecosystem-scale,
science-based forest management (Thomas 2008). One hallmark of the Plan was that
for the first time, scientists led development of land management alternatives and
played a key role in crafting federal policy (Bormann et al. 2007). FEMAT consisted
of a team of scientists with expertise in various aspects of forest management and its
economic, ecological and social impacts. The team was led by the “Gang of Four plus
Two” - wildlife and fisheries researchers from the Pacific Northwest aided by a cadre
of hundreds of consultants (FEMAT 1993). This unique set of expertise manifested
itself in a new management approach. The Plan was the first effort to manage a forest
ecosystem at the regional scale across an expansive area of ~10 million hectares (24.5
million acres) using a science-based, multi-resource approach. The original FEMAT
design conceptually ensured that management actions taken on a local scale would
not impair ecosystem function at a regional scale (FEMAT 1993). Fine-scaled
management elements were later integrated into the regional Plan, providing for a
multi-scaled management framework. ”Survey and manage” requirements were
added by President Clinton’s Council on Environmental Quality to manage sensitive
species having restricted distributions that were not otherwise protected by the Plan.
Fine-scale survey and manage requirements combined with coarser-scale land use
allocations (LUAs) were intended to facilitate Plan implementation without triggering
additional environmental regulations for species protections. With these requirements
in place, the intent of the plan was to assess projects by their outcomes as part of the
interconnected regional ecosystem rather than assessing management impacts only on
individual stand-level land parcels (Diaz 2004).
15
The integrated management of a regional ecosystem required that federal agencies,
which had previously operated as fragmented administrative units, become partners.
Integrated management however, led to several operational challenges. Previous
forest management actions had been planned through the local district personnel,
including foresters and silviculturists, and were approved based on District or Area
plans for inventory and timber harvests. When the first comprehensive meetings
between federal and state land managers and researchers were convened to discuss
the spotted owl and old-growth management, participants lacked standardized
approaches among agencies to assess multi-ownership forest blocks. For example,
region-wide maps of forest resources were not available, and local or agency-specific
mapping conventions made existing smaller-scale maps incompatible (Yaffee 1994).
Plan implementation was delayed as procedural issues were worked out.
The Density Management and Riparian Buffer Study as an adaptive learning
platform
Much of what the FEMAT discussed was founded in research conducted in the 1980s
to 1990s to address characterization of old-growth forests (e.g., Franklin and Spies
1991), and to understand its development (Spies and Franklin 1988). Other
researchers sought to understand how to manage young Douglas-fir (Pseudotsuga
menziesii) plantations for eventual old-growth characteristics. Dr. John Tappeiner, a
Senior Research Forester of the Bureau of Land Management Cooperative Research
Unit and Oregon State University Professor, wanted to investigate new restoration
management practices on BLM lands. Contemporary studies such as forest thinning
16
in the Black Rock Forest Research Area (Del Rio and Berg 1979) or the Young Stand
Thinning and Diversity Study (Hunter 1993) were designed to contribute to this topic.
However, those investigations were narrowly focused case studies because they were
implemented in relatively small study areas (0.04 to 50 ha treatments) or did not
represent the variability of managed forest conditions on federal lands in western
Oregon (J. Tappeiner, emeritus professor, Oregon State University College of
Forestry, personal communication). The need for studies with broadened scope, such
as the DMS, was apparent as other large-scale silvicultural experiments also were
implemented during this time (Poage and Anderson 2007). The DMS treatments were
designed to be implemented at the large-stand scale (50- to 300-ha treatments), with
study sites encompassing the range of stand conditions more representative of
western Oregon federally managed forests.
The Riparian Buffer Study component was added to the initial density management
study objectives in 1994 (Cissel et al. 2006; Olson 2013). This component addressed
the “interim” provision in the NWFP regarding Riparian Reserves. The Riparian
Reserve land-use allocation in the Northwest Forest Plan was defined to provide
specific conservation and mitigation for aquatic and riparian resources. The interim
Riparian Reserve widths (ranging from two site-potential tree heights for fish-bearing
stream reaches to one site-potential tree height for intermittent streams) were intended
to be adjusted after watershed and project-specific analyses of aquatic-riparian
resources. The riparian buffer aspect of DMS was designed specifically to test various
widths of riparian buffers in conjunction with upland thinning according to Plan
Standards and Guidelines (USDA and USDI 1994b), as well as possible management
17
options within Riparian Reserve boundaries (Hohler et al. 2001; Cunningham 2002;
Diaz and Haynes 2002).
Thus, by 1994, the DMS had become the first stand-scale test of joint riparian and
upland management prescriptions in the Pacific Northwest, and sought to answer
knowledge gaps identified during Plan development. In summary, the DMS was
implemented to address two general questions: 1) How well do alternative thinning
pathways accelerate the development of late successional forests? and 2) What are
effects on aquatic-riparian resources of riparian buffers of varying widths in
conjunction upland thinning?
The DMS presented logistical and organizational challenges that echoed other hurdles
in implementing the Plan. Processes developed during early DMS implementation
were later used to address questions and concerns about Plan implementation. For
example, DMS site selection and study implementation processes helped to frame
later discourse about procedures for forest management planning under the Plan.
During study-site selection for the DMS, several criteria were weighed. These
included geographic representation, forest type and condition, and land-use allocation
as described in the Plan on lands owned by the BLM. In the early 1990s, much of the
federal landscape (52 percent of BLM holdings) in western Oregon consisted of
Douglas-fir plantations younger than 80 years old (Cissel et al. 2006), with 35 percent
of BLM holdings younger than 40 years (Muir et al. 2002). Thinning projects in
young Douglas-fir stands had already been proposed on some national forests and
BLM resource areas, and hence some areas suitable for the study were easily found.
18
Some administrators expressed doubt about the efficacy of the heavy thinning
prescription proposed in the variable density treatment (Cissel et al. 2006), and local
natural-resource managers raised concerns about the use of treatments within the
range of the spotted owl or the Marbled Murrelet (Brachyramphus marmoratus). On
the heels of the forest management paradigm shift signaled by the entire Plan, which
included a new set of land-use allocations for the federal lands of the Pacific
Northwest, Field-level personnel also raised concerns about the DMS potentially
locking-up lands that were intended to be managed for later regeneration harvests
(“matrix” land-use allocation) into a long-term study (C. Thompson, BLM, personal
communication). Others held the opinion that the study aims might become irrelevant
due to a belief that “the whole spotted owl thing will blow over and they will get back
to clearcuts with a new [federal] administration” (J. Tappeiner, emeritus professor,
OSU College of Forestry, personal communication). Eventually, the scope of the
study was narrowed, when the Medford BLM district was dropped from the study
because the forest types and stand conditions in this sub-region were deemed to be
too different from the other BLM ownerships to warrant inclusion in the study. DMS
sites were selected from four western Oregon BLM districts, including seven stands
in lands allocated as matrix and three stands in late successional reserves (LSR).
Three additional sites on LSR-designated lands managed by the Siuslaw National
Forest were selected specifically for implementation of the Riparian Buffer Study
component, with elements of the moderate upslope thinning (Cissel et al. 2006). DMS
study sites were among the first forest management projects implemented by field
19
units after finalization of the Northwest Forest Plan, and in many cases, set the stage
for later Plan management-unit implementation.
The study was conducted at an operational scale, addressing many logistical issues
that arose later in other thinning operations. The DMS treatments were designed to
diversify stands with gaps, leave islands, as well as variable-density thinning.
Consequently, sale-preparation crews had to be trained in new methods to select trees
for harvest to achieve a heavier and more spatially variable thinning, compared to
more conventional thinning operations. Marking crews were quick to adapt to the
challenge, with most stands being adequately marked to meet prescriptions with a
single effort (C. Thompson, BLM, personal communication). Additionally, methods
for marking riparian buffers also needed to be developed. Researchers from the PNW
along with BLM personnel developed approaches at DMS sites that were
subsequently used along headwater streams in other areas of western forests. These
have been general guidelines, however, and many districts still implement a buffer
width of one or two site-potential tree heights for riparian buffers as proposed in the
Plan (USDA and USDI 1994a). Study site coordinators from the BLM also were able
to resolve concerns about sensitive species and microhabitats by adjusting how leave
islands were placed. Although random selection of upland treatment units and reaches
for buffer treatments was preferred, site-specific delineation adjustments to address
implementation barriers allowed the DMS to act as a model for resolving project
conflicts at multiple scales (Olson et al. 2002). Loggers and other equipment
20
operators adjusted to felling and maneuvering trees in and around riparian reserves
and leave islands.
Furthermore, socioeconomic considerations of implementing the DMS were projected
relative to both operational and implementation costs. To make DMS sales profitable,
patch openings and off-site parcels were included in some DMS sales, as they
increased harvest amounts and market values (Olson et al. 2002). Wood harvest as a
result of DMS implementation at individual BLM study sites was estimated to range
from ~1 to 8.5 mmbf (million board feet) from site-specific project areas ranging
from 73 to 162 ha (Olson et al. 2002). DMS harvest volumes exceeded those of
conventional thinnings, largely due to inclusion of patch cuts. As with any profitable
harvest on BLM-administered land at the time, the DMS harvests provided timber
benefits to the economy of local counties (Olson et al. 2002). The DMS demonstrated
that despite various hurdles, complicated management prescriptions can be
implemented in a way that provides silvicultural and operational learning
opportunities. Subsequent timber-project planning efforts by managers in the BLM
and Forest Service benefited from the lessons learned from DMS implementation.
In contrast, reviews of the success of adaptive management in the Northwest Forest
Plan have mostly identified short-comings in adaptive management areas. Problems
with the application of adaptive management generally lie in the lack of a learning
framework for management and a failure to close the learning loop even after
monitoring is completed. Adaptive management areas included flexible provisions to
21
address the need for development of innovative management approaches and
integration of site-specific contexts into local projects. Surveys of federal land
managers who have operated designated adaptive management areas identified
factors limiting successful implementation, such as regulations, especially the
Endangered Species Act, a lack of support from higher-level administrators, and a
lack of cooperation between researchers and managers (Stankey et al. 2003). Other
criticisms such as risk aversion, poor record keeping and budget misallocation also
were identified as hurdles to implementation (Bormann et al. 2007). The primary
failures of adaptive management were the lack of integrating learning opportunities
from project inception through to final monitoring and the testing of alternative
strategies. These aspects would heighten the efficacy of adaptive management
approaches, and go beyond the collection of just monitoring data (Bormann et al.
2007).
However, there were some bright spots in implementing adaptive management, such
as the creation of a formal monitoring program (Haynes et al. 2006), increased
stakeholder involvement (Stankey et al. 2006), and increased respect and cooperation
between researchers and decision makers (Bormann et al. 2007). Suggestions to
improve the functioning of designated adaptive management areas included defined
leadership and support roles, continuity, standardized documentation, and a
commitment to learning (Stankey et al. 2003, 2006). After receiving the interpretive
report (Haynes et al. 2006), federal regional managers developed and approved a
formal adaptive management framework, and refocused questions for future efforts.
22
This completed the cycle of adaptive management for the first 10 years of the Plan.
The DMS sets forth a model of how a more formal question-focused learning effort
can be carried out and fit within a learning environment across multiple land
allocations.
Application of the DMS
The DMS study is an example of effective active management and research in support
of the Plan. In line with the objectives of the study, the DMS has: shown that
variable-density thinning, including gaps and heavy thinning, moved canopy
heterogeneity closer to old-growth conditions (Wilson and Puettmann 2007);
demonstrated that 15-m buffers largely maintained the stream microclimate within the
upland thinning context (Anderson et al. 2007); and identified headwater-dependent
aquatic species that may warrant consideration during forest management, for their
habitat maintenance and landscape connectivity considerations (Olson et al. 2007;
Olson and Burnett 2009, 2013; Olson 2013). Although set up to primarily to answer
basic questions about forest and riparian management, the study has included
additional studies on bryophytes, lichens, songbirds, macroinvertebrates, and other
natural-resource elements (Cissel et al. 2006). Over 100 research papers and
technology-transfer products have resulted from the study
(http://ocid.nacse.org/nbii/density/pubs.php). Additionally, through field trips,
presentations, and outreach programs such as “Teachers in the Woods” over 5000
people were exposed to the DMS or visited the study sites from 2003-2006 (USDI,
23
unpublished data). It has provided an opportunity for prolific knowledge discovery,
and has become a platform for diverse discussions regarding forest management.
From the outset, the DMS was designed to be relevant to forestry operations. Many of
the original study designers from the BLM and PNW are still involved in the study.
The result of this continuity has been a steady stream of management-relevant
products and face-to-face workshops to disseminate the results of the study. In this
way, the DMS has been, by design, not only relevant to the researchers, but has
intimately involved managers and resource specialists as well. As a result of effective
technology transfer, the DMS study has had a direct effect on the way forest thinning
projects are planned by field management units. Research produced by the DMS has
been cited in several recent Environmental Assessments to provide evidence that the
planned management is scientifically sound. BLM districts with DMS experience as
well as Forest Service projects in western Oregon have used the DMS in project
planning. Projects in the Salem BLM district like the Gordon Creek Restoration
Project (USDI BLM 2009) cited the district’s experience with the DMS as evidence
for the effectiveness of variable-density thinning treatments. The Highland Fling
Project used the DMS to predict effects on riparian species (USDI BLM 2010). The
Forest Service also used results from the DMS. The Siuslaw National Forest used
results from the DMS to predict the response of the understory to project actions in
the environmental assessment for the Salmon/Neskowin Project (USDA FS 2011).
In addition to the technology transfer provided by the DMS, direct alliances among
DMS researchers, field managers, and natural-resource specialists have aided in
24
bridging science and application to on-the-ground forest thinning and riparian
management. The combined effort from the DMS and other large-scale silvicultural
experiments provides a wealth of useful information for project planners and
implementers (Poage and Anderson 2007; Anderson 2013). The sustained flow of
technology transfer, including science publications as well as summary articles and
workshops geared for managers, is an essential part of applied research programs.
Furthermore, DMS has been used as a demonstration project for broader learning
opportunities. University classes, natural-resource specialist training workshops, and
national to international forestry and natural-resource specialists have visited the sites
in order to understand the relevance of the variety of treatments established there.
Flyers, brochures, and Web-site materials have allowed further outreach to the broad
forestry community. DMS concepts have been presented to forestry organizations
such as the Society of American Foresters and the International Union for Forest
Research Organizations, raising the profile of DMS research applications.
The DMS is also an example of the evolving concept of adaptive management.
Findings from the first 10 years of the study have led to “phase 2” treatments, with
heavier thinning treatments and further-modified riparian buffers. Phase 2 of the
DMS now tests the application of an additional, second entry thinning harvest
conducted within the original treatments, and includes a case study of thinning
without a riparian buffer. Additional response variables could be investigated within
this new context to expand the learning paradigm offered by the overarching study
template. If DMS sites can be retained as designated research areas beyond the
25
designated end point of current studies, they could continue to be used to refine the
ideas of adaptive management in the future.
Although the DMS has shown its relevance to individual projects conducted by local
administrative units, it has had less impact on recent regional planning like BLM’s
Western Oregon Plan Revision (WOPR; USDI BLM 2008) or national-level planning
such as the recently proposed Planning Rule Revision from the Forest Service. The
WOPR included provisions to increase timber harvest on BLM lands in western
Oregon. Increased harvest would be accomplished by increasing the amount of land
where harvest was allowed, reducing the width of riparian buffers and streamlining
the process to approve higher thinning intensities. Although directly relevant to many
WOPR issues, with exception of microclimate results, DMS findings were seldom
referenced (USDI BLM 2008). The DMS findings can contribute more to sciencebased forest management decisions in western Oregon; closing this apparent gap is a
challenge to the DMS researchers and their partners who directly navigate this
science-management interface. This is an area for further development as DMS
outreach to managers and decision-makers continues into the future.
Whereas the vacated WOPR was unsuccessful in altering provisions in the Northwest
Forest Plan on the regional level, the Forest Service is working to alter how forest
management decisions are being made at the local level. In the last decade, the Forest
Service has tried to implement policies to make it easier to conduct thinning projects
or other forest management activities. A sample of these policies includes President
Bush’s Healthy Forests Initiative in 2003, and the 2008 Planning Rule Revision
26
(USDA FS 2008), which was suspended in 2009. A new final planning rule was
recently released (http://www.fs.usda.gov/planningrule) and includes national-level
process guidance for programmatic planning at the unit (national forests and
grasslands) and regional scales, to set the context for local project and landscapescale decision-making. It also emphasizes learning while planning and direct
stakeholder involvement in collaborative assessments. Local project National
Environmental Policy Act (NEPA) compliance will need to be constrained within
regional and individual national forest plans, but will most likely be less prescriptive
than the Northwest Forest Plan (B. Bormann, USFS, PNW Research Station, personal
communication). The proposed Planning Rule Revision also emphasizes
consideration of climate change and carbon storage in management decisions, and
increasing public participation in project planning (USDA FS 2011). Approval of the
revision will increase opportunities for DMS findings to significantly inform project
planning, especially in west-side national forests. Continued development of
informational products geared towards field managers will be important for DMS
technology transfer to stay current. Bridging science and management across
organizational scales of local areas to regions presents new challenges for studies
such as the DMS and their proponents.
Finally, the DMS and density management research must stay relevant in the face of
trends of declining research support from the federal government. With the DMS, the
BLM has shown that it can incorporate research and sound management practices
using agency personnel and agency land. By involving researchers and management
personnel from multiple agencies, field offices, and districts, management projects
27
have been funded and implemented to benefit all parties. New research paradigms
may be needed to expand research capacity because funding may be lacking in an
economically austere environment. However, by partnering researchers with
specialists at field units who manage lands, new projects can be conceived and
implemented by a suite of collaborators, not by scientists alone: DMS has been a
model in this regard. The DMS has shown how multiple agencies can work together
and solve problems at study, design, implementation, and information delivery stages
(Olson et al. 2002).
Several ongoing and emerging research questions included or brought about by the
DMS still need attention. Further study is needed on the management of young stands
for late-successional and old-growth characteristics, including the age-range within
which management toward the development of old-growth characteristics can be
effective; on how to manage fuel loads; and on how to work within the “80-year rule”
(Tappeiner 2009). There is also a need to use matrix lands, especially those controlled
by the BLM, for biodiversity management research, especially on early-seral habitat
(Spies et al. 2007). Many questions remain about the efficacy of riparian reserve
widths with upland regeneration harvests. Most managers do not vary from widths
prescribed in the Plan, although studies from western Washington including those by
Bisson et al. 2013 (this volume) and Raphael et al. 2013 (this volume) are
contributing to this arena. In recent years, the initial success of the DMS has carried
the momentum to other inquiries, such as the interplay between upslope treatment and
water availability in riparian areas (Burton et al. 2013; K. Ruzicka, unpublished data).
These projects rely on the long-standing relationships among Oregon State
28
University, the Forest Service Pacific Northwest Research Station, and the BLM.
Cooperation between researchers and land management agencies is more important
than ever.
Many collaborative land-management research projects, including the DMS, can
improve their communication and partnering efforts with regional to national interest
groups. Local managers of BLM and Forest Service lands understand the importance
of research findings for the development of science-based management proposals.
They can apply research results directly to management plans, including citing
appropriate publications in NEPA documents. The absence of integration of DMS
findings in recent national and regional management planning efforts suggests that
research communication and advocacy to higher-level managers and policy-makers
needs more attention. Project scientists and their science managers need to consider
development of more effective mechanisms to actively engage regional administrators
in dialogues about the outcomes and impacts of their work for regional-to-national
scale management and policy development. Ensuring the integration of science
findings at higher administrative scales would result in improved science-based
policy decisions. Science communication across geographic scales of natural resource
management and administration, and across agencies, makes this a complex task.
Without such liaisons, including both the processes and the personnel such liaisons
would entail, the outcome and impact of valuable studies such as DMS are not fully
realized. In an era of constantly emerging issues, such as global climate change,
invasive species, disease, and fire and pest management, science communication
across administrative boundaries has never been more important.
29
Conclusion
The Northwest Forest Plan was the first regional ecosystem management plan, and
tested the logistical, operational, and cooperative capacities of land managers in the
region. The Density Management Study originated in an atmosphere of new forest
and riparian management questions, with important policy implications proposed to
be answered through adaptive management. Over a decade of research in adaptive
management has identified that the opportunity for learning must be realized at all
stages of the project for management to be successful. The DMS also has provided a
set of lessons-learned for how to successfully plan, implement, and communicate
operational-scale research to a wide audience. With climate change and other
emerging natural resource issues on our horizon, increased uncertainty in research
funding, and anticipated changes in forest policy, the importance of the DMS will
only increase. The DMS can serve as a model for integrative studies conducted jointly
by collaborative partners from diverse research communities and land-management
agencies. Furthermore, DMS study sites can continue to be relevant as documented
locations that provide opportunities to efficiently address new, emerging forestmanagement questions. The DMS remains a golden opportunity for cutting-edge
forest research in the future.
Acknowledgments
We thank John Tappeiner, Charley Thompson and Hugh Snook for their
conversations about these topics, their recollections of past events, and their key roles
30
in the DMS implementation. Wesley Mouw provided comments on an early draft of
the manuscript. Paul Anderson, Bernard Bormann and an anonymous reviewer
commented on the accepted version. We also thank the BLM State Office and Forest
Service Pacific Northwest Research Station for support.
31
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Spies, T. A.; Johnson, K.N.; Burnett, K.M.; Ohmann, J.L.; McComb, B.C.; Reeves,
G.H.; Bettinger, P.; Kline, J.D.; Garber-Yonts, B. 2007. Cumulative ecological and
socioeconomic effects of forest policies in coastal Oregon. Ecological Applications
17(1): 5-17.
36
Stankey, G.H.; Bormann, B.T.; Ryan, C.; Shindler, B.; Turtevant, V.; Clark, R.N.;
Philpot, C. 2003. Adaptive management and the Northwest Forest Plan: rhetoric and
reality. Journal of Forestry 101(1): 40-46.
Stankey, G.H.; Clark, R.N.; Bormann, B.T., eds. 2006. Learning to manage a complex
ecosystem: adaptive management and the Northwest Forest Plan. Res. Pap. PNW-RP567. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest
Research Station.
Tappeiner, J.C. 2009 Managing young stands to develop old-forest characteristics. In:
Spies, T.A.; Duncan, S.L., eds., Old growth in a new world: a Pacific Northwest icon
reexamined. Washington, DC: Island Press: 261-273
Thomas, J.W. 2008. From the management of single species to ecosystem
management. In: Fulbright, T.E.; Hewitt, D.G., eds., Wildlife science: linking
ecological theory and management applications. Boca Raton, FL: CRC Press:225-240
Thomas, J.W. 2009. Increasing difficulty of active management on national forests.
In: Spies, T.A.; Duncan, S.L., eds., Old growth in a new world: a Pacific Northwest
icon reexamined. Washington, DC: Island Press. : 189-200
Thomas, J.W.; Franklin, J.F.; Gordon, J.; Johnson, K.N. 2006. The Northwest Forest
Plan: origins, components, implementation experience, and suggestions for change.
Conservation Biology 20(2): 277-287.
U.S. Department of Agriculture, Forest Service [USDA FS]. 2008. Environmental
impact statement National Forest System land management planning. Washington,
DC: U.S. Department of Agriculture, Forest Service.
37
U.S. Department of Agriculture, Forest Service [USDA FS]. 2011. Draft
programmatic environmental impact statement National Forest System land
management planning. Washington, DC: U.S. Department of Agriculture, Forest
Service.
U.S. Department of Agriculture, Forest Service; U.S. Department of the Interior,
Bureau of Land Management [USDA and; USDI]. 1994a. Final supplemental
environmental impact statement on management of habitat for late-successional and
old-growth forest related species within the range of the Northern Spotted Owl.
Portland, OR and Moscow, ID: U.S. Department of Agriculture, Forest Service, , and
U.S. Department of Interior, Bureau of Land Management.
U.S. Department of Agriculture, Forest Service; U.S. Department of the Interior,
Bureau of Land Management [USDA and USDI]. 1994b. Record of decision for
amendments to Forest Service and Bureau of Land Management planning documents
within the range of the northern spotted owl. Portland, OR and Moscow, ID: U.S.
Department of Agriculture, Forest Service, and U.S. Department of the Interior,
Bureau of Land Management. [plus attachment A: standards and guidelines]
U.S. Department of the Interior, Bureau of Land Management [USDI BLM]. 2008.
Final environmental impact statement for the revision of the resource management
plans of the western Oregon Bureau of Land Management districts. Portland, OR:
U.S. Department of the Interior, Bureau of Land Management, Oregon and
Washington State Office.U.S. Department of the Interior, Bureau of Land
Management [USDI BLM]. 2009. December 2009 Gordon Creek thinning: revised
environmental assessment and finding of no additional significant impact.
38
Environmental assessment number OR080-07-05. Salem, OR: U.S. Department of the
Interior, Bureau of Land Management, Salem District Office.
U.S. Department of the Interior, Bureau of Land Management [USDI BLM]. 2010.
Highland Fling thinning: environmental assessment and finding of no significant
impact. Environmental assessment number OR080-08-05. Salem, OR: U.S.
Department of the Interior, Bureau of Land Management, Salem District Office.
Williams G.W. 2009. The U.S. Forest Service in the Pacific Northwest: a history.
Corvallis, OR: Oregon State University Press.
Wilson, D.; Puettmann, K.J. 2007. Density management and biodiversity in young
Douglas-fir forests: challenges of managing across scales. Forest Ecology and
Management 246:123-134.
Yaffee, S.L. 1994. The wisdom of the spotted owl. Washington, DC: Island Press.
39
Chapter 3:
Management of riparian buffers: Upslope thinning with downslope impacts
Kenneth J. Ruzicka, Jr., Klaus J. Puettmann, and Deanna H. Olson
Kenneth J. Ruzicka Jr. is a doctoral candidate, Department of Forest Ecosystems
and Society, Oregon State University, Corvallis, OR 97331
Klaus J. Puettmann is Edmund Hayes Professor in Silviculture Alternatives,
Department of Forest Ecosystems and Society, Oregon State University, Corvallis,
OR 97331
Deanna H. Olson is a research ecologist, Forestry Sciences Laboratory, 3200 SW
Jefferson Way, Corvallis, OR 97331
This chapter has been modified from its published form. Table 2 had been updated to
add units and a better explanation of the results. Additional typographical and
spelling errors were fixed throughout the mchapter.
Ruzicka, K., D. Oldson, and K.J. Puettmann. 2014. Management of riparian buffers:
upslope thinning with downslope impacts. Forest Science. 60(5):881-892.
40
Abstract
We examined the potential of using upslope density management to influence growth
and drought tolerance of trees in untreated downslope riparian forests. Increment
cores from Douglas-fir trees in three mature stands in western Oregon, USA were
collected and measured. Trees responded to an apparent edge effect up to 15 m
downslope of thinned areas but not downslope of gaps. Growth responses in riparian
trees were not affected by slope or potential solar radiation (as a function of location
and topography). Additionally, in a retrospective analysis of tree growth and
allocation patterns (represented by ratio of early to latewood) and climate after
treatment over a 12-year period, trees in our study area did not appear to be water
limited and did not show a strong correlation with regional drought metrics. We
hypothesize that vegetation layers in these riparian forest stands responded
differentially to additional resources becoming available as a result of thinning, with
overstory trees in riparian areas responding downslope of thinned uplands and
subdominant canopy layers responding downslope of gaps. Our study demonstrates
that managers can affect riparian forests with upland treatments to a limited spatial
extent, which may be the only option in areas where direct riparian management is
restricted due to concerns for other ecosystem services.
Keywords: riparian zone; thinning; drought; climate; western Oregon; riparian
management
41
Introduction
Forested riparian areas serve as ecotones between upland and aquatic habitats
(Gregory et al. 1991) and as discrete habitat types providing a unique set of
ecological functions (Naiman and Decamps 1997). Riparian forests provide habitat
for a suite of plant and animal species (Brooks et al. 2012; Richardson and Danehy
2007; Sabo et al. 2005). Riparian areas also provide important ecological subsidies to
aquatic habitats, including down wood, sediment, litter, shade, and prey (Naiman et
al. 2000; Wipfli et al. 2007). Protecting these important stream-riparian ecological
functions and aquatic-riparian sensitive species in managed forest landscapes has
been the topic of much research (Marczak et al. 2010; Olson et al. 2007; Richardson
et al. 2012).
There is no apparent lack of woody debris in streams from late-successional
riparian forests in the Appalachian mountains (Hedman et al. 1996; Keeton et al.
2007) or Iberian peninsula (Diez et al. 2001), or potential snags and downed wood in
mixed-conifer forests (Romme and Knight 1981). In contrast, researchers in the
Pacific Northwest of North America and elsewhere have identified successional
trajectories for current second-growth stands that may result in eventual replacement
of conifer-dominated riparian areas with hardwood and later shrub- and herbaceousdominated communities (Goebel et al. 2012; Pabst and Spies 1999; Villarin et al.
2009). The likely lack of large conifers in future riparian areas in such managed
stands has potential negative implications for many riparian functions and processes,
including sustained delivery of down wood for fish habitat, stream shading, and
nutrient inputs (Gregory 1997; Naiman et al. 2000; Pollock et al. 2012).
42
Compositional and structural complexity of Pacific Northwest riparian forests,
including stem density and basal area, are legacies of historical clearcut harvesting
and reforestation activities. Consequently, managed, currently dense stands are not on
a trajectory to develop quickly into “old-growth” riparian areas (Acker et al. 2003),
with consequent interruptions of key riparian ecological functions and processes.
Maintenance of large conifers and accelerated growth of young conifers in riparian
zones are two common management goals for riparian forest restoration. In particular,
production of large woody debris is related to the successional processes of conifer
recruitment and growth via regeneration in riparian areas. Limited conifer
regeneration is a concern in many riparian areas in the northwestern United States
(Hibbs and Bower 2001; Pabst and Spies 1999).
Considering the importance of large conifers in riparian areas, the absence of
management options in riparian zones with dense forests has raised concerns about
future growth and vigor of trees in these areas. Density reductions through
silvicultural manipulations could benefit trees in high density patches with delayed
size differentiation. Active management in riparian areas could reduce concerns about
tree vigor, unstable soil conditions, and high competition-related mortality and
accelerate the development of large wood for potential instream recruitment (Palik et
al. 2012; Zenner et al. 2012). Conversely, establishment of riparian set-aside zones
may be warranted to protect water and wildlife habitat quality, including maintenance
of cool stream temperatures, and reducing erosion and the risk of stream
sedimentation (e.g., review in USDA and USDI 1993). Collectively, these diverse
processes and functions provide compelling rationale for both riparian protection and
43
active management, complicating a one-size fit all strategy for riparian forest
management.
The forest management ‘toolbox’ for maintenance or restoration of riparian
conditions includes management of adjacent uplands, with edge effects influencing
neighboring no-entry forests. For example, clearcuts influence the microclimate of
adjacent riparian forested areas (Brosofske et al. 1997; Davies-Colley et al. 2000;
Moore et al. 2005). Also, variable density thinning has been shown to affect
microclimates in adjacent riparian areas (Anderson 2007; Rykken et al. 2007).
Microclimate edge effects are influenced by a variety of factors including species,
topography, edge processes such as temperature changes and time (Chen et al. 1999).
As forested riparian areas on small streams are typically on steeper slopes,
water movement may be a proximate driver through which alteration of upland
conditions can influence tree growth and vigor in downslope riparian areas (Barnard
et al. 2010; Govind et al. 2011). Thinning has been shown to increase water
availability to remaining trees (Aussenac 1988; Bréda et al. 1995; Brix and Mitchell
1986). Water availability is also a major concern as climate change models predict
increased incidences and severity of drought periods, e.g., in the Pacific Northwest
(Mote and Salathé 2010). Thinning has been proposed as a method to reduce forest’s
vulnerability to drought (D'Amato et al. 2013). In areas where active riparian
management is not allowed, the potential may exist to use thinning in upland areas to
indirectly influence responses of trees in riparian areas to future water-availability
scenarios. The potential of lateral water movement through the soil of a hillslope has
been shown to be related to slope steepness and soil depth to bedrock (Hopp and
44
McDonnell 2009) as well as subsurface texture and availability of macropores
(Asbjornsen et al. 2011; Bachmair and Weiler 2011). Consequently, effects of upland
forest management on riparian tree growth within protected riparian zones in a
context of variable water availability warrants further consideration.
Furthermore, to address the proximate mechanisms influencing stand
development, the availability of resources, especially water, will change physiological
processes of trees as they adjust crowns, root systems, leaf morphology and sapwood
area to new conditions (Aussenac 2000; Jozsa and Brix 1989; McDowell et al. 2006).
For example, trees with lower water stress have lower sapwood:leaf area ratios
(Barnard et al. 2011). Latewood also stores more trunk water and allows easier
horizontal water exchange between rings in sapwood (Domec and Gartner 2002).
Thus, current water availability has important implications for the potential for trees
to adapt physiologically to future droughts (McDowell et al. 2008; Niinemets 2010).
Growth allocation, e.g., the ratio between early wood and late wood in year ring, can
also provide evidence of changing water availability after disturbances such as
thinning.
In our study, we test the hypotheses that management in forest uplands can
affect riparian tree growth. We hypothesize that this relationship is reflected in the
trees’ reaction to water availability, as evidenced by climate-growth relationships and
ring-growth allocation to earlywood or latewood. Specifically, we asked:
1) does manipulating upslope growing conditions affect growth of trees in
downslope riparian areas, and if so, does this effect vary with downhill distance from
treatment edge, and topographic factors such as slope steepness or light availability?
45
2) does reducing upland stem density alter the growing conditions for
downslope riparian trees to the point that they are less influenced by drought
conditions? i.e., does it “decouple” the relationship between annual water availability
and tree growth?
3) does reduced upland stem density affect riparian stem growth
characteristics of Douglas-fir, such as latewood:earlywood ratio, and if so, what
potential factors such as topography influence these relationships?
Methods
Our study utilizes sites established as part of the Density Management and
Riparian Buffer Study of western Oregon, USA (DMS; (Cissel et al. 2006) (Figure 1).
The DMS is an operational-scale experiment to investigate the potential of alternative
thinning treatments to accelerate late-successional forest structures in young evenaged stands. The Riparian Buffer Study component examines effects of alternate
widths of streamside management zones on aquatic-riparian species and habitat
elements. Overall, DMS sites were chosen to be representative of young managed
stands on west-side federal forests, from Mount Hood to Coos Bay, Oregon (Cissel et
al. 2006). For our study, we chose three DMS study sites (North Soup, OM Hubbard,
and Keel Mountain; elevation 176 to 783 m) to represent a climatic and latitudinal
gradient (Table 1). These sites were dominated by conifer species, primarily Douglasfir (Pseudotsuga menziesii (Mirb.) Franco) with a smaller component of western
hemlock (Tsuga heterophylla (Raf.) Sarg.) and western redcedar (Thuja plicata Donn
ex D. Don). Subdominant conifers were more prevalent at the Keel Mountain site in
46
the western Cascade Range (Figure 1). Soils were primarily humic ultisols and
inceptisols with high infiltration rates typical of western Oregon (Table 1). Western
Oregon experiences a Mediterranean climate with cool wet winters and warm dry
summers. Average precipitation at our sites ranged from 1417 to 1968 mm per year,
mostly occurring from November through April. Cissel et al. (2006) contains a
complete DMS overview including individual site histories and descriptions.
The DMS upland treatments were applied over large areas, ranging 20-49 ha
at our three sites. DMS treatments and buffer widths were assigned in a randomized
block design with some limitations on complete randomization; e.g., stream
occurrences often biased delineation of the moderate upslope treatment and the
untreated reference area (Olson et al. 2002). These constraints are not expected to
lead to biased results for our study as they were based on other site selection criteria
and due to the variety of sites selected (Dodson et al. 2012). We investigated the
treatment with the most drastic tree density reductions in the DMS which were
established within the variable density upland treatment (Cissel et al. 2006). We
examined two different tree densities within the variable density treatment area: 1)
areas thinned to a residual density of 100 trees per hectare (tph; “thin” treatment
herein); and 2) 0.4-ha circular gaps (“gap” treatment herein) in which all trees were
harvested in the gap and the matrix was thinned. There was also an unthinned
reference treatment (“control”). The average residual basal area in the second year
after treatment was 17.25 m2 ha-1in thinned, 53.95 m2 ha-1 in controls and 34.73 m2
ha-1 in riparian buffers (Anderson 2002). All thinning was done from below where
47
smaller trees were removed but minority species, mostly hardwoods, were retained
for structural and species diversity.
At each site we collected data from overstory trees at plots along 13 preestablished trans-riparian transects (Anderson 2007; Cissel et al. 2006), aligned
perpendicular to headwater streams. Riparian buffer widths ranged from 16 m to 32 m
from the middle of the stream channel. This buffer width was designed to have a 15
m minimum width, but could be wider to accommodate local conditions of riparian
vegetation and topography. In the riparian area, the first and second plot centers were
4.5 m and 14 m from the stream center, respectively. In the upland thinned and
unthinned treatments, the third plot was 9.1 m upslope of the second riparian plot
(22.7 m from stream center), and the fourth and final plot was 18 m upslope of the
third plot (41 m from stream center). Four of the “final, 41 m plots” were not in the
same headwater drainage as the rest of the respective transect and no trees were
sampled in these plots. One plot did not have any Douglas-fir, resulting in a total of
36 plots. In June to August 2011, we selected and recorded locations of the three codominant and apparently healthy Douglas-firs closest to plot centers. At each tree we
used a laser or acoustic rangefinder depending on understory visibility to measure
distance from treatment/buffer edge, which was defined as closest cut stump.
Distance from ridge top also was measured and the number of co-dominant trees
within a 11.5 m circle around each tree was counted. We did not account for slope in
these distance measurements in order to capture the physical length of soil between
the edge and ridge top. Trees in riparian buffers were between 2 m and 30 m
downslope of treatment/buffer edges.
48
We collected one 7-mm increment core to the pith and three 12-mm cores to
capture at least 30 annual rings, covering each cardinal direction side of trees. Each
core was sanded until rings and early-latewood boundaries were clearly visible. Tree
rings were crossdated visually using pointer years and statistically using the
cross.date function in the dplR program library (Bunn 2010). After crossdating to
ensure intra- and inter-tree accuracy the four cores from each tree were averaged
together by year into a raw ring width chronology for each tree. Raw tree ring widths
were mathematically converted to basal area increment (BAI) for earlywood,
latewood, and whole ring growth (Phipps 2005) after subtracting the width of the bark
to the final diameter (Larsen and Hann 1985). Trees that could not have their ages
verified correctly by crossdating were excluded from analysis resulting in a total of
n=98 trees.
To assess relationships between growth and water availability we used the
Palmer Drought Severity Index (PDSI). PDSI is a general index of drought severity
that can be compared across regions as it takes into account precipitation, potential
evapotranspiration and soil moisture (Alley 1984). PDSI data were downloaded from
the National Climatic Data Center (http://www.ncdc.noaa.gov/) and divided into early
(April-June), late (June-Sept) and winter (November – March) seasons (Barnard et
al. 2012).
We used the hillshade tool in ArcView (Environmental Systems Research
Institute 1996) to calculate potential solar illumination at ground levels for all trees.
This index takes into account the elevation and latitude to calculate the position of the
sun as well as mapping and accounting for topographic features that may shade a
49
given point. We selected June 21, 2010 for calculation of potential solar radiation to
capture the sun at its highest position (Suzuki et al. 2008). Our potential solar
illumination values ranged from 350,000 to 570,000 W m-2, where smaller values
represent north-facing aspects or heavily shaded drainages.
Data Analysis
To examine the effect of upslope thinning on tree growth, we first developed a
process to determine the time periods that best represent pre-and post-treatment
conditions. We plotted average BAI over time for each treatment to visually assess
patterns of change in growth (Figure 2). Trees growing in and downslope of thinned
areas grew at increased rates following thinning. In contrast, the BAI of trees in
unthinned “control” areas appeared unchanged. The data support the notion of several
years of physiological adaptation to new growing conditions (Aussenac 2000;
Harrington and Reukema 1983). Based on these results, we determined that stabilized
growth reflecting post-treatment conditions was reached four years post-treatment
and classified “after” treatment as the time period from year four after treatment to
year 12 after treatment. Accordingly, we classified “before” treatment as the time
period from 10 years before treatment to year zero (treatment year). We then
calculated differences between average tree growth rates (after - before) to examine
patterns with treatment type. In all models described below, an alpha level of 0.05
was selected as indication of significant differences.
To examine effects of upslope tree density reduction on downslope tree
growth (below thin, below gap, and unthinned) in riparian areas, we used a mixed
effects model with a nested random effect structure. Only downslope trees were
50
included in the model (n=81). The model examined treatment effects, and influences
of topographical features (distance downslope, percent slope) and local conditions
(potential solar radiation, neighborhood tree density) on differences in tree BAI
(Equation 1):
(1) Y ijkl = α i + β 1-4 + β 5 + γ j + λ jk + δ jkl + ε ijklm
Where
Y ijklm
is the mean value of the difference in BAI (after minus before)
αi
is the fixed effect of the upslope treatment i = below thin,
below gap, unthinned
β 1-4
is the fixed effect of 1-4 = distance downlsope from
treatment/buffer edge, solar radiation, percent slope and
neighborhood density
β5
is the slope of the interaction between distance downslope and
percent slope
γj
is the random effect of the j site (NS, KM, OM)
λ jk
is the random effect of the k transect in the i site (1-13)
δ jkl
is the random effect of the l plot in the k transect at the i site
(1-29)
ε ijklm
is the random effect of the individual tree m in the i treatment
in the j site in the k transect and l plot (1-81)
and β ~ N(0, σβ) and Cov(β,β’)=0, γ ij ~ N(0, σγ2) and Cov(γij,γi’j’)=0,
ε ijklm is independent
51
The model was fitted using the lme function from the nlme package (Pinheiro
et al. 2011) and computed in R 2.15.2(R Core Team 2012). We used the function
varIdent to allow for variance in sample size between treatment groups (Zuur 2009).
The assumptions of homogenous variance and normality were confirmed graphically.
Effects of upslope treatments (below thin, below gap, and unthinned) on
relationships between downslope tree growth and PDSI (drought severity index) were
tested by comparing parameters in multiple linear regression models that were fitted
for BAI growth before and after treatment. This model was used to compare the
growth of the entire ring as well as the early wood and late wood segments
individually to the different seasonal drought measures of growing season, early, late
and winter. Statistically different slope parameters after treatment indicate that
upslope treatments had resulted in different PDSI-growth relationships compared to
pre-treatment conditions. To test this, the model was fitted to the data from each
treatment (Equation 2):
(2) Y ijklm = β 0 + β 1 ΙBi + β 2 β i + β 3 Χ i ΙBi + γ j + λ jk + δ jkl + ε ijklm
Where
Y ijklm
is the value of the growth in BAI for tree (1-98)
Ι Bi
is an indicator for before and after treatment Ι B =1 for before
Xi
is the value of the continuous covariate PDSI (entire growing
season, spring, summer and winter tested individually)
γj
is the random effect of the j site (NS, KM, OM)
λ jk
is the random effect of the k transect in the i site (1-13)
52
δ jkl
is the random effect of the l plot in the k transect at the i site
(1-29)
ε ijklm
is the random effect of the individual tree m in the i treatment
in the j site in the k transect and l plot (1-98)
The model was fit using the lme function from the nlme package (Pinheiro et
al. 2011) and computed in R 2.15.2(R Core Team 2012). Growth (BAI) was log
transformed in models for each treatment to meet assumptions of normality and
homogenous variance.
To analyze potential relationships between upslope tree density and
physiological changes in growth allocation, we tested whether latewood:earlywood
ratios differed between pre- and post-treatment growth. We calculated ratios of BAI
of latewood:earlywood for each year and calculated average ratios for pre and post
treatment growth (year -10 to 0 and year 4 to 12). We then calculated differences
between post and pre-treatment averages to test for differences in temporal growth
patterns.
A mixed-effects model was used to test for treatment effects and influences of
different topographical features on growth patterns (Equation 3):
(3) Y ijklm = α i + β 1-4 + β 5 + γ j + λ jk + δ jkl + ε ijklm
Where
Y ijklm
is the mean value of the difference in latewood:earlywood ratio
αi
is the fixed effect of the upslope treatment i = below thin,
below gap, unthinned
53
Β 1-4
is the fixed effect of 1-4 = distance downslope, solar radiation,
percent slope and neighborhood density
B5
is the slope of the interaction between distance downslope and
percent slope
γj
is the random effect of the j site (NS, KM, OM)
λ jk
is the random effect of the k transect in the i site (1-13)
δ jkl
is the random effect of the l plot in the k transect at the i site
(1-29)
ε ijklm
is the random effect of the individual tree m in the i treatment
in the j site in the k transect and l plot (1-81)
and β ~ N(0, σβ) and Cov(β,β’)=0, γ ij ~ N(0, σγ2) and Cov(γij,γi’j’)=0,
ε ijklm is independent
The model was fit using the lme function from the nlme package (Pinheiro et
al. 2011) and computed in R 2.15.2 (R Core Team 2012). We used the function
varIdent to allow for different variances in sample size between treatment groups
(Zuur 2009).
The explanatory variables were selected to test our stated hypotheses except
neighborhood density (number of trees within 12 m) which was added as a covariate
to account for different competitive neighborhoods. The assumptions of homogenous
variance and normality were confirmed graphically for each model fitting.
54
Results
Our results suggested that upslope thinning increased downslope riparian tree
growth whereas upslope gaps did not (Figure 3). Post-thinning, trees growing
downslope of thinned areas had an increased basal area growth, which averaged more
than double (2710 mm2) the post-pretreatment growth difference of trees below
unthinned areas. In contrast, trees growing downslope of gaps within a thinned matrix
showed no difference between pre- and post-treatment growth. Also, trees growing
within riparian areas downslope of unthinned uplands did not appear to experience a
change in growing conditions (Table 2). The magnitude of growth responses due to
upland thinning decreased with distance downhill from the treatment edge and was
limited to trees growing within about 15 meters of treatment-buffer edges (Figure 4).
Steepness of the topography also affected BAI, as trees on steeper slopes showed less
growth response below the unthinned stands compared to trees below the two
thinning treatments (Figure 5). However, the significance of slope steepness
downslope of the unthinned area was the result of a single outlier in the unthinned
riparian area that experienced a significant growth reduction after thinning. When this
tree was removed from the analysis, slope steepness did not affect growth of trees in
riparian areas. Potential solar radiation, neighborhood density and the interaction
between slope and distance to treatment-buffer edge did not affect growth of trees in
our models.
Our data did not indicate a tree-growth response to water availability during
the study period. The slopes of regression lines relating growing season drought
severity (PDSI) and BAI were not significantly different from zero (Figure 6). The
55
regressions did reinforce the mixed model analysis that trees growing in thinned and
below thinned treatments had increased growth after treatment. Our study showed
similar results for seasonal drought patterns, including spring, summer, and winter
droughts (data not shown). In general, trees at our study sites did not change growth
patterns in response to PDSI (Table 3).
Trees in riparian areas did not change their timing of growth patterns after
upslope density reduction. Latewood:earlywood ratios did not appear to differ
between pre- and post-treatment conditions (Data not shown).
Discussion
Our findings indicate that forest managers can affect tree growth inside
untreated riparian buffers by upland forest management, at least within a short
distance of the treatment-buffer edge. Previous work in clearcuts indicated that edge
effects on tree growth are dependent on interacting factors including distance from
the edge, landform, species, and climatic conditions (Cadenasso et al. 1997; Harper et
al. 2007; Harper et al. 2005). Our results confirm that earlier findings of the spatial
extent of upland tree-to-tree interactions (e.g. 10 to 20 m for Douglas-fir) (D’Amato
and Puettmann 2004; Puettmann et al. 2009b; Wimberly and Bare 1996) are also
relevant when assessing effects of upland treatments on tree growth in riparian areas.
This suggests that plant interactions, such as competition for water and growing
space, may drive these patterns, as microclimate edge effects may extend further, up
to 62 m from the treatment-buffer edge (Anderson 2007; Brosofske et al. 1997; Chen
et al. 1995). Edge effects on tree diameter growth and understory plant communities
56
have been shown in other studies for variable density thinning including gaps
(Dodson et al. 2012; Fahey and Puettmann 2007; Roberts and Harrington 2008).
In contrast to our results, other riparian studies testing effects of clearcuts on
riparian tree growth did not show an edge effect and attributed it to a quick response
of the shrub layer and regeneration in the gap to block edge light availability and
wind (Hibbs and Bower 2001). In western North Carolina, a shelterwood harvest
increased nutrient availability in the harvested areas, but not in adjacent unharvested
riparian forests; however, an increase in organic nitrogen in deeper soil layers was
observed in the harvested unit suggesting some movement of nutrients (Knoepp and
Clinton 2009). We do not have any direct evidence from our study for specific
mechanisms tied to the increase in riparian tree growth. Increased growth may be due
to a combination of higher light, water, and nutrient availability (Lasky et al. 2013;
Powers 2009), whereby nutrient availability is intrinsically linked to moisture
conditions. However, it does not appear that increased availability of moisture to trees
is driving the growth response in our study as discussed below. When management
and protection of riparian zones are contentious, our results suggest that growth and
vigor of trees in riparian buffers can be improved by upslope thinning, albeit to a
limited spatial extent (Lee et al. 2004; Rambo and North 2009).
The extent of edge effects from upslope management will be influenced by
local vegetation, climatic, and topographic factors (Chen et al. 1999). For example,
the understory species composition of riparian areas in the Pacific Northwest varies
with local factors such as the distance from stream, aspect, slope position and percent
slope (Sarr and Hibbs 2007a; Sarr and Hibbs 2007b; Sarr et al. 2011). Douglas-fir are
57
often more prevalent on steeper slopes in the Pacific Northwest than gentler sloped
floodplains (Barker et al. 2002; Hibbs and Bower 2001). Although topographic
factors, such as slope steepness, did not appear to impact tree growth responses to
upslope thinning in our study, other studies suggested that management in uplands
designed to impact trees in riparian areas needs to account for local topography. Trees
adjust their canopy structure and live crown ratios in response to light from different
angles, due to different azimuth, slopes and edge effects (Muth and Bazzaz 2002;
Šálek et al. 2013). Thus, exploration of how canopy structure and tree growth can be
manipulated without active treatments directly within riparian areas may provide
information how to create desirable late-successional stand conditions in areas with
forest management constraints due to regulatory restrictions (Bauhus et al. 2009;
Franklin et al. 2002).
The gaps embedded in variable density thinning treatments in our study have
different effects on surrounding vegetation than thinning alone depending on gap size
and species (Davis et al. 2007; Fahey and Puettmann 2007; Fahey and Puettmann
2008). Gaps can also lead to different microclimate conditions depending on the
prevailing aspect (Gray et al. 2002). However, none of these factors is an obvious
candidate to explain why trees downslope of gaps did not demonstrate increased
growth, even though presumably more resources would be available below gaps than
below thinned areas. As reflected in amount and composition of understory
vegetation, gap effects were initially limited to the area inside gaps and did not
infiltrate adjacent forest interior (Fahey and Puettmann 2007; Fahey and Puettmann
2008). However, on steep hillsides in riparian areas the gaps may have benefited
58
smaller adjacent trees in the midstory (Gray et al. 2012; Hibbs and Bower 2001) and
also the shrub layers (Montgomery et al. 2010; Sarr and Hibbs 2007b), both of which
were not formally sampled in our study. However, personal observations taken during
data collection support that understory vegetation in gaps responded with increased
growth compared with thinned areas (K. Ruzicka personal observation). Other
studies using the DMS sites showed that the amount of understory vegetation was
inversely proportional to overstory density (Neill and Puettmann 2013). Also,
vegetation in gaps at DMS sites responded quickly in terms of plant cover compared
to the thinned stand interior (Fahey and Puettmann 2008). Other studies in
shelterwoods in Minnesota have found that increased light transmittance through the
canopy was correlated with increased shrub cover (Smidt and Puettmann 1998).
Studies in western Oregon have shown that management actions initially reduced
shrub cover, likely through mechanical breaking, but that smaller shrubs recovered
quickly while larger shrubs responded more slowly (Berger et al. 2012). Overstory
trees are tightly coupled with understory vegetation through a variety of mechanism
such as light transmission, precipitation throughfall patterns and soil moisture status
(Barbier et al. 2008). Overstory structure (Nagaike et al. 1999) and species
composition (Berger and Puettmann 2000) is reflected in composition of understory
vegetation. The effect of the overstory on the shrub layer varies by time and
successional status (McKenzie et al. 2000). Conversely, neighboring shrub cover (non
N-fixing species) has been linked to reduced nitrogen in overstory tree foliage as well
as reduced soil moisture status and temperature reducing photosysthesis (see review
by (Li et al. 2012). It is possible that subdominant species were situated to take
59
advantage of increased aboveground resources, such as light (Comfort et al. 2010;
Shatford et al. 2009) co-opting other belowground resources released by gap creation
(Montgomery et al. 2010).
Our study did not show that potential drought effects (within the sampled
conditions) were mitigated by reduced stem density. We hypothesized that lateral soil
water flow would have potentially led to an increase of available water to downslope
riparian trees because riparian trees have been shown to reduce stream flows through
evapotranspiration on a diel scale (Barnard et al. 2010). In areas with a less expressed
summer dry season, e.g., the southeastern US, harvesting in headwater riparian
communities resulted in higher water tables and increased composition of hydrophilic
understory vegetation communities (Choi et al. 2012; Clinton 2011). For species that
are able to directly access stream water, low stream flows were correlated with
reduced tree growth (Coble and Kolb 2012) and fertilized forests area able to reduce
stream flow through increased evapotranspiration. Riparian vegetation also influences
stream flow and nutrient loads in headwater streams. In agricultural landscapes,
steeper slopes in riparian buffers were correlated with higher levels of in-stream
nitrogen due to increased subsurface flow (Burt et al. 2002; Vidon and Hill 2004a, b).
It is likely that thinning would not alter the relationship between water
availability (PDSI) and tree growth if trees are not water stressed sufficiently except
in extreme drought conditions (≤-4 PDSI) (Alley 1984). Thus, they are not tightly
coupled to regional patterns in water availability (Barnard et al. 2012; Niinemets
2010). Extreme weather events are important drivers of vegetation mortality (Jentsch
et al. 2007; Parmesan et al. 2000; Thompson et al. 2013) as well as long term drought
60
stress on trees (McDowell 2011). It is possible that the drought events at our study
sites during the period investigated were not severe enough to be reflected in the
growth of dominant tree species (Lloret et al. 2012). While the trees at our sites
experienced several years with moderate drought (PDSI -3.9 (1992, 2001), they were
interspersed with very wet years of up to PDSI 4.7 (1996, 2006). Prolonged multiyear drought has not been a potential stressor at our sites.
Deciduous Quercus and Fagus species in Germany as well as mixed
Mediterranean tree species in Spain showed decreasing growth sensitivity to drought
from xeric to mesic habitats (Pasho et al. 2011; Scharnweber et al. 2011). Growth
models of lodgepole pine in southwest Canada have predicted that provenances
adapted to temperature and precipitation extremes, high or low, would respond
strongly to climate change while trees in more adaptable moderate environments were
more adaptable and do not strongly respond to climatic pressures (McLane et al.
2011). In the Pacific Northwest, growth of conifers, especially Douglas-fir is more
strongly correlated with temperature and precipitation at higher altitudes and at the
edge of its climatic range than in lower elevation forests (Ettinger et al. 2011). Trees
at our sites were growing in moderate climates at low elevation for Douglas-fir and as
such we expect they were adapted to the moderate drought conditions exhibited
during our study and could alter other physiological mechanisms to maintain
relatively constant growth. It is also possible that trees at our sites were limited by
factors other than water (Aussenac 2000; Brooks and Mitchell 2011) and instead
responded to the availability of nitrogen (Brooks and Coulombe 2009; Kastendick et
61
al. 2012). Nitrogen is the primary limiting nutrient for Douglas-fir in the Pacific
Northwest (Vance et al. 2010).
This argument was also supported by the fact that trees at our sites did not
show changes in the temporal diameter growth patterns in trees downslope of
treatment, regardless of slope steepness and light intensity. Shifting growth allocation
to latewood can be a physiological adaptation to drought by changing the hydraulic
conductance of water transport xylem and horizontal water flow processes in tree
conductive tissue (Barnard et al. 2011; Wang et al. 2012). If trees had been extremely
drought stressed, we had expected to detect changes in timing of growth allocation
(Peñuelas et al. 2011). Further study explicitly examining the water status of these
trees is needed to support or conclusively reject our hypothesis.
Conclusion
Riparian areas are an important component of the forested landscape and
provide many ecological subsidies to uplands, aquatic environments, and downstream
users. Active silviculture treatments in riparian forests can be contentious although it
may be desirable for specific goals, such as provision of large trees. We found an
edge effect below upland thinning treatments which extended up to 15 meters into
untreated riparian buffers. There was no similar edge effect for trees downslope of
gaps. We speculate that this difference may be due to the understory, shrub and
subdominant canopy layers responding more strongly to gap creation than thinning.
Our study demonstrates that upland management can be used to influence riparian
forests at the upland edge but only to a limited spatial extent. Such management
practices may be enough to support the functional goals of riparian buffers such as
62
supporting potential in-stream coarse woody debris, stream temperature moderation
and nutrient uptake. Maintaining lower tree densities directly upslope of riparian
areas may be especially beneficial if other methods to increase tree growth and vigor
such as thinning are not allowed directly in riparian management areas. We did not
find evidence in our study that upslope thinning decreased the impact of drought in
riparian trees but that may be due to the limited extent of drought over the time frame
in our study. However, managing for increased tree vigor seems to be a reasonable
goal considering an uncertain future with global change.
Acknowledgements
Our research was supported by the American Recovery and Reinvestment
Act, the United States Forest Service, Pacific Northwest Research Station, the United
States Environmental Protection Agency, The Bureau of Land Management and the
Edmund Hayes Silvicultural Fellowship. We would like to thank Paul Puettmann for
help with data collection and Kelly Christiansen for help with figures. We also thank
two anonymous reviewers for their helpful comments.
63
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Table 1. Study site description including physical characteristics and treatment
details. Additional information can be found in (Cissel et al. 2006).
Latitude
Longitude
Elevation (m)
North Soup
Keel Mountain
OM Hubbard
N43°33’57.0”
44°31’41.0”
43°17’30.0”
W123°46’38.0”
122°37’55.0”
123°35’00.0”
176-411
654-756
436-783
Absaquil-Blachy-
Kinney / Blachly
Orford / Gustin-
McDuff / Digger-
Orford
Soils (major types)
BohannonUmpcoos
Mean annual precip.
1735
1968
1417
Oregon Coast
Willamette Valley
Southwest
(mm)
Oregon Climate
Division
Site index (m) (King
Valleys
40
39
36
August 1998
December 1997
September 1997
48
44
39
Cable
Ground
Cable + Ground
1966)
Treatment date
Stand age at
treatment
Harvesting method
79
Table 2. Effect sizes and 95 percent confidence intervals for treatments and
topographic covariates on the difference in growth for trees growing in riparian areas.
Mean values indicate the average BAI difference in post-pre growth in the entire ring
after treatment for trees within or downslope of treatments. Values of 0 would
indicate no change in BAI after treatment. Significant values indicate differences in
mean growth after treatment. Results were similar for tests completed with early and
latewood.
Mean
Std
Effect
Difference
DF
p-
Lower
Upper
value*
95% CI
95% CI
t-value
Error
(mm2)
Below
2710.28
1307.13
48
2.07
0.04
82.21
5338.45
-327.71
364.28
8
-0.90
0.39
-1167.74
512.32
-1337.96
479.33
8
-2.72
0.02
-2442.69
-232.03
Distance
-88.31
32.45
48
-0.56
0.01
-153.55
-23.06
Radiation
-0.00
0.00
48
-2.27
0.58
-6390.13
3607.70
Slope
-17.38
7.66
15
-0.89
0.04
-33.70
-1.05
Density
-14.57
16.37
48
1.42
0.37
-47.48
18.34
thin
Below
gap
Unthinned
*significant results in Bold
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Table 3. Significant trends for comparison of multiple regression lines using yearly
growth and growing season Palmer Drought Severity Index (PDSI). Regression
slopes were compared for growth before and after within each treatment. A (+)
indicates a significant positive correlation. A (-) indicates a significant negative
correlation. NS indicates a non-significant relationship. Within each treatment the
slope of the line was not significantly different for growth before and after treatment.
Results were similar for all combinations of seasonal PDSI and early or late wood.
Parameter
Upland thinned
Below thinned
Below gap
Control
Growth ~ PDSI
NS (p=0.1857)
NS (p=0.7314)
NS (p=0.5415)
NS (p=0.2430)
After - Before
+ (p<0.0001)
+ (p=0.0001)
NS (p=0.4543)
NS (p=0.1744)
Interaction
+ (p=0.0086)
+ (p=0.0103)
NS (p=0.0851)
NS (p=0.0598)
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Figure 1. Study sites from the Density Management and Riparian Buffer Study in
western Oregon, USA.
82
Figure 2. Annual basal area increment (BAI) for trees growing in different treated
areas in our western Oregon study. Below thin and below gap indicates trees growing
in riparian areas downslope of the treatments. Year 0 indicates the year treatments
were applied to the upslope.
83
Figure 3. Boxplots of the differences in tree growth between pre- and post-treatment,
of trees growing in riparian areas downslope of the treatments (thin and gap) and in
the riparian area of unthinned controls. The center line represents the median while
the lower and upper edges represent the 25 and 75 percent quartiles. Whiskers
represent the extent of 90 and 10 percentiles with open circles showing trees outside
of these values.
84
Figure 4. Tree growth difference between pre- and post-treatment and distance
downslope of the treatment edge for trees growing in riparian areas downslope of the
treatments. Distance 0 is the nearest cut stump in the treatment area.
85
Figure 5. Growth difference as influenced by slope for trees growing in riparian areas
downslope of the treatments in our western Oregon study.
86
Figure 6. Comparison of regression lines of basal area growth and growing season
drought between post- and pretreatment in our western Oregon study.
87
Chapter 4: Cross-scale interactions and the importance of spatial context in land
management
Kenneth J. Ruzicka, Jr., Klaus J. Puettmann, and J. Renée Brooks
Kenneth J. Ruzicka Jr. is a doctoral candidate, Department of Forest Ecosystems
and Society, Oregon State University, Corvallis, OR 97331
Klaus J. Puettmann is Edmund Hayes Professor in Silviculture Alternatives,
Department of Forest Ecosystems and Society, Oregon State University, Corvallis,
OR 97331
J. Renée Brooks is Plant Physiologist, U.S. Environmental Protection Agency,
Western Ecology Division, Corvallis, OR 97331
88
Abstract
We investigated the potential of cross-scale interactions to affect the outcome
of density reduction in a large-scale silvicultural experiment. Using
dendrochronology and stable isotope analysis, we measured tree growth and wateruse efficiency to investigate the impacts of density through progressively finer spatial
scales. Trees at the wettest site responded more strongly to density reduction than
trees at the driest site. Additionally, trees in treated stands at the driest site responded
with increased water-use efficiency while trees in treated stands at the wettest site
showed no change in water-use efficiency. We hypothesized that water is not the
primary limiting factor for growth at our sites, but that treatment released other
resources, such as growing space or nutrients to drive the growth response. At
progressively finer spatial scales we found that trees downslope of treated stands
responded primarily to changes in local density but this response was limited locally
to the edge of treatment stands. The results of this study demonstrate that although the
same density reduction treatments were applied to different sites, the response of trees
at those sites will depend on processes operating at multiple spatial scales.
Keywords: density reduction; cross-scale interactions; climate; western Oregon;
stable isotope analysis
89
Introduction
Global climate change is projected to affect ecosystems and valuable services
at multiple scales and processes (Garcia et al. 2014). One of the main challenges in
planning for global change is to identify over which scales important processes will
be impacted. Global-scale processes such as CO 2 fertilization or temperature
increase affects water-use efficiency and biomass-allocation patterns (Keenan et al.
2013; Reich et al. 2014). Regional processes such as drought and species migration
affect plant growth, mortality and disturbance patterns (McDowell et al. 2011; Raffa
et al. 2008). Just as, or even more important, factors at different scales interact, e.g.,
global and regional impacts can be moderated through decoupling of the environment
at finer, local scales. For example, regional warming can be moderated by local
topographic features (Daly et al. 2010) or site quality (Lévesque et al. 2014), and
plant interactions (De Frenne et al. 2013; Spasojevic et al. 2014).
Assessing these scales and impacts separately may lead to mismatches of
potential management interventions with processes that affect ecosystem services
such as water yield and carbon sequestration. Forest and rangeland vegetation
management at local scales can affect above- and below-ground carbon storage
(Thébault et al. 2014), habitat availability (Wilson and Puettmann 2007) and may
effect regional weather patterns (Morris et al. 2014; Peters et al. 2004) Only
examining factors at one spatial scale in isolation can lead managers to miss
important options or potential synergies. It also ignores cross-scale interactions,
where processes at one scale, like regional climate, interact with processes at another
scale such as local, small scale plant competition (Peters et al. 2007). Cross-scale
90
interactions can give rise to nonlinear or threshold responses that overwhelm the
effects of processes at other scales (Peters et al. 2004; Raffa et al. 2008)
The intensity and importance of processes that act at small spatial scales, such
as competition, are strongly influenced by plant density (Brooker et al. 2005; Stevens
and Carson 1999). Consequently, natural resource managers have commonly
managed density by regulating plant spacing through seeding and planting or
reducing post-establishment plant density by thinning operations (Puettmann et al.
2009a). By managing competition, density reduction has been used to achieve a
variety of objectives such as to increase tree growth and vigor (Bréda et al. 1995;
Waring and Pitman 1985) and understory amount and adaptive capacity (Neill and
Puettmann 2013; Reich et al. 2012), diversifying overstory structure (Bauhus et al.
2009; D’Amato et al. 2011), or reducing fuel (Allen et al. 2002). The primary effect
of density reduction in forests is to increase the amount of resources (water, light,
nutrients, growing space) available to residual vegetation (Bréda, Granier et al. 1995,
Boyden, Montgomery et al. 2012). In response to increased resource availability,
trees will adjust crown architecture, and leaf, sapwood and root areas to capture these
resources (Aussenac 2000). Thus the impact of density reduction is not only
dependent on the spatial heterogeneity of residual available resources, but varies over
time. Through the increase in available resources, especially water, thinning has been
proposed as an option to reduce the effects of climate change on forests, specifically
drought (Chmura et al. 2011; D'Amato et al. 2013; Kohler et al. 2010).
Douglas-fir forests managed for commodity production and other ecosystem
services provide an ideal opportunity to study the influence of density reduction at
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multiple spatial scales. Douglas-fir has a broad natural distribution and is widely
planted in many different ecosystems, including outside its natural range, with a
correspondingly wide range of climate conditions (Chen et al. 2010; Chmura et al.
2011; Lévesque et al. 2013). There is also an abundance of research on potential
management options for forests where this species is a key component in uncertain
future climates (review: (Chmura et al. 2011).
Retrospective studies on the effects of thinning, other disturbances and
climate variability on forests are often accomplished using dendrochronology
(Beedlow et al. 2013; Keim and Amos 2012; Sangüesa-Barreda et al. 2013).
Additionally, recent advances in isotopic analysis of tissue composition allow more
detailed investigation of climate and thinning influences on plant physiological
performance. Specifically, stable carbon isotope analysis (δ13C) from tree rings,
combined with dendrochronology, is now a common tool used to retrospectively
investigate impacts of forest management on the water status of trees (Brooks and
Coulombe 2009; Brooks and Mitchell 2011; Di Matteo et al. 2010; McDowell et al.
2006; Powers et al. 2010). Using the stable isotopes of carbon to calculate intrinsic
water-use efficiency facilitates comparisons of the physiological responses to
changing resources over time among trees growing at different sites (Lévesque et al.
2013; McCarroll and Loader 2004). In general, higher water-use efficiency indicates
drier environmental conditions so changes in-water use efficiency evident in the
chronology of tree rings can indicate moisture conditions at the time the ring was
made.
92
Specifically, isotope analysis allows researchers to better understand the
physiological processes underlying a response to thinning, such as changes in leaf and
sapwood area ratios (McDowell et al. 2006). This is especially useful when finding
counterintuitive results such as thinning increasing water stress on residual vegetation
(Brooks and Coulombe 2009). In a previous study on Douglas-fir in western Oregon,
Ruzicka et al. (2014) identified that trees in thinned and downslope of thinned stands
showed higher post-treatment growth than trees in the unthinned stands and
downslope of gaps. We initiated the current study to investigate if examining multiscale processes can help understand the mechanisms responsible for tree responses to
density reduction. Specifically we used changes and growth and water-use efficiency
to examine if cross-scale interactions provide insights into management options to
reduce the impact of climate change on forests. Our analysis is based on a hierarchy
of scales, where we first examine large-scale patterns and then examine finer scales
processes to investigate potential cross-scale interactions. We first investigated basic
information about growth differences across three sites which varied in local climate.
Next, we examined potential cross-scale interactions by comparing tree growth in
thinned and unthinned stands within sites. To understand stand-level responses, we
then examined a series of finer-grained interactions from hillslope location to the
local-neighborhood scale.
Methods
Our study utilized sites established as part of the Density Management and
Riparian Buffer Study of western Oregon, USA (DMS; (Cissel et al. 2006; Ruzicka et
al. 2014a) (Figure 1). We chose three DMS study sites (Keel Mountain, North Soup,
93
OM Hubbard) to represent a climate gradient (wet, intermediate, dry, respectively;
Table 1). All three sites experience a Mediterranean climate with cool wet winters
and warm dry summers. Average precipitation ranged from 1417 to 1968 mm per
year, mostly occurring from November through April. Keel Mountain was the wettest
site experiencing, on average, the most precipitation and lowest growing season vapor
pressure deficit (VPD) while OM Hubbard was the driest with the lowest yearly
rainfall and highest VPD. The three sites in this study were over 200 ha in area and
approximately 200 km apart from each other. Forests on all sites were dominated by
conifer species, primarily Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) with a
smaller component of western hemlock (Tsuga heterophylla (Raf.) Sarg.) and western
redcedar (Thuja plicata Donn ex D. Don). Less common conifers were more
prevalent at the Keel Mountain site in the western Cascade Range (Figure 1). Soils
were primarily humic ultisols and inceptisols with high infiltration rates typical of
western Oregon. The soils at OM Hubbard contain a higher clay content than the
other two sites. Cissel et al. (2006) contains a complete DMS overview including
detailed site histories and descriptions.
Upland treatments were applied over large areas, ranging 20-49 ha creating
the different stands used in this study. We examined two different tree densities
within the variable density treatment area: 1) areas thinned to a residual density of
100 trees per hectare (tph; “thin” stand) also creating a downslope of thinned
(“downslope thin”) stand in the adjacent riparian area; and 2) 0.4-ha circular gaps in
which all trees were harvested in the gap creating a riparian stand below the gap
(“gap” stand). There was also an unthinned reference stand (“control”). All thinning
94
preferentially removed smaller trees, except for minority species, mostly hardwoods,
which were retained for structural and species diversity. Treatments were completed
at OM Hubbard and Keel Mountain in 1997 while North Soup was treated in 1998.
At all sites we collected data from overstory trees in plots that were part of 13
pre-established trans-riparian transects (Anderson et al. 2007; Cissel et al. 2006),
aligned perpendicular to headwater streams. Riparian buffer widths ranged from 16 m
to 32 m from the middle of the stream channel. The buffer was designed to have a 15
m minimum width, but could be wider to accommodate local conditions of riparian
vegetation and topography. The first and second plot centers were 4.5 m and 14 m
from the stream center, respectively, and typically were located in the riparian buffer.
In the upland of the thinned and control stands, the third plot was 22.7 m from stream
center, and the fourth and final plot was 41 m upslope of the stream center. Four
transects went into neighboring headwater drainages as the ridge top was less than
41m from the stream center. For these transects, only trees in the three plots in the
study headwater drainage were measured. In addition, one plot at Keel Mountain did
not have any Douglas-fir, resulting in a total of 36 plots. From June to August 2011,
we selected and recorded locations of the three co-dominant and apparently healthy
Douglas-firs closest to plot centers. For each tree we measured distance from
treatment edge, which was defined as the closest cut stump. Distance from stream
also was measured and the number of co-dominant trees within 11.5 m of each tree
was counted. We did not correct for slope in these distance measurements to capture
the physical length of soil between the edge and stream. Trees in riparian buffers
were between 2 m and 30 m downslope of stand edges.
95
We collected one 7-mm increment core to the pith and three 12-mm cores to
capture at least 30 annual rings, covering each cardinal direction side of trees. Each
core was sanded until rings and early-latewood boundaries were clearly visible. Ring
width was measured using an optical scanner with 2400 DPI resolution and loaded
into WinDendro to measure early, late and entire ring width. Tree rings were
crossdated visually using pointer years and statistically using the cross.date function
in the dplR program library (Bunn 2010). After crossdating to ensure intra- and intertree accuracy the four cores from each tree were averaged together by year into a raw
ring width chronology for each tree. Raw tree ring widths were converted to basal
area increment (BAI) for earlywood, latewood, and whole ring growth (Phipps 2005)
after subtracting the width of the bark from the final tree diameter (Larsen and Hann
1985). Trees that could not be accurately dated and measured were excluded from
analysis.
The early and latewood from the three 12-mm cores from each tree were
separated along ring boundaries established in the dating process using a grinding bit
in a handheld rotary tool which transformed the wood tissue to a fine powder. The
powdered samples from the replicate cores were combined for each tree by year and
then extracted to α–cellulose (Leavitt and Danzer 1993). The values of δ13C in tree
ring cellulose were determined by the stable isotope lab at Oregon State University
College of Earth, Oceanic, Atmospheric Sciences in Corvallis, OR. Samples were
combusted to CO 2 in a Carlo Erba NA1500 elemental analyzer then introduced into a
DeltaPlusXL isotope ratio mass spectrometer. Measurement precision was better than
0.1 ‰ as determined from repeated measures from sample replicates.
96
δ13C values are reported relative to the Pedee belemite standard (PDB) where
the ratio of 13C to 12C atoms in a sample (R sample ) were compared with the ratio of the
standard (R standard ):
𝑅𝑅
(1) 𝛿𝛿 13 𝐶𝐶 = �𝑅𝑅 sample − 1�
standard
Only latewood for δ13C was analyzed because latewood has been found to provide a
more robust indicator of the growing conditions for a particular year and provides a
record of the growing conditions for the water-limited season in a Mediterranean
climate. Earlywood retains the isotopic signature of photosynthesis carried out in the
previous growing season, winter and other non-structural carbohydrates (McCarroll
and Loader 2004). Some samples and trees were excluded in our final analysis due to
an inability to crossdate cores, too little sample to collect, sample corruption due to
separation technique or not enough sample to accurately measure for isotope
composition. The majority of trees excluded not used were downslope of gap and thin
stands at the dry (6) and intermediate (14) sites. They tended to have narrow growth
rings that lead to sample corruption when ground using a ball mill. We used samples
from 5 years pre-treatment to 12 years post-treatment in our analysis. Our final
sample size was 76 trees over three sites for a total of 1347 observations. The
numbers of sample trees by treatment was 9 in the thinned stand, 14 downslope of
thinning, 21 downslope of gaps and 32 in untinned, control stands.
Isotope theory
Tree tissues are depleted in 13C compared to the atmosphere because of
fractionation during photosynthesis and incorporation into plant tissues (McCarroll
and Loader 2004). We modeled the fractionations occurring in the leaf, specifically
97
diffusion through the stomata and discrimination against the heavier isotope by
rubisco, the primary carboxylation enzyme (Farquhar et al 1982). When the stomata
aperture decreases, the efflux of H 2 O and influx CO 2 into the leaf decreases. If CO 2
is limiting to photosynthesis, more of the heavier carbon isotope is incorporated into
sugars. Thus, a heavier carbon isotope ratio is associated with lower water loss per
unit carbon gained, a measure of water-use efficiency (WUE). For example, an
increase in the photosynthetic rate due to increased light with constant transpiration
will result in higher water use efficiency and heavier carbon isotope ratio.
To model WUE i using δ13C we first calculated the discrimination (Δ13C)
against the heavier isotope to remove year to year variation of δ13C in the atmosphere:
(Farquhar et al. 1982)
(2)
Δ13 C =
δ13 Cair − δ13 Ccell
1 + δ13 Ccell
The atmospheric values of δ13C and the concentration of [CO 2 ] were obtained from
the National Oceanic and Atmospheric Administration Cooperative air sampling
network, Midway Island station for the years 1991-2011
(http://www.esrl.noaa.gov/gmd/ccgg/flask.php). To account for seasonal changes in
the concentration of CO 2 and δ13C we converted monthly data to seasonal averages
corresponding to the seasonal climate averages associated with early and late periods
of carbon fixation (April-June and late July-Sept). ∆13C is regulated by the ratio of
internal [CO 2 ] to atmospheric [CO 2 ] (c i /c a) as described by the following equation
(Farquhar et al. 1989):
98
(3)
𝑐𝑐𝑖𝑖
Δ13 C = 𝑎𝑎 + (𝑏𝑏 − 𝑎𝑎)( )
𝑐𝑐𝑎𝑎
where a is the fractionation from diffusion through the stomata (4.4 ‰) and b is the
fractionation by rubisco (~27 ‰). Intrinsic water use efficiency (WUE i ) is then
estimated using the atmospheric concentration of CO 2 (c a ) and the difference in
diffusivity of CO 2 and water in air (1.6): (Farquhar et al. 1989)
(4)
𝑊𝑊𝑊𝑊𝐸𝐸𝑖𝑖 =
A
(𝑐𝑐𝑎𝑎 − 𝑐𝑐𝑖𝑖 )
=
𝑔𝑔𝑠𝑠
1.6
WUE i is defined as the ratio of net photosynthesis (A) to stomatal
conductance (g s ). Isotopically derived values of WUE i from tree rings facilitates
comparison of long-term trends because the carbon in latewood is integrated over the
period of latewood formation, averaging WUE i over the late growing season
(McCarroll and Loader 2004). Year-to-year values of WUEi can then be compared to
indicate different water availability or changing growing conditions. Implicit in these
models are the assumptions that mesophyll conductance remains constant and nonlimiting in leaves throughout the study (Seibt et al. 2008), and any fractionation
events in the trunk or in phloem are consistent (Offermann et al. 2011) in the effect
on δ13C in tree rings. Other recent studies using stable isotopes in Douglas-fir have
found these assumptions to be valid (Barnard et al. 2012; Brooks and Coulombe
2009; Brooks and Mitchell 2011).
99
Data analysis
We used a hierarchal framework to plan our data analysis at multiple scales.
The results of analysis at larger scales were used to inform the analysis at finer scales.
Data analysis in this framework allowed us to explore variation found at larger scales
in the search of a more parsimonious explanation for the patterns observed at each
scale.
Site Scale
The first and largest scale analyzed was at the site level to assess site
differences in tree growth and water-use efficiency. Trees growing in the unthinned
riparian stands were included to avoid any treatment effects on this analysis (n=32).
Two-way repeated measures ANOVA was used to investigate inherent site
differences in five metrics: entire ring growth (BAI), earlywood, latewood BAI,
latewood WUE i and ratio if earlywood to entire ring width (earlywood proportion). A
random error structure was included to account for our nested design (plots within
transects) and an autoregressive covariance structure to model the autocorrelation
present due to repeated measures by year. The five models were fitted using the lme
function from the nlme package (Pinheiro et al. 2011) and computed in R 3.1.1 (R
Core Team 2012). The function varIdent was used to allow for different variances in
sample size between sites (Zuur 2009). The Bonferroni method was used for multiple
comparisons to obtain correct p-values when computing contrasts.
Stand scale
The second scale analyzed for this study was at the stand level. Multiple
stands were created at each site using the treatments described in the methods (thin,
100
downslope thin, gap, control). A two-way repeated measures ANOVA was used to
investigate stand differences among sites after treatment in four metrics: entire ring
growth (BAI), earlywood, latewood BAI, latewood WUE i . The interaction between
stand and site was included in the model. A random error structure was used to
account for the nested experimental design (plots within transects). An autoregressive
covariance structure was also included to model the autocorrelation present due to
repeated measures by year. The two models were fitted using the lme function from
the nlme package (Pinheiro et al. 2011) and computed in R 3.1.1 (R Core Team
2012). The function varIdent was used to allow for different variances in sample size
between stands (Zuur 2009). Contrasts of stands among sites was calculated using the
testInteractions function from the phia package (De Rosario-Martinez and Fox 2013).
The Bonferroni method was used for multiple comparisons to obtain correct p-values
when computing site contrasts.
Hillslope location and local neighborhood scales
Analysis at the stand scale highlighted the difference between stands among
sites, but also suggested that finer scale processes might explain differences at the
stand scale better than by only examining at the stand level. As previously identified,
treatment effects from thinning extended about 15 m from the edge into the stands
downslope of thinning (Ruzicka et al. 2014). To test for possible cross-scale
interactions that influence stand-level responses, we focused the next investigations
on the stands downslope of thinning and gaps. The finer scales analyzed for this study
used only trees downslope of thinned and gap stands (n=35) to investigate differences
between these stands.
101
To investigate the interaction of factors acting at the hillslope and stands, we
focused only on trees growing in stands below the thinned and gaps. Trees in these
stands were divided into groups; trees that grew within 15 m from the edge of the
treated stand, and those that did not. A type II ANOVA was used to compare
differences in post-pretreatment normalized growth between the two groups. A 0
value in the difference would indicate no change in growth after treatment. An
interaction between hillslope location (stand edge and not edge) and site that was
included to examine if any difference between locations was based on the sites. The
model was fitted using the aov function in R 3.1.1 (R Core Team 2012).
The interaction of the factors acting local neighborhood and hillslope scales
was investigated to determine why some trees within 15 m of the stand edge in the
downslope thin and gap stands responded with increased growth, and others did not.
All trees in the downslope thin and gap stands were separated into trees that
responded with increased ringwood growth (post-pretreatment growth >0) those that
did not. The growth difference was used to separate trees that responded to treated
stands (0 difference indicates no difference in response between pre and post
treatment years). A type II ANOVA was used to compare the difference in local
neighborhood density growth between the two groups. An interaction was included
to examine if any difference was based on the sites. The model was fitted using the
aov function in R 3.1.1 (R Core Team 2012).
The final investigation was to determine if the response of trees at the local
scale was related to changes in the tree’s water-use efficiency. The trees were divided
into two groups; trees within 15m that responded with increased growth to density
102
reduction from treatments and those that did not. A type II ANOVA was used to
compare the difference (post-pretreatment) in latewood WUEi between the two
groups. An interaction was included to examine if any difference was based on the
site. The model was fit using the aov function in R 3.1.1 (R Core Team 2012). The
assumptions of normality and homogeneity of variances was tested graphically for all
ANOVA tests at the hillslope location and local scale. Additionally, p-values from
tests done at the hillslope location and local scale were corrected using the false
discovery rate to account for multiple tests (Verhoeven et al. 2005)
Results
In summary, using a hierarchal analysis framework, our results indicate that
cross-scale interactions control the growth response to density reduction at larger
scales. Density at the local neighborhood scale can override impacts of larger scales.
Factors acting at the largest scale observed in our study (site) moderated the
magnitude of growth response at the stand scale. Trees in stands at the wet site
experienced increased growth compared to the dry site. At smaller scales, we
discovered that hillslope location was not important as the observed differences in
stand growth were reflected in the response to density reduction only in
neighborhoods were the density had been lowered due to thinning. Rather than an
edge effect we found that hillslope location was only important if the local density
changed.Trees that grew in less dense environments after thinning were more likely
to respond to treatment within both downslope of gaps and downslope thinned stands.
This suggests that a cross-scale interaction, where the patterns observed at larger
103
scales (stand) were mediated by patterns observed at a small scale conditions (local
neighborhood).
Site-level differences
Site-scale differences (e.g., precipitation, growing season vapor pressure
deficit: Table 1) were reflected in the growth and water-use efficiency of trees
(Figures 2 and 3). Trees growing at the wet site (Keel Mountain) had the highest
average annual growth, while trees at the dry site (OM Hubbard) had the lowest
(Table 2). Annual ring BAI and seasonal growth allocation were different in the
unthinned riparian stands among the three sites. Earlywood growth in trees was not
different between the three sites (Table 2). In contrast, trees growing at the dry site
had significantly less average yearly latewood growth with almost 60 percent of
growth completed before the seasonal summer drought (Brix 1972) (Table 2). Tree
water-use efficiency was also higher at the dry and intermediate sites but only the
intermediate site was significantly higher than the wet site (Table 2). Latewood
WUE i at the dry site was not significantly different from the other two sites as a result
of two trees at that site having the lowest average water-use efficiency from all trees
in this study. When these trees were removed from analysis trees on the dry site had
significantly higher water-use efficiency than wet site trees but were not significantly
different from intermediate site trees (data not shown).
Stand-level differences
Stand-level differences indicated a cross-scale interaction with site scale as
trees responded differently to stand-level treatments at each site (Figure 4). Entire
ring BAI was significantly higher in trees in the thinned stand after thinning at the
104
wet site compared to trees in the thinned stand in the dry site (Table 3). Similar
patterns were found when examining earlywood and latewood BAI (data not shown).
Trees in stands downslope of gaps showed an increase in growth after treatment at the
intermediate site compared to trees at the wet and dry stands (Table 3). The large
variation in thinned-stand responses as well as the site differences, indicated that
cross-scale interactions at finer spatial scales likely affected tree growth.
Trees in the thinned stand at the dry site increased in WUE i after density
reduction (Figure 5). Trees downslope of the thinned stand at the intermediate site
also had a significantly higher WUE i than trees on the wet site but not the dry site
(Table 4). An increase in WUE i indicated that the photosynthetic rate increased more
than stomatal conductance. Also, the WUE i for trees on the wet site for all stands
was unchanged after density reduction. Together these two lines of evidence
suggested that increased water from density reduction was not driving the growth
increase of trees in this study. WUE i would likely have decreased after density
reduction if an increase in water led to increased growth. In this scenario, stomatal
conductance would have increased relative to the rate of photosynthesis indicating
that water was no longer limiting tree growth.
Hillslope location differences
Only trees within 15 m of the edge increased entire ring BAI growth in
response to upland density reduction (Figure 6). However, we did not have the plots
to test if the edge effect was related to distance from the edge or is the responses were
only relayed to changing local density i.e., there were no density reduction in riparian
areas so the density didn’t change beyond the treatment areas. There was a
105
significant edge effect evidenced by trees within 15 m of density reduction treatments
responding with increased growth (Table 5). Additionally, growth response was
consistently greater in trees in the edge at the wet and intermediate sites where as the
response was more variable at the dry site. It is likely that the variability between and
within stands indicates that resources were not released uniformly across a stand after
density reduction. Higher variability observed in the edge response at the hillslope
location scale, especially at the dry site, suggested that interactions with finer spatial
scales can explain the apparent edge effect.
Local neighborhood scale differences
Trees that responded to upland density reductions within 15 m of the
treatment edge lived in significantly less dense local neighborhoods after treatment
(Table 6). The effect was consistent at all three sites, whereas trees at the dry site
experienced the densest neighborhoods and most variation in density (Figure 7).
The effect of the cross-scale interaction between stand and local neighborhood
was reflected by variability in tree physiological response. Trees that increased
growth within the 15 m edge showed a highly variable WUEi response to upland
forest management. WUEi generally increased in trees at the intermediate and dry
sites, with more dramatic increases at the intermediate site and more variability at the
dry site (Figure 8), although the differences or the site interaction were not significant
(Table 7). This result provided further evidence that changes in water availability
from density reduction was not the primary driver of the growth response in trees.
106
Discussion
Our study provided support for the importance of assessing cross-scale
interactions to understand ecological processes, such as tree growth. This is especially
important in an era of climate change uncertainty, when mitigation at the global scale
is not likely to be effective. However, land managers are typically limited to
managing at smaller scales, e.g., stand levels or smaller in forestry operations
(Puettmann et al. 2009a) . Our study adds to the growing body of evidence that
suggests that impacts of large-scale phenomena, such as climate change, can be
mediated by fine-scale processes, such as competition or facilitation (De Frenne et al.
2013; Spasojevic et al. 2014). In this study we have identified that multiple processes
interact across scales to affect the response of trees to density reduction. Site-scale
differences impacted the magnitude of the growth response of different stands to
density reduction with a larger response in trees at wet sites. However, local
neighborhood density was the primary driver of the larger-scale patterns when
investigating whether or not a tree responds with increased growth to density
reduction (Figure 9).
The effects of global and continental scale processes are relevant to the scales
interacting in our study when managing to create climate-smart landscapes (Hansen et
al. 2010). Douglas-fir obtain maximum size and competitive advantage due to the
cool wet winters followed by relatively cool dry summers of the Pacific Northwest
(Waring and Franklin 1979). However, due to widespread planting, Douglas-fir
growth in the Pacific Northwest can also be compared across different climate
patterns. For example, modeling studies have hypothesized that Douglas-fir in New
107
Zealand grow faster due to reduced summer evaporative demand and increased
summer moisture availability (Waring et al. 2008). Additionally in Europe planted
Douglas-fir grow larger at a mesic, continental site with summer rains than in a
Mediterranean climate with a summer moisture deficit (Lévesque et al. 2013). For
our study, growth responses to density reduction depend on seasonal moisture
patterns of a Mediterranean climate. The climate in the Pacific Northwest, with
relatively warm, wet winters and dry summers, provides a unique setting that is not
common in other regions where Douglas-fir is found naturally (e.g., the Rocky
mountains) or was introduced by humans. Higher summer moisture availability in
many other regions would change the results found in this study. In such climates,
trees would be expected to have lower water use efficiency and increased growth
associated with higher water availability, assuming freezing winter temperatures did
not damage the trees.
Our study suggested that even within the Pacific Northwest, sensitivity of
Douglas-fir growth response to density reduction varies along a fairly narrow range of
site conditions compared to the natural distribution of the species. In our study, trees
on different sites have established different sensitivities to local climate. Trees
growing at our dry site likely experienced more stress and less growth with changes,
such as increased drought, that lower precipitation in the winter and spring because
they did the majority of their diameter growth in the spring prior to summer dry
periods. It is also possible that trees respond to deep summer droughts by closing
stomata and thus decoupling growth from regional climate measures for a time
(Panek and Waring 1997). Our results supported the findings from other studies that
108
report differences in growth rates and water-use efficiency along a climate gradient
elsewhere at the regional Pacific Northwest and sub-continental western North
American scales (Littell et al. 2008; Nakawatase and Peterson 2006). In general trees
growing at drier sites had a lower growth and higher water-use efficiency (higher
δ13C) when compared with wetter sites (Panek and Waring 1997; Roden et al. 2005).
Higher water-use efficiency in trees on drier site is relatively consistent when
examined at continental scales for Douglas-fir (Chen et al. 2010). Photosynthesis in
the winter and early spring contribute large amount of carbon to tree reserves (Waring
and Franklin 1979) and substantial water can be stored in tree trunks of Douglas-fir
for use during the seasonal summer drought (Čermák et al. 2007; Phillips et al. 2003).
Our results suggested that Douglas-fir at our study sites were adapted to site-level
conditions which must be taken into account when examining changes in growth or
water-use efficiency at finer spatial scales because response can vary depending on
site wetness (Chen et al. 2010; Littell et al. 2008).
An increase in growth without a change in water-use efficiency in the thinned
stand at the wet and intermediate sites in our study suggested that photosynthesis
increased due to an increase in available resources such as light and nutrients
(Farquhar et al. 1989). There was also a proportional increase in water availability, as
indicated by water-use efficiency staying similar to trees in the control stand. Trees in
the thinned stand at the dry site showed increased latewood growth and water-use
efficiency in response to treatment indicating that an increase in available growing
space or other resources was not concurrent with an increase in water (Brooks and
Coulombe 2009). Our results indicated that thinning did not lead to increased water
109
availability in the late summer and thus increased water resources was not driving the
growth response after thinning. Since water-use efficiency (A/g s ) increased, we
suspected that nutrient and/or light availability led to an increased photosynthetic rate
relative to stomatal conductance. Other studies have shown an increase in water use
efficiency in trees due to nitrogen addition (Brooks and Coulombe 2009; Brooks and
Mitchell 2011) in Douglas-fir and in other species (Macfarlane et al. 2004). Thinning
typically increases growth in residual stress due to an increase in available resources
with larger growth increases at better sites (Aussenac 2000). Physiologically, trees in
water limited environments tend to show lower water-use efficiency due to increased
water availability (McDowell et al. 2006) while trees in more mesic sites may
increase water use efficiency in the summer in association with increased leaf area
(Brooks and Coulombe 2009). Our results suggest that trees in the thinned stand at
the dry site increased leaf area to take advantage new growing space. Potentially, soil
water was available in the winter and early growing season and could result in
increased leaf area. However, during subsequent seasonal water limitation, the
increased leaf areas may have resulted in increased foliage moisture deficits
accompanied by decreased stomatal conductance and increased water use efficiency
during latewood formation (Betson et al. 2007; Brooks and Coulombe 2009). These
results highlight the importance of cross-scale interactions when different processes
can also affect plant physiology, e.g., stomata response.
Cross-scale interactions can also help interpret confusing or contradictory
findings from other studies (Peters et al. 2007; Raffa et al. 2008). For example at our
sites, when the growth in stands was averaged across sites, trees downslope of
110
thinning responded with increased growth, while trees in stands downslope of gaps
did not. In our previous work, we hypothesized that the mechanism for this was a
strong response by understory plants in the gaps as they were competing for nutrients,
especially nitrogen, with the trees growing at the treatment edge (Ruzicka et al.
2014). More detailed investigations at smaller spatial scales and information about
water-use efficiency presented in this study failed to support this hypothesis. Instead,
results from this study suggested that competition in areas with high local
neighborhood densities may override any potential benefits from uphill thinning
treatments. In this study, the edge at the hillslope location scale was likely a proxy
for competitive processes operating at the local neighborhood scale. Other studies
have also found that processes such as habitat availability operating at forest edges
are dependent on the spatial scale examined (Donovan et al. 1997), as well as at
cross-scale interactions which can lead to the apparent variability of responses when
investigating edge effects (Ries et al. 2004). Previous studies at our sites
demonstrated that gap creation increased the amount and variability of nitrogen at the
edge of gaps (Thiel and Perakis 2009). Increased variability in nutrient availability
has been hypothesized as a factor in another study that demonstrated that understory
response depends on small-scale processes operating at the edge of gaps (Fahey and
Puettmann 2008). Our study reported variability not just in the edge response, but
different variability at different sites where the dry site had more variability in
response than the wet site. At our sites, the location along the hillslope was not
important as edge trees responded, or not, to resource availability and competitive
ability through local neighborhood interactions.
111
Competition and other plant interactions that can influence tree growth act at
small spatial scales (D’Amato and Puettmann 2004; Puettmann et al. 2009b). Our
study suggested a cross-scale interaction between competition operating at the local
scale and the growth response in the gap and downslope of thinned stand edges.
Although competition was reduced in the overstory in stands with lower density and
near the edge, not all trees experienced the same change in conditions. Gap creation
and thinning create spatial heterogeneity in resource availability driven by residual
plant structure, substrate and thus plant response (Boyden et al. 2012; Gray et al.
2002). The spatial impacts of thinning on residual trees will depend on residual stand
density (Aussenac 2000; Boyden et al. 2012) but also the size and configuration of
gaps (Kern et al. 2013). For example, regional-scale patterns in regeneration and
understory plant diversity after thinning varied based on local competitive
neighborhoods and cross-scale interactions between overstory cover and understory
competition (western Oregon: Burton et al. 2014; Dodson et al. 2014). At the local
neighborhood scale, trees in our study exhibited slightly different patterns of growth
and water-use efficiency after treatment depending on a cross-scale interaction with
site. Other studies have also found that response to climate gradients can change
based on interactions at different spatial scales (Galván et al. 2014) or plant processes
(Reyer et al. 2013). Potential mechanisms for this disparity are rooted in changes in
resource availability over space and time as plants adjust to different growing
conditions (Aussenac 2000; Reich 2014). Variability at each spatial scale
investigated in our study has been driven by cross-scale interactions with the other
spatial scales examined. It was likely that variability in the response of trees due to
112
changes in neighborhood density, especially at the different sites was due to
microclimate, soil resource availability, and genetics not investigated in this study.
Conclusions and management utilizing cross-scale interactions
Our study identified a hierarchy of cross-scale interactions that reflect the
changes in growth and water-use efficiency following density reduction. At the site
scale, wetter sites had increased growth and lower water-use efficiency than drier
sites. In treated stands the interaction with site-level processes led to higher growth
in thinned, wetter stands compared to stands in the drier site. Tree response to
density reduction may have been influenced through an edge effect but the proximate
mechanism of this response appeared to be that individual trees responded to changes
in their local growing conditions. Finally, there is great value in placing a project
area and organisms within the context of the larger landscape. Our study
demonstrated patterns observed at one spatial scale are best understood when
observed in connection with other spatial scales
Identifying the different scales relevant to different ecosystem goods and
services can determine when to intervene, in what ecological process and with what
management action (Raffa et al. 2008). Management actions to create a robust,
climate-smart landscape need to examine the processes affected at multiple spatial
and temporal scales. Our study identifies several potential avenues where cross scale
interactions can be utilized for management actions. Managers can prioritize stands
for treatment based on different management goals and current conditions. Wet sites
can be targeted for density reduction if increased growth and vigor of trees for climate
113
change adaptation is the primary objective. Trees in dry or dense stands may respond
to density reduction at lower levels of residual density compared to wet stands.
Management techniques used to create structural heterogeneity will need to account
for changes in the local neighborhood of individual trees to understand stand level
responses (Dodson et al. 2012). Local density controls can be used to create a mosaic
of different conditions that will shift over different spatial and temporal scales
(Chambers et al. 2013). Additionally, changes in temporal response will complicate
reducing density to encourage drought tolerance (D'Amato et al. 2013). Trees will
respond to local conditions by growing to capture available resources, thereby
creating larger maintenance costs.
Finally, the finding that cross-scale interactions are important in
understanding vegetation response suggests that the inference scope for studies of tree
response to density reductions need to be placed into a larger spatial context (Hill and
Hamer 2004; Raffa et al. 2008). Studies using single stands or by averaging across
continents cannot be simply extrapolated to global scales or other regions of the
world or easily downscaled to local management options within placing the stand of
interest into the context of multiple interacting scales (Peters et al. 2004).
Acknowledgements
Our research was supported by the American Recovery and Reinvestment
Act, the United States Forest Service, Pacific Northwest Research Station, the United
States Environmental Protection Agency, The Bureau of Land Management and the
Edmund Hayes Silvicultural Fellowship. This manuscript has been subjected to the
114
Environmental Protection Agency's (EPA) peer and administrative review, and it has
been approved for publication as an EPA document. Mention of trade names or
commercial products does not constitute endorsement or recommendation for use.
Paul Anderson helped with the development of our analysis. We would like to thank
Paul Puettmann and Fritz Kraetcher for help with data collection. We would also like
to thank the team at the ISIRF facility for expert advice and lab help for isotope
extraction. Numerous undergraduate students also helped with isotope sample
preparation and weighing.
115
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Table 1. Physical habitat characteristics and treatment conditions for three western
Oregon, USA study sites.
Keel Mountain
(wet)
North Soup
(intermediate)
OM Hubbard
(dry)
Latitude
44°31’41.0”
N43°33’57.0”
43°17’30.0”
Longitude
122°37’55.0”
W123°46’38.0”
123°35’00.0”
Elevation (m)
654-756
176-411
436-783
Mean annual precip.
(mm)
1968
1735
1417
Mean growing
season vapor
pressure deficit
(kPA)
Site index (m) (King
1966)
1.23
1.5
1.64
39
40
36
Treatment start
Dec-1997
Aug-1998
Sep-1997
Stand age at
treatment
44
48
39
128
Table 2. Western Oregon, USA study site differences in Douglas-fir growth and
water-use efficiency. Type III Repeated Measures ANOVA was used to determine
differences between sites for all five comparisons. Mean annual values (site means)
as indicated in each column. Significant contrasts between the sites are indicated by
stars (*p<0.1, **p<0.05) after Bonferroni correction while values with the same
letters are not different.
Site
Wet
Intermediate
Dry
Ring
Earlywood
Latewood
Proportion
Latewood
growth
growth
growth
of
WUE
(mm2)
(mm2)
(mm2)
earlywood
3759a*
1821a
1916a**
0.47a**
103a**
(457)
(284)
(220)
(0.02)
(2)
2887ab
1397a
1554ab
0.46a**
112b**
(645)
(398)
(313)
(0.03)
(2)
2378b*
1445a
904b**
0.60b**
107ab
(739)
(466)
(354)
(0.03)
(3)
129
Table 3. Mixed model Type III Repeated measures ANOVA results for the difference
in entire ring BAI growth for Douglas-fir stands among three western Oregon, USA
study sites. Mean values (standard error) indicate the average entire ring growth after
treatment for trees within stands. Values were normalized using the pretreatment
means so a 0 value would indicate no change in growth after treatment. Stands were
“thinned”, “downslope of thinning” and “downslope of gaps” and “unthinned”.
Significant differences in contrasts between stands among sites are indicated by stars
(*p<0.1, **p<0.05) after Bonferroni correction. Values with the same letters are not
significantly different.
Stand X Site
Thinned
Downslope thinned
Downslope gap
Unthinned
Wet
Middle
Dry
BAI
BAI
BAI
(mm2)
(mm2)
(mm2)
2871.6a
1933.7ab
787.6b**
(364.5)
(183.7)
(166.10)
798.5a
971.6a
1382.8a
(166.8)
(110.7)
(122.82)
104.9a
626.3a
-181.4a
(81.6)
(152.4)
(50.7)
52.4a
194.2a
55.1a
(73.1)
(87.2)
(58.2)
130
Table 4. Mixed model Type III Repeated Measures contrasts for the difference in
latewood WUEi for Douglas-fir stands among three western Oregon, USA study
sites. Mean values (standard error) indicate the average latewood WUEi after
treatment for trees within stands. Values were normalized using the pretreatment
means so a 0 value would indicate no change in WUEi after treatment. Stands were
thinned, downslope of thinning and downslope of gaps. Significant contrast
differences are indicated by stars (*p<0.1, **p<0.05) after bonferroni correction.
Values with the same letters are not different.
Stand X Site
Thinned
Wet
Intermediate
Dry
WUEi
WUEi
WUEi
4.54a
6.99a
22.27b**
(1.11)
(0.98)
(1.22)
14.01b*
11.52ab
(0.65)
(0.95)
(1.23)
7.49a
11.98a
8.54a
(0.83)
(0.63)
(0.67)
6.28a
8.29a
10.26a
(0.48)
(0.46)
(0.69)
Downslope thinned 5.93a
Downslope gap
Unthinned
131
Table 5: Type I ANOVA results showing differences in the response of edge (<15 m
downslope) and not edge (>15m downslope) in the entire ring growth BAI response
of Douglas-fir trees in stands downslope of thinned and downslope of gap in western
Oregon, USA. Significant differences are indicated by stars (*p<0.1, **p<0.05) after
correction with the false discovery rate. A significant interaction term would indicate
that the growth response of trees within the edge changed at the different sites.
Sum Sq.
Mean Sq
F-value
p-value
Edge
1159600
1159600
5.62
0.027*
Site
153547
76774
0.3722
0.692
Interaction
62275
31137
0.1510
0.8603
Residuals
42
8662879
206259
132
Table 6: Type I ANOVA results showing differences in the neighborhood densities of
responder Douglas-fir trees (>100 mm2 BAI mean post-pre growth difference) and
non-responder trees (<100 mm2 mean post-pre growth difference) in stands
downslope of thinned and downslope of gap in western Oregon, USA. Significant
differences are indicated by stars (*p<0.1, **p<0.05) after correction with the false
discovery rate. A significant interaction term would indicate that the growth response
of trees to neighborhood density changed at the different sites.
Sum Sq.
Neighborhood 184.61
Mean Sq
F-value
p-value
184.61
7.97
0.001*
density
Site
523.06
261.52
11.29
0.0044*
Interaction
16.19
8.10
0.35
0.707
Residuals
42
973.11
23.17
133
Table 7. Type I ANOVA results showing differences in the latewood WUEi of
responder trees (>100 mm2 BAI mean post-pre growth difference) and non-responder
trees (<100 mm2 mean post-pre growth difference) in Douglas-fir stands downslope
of thinned and downslope of gap in western Oregon, USA. Significant differences are
indicated by stars (*p<0.1, **p<0.05) after correction with the false discovery rate. A
significant interaction term would indicate that the WUEi response of trees changed
at the different sites.
Sum Sq.
Mean Sq
F-value
p-value
3.27
3.33
0.24
0.633
Site
19.38
9.69
0.69
0.515
Interaction
17.86
8.93
0.64
0.541
Residuals
15
209.41
13.96
Responder
trees
134
Figure 1. Location of three study sites in western Oregon, USA.
135
Figure 2: Basal area increment by year for the entire ring, earlywood and latewood
for Douglas-fir trees growing in the unthinned stands at each western Oregon, USA
study site.
136
Figure 3. Proportion of earlywood and the WUEi by year for Douglas-fir trees
growing in the unthinned stands of each western Oregon, USA study site.
137
Figure 4: Stand level entire ring BAI by year for Douglas-fir trees within our western
Oregon, USA study sites. Growth was normalized by post-pre to account for inherent
differences in tree growth (see methods). Error bars represent standard errors. A value
of 0 for growth would indicate no difference from the pretreatment average. Year 0
represents the year that density reduction treatments were applied to each stand.
138
Figure 5: Stand-level latewood WUEi by year for Douglas-fir trees within three
western Oregon, USA study sites. WUEi was normalized by post-pre to account for
inherent differences in tree physiology (see methods). Error bars represent standard
errors. A value of 0 for WUEi would indicate no difference from the pretreatment
average. Year 0 represents the year different density reduction treatments were
applied to each stand.
139
Figure 6. Boxplots of post-pretreatment entire ring BAI for Douglas-fir trees
downslope of the thinned and gap treatments in our western Oregon, USA study.
Trees within 15 m of the treatment edge are (edge) while trees further downslope are
(> 15 m). A value of 0 would indicate no change from the pretreatment average.
140
Figure 7. Boxplots of local neighborhood density (number of trees within 11.5m) for
Douglas-fir trees that responded to density reduction with increased growth (yes) and
those that did not respond (no). Data are from all trees downslope of the thinned and
gap stands. Solid lines show the mean density found in the control stand for each site.
141
Figure 8: Boxplots of post-pre difference in normalized WUEi at each western
Oregon, USA study site. Data are from Douglas-fir trees downslope of the
thinned and gap treatments. Trees responded to upslope density reductions
with increased growth (yes) or did not (no). A value of 0 would indicate no
difference from the pretreatment average WUEi for each tree and controls.
Values were normalized by controls to account for increasing WUEi over time
in response to CO 2 fertilizations.
142
Figure 9: Conceptual model for cross-scale interactions examined in our study
from the largest scale in the upper left. Site scale differences in growth impact
the magnitude of the growth response of different stands to density reduction.
Local neighborhood density can override larger scale processes when
investigating tree response to density reduction. Arrow width represents the
magnitude of the cross-scale interaction for the response of Douglas-fir wateruse efficiency or growth. The dry site (orange arrow) responded to thinning
with increased water-use efficiency. The wet site (blue arrow) responded to
thinning with the greatest growth increase followed by the intermediate site
(green arrow) and dry site. Multiple site trajectories of “yes” responses are
shown by black arrows.
143
Chapter 5: Synthesis
Research summary
My research used the Density Management and Riparian Buffer Study (DMS)
to examine three questions pertinent to forest management: the working relationship
between researchers and forest managers, managing riparian buffers with upslope
thinning, and cross scale interactions that affect how trees respond to density
reduction at multiple scales.
In chapter 2 I examined the socio-political context in which the DMS was
initiated and what led to the development of the research questions and experimental
set up. The DMS was set up as an operational scale silvicultural experiment. It
investigated unconventional thinning methods as ways to increase the structural
diversity of young forests that were regenerated as plantations. The DMS faced
numerous institutional and operational challenges in its implementation such as
questions about its fundamental necessity or problems training tree-marking crews for
novel prescriptions. The study has contributed enormously to the scientific literature
and local public land management. The effect on regional or national forest policy is
harder to detect. However, after over 15 years, the DMS has proven to be a valuable
contributor to forest management and policy in the Pacific Northwest.
In chapter 3 I investigated the impacts of upslope density reduction on the
growth of trees in downslope riparian buffers. I used linear mixed models to
determine if downslope trees would respond to upslope thinning potentially, the
144
distance over which resource freed up by thinning would be utilized by trees. My
results suggested that an increase in growth downslope of treatment was negatively
correlated with distance from the treatment edge. There were no significant
relationships with other indicators such available sunlight or density. I also did not
find significant correlations between a regional indicator of drought (PDSI) so I could
not determine if thinning could potentially reduce the impact of drought (at least in
western Oregon) on trees in my study.
In chapter 4 I investigated cross-scale interactions which govern the response
of trees to thinning at multiple scales. I used linear mixed models and ANOVA at
progressively finer scales from the site to the local neighborhood to test the scales
over which thinning affects tree growth and water use efficiency. I found that trees
growing at wet sites in unthinned stands had higher growth than trees at dry sites. I
also found a cross-scale interaction between site and stand where trees at wet sites in
thinned stands increased growth more than trees at dry sites in response to thinning
treatment. Using tree-ring carbon isotopic signatures, we found increased water use
efficiency for trees in thinned stands at dry sites but not at wet sites. This evidence
suggests that water is not what is driving the growth response. The trees are likely
responding to other factors such as increased light or nutrients released by density
reduction, especially at the dry site. A cross scale interaction was evident as trees
within the 15 m edge downslope of density reductions only increased growth when
the local neighborhood density was reduced. Trees growing in distances further
downhill could not be tested, as our dataset did not contain plots with reduced density
within riparian buffers. I concluded by examining different processes that can affect
145
tree response to forest management at different scales. Forest managers need to
examine the context of their stand before prescriptions are implemented.
Although beyond the scope of this work, further research investigating the
mechanisms behind observed patterns can be inferred by our results. For example, do
site differences in tree water use efficiency after density reduction follow a linear
response? If so, this would imply that trees found at sites in a gradient from wet to dry
climates would exhibit no change (Chapter 4) or a slight decrease in water use
efficiency at wet sites (Brooks and Coulombe 2009) to an increase in water use
efficiency in drier sites (Chapter 4) (for western Oregon). However, we also find a a
decrease in water use efficiency for very dry climates (McDowell et al. 2006).
Studies on water use efficiency in trees on unmanaged sites show a gradient of
increasing water use efficiency (through δ13C) from west to east (wet to dry) in
Oregon (Roden et al. 2005). However, the fallacy of linear extrapolation postulates
that assuming observations at one scale (western Oregon) may miss important
thresholds or patterns when cross-scale interactions are considered (Peters et al.
2004). There might not be a gradient and different trees on different sites may
respond according to local conditions not investigated in this study. A study
investigating these effects could also potentially determine a range of density
reductions that would cause a similar magnitude increase in growth of trees at dry
sites to match those of wet sites.
146
Emergent themes
The primary theme of my dissertation is that context and cross-scale
interactions matter. Examination of any one aspect of a system will result in an
incomplete understanding unless placed in context of the whole. The DMS study and
its implications only make sense when placed in the context of the Northwest Forest
Plan, which in turn only makes sense in the context of the larger relationship between
society and forest management (Chapter 2). Large-scale patterns in growth averaged
across sites (Chapter 3) were explained when examined in the context of multiple
scales and physiological response (Chapter 4).
Likewise, cross-scale-interactions may not be apparent if only looking at one
scale. For example, in Chapter 2 I identified that local neighborhood density was not
significantly related to the growth response of trees to upslope density management.
This result came from an analysis in which the sites are averaged together. However,
when examining finer spatial scales, the interaction of local neighborhood density and
location within the edge could be used to better predict tree response to upslope
thinning. Researchers can miss potential cross-scale interactions if we do not place
different processes (such as competition) in the correct scale or do not realize the
appropriate scale of investigation (Raffa et al. 2008).
Management implications
The primary management implication from this dissertation is that the scale of
management actions should account for interactions among the scales of the
ecosystem processes to be managed (Peters et al. 2011). Our study has shown that
managers are able to influence tree growth in riparian buffers by altering the local
147
density within the riparian buffer edge. However, the magnitude of response to
density reduction will depend on a cross-scale interaction at the site scale where site
wetness is a factor. Additionally, trees at my study sites appear to be responding to
other, potentially more limiting resources, than the increased water availability.
Identifying the scale over which density reduction operates has important
implications for biodiversity and habitat connectivity as well. Drier sites may
experience different changes to understory communities than wet sites (Neill and
Puettmann 2013). If microclimate is a critical factor for wildlife habitat, connectivity
over different habitat areas may need to be expanded or designed with different
configurations depending on how local tree density affects microclimate.
Recognizing the importance of cross-scale interactions is also important in
managing other ecosystems. For example, the oak-fire hypothesis has been used to
explain the prevalence of oak in continental climates and its potential fall from the
dominant overstory group due to the lack of anthropogenic fire (Abrams 1992). This
has led to a proliferation of studies and management operations using fire and density
treatments to regenerate oak with varying degrees of success (Nuttle et al. 2013).
However, understanding dynamic ecosystem processes, such as oak decline becomes
much more complicated when processes at multiple scales including regional climate
patterns or damage to individual tree crowns are taken into account (McEwan et al.
2011). Similarly, in the tropics, gamma biodiversity at the landscape scale is related
to the frequency of old-growth habitat interspersed in a matrix of palm-oil plantations
but is ultimately controlled by local micro-habitat availability within stands (Koh
2008). Potential mismatches of scale and processes over many different management
148
regimes suggests that managing for a suite of habitat structures and conditions might
be an effecting strategy for climate change mitigation (Puettmann 2011).
Management to increase structural diversity at the DMS is based on using
variable retention harvesting and thinning and gap creation to mimic a range of small
scale disturbances (Franklin et al. 2002; Sibley et al. 2012). These practices created
diversity of conditions that allow for processes acting at multiple scales, like wildlife
habitat or a range of microclimate conditions for tree regeneration. Using unevenaged and retention silviculture coupled with variable-density thinning can maximize
structural heterogeneity at the stand scale (Carey 2003; Gustafsson et al. 2012). The
overall goal of forest management in this fashion may be used to increase adaptability
to future conditions (Puettmann 2011; Puettmann et al. 2009a) through the retention
and creation of a diverse suite of forest habitats and structures (Messier et al. 2013;
Turner et al. 2012) for a variety of human needs (Bradford and D'Amato 2011; Seidl
et al. 2011). Building adaptive capacity in this fashion will increase the ability of
forests to persist and continue to provide important ecosystem functions under global
climate change (Cardinale et al. 2012; Chmura et al. 2011; Ives and Carpenter 2007;
Vicente-Serrano et al. 2013).
Final thoughts
Silviculture, much like this dissertation, only provides a brief interaction
between the forest and humans. Some effects are carried on in ecosystem memory
throughout further cycles of collapse and reorganization. Some, by design, will only
constitute a blip in the existence of an entire forest. I purposefully chose the theme
that context matters because of the broad applicability to a rapidly changing world. I
149
have shown how the expectations of forest management have changed, and how
silviculture experiments are adapting. I have shown how using those experiments can
yield insights into ecological processes and cross-scale interactions with implications
for riparian management. However these findings are contingent upon the spatial
scale examined, and this might be the most important finding of my dissertation. The
world around us is a rapidly changing mess called the Anthropocene. Invasive
species used to be a scourge to be combated; at the same time they can be part of
new, interesting, and sometimes productive novel ecosystem. Forest management
used to be based on forest science for sustained yield and efficient production, but
now ecological science with its inherent bias towards complexity is ascendant. Maybe
the only thing constant in how humans interact with our world is that we view it
through the lens of current needs and understanding. An emphasis on riparian
management or the importance of correlations of tree growth with a useful measure of
climate might be ephemeral, however, context matters.
150
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